From dec06e0e189b201ca2a8d87fe10db26f94fcb781 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 04:50:38 +0530
Subject: [PATCH 01/36] chore: add E2E tests workflow configuration

---
 .github/workflows/e2e-tests.yml | 287 ++++++++++++++++++++++++++++++++
 1 file changed, 287 insertions(+)
 create mode 100644 .github/workflows/e2e-tests.yml

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
new file mode 100644
index 000000000..9b04a28b2
--- /dev/null
+++ b/.github/workflows/e2e-tests.yml
@@ -0,0 +1,287 @@
+name: E2E Tests
+
+on:
+  pull_request:
+    branches: [main, dev]
+    types: [opened, synchronize, reopened, ready_for_review]
+    paths:
+      - 'surfsense_web/**'
+      - 'surfsense_backend/**'
+      - '.github/workflows/e2e-tests.yml'
+  workflow_dispatch:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  e2e:
+    name: Playwright E2E
+    runs-on: ubuntu-latest
+    if: github.event.pull_request.draft == false
+    timeout-minutes: 45
+
+    services:
+      postgres:
+        image: pgvector/pgvector:pg17
+        env:
+          POSTGRES_USER: postgres
+          POSTGRES_PASSWORD: postgres
+          POSTGRES_DB: surfsense_e2e
+        ports:
+          - 5432:5432
+        options: >-
+          --health-cmd "pg_isready -U postgres -d surfsense_e2e"
+          --health-interval 10s
+          --health-timeout 5s
+          --health-retries 5
+      # Required by Celery (broker + result backend) AND by the app's
+      # own Redis-backed features (heartbeats, podcast markers, anon
+      # quota). The previous workflow omitted this and indexing journeys
+      # silently hung.
+      redis:
+        image: redis:8-alpine
+        ports:
+          - 6379:6379
+        options: >-
+          --health-cmd "redis-cli ping"
+          --health-interval 10s
+          --health-timeout 5s
+          --health-retries 5
+
+    env:
+      # ---- Backend ------------------------------------------------------
+      DATABASE_URL: postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense_e2e
+      CELERY_BROKER_URL: redis://localhost:6379/0
+      CELERY_RESULT_BACKEND: redis://localhost:6379/0
+      REDIS_APP_URL: redis://localhost:6379/0
+      SECRET_KEY: ci-test-secret-key-not-for-production
+      AUTH_TYPE: LOCAL
+      REGISTRATION_ENABLED: "TRUE"
+      ETL_SERVICE: DOCLING
+      EMBEDDING_MODEL: sentence-transformers/all-MiniLM-L6-v2
+      NEXT_FRONTEND_URL: http://localhost:3000
+
+      # ---- Composio sentinel -------------------------------------------
+      # Production code does `from composio import Composio` at import
+      # time. `tests/e2e/run_backend.py` and `run_celery.py` hijack
+      # sys.modules BEFORE that import resolves, so the real SDK is
+      # never loaded. This sentinel API key is defense layer 3 from
+      # surfsense_backend/tests/e2e/README.md: if the hijack ever
+      # silently breaks, any real Composio call will 401 loudly with
+      # this token instead of using a stray developer key.
+      COMPOSIO_API_KEY: e2e-deny-real-call-sentinel
+      COMPOSIO_ENABLED: "TRUE"
+
+      # ---- Frontend (read by `next dev` via playwright.config.ts) -----
+      NEXT_PUBLIC_FASTAPI_BACKEND_URL: http://localhost:8000
+      NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: LOCAL
+
+      # ---- Playwright --------------------------------------------------
+      PLAYWRIGHT_TEST_EMAIL: e2e-test@surfsense.net
+      PLAYWRIGHT_TEST_PASSWORD: E2eTestPassword123!
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v6
+
+      # =================================================================
+      # Backend: Python + uv + dependencies + migrations
+      # =================================================================
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v8.1.0
+
+      - name: Cache backend dependencies
+        uses: actions/cache@v5
+        with:
+          path: |
+            ~/.cache/uv
+            surfsense_backend/.venv
+          key: python-deps-${{ hashFiles('surfsense_backend/uv.lock') }}
+          restore-keys: |
+            python-deps-
+
+      - name: Cache HuggingFace models
+        uses: actions/cache@v5
+        with:
+          path: ~/.cache/huggingface
+          key: hf-models-${{ env.EMBEDDING_MODEL }}-${{ env.ETL_SERVICE }}
+
+      - name: Install backend dependencies
+        working-directory: surfsense_backend
+        run: uv sync
+
+      - name: Run database migrations
+        working-directory: surfsense_backend
+        run: uv run alembic upgrade head
+
+      # =================================================================
+      # Boot the E2E backend.
+      #
+      # CRITICAL: do NOT run `uvicorn main:app` here. Production code
+      # binds `from composio import Composio` (and friends) at import
+      # time. `tests/e2e/run_backend.py` is the test-only entrypoint
+      # that hijacks sys.modules before that import — without it, every
+      # connector journey would call the real SDK.
+      # See surfsense_backend/tests/e2e/README.md.
+      # =================================================================
+      - name: Start backend (E2E entrypoint with sys.modules hijack)
+        working-directory: surfsense_backend
+        run: |
+          uv run python tests/e2e/run_backend.py \
+            > backend.log 2>&1 &
+          echo $! > backend.pid
+
+      # Celery runs in its own interpreter, so the hijack from
+      # run_backend.py does NOT carry over. run_celery.py reapplies it
+      # before importing celery_app. Without this worker, indexing
+      # tasks queue but never execute and journey specs hang.
+      - name: Start Celery worker (E2E entrypoint)
+        working-directory: surfsense_backend
+        run: |
+          uv run python tests/e2e/run_celery.py \
+            > celery.log 2>&1 &
+          echo $! > celery.pid
+
+      - name: Wait for backend readiness
+        run: |
+          for i in $(seq 1 60); do
+            if curl -sf http://localhost:8000/openapi.json > /dev/null; then
+              echo "Backend up after ${i} attempts"
+              exit 0
+            fi
+            sleep 2
+          done
+          echo "::error::Backend failed to start within 120s"
+          echo "===== backend.log (tail 200) ====="
+          tail -200 surfsense_backend/backend.log || true
+          echo "===== celery.log (tail 200) ====="
+          tail -200 surfsense_backend/celery.log || true
+          exit 1
+
+      - name: Wait for Celery worker readiness
+        working-directory: surfsense_backend
+        run: |
+          for i in $(seq 1 30); do
+            if uv run celery -A app.celery_app inspect ping --timeout 2 \
+                > /dev/null 2>&1; then
+              echo "Celery worker up after ${i} attempts"
+              exit 0
+            fi
+            sleep 2
+          done
+          echo "::error::Celery worker failed to start within 60s"
+          echo "===== celery.log (tail 200) ====="
+          tail -200 celery.log || true
+          exit 1
+
+      - name: Register E2E test user
+        run: |
+          # Idempotent: 200/201 = created, 400 = already exists (also OK)
+          STATUS=$(curl -s -o /tmp/register.json -w "%{http_code}" \
+            -X POST http://localhost:8000/auth/register \
+            -H "Content-Type: application/json" \
+            -d "{\"email\":\"${PLAYWRIGHT_TEST_EMAIL}\",\"password\":\"${PLAYWRIGHT_TEST_PASSWORD}\"}")
+          echo "Register status: ${STATUS}"
+          cat /tmp/register.json
+          if [ "${STATUS}" != "200" ] && [ "${STATUS}" != "201" ] && [ "${STATUS}" != "400" ]; then
+            echo "::error::Failed to register test user (status ${STATUS})"
+            exit 1
+          fi
+
+      # =================================================================
+      # Frontend: Node + pnpm + Playwright
+      # =================================================================
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: '20'
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v6
+        with:
+          version: 10
+
+      - name: Get pnpm store directory
+        id: pnpm-cache
+        shell: bash
+        run: echo "STORE_PATH=$(pnpm store path --silent)" >> $GITHUB_OUTPUT
+
+      - name: Cache pnpm store
+        uses: actions/cache@v5
+        with:
+          path: ${{ steps.pnpm-cache.outputs.STORE_PATH }}
+          key: pnpm-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}
+          restore-keys: |
+            pnpm-${{ runner.os }}-
+
+      - name: Install web dependencies
+        working-directory: surfsense_web
+        run: pnpm install --frozen-lockfile
+
+      - name: Cache Playwright browsers
+        id: playwright-cache
+        uses: actions/cache@v5
+        with:
+          path: ~/.cache/ms-playwright
+          key: playwright-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}
+
+      - name: Install Playwright browsers
+        if: steps.playwright-cache.outputs.cache-hit != 'true'
+        working-directory: surfsense_web
+        run: pnpm exec playwright install --with-deps chromium
+
+      - name: Install Playwright system deps (cache hit)
+        if: steps.playwright-cache.outputs.cache-hit == 'true'
+        working-directory: surfsense_web
+        run: pnpm exec playwright install-deps chromium
+
+      # playwright.config.ts boots `pnpm exec next dev` automatically
+      # via webServer config (skipped when PLAYWRIGHT_NO_WEB_SERVER set).
+      - name: Run Playwright tests
+        working-directory: surfsense_web
+        run: pnpm test:e2e
+
+      # =================================================================
+      # Diagnostics
+      # =================================================================
+      - name: Upload Playwright HTML report
+        if: always()
+        uses: actions/upload-artifact@v7
+        with:
+          name: playwright-report
+          path: surfsense_web/playwright-report/
+          retention-days: 14
+
+      - name: Upload Playwright traces / videos
+        if: failure()
+        uses: actions/upload-artifact@v7
+        with:
+          name: playwright-traces
+          path: surfsense_web/test-results/
+          retention-days: 14
+
+      - name: Upload backend + celery logs
+        if: failure()
+        uses: actions/upload-artifact@v7
+        with:
+          name: backend-celery-logs
+          path: |
+            surfsense_backend/backend.log
+            surfsense_backend/celery.log
+          retention-days: 7
+
+      - name: Stop backend + Celery worker
+        if: always()
+        working-directory: surfsense_backend
+        run: |
+          for f in backend.pid celery.pid; do
+            if [ -f "$f" ]; then
+              kill "$(cat $f)" 2>/dev/null || true
+            fi
+          done

From 10212f3d5a21401c03c7e622fb8301138a7e2524 Mon Sep 17 00:00:00 2001
From: guangyang1206 <guangyang1206@users.noreply.github.com>
Date: Sun, 10 May 2026 12:05:10 +0800
Subject: [PATCH 02/36] feat(shared): extract formatThreadTimestamp helper for
 chats sidebars (fixes #1376)

- Add formatThreadTimestamp() to surfsense_web/lib/format-date.ts
- Use shared helper in AllPrivateChatsSidebar and AllSharedChatsSidebar
- Remove unused date-fns format import from both sidebar files
- Centralises timestamp formatting policy for future i18n/relative-time changes
---
 .../layout/ui/sidebar/AllPrivateChatsSidebar.tsx          | 4 ++--
 .../layout/ui/sidebar/AllSharedChatsSidebar.tsx           | 4 ++--
 surfsense_web/lib/format-date.ts                          | 8 ++++++++
 3 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx b/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx
index ab5213db2..6bd2275fc 100644
--- a/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx
+++ b/surfsense_web/components/layout/ui/sidebar/AllPrivateChatsSidebar.tsx
@@ -1,7 +1,7 @@
 "use client";
 
 import { useQuery, useQueryClient } from "@tanstack/react-query";
-import { format } from "date-fns";
+import { formatThreadTimestamp } from "@/lib/format-date";
 import { useSetAtom } from "jotai";
 import {
 	ArchiveIcon,
@@ -390,7 +390,7 @@ export function AllPrivateChatsSidebarContent({
 											<TooltipContent side="bottom" align="start">
 												<p>
 													{t("updated") || "Updated"}:{" "}
-													{format(new Date(thread.updatedAt), "MMM d, yyyy 'at' h:mm a")}
+													{formatThreadTimestamp(thread.updatedAt)}
 												</p>
 											</TooltipContent>
 										</Tooltip>
diff --git a/surfsense_web/components/layout/ui/sidebar/AllSharedChatsSidebar.tsx b/surfsense_web/components/layout/ui/sidebar/AllSharedChatsSidebar.tsx
index ab1072459..81d173f36 100644
--- a/surfsense_web/components/layout/ui/sidebar/AllSharedChatsSidebar.tsx
+++ b/surfsense_web/components/layout/ui/sidebar/AllSharedChatsSidebar.tsx
@@ -1,7 +1,7 @@
 "use client";
 
 import { useQuery, useQueryClient } from "@tanstack/react-query";
-import { format } from "date-fns";
+import { formatThreadTimestamp } from "@/lib/format-date";
 import { useSetAtom } from "jotai";
 import {
 	ArchiveIcon,
@@ -389,7 +389,7 @@ export function AllSharedChatsSidebarContent({
 											<TooltipContent side="bottom" align="start">
 												<p>
 													{t("updated") || "Updated"}:{" "}
-													{format(new Date(thread.updatedAt), "MMM d, yyyy 'at' h:mm a")}
+													{formatThreadTimestamp(thread.updatedAt)}
 												</p>
 											</TooltipContent>
 										</Tooltip>
diff --git a/surfsense_web/lib/format-date.ts b/surfsense_web/lib/format-date.ts
index ee60d113d..9decd3402 100644
--- a/surfsense_web/lib/format-date.ts
+++ b/surfsense_web/lib/format-date.ts
@@ -22,3 +22,11 @@ export function formatRelativeDate(dateString: string): string {
 	if (daysAgo < 7) return `${daysAgo}d ago`;
 	return format(date, "MMM d, yyyy");
 }
+
+/**
+ * Format a thread's last-updated timestamp for the chats sidebars.
+ * Example: "Mar 23, 2026 at 4:30 PM"
+ */
+export function formatThreadTimestamp(dateString: string): string {
+	return format(new Date(dateString), "MMM d, yyyy 'at' h:mm a");
+}

From cf9e702bee7c8b1daa5af1632d7fbdd6a23e7f96 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 13:09:50 +0530
Subject: [PATCH 03/36] chore: refine E2E tests workflow by updating Redis
 configuration and adding fake API keys for various services

---
 .github/workflows/e2e-tests.yml | 64 ++++++++++++---------------------
 1 file changed, 23 insertions(+), 41 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 9b04a28b2..5db9b3519 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -35,10 +35,6 @@ jobs:
           --health-interval 10s
           --health-timeout 5s
           --health-retries 5
-      # Required by Celery (broker + result backend) AND by the app's
-      # own Redis-backed features (heartbeats, podcast markers, anon
-      # quota). The previous workflow omitted this and indexing journeys
-      # silently hung.
       redis:
         image: redis:8-alpine
         ports:
@@ -50,7 +46,6 @@ jobs:
           --health-retries 5
 
     env:
-      # ---- Backend ------------------------------------------------------
       DATABASE_URL: postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense_e2e
       CELERY_BROKER_URL: redis://localhost:6379/0
       CELERY_RESULT_BACKEND: redis://localhost:6379/0
@@ -62,22 +57,29 @@ jobs:
       EMBEDDING_MODEL: sentence-transformers/all-MiniLM-L6-v2
       NEXT_FRONTEND_URL: http://localhost:3000
 
-      # ---- Composio sentinel -------------------------------------------
-      # Production code does `from composio import Composio` at import
-      # time. `tests/e2e/run_backend.py` and `run_celery.py` hijack
-      # sys.modules BEFORE that import resolves, so the real SDK is
-      # never loaded. This sentinel API key is defense layer 3 from
-      # surfsense_backend/tests/e2e/README.md: if the hijack ever
-      # silently breaks, any real Composio call will 401 loudly with
-      # this token instead of using a stray developer key.
+      # Sentinel keys — fakes never read them; turns leaked real calls into 401s.
       COMPOSIO_API_KEY: e2e-deny-real-call-sentinel
       COMPOSIO_ENABLED: "TRUE"
+      OPENAI_API_KEY: e2e-deny-real-call-sentinel
+      ANTHROPIC_API_KEY: e2e-deny-real-call-sentinel
+      LITELLM_API_KEY: e2e-deny-real-call-sentinel
+
+      MICROSOFT_CLIENT_ID: fake-microsoft-client-id
+      MICROSOFT_CLIENT_SECRET: fake-microsoft-client-secret
+      ONEDRIVE_REDIRECT_URI: http://localhost:8000/api/v1/auth/onedrive/connector/callback
+      DROPBOX_APP_KEY: fake-dropbox-app-key
+      DROPBOX_APP_SECRET: fake-dropbox-app-secret
+      DROPBOX_REDIRECT_URI: http://localhost:8000/api/v1/auth/dropbox/connector/callback
+
+      # NO_PROXY must keep huggingface — embedding + Docling models lazy-download
+      # there on cold cache. Embedding fakes patch callsites, not the loader.
+      HTTPS_PROXY: http://127.0.0.1:1
+      HTTP_PROXY: http://127.0.0.1:1
+      NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
 
-      # ---- Frontend (read by `next dev` via playwright.config.ts) -----
       NEXT_PUBLIC_FASTAPI_BACKEND_URL: http://localhost:8000
       NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: LOCAL
 
-      # ---- Playwright --------------------------------------------------
       PLAYWRIGHT_TEST_EMAIL: e2e-test@surfsense.net
       PLAYWRIGHT_TEST_PASSWORD: E2eTestPassword123!
 
@@ -85,9 +87,6 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v6
 
-      # =================================================================
-      # Backend: Python + uv + dependencies + migrations
-      # =================================================================
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
@@ -120,16 +119,9 @@ jobs:
         working-directory: surfsense_backend
         run: uv run alembic upgrade head
 
-      # =================================================================
-      # Boot the E2E backend.
-      #
-      # CRITICAL: do NOT run `uvicorn main:app` here. Production code
-      # binds `from composio import Composio` (and friends) at import
-      # time. `tests/e2e/run_backend.py` is the test-only entrypoint
-      # that hijacks sys.modules before that import — without it, every
-      # connector journey would call the real SDK.
-      # See surfsense_backend/tests/e2e/README.md.
-      # =================================================================
+      # Do NOT replace with `uvicorn main:app`. run_backend.py hijacks
+      # sys.modules["composio"] before app import; production binds it
+      # at import time so plain uvicorn would call the real SDK.
       - name: Start backend (E2E entrypoint with sys.modules hijack)
         working-directory: surfsense_backend
         run: |
@@ -137,10 +129,8 @@ jobs:
             > backend.log 2>&1 &
           echo $! > backend.pid
 
-      # Celery runs in its own interpreter, so the hijack from
-      # run_backend.py does NOT carry over. run_celery.py reapplies it
-      # before importing celery_app. Without this worker, indexing
-      # tasks queue but never execute and journey specs hang.
+      # Worker runs in a separate interpreter, so the hijack must be
+      # reapplied here. Without it, indexing tasks queue but never run.
       - name: Start Celery worker (E2E entrypoint)
         working-directory: surfsense_backend
         run: |
@@ -182,7 +172,7 @@ jobs:
 
       - name: Register E2E test user
         run: |
-          # Idempotent: 200/201 = created, 400 = already exists (also OK)
+          # 200/201 = created, 400 = already exists (idempotent across reruns).
           STATUS=$(curl -s -o /tmp/register.json -w "%{http_code}" \
             -X POST http://localhost:8000/auth/register \
             -H "Content-Type: application/json" \
@@ -194,9 +184,6 @@ jobs:
             exit 1
           fi
 
-      # =================================================================
-      # Frontend: Node + pnpm + Playwright
-      # =================================================================
       - name: Setup Node.js
         uses: actions/setup-node@v6
         with:
@@ -241,15 +228,10 @@ jobs:
         working-directory: surfsense_web
         run: pnpm exec playwright install-deps chromium
 
-      # playwright.config.ts boots `pnpm exec next dev` automatically
-      # via webServer config (skipped when PLAYWRIGHT_NO_WEB_SERVER set).
       - name: Run Playwright tests
         working-directory: surfsense_web
         run: pnpm test:e2e
 
-      # =================================================================
-      # Diagnostics
-      # =================================================================
       - name: Upload Playwright HTML report
         if: always()
         uses: actions/upload-artifact@v7

From 21d3be14c97c2fb5ecc95fa509930c5e9f3ef6ac Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 21:13:57 +0530
Subject: [PATCH 04/36] chore: update E2E tests workflow name and adjust video
 recording settings

---
 .github/workflows/e2e-tests.yml    | 6 +++---
 surfsense_web/playwright.config.ts | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 5db9b3519..361cce95e 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -16,7 +16,7 @@ concurrency:
 
 jobs:
   e2e:
-    name: Playwright E2E
+    name: Journey
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
     timeout-minutes: 45
@@ -71,7 +71,7 @@ jobs:
       DROPBOX_APP_SECRET: fake-dropbox-app-secret
       DROPBOX_REDIRECT_URI: http://localhost:8000/api/v1/auth/dropbox/connector/callback
 
-      # NO_PROXY must keep huggingface — embedding + Docling models lazy-download
+      # NO_PROXY must keep huggingface, embedding + Docling models lazy-download
       # there on cold cache. Embedding fakes patch callsites, not the loader.
       HTTPS_PROXY: http://127.0.0.1:1
       HTTP_PROXY: http://127.0.0.1:1
@@ -240,7 +240,7 @@ jobs:
           path: surfsense_web/playwright-report/
           retention-days: 14
 
-      - name: Upload Playwright traces / videos
+      - name: Upload Playwright traces
         if: failure()
         uses: actions/upload-artifact@v7
         with:
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index 511db6b09..189916f02 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -31,7 +31,7 @@ export default defineConfig({
 		baseURL,
 		trace: "on-first-retry",
 		screenshot: "only-on-failure",
-		video: "retain-on-failure",
+		video: process.env.CI ? "off" : "retain-on-failure",
 		extraHTTPHeaders: {
 			"x-playwright-test": "true",
 		},

From 288c18bdf7db93a120b201e577aaa5e138c28960 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 21:34:07 +0530
Subject: [PATCH 05/36] chore: update E2E tests workflow to include scoped
 proxy settings for backend and Celery worker

---
 .github/workflows/e2e-tests.yml | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 361cce95e..cb3cfa275 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -71,12 +71,6 @@ jobs:
       DROPBOX_APP_SECRET: fake-dropbox-app-secret
       DROPBOX_REDIRECT_URI: http://localhost:8000/api/v1/auth/dropbox/connector/callback
 
-      # NO_PROXY must keep huggingface, embedding + Docling models lazy-download
-      # there on cold cache. Embedding fakes patch callsites, not the loader.
-      HTTPS_PROXY: http://127.0.0.1:1
-      HTTP_PROXY: http://127.0.0.1:1
-      NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
-
       NEXT_PUBLIC_FASTAPI_BACKEND_URL: http://localhost:8000
       NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: LOCAL
 
@@ -124,6 +118,10 @@ jobs:
       # at import time so plain uvicorn would call the real SDK.
       - name: Start backend (E2E entrypoint with sys.modules hijack)
         working-directory: surfsense_backend
+        env:
+          HTTPS_PROXY: http://127.0.0.1:1
+          HTTP_PROXY: http://127.0.0.1:1
+          NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
         run: |
           uv run python tests/e2e/run_backend.py \
             > backend.log 2>&1 &
@@ -131,8 +129,13 @@ jobs:
 
       # Worker runs in a separate interpreter, so the hijack must be
       # reapplied here. Without it, indexing tasks queue but never run.
+      # Same proxy-scoping rationale as the backend step above.
       - name: Start Celery worker (E2E entrypoint)
         working-directory: surfsense_backend
+        env:
+          HTTPS_PROXY: http://127.0.0.1:1
+          HTTP_PROXY: http://127.0.0.1:1
+          NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
         run: |
           uv run python tests/e2e/run_celery.py \
             > celery.log 2>&1 &

From 548e574f1a3ff6d96247d0c3c2f047ecd6e18dc8 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 21:47:59 +0530
Subject: [PATCH 06/36] chore: refactor E2E tests workflow to start Postgres as
 a container and add readiness check

---
 .github/workflows/e2e-tests.yml | 51 ++++++++++++++++++++++-----------
 1 file changed, 35 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index cb3cfa275..63db5f4f1 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -21,20 +21,9 @@ jobs:
     if: github.event.pull_request.draft == false
     timeout-minutes: 45
 
+    # Postgres runs as a step (not a service) so we can pass `-c wal_level=logical`,
+    # required for migration 117's zero-cache publications.
     services:
-      postgres:
-        image: pgvector/pgvector:pg17
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-          POSTGRES_DB: surfsense_e2e
-        ports:
-          - 5432:5432
-        options: >-
-          --health-cmd "pg_isready -U postgres -d surfsense_e2e"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
       redis:
         image: redis:8-alpine
         ports:
@@ -81,6 +70,21 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v6
 
+      # Started early so it warms up while Python deps install.
+      - name: Start Postgres
+        run: |
+          docker run -d \
+            --name surfsense_postgres \
+            -p 5432:5432 \
+            -e POSTGRES_USER=postgres \
+            -e POSTGRES_PASSWORD=postgres \
+            -e POSTGRES_DB=surfsense_e2e \
+            pgvector/pgvector:pg17 \
+            postgres \
+              -c wal_level=logical \
+              -c max_wal_senders=10 \
+              -c max_replication_slots=10
+
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
@@ -109,6 +113,19 @@ jobs:
         working-directory: surfsense_backend
         run: uv sync
 
+      - name: Wait for Postgres readiness
+        run: |
+          for i in $(seq 1 30); do
+            if docker exec surfsense_postgres pg_isready -U postgres -d surfsense_e2e > /dev/null 2>&1; then
+              echo "Postgres ready after ${i} attempts"
+              exit 0
+            fi
+            sleep 2
+          done
+          echo "::error::Postgres failed to become ready within 60s"
+          docker logs surfsense_postgres --tail 100
+          exit 1
+
       - name: Run database migrations
         working-directory: surfsense_backend
         run: uv run alembic upgrade head
@@ -127,9 +144,7 @@ jobs:
             > backend.log 2>&1 &
           echo $! > backend.pid
 
-      # Worker runs in a separate interpreter, so the hijack must be
-      # reapplied here. Without it, indexing tasks queue but never run.
-      # Same proxy-scoping rationale as the backend step above.
+      # Worker is a separate interpreter, so the composio hijack must be reapplied.
       - name: Start Celery worker (E2E entrypoint)
         working-directory: surfsense_backend
         env:
@@ -270,3 +285,7 @@ jobs:
               kill "$(cat $f)" 2>/dev/null || true
             fi
           done
+
+      - name: Stop Postgres
+        if: always()
+        run: docker rm -f surfsense_postgres 2>/dev/null || true

From 292b4d70ac4da2355acba9fe28c4638af3c2d248 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 22:21:06 +0530
Subject: [PATCH 07/36] chore: enhance E2E tests workflow by adding caching for
 Next.js build and updating test command

---
 .github/workflows/e2e-tests.yml    | 13 ++++++++++---
 surfsense_backend/alembic/env.py   |  6 +++++-
 surfsense_web/package.json         |  1 +
 surfsense_web/playwright.config.ts |  8 +++++---
 4 files changed, 21 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 63db5f4f1..a807603b8 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -21,8 +21,7 @@ jobs:
     if: github.event.pull_request.draft == false
     timeout-minutes: 45
 
-    # Postgres runs as a step (not a service) so we can pass `-c wal_level=logical`,
-    # required for migration 117's zero-cache publications.
+    # Postgres runs as a step (not a service)
     services:
       redis:
         image: redis:8-alpine
@@ -246,9 +245,17 @@ jobs:
         working-directory: surfsense_web
         run: pnpm exec playwright install-deps chromium
 
+      - name: Cache Next.js build
+        uses: actions/cache@v5
+        with:
+          path: surfsense_web/.next/cache
+          key: nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-${{ hashFiles('surfsense_web/**/*.{js,jsx,ts,tsx}') }}
+          restore-keys: |
+            nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-
+
       - name: Run Playwright tests
         working-directory: surfsense_web
-        run: pnpm test:e2e
+        run: pnpm test:e2e:prod
 
       - name: Upload Playwright HTML report
         if: always()
diff --git a/surfsense_backend/alembic/env.py b/surfsense_backend/alembic/env.py
index bd8c20356..5354211aa 100644
--- a/surfsense_backend/alembic/env.py
+++ b/surfsense_backend/alembic/env.py
@@ -67,7 +67,11 @@ def run_migrations_offline() -> None:
 
 
 def do_run_migrations(connection: Connection) -> None:
-    context.configure(connection=connection, target_metadata=target_metadata)
+    context.configure(
+        connection=connection,
+        target_metadata=target_metadata,
+        transaction_per_migration=True,
+    )
 
     with context.begin_transaction():
         context.run_migrations()
diff --git a/surfsense_web/package.json b/surfsense_web/package.json
index fa8d50cdc..d9f836ea9 100644
--- a/surfsense_web/package.json
+++ b/surfsense_web/package.json
@@ -20,6 +20,7 @@
 		"db:studio": "drizzle-kit studio",
 		"format:fix": "npx @biomejs/biome check --fix",
 		"test:e2e": "playwright test",
+		"test:e2e:prod": "cross-env CI=1 playwright test",
 		"test:e2e:ui": "playwright test --ui",
 		"test:e2e:headed": "playwright test --headed",
 		"test:e2e:debug": "playwright test --debug",
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index 189916f02..0dfdf80bf 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -53,11 +53,13 @@ export default defineConfig({
 	webServer: process.env.PLAYWRIGHT_NO_WEB_SERVER
 		? undefined
 		: {
-				// Pin to webpack dev (Turbopack has caused stale-lock panics in E2E).
-				command: "pnpm exec next dev",
+				// Local stays on webpack dev (Turbopack caused stale-lock panics in E2E).
+				command: process.env.CI
+					? "pnpm build && pnpm start"
+					: "pnpm exec next dev",
 				url: `http://localhost:${PORT}`,
 				reuseExistingServer: !process.env.CI,
-				timeout: 180_000,
+				timeout: process.env.CI ? 300_000 : 180_000,
 				env: {
 					NEXT_PUBLIC_FASTAPI_BACKEND_URL: `http://localhost:${BACKEND_PORT}`,
 					NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: "LOCAL",

From 319923fb40f3201edd43d8e5acbf7db51850120a Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 22:40:29 +0530
Subject: [PATCH 08/36] fix: add checks for existing tables and indexes before
 creating them in alembic migrations for idempotency

---
 .../versions/130_add_agent_action_log.py      |  4 ++
 .../versions/131_add_document_revisions.py    | 17 ++++++
 .../132_add_agent_permission_rules.py         |  4 ++
 .../135_action_log_correlation_ids.py         | 52 +++++++++++--------
 .../137_unique_reverse_of_in_action_log.py    |  7 +++
 .../141_unique_chat_message_turn_role.py      |  5 ++
 6 files changed, 68 insertions(+), 21 deletions(-)

diff --git a/surfsense_backend/alembic/versions/130_add_agent_action_log.py b/surfsense_backend/alembic/versions/130_add_agent_action_log.py
index f86a8a3b5..5978848d0 100644
--- a/surfsense_backend/alembic/versions/130_add_agent_action_log.py
+++ b/surfsense_backend/alembic/versions/130_add_agent_action_log.py
@@ -26,6 +26,10 @@ depends_on: str | Sequence[str] | None = None
 
 
 def upgrade() -> None:
+    bind = op.get_bind()
+    if sa.inspect(bind).has_table("agent_action_log"):
+        return
+
     op.create_table(
         "agent_action_log",
         sa.Column("id", sa.Integer(), primary_key=True, index=True),
diff --git a/surfsense_backend/alembic/versions/131_add_document_revisions.py b/surfsense_backend/alembic/versions/131_add_document_revisions.py
index 95ce0e032..c1e9b6068 100644
--- a/surfsense_backend/alembic/versions/131_add_document_revisions.py
+++ b/surfsense_backend/alembic/versions/131_add_document_revisions.py
@@ -29,6 +29,21 @@ depends_on: str | Sequence[str] | None = None
 
 
 def upgrade() -> None:
+    bind = op.get_bind()
+    inspector = sa.inspect(bind)
+
+    if inspector.has_table("document_revisions") and inspector.has_table(
+        "folder_revisions"
+    ):
+        return
+
+    if not inspector.has_table("document_revisions"):
+        _create_document_revisions()
+    if not inspector.has_table("folder_revisions"):
+        _create_folder_revisions()
+
+
+def _create_document_revisions() -> None:
     op.create_table(
         "document_revisions",
         sa.Column("id", sa.Integer(), primary_key=True, index=True),
@@ -74,6 +89,8 @@ def upgrade() -> None:
         ),
     )
 
+
+def _create_folder_revisions() -> None:
     op.create_table(
         "folder_revisions",
         sa.Column("id", sa.Integer(), primary_key=True, index=True),
diff --git a/surfsense_backend/alembic/versions/132_add_agent_permission_rules.py b/surfsense_backend/alembic/versions/132_add_agent_permission_rules.py
index ff5b52e18..1ee3cd2f0 100644
--- a/surfsense_backend/alembic/versions/132_add_agent_permission_rules.py
+++ b/surfsense_backend/alembic/versions/132_add_agent_permission_rules.py
@@ -26,6 +26,10 @@ depends_on: str | Sequence[str] | None = None
 
 
 def upgrade() -> None:
+    bind = op.get_bind()
+    if sa.inspect(bind).has_table("agent_permission_rules"):
+        return
+
     op.create_table(
         "agent_permission_rules",
         sa.Column("id", sa.Integer(), primary_key=True, index=True),
diff --git a/surfsense_backend/alembic/versions/135_action_log_correlation_ids.py b/surfsense_backend/alembic/versions/135_action_log_correlation_ids.py
index 9ae368b81..e40c4fb26 100644
--- a/surfsense_backend/alembic/versions/135_action_log_correlation_ids.py
+++ b/surfsense_backend/alembic/versions/135_action_log_correlation_ids.py
@@ -50,29 +50,39 @@ depends_on: str | Sequence[str] | None = None
 
 
 def upgrade() -> None:
-    op.add_column(
-        "agent_action_log",
-        sa.Column("tool_call_id", sa.String(length=64), nullable=True),
-    )
-    op.add_column(
-        "agent_action_log",
-        sa.Column("chat_turn_id", sa.String(length=64), nullable=True),
-    )
+    bind = op.get_bind()
+    inspector = sa.inspect(bind)
+    columns = {c["name"] for c in inspector.get_columns("agent_action_log")}
+    indexes = {i["name"] for i in inspector.get_indexes("agent_action_log")}
 
-    op.create_index(
-        "ix_agent_action_log_tool_call_id",
-        "agent_action_log",
-        ["tool_call_id"],
-    )
-    op.create_index(
-        "ix_agent_action_log_chat_turn_id",
-        "agent_action_log",
-        ["chat_turn_id"],
-    )
+    if "tool_call_id" not in columns:
+        op.add_column(
+            "agent_action_log",
+            sa.Column("tool_call_id", sa.String(length=64), nullable=True),
+        )
+    if "chat_turn_id" not in columns:
+        op.add_column(
+            "agent_action_log",
+            sa.Column("chat_turn_id", sa.String(length=64), nullable=True),
+        )
 
-    op.execute(
-        "UPDATE agent_action_log SET tool_call_id = turn_id WHERE tool_call_id IS NULL"
-    )
+    if "ix_agent_action_log_tool_call_id" not in indexes:
+        op.create_index(
+            "ix_agent_action_log_tool_call_id",
+            "agent_action_log",
+            ["tool_call_id"],
+        )
+    if "ix_agent_action_log_chat_turn_id" not in indexes:
+        op.create_index(
+            "ix_agent_action_log_chat_turn_id",
+            "agent_action_log",
+            ["chat_turn_id"],
+        )
+
+    if "turn_id" in columns:
+        op.execute(
+            "UPDATE agent_action_log SET tool_call_id = turn_id WHERE tool_call_id IS NULL"
+        )
 
 
 def downgrade() -> None:
diff --git a/surfsense_backend/alembic/versions/137_unique_reverse_of_in_action_log.py b/surfsense_backend/alembic/versions/137_unique_reverse_of_in_action_log.py
index d606a00f9..47421e712 100644
--- a/surfsense_backend/alembic/versions/137_unique_reverse_of_in_action_log.py
+++ b/surfsense_backend/alembic/versions/137_unique_reverse_of_in_action_log.py
@@ -27,6 +27,8 @@ from __future__ import annotations
 
 from collections.abc import Sequence
 
+import sqlalchemy as sa
+
 from alembic import op
 
 revision: str = "137"
@@ -39,6 +41,11 @@ _INDEX_NAME = "ux_agent_action_log_reverse_of"
 
 
 def upgrade() -> None:
+    bind = op.get_bind()
+    indexes = {i["name"] for i in sa.inspect(bind).get_indexes("agent_action_log")}
+    if _INDEX_NAME in indexes:
+        return
+
     # Defensively de-dup any pre-existing double-revert rows before
     # adding the unique index. Keeps the OLDEST row (smallest id) and
     # NULLs out the duplicates' ``reverse_of`` so they survive as audit
diff --git a/surfsense_backend/alembic/versions/141_unique_chat_message_turn_role.py b/surfsense_backend/alembic/versions/141_unique_chat_message_turn_role.py
index 9a27e7ed0..1226a59b4 100644
--- a/surfsense_backend/alembic/versions/141_unique_chat_message_turn_role.py
+++ b/surfsense_backend/alembic/versions/141_unique_chat_message_turn_role.py
@@ -53,6 +53,11 @@ TABLE_NAME = "new_chat_messages"
 
 
 def upgrade() -> None:
+    bind = op.get_bind()
+    indexes = {i["name"] for i in sa.inspect(bind).get_indexes(TABLE_NAME)}
+    if INDEX_NAME in indexes:
+        return
+
     op.create_index(
         INDEX_NAME,
         TABLE_NAME,

From 2c8828f60cc9ff2fee4c47f07fb1af046cff8cc2 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Sun, 10 May 2026 22:45:26 +0530
Subject: [PATCH 09/36] fix: ensure idempotency in alembic migrations by
 checking for existing columns and indexes before creation

---
 .../versions/136_new_chat_message_turn_id.py  | 25 ++++++++++++-------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/surfsense_backend/alembic/versions/136_new_chat_message_turn_id.py b/surfsense_backend/alembic/versions/136_new_chat_message_turn_id.py
index 8d4350424..ee02e453c 100644
--- a/surfsense_backend/alembic/versions/136_new_chat_message_turn_id.py
+++ b/surfsense_backend/alembic/versions/136_new_chat_message_turn_id.py
@@ -36,15 +36,22 @@ depends_on: str | Sequence[str] | None = None
 
 
 def upgrade() -> None:
-    op.add_column(
-        "new_chat_messages",
-        sa.Column("turn_id", sa.String(length=64), nullable=True),
-    )
-    op.create_index(
-        "ix_new_chat_messages_turn_id",
-        "new_chat_messages",
-        ["turn_id"],
-    )
+    bind = op.get_bind()
+    inspector = sa.inspect(bind)
+    columns = {c["name"] for c in inspector.get_columns("new_chat_messages")}
+    indexes = {i["name"] for i in inspector.get_indexes("new_chat_messages")}
+
+    if "turn_id" not in columns:
+        op.add_column(
+            "new_chat_messages",
+            sa.Column("turn_id", sa.String(length=64), nullable=True),
+        )
+    if "ix_new_chat_messages_turn_id" not in indexes:
+        op.create_index(
+            "ix_new_chat_messages_turn_id",
+            "new_chat_messages",
+            ["turn_id"],
+        )
 
 
 def downgrade() -> None:

From 68f45335bc1d5cf214eec854a924b82d8ba5775b Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 03:09:01 +0530
Subject: [PATCH 10/36] chore: implement E2E testing setup with Docker Compose
 and update workflow for backend and Redis services

---
 .github/workflows/e2e-tests.yml               | 231 ++++--------------
 docker/docker-compose.e2e.yml                 | 168 +++++++++++++
 surfsense_backend/Dockerfile                  | 134 ++++++----
 .../scripts/docker/entrypoint.e2e.sh          |  52 ++++
 surfsense_backend/tests/e2e/run_backend.py    |  23 ++
 surfsense_backend/tests/e2e/run_celery.py     |  32 ++-
 surfsense_web/playwright.config.ts            |  13 +-
 surfsense_web/tests/auth.setup.ts             |   9 +-
 surfsense_web/tests/helpers/api/auth.ts       |   4 +-
 9 files changed, 433 insertions(+), 233 deletions(-)
 create mode 100644 docker/docker-compose.e2e.yml
 create mode 100755 surfsense_backend/scripts/docker/entrypoint.e2e.sh

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index a807603b8..20c79c42d 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -7,6 +7,7 @@ on:
     paths:
       - 'surfsense_web/**'
       - 'surfsense_backend/**'
+      - 'docker/docker-compose.e2e.yml'
       - '.github/workflows/e2e-tests.yml'
   workflow_dispatch:
 
@@ -19,173 +20,36 @@ jobs:
     name: Journey
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
-    timeout-minutes: 45
-
-    # Postgres runs as a step (not a service)
-    services:
-      redis:
-        image: redis:8-alpine
-        ports:
-          - 6379:6379
-        options: >-
-          --health-cmd "redis-cli ping"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
+    timeout-minutes: 30
 
     env:
-      DATABASE_URL: postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense_e2e
-      CELERY_BROKER_URL: redis://localhost:6379/0
-      CELERY_RESULT_BACKEND: redis://localhost:6379/0
-      REDIS_APP_URL: redis://localhost:6379/0
-      SECRET_KEY: ci-test-secret-key-not-for-production
-      AUTH_TYPE: LOCAL
-      REGISTRATION_ENABLED: "TRUE"
-      ETL_SERVICE: DOCLING
-      EMBEDDING_MODEL: sentence-transformers/all-MiniLM-L6-v2
-      NEXT_FRONTEND_URL: http://localhost:3000
-
-      # Sentinel keys — fakes never read them; turns leaked real calls into 401s.
-      COMPOSIO_API_KEY: e2e-deny-real-call-sentinel
-      COMPOSIO_ENABLED: "TRUE"
-      OPENAI_API_KEY: e2e-deny-real-call-sentinel
-      ANTHROPIC_API_KEY: e2e-deny-real-call-sentinel
-      LITELLM_API_KEY: e2e-deny-real-call-sentinel
-
-      MICROSOFT_CLIENT_ID: fake-microsoft-client-id
-      MICROSOFT_CLIENT_SECRET: fake-microsoft-client-secret
-      ONEDRIVE_REDIRECT_URI: http://localhost:8000/api/v1/auth/onedrive/connector/callback
-      DROPBOX_APP_KEY: fake-dropbox-app-key
-      DROPBOX_APP_SECRET: fake-dropbox-app-secret
-      DROPBOX_REDIRECT_URI: http://localhost:8000/api/v1/auth/dropbox/connector/callback
-
+      # Test user that the backend creates via /auth/register before Playwright runs.
+      PLAYWRIGHT_TEST_EMAIL: e2e-test@surfsense.net
+      PLAYWRIGHT_TEST_PASSWORD: E2eTestPassword123!
+      # Frontend env: Playwright's webServer (surfsense_web/playwright.config.ts)
+      # spawns `pnpm build && pnpm start` in CI; these get baked into the build.
       NEXT_PUBLIC_FASTAPI_BACKEND_URL: http://localhost:8000
       NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: LOCAL
 
-      PLAYWRIGHT_TEST_EMAIL: e2e-test@surfsense.net
-      PLAYWRIGHT_TEST_PASSWORD: E2eTestPassword123!
-
     steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
+      - uses: actions/checkout@v6
 
-      # Started early so it warms up while Python deps install.
-      - name: Start Postgres
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      # ─── Backend stack ─────────────────────────────────────────────────
+      # Builds the e2e image (multi-stage, deps cached via GHA), brings up
+      # db + redis + backend + celery_worker, blocks until every healthcheck
+      # is green. No `uv` invocation on the runner; no PID files; no curl
+      # polling loops; readiness is gated by Docker healthchecks.
+      - name: Build & start backend stack
         run: |
-          docker run -d \
-            --name surfsense_postgres \
-            -p 5432:5432 \
-            -e POSTGRES_USER=postgres \
-            -e POSTGRES_PASSWORD=postgres \
-            -e POSTGRES_DB=surfsense_e2e \
-            pgvector/pgvector:pg17 \
-            postgres \
-              -c wal_level=logical \
-              -c max_wal_senders=10 \
-              -c max_replication_slots=10
+          docker compose -f docker/docker-compose.e2e.yml \
+            up -d --build --wait --wait-timeout 300
 
-      - name: Set up Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.12'
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v8.1.0
-
-      - name: Cache backend dependencies
-        uses: actions/cache@v5
-        with:
-          path: |
-            ~/.cache/uv
-            surfsense_backend/.venv
-          key: python-deps-${{ hashFiles('surfsense_backend/uv.lock') }}
-          restore-keys: |
-            python-deps-
-
-      - name: Cache HuggingFace models
-        uses: actions/cache@v5
-        with:
-          path: ~/.cache/huggingface
-          key: hf-models-${{ env.EMBEDDING_MODEL }}-${{ env.ETL_SERVICE }}
-
-      - name: Install backend dependencies
-        working-directory: surfsense_backend
-        run: uv sync
-
-      - name: Wait for Postgres readiness
-        run: |
-          for i in $(seq 1 30); do
-            if docker exec surfsense_postgres pg_isready -U postgres -d surfsense_e2e > /dev/null 2>&1; then
-              echo "Postgres ready after ${i} attempts"
-              exit 0
-            fi
-            sleep 2
-          done
-          echo "::error::Postgres failed to become ready within 60s"
-          docker logs surfsense_postgres --tail 100
-          exit 1
-
-      - name: Run database migrations
-        working-directory: surfsense_backend
-        run: uv run alembic upgrade head
-
-      # Do NOT replace with `uvicorn main:app`. run_backend.py hijacks
-      # sys.modules["composio"] before app import; production binds it
-      # at import time so plain uvicorn would call the real SDK.
-      - name: Start backend (E2E entrypoint with sys.modules hijack)
-        working-directory: surfsense_backend
-        env:
-          HTTPS_PROXY: http://127.0.0.1:1
-          HTTP_PROXY: http://127.0.0.1:1
-          NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
-        run: |
-          uv run python tests/e2e/run_backend.py \
-            > backend.log 2>&1 &
-          echo $! > backend.pid
-
-      # Worker is a separate interpreter, so the composio hijack must be reapplied.
-      - name: Start Celery worker (E2E entrypoint)
-        working-directory: surfsense_backend
-        env:
-          HTTPS_PROXY: http://127.0.0.1:1
-          HTTP_PROXY: http://127.0.0.1:1
-          NO_PROXY: localhost,127.0.0.1,0.0.0.0,huggingface.co,*.huggingface.co,*.hf.co,cdn-lfs.huggingface.co
-        run: |
-          uv run python tests/e2e/run_celery.py \
-            > celery.log 2>&1 &
-          echo $! > celery.pid
-
-      - name: Wait for backend readiness
-        run: |
-          for i in $(seq 1 60); do
-            if curl -sf http://localhost:8000/openapi.json > /dev/null; then
-              echo "Backend up after ${i} attempts"
-              exit 0
-            fi
-            sleep 2
-          done
-          echo "::error::Backend failed to start within 120s"
-          echo "===== backend.log (tail 200) ====="
-          tail -200 surfsense_backend/backend.log || true
-          echo "===== celery.log (tail 200) ====="
-          tail -200 surfsense_backend/celery.log || true
-          exit 1
-
-      - name: Wait for Celery worker readiness
-        working-directory: surfsense_backend
-        run: |
-          for i in $(seq 1 30); do
-            if uv run celery -A app.celery_app inspect ping --timeout 2 \
-                > /dev/null 2>&1; then
-              echo "Celery worker up after ${i} attempts"
-              exit 0
-            fi
-            sleep 2
-          done
-          echo "::error::Celery worker failed to start within 60s"
-          echo "===== celery.log (tail 200) ====="
-          tail -200 celery.log || true
-          exit 1
+      - name: Show backend stack status
+        if: always()
+        run: docker compose -f docker/docker-compose.e2e.yml ps
 
       - name: Register E2E test user
         run: |
@@ -201,13 +65,14 @@ jobs:
             exit 1
           fi
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v6
+      # ─── Frontend (host-side) ──────────────────────────────────────────
+      # Playwright's webServer block in playwright.config.ts spawns
+      # `pnpm build && pnpm start` in CI mode and waits for :3000.
+      - uses: actions/setup-node@v6
         with:
           node-version: '20'
 
-      - name: Install pnpm
-        uses: pnpm/action-setup@v6
+      - uses: pnpm/action-setup@v6
         with:
           version: 10
 
@@ -221,8 +86,7 @@ jobs:
         with:
           path: ${{ steps.pnpm-cache.outputs.STORE_PATH }}
           key: pnpm-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}
-          restore-keys: |
-            pnpm-${{ runner.os }}-
+          restore-keys: pnpm-${{ runner.os }}-
 
       - name: Install web dependencies
         working-directory: surfsense_web
@@ -253,10 +117,26 @@ jobs:
           restore-keys: |
             nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-
 
+      # ─── Tests ─────────────────────────────────────────────────────────
       - name: Run Playwright tests
         working-directory: surfsense_web
         run: pnpm test:e2e:prod
 
+      # ─── Failure diagnostics ───────────────────────────────────────────
+      - name: Dump backend stack logs on failure
+        if: failure()
+        run: |
+          mkdir -p ./compose-logs
+          docker compose -f docker/docker-compose.e2e.yml logs --no-color --timestamps \
+            > ./compose-logs/all-services.log 2>&1 || true
+          for svc in db redis backend celery_worker; do
+            docker compose -f docker/docker-compose.e2e.yml logs --no-color --timestamps "$svc" \
+              > "./compose-logs/${svc}.log" 2>&1 || true
+          done
+          docker compose -f docker/docker-compose.e2e.yml ps \
+            > ./compose-logs/ps.txt 2>&1 || true
+
+      # ─── Artifacts ─────────────────────────────────────────────────────
       - name: Upload Playwright HTML report
         if: always()
         uses: actions/upload-artifact@v7
@@ -273,26 +153,15 @@ jobs:
           path: surfsense_web/test-results/
           retention-days: 14
 
-      - name: Upload backend + celery logs
+      - name: Upload backend stack logs
         if: failure()
         uses: actions/upload-artifact@v7
         with:
-          name: backend-celery-logs
-          path: |
-            surfsense_backend/backend.log
-            surfsense_backend/celery.log
+          name: backend-stack-logs
+          path: ./compose-logs/
           retention-days: 7
 
-      - name: Stop backend + Celery worker
+      # ─── Teardown ──────────────────────────────────────────────────────
+      - name: Tear down backend stack
         if: always()
-        working-directory: surfsense_backend
-        run: |
-          for f in backend.pid celery.pid; do
-            if [ -f "$f" ]; then
-              kill "$(cat $f)" 2>/dev/null || true
-            fi
-          done
-
-      - name: Stop Postgres
-        if: always()
-        run: docker rm -f surfsense_postgres 2>/dev/null || true
+        run: docker compose -f docker/docker-compose.e2e.yml down -v --remove-orphans
diff --git a/docker/docker-compose.e2e.yml b/docker/docker-compose.e2e.yml
new file mode 100644
index 000000000..87b4e7261
--- /dev/null
+++ b/docker/docker-compose.e2e.yml
@@ -0,0 +1,168 @@
+# =============================================================================
+# SurfSense — E2E Docker Compose stack
+# =============================================================================
+# Hermetic backend stack for Playwright E2E tests:
+#   - db / redis on an internal-only network (no internet egress)
+#   - backend (FastAPI) joins the internal network AND a separate ingress
+#     bridge so the host runner can reach :8000
+#   - celery_worker on the internal network only — zero egress surface
+#
+# The backend image is built from surfsense_backend/Dockerfile target=e2e,
+# which adds tests/ via the `tests-source` additional context (tests/ is
+# excluded from the main context by .dockerignore so production never ships
+# test fakes). See surfsense_backend/Dockerfile for stage layout.
+#
+# Usage from repo root:
+#   docker compose -f docker/docker-compose.e2e.yml up -d --build --wait
+#   curl -X POST http://localhost:8000/auth/register ...
+#   ( run Playwright on host, pointing at localhost:8000 + localhost:3000 )
+#   docker compose -f docker/docker-compose.e2e.yml down -v
+# =============================================================================
+
+name: surfsense-e2e
+
+x-backend-env: &backend-env
+  DATABASE_URL: postgresql+asyncpg://postgres:postgres@db:5432/surfsense_e2e
+  CELERY_BROKER_URL: redis://redis:6379/0
+  CELERY_RESULT_BACKEND: redis://redis:6379/0
+  REDIS_APP_URL: redis://redis:6379/0
+  CELERY_TASK_DEFAULT_QUEUE: surfsense
+  SECRET_KEY: ci-test-secret-key-not-for-production
+  AUTH_TYPE: LOCAL
+  REGISTRATION_ENABLED: "TRUE"
+  ETL_SERVICE: DOCLING
+  EMBEDDING_MODEL: sentence-transformers/all-MiniLM-L6-v2
+  NEXT_FRONTEND_URL: http://host.docker.internal:3000
+  # Sentinel keys — fakes never read them; turns leaked real calls into 401s.
+  COMPOSIO_API_KEY: e2e-deny-real-call-sentinel
+  COMPOSIO_ENABLED: "TRUE"
+  OPENAI_API_KEY: e2e-deny-real-call-sentinel
+  ANTHROPIC_API_KEY: e2e-deny-real-call-sentinel
+  LITELLM_API_KEY: e2e-deny-real-call-sentinel
+  MICROSOFT_CLIENT_ID: fake-microsoft-client-id
+  MICROSOFT_CLIENT_SECRET: fake-microsoft-client-secret
+  ONEDRIVE_REDIRECT_URI: http://localhost:8000/api/v1/auth/onedrive/connector/callback
+  DROPBOX_APP_KEY: fake-dropbox-app-key
+  DROPBOX_APP_SECRET: fake-dropbox-app-secret
+  DROPBOX_REDIRECT_URI: http://localhost:8000/api/v1/auth/dropbox/connector/callback
+  # Defense-in-depth: even though L3 egress is denied for the worker via
+  # `internal: true`, the backend still has a route via `ingress`. Setting
+  # HTTPS_PROXY to an unreachable port turns any leaked Python outbound HTTP
+  # call into a fast Connection refused. UNLIKE the old runner-shell setup,
+  # this proxy is set on the container env and `uv` is never invoked here,
+  # so there is no interaction with uv's implicit-sync behaviour.
+  HTTPS_PROXY: http://127.0.0.1:1
+  HTTP_PROXY: http://127.0.0.1:1
+  NO_PROXY: localhost,127.0.0.1,0.0.0.0,db,redis,host.docker.internal
+
+services:
+  db:
+    image: pgvector/pgvector:pg17
+    command: >
+      postgres
+        -c wal_level=logical
+        -c max_wal_senders=10
+        -c max_replication_slots=10
+    environment:
+      POSTGRES_USER: postgres
+      POSTGRES_PASSWORD: postgres
+      POSTGRES_DB: surfsense_e2e
+    # Ephemeral storage — every CI run gets a clean DB, no volume cleanup needed.
+    tmpfs:
+      - /var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U postgres -d surfsense_e2e"]
+      interval: 2s
+      timeout: 3s
+      retries: 30
+    networks: [internal]
+
+  redis:
+    image: redis:8-alpine
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 2s
+      timeout: 3s
+      retries: 30
+    networks: [internal]
+
+  backend:
+    build:
+      context: ../surfsense_backend
+      dockerfile: Dockerfile
+      target: e2e
+      additional_contexts:
+        # tests/ is excluded from the main context by .dockerignore;
+        # the e2e stage's `COPY --from=tests-source` pulls it in here.
+        tests-source: ../surfsense_backend/tests
+      cache_from:
+        - type=gha,scope=surfsense-e2e-backend
+      cache_to:
+        - type=gha,mode=max,scope=surfsense-e2e-backend
+    image: surfsense-e2e-backend:local
+    environment:
+      <<: *backend-env
+      SERVICE_ROLE: api
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    ports:
+      - "8000:8000"
+    depends_on:
+      db: { condition: service_healthy }
+      redis: { condition: service_healthy }
+    healthcheck:
+      # Use Python (already in the image) instead of curl/wget to avoid
+      # depending on either tool being installed in the runtime layers.
+      test:
+        - CMD
+        - python
+        - -c
+        - |
+          import sys, urllib.request
+          try:
+              r = urllib.request.urlopen("http://localhost:8000/openapi.json", timeout=2)
+              sys.exit(0 if r.status == 200 else 1)
+          except Exception:
+              sys.exit(1)
+      interval: 3s
+      timeout: 5s
+      retries: 60
+      start_period: 30s
+    networks:
+      - internal      # to reach db/redis
+      - ingress       # so host can reach :8000
+
+  celery_worker:
+    image: surfsense-e2e-backend:local
+    pull_policy: never
+    # No build: section — reuses the image built by the `backend` service.
+    # Compose v2 builds shared images exactly once across services that
+    # reference the same `image:` tag.
+    environment:
+      <<: *backend-env
+      SERVICE_ROLE: worker
+    depends_on:
+      backend: { condition: service_healthy }
+    healthcheck:
+      test:
+        - CMD-SHELL
+        - "celery -A app.celery_app inspect ping --timeout 2 | grep -q pong"
+      interval: 5s
+      timeout: 5s
+      retries: 12
+      start_period: 20s
+    networks: [internal]
+
+networks:
+  # Internal network: containers attached only to this network have NO route
+  # to the host or the internet. This is the L3 deny-egress mechanism that
+  # replaces the fragile HTTPS_PROXY-on-the-runner approach.
+  internal:
+    driver: bridge
+    internal: true
+
+  # Regular bridge network. Only the `backend` service joins it, solely so
+  # the host can reach :8000 via the published port. celery_worker / db /
+  # redis stay off this network entirely.
+  ingress:
+    driver: bridge
diff --git a/surfsense_backend/Dockerfile b/surfsense_backend/Dockerfile
index 73d5819b9..a5b391a1e 100644
--- a/surfsense_backend/Dockerfile
+++ b/surfsense_backend/Dockerfile
@@ -1,8 +1,23 @@
-FROM python:3.12-slim
+# =============================================================================
+# SurfSense Backend — Multi-stage Dockerfile
+# =============================================================================
+# Stages:
+#   base       — system deps + Pandoc 3.x
+#   deps       — Python deps frozen from uv.lock (no dev deps)
+#   models     — pre-baked offline assets (EasyOCR, Docling, Playwright)
+#   e2e        — adds tests/ via additional_contexts, swaps entrypoint
+#   production — production runtime (LAST stage = default `docker build` target)
+#
+# IMPORTANT: `production` MUST remain the last stage. .github/workflows/docker-build.yml
+# builds without `target:` and BuildKit defaults to the last stage. Reordering will
+# silently break ghcr.io/modsetter/surfsense-backend.
+# =============================================================================
+
+# ─── Stage 1: base (system deps, Pandoc, certificates) ──────────────────────
+FROM python:3.12-slim AS base
 
 WORKDIR /app
 
-# Install system dependencies including SSL tools, CUDA dependencies, and Tesseract OCR
 RUN apt-get update && apt-get install -y --no-install-recommends \
     gcc \
     python3-dev \
@@ -22,21 +37,24 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-# Install Pandoc 3.x from GitHub as a fallback for Linux where pypandoc_binary
-# may not bundle pandoc (apt ships 2.17 which has broken table rendering).
-# pypandoc_binary bundles pandoc on Windows/macOS; on Linux it picks this up.
+# Pandoc 3.x from GitHub Releases — apt ships 2.17 which has broken table rendering.
+# pypandoc_binary bundles pandoc on Windows/macOS; on Linux it picks up this binary.
 RUN ARCH=$(dpkg --print-architecture) && \
     wget -qO /tmp/pandoc.deb "https://github.com/jgm/pandoc/releases/download/3.9/pandoc-3.9-1-${ARCH}.deb" && \
     dpkg -i /tmp/pandoc.deb && \
     rm /tmp/pandoc.deb
 
-# Update certificates and install SSL tools
 RUN update-ca-certificates
 RUN pip install --upgrade certifi pip-system-certs
 
-# Copy requirements
-COPY pyproject.toml .
-COPY uv.lock .
+ENV SSL_CERT_FILE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
+ENV REQUESTS_CA_BUNDLE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
+
+
+# ─── Stage 2: deps (Python deps frozen from uv.lock) ────────────────────────
+FROM base AS deps
+
+COPY pyproject.toml uv.lock ./
 
 # Install all Python dependencies from uv.lock for deterministic builds.
 #
@@ -49,9 +67,7 @@ COPY uv.lock .
 # Note on torch/CUDA: we do NOT install torch from a separate cu* index here.
 # PyPI's torch wheels for Linux x86_64 already ship CUDA-enabled and pull
 # nvidia-cudnn-cu13, nvidia-nccl-cu13, triton, etc. as install deps (all
-# captured in uv.lock). Installing from cu121 first only wasted ~2GB of
-# downloads that the lock-based install immediately replaced. If a specific
-# CUDA version is needed (driver compatibility, etc.), wire it through
+# captured in uv.lock). If a specific CUDA version is needed, wire it through
 # [tool.uv.sources] in pyproject.toml so the lock stays the source of truth.
 RUN pip install --no-cache-dir uv && \
     uv export --frozen --no-dev --no-hashes --no-emit-project \
@@ -59,49 +75,32 @@ RUN pip install --no-cache-dir uv && \
     uv pip install --system --no-cache-dir -r /tmp/requirements.txt && \
     rm /tmp/requirements.txt
 
-# Set SSL environment variables dynamically
-RUN CERTIFI_PATH=$(python -c "import certifi; print(certifi.where())") && \
-    echo "Setting SSL_CERT_FILE to $CERTIFI_PATH" && \
-    echo "export SSL_CERT_FILE=$CERTIFI_PATH" >> /root/.bashrc && \
-    echo "export REQUESTS_CA_BUNDLE=$CERTIFI_PATH" >> /root/.bashrc
-ENV SSL_CERT_FILE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
-ENV REQUESTS_CA_BUNDLE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
+
+# ─── Stage 3: models (pre-baked offline assets) ─────────────────────────────
+FROM deps AS models
 
 # Pre-download EasyOCR models to avoid runtime SSL issues
-RUN mkdir -p /root/.EasyOCR/model
-RUN wget --no-check-certificate https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip -O /root/.EasyOCR/model/english_g2.zip || true
-RUN wget --no-check-certificate https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip -O /root/.EasyOCR/model/craft_mlt_25k.zip || true
-RUN cd /root/.EasyOCR/model && (unzip -o english_g2.zip || true) && (unzip -o craft_mlt_25k.zip || true)
+RUN mkdir -p /root/.EasyOCR/model && \
+    wget --no-check-certificate https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip      -O /root/.EasyOCR/model/english_g2.zip      || true && \
+    wget --no-check-certificate https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip -O /root/.EasyOCR/model/craft_mlt_25k.zip || true && \
+    cd /root/.EasyOCR/model && \
+    (unzip -o english_g2.zip || true) && \
+    (unzip -o craft_mlt_25k.zip || true)
 
 # Pre-download Docling models
 RUN python -c "try:\n    from docling.document_converter import DocumentConverter\n    conv = DocumentConverter()\nexcept:\n    pass" || true
 
-# Install Playwright browsers for web scraping (the playwright package itself
-# is already installed via uv.lock above)
+# Install Playwright browsers (the playwright python package itself is in deps)
 RUN playwright install chromium --with-deps
 
-# Copy source code
-COPY . .
-
-# Install the project itself in editable mode. Dependencies were already
-# installed deterministically from uv.lock above, so --no-deps prevents any
-# re-resolution that could pull newer versions.
-RUN uv pip install --system --no-cache-dir --no-deps -e .
-
-# Copy and set permissions for entrypoint script
-# Use dos2unix to ensure LF line endings (fixes CRLF issues from Windows checkouts)
-COPY scripts/docker/entrypoint.sh /app/scripts/docker/entrypoint.sh
-RUN dos2unix /app/scripts/docker/entrypoint.sh && chmod +x /app/scripts/docker/entrypoint.sh
-
 # Shared temp directory for file uploads between API and Worker containers.
 # Python's tempfile module uses TMPDIR, so uploaded files land here.
 # Mount the SAME volume at /shared_tmp on both API and Worker in Coolify.
 RUN mkdir -p /shared_tmp
-ENV TMPDIR=/shared_tmp
 
-# Prevent uvloop compatibility issues
 ENV PYTHONPATH=/app
 ENV UVICORN_LOOP=asyncio
+ENV TMPDIR=/shared_tmp
 
 # Tune glibc malloc to return freed memory to the OS more aggressively.
 # Without these, Python's gc.collect() frees objects but the underlying
@@ -110,6 +109,58 @@ ENV MALLOC_MMAP_THRESHOLD_=65536
 ENV MALLOC_TRIM_THRESHOLD_=131072
 ENV MALLOC_MMAP_MAX_=65536
 
+
+# ─── Stage 4: e2e (production source + tests/ + e2e entrypoint) ─────────────
+# Built via `docker buildx build --target e2e`. The default build target is
+# `production` (the last stage), so this stage is opt-in for CI only.
+#
+# `tests/` is excluded from the main build context by .dockerignore (so prod
+# can never accidentally ship test fakes). The e2e stage receives tests/
+# through an "additional context" passed by docker-compose.e2e.yml — see
+# https://docs.docker.com/reference/compose-file/build/#additional_contexts
+FROM models AS e2e
+
+# Same source copy as production. .dockerignore filters out tests/.
+COPY . .
+
+# Bring tests/ in via the named additional build context. CI passes
+#   --build-context tests-source=./tests
+# (or the equivalent additional_contexts entry in docker-compose.e2e.yml).
+COPY --from=tests-source . ./tests/
+
+# Install the project itself in editable mode. Dependencies were already
+# installed deterministically from uv.lock above, so --no-deps prevents any
+# re-resolution that could pull newer versions.
+RUN uv pip install --system --no-cache-dir --no-deps -e .
+
+COPY scripts/docker/entrypoint.e2e.sh /app/scripts/docker/entrypoint.e2e.sh
+RUN dos2unix /app/scripts/docker/entrypoint.e2e.sh && chmod +x /app/scripts/docker/entrypoint.e2e.sh
+
+# SERVICE_ROLE is overridden per service in docker-compose.e2e.yml (api / worker).
+ENV SERVICE_ROLE=api
+
+EXPOSE 8000-8001
+CMD ["/app/scripts/docker/entrypoint.e2e.sh"]
+
+
+# ─── Stage 5: production (LAST stage — default `docker build` target) ───────
+# Behavior is byte-identical to the previous single-stage Dockerfile.
+# .github/workflows/docker-build.yml builds without `target:` and BuildKit
+# defaults to the last stage, so this MUST stay last.
+FROM models AS production
+
+# Copy source code (tests/ excluded by .dockerignore — production never ships tests).
+COPY . .
+
+# Install the project itself in editable mode. Dependencies were already
+# installed deterministically from uv.lock above, so --no-deps prevents any
+# re-resolution that could pull newer versions.
+RUN uv pip install --system --no-cache-dir --no-deps -e .
+
+# Use dos2unix to ensure LF line endings (fixes CRLF issues from Windows checkouts)
+COPY scripts/docker/entrypoint.sh /app/scripts/docker/entrypoint.sh
+RUN dos2unix /app/scripts/docker/entrypoint.sh && chmod +x /app/scripts/docker/entrypoint.sh
+
 # SERVICE_ROLE controls which process this container runs:
 #   api     – FastAPI backend only (runs migrations on startup)
 #   worker  – Celery worker only
@@ -127,6 +178,5 @@ ENV CELERY_MAX_TASKS_PER_CHILD=50
 #   ""                       – both queues (default, for single-worker setups)
 ENV CELERY_QUEUES=""
 
-# Run
 EXPOSE 8000-8001
-CMD ["/app/scripts/docker/entrypoint.sh"]
\ No newline at end of file
+CMD ["/app/scripts/docker/entrypoint.sh"]
diff --git a/surfsense_backend/scripts/docker/entrypoint.e2e.sh b/surfsense_backend/scripts/docker/entrypoint.e2e.sh
new file mode 100755
index 000000000..84cfe2568
--- /dev/null
+++ b/surfsense_backend/scripts/docker/entrypoint.e2e.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+# =============================================================================
+# E2E entrypoint for the multi-stage Dockerfile's `e2e` target.
+#
+# Dispatches on SERVICE_ROLE to the test-only entrypoints under tests/e2e/.
+# Those scripts apply sys.modules hijacks and LLM/embedding patches BEFORE
+# importing production app code (see tests/e2e/run_backend.py for rationale).
+#
+# Production never sees this file: tests/ is excluded from the production
+# stage, and the production stage uses scripts/docker/entrypoint.sh.
+# =============================================================================
+set -euo pipefail
+
+SERVICE_ROLE="${SERVICE_ROLE:-api}"
+echo "[e2e-entrypoint] starting role=${SERVICE_ROLE}"
+
+wait_for_db() {
+    # Block until the database is reachable. We don't loop forever — Compose
+    # depends_on/healthchecks already gate on db readiness, this is just
+    # belt-and-suspenders so a slow first connection doesn't race migrations.
+    for i in {1..60}; do
+        if python -c "from app.db import engine; import asyncio; asyncio.run(engine.dispose())" 2>/dev/null; then
+            echo "[e2e-entrypoint] db reachable after ${i} attempts"
+            return 0
+        fi
+        sleep 1
+    done
+    echo "[e2e-entrypoint] ERROR: db not reachable after 60s" >&2
+    return 1
+}
+
+case "${SERVICE_ROLE}" in
+    api)
+        wait_for_db
+        echo "[e2e-entrypoint] running alembic upgrade head"
+        alembic upgrade head
+        # `exec` so SIGTERM from `docker stop` reaches Python directly,
+        # without a shell wrapper interposing.
+        exec python tests/e2e/run_backend.py
+        ;;
+    worker)
+        # Worker doesn't run migrations — the api role does that exactly once.
+        # We still wait for db so Celery's broker connection check doesn't
+        # race against an unready Postgres on cold start.
+        wait_for_db
+        exec python tests/e2e/run_celery.py
+        ;;
+    *)
+        echo "[e2e-entrypoint] ERROR: unknown SERVICE_ROLE='${SERVICE_ROLE}' (expected: api | worker)" >&2
+        exit 1
+        ;;
+esac
diff --git a/surfsense_backend/tests/e2e/run_backend.py b/surfsense_backend/tests/e2e/run_backend.py
index 4156a4ea4..a34327908 100644
--- a/surfsense_backend/tests/e2e/run_backend.py
+++ b/surfsense_backend/tests/e2e/run_backend.py
@@ -57,6 +57,29 @@ sys.modules["notion_client.errors"] = _fake_notion.errors
 from dotenv import load_dotenv  # noqa: E402
 
 load_dotenv()
+
+os.environ.setdefault(
+    "DATABASE_URL",
+    "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
+)
+os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
+os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
+os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
+os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
+os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
+os.environ.setdefault("AUTH_TYPE", "LOCAL")
+os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
+os.environ.setdefault("ETL_SERVICE", "DOCLING")
+os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
+
+# Sentinel keys — fakes never read them; turns leaked real calls into 401s.
+os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
+os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
+
 os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
 os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
 os.environ.setdefault(
diff --git a/surfsense_backend/tests/e2e/run_celery.py b/surfsense_backend/tests/e2e/run_celery.py
index 407fecde3..787eb5486 100644
--- a/surfsense_backend/tests/e2e/run_celery.py
+++ b/surfsense_backend/tests/e2e/run_celery.py
@@ -44,6 +44,29 @@ sys.modules["notion_client.errors"] = _fake_notion.errors
 from dotenv import load_dotenv  # noqa: E402
 
 load_dotenv()
+
+os.environ.setdefault(
+    "DATABASE_URL",
+    "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
+)
+os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
+os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
+os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
+os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
+os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
+os.environ.setdefault("AUTH_TYPE", "LOCAL")
+os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
+os.environ.setdefault("ETL_SERVICE", "DOCLING")
+os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
+
+# Sentinel keys — fakes never read them; turns leaked real calls into 401s.
+os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
+os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
+os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
+
 os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
 os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
 os.environ.setdefault(
@@ -198,12 +221,19 @@ def _main() -> None:
     # so Drive indexing tasks are picked up).
     queue_name = os.getenv("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
     queues = f"{queue_name},{queue_name}.connectors"
+
+    # macOS forks-after-MPS-init crash prefork workers; threads avoid it.
+    default_pool = "threads" if sys.platform == "darwin" else "prefork"
+    pool = os.getenv("CELERY_POOL", default_pool)
+    concurrency = os.getenv("CELERY_CONCURRENCY", "2")
+
     celery_app.worker_main(
         argv=[
             "worker",
             "--loglevel=info",
             f"--queues={queues}",
-            "--concurrency=2",
+            f"--pool={pool}",
+            f"--concurrency={concurrency}",
             "--without-gossip",
             "--without-mingle",
         ]
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index 0dfdf80bf..0fecc73ef 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -4,6 +4,11 @@ const PORT = process.env.PORT || "3000";
 const BACKEND_PORT = process.env.BACKEND_PORT || "8000";
 const baseURL = process.env.PLAYWRIGHT_BASE_URL || `http://localhost:${PORT}`;
 
+process.env.PLAYWRIGHT_TEST_EMAIL ??= "e2e-test@surfsense.net";
+process.env.PLAYWRIGHT_TEST_PASSWORD ??= "E2eTestPassword123!";
+process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL ??= `http://localhost:${BACKEND_PORT}`;
+process.env.NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE ??= "LOCAL";
+
 /**
  * Playwright configuration for SurfSense web E2E tests.
  *
@@ -60,9 +65,13 @@ export default defineConfig({
 				url: `http://localhost:${PORT}`,
 				reuseExistingServer: !process.env.CI,
 				timeout: process.env.CI ? 300_000 : 180_000,
+				stdout: "pipe",
+      			stderr: "pipe",
 				env: {
-					NEXT_PUBLIC_FASTAPI_BACKEND_URL: `http://localhost:${BACKEND_PORT}`,
-					NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: "LOCAL",
+					NEXT_PUBLIC_FASTAPI_BACKEND_URL:
+						process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL,
+					NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE:
+						process.env.NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE,
 				},
 			},
 });
diff --git a/surfsense_web/tests/auth.setup.ts b/surfsense_web/tests/auth.setup.ts
index e5d31a257..064552904 100644
--- a/surfsense_web/tests/auth.setup.ts
+++ b/surfsense_web/tests/auth.setup.ts
@@ -10,15 +10,14 @@ import { expect, test as setup } from "@playwright/test";
  *   POST /auth/jwt/login  ->  { access_token }
  *   localStorage.setItem("surfsense_bearer_token", access_token)
  *
- * Requires a seeded test user in the dev/test DB. Configure via env:
- *   PLAYWRIGHT_TEST_EMAIL, PLAYWRIGHT_TEST_PASSWORD
- *   NEXT_PUBLIC_FASTAPI_BACKEND_URL  (defaults to http://localhost:8000)
+ * Requires a seeded test user in the dev/test DB. Defaults match the
+ * docker/docker-compose.e2e.yml local stack and can be overridden via env.
  */
 
 const authFile = path.join(__dirname, "..", "playwright", ".auth", "user.json");
 
-const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "test@surfsense.net";
-const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "TestPassword123!";
+const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "e2e-test@surfsense.net";
+const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "E2eTestPassword123!";
 const BACKEND_URL = process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL || "http://localhost:8000";
 const STORAGE_KEY = "surfsense_bearer_token";
 
diff --git a/surfsense_web/tests/helpers/api/auth.ts b/surfsense_web/tests/helpers/api/auth.ts
index c912afedc..02aeb6d69 100644
--- a/surfsense_web/tests/helpers/api/auth.ts
+++ b/surfsense_web/tests/helpers/api/auth.ts
@@ -11,8 +11,8 @@ import type { APIRequestContext } from "@playwright/test";
 
 export const BACKEND_URL = process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL || "http://localhost:8000";
 
-const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "test@surfsense.net";
-const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "TestPassword123!";
+const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "e2e-test@surfsense.net";
+const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "E2eTestPassword123!";
 
 export async function loginAsTestUser(request: APIRequestContext): Promise<string> {
 	const response = await request.post(`${BACKEND_URL}/auth/jwt/login`, {

From 5344fa47e6b7036aa0be0b48e81ca6667548f58b Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 03:29:32 +0530
Subject: [PATCH 11/36] chore: update E2E test documentation for clarity and
 local setup instructions

---
 surfsense_backend/tests/e2e/README.md | 151 +++++++++++++++++++-------
 surfsense_web/tests/README.md         | 119 +++++++++++++-------
 2 files changed, 192 insertions(+), 78 deletions(-)

diff --git a/surfsense_backend/tests/e2e/README.md b/surfsense_backend/tests/e2e/README.md
index 800d61dfb..caa0f89b0 100644
--- a/surfsense_backend/tests/e2e/README.md
+++ b/surfsense_backend/tests/e2e/README.md
@@ -1,48 +1,48 @@
-# Backend E2E Test Harness
+# Backend E2E Harness
 
-Strict fakes + alternative entrypoints used **only** by Playwright E2E.
-Excluded from the production Docker image via `.dockerignore`.
+This directory contains the test-only backend entrypoints and fakes used by
+Playwright. They are not part of the production image: `.dockerignore` excludes
+`tests/`, and the E2E Docker stage copies this directory through a separate
+build context.
 
 ## Files
 
-| Path                             | Role                                                                            |
-| -------------------------------- | ------------------------------------------------------------------------------- |
-| `run_backend.py`                 | FastAPI entrypoint that hijacks `sys.modules` before importing `app.app:app`    |
-| `run_celery.py`                  | Celery worker entrypoint with the same hijack + patch logic                     |
-| `middleware/scenario.py`         | `X-E2E-Scenario` header → ContextVar (read by fakes)                            |
-| `fakes/composio_module.py`       | Strict drop-in for the `composio` package; raises on unknown surface            |
-| `fakes/llm.py`                   | `fake_get_user_long_context_llm` returning a `FakeListChatModel`                |
-| `fakes/embeddings.py`            | Deterministic 0.1-vector `embed_text` / `embed_texts`                           |
-| `fakes/fixtures/drive_files.json`| Canned Drive listings + file contents (incl. canary tokens)                     |
+| Path | Purpose |
+| --- | --- |
+| `run_backend.py` | Starts FastAPI after installing the test fakes into `sys.modules`. |
+| `run_celery.py` | Starts the Celery worker with the same fake setup. |
+| `middleware/scenario.py` | Reads `X-E2E-Scenario` into a request-scoped context var. |
+| `fakes/composio_module.py` | Fake `composio` package used by connector flows. |
+| `fakes/llm.py` | Fake chat model factory. |
+| `fakes/embeddings.py` | Deterministic embedding helpers. |
+| `fakes/fixtures/drive_files.json` | Drive fixture data and canary file contents. |
 
-## Why a sys.modules hijack?
+## Why the import hook exists
 
-Production code does `from composio import Composio` at module load
-time. By the time the FastAPI app object exists, that binding has
-already been resolved. The hijack runs **before** any `app.*` import,
-so the binding resolves to our strict fake. No production source
-changes; fakes are physically excluded from production images.
+Some production modules import SDK clients at module load time, for example
+`from composio import Composio`. By the time `app.app` has been imported, those
+bindings are already fixed.
 
-Belt + suspenders + no internet: the strict `__getattr__` in every
-fake raises `NotImplementedError` if a future production code path
-introduces a new SDK call. CI also sets `HTTPS_PROXY=http://127.0.0.1:1`
-plus sentinel API keys so any leaked outbound HTTP fails immediately.
+The E2E entrypoints install fake modules in `sys.modules` before importing any
+`app.*` module. That lets the normal production code run while SDK calls resolve
+to local fakes.
 
-## Adding a new fake
+The fakes should fail loudly. If production starts using a new SDK method that
+the fake does not implement, add that method to the fake instead of letting the
+test call the real service.
 
-1. Create `fakes/<sdk>_module.py` modelled on `composio_module.py`.
-2. In `run_backend.py` and `run_celery.py`, register
-   `sys.modules["<sdk>"] = _fake_<sdk>` before the `from app.app import app`
-   line.
-3. If the new fake needs scenario branching, read from
+## Adding a fake
+
+1. Add `fakes/<sdk>_module.py`.
+2. Register it in both `run_backend.py` and `run_celery.py` before importing
+   `app.app` or `app.celery_app`.
+3. If the fake needs per-test behavior, read the current scenario from
    `tests.e2e.middleware.scenario.current_scenario()`.
 
-## Reused by backend integration tests
+## Shared with backend integration tests
 
-The strict fakes are not only for Playwright. Backend route integration
-tests can import the same fake before importing `app.app`, so Composio
-route tests exercise production route code without touching the real
-SDK:
+Backend integration tests can use the same fakes when they need production route
+code without the real SDK:
 
 ```python
 from tests.e2e.fakes import composio_module as _fake_composio
@@ -50,20 +50,93 @@ sys.modules["composio"] = _fake_composio
 from app.app import app
 ```
 
-See `surfsense_backend/tests/integration/composio/conftest.py` for the
-current pattern.
+See `surfsense_backend/tests/integration/composio/conftest.py` for the current
+pattern.
 
 ## Running locally
 
+The recommended local flow runs only Postgres and Redis in Docker, and the
+backend + Celery worker on the host. No `.env` file is required: both
+entrypoints `setdefault` every variable they need (DB URL, Redis URL,
+sentinel API keys, etc.) to values that match `docker-compose.deps-only.yml`.
+
+### One-time setup
+
+From `surfsense_web/`:
+
 ```bash
-cd surfsense_backend
+pnpm install
+pnpm exec playwright install --with-deps chromium
+```
+
+### Each run
+
+**1. Bring up Postgres + Redis** from the repo root (the other deps-only
+services (SearXNG, Zero, pgAdmin) are not needed for E2E):
+
+```bash
+docker compose -f docker/docker-compose.deps-only.yml up -d db redis
+```
+
+**2. Start the backend** in `surfsense_backend/`, terminal A:
+
+```bash
+uv sync
+uv run alembic upgrade head
 uv run python tests/e2e/run_backend.py
-# in a second shell:
+```
+
+**3. Start the Celery worker** in `surfsense_backend/`, terminal B:
+
+```bash
 uv run python tests/e2e/run_celery.py
 ```
 
-Then in `surfsense_web`:
+**4. Register the Playwright user**:
 
 ```bash
-pnpm test:e2e
+curl -X POST http://localhost:8000/auth/register \
+  -H "Content-Type: application/json" \
+  -d '{"email":"e2e-test@surfsense.net","password":"E2eTestPassword123!"}'
 ```
+
+**5. Run Playwright** from `surfsense_web/`, terminal C:
+
+```bash
+pnpm test:e2e             # dev server (fast iteration)
+pnpm test:e2e:headed      # show the browser
+pnpm test:e2e:ui          # Playwright UI mode
+pnpm test:e2e:prod        # build + start (matches CI exactly)
+```
+
+`playwright.config.ts` and the run scripts share defaults, so this works on a
+fresh checkout. Set `PLAYWRIGHT_TEST_EMAIL`, `PLAYWRIGHT_TEST_PASSWORD`,
+`NEXT_PUBLIC_FASTAPI_BACKEND_URL`, or any backend env (e.g. `DATABASE_URL`)
+only when pointing tests at a different stack.
+
+### Cleanup
+
+```bash
+docker compose -f docker/docker-compose.deps-only.yml down
+```
+
+Add `-v` to also wipe the Postgres volume.
+
+### Hermetic alternative (matches CI)
+
+To reproduce the CI environment exactly — backend and Celery in containers,
+network egress denied at L3 — replace steps 1–3 with:
+
+```bash
+docker compose -f docker/docker-compose.e2e.yml up -d --build --wait
+```
+
+Then run steps 4 (curl register) and 5 (`pnpm test:e2e:prod`) as above. Tear
+down with:
+
+```bash
+docker compose -f docker/docker-compose.e2e.yml down -v --remove-orphans
+```
+
+This builds the ~9 GB `surfsense-e2e-backend:local` image, so the deps-only
+flow above is faster for day-to-day development.
diff --git a/surfsense_web/tests/README.md b/surfsense_web/tests/README.md
index 51fd35050..89aab1f9b 100644
--- a/surfsense_web/tests/README.md
+++ b/surfsense_web/tests/README.md
@@ -5,29 +5,6 @@ Celery + Postgres + Redis). Designed to scale from one connector
 (Composio Drive in Phase 1) to every connector + manual file upload
 without rewriting the harness.
 
-## Layout
-
-```
-tests/
-├── auth.setup.ts                    # one-time login, persists localStorage
-├── smoke/                           # tracer-bullet tests (dashboard renders)
-├── connectors/
-│   └── composio/
-│       └── drive/                   # Composio Google Drive — Phase 1
-│           └── journey.spec.ts      # connect -> select -> index -> canary assertion
-├── fixtures/                        # test.extend() fixtures
-│   ├── index.ts                     # named `test` exports per spec category
-│   ├── search-space.fixture.ts      # apiToken + per-test search space
-│   └── connectors/
-│       └── composio-drive.fixture.ts
-├── helpers/                         # reusable building blocks
-│   ├── api/                         # backend HTTP helpers
-│   ├── ui/                          # page-object selectors
-│   ├── waits/                       # deterministic polling
-│   └── canary.ts                    # canary tokens + fixed Drive file ids
-└── README.md                        # this file
-```
-
 ## How the deterministic harness works
 
 There are **three layers of defense** against accidental real-world
@@ -47,26 +24,90 @@ calls. None of them touch production code.
 
 ## Running locally
 
+The recommended flow runs only Postgres and Redis in Docker, and the backend
++ Celery worker on the host. The E2E entrypoints `setdefault` every backend
+variable they need, so no `.env` file is required on a fresh checkout.
+
+### One-time setup
+
+From `surfsense_web/`:
+
 ```bash
-# 1. Bring up Postgres + Redis (Docker compose, supabase, whatever you use)
-docker compose up -d postgres redis
-
-# 2. Backend with E2E entrypoint (note: NOT `uv run main.py`)
-cd surfsense_backend
-uv run alembic upgrade head
-uv run python tests/e2e/run_backend.py &
-
-# 3. Celery worker with the same entrypoint pattern
-uv run python tests/e2e/run_celery.py &
-
-# 4. Run Playwright tests (auto-starts `pnpm dev` via webServer config)
-cd ../surfsense_web
-pnpm test:e2e
+pnpm install
+pnpm exec playwright install --with-deps chromium
 ```
 
-For CI behavior in one go: `pnpm test:e2e:headless`.
+### Each run
 
-To debug the Drive journey: `pnpm test:e2e -- connectors/composio/drive/journey.spec.ts --headed`.
+**1. Bring up Postgres + Redis** from the repo root:
+
+```bash
+docker compose -f docker/docker-compose.deps-only.yml up -d db redis
+```
+
+**2. Start the backend** in `surfsense_backend/`, terminal A:
+
+```bash
+uv sync
+uv run alembic upgrade head
+uv run python tests/e2e/run_backend.py
+```
+
+**3. Start the Celery worker** in `surfsense_backend/`, terminal B:
+
+```bash
+uv run python tests/e2e/run_celery.py
+```
+
+**4. Register the Playwright user**:
+
+```bash
+curl -X POST http://localhost:8000/auth/register \
+  -H "Content-Type: application/json" \
+  -d '{"email":"e2e-test@surfsense.net","password":"E2eTestPassword123!"}'
+```
+
+**5. Run Playwright** from `surfsense_web/`, terminal C:
+
+```bash
+pnpm test:e2e             # dev server (fast iteration)
+pnpm test:e2e:headed      # show the browser
+pnpm test:e2e:ui          # Playwright UI mode
+pnpm test:e2e:debug       # Playwright Inspector
+pnpm test:e2e:prod        # build + start (matches CI exactly)
+pnpm test:e2e:report      # open the last HTML report
+```
+
+`playwright.config.ts` and the backend run scripts share defaults, so the
+above works without exporting any env vars. Override
+`PLAYWRIGHT_TEST_EMAIL`, `PLAYWRIGHT_TEST_PASSWORD`, or
+`NEXT_PUBLIC_FASTAPI_BACKEND_URL` only when pointing tests at a different
+stack.
+
+To debug a single journey:
+
+```bash
+pnpm test:e2e:headed connectors/composio/drive/journey.spec.ts
+```
+
+### Hermetic alternative (matches CI)
+
+To reproduce the CI environment exactly: backend and Celery in containers
+with L3 egress denied, replace steps 1–3 with:
+
+```bash
+docker compose -f docker/docker-compose.e2e.yml up -d --build --wait
+```
+
+Then run steps 4 (curl register) and 5 (`pnpm test:e2e:prod`) as above. Tear
+down with:
+
+```bash
+docker compose -f docker/docker-compose.e2e.yml down -v --remove-orphans
+```
+
+This builds the ~9 GB e2e backend image, so the deps-only flow is faster for
+day-to-day work.
 
 ## Adding a new connector
 

From f091182b9495f86df881c5d5b65c28f6cef92362 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 03:52:22 +0530
Subject: [PATCH 12/36] chore: update GitHub Actions workflows and Dockerfile
 to use latest action versions and improve build targets

---
 .github/workflows/docker-build.yml | 27 +++++++++++++++------------
 surfsense_backend/Dockerfile       | 19 +++++--------------
 2 files changed, 20 insertions(+), 26 deletions(-)

diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml
index 8de55ba91..224591d1f 100644
--- a/.github/workflows/docker-build.yml
+++ b/.github/workflows/docker-build.yml
@@ -31,7 +31,7 @@ jobs:
       new_tag: ${{ steps.tag_version.outputs.next_version }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           ref: ${{ github.event.inputs.branch }}
@@ -108,16 +108,18 @@ jobs:
             name: surfsense-backend
             context: ./surfsense_backend
             file: ./surfsense_backend/Dockerfile
+            target: production
           - image: web
             name: surfsense-web
             context: ./surfsense_web
             file: ./surfsense_web/Dockerfile
+            target: runner
     env:
       REGISTRY_IMAGE: ghcr.io/${{ github.repository_owner }}/${{ matrix.name }}
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set lowercase image name
         id: image
@@ -125,19 +127,19 @@ jobs:
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v6
         with:
           images: ${{ steps.image.outputs.name }}
 
       - name: Login to GitHub Container Registry
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4
 
       - name: Free up disk space
         run: |
@@ -149,10 +151,11 @@ jobs:
 
       - name: Build and push by digest ${{ matrix.name }} (${{ matrix.suffix }})
         id: build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: ${{ matrix.context }}
           file: ${{ matrix.file }}
+          target: ${{ matrix.target }}
           labels: ${{ steps.meta.outputs.labels }}
           tags: ${{ steps.image.outputs.name }}
           outputs: type=image,push-by-digest=true,name-canonical=true,push=true
@@ -174,7 +177,7 @@ jobs:
           touch "/tmp/digests/${digest#sha256:}"
 
       - name: Upload digest
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: digests-${{ matrix.image }}-${{ matrix.suffix }}
           path: /tmp/digests/*
@@ -205,22 +208,22 @@ jobs:
         run: echo "name=${REGISTRY_IMAGE,,}" >> $GITHUB_OUTPUT
 
       - name: Download amd64 digest
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v8
         with:
           name: digests-${{ matrix.image }}-amd64
           path: /tmp/digests
 
       - name: Download arm64 digest
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v8
         with:
           name: digests-${{ matrix.image }}-arm64
           path: /tmp/digests
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4
 
       - name: Login to GitHub Container Registry
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
@@ -239,7 +242,7 @@ jobs:
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v6
         with:
           images: ${{ steps.image.outputs.name }}
           tags: |
diff --git a/surfsense_backend/Dockerfile b/surfsense_backend/Dockerfile
index a5b391a1e..040200863 100644
--- a/surfsense_backend/Dockerfile
+++ b/surfsense_backend/Dockerfile
@@ -1,16 +1,9 @@
 # =============================================================================
 # SurfSense Backend — Multi-stage Dockerfile
 # =============================================================================
-# Stages:
-#   base       — system deps + Pandoc 3.x
-#   deps       — Python deps frozen from uv.lock (no dev deps)
-#   models     — pre-baked offline assets (EasyOCR, Docling, Playwright)
-#   e2e        — adds tests/ via additional_contexts, swaps entrypoint
-#   production — production runtime (LAST stage = default `docker build` target)
-#
-# IMPORTANT: `production` MUST remain the last stage. .github/workflows/docker-build.yml
-# builds without `target:` and BuildKit defaults to the last stage. Reordering will
-# silently break ghcr.io/modsetter/surfsense-backend.
+# Graph: base → deps → models → {e2e, production}
+#   e2e        — tests/ via additional_contexts (docker-compose.e2e.yml)
+#   production — published ghcr.io image (docker-build.yml pins target)
 # =============================================================================
 
 # ─── Stage 1: base (system deps, Pandoc, certificates) ──────────────────────
@@ -143,10 +136,8 @@ EXPOSE 8000-8001
 CMD ["/app/scripts/docker/entrypoint.e2e.sh"]
 
 
-# ─── Stage 5: production (LAST stage — default `docker build` target) ───────
-# Behavior is byte-identical to the previous single-stage Dockerfile.
-# .github/workflows/docker-build.yml builds without `target:` and BuildKit
-# defaults to the last stage, so this MUST stay last.
+# ─── Stage 5: production (published ghcr.io image) ──────────────────────────
+# CI pins `target: production`; also the default for `docker build` / dev compose.
 FROM models AS production
 
 # Copy source code (tests/ excluded by .dockerignore — production never ships tests).

From 65fecb3337398e5c5e3e607c85b1f4960e78956b Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 03:53:47 +0530
Subject: [PATCH 13/36] chore: update Docker Buildx action to version 4 in E2E
 tests workflow

---
 .github/workflows/e2e-tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 20c79c42d..d6a695b29 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v6
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4
 
       # ─── Backend stack ─────────────────────────────────────────────────
       # Builds the e2e image (multi-stage, deps cached via GHA), brings up

From 18de0136bc05516dbbc7e946628f3b2dcf4161f4 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 04:02:39 +0530
Subject: [PATCH 14/36] chore: add ffmpeg to Dockerfile for audio processing
 capabilities

---
 surfsense_backend/Dockerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/surfsense_backend/Dockerfile b/surfsense_backend/Dockerfile
index 040200863..bb8466ab0 100644
--- a/surfsense_backend/Dockerfile
+++ b/surfsense_backend/Dockerfile
@@ -19,6 +19,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     wget \
     unzip \
     gnupg2 \
+    ffmpeg \
     espeak-ng \
     libsndfile1 \
     libgl1 \

From efff7ab2a219d84fc53c29839a982398cfcdd708 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 04:51:19 +0530
Subject: [PATCH 15/36] chore: enhance Dockerfile and config to support
 conditional static ffmpeg import

---
 surfsense_backend/Dockerfile             | 11 ++++++++++-
 surfsense_backend/app/config/__init__.py | 11 ++++++++---
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/surfsense_backend/Dockerfile b/surfsense_backend/Dockerfile
index bb8466ab0..93a923ea3 100644
--- a/surfsense_backend/Dockerfile
+++ b/surfsense_backend/Dockerfile
@@ -31,6 +31,8 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     git \
     && rm -rf /var/lib/apt/lists/*
 
+RUN which ffmpeg && ffmpeg -version
+
 # Pandoc 3.x from GitHub Releases — apt ships 2.17 which has broken table rendering.
 # pypandoc_binary bundles pandoc on Windows/macOS; on Linux it picks up this binary.
 RUN ARCH=$(dpkg --print-architecture) && \
@@ -43,6 +45,7 @@ RUN pip install --upgrade certifi pip-system-certs
 
 ENV SSL_CERT_FILE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
 ENV REQUESTS_CA_BUNDLE=/usr/local/lib/python3.12/site-packages/certifi/cacert.pem
+ENV SURFSENSE_ALLOW_STATIC_FFMPEG_DOWNLOAD=FALSE
 
 
 # ─── Stage 2: deps (Python deps frozen from uv.lock) ────────────────────────
@@ -82,7 +85,13 @@ RUN mkdir -p /root/.EasyOCR/model && \
     (unzip -o craft_mlt_25k.zip || true)
 
 # Pre-download Docling models
-RUN python -c "try:\n    from docling.document_converter import DocumentConverter\n    conv = DocumentConverter()\nexcept:\n    pass" || true
+RUN printf '%s\n' \
+    'try:' \
+    '    from docling.document_converter import DocumentConverter' \
+    '    DocumentConverter()' \
+    'except Exception:' \
+    '    pass' \
+    | python || true
 
 # Install Playwright browsers (the playwright python package itself is in deps)
 RUN playwright install chromium --with-deps
diff --git a/surfsense_backend/app/config/__init__.py b/surfsense_backend/app/config/__init__.py
index f6f0c7f62..724762854 100644
--- a/surfsense_backend/app/config/__init__.py
+++ b/surfsense_backend/app/config/__init__.py
@@ -473,10 +473,15 @@ def initialize_vision_llm_router():
 class Config:
     # Check if ffmpeg is installed
     if not is_ffmpeg_installed():
-        import static_ffmpeg
+        allow_static_ffmpeg = (
+            os.getenv("SURFSENSE_ALLOW_STATIC_FFMPEG_DOWNLOAD", "TRUE").upper() == "TRUE"
+        )
+        if allow_static_ffmpeg:
+            import static_ffmpeg
+
+            # ffmpeg installed on first call to add_paths(), threadsafe.
+            static_ffmpeg.add_paths()
 
-        # ffmpeg installed on first call to add_paths(), threadsafe.
-        static_ffmpeg.add_paths()
         # check if ffmpeg is installed again
         if not is_ffmpeg_installed():
             raise ValueError(

From 242925d8e52d21941d72a279488fb36f6c32ce22 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 12:31:15 +0530
Subject: [PATCH 16/36] chore: update Docker configurations to streamline
 backend build and enhance E2E testing environment

---
 docker/docker-compose.dev.yml                      | 11 ++++++++---
 docker/docker-compose.e2e.yml                      |  4 ++++
 surfsense_backend/Dockerfile                       |  4 ++++
 surfsense_backend/scripts/docker/entrypoint.e2e.sh |  3 ++-
 4 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/docker/docker-compose.dev.yml b/docker/docker-compose.dev.yml
index bbe758d4f..b974f7e3d 100644
--- a/docker/docker-compose.dev.yml
+++ b/docker/docker-compose.dev.yml
@@ -10,6 +10,11 @@
 
 name: surfsense-dev
 
+x-backend-build: &backend-build
+  context: ../surfsense_backend
+  args:
+    EMBEDDING_MODEL: ${EMBEDDING_MODEL:-sentence-transformers/all-MiniLM-L6-v2}
+
 services:
   db:
     image: pgvector/pgvector:pg17
@@ -69,7 +74,7 @@ services:
       retries: 5
 
   backend:
-    build: ../surfsense_backend
+    build: *backend-build
     ports:
       - "${BACKEND_PORT:-8000}:8000"
     volumes:
@@ -114,7 +119,7 @@ services:
       start_period: 200s
 
   celery_worker:
-    build: ../surfsense_backend
+    build: *backend-build
     volumes:
       - ../surfsense_backend/app:/app/app
       - shared_temp:/shared_tmp
@@ -140,7 +145,7 @@ services:
         condition: service_healthy
 
   celery_beat:
-    build: ../surfsense_backend
+    build: *backend-build
     env_file:
       - ../surfsense_backend/.env
     environment:
diff --git a/docker/docker-compose.e2e.yml b/docker/docker-compose.e2e.yml
index 87b4e7261..a752262cb 100644
--- a/docker/docker-compose.e2e.yml
+++ b/docker/docker-compose.e2e.yml
@@ -54,6 +54,8 @@ x-backend-env: &backend-env
   HTTPS_PROXY: http://127.0.0.1:1
   HTTP_PROXY: http://127.0.0.1:1
   NO_PROXY: localhost,127.0.0.1,0.0.0.0,db,redis,host.docker.internal
+  HF_HUB_OFFLINE: "1"
+  TRANSFORMERS_OFFLINE: "1"
 
 services:
   db:
@@ -95,6 +97,8 @@ services:
         # tests/ is excluded from the main context by .dockerignore;
         # the e2e stage's `COPY --from=tests-source` pulls it in here.
         tests-source: ../surfsense_backend/tests
+      args:
+        EMBEDDING_MODEL: sentence-transformers/all-MiniLM-L6-v2
       cache_from:
         - type=gha,scope=surfsense-e2e-backend
       cache_to:
diff --git a/surfsense_backend/Dockerfile b/surfsense_backend/Dockerfile
index 93a923ea3..6e1b2481e 100644
--- a/surfsense_backend/Dockerfile
+++ b/surfsense_backend/Dockerfile
@@ -93,6 +93,9 @@ RUN printf '%s\n' \
     '    pass' \
     | python || true
 
+ARG EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
+RUN python -c "from chonkie import AutoEmbeddings; AutoEmbeddings.get_embeddings('${EMBEDDING_MODEL}')"
+
 # Install Playwright browsers (the playwright python package itself is in deps)
 RUN playwright install chromium --with-deps
 
@@ -104,6 +107,7 @@ RUN mkdir -p /shared_tmp
 ENV PYTHONPATH=/app
 ENV UVICORN_LOOP=asyncio
 ENV TMPDIR=/shared_tmp
+ENV PYTHONUNBUFFERED=1
 
 # Tune glibc malloc to return freed memory to the OS more aggressively.
 # Without these, Python's gc.collect() frees objects but the underlying
diff --git a/surfsense_backend/scripts/docker/entrypoint.e2e.sh b/surfsense_backend/scripts/docker/entrypoint.e2e.sh
index 84cfe2568..b44e1ee95 100755
--- a/surfsense_backend/scripts/docker/entrypoint.e2e.sh
+++ b/surfsense_backend/scripts/docker/entrypoint.e2e.sh
@@ -19,7 +19,8 @@ wait_for_db() {
     # depends_on/healthchecks already gate on db readiness, this is just
     # belt-and-suspenders so a slow first connection doesn't race migrations.
     for i in {1..60}; do
-        if python -c "from app.db import engine; import asyncio; asyncio.run(engine.dispose())" 2>/dev/null; then
+        echo "[e2e-entrypoint] db check attempt ${i}/60"
+        if python -c "from app.db import engine; import asyncio; asyncio.run(engine.dispose())"; then
             echo "[e2e-entrypoint] db reachable after ${i} attempts"
             return 0
         fi

From b92cc963cecad06548a4767615fca8c254c0c9d9 Mon Sep 17 00:00:00 2001
From: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Date: Mon, 11 May 2026 00:11:05 -0700
Subject: [PATCH 17/36] refactor(use-logs): use canonical log types from
 contracts/types/log.types

Removes duplicated LogLevel, LogStatus, Log, LogFilters and LogSummary
definitions from surfsense_web/hooks/use-logs.ts. These shapes already
live as Zod-derived types in contracts/types/log.types.ts, which is the
source of truth used by logs-api.service.ts and log-mutation.atoms.ts.

Adds LogLevel and LogStatus aliases for LogLevelEnum/LogStatusEnum in
log.types.ts so the existing public surface from use-logs is preserved
without per-hook re-exports. The hook re-exports the canonical names so
callers (app/dashboard/[search_space_id]/logs/(manage)/page.tsx) do not
need to change.

Closes #1372
---
 surfsense_web/contracts/types/log.types.ts |  2 +
 surfsense_web/hooks/use-logs.ts            | 54 ++++------------------
 2 files changed, 10 insertions(+), 46 deletions(-)

diff --git a/surfsense_web/contracts/types/log.types.ts b/surfsense_web/contracts/types/log.types.ts
index eb9f7fe6c..95aa33319 100644
--- a/surfsense_web/contracts/types/log.types.ts
+++ b/surfsense_web/contracts/types/log.types.ts
@@ -117,6 +117,8 @@ export const getLogSummaryResponse = logSummary;
 export type Log = z.infer<typeof log>;
 export type LogLevelEnum = z.infer<typeof logLevelEnum>;
 export type LogStatusEnum = z.infer<typeof logStatusEnum>;
+export type LogLevel = LogLevelEnum;
+export type LogStatus = LogStatusEnum;
 export type LogFilters = z.infer<typeof logFilters>;
 export type CreateLogRequest = z.infer<typeof createLogRequest>;
 export type CreateLogResponse = z.infer<typeof createLogResponse>;
diff --git a/surfsense_web/hooks/use-logs.ts b/surfsense_web/hooks/use-logs.ts
index 0cf5975b6..b646b078d 100644
--- a/surfsense_web/hooks/use-logs.ts
+++ b/surfsense_web/hooks/use-logs.ts
@@ -1,55 +1,17 @@
 "use client";
 import { useQuery } from "@tanstack/react-query";
 import { useCallback, useMemo } from "react";
+import type { LogFilters } from "@/contracts/types/log.types";
 import { logsApiService } from "@/lib/apis/logs-api.service";
 import { cacheKeys } from "@/lib/query-client/cache-keys";
 
-export type LogLevel = "DEBUG" | "INFO" | "WARNING" | "ERROR" | "CRITICAL";
-export type LogStatus = "IN_PROGRESS" | "SUCCESS" | "FAILED";
-
-export interface Log {
-	id: number;
-	level: LogLevel;
-	status: LogStatus;
-	message: string;
-	source?: string;
-	log_metadata?: Record<string, unknown>;
-	created_at: string;
-	search_space_id: number;
-}
-
-export interface LogFilters {
-	search_space_id?: number;
-	level?: LogLevel;
-	status?: LogStatus;
-	source?: string;
-	start_date?: string;
-	end_date?: string;
-}
-
-export interface LogSummary {
-	total_logs: number;
-	time_window_hours: number;
-	by_status: Record<string, number>;
-	by_level: Record<string, number>;
-	by_source: Record<string, number>;
-	active_tasks: Array<{
-		id: number;
-		task_name: string;
-		message: string;
-		started_at: string;
-		source?: string;
-		document_id?: number;
-	}>;
-	recent_failures: Array<{
-		id: number;
-		task_name: string;
-		message: string;
-		failed_at: string;
-		source?: string;
-		error_details?: string;
-	}>;
-}
+export type {
+	Log,
+	LogFilters,
+	LogLevel,
+	LogStatus,
+	LogSummary,
+} from "@/contracts/types/log.types";
 
 export function useLogs(searchSpaceId?: number, filters: LogFilters = {}) {
 	const filtersKey = JSON.stringify(filters);

From 99e667f3f9b0e369e88f62cfa246ec9bdc457794 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 13:01:20 +0530
Subject: [PATCH 18/36] chore: refine E2E tests workflow by removing pnpm
 version specification and updating Docker Compose for backend build reference

---
 .github/workflows/e2e-tests.yml | 2 --
 docker/docker-compose.dev.yml   | 2 +-
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index d6a695b29..fd35455b0 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -73,8 +73,6 @@ jobs:
           node-version: '20'
 
       - uses: pnpm/action-setup@v6
-        with:
-          version: 10
 
       - name: Get pnpm store directory
         id: pnpm-cache
diff --git a/docker/docker-compose.dev.yml b/docker/docker-compose.dev.yml
index b974f7e3d..28b00a044 100644
--- a/docker/docker-compose.dev.yml
+++ b/docker/docker-compose.dev.yml
@@ -164,7 +164,7 @@ services:
         condition: service_started
 
   # flower:
-  #   build: ../surfsense_backend
+  #   build: *backend-build
   #   ports:
   #     - "${FLOWER_PORT:-5555}:5555"
   #   env_file:

From 3b345e709121b94469041d7a565f6c2556b24948 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 13:41:38 +0530
Subject: [PATCH 19/36] chore: add pnpm configuration for only built
 dependencies in package.json

---
 surfsense_web/package.json | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/surfsense_web/package.json b/surfsense_web/package.json
index d9f836ea9..9a3f5ec7c 100644
--- a/surfsense_web/package.json
+++ b/surfsense_web/package.json
@@ -176,5 +176,17 @@
 		"tsx": "^4.20.6",
 		"typescript": "^5.8.3",
 		"vite": "^7.3.1"
+	},
+	"pnpm": {
+		"onlyBuiltDependencies": [
+			"@parcel/watcher",
+			"@rocicorp/zero-sqlite3",
+			"@swc/core",
+			"core-js",
+			"esbuild",
+			"protobufjs",
+			"sharp",
+			"unrs-resolver"
+		]
 	}
 }

From 83e40c5aea83c2fa5915e19875adde7202f41519 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 15:31:24 +0530
Subject: [PATCH 20/36] chore: update Docker configuration to include pnpm
 workspace and refine dependency management

---
 docker/.env.example               |  2 +-
 surfsense_web/Dockerfile          |  2 +-
 surfsense_web/package.json        | 12 ------------
 surfsense_web/pnpm-workspace.yaml | 11 +++++++++++
 4 files changed, 13 insertions(+), 14 deletions(-)
 create mode 100644 surfsense_web/pnpm-workspace.yaml

diff --git a/docker/.env.example b/docker/.env.example
index aba15f13f..4de35a5e9 100644
--- a/docker/.env.example
+++ b/docker/.env.example
@@ -4,7 +4,7 @@
 # Database, Redis, and internal service wiring are handled automatically.
 # ==============================================================================
 
-# SurfSense version (use "latest", a clean version like "0.0.14", or a specific build like "0.0.14.1")
+# SurfSense version (use "latest" or a specific version like "0.0.14")
 SURFSENSE_VERSION=latest
 
 # ------------------------------------------------------------------------------
diff --git a/surfsense_web/Dockerfile b/surfsense_web/Dockerfile
index da6bc8b7e..0e3ed11de 100644
--- a/surfsense_web/Dockerfile
+++ b/surfsense_web/Dockerfile
@@ -12,7 +12,7 @@ WORKDIR /app
 RUN corepack enable pnpm
 
 # Copy package files
-COPY package.json pnpm-lock.yaml* .npmrc* ./
+COPY package.json pnpm-lock.yaml* pnpm-workspace.yaml* .npmrc* ./
 
 # First copy the config file and content to avoid fumadocs-mdx postinstall error
 COPY source.config.ts ./
diff --git a/surfsense_web/package.json b/surfsense_web/package.json
index 9a3f5ec7c..d9f836ea9 100644
--- a/surfsense_web/package.json
+++ b/surfsense_web/package.json
@@ -176,17 +176,5 @@
 		"tsx": "^4.20.6",
 		"typescript": "^5.8.3",
 		"vite": "^7.3.1"
-	},
-	"pnpm": {
-		"onlyBuiltDependencies": [
-			"@parcel/watcher",
-			"@rocicorp/zero-sqlite3",
-			"@swc/core",
-			"core-js",
-			"esbuild",
-			"protobufjs",
-			"sharp",
-			"unrs-resolver"
-		]
 	}
 }
diff --git a/surfsense_web/pnpm-workspace.yaml b/surfsense_web/pnpm-workspace.yaml
new file mode 100644
index 000000000..a822cfab5
--- /dev/null
+++ b/surfsense_web/pnpm-workspace.yaml
@@ -0,0 +1,11 @@
+packages:
+  - "."
+onlyBuiltDependencies:
+  - "@parcel/watcher"
+  - "@rocicorp/zero-sqlite3"
+  - "@swc/core"
+  - core-js
+  - esbuild
+  - protobufjs
+  - sharp
+  - unrs-resolver

From 6501e32b4fbb13ef02736b5fe398daeadc348cfc Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 16:27:35 +0530
Subject: [PATCH 21/36] chore: bump pinned pnpm version to 10.26.0

---
 package.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/package.json b/package.json
index 8a1a6add8..1e45c1706 100644
--- a/package.json
+++ b/package.json
@@ -1,5 +1,5 @@
 {
   "name": "surfsense",
   "private": true,
-  "packageManager": "pnpm@10.24.0"
+  "packageManager": "pnpm@10.26.0"
 }

From 741d6e7eea6eef1a06eac93db7bfba71690831ac Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 17:02:06 +0530
Subject: [PATCH 22/36] chore: update pnpm workspace configuration to allow
 builds for specified dependencies

---
 surfsense_web/pnpm-workspace.yaml | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/surfsense_web/pnpm-workspace.yaml b/surfsense_web/pnpm-workspace.yaml
index a822cfab5..69e46cf7a 100644
--- a/surfsense_web/pnpm-workspace.yaml
+++ b/surfsense_web/pnpm-workspace.yaml
@@ -1,11 +1,9 @@
-packages:
-  - "."
-onlyBuiltDependencies:
-  - "@parcel/watcher"
-  - "@rocicorp/zero-sqlite3"
-  - "@swc/core"
-  - core-js
-  - esbuild
-  - protobufjs
-  - sharp
-  - unrs-resolver
+allowBuilds:
+  "@parcel/watcher": true
+  "@rocicorp/zero-sqlite3": true
+  "@swc/core": true
+  core-js: true
+  esbuild: true
+  protobufjs: true
+  sharp: true
+  unrs-resolver: true

From b247ff37df4c84d19bd6910065a5f4dd19fd591d Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Mon, 11 May 2026 19:48:18 +0530
Subject: [PATCH 23/36] chore: implement test-only token mint endpoint and
 update E2E test authentication flow

---
 .github/workflows/e2e-tests.yml               |   8 +
 docker/docker-compose.e2e.yml                 |   2 +
 surfsense_backend/tests/e2e/auth_mint.py      |  68 ++++
 surfsense_backend/tests/e2e/run_backend.py    | 307 ++++++++++--------
 surfsense_web/playwright.config.ts            |   2 +-
 surfsense_web/tests/auth.setup.ts             |  39 +--
 .../tests/fixtures/search-space.fixture.ts    |  39 ++-
 surfsense_web/tests/helpers/api/auth.ts       |  49 +++
 8 files changed, 344 insertions(+), 170 deletions(-)
 create mode 100644 surfsense_backend/tests/e2e/auth_mint.py

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index fd35455b0..2b7b6f1a7 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -30,6 +30,9 @@ jobs:
       # spawns `pnpm build && pnpm start` in CI; these get baked into the build.
       NEXT_PUBLIC_FASTAPI_BACKEND_URL: http://localhost:8000
       NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: LOCAL
+      # Shared secret for the test-only POST /__e2e__/auth/token endpoint.
+      # Must match docker-compose.e2e.yml's backend env (x-backend-env).
+      E2E_MINT_SECRET: e2e-mint-secret-not-for-production
 
     steps:
       - uses: actions/checkout@v6
@@ -65,6 +68,11 @@ jobs:
             exit 1
           fi
 
+          # Flush auth rate-limit counters so Playwright starts clean.
+          docker compose -f docker/docker-compose.e2e.yml exec -T redis \
+            sh -c "redis-cli --scan --pattern 'surfsense:auth_rate_limit:*' \
+              | xargs -r redis-cli DEL" || true
+
       # ─── Frontend (host-side) ──────────────────────────────────────────
       # Playwright's webServer block in playwright.config.ts spawns
       # `pnpm build && pnpm start` in CI mode and waits for :3000.
diff --git a/docker/docker-compose.e2e.yml b/docker/docker-compose.e2e.yml
index a752262cb..b34d8d82d 100644
--- a/docker/docker-compose.e2e.yml
+++ b/docker/docker-compose.e2e.yml
@@ -56,6 +56,8 @@ x-backend-env: &backend-env
   NO_PROXY: localhost,127.0.0.1,0.0.0.0,db,redis,host.docker.internal
   HF_HUB_OFFLINE: "1"
   TRANSFORMERS_OFFLINE: "1"
+  # Test-only token-mint endpoint secret (see tests/e2e/run_backend.py).
+  E2E_MINT_SECRET: e2e-mint-secret-not-for-production
 
 services:
   db:
diff --git a/surfsense_backend/tests/e2e/auth_mint.py b/surfsense_backend/tests/e2e/auth_mint.py
new file mode 100644
index 000000000..a80e68fc1
--- /dev/null
+++ b/surfsense_backend/tests/e2e/auth_mint.py
@@ -0,0 +1,68 @@
+"""Test-only token mint endpoint for the E2E backend entrypoint.
+
+Mounted by ``tests/e2e/run_backend.py`` so Playwright can authenticate
+the seeded e2e user without hitting ``/auth/jwt/login`` (rate-limited
+to 5/min/IP in production). NEVER ships to production: this whole
+``tests/`` tree is excluded from the production Docker image by
+``surfsense_backend/.dockerignore``.
+
+Authn: shared secret in ``X-E2E-Mint-Secret``. Same value is set on the
+backend container env (``docker/docker-compose.e2e.yml``) and exported
+to the Playwright runner (``.github/workflows/e2e-tests.yml``).
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+from fastapi import APIRouter, FastAPI, Header, HTTPException
+from pydantic import BaseModel
+from sqlalchemy import select
+
+from app.db import User, async_session_maker
+from app.users import get_jwt_strategy
+
+_logger = logging.getLogger("surfsense.e2e.auth_mint")
+
+
+class MintRequest(BaseModel):
+    email: str = "e2e-test@surfsense.net"
+
+
+class MintResponse(BaseModel):
+    access_token: str
+    token_type: str = "bearer"
+
+
+def _expected_secret() -> str:
+    return os.environ.get(
+        "E2E_MINT_SECRET", "local-e2e-mint-secret-not-for-production"
+    )
+
+
+router = APIRouter(prefix="/__e2e__", tags=["__e2e__"])
+
+
+@router.post("/auth/token", response_model=MintResponse)
+async def mint_test_token(
+    body: MintRequest,
+    x_e2e_mint_secret: str = Header(..., alias="X-E2E-Mint-Secret"),
+) -> MintResponse:
+    if x_e2e_mint_secret != _expected_secret():
+        raise HTTPException(status_code=403, detail="invalid e2e mint secret")
+    async with async_session_maker() as session:
+        result = await session.execute(select(User).where(User.email == body.email))
+        user = result.scalar_one_or_none()
+    if user is None:
+        raise HTTPException(
+            status_code=404, detail=f"e2e user {body.email!r} not seeded"
+        )
+    token = await get_jwt_strategy().write_token(user)
+    return MintResponse(access_token=token)
+
+
+def install(app: FastAPI) -> None:
+    """Mount the test-only mint router onto the given FastAPI app."""
+    app.include_router(router)
+    _logger.warning("[e2e] mounted POST /__e2e__/auth/token (test-only token mint)")
diff --git a/surfsense_backend/tests/e2e/run_backend.py b/surfsense_backend/tests/e2e/run_backend.py
index a34327908..c5cb163a1 100644
--- a/surfsense_backend/tests/e2e/run_backend.py
+++ b/surfsense_backend/tests/e2e/run_backend.py
@@ -23,15 +23,12 @@ Usage:
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import os
 import sys
 
-# ---------------------------------------------------------------------------
-# 1) Hijack sys.modules BEFORE any production import.
-#    Production: composio_service.py:11 does `from composio import Composio`.
-#    With this hijack in place, that import resolves to our strict fake.
-# ---------------------------------------------------------------------------
+import uvicorn
 
 # Make the surfsense_backend root importable as a top-level package so
 # `import tests.e2e.fakes...` works regardless of how the entrypoint is
@@ -42,120 +39,113 @@ _BACKEND_ROOT = os.path.abspath(os.path.join(_THIS_DIR, "..", ".."))
 if _BACKEND_ROOT not in sys.path:
     sys.path.insert(0, _BACKEND_ROOT)
 
-import tests.e2e.fakes.composio_module as _fake_composio  # noqa: E402
-import tests.e2e.fakes.notion_module as _fake_notion  # noqa: E402
 
-sys.modules["composio"] = _fake_composio
-sys.modules["notion_client"] = _fake_notion
-sys.modules["notion_client.errors"] = _fake_notion.errors
-
-
-# ---------------------------------------------------------------------------
-# 2) Standard logging + dotenv so the rest of the app behaves like main.py.
-# ---------------------------------------------------------------------------
-
-from dotenv import load_dotenv  # noqa: E402
-
-load_dotenv()
-
-os.environ.setdefault(
-    "DATABASE_URL",
-    "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
-)
-os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
-os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
-os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
-os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
-os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
-os.environ.setdefault("AUTH_TYPE", "LOCAL")
-os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
-os.environ.setdefault("ETL_SERVICE", "DOCLING")
-os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
-os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
-
-# Sentinel keys — fakes never read them; turns leaked real calls into 401s.
-os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
-os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
-
-os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
-os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
-os.environ.setdefault(
-    "CONFLUENCE_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/confluence/connector/callback",
-)
-os.environ.setdefault("NOTION_CLIENT_ID", "fake-notion-client-id")
-os.environ.setdefault("NOTION_CLIENT_SECRET", "fake-notion-client-secret")
-os.environ.setdefault(
-    "NOTION_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/notion/connector/callback",
-)
-os.environ.setdefault("MICROSOFT_CLIENT_ID", "fake-microsoft-client-id")
-os.environ.setdefault("MICROSOFT_CLIENT_SECRET", "fake-microsoft-client-secret")
-os.environ.setdefault(
-    "ONEDRIVE_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/onedrive/connector/callback",
-)
-os.environ.setdefault("DROPBOX_APP_KEY", "fake-dropbox-app-key")
-os.environ.setdefault("DROPBOX_APP_SECRET", "fake-dropbox-app-secret")
-os.environ.setdefault(
-    "DROPBOX_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
-)
-os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
-os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
 logger = logging.getLogger("surfsense.e2e.backend")
-logger.warning(
-    "*** SURFSENSE E2E BACKEND ENTRYPOINT — fake Composio + LLM + embeddings ***"
-)
-
-
-# ---------------------------------------------------------------------------
-# 3) Now import the production app. Every module in app.* loads here,
-#    creating their bindings (some of which we will patch in step 4).
-# ---------------------------------------------------------------------------
-
-# ---------------------------------------------------------------------------
-# 4) Patch LLM + embedding bindings at every consumer site.
-#    Composio is already covered by the sys.modules hijack in step 1.
-# ---------------------------------------------------------------------------
-from unittest.mock import patch  # noqa: E402
-
-from app.app import app  # noqa: E402
-from tests.e2e.fakes import (  # noqa: E402
-    clickup_module as _fake_clickup_module,
-    confluence_indexer as _fake_confluence_indexer,
-    confluence_oauth as _fake_confluence_oauth,
-    dropbox_api as _fake_dropbox_api,
-    embeddings as _fake_embeddings,
-    jira_module as _fake_jira_module,
-    linear_module as _fake_linear_module,
-    mcp_oauth_runtime as _fake_mcp_oauth_runtime,
-    mcp_runtime as _fake_mcp_runtime,
-    native_google as _fake_native_google,
-    notion_module as _fake_notion_module,
-    onedrive_graph as _fake_onedrive_graph,
-    slack_module as _fake_slack_module,
-)
-from tests.e2e.fakes.chat_llm import (  # noqa: E402
-    fake_create_chat_litellm_from_agent_config,
-    fake_create_chat_litellm_from_config,
-)
-from tests.e2e.fakes.llm import fake_get_user_long_context_llm  # noqa: E402
 
+# Patches started during bootstrap are kept alive for the lifetime of the
+# process. We never call .stop() on them.
 _active_patches: list = []
 
 
+def _hijack_external_sdks() -> None:
+    """Replace composio + notion_client in sys.modules.
+
+    Production does ``from composio import Composio`` and
+    ``import notion_client`` at import time. With this hijack in place,
+    those imports resolve to our strict fakes.
+
+    MUST run before _import_production_app().
+    """
+    import tests.e2e.fakes.composio_module as _fake_composio
+    import tests.e2e.fakes.notion_module as _fake_notion
+
+    sys.modules["composio"] = _fake_composio
+    sys.modules["notion_client"] = _fake_notion
+    sys.modules["notion_client.errors"] = _fake_notion.errors
+
+
+def _load_dotenv_and_set_env_defaults() -> None:
+    """Load .env and set every env var the production config reads on import.
+
+    MUST run before _import_production_app(), since app.config consumes
+    these values at import time.
+    """
+    from dotenv import load_dotenv
+
+    load_dotenv()
+
+    os.environ.setdefault(
+        "DATABASE_URL",
+        "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
+    )
+    os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
+    os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
+    os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
+    os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
+    os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
+    os.environ.setdefault("AUTH_TYPE", "LOCAL")
+    os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
+    os.environ.setdefault("ETL_SERVICE", "DOCLING")
+    os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
+
+    # Sentinel keys — fakes never read them; turns leaked real calls into 401s.
+    os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
+    os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
+
+    os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
+    os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
+    os.environ.setdefault(
+        "CONFLUENCE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/confluence/connector/callback",
+    )
+    os.environ.setdefault("NOTION_CLIENT_ID", "fake-notion-client-id")
+    os.environ.setdefault("NOTION_CLIENT_SECRET", "fake-notion-client-secret")
+    os.environ.setdefault(
+        "NOTION_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/notion/connector/callback",
+    )
+    os.environ.setdefault("MICROSOFT_CLIENT_ID", "fake-microsoft-client-id")
+    os.environ.setdefault("MICROSOFT_CLIENT_SECRET", "fake-microsoft-client-secret")
+    os.environ.setdefault(
+        "ONEDRIVE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/onedrive/connector/callback",
+    )
+    os.environ.setdefault("DROPBOX_APP_KEY", "fake-dropbox-app-key")
+    os.environ.setdefault("DROPBOX_APP_SECRET", "fake-dropbox-app-secret")
+    os.environ.setdefault(
+        "DROPBOX_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
+    )
+    os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
+    os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
+
+
+def _import_production_app():
+    """Import and return the production FastAPI app.
+
+    Every module under ``app.*`` loads here, creating their bindings.
+    The LLM/embedding factories captured at this point will be replaced
+    by patches in _patch_llm_bindings() below.
+    """
+    from app.app import app as production_app
+
+    return production_app
+
+
 def _patch_llm_bindings() -> None:
     """Replace LLM factories at every known binding site."""
+    from unittest.mock import patch
+
+    from tests.e2e.fakes.chat_llm import (
+        fake_create_chat_litellm_from_agent_config,
+        fake_create_chat_litellm_from_config,
+    )
+    from tests.e2e.fakes.llm import fake_get_user_long_context_llm
+
     targets = [
         "app.services.llm_service.get_user_long_context_llm",
         "app.tasks.connector_indexers.confluence_indexer.get_user_long_context_llm",
@@ -213,38 +203,85 @@ def _patch_llm_bindings() -> None:
             logger.warning("[fake-chat-llm] could not patch %s: %s.", target, exc)
 
 
-_patch_llm_bindings()
-_fake_embeddings.install(_active_patches)
-_fake_confluence_oauth.install(_active_patches)
-_fake_confluence_indexer.install(_active_patches)
-_fake_native_google.install(_active_patches)
-_fake_onedrive_graph.install(_active_patches)
-_fake_dropbox_api.install(_active_patches)
-_fake_notion_module.install(_active_patches)
-_fake_linear_module.install(_active_patches)
-_fake_jira_module.install(_active_patches)
-_fake_clickup_module.install(_active_patches)
-_fake_mcp_runtime.install(_active_patches)
-_fake_mcp_oauth_runtime.install(_active_patches)
-_fake_slack_module.install(_active_patches)
+def _install_runtime_fakes() -> None:
+    """Run each fake's install() against the active patch stack."""
+    from tests.e2e.fakes import (
+        clickup_module as _fake_clickup_module,
+        confluence_indexer as _fake_confluence_indexer,
+        confluence_oauth as _fake_confluence_oauth,
+        dropbox_api as _fake_dropbox_api,
+        embeddings as _fake_embeddings,
+        jira_module as _fake_jira_module,
+        linear_module as _fake_linear_module,
+        mcp_oauth_runtime as _fake_mcp_oauth_runtime,
+        mcp_runtime as _fake_mcp_runtime,
+        native_google as _fake_native_google,
+        notion_module as _fake_notion_module,
+        onedrive_graph as _fake_onedrive_graph,
+        slack_module as _fake_slack_module,
+    )
+
+    _fake_embeddings.install(_active_patches)
+    _fake_confluence_oauth.install(_active_patches)
+    _fake_confluence_indexer.install(_active_patches)
+    _fake_native_google.install(_active_patches)
+    _fake_onedrive_graph.install(_active_patches)
+    _fake_dropbox_api.install(_active_patches)
+    _fake_notion_module.install(_active_patches)
+    _fake_linear_module.install(_active_patches)
+    _fake_jira_module.install(_active_patches)
+    _fake_clickup_module.install(_active_patches)
+    _fake_mcp_runtime.install(_active_patches)
+    _fake_mcp_oauth_runtime.install(_active_patches)
+    _fake_slack_module.install(_active_patches)
 
 
-# ---------------------------------------------------------------------------
-# 5) Mount test-only middleware. Production never reaches this code.
-# ---------------------------------------------------------------------------
+def _install_test_only_app_extensions(app) -> None:
+    """Mount test-only middleware + the /__e2e__ token mint router.
 
-from tests.e2e.middleware.scenario import ScenarioMiddleware  # noqa: E402
+    POST /__e2e__/auth/token bypasses /auth/jwt/login's 5/min/IP rate
+    limit so Playwright workers can authenticate without thrashing the
+    production auth surface. See tests/e2e/auth_mint.py.
+    """
+    from tests.e2e.auth_mint import install as install_e2e_mint
+    from tests.e2e.middleware.scenario import ScenarioMiddleware
 
-app.add_middleware(ScenarioMiddleware)
+    app.add_middleware(ScenarioMiddleware)
+    install_e2e_mint(app)
 
 
-# ---------------------------------------------------------------------------
-# 6) Start uvicorn, mirroring main.py's behaviour.
-# ---------------------------------------------------------------------------
+def _bootstrap():
+    """Run the full E2E bootstrap and return the production FastAPI app.
 
-import asyncio  # noqa: E402
+    Ordering is load-bearing:
+      1) Hijack composio + notion_client in sys.modules.
+      2) Load .env + set env defaults (app.config reads env on import).
+      3) Configure logging.
+      4) Import production app (which transitively imports the now-faked
+         external SDKs and reads the env defaults).
+      5) Patch LLM / embedding bindings at every consumer site.
+      6) Mount test-only middleware + /__e2e__ routes onto the app.
+    """
+    _hijack_external_sdks()
+    _load_dotenv_and_set_env_defaults()
 
-import uvicorn  # noqa: E402
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+    logger.warning(
+        "*** SURFSENSE E2E BACKEND ENTRYPOINT — fake Composio + LLM + embeddings ***"
+    )
+
+    production_app = _import_production_app()
+    _patch_llm_bindings()
+    _install_runtime_fakes()
+    _install_test_only_app_extensions(production_app)
+    return production_app
+
+
+app = _bootstrap()
 
 
 def _main() -> None:
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index 0fecc73ef..d645e978f 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -28,7 +28,7 @@ export default defineConfig({
 	fullyParallel: true,
 	forbidOnly: !!process.env.CI,
 	retries: process.env.CI ? 2 : 0,
-	workers: process.env.CI ? 1 : undefined,
+	workers: 1,
 	reporter: process.env.CI
 		? [["html", { open: "never" }], ["github"], ["list"]]
 		: [["html", { open: "on-failure" }], ["list"]],
diff --git a/surfsense_web/tests/auth.setup.ts b/surfsense_web/tests/auth.setup.ts
index 064552904..a33a81b3c 100644
--- a/surfsense_web/tests/auth.setup.ts
+++ b/surfsense_web/tests/auth.setup.ts
@@ -1,46 +1,21 @@
 import path from "node:path";
 import { expect, test as setup } from "@playwright/test";
+import { acquireTestToken } from "./helpers/api/auth";
 
 /**
- * One-time authentication setup. Logs in via the FastAPI backend directly
- * (skipping the UI) and persists the resulting localStorage token so every
- * test in the chromium project starts already authenticated.
- *
- * Mirrors the real auth flow in `lib/apis/auth-api.service.ts`:
- *   POST /auth/jwt/login  ->  { access_token }
- *   localStorage.setItem("surfsense_bearer_token", access_token)
- *
- * Requires a seeded test user in the dev/test DB. Defaults match the
- * docker/docker-compose.e2e.yml local stack and can be overridden via env.
+ * One-time authentication setup. Acquires a bearer token for the seeded
+ * e2e user (rate-limit-free /__e2e__/auth/token first, /auth/jwt/login
+ * fallback) and persists it via localStorage so every test in the
+ * chromium project starts already authenticated.
  */
 
 const authFile = path.join(__dirname, "..", "playwright", ".auth", "user.json");
 
-const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "e2e-test@surfsense.net";
-const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "E2eTestPassword123!";
-const BACKEND_URL = process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL || "http://localhost:8000";
 const STORAGE_KEY = "surfsense_bearer_token";
 
 setup("authenticate", async ({ page, request }) => {
-	const response = await request.post(`${BACKEND_URL}/auth/jwt/login`, {
-		form: {
-			username: TEST_USER_EMAIL,
-			password: TEST_USER_PASSWORD,
-			grant_type: "password",
-		},
-		headers: { "Content-Type": "application/x-www-form-urlencoded" },
-	});
-
-	expect(
-		response.ok(),
-		`Login to ${BACKEND_URL}/auth/jwt/login failed (${response.status()}). ` +
-			`Check that the backend is running and that PLAYWRIGHT_TEST_EMAIL ` +
-			`(${TEST_USER_EMAIL}) is seeded with PLAYWRIGHT_TEST_PASSWORD. ` +
-			`Body: ${await response.text()}`
-	).toBeTruthy();
-
-	const { access_token } = (await response.json()) as { access_token: string };
-	expect(access_token, "Backend response missing access_token").toBeTruthy();
+	const access_token = await acquireTestToken(request);
+	expect(access_token, "Failed to acquire e2e bearer token").toBeTruthy();
 
 	await page.addInitScript(
 		({ key, token }) => {
diff --git a/surfsense_web/tests/fixtures/search-space.fixture.ts b/surfsense_web/tests/fixtures/search-space.fixture.ts
index defde7048..62958caf4 100644
--- a/surfsense_web/tests/fixtures/search-space.fixture.ts
+++ b/surfsense_web/tests/fixtures/search-space.fixture.ts
@@ -1,5 +1,7 @@
+import fs from "node:fs";
+import path from "node:path";
 import { test as base } from "@playwright/test";
-import { loginAsTestUser } from "../helpers/api/auth";
+import { acquireTestToken } from "../helpers/api/auth";
 import {
 	createSearchSpace,
 	deleteSearchSpace,
@@ -20,12 +22,45 @@ export type SearchSpaceFixtures = {
 	searchSpace: SearchSpaceRow;
 };
 
+const STORAGE_KEY = "surfsense_bearer_token";
+
+// Reuse the token written by tests/auth.setup.ts; on cache miss we
+// mint a fresh one via /__e2e__/auth/token (rate-limit-free).
+const AUTH_STATE_PATH = path.join(__dirname, "..", "..", "playwright", ".auth", "user.json");
+
+function loadCachedBearerToken(): string | null {
+	try {
+		const raw = fs.readFileSync(AUTH_STATE_PATH, "utf8");
+		const parsed = JSON.parse(raw) as {
+			origins?: Array<{
+				origin?: string;
+				localStorage?: Array<{ name?: string; value?: string }>;
+			}>;
+		};
+		for (const origin of parsed.origins ?? []) {
+			for (const entry of origin.localStorage ?? []) {
+				if (entry.name === STORAGE_KEY && entry.value) {
+					return entry.value;
+				}
+			}
+		}
+	} catch {
+		// Fall back to a fresh login.
+	}
+	return null;
+}
+
 export const searchSpaceFixtures = base.extend<SearchSpaceFixtures, { apiTokenWorker: string }>({
 	apiTokenWorker: [
 		async ({ playwright }, use) => {
+			const cached = loadCachedBearerToken();
+			if (cached) {
+				await use(cached);
+				return;
+			}
 			const ctx = await playwright.request.newContext();
 			try {
-				const token = await loginAsTestUser(ctx);
+				const token = await acquireTestToken(ctx);
 				await use(token);
 			} finally {
 				await ctx.dispose();
diff --git a/surfsense_web/tests/helpers/api/auth.ts b/surfsense_web/tests/helpers/api/auth.ts
index 02aeb6d69..2071a80f4 100644
--- a/surfsense_web/tests/helpers/api/auth.ts
+++ b/surfsense_web/tests/helpers/api/auth.ts
@@ -13,6 +13,38 @@ export const BACKEND_URL = process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL || "http:
 
 const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "e2e-test@surfsense.net";
 const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "E2eTestPassword123!";
+const E2E_MINT_SECRET =
+	process.env.E2E_MINT_SECRET || "local-e2e-mint-secret-not-for-production";
+
+/**
+ * Mints a JWT for the seeded e2e user via the test-only endpoint mounted
+ * by surfsense_backend/tests/e2e/run_backend.py. Bypasses the production
+ * /auth/jwt/login rate limit (5/min/IP), so it's safe to call from any
+ * worker / retry. Returns 404 from the backend when the endpoint isn't
+ * mounted (i.e. someone is pointing the suite at a non-e2e backend).
+ */
+export async function mintTestToken(
+	request: APIRequestContext,
+	email: string = TEST_USER_EMAIL
+): Promise<string> {
+	const response = await request.post(`${BACKEND_URL}/__e2e__/auth/token`, {
+		data: { email },
+		headers: {
+			"Content-Type": "application/json",
+			"X-E2E-Mint-Secret": E2E_MINT_SECRET,
+		},
+	});
+	if (!response.ok()) {
+		throw new Error(
+			`Mint token at ${BACKEND_URL}/__e2e__/auth/token failed (${response.status()}): ${await response.text()}`
+		);
+	}
+	const { access_token } = (await response.json()) as { access_token: string };
+	if (!access_token) {
+		throw new Error("Mint response missing access_token");
+	}
+	return access_token;
+}
 
 export async function loginAsTestUser(request: APIRequestContext): Promise<string> {
 	const response = await request.post(`${BACKEND_URL}/auth/jwt/login`, {
@@ -37,6 +69,23 @@ export async function loginAsTestUser(request: APIRequestContext): Promise<strin
 	return access_token;
 }
 
+/**
+ * Get a bearer token by trying the rate-limit-free mint endpoint first
+ * and falling back to /auth/jwt/login if the e2e endpoint isn't mounted
+ * (e.g. running against a non-e2e backend in local dev).
+ */
+export async function acquireTestToken(request: APIRequestContext): Promise<string> {
+	try {
+		return await mintTestToken(request);
+	} catch (err) {
+		const msg = err instanceof Error ? err.message : String(err);
+		if (msg.includes("(404)") || msg.includes("(405)")) {
+			return loginAsTestUser(request);
+		}
+		throw err;
+	}
+}
+
 /**
  * Standard auth headers for backend API calls. Optionally injects an
  * X-E2E-Scenario header that the test-only ScenarioMiddleware in

From c052fc9304e07e3891ff49b0edb6335c629d3026 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 00:30:16 +0530
Subject: [PATCH 24/36] chore: add fake DoclingService for E2E tests and
 integrate into runtime fakes

---
 .../tests/e2e/fakes/docling_service.py        | 139 ++++++++++++++++++
 surfsense_backend/tests/e2e/run_backend.py    |   2 +
 surfsense_backend/tests/e2e/run_celery.py     |   2 +
 3 files changed, 143 insertions(+)
 create mode 100644 surfsense_backend/tests/e2e/fakes/docling_service.py

diff --git a/surfsense_backend/tests/e2e/fakes/docling_service.py b/surfsense_backend/tests/e2e/fakes/docling_service.py
new file mode 100644
index 000000000..2486f5db6
--- /dev/null
+++ b/surfsense_backend/tests/e2e/fakes/docling_service.py
@@ -0,0 +1,139 @@
+"""Stub DoclingService.process_document for E2E.
+
+The real ``DoclingService.process_document`` calls
+``DocumentConverter.convert(file_path)`` which lazily downloads the
+``docling-project/docling-layout-heron`` model from Hugging Face Hub.
+The hermetic E2E container sets ``HF_HUB_OFFLINE=1`` (see
+``docker/docker-compose.e2e.yml``), so that download fails with
+``LocalEntryNotFoundError`` and the indexing Celery task retries until
+the Playwright test hits its ~4-minute step timeout. In CI that is the
+difference between the suite finishing and the 30-minute job timeout
+killing the run before any report can upload.
+
+Stubbing ``process_document`` bypasses ``DocumentConverter.convert()``
+entirely. ``DoclingService.__init__`` is intentionally left untouched
+because constructing ``DocumentConverter(...)`` is cheap and offline —
+it is only ``.convert()`` that triggers the offline-model download.
+
+Every canary PDF under ``tests/e2e/fakes/fixtures/binary/`` is produced
+by ``generate_canary_pdfs.py`` and embeds its canary token as plain
+``(text) Tj`` PDF text operators. Extracting those operators gives us
+the canary string back, which is what the Playwright assertions look
+for in the resulting Document row.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+# Matches the `(escaped text) Tj` text-show operator emitted by
+# generate_canary_pdfs.py. Inside the parens, the escape rules are:
+#   \\  -> backslash
+#   \(  -> literal (
+#   \)  -> literal )
+# The character class [^\\()] consumes any non-escape byte; \\. consumes
+# an escape sequence. Sufficient for our synthetic fixtures.
+_TJ_PATTERN = re.compile(rb"\(((?:[^\\()]|\\.)*)\)\s*Tj")
+
+
+def _extract_text_from_synthetic_pdf(file_path: str) -> str:
+    """Pull every ``(text) Tj`` payload out of a fixture PDF in order.
+
+    Returns an empty string if the file cannot be read. We do not try to
+    handle arbitrary PDFs because the fake is only ever invoked against
+    fixtures we generate ourselves.
+    """
+    try:
+        data = Path(file_path).read_bytes()
+    except OSError as exc:
+        logger.warning("[fake-docling] could not read %s: %s", file_path, exc)
+        return ""
+
+    lines: list[str] = []
+    for match in _TJ_PATTERN.finditer(data):
+        raw = match.group(1)
+        # Order-sensitive unescape via sentinel: protect `\\` first so
+        # the subsequent `\(` / `\)` passes do not corrupt it.
+        text = (
+            raw.replace(rb"\\", b"\x00")
+            .replace(rb"\(", b"(")
+            .replace(rb"\)", b")")
+            .replace(b"\x00", b"\\")
+        )
+        try:
+            lines.append(text.decode("utf-8"))
+        except UnicodeDecodeError:
+            lines.append(text.decode("latin-1"))
+    return "\n".join(lines)
+
+
+async def fake_process_document(
+    self,
+    file_path: str,
+    filename: str | None = None,
+) -> dict[str, Any]:
+    """Drop-in replacement for ``DoclingService.process_document``.
+
+    Returns the same dict shape as the production method so callers
+    (``app/etl_pipeline/parsers/docling.py``) can keep reading
+    ``result["content"]`` without changes.
+    """
+    extracted = _extract_text_from_synthetic_pdf(file_path)
+    display_name = filename or Path(file_path).name
+
+    if extracted:
+        content = f"# {display_name}\n\n{extracted}\n"
+    else:
+        # Empty fallback so the indexing pipeline does not error out on
+        # an unexpected payload. A failing canary assertion is a much
+        # clearer failure mode than a hard parser exception.
+        content = f"# {display_name}\n\n(empty docling fake — no text-show operators found)\n"
+
+    logger.info(
+        "[fake-docling] returning %d chars for %s",
+        len(content),
+        display_name,
+    )
+
+    return {
+        "content": content,
+        "full_text": content,
+        "service_used": "docling-fake",
+        "status": "success",
+        "processing_notes": "e2e fake DoclingService — no real PDF parsing",
+    }
+
+
+def install(patches: list[Any]) -> None:
+    """Patch ``DoclingService.process_document`` at the class level.
+
+    Patching the class method (rather than each call site) is correct
+    here because every consumer goes through
+    ``create_docling_service()`` → ``DoclingService()`` → instance method
+    dispatch, so the descriptor protocol picks up our replacement. There
+    is exactly one such consumer today
+    (``app/etl_pipeline/parsers/docling.py``), but patching the class is
+    future-proof.
+
+    Fails loud rather than warning, because a silent passthrough means
+    real Docling + ``HF_HUB_OFFLINE=1`` = 4 minutes of CI hang per test.
+    """
+    from unittest.mock import patch as _patch
+
+    target = "app.services.docling_service.DoclingService.process_document"
+    try:
+        p = _patch(target, fake_process_document)
+        p.start()
+        patches.append(p)
+        logger.info("[fake-docling] patched %s", target)
+    except (ModuleNotFoundError, AttributeError) as exc:
+        raise RuntimeError(
+            f"Could not patch Docling binding {target!r}: {exc!s}. "
+            f"Update surfsense_backend/tests/e2e/fakes/docling_service.py "
+            f"to point at the new binding site."
+        ) from exc
diff --git a/surfsense_backend/tests/e2e/run_backend.py b/surfsense_backend/tests/e2e/run_backend.py
index c5cb163a1..7419173a7 100644
--- a/surfsense_backend/tests/e2e/run_backend.py
+++ b/surfsense_backend/tests/e2e/run_backend.py
@@ -209,6 +209,7 @@ def _install_runtime_fakes() -> None:
         clickup_module as _fake_clickup_module,
         confluence_indexer as _fake_confluence_indexer,
         confluence_oauth as _fake_confluence_oauth,
+        docling_service as _fake_docling_service,
         dropbox_api as _fake_dropbox_api,
         embeddings as _fake_embeddings,
         jira_module as _fake_jira_module,
@@ -222,6 +223,7 @@ def _install_runtime_fakes() -> None:
     )
 
     _fake_embeddings.install(_active_patches)
+    _fake_docling_service.install(_active_patches)
     _fake_confluence_oauth.install(_active_patches)
     _fake_confluence_indexer.install(_active_patches)
     _fake_native_google.install(_active_patches)
diff --git a/surfsense_backend/tests/e2e/run_celery.py b/surfsense_backend/tests/e2e/run_celery.py
index 787eb5486..3b7c75bb1 100644
--- a/surfsense_backend/tests/e2e/run_celery.py
+++ b/surfsense_backend/tests/e2e/run_celery.py
@@ -117,6 +117,7 @@ from tests.e2e.fakes import (  # noqa: E402
     clickup_module as _fake_clickup_module,
     confluence_indexer as _fake_confluence_indexer,
     confluence_oauth as _fake_confluence_oauth,
+    docling_service as _fake_docling_service,
     dropbox_api as _fake_dropbox_api,
     embeddings as _fake_embeddings,
     jira_module as _fake_jira_module,
@@ -197,6 +198,7 @@ def _patch_llm_bindings() -> None:
 
 _patch_llm_bindings()
 _fake_embeddings.install(_active_patches)
+_fake_docling_service.install(_active_patches)
 _fake_confluence_oauth.install(_active_patches)
 _fake_confluence_indexer.install(_active_patches)
 _fake_native_google.install(_active_patches)

From 315329f344fe22837444515526041fbfff7d0b2a Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 01:35:33 +0530
Subject: [PATCH 25/36] chore: update E2E tests workflow to capture logs on
 cancellation and add shared volume for backend services

---
 .github/workflows/e2e-tests.yml | 4 ++--
 docker/docker-compose.e2e.yml   | 7 +++++++
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index 2b7b6f1a7..d2338f092 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -130,7 +130,7 @@ jobs:
 
       # ─── Failure diagnostics ───────────────────────────────────────────
       - name: Dump backend stack logs on failure
-        if: failure()
+        if: ${{ failure() || cancelled() }}
         run: |
           mkdir -p ./compose-logs
           docker compose -f docker/docker-compose.e2e.yml logs --no-color --timestamps \
@@ -160,7 +160,7 @@ jobs:
           retention-days: 14
 
       - name: Upload backend stack logs
-        if: failure()
+        if: ${{ failure() || cancelled() }}
         uses: actions/upload-artifact@v7
         with:
           name: backend-stack-logs
diff --git a/docker/docker-compose.e2e.yml b/docker/docker-compose.e2e.yml
index b34d8d82d..2d55595f7 100644
--- a/docker/docker-compose.e2e.yml
+++ b/docker/docker-compose.e2e.yml
@@ -109,6 +109,8 @@ services:
     environment:
       <<: *backend-env
       SERVICE_ROLE: api
+    volumes:
+      - shared_temp:/shared_tmp
     extra_hosts:
       - "host.docker.internal:host-gateway"
     ports:
@@ -147,6 +149,8 @@ services:
     environment:
       <<: *backend-env
       SERVICE_ROLE: worker
+    volumes:
+      - shared_temp:/shared_tmp
     depends_on:
       backend: { condition: service_healthy }
     healthcheck:
@@ -172,3 +176,6 @@ networks:
   # redis stay off this network entirely.
   ingress:
     driver: bridge
+
+volumes:
+  shared_temp:

From 650b691a398d9f78b1875bdaf76b221aa10a8a94 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 02:37:39 +0530
Subject: [PATCH 26/36] chore: enhance E2E tests by adding synthetic global LLM
 config and updating environment variables for Google OAuth

---
 .github/workflows/e2e-tests.yml               |  3 +-
 surfsense_backend/.gitignore                  |  2 +-
 .../tests/e2e/fixtures/global_llm_config.yaml | 45 +++++++++++
 surfsense_backend/tests/e2e/run_backend.py    | 75 ++++++++++++++++++-
 surfsense_backend/tests/e2e/run_celery.py     | 48 ++++++++++++
 surfsense_web/playwright.config.ts            |  2 +-
 .../documents/file-upload/journey.spec.ts     |  4 +-
 7 files changed, 170 insertions(+), 9 deletions(-)
 create mode 100644 surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml

diff --git a/.github/workflows/e2e-tests.yml b/.github/workflows/e2e-tests.yml
index d2338f092..b87537dab 100644
--- a/.github/workflows/e2e-tests.yml
+++ b/.github/workflows/e2e-tests.yml
@@ -119,9 +119,10 @@ jobs:
         uses: actions/cache@v5
         with:
           path: surfsense_web/.next/cache
-          key: nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-${{ hashFiles('surfsense_web/**/*.{js,jsx,ts,tsx}') }}
+          key: nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-${{ github.sha }}
           restore-keys: |
             nextjs-${{ runner.os }}-${{ hashFiles('surfsense_web/pnpm-lock.yaml') }}-
+            nextjs-${{ runner.os }}-
 
       # ─── Tests ─────────────────────────────────────────────────────────
       - name: Run Playwright tests
diff --git a/surfsense_backend/.gitignore b/surfsense_backend/.gitignore
index 1cd7fd32c..47fd53aef 100644
--- a/surfsense_backend/.gitignore
+++ b/surfsense_backend/.gitignore
@@ -13,5 +13,5 @@ celerybeat-schedule*
 celerybeat-schedule.*
 celerybeat-schedule.dir
 celerybeat-schedule.bak
-global_llm_config.yaml
+/app/config/global_llm_config.yaml
 app/templates/_generated/
\ No newline at end of file
diff --git a/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
new file mode 100644
index 000000000..ef00ac0c4
--- /dev/null
+++ b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
@@ -0,0 +1,45 @@
+# Synthetic Global LLM configuration for E2E ONLY.
+#
+# Why this file exists:
+#   surfsense_backend/app/config/global_llm_config.yaml is gitignored
+#   (operators ship real API keys there). In CI that file does not exist,
+#   so app.config.load_global_llm_configs() returns [], every chat-stream
+#   test fails fast with "No usable global LLM configs are available for
+#   Auto mode" raised by auto_model_pin_service._global_candidates().
+#
+# What this file does:
+#   tests/e2e/run_backend.py and tests/e2e/run_celery.py copy this file
+#   to app/config/global_llm_config.yaml at startup, BEFORE app.config
+#   is imported. The copy lives only inside the E2E Docker container.
+#
+# Why a fake api_key is safe:
+#   tests.e2e.fakes.chat_llm patches
+#     app.tasks.chat.stream_new_chat.create_chat_litellm_from_agent_config
+#     app.tasks.chat.stream_new_chat.create_chat_litellm_from_config
+#   so the resolved auto-pin id is never sent to a real LLM provider.
+#   The values below only need to pass
+#   auto_model_pin_service._is_usable_global_config()
+#   which requires id / model_name / provider / api_key all truthy.
+
+router_settings:
+  routing_strategy: "simple-shuffle"
+  num_retries: 0
+  allowed_fails: 1
+  cooldown_time: 1
+
+global_llm_configs:
+  - id: 1001
+    name: "E2E Fake Auto Model"
+    billing_tier: "free"
+    anonymous_enabled: false
+    seo_enabled: false
+    quality_score: 1.0
+    provider: "OPENAI"
+    model_name: "fake-e2e-model"
+    api_key: "fake-e2e-api-key-not-for-production"
+    supports_image_input: false
+    quota_reserve_tokens: 1024
+    rpm: 1000
+    tpm: 100000
+    litellm_params:
+      model: "openai/fake-e2e-model"
diff --git a/surfsense_backend/tests/e2e/run_backend.py b/surfsense_backend/tests/e2e/run_backend.py
index 7419173a7..d0c734751 100644
--- a/surfsense_backend/tests/e2e/run_backend.py
+++ b/surfsense_backend/tests/e2e/run_backend.py
@@ -120,10 +120,74 @@ def _load_dotenv_and_set_env_defaults() -> None:
         "DROPBOX_REDIRECT_URI",
         "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
     )
+    # Native Google OAuth — fake Flow in tests.e2e.fakes.native_google
+    # raises "Fake Google Flow requires redirect_uri." if these are empty,
+    # so connector/add routes return 500 in CI where no .env supplies them.
+    os.environ.setdefault(
+        "GOOGLE_DRIVE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/drive/connector/callback",
+    )
+    os.environ.setdefault(
+        "GOOGLE_GMAIL_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/gmail/connector/callback",
+    )
+    os.environ.setdefault(
+        "GOOGLE_CALENDAR_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/calendar/connector/callback",
+    )
     os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
     os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
 
 
+def _install_synthetic_global_llm_config() -> None:
+    """Materialise a fake ``app/config/global_llm_config.yaml`` for E2E.
+
+    The real file is gitignored (production operators ship their own with
+    real API keys), so a fresh CI checkout has no YAML at the path
+    ``app.config.load_global_llm_configs()`` reads. With an empty
+    ``GLOBAL_LLM_CONFIGS`` list, ``auto_model_pin_service`` raises
+    ``"No usable global LLM configs are available for Auto mode"`` on
+    every chat-stream request.
+
+    We copy the synthetic fixture from ``tests/e2e/fixtures/`` into the
+    production-expected location BEFORE ``_import_production_app()`` so
+    ``app.config`` picks it up on import. Production code is untouched —
+    this is purely a test-time scaffold.
+
+    Only installs when the destination is missing. A developer running
+    the E2E entrypoint locally keeps their real ``global_llm_config.yaml``
+    intact (the patched ``create_chat_litellm_from_*`` factories make the
+    actual model values irrelevant either way).
+
+    MUST run before _import_production_app().
+    """
+    import shutil
+
+    src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
+    dst = os.path.join(
+        _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
+    )
+
+    if not os.path.exists(src):
+        raise RuntimeError(
+            f"E2E synthetic global LLM config fixture missing at {src!r}. "
+            f"This file is checked into tests/e2e/fixtures/ — if it has gone "
+            f"missing, restore it from VCS before running the E2E entrypoint."
+        )
+
+    if os.path.exists(dst):
+        logger.info(
+            "[e2e-global-llm-config] %s already exists; leaving it alone "
+            "(local dev config preserved)",
+            dst,
+        )
+        return
+
+    os.makedirs(os.path.dirname(dst), exist_ok=True)
+    shutil.copyfile(src, dst)
+    logger.info("[e2e-global-llm-config] installed %s -> %s", src, dst)
+
+
 def _import_production_app():
     """Import and return the production FastAPI app.
 
@@ -259,10 +323,12 @@ def _bootstrap():
       1) Hijack composio + notion_client in sys.modules.
       2) Load .env + set env defaults (app.config reads env on import).
       3) Configure logging.
-      4) Import production app (which transitively imports the now-faked
-         external SDKs and reads the env defaults).
-      5) Patch LLM / embedding bindings at every consumer site.
-      6) Mount test-only middleware + /__e2e__ routes onto the app.
+      4) Materialise the synthetic global_llm_config.yaml so Auto-mode
+         pin resolution finds at least one usable candidate.
+      5) Import production app (which transitively imports the now-faked
+         external SDKs and reads the env defaults + YAML).
+      6) Patch LLM / embedding bindings at every consumer site.
+      7) Mount test-only middleware + /__e2e__ routes onto the app.
     """
     _hijack_external_sdks()
     _load_dotenv_and_set_env_defaults()
@@ -276,6 +342,7 @@ def _bootstrap():
         "*** SURFSENSE E2E BACKEND ENTRYPOINT — fake Composio + LLM + embeddings ***"
     )
 
+    _install_synthetic_global_llm_config()
     production_app = _import_production_app()
     _patch_llm_bindings()
     _install_runtime_fakes()
diff --git a/surfsense_backend/tests/e2e/run_celery.py b/surfsense_backend/tests/e2e/run_celery.py
index 3b7c75bb1..56480a295 100644
--- a/surfsense_backend/tests/e2e/run_celery.py
+++ b/surfsense_backend/tests/e2e/run_celery.py
@@ -91,6 +91,20 @@ os.environ.setdefault(
     "DROPBOX_REDIRECT_URI",
     "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
 )
+# Native Google OAuth — fake Flow in tests.e2e.fakes.native_google raises
+# "Fake Google Flow requires redirect_uri." when these are empty.
+os.environ.setdefault(
+    "GOOGLE_DRIVE_REDIRECT_URI",
+    "http://localhost:8000/api/v1/auth/google/drive/connector/callback",
+)
+os.environ.setdefault(
+    "GOOGLE_GMAIL_REDIRECT_URI",
+    "http://localhost:8000/api/v1/auth/google/gmail/connector/callback",
+)
+os.environ.setdefault(
+    "GOOGLE_CALENDAR_REDIRECT_URI",
+    "http://localhost:8000/api/v1/auth/google/calendar/connector/callback",
+)
 os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
 os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
 
@@ -103,6 +117,40 @@ logger = logging.getLogger("surfsense.e2e.celery")
 logger.warning("*** SURFSENSE E2E CELERY WORKER — fake Composio + LLM + embeddings ***")
 
 
+# ---------------------------------------------------------------------------
+# 2.5) Materialise the synthetic global_llm_config.yaml so the worker's
+#      view of app.config.GLOBAL_LLM_CONFIGS matches the API container.
+#      Must run BEFORE the production celery_app import below, which
+#      transitively imports app.config. Install-only-if-missing so a
+#      developer's local config (with real API keys) is preserved.
+# ---------------------------------------------------------------------------
+import shutil as _shutil  # noqa: E402
+
+_e2e_llm_cfg_src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
+_e2e_llm_cfg_dst = os.path.join(
+    _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
+)
+if not os.path.exists(_e2e_llm_cfg_src):
+    raise RuntimeError(
+        f"E2E synthetic global LLM config fixture missing at {_e2e_llm_cfg_src!r}. "
+        f"Restore tests/e2e/fixtures/global_llm_config.yaml from VCS."
+    )
+if os.path.exists(_e2e_llm_cfg_dst):
+    logger.info(
+        "[e2e-global-llm-config] %s already exists; leaving it alone "
+        "(local dev config preserved)",
+        _e2e_llm_cfg_dst,
+    )
+else:
+    os.makedirs(os.path.dirname(_e2e_llm_cfg_dst), exist_ok=True)
+    _shutil.copyfile(_e2e_llm_cfg_src, _e2e_llm_cfg_dst)
+    logger.info(
+        "[e2e-global-llm-config] installed %s -> %s",
+        _e2e_llm_cfg_src,
+        _e2e_llm_cfg_dst,
+    )
+
+
 # ---------------------------------------------------------------------------
 # 3) Import the production celery_app. All task modules load here.
 # ---------------------------------------------------------------------------
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index d645e978f..eb287635d 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -27,7 +27,7 @@ export default defineConfig({
 	expect: { timeout: 15_000 },
 	fullyParallel: true,
 	forbidOnly: !!process.env.CI,
-	retries: process.env.CI ? 2 : 0,
+	retries: process.env.CI ? 1 : 0,
 	workers: 1,
 	reporter: process.env.CI
 		? [["html", { open: "never" }], ["github"], ["list"]]
diff --git a/surfsense_web/tests/documents/file-upload/journey.spec.ts b/surfsense_web/tests/documents/file-upload/journey.spec.ts
index 6ddfb522f..711963bf0 100644
--- a/surfsense_web/tests/documents/file-upload/journey.spec.ts
+++ b/surfsense_web/tests/documents/file-upload/journey.spec.ts
@@ -107,14 +107,14 @@ test.describe("Manual file upload journey", () => {
 		});
 	});
 
-	test("user uploads a PDF (DOCUMENT branch via real Docling)", async ({
+	test("user uploads a PDF (DOCUMENT branch)", async ({
 		page,
 		request,
 		apiToken,
 		searchSpace,
 		chatThread,
 	}) => {
-		test.setTimeout(240_000); // Docling cold-start can take 30-60s on first invocation.
+		test.setTimeout(180_000);
 
 		await uploadAndAssert({
 			page,

From 0b9fc00663510d3c9a99023928770711be2ffa56 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 03:00:35 +0530
Subject: [PATCH 27/36] chore: update global LLM config fixture to include both
 premium and free models for comprehensive E2E testing

---
 .../tests/e2e/fixtures/global_llm_config.yaml | 34 ++++++++++++++++---
 1 file changed, 30 insertions(+), 4 deletions(-)

diff --git a/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
index ef00ac0c4..f35974957 100644
--- a/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
+++ b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
@@ -20,6 +20,16 @@
 #   The values below only need to pass
 #   auto_model_pin_service._is_usable_global_config()
 #   which requires id / model_name / provider / api_key all truthy.
+#
+# Why TWO entries (premium + free):
+#   auto_model_pin_service.resolve_or_get_pinned_llm_config_id() splits
+#   candidates by billing_tier based on _is_premium_eligible(user):
+#     premium_eligible == True  -> keeps only tier=="premium" configs
+#     premium_eligible == False -> keeps only tier!="premium" configs
+#   A single-tier fixture would fail one of the two branches with
+#   "Auto mode could not find an eligible LLM config for this user and
+#   quota state". Shipping one of each guarantees every quota state
+#   resolves to a viable pin in E2E.
 
 router_settings:
   routing_strategy: "simple-shuffle"
@@ -29,17 +39,33 @@ router_settings:
 
 global_llm_configs:
   - id: 1001
-    name: "E2E Fake Auto Model"
-    billing_tier: "free"
+    name: "E2E Fake Auto Model (premium)"
+    billing_tier: "premium"
     anonymous_enabled: false
     seo_enabled: false
     quality_score: 1.0
     provider: "OPENAI"
-    model_name: "fake-e2e-model"
+    model_name: "fake-e2e-model-premium"
     api_key: "fake-e2e-api-key-not-for-production"
     supports_image_input: false
     quota_reserve_tokens: 1024
     rpm: 1000
     tpm: 100000
     litellm_params:
-      model: "openai/fake-e2e-model"
+      model: "openai/fake-e2e-model-premium"
+
+  - id: 1002
+    name: "E2E Fake Auto Model (free)"
+    billing_tier: "free"
+    anonymous_enabled: false
+    seo_enabled: false
+    quality_score: 1.0
+    provider: "OPENAI"
+    model_name: "fake-e2e-model-free"
+    api_key: "fake-e2e-api-key-not-for-production"
+    supports_image_input: false
+    quota_reserve_tokens: 1024
+    rpm: 1000
+    tpm: 100000
+    litellm_params:
+      model: "openai/fake-e2e-model-free"

From bed2041a1b123012202a9dcc75f372d32b66b93e Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 03:30:01 +0530
Subject: [PATCH 28/36] chore: modify E2E test configuration by updating global
 LLM model IDs to negative values for improved test isolation

---
 .../tests/e2e/fixtures/global_llm_config.yaml |   4 +-
 surfsense_backend/tests/e2e/run_celery.py     | 399 ++++++++++--------
 2 files changed, 225 insertions(+), 178 deletions(-)

diff --git a/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
index f35974957..017fa1eb3 100644
--- a/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
+++ b/surfsense_backend/tests/e2e/fixtures/global_llm_config.yaml
@@ -38,7 +38,7 @@ router_settings:
   cooldown_time: 1
 
 global_llm_configs:
-  - id: 1001
+  - id: -9001
     name: "E2E Fake Auto Model (premium)"
     billing_tier: "premium"
     anonymous_enabled: false
@@ -54,7 +54,7 @@ global_llm_configs:
     litellm_params:
       model: "openai/fake-e2e-model-premium"
 
-  - id: 1002
+  - id: -9002
     name: "E2E Fake Auto Model (free)"
     billing_tier: "free"
     anonymous_enabled: false
diff --git a/surfsense_backend/tests/e2e/run_celery.py b/surfsense_backend/tests/e2e/run_celery.py
index 56480a295..fd6cc88cf 100644
--- a/surfsense_backend/tests/e2e/run_celery.py
+++ b/surfsense_backend/tests/e2e/run_celery.py
@@ -25,168 +25,168 @@ if _BACKEND_ROOT not in sys.path:
     sys.path.insert(0, _BACKEND_ROOT)
 
 
-# ---------------------------------------------------------------------------
-# 1) Hijack sys.modules BEFORE production celery imports anything.
-# ---------------------------------------------------------------------------
-
-import tests.e2e.fakes.composio_module as _fake_composio  # noqa: E402
-import tests.e2e.fakes.notion_module as _fake_notion  # noqa: E402
-
-sys.modules["composio"] = _fake_composio
-sys.modules["notion_client"] = _fake_notion
-sys.modules["notion_client.errors"] = _fake_notion.errors
-
-
-# ---------------------------------------------------------------------------
-# 2) Logging + dotenv.
-# ---------------------------------------------------------------------------
-
-from dotenv import load_dotenv  # noqa: E402
-
-load_dotenv()
-
-os.environ.setdefault(
-    "DATABASE_URL",
-    "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
-)
-os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
-os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
-os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
-os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
-os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
-os.environ.setdefault("AUTH_TYPE", "LOCAL")
-os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
-os.environ.setdefault("ETL_SERVICE", "DOCLING")
-os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
-os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
-
-# Sentinel keys — fakes never read them; turns leaked real calls into 401s.
-os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
-os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
-os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
-
-os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
-os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
-os.environ.setdefault(
-    "CONFLUENCE_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/confluence/connector/callback",
-)
-os.environ.setdefault("NOTION_CLIENT_ID", "fake-notion-client-id")
-os.environ.setdefault("NOTION_CLIENT_SECRET", "fake-notion-client-secret")
-os.environ.setdefault(
-    "NOTION_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/notion/connector/callback",
-)
-os.environ.setdefault("MICROSOFT_CLIENT_ID", "fake-microsoft-client-id")
-os.environ.setdefault("MICROSOFT_CLIENT_SECRET", "fake-microsoft-client-secret")
-os.environ.setdefault(
-    "ONEDRIVE_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/onedrive/connector/callback",
-)
-os.environ.setdefault("DROPBOX_APP_KEY", "fake-dropbox-app-key")
-os.environ.setdefault("DROPBOX_APP_SECRET", "fake-dropbox-app-secret")
-os.environ.setdefault(
-    "DROPBOX_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
-)
-# Native Google OAuth — fake Flow in tests.e2e.fakes.native_google raises
-# "Fake Google Flow requires redirect_uri." when these are empty.
-os.environ.setdefault(
-    "GOOGLE_DRIVE_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/google/drive/connector/callback",
-)
-os.environ.setdefault(
-    "GOOGLE_GMAIL_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/google/gmail/connector/callback",
-)
-os.environ.setdefault(
-    "GOOGLE_CALENDAR_REDIRECT_URI",
-    "http://localhost:8000/api/v1/auth/google/calendar/connector/callback",
-)
-os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
-os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
-)
 logger = logging.getLogger("surfsense.e2e.celery")
-logger.warning("*** SURFSENSE E2E CELERY WORKER — fake Composio + LLM + embeddings ***")
-
-
-# ---------------------------------------------------------------------------
-# 2.5) Materialise the synthetic global_llm_config.yaml so the worker's
-#      view of app.config.GLOBAL_LLM_CONFIGS matches the API container.
-#      Must run BEFORE the production celery_app import below, which
-#      transitively imports app.config. Install-only-if-missing so a
-#      developer's local config (with real API keys) is preserved.
-# ---------------------------------------------------------------------------
-import shutil as _shutil  # noqa: E402
-
-_e2e_llm_cfg_src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
-_e2e_llm_cfg_dst = os.path.join(
-    _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
-)
-if not os.path.exists(_e2e_llm_cfg_src):
-    raise RuntimeError(
-        f"E2E synthetic global LLM config fixture missing at {_e2e_llm_cfg_src!r}. "
-        f"Restore tests/e2e/fixtures/global_llm_config.yaml from VCS."
-    )
-if os.path.exists(_e2e_llm_cfg_dst):
-    logger.info(
-        "[e2e-global-llm-config] %s already exists; leaving it alone "
-        "(local dev config preserved)",
-        _e2e_llm_cfg_dst,
-    )
-else:
-    os.makedirs(os.path.dirname(_e2e_llm_cfg_dst), exist_ok=True)
-    _shutil.copyfile(_e2e_llm_cfg_src, _e2e_llm_cfg_dst)
-    logger.info(
-        "[e2e-global-llm-config] installed %s -> %s",
-        _e2e_llm_cfg_src,
-        _e2e_llm_cfg_dst,
-    )
-
-
-# ---------------------------------------------------------------------------
-# 3) Import the production celery_app. All task modules load here.
-# ---------------------------------------------------------------------------
-
-# ---------------------------------------------------------------------------
-# 4) Patch LLM + embedding bindings inside the worker process.
-# ---------------------------------------------------------------------------
-from unittest.mock import patch  # noqa: E402
-
-from app.celery_app import celery_app  # noqa: E402
-from tests.e2e.fakes import (  # noqa: E402
-    clickup_module as _fake_clickup_module,
-    confluence_indexer as _fake_confluence_indexer,
-    confluence_oauth as _fake_confluence_oauth,
-    docling_service as _fake_docling_service,
-    dropbox_api as _fake_dropbox_api,
-    embeddings as _fake_embeddings,
-    jira_module as _fake_jira_module,
-    linear_module as _fake_linear_module,
-    mcp_oauth_runtime as _fake_mcp_oauth_runtime,
-    mcp_runtime as _fake_mcp_runtime,
-    native_google as _fake_native_google,
-    notion_module as _fake_notion_module,
-    onedrive_graph as _fake_onedrive_graph,
-    slack_module as _fake_slack_module,
-)
-from tests.e2e.fakes.chat_llm import (  # noqa: E402
-    fake_create_chat_litellm_from_agent_config,
-    fake_create_chat_litellm_from_config,
-)
-from tests.e2e.fakes.llm import fake_get_user_long_context_llm  # noqa: E402
 
+# Patches started during bootstrap are kept alive for the lifetime of the
+# process. We never call .stop() on them.
 _active_patches: list = []
 
 
+def _hijack_external_sdks() -> None:
+    """Replace composio + notion_client in sys.modules.
+
+    Production does ``from composio import Composio`` and
+    ``import notion_client`` at import time. With this hijack in place,
+    those imports resolve to our strict fakes.
+
+    MUST run before _import_celery_app().
+    """
+    import tests.e2e.fakes.composio_module as _fake_composio
+    import tests.e2e.fakes.notion_module as _fake_notion
+
+    sys.modules["composio"] = _fake_composio
+    sys.modules["notion_client"] = _fake_notion
+    sys.modules["notion_client.errors"] = _fake_notion.errors
+
+
+def _load_dotenv_and_set_env_defaults() -> None:
+    """Load .env and set every env var the production config reads on import.
+
+    MUST run before _import_celery_app(), since app.config consumes
+    these values at import time.
+    """
+    from dotenv import load_dotenv
+
+    load_dotenv()
+
+    os.environ.setdefault(
+        "DATABASE_URL",
+        "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense",
+    )
+    os.environ.setdefault("CELERY_BROKER_URL", "redis://localhost:6379/0")
+    os.environ.setdefault("CELERY_RESULT_BACKEND", "redis://localhost:6379/0")
+    os.environ.setdefault("REDIS_APP_URL", "redis://localhost:6379/0")
+    os.environ.setdefault("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
+    os.environ.setdefault("SECRET_KEY", "local-e2e-secret-not-for-production")
+    os.environ.setdefault("AUTH_TYPE", "LOCAL")
+    os.environ.setdefault("REGISTRATION_ENABLED", "TRUE")
+    os.environ.setdefault("ETL_SERVICE", "DOCLING")
+    os.environ.setdefault("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    os.environ.setdefault("NEXT_FRONTEND_URL", "http://localhost:3000")
+
+    # Sentinel keys — fakes never read them; turns leaked real calls into 401s.
+    os.environ.setdefault("COMPOSIO_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("COMPOSIO_ENABLED", "TRUE")
+    os.environ.setdefault("OPENAI_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("ANTHROPIC_API_KEY", "local-deny-real-call-sentinel")
+    os.environ.setdefault("LITELLM_API_KEY", "local-deny-real-call-sentinel")
+
+    os.environ.setdefault("ATLASSIAN_CLIENT_ID", "fake-atlassian-client-id")
+    os.environ.setdefault("ATLASSIAN_CLIENT_SECRET", "fake-atlassian-client-secret")
+    os.environ.setdefault(
+        "CONFLUENCE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/confluence/connector/callback",
+    )
+    os.environ.setdefault("NOTION_CLIENT_ID", "fake-notion-client-id")
+    os.environ.setdefault("NOTION_CLIENT_SECRET", "fake-notion-client-secret")
+    os.environ.setdefault(
+        "NOTION_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/notion/connector/callback",
+    )
+    os.environ.setdefault("MICROSOFT_CLIENT_ID", "fake-microsoft-client-id")
+    os.environ.setdefault("MICROSOFT_CLIENT_SECRET", "fake-microsoft-client-secret")
+    os.environ.setdefault(
+        "ONEDRIVE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/onedrive/connector/callback",
+    )
+    os.environ.setdefault("DROPBOX_APP_KEY", "fake-dropbox-app-key")
+    os.environ.setdefault("DROPBOX_APP_SECRET", "fake-dropbox-app-secret")
+    os.environ.setdefault(
+        "DROPBOX_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/dropbox/connector/callback",
+    )
+    # Native Google OAuth — fake Flow in tests.e2e.fakes.native_google raises
+    # "Fake Google Flow requires redirect_uri." when these are empty.
+    os.environ.setdefault(
+        "GOOGLE_DRIVE_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/drive/connector/callback",
+    )
+    os.environ.setdefault(
+        "GOOGLE_GMAIL_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/gmail/connector/callback",
+    )
+    os.environ.setdefault(
+        "GOOGLE_CALENDAR_REDIRECT_URI",
+        "http://localhost:8000/api/v1/auth/google/calendar/connector/callback",
+    )
+    os.environ["SLACK_CLIENT_ID"] = "fake-slack-mcp-client-id"
+    os.environ["SLACK_CLIENT_SECRET"] = "fake-slack-mcp-client-secret"
+
+
+def _install_synthetic_global_llm_config() -> None:
+    """Materialise a fake ``app/config/global_llm_config.yaml`` for E2E.
+
+    The real file is gitignored (production operators ship their own with
+    real API keys), so a fresh CI checkout has no YAML at the path
+    ``app.config.load_global_llm_configs()`` reads. With an empty
+    ``GLOBAL_LLM_CONFIGS`` list, the worker's view of the config diverges
+    from the API container.
+
+    We copy the synthetic fixture from ``tests/e2e/fixtures/`` into the
+    production-expected location BEFORE _import_celery_app() so
+    ``app.config`` picks it up on import. Install-only-if-missing so a
+    developer's local config (with real API keys) is preserved.
+
+    MUST run before _import_celery_app().
+    """
+    import shutil
+
+    src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
+    dst = os.path.join(
+        _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
+    )
+
+    if not os.path.exists(src):
+        raise RuntimeError(
+            f"E2E synthetic global LLM config fixture missing at {src!r}. "
+            f"Restore tests/e2e/fixtures/global_llm_config.yaml from VCS."
+        )
+
+    if os.path.exists(dst):
+        logger.info(
+            "[e2e-global-llm-config] %s already exists; leaving it alone "
+            "(local dev config preserved)",
+            dst,
+        )
+        return
+
+    os.makedirs(os.path.dirname(dst), exist_ok=True)
+    shutil.copyfile(src, dst)
+    logger.info("[e2e-global-llm-config] installed %s -> %s", src, dst)
+
+
+def _import_celery_app():
+    """Import and return the production Celery app.
+
+    Every module under ``app.*`` (including all task modules) loads here,
+    creating their bindings. The LLM/embedding factories captured at this
+    point will be replaced by patches in _patch_llm_bindings() below.
+    """
+    from app.celery_app import celery_app
+
+    return celery_app
+
+
 def _patch_llm_bindings() -> None:
+    """Replace LLM factories at every known binding site in worker tasks."""
+    from unittest.mock import patch
+
+    from tests.e2e.fakes.chat_llm import (
+        fake_create_chat_litellm_from_agent_config,
+        fake_create_chat_litellm_from_config,
+    )
+    from tests.e2e.fakes.llm import fake_get_user_long_context_llm
+
     targets = [
         "app.services.llm_service.get_user_long_context_llm",
         "app.tasks.connector_indexers.confluence_indexer.get_user_long_context_llm",
@@ -244,31 +244,78 @@ def _patch_llm_bindings() -> None:
             )
 
 
-_patch_llm_bindings()
-_fake_embeddings.install(_active_patches)
-_fake_docling_service.install(_active_patches)
-_fake_confluence_oauth.install(_active_patches)
-_fake_confluence_indexer.install(_active_patches)
-_fake_native_google.install(_active_patches)
-_fake_onedrive_graph.install(_active_patches)
-_fake_dropbox_api.install(_active_patches)
-_fake_notion_module.install(_active_patches)
-_fake_linear_module.install(_active_patches)
-_fake_jira_module.install(_active_patches)
-_fake_clickup_module.install(_active_patches)
-_fake_mcp_runtime.install(_active_patches)
-_fake_mcp_oauth_runtime.install(_active_patches)
-_fake_slack_module.install(_active_patches)
+def _install_runtime_fakes() -> None:
+    """Run each fake's install() against the active patch stack."""
+    from tests.e2e.fakes import (
+        clickup_module as _fake_clickup_module,
+        confluence_indexer as _fake_confluence_indexer,
+        confluence_oauth as _fake_confluence_oauth,
+        docling_service as _fake_docling_service,
+        dropbox_api as _fake_dropbox_api,
+        embeddings as _fake_embeddings,
+        jira_module as _fake_jira_module,
+        linear_module as _fake_linear_module,
+        mcp_oauth_runtime as _fake_mcp_oauth_runtime,
+        mcp_runtime as _fake_mcp_runtime,
+        native_google as _fake_native_google,
+        notion_module as _fake_notion_module,
+        onedrive_graph as _fake_onedrive_graph,
+        slack_module as _fake_slack_module,
+    )
+
+    _fake_embeddings.install(_active_patches)
+    _fake_docling_service.install(_active_patches)
+    _fake_confluence_oauth.install(_active_patches)
+    _fake_confluence_indexer.install(_active_patches)
+    _fake_native_google.install(_active_patches)
+    _fake_onedrive_graph.install(_active_patches)
+    _fake_dropbox_api.install(_active_patches)
+    _fake_notion_module.install(_active_patches)
+    _fake_linear_module.install(_active_patches)
+    _fake_jira_module.install(_active_patches)
+    _fake_clickup_module.install(_active_patches)
+    _fake_mcp_runtime.install(_active_patches)
+    _fake_mcp_oauth_runtime.install(_active_patches)
+    _fake_slack_module.install(_active_patches)
 
 
-# ---------------------------------------------------------------------------
-# 5) Start the worker.
-# ---------------------------------------------------------------------------
+def _bootstrap():
+    """Run the full E2E bootstrap and return the production Celery app.
+
+    Ordering is load-bearing:
+      1) Hijack composio + notion_client in sys.modules.
+      2) Load .env + set env defaults (app.config reads env on import).
+      3) Configure logging.
+      4) Materialise the synthetic global_llm_config.yaml so the worker's
+         view of GLOBAL_LLM_CONFIGS matches the API container.
+      5) Import production celery_app (which transitively imports the
+         now-faked external SDKs and reads the env defaults + YAML).
+      6) Patch LLM / embedding bindings at every consumer site.
+      7) Install runtime fakes for connectors and chat backends.
+    """
+    _hijack_external_sdks()
+    _load_dotenv_and_set_env_defaults()
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+    logger.warning(
+        "*** SURFSENSE E2E CELERY WORKER — fake Composio + LLM + embeddings ***"
+    )
+
+    _install_synthetic_global_llm_config()
+    celery_app = _import_celery_app()
+    _patch_llm_bindings()
+    _install_runtime_fakes()
+    return celery_app
+
+
+celery_app = _bootstrap()
 
 
 def _main() -> None:
-    # Default queues mirror production (default queue + connectors queue
-    # so Drive indexing tasks are picked up).
     queue_name = os.getenv("CELERY_TASK_DEFAULT_QUEUE", "surfsense")
     queues = f"{queue_name},{queue_name}.connectors"
 

From 4dbadbf159cfb762b89ff30defd2be71409050e5 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 03:59:52 +0530
Subject: [PATCH 29/36] chore: update .gitignore and biome.json to include
 additional test-related directories and files for improved E2E testing

---
 surfsense_web/.gitignore |  7 +++++--
 surfsense_web/biome.json | 14 +++++++++++++-
 2 files changed, 18 insertions(+), 3 deletions(-)

diff --git a/surfsense_web/.gitignore b/surfsense_web/.gitignore
index 6ae7fe0c4..3ae7683d3 100644
--- a/surfsense_web/.gitignore
+++ b/surfsense_web/.gitignore
@@ -12,6 +12,10 @@
 
 # testing
 /coverage
+/playwright/.auth/
+/playwright-report/
+/test-results/
+/blob-report/
 
 # next.js
 /.next/
@@ -48,5 +52,4 @@ next-env.d.ts
 # source
 /.source/
 
-.pnpm-store/
-
+.pnpm-store/
\ No newline at end of file
diff --git a/surfsense_web/biome.json b/surfsense_web/biome.json
index 738a3636d..aa71f509e 100644
--- a/surfsense_web/biome.json
+++ b/surfsense_web/biome.json
@@ -7,7 +7,19 @@
 	},
 	"files": {
 		"ignoreUnknown": true,
-		"includes": ["**", "!!node_modules", "!!.git", "!!.next", "!!dist", "!!build", "!!coverage"],
+		"includes": [
+			"**",
+			"!!node_modules",
+			"!!.git",
+			"!!.next",
+			"!!dist",
+			"!!build",
+			"!!coverage",
+			"!!test-results",
+			"!!playwright-report",
+			"!!blob-report",
+			"!!playwright/.auth"
+		],
 		"maxSize": 1048576
 	},
 	"formatter": {

From 275e2c9e83dca350317e56f950f5682bc19de034 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 04:00:04 +0530
Subject: [PATCH 30/36] chore: fix linting

---
 surfsense_backend/app/config/__init__.py          |  3 ++-
 surfsense_backend/tests/e2e/auth_mint.py          |  4 +---
 .../tests/e2e/fakes/docling_service.py            |  4 +++-
 surfsense_backend/tests/e2e/run_backend.py        |  4 +---
 surfsense_backend/tests/e2e/run_celery.py         |  4 +---
 .../new-chat/[[...chat_id]]/page.tsx              | 15 +++++++--------
 .../atoms/chat/mentioned-documents.atom.ts        |  4 +---
 .../assistant-ui/inline-mention-editor.tsx        |  8 ++------
 .../components/assistant-ui/markdown-text.tsx     |  8 ++------
 .../components/assistant-ui/user-message.tsx      |  6 +-----
 surfsense_web/components/editor/plate-editor.tsx  | 15 +++------------
 .../components/editor/utils/safe-deserialize.ts   |  5 +----
 .../layout/ui/sidebar/DocumentsSidebar.tsx        |  5 +----
 .../new-chat/document-mention-picker.tsx          |  7 ++-----
 surfsense_web/playwright.config.ts                | 12 ++++--------
 surfsense_web/tests/helpers/api/auth.ts           |  3 +--
 16 files changed, 33 insertions(+), 74 deletions(-)

diff --git a/surfsense_backend/app/config/__init__.py b/surfsense_backend/app/config/__init__.py
index 724762854..448818e88 100644
--- a/surfsense_backend/app/config/__init__.py
+++ b/surfsense_backend/app/config/__init__.py
@@ -474,7 +474,8 @@ class Config:
     # Check if ffmpeg is installed
     if not is_ffmpeg_installed():
         allow_static_ffmpeg = (
-            os.getenv("SURFSENSE_ALLOW_STATIC_FFMPEG_DOWNLOAD", "TRUE").upper() == "TRUE"
+            os.getenv("SURFSENSE_ALLOW_STATIC_FFMPEG_DOWNLOAD", "TRUE").upper()
+            == "TRUE"
         )
         if allow_static_ffmpeg:
             import static_ffmpeg
diff --git a/surfsense_backend/tests/e2e/auth_mint.py b/surfsense_backend/tests/e2e/auth_mint.py
index a80e68fc1..f489ed274 100644
--- a/surfsense_backend/tests/e2e/auth_mint.py
+++ b/surfsense_backend/tests/e2e/auth_mint.py
@@ -36,9 +36,7 @@ class MintResponse(BaseModel):
 
 
 def _expected_secret() -> str:
-    return os.environ.get(
-        "E2E_MINT_SECRET", "local-e2e-mint-secret-not-for-production"
-    )
+    return os.environ.get("E2E_MINT_SECRET", "local-e2e-mint-secret-not-for-production")
 
 
 router = APIRouter(prefix="/__e2e__", tags=["__e2e__"])
diff --git a/surfsense_backend/tests/e2e/fakes/docling_service.py b/surfsense_backend/tests/e2e/fakes/docling_service.py
index 2486f5db6..9dd09d603 100644
--- a/surfsense_backend/tests/e2e/fakes/docling_service.py
+++ b/surfsense_backend/tests/e2e/fakes/docling_service.py
@@ -92,7 +92,9 @@ async def fake_process_document(
         # Empty fallback so the indexing pipeline does not error out on
         # an unexpected payload. A failing canary assertion is a much
         # clearer failure mode than a hard parser exception.
-        content = f"# {display_name}\n\n(empty docling fake — no text-show operators found)\n"
+        content = (
+            f"# {display_name}\n\n(empty docling fake — no text-show operators found)\n"
+        )
 
     logger.info(
         "[fake-docling] returning %d chars for %s",
diff --git a/surfsense_backend/tests/e2e/run_backend.py b/surfsense_backend/tests/e2e/run_backend.py
index d0c734751..5a787ac52 100644
--- a/surfsense_backend/tests/e2e/run_backend.py
+++ b/surfsense_backend/tests/e2e/run_backend.py
@@ -164,9 +164,7 @@ def _install_synthetic_global_llm_config() -> None:
     import shutil
 
     src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
-    dst = os.path.join(
-        _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
-    )
+    dst = os.path.join(_BACKEND_ROOT, "app", "config", "global_llm_config.yaml")
 
     if not os.path.exists(src):
         raise RuntimeError(
diff --git a/surfsense_backend/tests/e2e/run_celery.py b/surfsense_backend/tests/e2e/run_celery.py
index fd6cc88cf..e4091d689 100644
--- a/surfsense_backend/tests/e2e/run_celery.py
+++ b/surfsense_backend/tests/e2e/run_celery.py
@@ -142,9 +142,7 @@ def _install_synthetic_global_llm_config() -> None:
     import shutil
 
     src = os.path.join(_THIS_DIR, "fixtures", "global_llm_config.yaml")
-    dst = os.path.join(
-        _BACKEND_ROOT, "app", "config", "global_llm_config.yaml"
-    )
+    dst = os.path.join(_BACKEND_ROOT, "app", "config", "global_llm_config.yaml")
 
     if not os.path.exists(src):
         raise RuntimeError(
diff --git a/surfsense_web/app/dashboard/[search_space_id]/new-chat/[[...chat_id]]/page.tsx b/surfsense_web/app/dashboard/[search_space_id]/new-chat/[[...chat_id]]/page.tsx
index c431ab304..0ebd8dc9a 100644
--- a/surfsense_web/app/dashboard/[search_space_id]/new-chat/[[...chat_id]]/page.tsx
+++ b/surfsense_web/app/dashboard/[search_space_id]/new-chat/[[...chat_id]]/page.tsx
@@ -208,7 +208,10 @@ const MentionedDocumentInfoSchema = z.object({
 	id: z.number(),
 	title: z.string(),
 	document_type: z.string(),
-	kind: z.union([z.literal("doc"), z.literal("folder")]).optional().default("doc"),
+	kind: z
+		.union([z.literal("doc"), z.literal("folder")])
+		.optional()
+		.default("doc"),
 });
 
 const MentionedDocumentsPartSchema = z.object({
@@ -1029,9 +1032,7 @@ export default function NewChatPage() {
 							mentioned_surfsense_doc_ids: hasSurfsenseDocIds
 								? mentionedDocumentIds.surfsense_doc_ids
 								: undefined,
-							mentioned_folder_ids: hasFolderIds
-								? mentionedDocumentIds.folder_ids
-								: undefined,
+							mentioned_folder_ids: hasFolderIds ? mentionedDocumentIds.folder_ids : undefined,
 							// Full mention metadata (docs + folders, with
 							// ``kind`` discriminator) so the BE can embed a
 							// ``mentioned-documents`` ContentPart on the
@@ -1900,12 +1901,10 @@ export default function NewChatPage() {
 					filesystem_mode: selection.filesystem_mode,
 					client_platform: selection.client_platform,
 					local_filesystem_mounts: selection.local_filesystem_mounts,
-					mentioned_document_ids:
-						regenerateDocIds.length > 0 ? regenerateDocIds : undefined,
+					mentioned_document_ids: regenerateDocIds.length > 0 ? regenerateDocIds : undefined,
 					mentioned_surfsense_doc_ids:
 						regenerateSurfsenseDocIds.length > 0 ? regenerateSurfsenseDocIds : undefined,
-					mentioned_folder_ids:
-						regenerateFolderIds.length > 0 ? regenerateFolderIds : undefined,
+					mentioned_folder_ids: regenerateFolderIds.length > 0 ? regenerateFolderIds : undefined,
 					// Full mention metadata for the regenerate-specific
 					// source list. Only meaningful for edit (the BE only
 					// re-persists a user row when ``user_query`` is set);
diff --git a/surfsense_web/atoms/chat/mentioned-documents.atom.ts b/surfsense_web/atoms/chat/mentioned-documents.atom.ts
index eafdaf87e..9163960f4 100644
--- a/surfsense_web/atoms/chat/mentioned-documents.atom.ts
+++ b/surfsense_web/atoms/chat/mentioned-documents.atom.ts
@@ -97,9 +97,7 @@ export const mentionedDocumentIdsAtom = atom((get) => {
 		surfsense_doc_ids: docs
 			.filter((doc) => doc.document_type === "SURFSENSE_DOCS")
 			.map((doc) => doc.id),
-		document_ids: docs
-			.filter((doc) => doc.document_type !== "SURFSENSE_DOCS")
-			.map((doc) => doc.id),
+		document_ids: docs.filter((doc) => doc.document_type !== "SURFSENSE_DOCS").map((doc) => doc.id),
 		folder_ids: folders.map((f) => f.id),
 	};
 });
diff --git a/surfsense_web/components/assistant-ui/inline-mention-editor.tsx b/surfsense_web/components/assistant-ui/inline-mention-editor.tsx
index e12556486..c7893b6ac 100644
--- a/surfsense_web/components/assistant-ui/inline-mention-editor.tsx
+++ b/surfsense_web/components/assistant-ui/inline-mention-editor.tsx
@@ -47,10 +47,7 @@ export interface InlineMentionEditorRef {
 	setText: (text: string) => void;
 	getText: () => string;
 	getMentionedDocuments: () => MentionedDocument[];
-	insertMentionChip: (
-		mention: MentionChipInput,
-		options?: { removeTriggerText?: boolean }
-	) => void;
+	insertMentionChip: (mention: MentionChipInput, options?: { removeTriggerText?: boolean }) => void;
 	/**
 	 * @deprecated Use ``insertMentionChip``. Kept for one transition
 	 * cycle so we don't break ad-hoc callers; prefer the new name.
@@ -364,8 +361,7 @@ export const InlineMentionEditor = forwardRef<InlineMentionEditorRef, InlineMent
 				const selection = editor.selection;
 				const kind: MentionKind = mention.kind ?? "doc";
 				const document_type =
-					mention.document_type ??
-					(kind === "folder" ? FOLDER_MENTION_DOCUMENT_TYPE : undefined);
+					mention.document_type ?? (kind === "folder" ? FOLDER_MENTION_DOCUMENT_TYPE : undefined);
 				const mentionNode: MentionElementNode = {
 					type: MENTION_TYPE,
 					id: mention.id,
diff --git a/surfsense_web/components/assistant-ui/markdown-text.tsx b/surfsense_web/components/assistant-ui/markdown-text.tsx
index fba80c09f..1bb7cf2d9 100644
--- a/surfsense_web/components/assistant-ui/markdown-text.tsx
+++ b/surfsense_web/components/assistant-ui/markdown-text.tsx
@@ -33,8 +33,8 @@ import {
 } from "@/components/ui/table";
 import { useElectronAPI } from "@/hooks/use-platform";
 import { documentsApiService } from "@/lib/apis/documents-api.service";
-import { type CitationUrlMap, preprocessCitationMarkdown } from "@/lib/citations/citation-parser";
 import { getVirtualPathDisplay } from "@/lib/chat/virtual-path-display";
+import { type CitationUrlMap, preprocessCitationMarkdown } from "@/lib/citations/citation-parser";
 import { cn } from "@/lib/utils";
 
 function MarkdownCodeBlockSkeleton() {
@@ -222,11 +222,7 @@ function FilePathLink({ path, className }: { path: string; className?: string })
 		: undefined;
 
 	const { displayName, isFolder } = getVirtualPathDisplay(path);
-	const icon = isFolder ? (
-		<FolderIcon className="size-3.5" />
-	) : (
-		<FileIcon className="size-3.5" />
-	);
+	const icon = isFolder ? <FolderIcon className="size-3.5" /> : <FileIcon className="size-3.5" />;
 
 	const handleClick = useCallback(
 		(event: React.MouseEvent<HTMLButtonElement>) => {
diff --git a/surfsense_web/components/assistant-ui/user-message.tsx b/surfsense_web/components/assistant-ui/user-message.tsx
index b09aa7680..708cefbc0 100644
--- a/surfsense_web/components/assistant-ui/user-message.tsx
+++ b/surfsense_web/components/assistant-ui/user-message.tsx
@@ -111,11 +111,7 @@ const UserTextPart: FC = () => {
 						icon={icon}
 						label={segment.doc.title}
 						tooltip={isFolder ? `Folder: ${segment.doc.title}` : segment.doc.title}
-						onClick={
-							isFolder
-								? undefined
-								: () => handleOpenDoc(segment.doc.id, segment.doc.title)
-						}
+						onClick={isFolder ? undefined : () => handleOpenDoc(segment.doc.id, segment.doc.title)}
 						className="mx-0.5"
 					/>
 				);
diff --git a/surfsense_web/components/editor/plate-editor.tsx b/surfsense_web/components/editor/plate-editor.tsx
index 51ad7d700..77845ad2a 100644
--- a/surfsense_web/components/editor/plate-editor.tsx
+++ b/surfsense_web/components/editor/plate-editor.tsx
@@ -170,16 +170,10 @@ export function PlateEditor({
 			: markdown
 				? (editor) => {
 						if (!enableCitations) {
-							return safeDeserializeMarkdown(
-								editor,
-								escapeMdxExpressions(markdown)
-							) as Value;
+							return safeDeserializeMarkdown(editor, escapeMdxExpressions(markdown)) as Value;
 						}
 						const { content: rewritten, urlMap } = preprocessCitationMarkdown(markdown);
-						const value = safeDeserializeMarkdown(
-							editor,
-							escapeMdxExpressions(rewritten)
-						);
+						const value = safeDeserializeMarkdown(editor, escapeMdxExpressions(rewritten));
 						return injectCitationNodes(value, urlMap) as Value;
 					}
 				: undefined,
@@ -203,10 +197,7 @@ export function PlateEditor({
 			let newValue: Descendant[];
 			if (enableCitations) {
 				const { content: rewritten, urlMap } = preprocessCitationMarkdown(markdown);
-				const deserialized = safeDeserializeMarkdown(
-					editor,
-					escapeMdxExpressions(rewritten)
-				);
+				const deserialized = safeDeserializeMarkdown(editor, escapeMdxExpressions(rewritten));
 				newValue = injectCitationNodes(deserialized, urlMap);
 			} else {
 				newValue = safeDeserializeMarkdown(editor, escapeMdxExpressions(markdown));
diff --git a/surfsense_web/components/editor/utils/safe-deserialize.ts b/surfsense_web/components/editor/utils/safe-deserialize.ts
index e359a7791..8f3e6275b 100644
--- a/surfsense_web/components/editor/utils/safe-deserialize.ts
+++ b/surfsense_web/components/editor/utils/safe-deserialize.ts
@@ -49,10 +49,7 @@ export function safeDeserializeMarkdown(
 		return api.deserialize(markdown, { remarkPlugins: STRICT_PLUGINS }) as Descendant[];
 	} catch (mdxError) {
 		if (process.env.NODE_ENV !== "production") {
-			console.warn(
-				"[plate-editor] MDX parse failed, retrying without remark-mdx:",
-				mdxError
-			);
+			console.warn("[plate-editor] MDX parse failed, retrying without remark-mdx:", mdxError);
 		}
 		try {
 			return api.deserialize(markdown, { remarkPlugins: LENIENT_PLUGINS }) as Descendant[];
diff --git a/surfsense_web/components/layout/ui/sidebar/DocumentsSidebar.tsx b/surfsense_web/components/layout/ui/sidebar/DocumentsSidebar.tsx
index 958941928..3ecf046bb 100644
--- a/surfsense_web/components/layout/ui/sidebar/DocumentsSidebar.tsx
+++ b/surfsense_web/components/layout/ui/sidebar/DocumentsSidebar.tsx
@@ -24,10 +24,7 @@ import type React from "react";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
 import { toast } from "sonner";
 import { agentFlagsAtom } from "@/atoms/agent/agent-flags-query.atom";
-import {
-	makeFolderMention,
-	mentionedDocumentsAtom,
-} from "@/atoms/chat/mentioned-documents.atom";
+import { makeFolderMention, mentionedDocumentsAtom } from "@/atoms/chat/mentioned-documents.atom";
 import { connectorDialogOpenAtom } from "@/atoms/connector-dialog/connector-dialog.atoms";
 import { connectorsAtom } from "@/atoms/connectors/connector-query.atoms";
 import { deleteDocumentMutationAtom } from "@/atoms/documents/document-mutation.atoms";
diff --git a/surfsense_web/components/new-chat/document-mention-picker.tsx b/surfsense_web/components/new-chat/document-mention-picker.tsx
index 0881b11b6..0d68c8df8 100644
--- a/surfsense_web/components/new-chat/document-mention-picker.tsx
+++ b/surfsense_web/components/new-chat/document-mention-picker.tsx
@@ -301,8 +301,7 @@ export const DocumentMentionPicker = forwardRef<
 	// folder entries lift the existing kind-aware key so the same
 	// matchers used by the chip atom apply unchanged.
 	const selectedKeys = useMemo(
-		() =>
-			new Set(initialSelectedDocuments.map((d) => getMentionDocKey(d))),
+		() => new Set(initialSelectedDocuments.map((d) => getMentionDocKey(d))),
 		[initialSelectedDocuments]
 	);
 
@@ -583,9 +582,7 @@ export const DocumentMentionPicker = forwardRef<
 								{(surfsenseDocsList.length > 0 || userDocsList.length > 0) && (
 									<div className="mx-2 my-4 border-t border-border dark:border-white/5" />
 								)}
-								<div className="px-3 py-2 text-xs font-bold text-muted-foreground/55">
-									Folders
-								</div>
+								<div className="px-3 py-2 text-xs font-bold text-muted-foreground/55">Folders</div>
 								{folderMentions.map((folder) => {
 									const folderKey = getMentionDocKey(folder);
 									const isAlreadySelected = selectedKeys.has(folderKey);
diff --git a/surfsense_web/playwright.config.ts b/surfsense_web/playwright.config.ts
index eb287635d..ef066a9be 100644
--- a/surfsense_web/playwright.config.ts
+++ b/surfsense_web/playwright.config.ts
@@ -59,19 +59,15 @@ export default defineConfig({
 		? undefined
 		: {
 				// Local stays on webpack dev (Turbopack caused stale-lock panics in E2E).
-				command: process.env.CI
-					? "pnpm build && pnpm start"
-					: "pnpm exec next dev",
+				command: process.env.CI ? "pnpm build && pnpm start" : "pnpm exec next dev",
 				url: `http://localhost:${PORT}`,
 				reuseExistingServer: !process.env.CI,
 				timeout: process.env.CI ? 300_000 : 180_000,
 				stdout: "pipe",
-      			stderr: "pipe",
+				stderr: "pipe",
 				env: {
-					NEXT_PUBLIC_FASTAPI_BACKEND_URL:
-						process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL,
-					NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE:
-						process.env.NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE,
+					NEXT_PUBLIC_FASTAPI_BACKEND_URL: process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL,
+					NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE: process.env.NEXT_PUBLIC_FASTAPI_BACKEND_AUTH_TYPE,
 				},
 			},
 });
diff --git a/surfsense_web/tests/helpers/api/auth.ts b/surfsense_web/tests/helpers/api/auth.ts
index 2071a80f4..6492b09ba 100644
--- a/surfsense_web/tests/helpers/api/auth.ts
+++ b/surfsense_web/tests/helpers/api/auth.ts
@@ -13,8 +13,7 @@ export const BACKEND_URL = process.env.NEXT_PUBLIC_FASTAPI_BACKEND_URL || "http:
 
 const TEST_USER_EMAIL = process.env.PLAYWRIGHT_TEST_EMAIL || "e2e-test@surfsense.net";
 const TEST_USER_PASSWORD = process.env.PLAYWRIGHT_TEST_PASSWORD || "E2eTestPassword123!";
-const E2E_MINT_SECRET =
-	process.env.E2E_MINT_SECRET || "local-e2e-mint-secret-not-for-production";
+const E2E_MINT_SECRET = process.env.E2E_MINT_SECRET || "local-e2e-mint-secret-not-for-production";
 
 /**
  * Mints a JWT for the seeded e2e user via the test-only endpoint mounted

From 6eb900cb0f238f7da0acab9a819bd4859c3a52f9 Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Tue, 12 May 2026 23:59:58 +0530
Subject: [PATCH 31/36] chore: update packageManager version to pnpm@10.26.0 in
 both desktop and web projects

---
 surfsense_desktop/package.json | 2 +-
 surfsense_web/package.json     | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/surfsense_desktop/package.json b/surfsense_desktop/package.json
index 4ef624760..b1fff79a5 100644
--- a/surfsense_desktop/package.json
+++ b/surfsense_desktop/package.json
@@ -21,7 +21,7 @@
     "email": "rohan@surfsense.com"
   },
   "license": "MIT",
-  "packageManager": "pnpm@10.24.0",
+  "packageManager": "pnpm@10.26.0",
   "devDependencies": {
     "@electron/rebuild": "^4.0.3",
     "@types/node": "^25.5.0",
diff --git a/surfsense_web/package.json b/surfsense_web/package.json
index d9f836ea9..95894d2f2 100644
--- a/surfsense_web/package.json
+++ b/surfsense_web/package.json
@@ -2,6 +2,7 @@
 	"name": "surfsense_web",
 	"version": "0.0.23",
 	"private": true,
+	"packageManager": "pnpm@10.26.0",
 	"description": "SurfSense Frontend",
 	"scripts": {
 		"dev": "next dev --turbopack",

From 883c72396c98c0c154d3d4af0dc9ff7d5340e97f Mon Sep 17 00:00:00 2001
From: Anish Sarkar <104695310+AnishSarkar22@users.noreply.github.com>
Date: Wed, 13 May 2026 03:38:04 +0530
Subject: [PATCH 32/36] chore: add minimumReleaseAge configuration to pnpm
 workspace for dependency management

---
 surfsense_web/pnpm-workspace.yaml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/surfsense_web/pnpm-workspace.yaml b/surfsense_web/pnpm-workspace.yaml
index 69e46cf7a..5f1b93969 100644
--- a/surfsense_web/pnpm-workspace.yaml
+++ b/surfsense_web/pnpm-workspace.yaml
@@ -7,3 +7,5 @@ allowBuilds:
   protobufjs: true
   sharp: true
   unrs-resolver: true
+
+minimumReleaseAge: 10080
\ No newline at end of file

From b7b4443276af1d7260711e186bad03c2140fec96 Mon Sep 17 00:00:00 2001
From: guangyang1206 <guangyang1206@users.noreply.github.com>
Date: Wed, 13 May 2026 20:59:08 +0800
Subject: [PATCH 33/36] fix(web): invalidate all log cache keys on log
 mutations
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Fixes #1369 — log create/update/delete mutations did not invalidate
the query keys that useLogs actually subscribes to, causing UI staleness.

Replace narrow invalidations (list, summary) with prefix-level
invalidation (["logs"]) to cover withQueryParams, list, summary
and detail in one shot.
---
 .../atoms/logs/log-mutation.atoms.ts          | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/surfsense_web/atoms/logs/log-mutation.atoms.ts b/surfsense_web/atoms/logs/log-mutation.atoms.ts
index e17b42fb6..f9d73f6e0 100644
--- a/surfsense_web/atoms/logs/log-mutation.atoms.ts
+++ b/surfsense_web/atoms/logs/log-mutation.atoms.ts
@@ -19,10 +19,8 @@ export const createLogMutationAtom = atomWithMutation((get) => {
 		enabled: !!searchSpaceId,
 		mutationFn: async (request: CreateLogRequest) => logsApiService.createLog(request),
 		onSuccess: () => {
-			queryClient.invalidateQueries({ queryKey: cacheKeys.logs.list(searchSpaceId ?? undefined) });
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.logs.summary(searchSpaceId ?? undefined),
-			});
+			// Invalidate all log-related queries (list, summary, detail, withQueryParams)
+			queryClient.invalidateQueries({ queryKey: ["logs"] });
 		},
 	};
 });
@@ -38,11 +36,7 @@ export const updateLogMutationAtom = atomWithMutation((get) => {
 		mutationFn: async ({ logId, data }: { logId: number; data: UpdateLogRequest }) =>
 			logsApiService.updateLog(logId, data),
 		onSuccess: (_data, variables) => {
-			queryClient.invalidateQueries({ queryKey: cacheKeys.logs.detail(variables.logId) });
-			queryClient.invalidateQueries({ queryKey: cacheKeys.logs.list(searchSpaceId ?? undefined) });
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.logs.summary(searchSpaceId ?? undefined),
-			});
+			queryClient.invalidateQueries({ queryKey: ["logs"] });
 		},
 	};
 });
@@ -57,12 +51,7 @@ export const deleteLogMutationAtom = atomWithMutation((get) => {
 		enabled: !!searchSpaceId,
 		mutationFn: async (request: DeleteLogRequest) => logsApiService.deleteLog(request),
 		onSuccess: (_data, request) => {
-			queryClient.invalidateQueries({ queryKey: cacheKeys.logs.list(searchSpaceId ?? undefined) });
-			queryClient.invalidateQueries({
-				queryKey: cacheKeys.logs.summary(searchSpaceId ?? undefined),
-			});
-			if (request?.id)
-				queryClient.invalidateQueries({ queryKey: cacheKeys.logs.detail(request.id) });
+			queryClient.invalidateQueries({ queryKey: ["logs"] });
 		},
 	};
 });

From 373711805045115525dc91cc3a102e091bb2b740 Mon Sep 17 00:00:00 2001
From: "DESKTOP-RTLN3BA\\$punk" <vermarohanfinal@gmail.com>
Date: Wed, 13 May 2026 14:02:26 -0700
Subject: [PATCH 34/36] chore: evals

---
 .gitignore                                    |    2 +
 .../app/etl_pipeline/etl_pipeline_service.py  |   80 +
 .../app/etl_pipeline/parsers/vision_llm.py    |   60 +-
 .../app/etl_pipeline/picture_describer.py     |  678 +++++++
 .../app/services/docling_service.py           |   12 +-
 .../document_processors/file_processors.py    |   27 +-
 .../etl_pipeline/test_etl_pipeline_service.py |  366 ++++
 .../etl_pipeline/test_picture_describer.py    |  967 +++++++++
 .../unit/etl_pipeline/test_vision_llm.py      |  146 ++
 surfsense_evals/.env.example                  |   65 +
 surfsense_evals/.gitignore                    |   29 +
 surfsense_evals/README.md                     |  228 +++
 surfsense_evals/data/.gitignore               |    2 +
 surfsense_evals/pyproject.toml                |   60 +
 surfsense_evals/reports/.gitignore            |    4 +
 .../scripts/download_crag_task3.py            |   97 +
 surfsense_evals/scripts/peek_crag_run.py      |   37 +
 surfsense_evals/scripts/peek_disagreements.py |   64 +
 surfsense_evals/scripts/peek_t3_doc_map.py    |   40 +
 surfsense_evals/scripts/summarise_crag_run.py |   65 +
 .../src/surfsense_evals/__init__.py           |   10 +
 .../src/surfsense_evals/__main__.py           |   13 +
 .../src/surfsense_evals/core/__init__.py      |    8 +
 .../src/surfsense_evals/core/arms/__init__.py |   44 +
 .../src/surfsense_evals/core/arms/bare_llm.py |  100 +
 .../src/surfsense_evals/core/arms/base.py     |   93 +
 .../surfsense_evals/core/arms/native_pdf.py   |  104 +
 .../surfsense_evals/core/arms/surfsense.py    |  104 +
 .../src/surfsense_evals/core/auth.py          |  273 +++
 .../src/surfsense_evals/core/cli.py           |  790 ++++++++
 .../surfsense_evals/core/clients/__init__.py  |   14 +
 .../surfsense_evals/core/clients/documents.py |  277 +++
 .../surfsense_evals/core/clients/new_chat.py  |  280 +++
 .../core/clients/search_space.py              |  207 ++
 .../src/surfsense_evals/core/config.py        |  279 +++
 .../surfsense_evals/core/ingest_settings.py   |  311 +++
 .../surfsense_evals/core/metrics/__init__.py  |   50 +
 .../core/metrics/comparison.py                |  258 +++
 .../core/metrics/mc_accuracy.py               |  130 ++
 .../surfsense_evals/core/metrics/retrieval.py |  132 ++
 .../surfsense_evals/core/parse/__init__.py    |   21 +
 .../core/parse/answer_letter.py               |  122 ++
 .../surfsense_evals/core/parse/citations.py   |  110 ++
 .../core/parse/freeform_answer.py             |   85 +
 .../src/surfsense_evals/core/parse/sse.py     |   72 +
 .../src/surfsense_evals/core/pdf/__init__.py  |   31 +
 .../src/surfsense_evals/core/pdf/render.py    |  351 ++++
 .../core/providers/__init__.py                |   22 +
 .../core/providers/openrouter_chat.py         |  118 ++
 .../core/providers/openrouter_pdf.py          |  231 +++
 .../src/surfsense_evals/core/registry.py      |  265 +++
 .../surfsense_evals/core/report/__init__.py   |   18 +
 .../src/surfsense_evals/core/report/writer.py |   89 +
 .../src/surfsense_evals/core/scenarios.py     |   58 +
 .../src/surfsense_evals/core/vision_llm.py    |  127 ++
 .../src/surfsense_evals/suites/__init__.py    |   66 +
 .../surfsense_evals/suites/_demo/__init__.py  |    8 +
 .../suites/_demo/hello/__init__.py            |   46 +
 .../suites/medical/__init__.py                |    7 +
 .../suites/medical/cure/__init__.py           |   18 +
 .../suites/medical/cure/ingest.py             |  239 +++
 .../suites/medical/cure/runner.py             |  397 ++++
 .../suites/medical/medxpertqa/__init__.py     |   25 +
 .../suites/medical/medxpertqa/ingest.py       |  394 ++++
 .../suites/medical/medxpertqa/prompt.py       |   54 +
 .../suites/medical/medxpertqa/runner.py       |  681 +++++++
 .../suites/medical/mirage/__init__.py         |   17 +
 .../suites/medical/mirage/ingest.py           |  548 ++++++
 .../suites/medical/mirage/prompt.py           |   44 +
 .../suites/medical/mirage/runner.py           |  332 ++++
 .../suites/multimodal_doc/__init__.py         |   14 +
 .../multimodal_doc/mmlongbench/__init__.py    |   19 +
 .../multimodal_doc/mmlongbench/grader.py      |  236 +++
 .../multimodal_doc/mmlongbench/ingest.py      |  365 ++++
 .../multimodal_doc/mmlongbench/prompt.py      |   60 +
 .../multimodal_doc/mmlongbench/runner.py      |  704 +++++++
 .../suites/research/__init__.py               |   18 +
 .../suites/research/crag/__init__.py          |   57 +
 .../suites/research/crag/dataset.py           |  335 ++++
 .../suites/research/crag/dataset_task3.py     |  263 +++
 .../suites/research/crag/grader.py            |  540 +++++
 .../suites/research/crag/html_extract.py      |  206 ++
 .../suites/research/crag/ingest.py            |  447 +++++
 .../suites/research/crag/ingest_task3.py      |  191 ++
 .../suites/research/crag/prompt.py            |  146 ++
 .../suites/research/crag/runner.py            | 1053 ++++++++++
 .../suites/research/frames/__init__.py        |   29 +
 .../suites/research/frames/dataset.py         |  174 ++
 .../suites/research/frames/grader.py          |  341 ++++
 .../suites/research/frames/ingest.py          |  341 ++++
 .../suites/research/frames/prompt.py          |   71 +
 .../suites/research/frames/runner.py          |  686 +++++++
 .../suites/research/frames/wiki_fetch.py      |  241 +++
 surfsense_evals/tests/__init__.py             |    1 +
 surfsense_evals/tests/conftest.py             |   34 +
 surfsense_evals/tests/core/__init__.py        |    1 +
 surfsense_evals/tests/core/test_auth.py       |   95 +
 surfsense_evals/tests/core/test_clients.py    |  262 +++
 surfsense_evals/tests/core/test_config.py     |  160 ++
 .../tests/core/test_ingest_settings.py        |  269 +++
 surfsense_evals/tests/core/test_metrics.py    |  153 ++
 .../tests/core/test_parse_answer_letter.py    |   27 +
 .../tests/core/test_parse_citations.py        |  108 +
 .../tests/core/test_parse_freeform_answer.py  |   73 +
 surfsense_evals/tests/core/test_parse_sse.py  |   84 +
 surfsense_evals/tests/core/test_pdf_render.py |   51 +
 .../tests/core/test_pdf_render_with_images.py |   73 +
 .../tests/core/test_provider_openrouter.py    |  121 ++
 surfsense_evals/tests/core/test_registry.py   |   58 +
 surfsense_evals/tests/core/test_scenarios.py  |   68 +
 surfsense_evals/tests/core/test_vision_llm.py |  121 ++
 surfsense_evals/tests/suites/__init__.py      |    1 +
 .../tests/suites/test_crag_dataset.py         |  224 +++
 .../tests/suites/test_crag_dataset_task3.py   |  259 +++
 .../tests/suites/test_crag_grader.py          |  248 +++
 .../tests/suites/test_crag_html_extract.py    |  149 ++
 .../tests/suites/test_frames_dataset.py       |  154 ++
 .../tests/suites/test_frames_grader.py        |  160 ++
 .../tests/suites/test_frames_wiki_fetch.py    |  112 ++
 .../tests/suites/test_mmlongbench_grader.py   |  129 ++
 .../tests/test_integration_smoke.py           |   35 +
 surfsense_evals/uv.lock                       | 1742 +++++++++++++++++
 122 files changed, 22598 insertions(+), 13 deletions(-)
 create mode 100644 surfsense_backend/app/etl_pipeline/picture_describer.py
 create mode 100644 surfsense_backend/tests/unit/etl_pipeline/test_picture_describer.py
 create mode 100644 surfsense_backend/tests/unit/etl_pipeline/test_vision_llm.py
 create mode 100644 surfsense_evals/.env.example
 create mode 100644 surfsense_evals/.gitignore
 create mode 100644 surfsense_evals/README.md
 create mode 100644 surfsense_evals/data/.gitignore
 create mode 100644 surfsense_evals/pyproject.toml
 create mode 100644 surfsense_evals/reports/.gitignore
 create mode 100644 surfsense_evals/scripts/download_crag_task3.py
 create mode 100644 surfsense_evals/scripts/peek_crag_run.py
 create mode 100644 surfsense_evals/scripts/peek_disagreements.py
 create mode 100644 surfsense_evals/scripts/peek_t3_doc_map.py
 create mode 100644 surfsense_evals/scripts/summarise_crag_run.py
 create mode 100644 surfsense_evals/src/surfsense_evals/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/__main__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/arms/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/arms/base.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/arms/surfsense.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/auth.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/cli.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/clients/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/clients/documents.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/clients/new_chat.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/clients/search_space.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/config.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/ingest_settings.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/metrics/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/metrics/comparison.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parse/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parse/citations.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parse/sse.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/pdf/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/pdf/render.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/providers/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/registry.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/report/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/report/writer.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/scenarios.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/vision_llm.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/dataset.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/grader.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/runner.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/research/frames/wiki_fetch.py
 create mode 100644 surfsense_evals/tests/__init__.py
 create mode 100644 surfsense_evals/tests/conftest.py
 create mode 100644 surfsense_evals/tests/core/__init__.py
 create mode 100644 surfsense_evals/tests/core/test_auth.py
 create mode 100644 surfsense_evals/tests/core/test_clients.py
 create mode 100644 surfsense_evals/tests/core/test_config.py
 create mode 100644 surfsense_evals/tests/core/test_ingest_settings.py
 create mode 100644 surfsense_evals/tests/core/test_metrics.py
 create mode 100644 surfsense_evals/tests/core/test_parse_answer_letter.py
 create mode 100644 surfsense_evals/tests/core/test_parse_citations.py
 create mode 100644 surfsense_evals/tests/core/test_parse_freeform_answer.py
 create mode 100644 surfsense_evals/tests/core/test_parse_sse.py
 create mode 100644 surfsense_evals/tests/core/test_pdf_render.py
 create mode 100644 surfsense_evals/tests/core/test_pdf_render_with_images.py
 create mode 100644 surfsense_evals/tests/core/test_provider_openrouter.py
 create mode 100644 surfsense_evals/tests/core/test_registry.py
 create mode 100644 surfsense_evals/tests/core/test_scenarios.py
 create mode 100644 surfsense_evals/tests/core/test_vision_llm.py
 create mode 100644 surfsense_evals/tests/suites/__init__.py
 create mode 100644 surfsense_evals/tests/suites/test_crag_dataset.py
 create mode 100644 surfsense_evals/tests/suites/test_crag_dataset_task3.py
 create mode 100644 surfsense_evals/tests/suites/test_crag_grader.py
 create mode 100644 surfsense_evals/tests/suites/test_crag_html_extract.py
 create mode 100644 surfsense_evals/tests/suites/test_frames_dataset.py
 create mode 100644 surfsense_evals/tests/suites/test_frames_grader.py
 create mode 100644 surfsense_evals/tests/suites/test_frames_wiki_fetch.py
 create mode 100644 surfsense_evals/tests/suites/test_mmlongbench_grader.py
 create mode 100644 surfsense_evals/tests/test_integration_smoke.py
 create mode 100644 surfsense_evals/uv.lock

diff --git a/.gitignore b/.gitignore
index 6c80c95c3..ac2ff94c9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,5 @@ surfsense_web/test-results/
 surfsense_web/blob-report/
 hermes-agent
 hermes-agent/
+
+content_research/
diff --git a/surfsense_backend/app/etl_pipeline/etl_pipeline_service.py b/surfsense_backend/app/etl_pipeline/etl_pipeline_service.py
index d45bd780c..7fe3c94df 100644
--- a/surfsense_backend/app/etl_pipeline/etl_pipeline_service.py
+++ b/surfsense_backend/app/etl_pipeline/etl_pipeline_service.py
@@ -134,12 +134,92 @@ class EtlPipelineService:
         else:
             raise EtlServiceUnavailableError(f"Unknown ETL_SERVICE: {etl_service}")
 
+        # When the operator opts into vision-LLM at ingest, walk the
+        # original file's embedded images and append a structured
+        # "Image Content" section. The parser's own OCR (Docling
+        # do_ocr=True, Azure DI prebuilt-read, etc.) handles text-in-
+        # image; this side handles the *visual* description which the
+        # parsers all drop today.
+        content = await self._maybe_append_picture_descriptions(request, content)
+
         return EtlResult(
             markdown_content=content,
             etl_service=etl_service,
             content_type="document",
         )
 
+    async def _maybe_append_picture_descriptions(
+        self, request: EtlRequest, markdown: str
+    ) -> str:
+        if self._vision_llm is None:
+            return markdown
+
+        from app.etl_pipeline.picture_describer import (
+            describe_pictures,
+            merge_descriptions_into_markdown,
+        )
+
+        # Per-image OCR runner: re-feed each extracted image through
+        # the ETL pipeline *as a standalone image* (no vision LLM, so
+        # the IMAGE branch falls through to the document parser, which
+        # OCRs the image with the configured backend -- Docling /
+        # Azure DI / LlamaCloud). This gives us per-image OCR text
+        # attached to the inline image block, in addition to the
+        # page-level OCR that the parser already merges into the main
+        # markdown stream. The fresh sub-service gets vision_llm=None
+        # so this call cannot recurse back into picture_describer.
+        async def _ocr_image(image_path: str, image_name: str) -> str:
+            try:
+                sub = EtlPipelineService(vision_llm=None)
+                ocr_result = await sub.extract(
+                    EtlRequest(file_path=image_path, filename=image_name)
+                )
+            except (
+                EtlUnsupportedFileError,
+                EtlServiceUnavailableError,
+            ) as exc:
+                # Common case: the configured ETL service can't OCR
+                # this image format (or no service is configured at
+                # all). Don't spam warnings -- just no OCR for it.
+                logging.debug(
+                    "Skipping per-image OCR for %s: %s", image_name, exc
+                )
+                return ""
+            return ocr_result.markdown_content
+
+        try:
+            result = await describe_pictures(
+                request.file_path,
+                request.filename,
+                self._vision_llm,
+                ocr_runner=_ocr_image,
+            )
+        except Exception:
+            # Picture description is additive; never let it fail an
+            # otherwise-successful document extraction.
+            logging.warning(
+                "Picture description failed for %s, returning parser output unchanged",
+                request.filename,
+                exc_info=True,
+            )
+            return markdown
+
+        if not result.descriptions:
+            return markdown
+
+        merged = merge_descriptions_into_markdown(markdown, result)
+        logging.info(
+            "Vision LLM described %d image(s) in %s "
+            "(skipped: %d small / %d large / %d duplicate, %d failed)",
+            len(result.descriptions),
+            request.filename,
+            result.skipped_too_small,
+            result.skipped_too_large,
+            result.skipped_duplicate,
+            result.failed,
+        )
+        return merged
+
     async def _extract_with_llamacloud(self, request: EtlRequest) -> str:
         """Try Azure Document Intelligence first (when configured) then LlamaCloud.
 
diff --git a/surfsense_backend/app/etl_pipeline/parsers/vision_llm.py b/surfsense_backend/app/etl_pipeline/parsers/vision_llm.py
index c80fbca0a..8ae0715f3 100644
--- a/surfsense_backend/app/etl_pipeline/parsers/vision_llm.py
+++ b/surfsense_backend/app/etl_pipeline/parsers/vision_llm.py
@@ -4,12 +4,34 @@ import os
 
 from langchain_core.messages import HumanMessage
 
+# Single-shot prompt used by standalone image uploads (.png/.jpg/etc).
+# A standalone image IS the document, so we want everything: visual
+# content plus any text the model can read off it. The output is
+# combined markdown that the chunker treats as the full document body.
 _PROMPT = (
     "Describe this image in markdown. "
     "Transcribe any visible text verbatim. "
     "Be concise but complete — let the image content guide the level of detail."
 )
 
+# Per-image-in-PDF prompt. Here the image is *inside* a larger
+# document, and the ETL service (Docling/Azure DI/LlamaCloud/...) is
+# already running OCR over the whole page — including text rendered
+# into images. So we explicitly tell the model NOT to transcribe text
+# and to focus only on visual interpretation. This avoids paying
+# output tokens for OCR content the ETL pipeline already captured.
+_DESCRIPTION_PROMPT = (
+    "Describe what this image visually depicts in concise markdown. "
+    "Focus on visual content — anatomy, structures, charts, diagrams, "
+    "spatial relationships, colors, modality (e.g. axial CT, ECG strip, "
+    "histology slide), and any clinically or structurally relevant "
+    "findings.\n\n"
+    "Do NOT transcribe text from the image. Any text in the image "
+    "(axis labels, annotations, scale bars, lab values, etc.) is "
+    "already extracted by a separate OCR pipeline; duplicating it "
+    "here would be redundant. Stick to the visual interpretation."
+)
+
 _MAX_IMAGE_BYTES = (
     5 * 1024 * 1024
 )  # 5 MB (Anthropic Claude's limit, the most restrictive)
@@ -47,11 +69,10 @@ def _image_to_data_url(file_path: str) -> str:
     return f"data:{mime_type};base64,{encoded}"
 
 
-async def parse_with_vision_llm(file_path: str, filename: str, llm) -> str:
-    data_url = _image_to_data_url(file_path)
+async def _invoke_vision(llm, prompt: str, data_url: str, filename: str) -> str:
     message = HumanMessage(
         content=[
-            {"type": "text", "text": _PROMPT},
+            {"type": "text", "text": prompt},
             {"type": "image_url", "image_url": {"url": data_url}},
         ]
     )
@@ -62,3 +83,36 @@ async def parse_with_vision_llm(file_path: str, filename: str, llm) -> str:
     if not text or not text.strip():
         raise ValueError(f"Vision LLM returned empty content for {filename}")
     return text.strip()
+
+
+async def parse_with_vision_llm(file_path: str, filename: str, llm) -> str:
+    """Single-shot: returns combined markdown for a standalone image upload.
+
+    Used when the operator uploads an image file directly (jpg/png/etc).
+    The image is the document, so the prompt asks for both visual
+    description and verbatim text in one go.
+    """
+    data_url = _image_to_data_url(file_path)
+    return await _invoke_vision(llm, _PROMPT, data_url, filename)
+
+
+async def parse_image_for_description(
+    file_path: str, filename: str, llm
+) -> str:
+    """Visual-description-only call for per-image-in-PDF use.
+
+    Used by ``picture_describer`` when an image is embedded inside a
+    larger document. Returns a markdown description of what the image
+    visually depicts; deliberately does NOT include text-in-image OCR
+    because the ETL service (Docling, Azure DI, LlamaCloud, ...) is
+    already running OCR over the entire page and would duplicate that
+    text content.
+    """
+    data_url = _image_to_data_url(file_path)
+    return await _invoke_vision(llm, _DESCRIPTION_PROMPT, data_url, filename)
+
+
+__all__ = [
+    "parse_image_for_description",
+    "parse_with_vision_llm",
+]
diff --git a/surfsense_backend/app/etl_pipeline/picture_describer.py b/surfsense_backend/app/etl_pipeline/picture_describer.py
new file mode 100644
index 000000000..f6bda2d4e
--- /dev/null
+++ b/surfsense_backend/app/etl_pipeline/picture_describer.py
@@ -0,0 +1,678 @@
+"""Extract embedded images from PDFs, describe them, and inject the
+descriptions inline into the parser's markdown.
+
+When the operator passes ``use_vision_llm=True`` for a PDF, the document
+parsers (DOCLING / LLAMACLOUD / Azure DI / UNSTRUCTURED) extract text
+but mostly drop the actual image content -- a CT scan inside a clinical
+PDF becomes (at best) a ``<!-- image -->`` placeholder in the markdown,
+and the caption text below it.
+
+This module fills that gap. After the document parser produces markdown
+text, we:
+
+1. Walk the original PDF with :mod:`pypdf`, pulling out each embedded
+   image (deduped by sha256, size-capped to match the vision LLM's own
+   limits).
+2. Run the vision LLM on each unique image (visual description) and,
+   in parallel when an OCR runner is provided, re-feed the same image
+   through the ETL service for per-image OCR.
+3. **Inject** a horizontal-rule-delimited markdown section -- with
+   named "OCR text" and "Visual description" sub-sections -- where the
+   image actually appears in the parser's markdown. Two splice modes,
+   chosen by which marker the parser emitted:
+
+   - **Replace** Docling-style ``<!-- image -->`` placeholders (and an
+     optional ``Image: <filename>`` caption line). The placeholder
+     carries no useful content of its own, so we substitute our block
+     for it.
+   - **Append after** layout-aware ``<figure>...</figure>`` blocks
+     (Azure DI ``prebuilt-layout``, LlamaCloud premium). Those blocks
+     already contain parser-extracted chart values / OCR'd labels /
+     captions, which are themselves useful for retrieval -- so we
+     PRESERVE the figure verbatim and add our vision-LLM block
+     immediately after it. The chunk then contains both the parser's
+     structured numbers AND the VLM's semantic interpretation.
+
+   Either way, the image content stays in context with the surrounding
+   document body rather than getting orphaned at the end -- crucial for
+   retrieval, where a single chunk should contain the question, the
+   image content, and the answer options together.
+
+If no placeholders, figures, or captions can be matched (e.g. an
+unusual parser output), we fall back to appending an
+``## Image Content`` section so no image content is silently lost.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import hashlib
+import logging
+import re
+import tempfile
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+# Type alias for the OCR callback. Takes (file_path, filename), returns
+# the OCR'd markdown text -- or empty string if no text was found, or
+# raises if OCR failed unrecoverably (which the describer catches and
+# treats as "no OCR for this image" rather than failing the whole doc).
+OcrRunner = Callable[[str, str], Awaitable[str]]
+
+logger = logging.getLogger(__name__)
+
+
+# Bound how many vision LLM calls we make in parallel for a single
+# document. Vision models are typically rate-limited; 4 concurrent
+# calls is a safe default that respects most provider limits while
+# keeping wall-clock manageable for image-heavy PDFs.
+_VISION_CONCURRENCY = 4
+
+# Match parse_with_vision_llm's per-image cap so we don't even attempt
+# images that the vision LLM would reject anyway (Anthropic's 5 MB
+# limit is the most restrictive among the major providers).
+_MAX_IMAGE_BYTES = 5 * 1024 * 1024
+
+# Skip degenerate images: tracking pixels, very small decorative dots,
+# scanner-introduced artefacts. We can't cheaply check pixel dimensions
+# without decoding the image, so we approximate: anything under 1 KB is
+# almost certainly not informative content.
+_MIN_IMAGE_BYTES = 1024
+
+
+@dataclass
+class PictureDescription:
+    """A single extracted image with its visual description and (optionally) OCR.
+
+    Two content fields by design, each produced by the *right* tool:
+
+    - ``description``: the vision LLM's visual interpretation. What the
+      image depicts (anatomy, charts, layout, etc.) -- the semantic
+      content that only a vision model can produce.
+    - ``ocr_text``: text-in-image extracted by re-feeding the image
+      through the configured ETL service (Docling/Azure DI/LlamaCloud)
+      *as if it were a standalone image upload*. Specialist OCR engine,
+      per-image attribution, no vision LLM tokens spent on text. None
+      when no OCR was requested or OCR found no text.
+    """
+
+    page_number: int                # 1-indexed
+    ordinal_in_page: int            # 0-indexed within the page
+    name: str                       # name pypdf assigned (e.g. "Im0")
+    sha256: str                     # hash of the raw image bytes
+    description: str                # visual description (markdown)
+    ocr_text: str | None = None     # OCR text from the ETL service, if any
+
+
+@dataclass
+class PictureExtractionResult:
+    """Aggregate result of extracting all pictures from a document."""
+
+    descriptions: list[PictureDescription] = field(default_factory=list)
+    skipped_too_small: int = 0
+    skipped_too_large: int = 0
+    skipped_duplicate: int = 0
+    failed: int = 0
+
+    @property
+    def has_content(self) -> bool:
+        return bool(self.descriptions)
+
+
+def _is_pdf(filename: str) -> bool:
+    return filename.lower().endswith(".pdf")
+
+
+def _pick_suffix(name: str) -> str:
+    lower = name.lower()
+    for ext in (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif", ".webp"):
+        if lower.endswith(ext):
+            return ".jpeg" if ext == ".jpg" else ext
+    return ".png"
+
+
+def _extract_pdf_images(file_path: str) -> list[tuple[int, int, str, bytes]]:
+    """Pull every embedded image out of a PDF.
+
+    Returns ``(page_number_1_indexed, ordinal_in_page, name, bytes)``.
+    Per-page and per-image failures are logged and skipped -- one bad
+    image must not fail the whole document.
+    """
+
+    from pypdf import PdfReader
+
+    out: list[tuple[int, int, str, bytes]] = []
+    try:
+        reader = PdfReader(file_path)
+    except Exception:
+        logger.warning(
+            "pypdf failed to open %s for image extraction",
+            file_path,
+            exc_info=True,
+        )
+        return out
+
+    for page_idx, page in enumerate(reader.pages):
+        try:
+            images = list(page.images)
+        except Exception:
+            logger.warning(
+                "pypdf failed to enumerate images on page %d of %s",
+                page_idx + 1,
+                file_path,
+                exc_info=True,
+            )
+            continue
+        for img_idx, img in enumerate(images):
+            try:
+                name = getattr(img, "name", None) or f"page{page_idx + 1}_img{img_idx}"
+                data = img.data
+            except Exception:
+                logger.warning(
+                    "pypdf failed to read image %d on page %d of %s",
+                    img_idx,
+                    page_idx + 1,
+                    file_path,
+                    exc_info=True,
+                )
+                continue
+            out.append((page_idx + 1, img_idx, name, data))
+    return out
+
+
+async def _describe_one(
+    page_number: int,
+    ordinal: int,
+    name: str,
+    sha256: str,
+    data: bytes,
+    vision_llm: Any,
+    semaphore: asyncio.Semaphore,
+    ocr_runner: OcrRunner | None,
+) -> PictureDescription | None:
+    from app.etl_pipeline.parsers.vision_llm import parse_image_for_description
+
+    suffix = _pick_suffix(name)
+    # NamedTemporaryFile + delete=False because the vision-LLM helper
+    # and the OCR runner each open the path themselves; we clean up in
+    # the finally. Same temp file feeds both, which is correct: vision
+    # LLM and OCR are looking at the same image, just asking different
+    # questions of it.
+    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
+        tmp.write(data)
+        tmp_path = tmp.name
+    try:
+        async with semaphore:
+            tasks: list[Awaitable[Any]] = [
+                parse_image_for_description(tmp_path, name, vision_llm),
+            ]
+            if ocr_runner is not None:
+                tasks.append(ocr_runner(tmp_path, name))
+
+            # return_exceptions=True so a failure in one branch (most
+            # often OCR) doesn't poison the other.
+            results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        description_result = results[0]
+        if isinstance(description_result, BaseException):
+            logger.warning(
+                "Vision LLM failed for image %s on page %d, skipping",
+                name,
+                page_number,
+                exc_info=description_result,
+            )
+            return None
+        description = str(description_result)
+
+        ocr_text: str | None = None
+        if ocr_runner is not None and len(results) > 1:
+            ocr_result = results[1]
+            if isinstance(ocr_result, BaseException):
+                logger.warning(
+                    "Per-image OCR failed for image %s on page %d, "
+                    "omitting OCR field for this image",
+                    name,
+                    page_number,
+                    exc_info=ocr_result,
+                )
+            else:
+                stripped = str(ocr_result).strip()
+                # Empty OCR (or whitespace-only) means the OCR engine
+                # found no text in this image. Record that as None so
+                # the rendered block doesn't include a useless empty tag.
+                ocr_text = stripped or None
+    finally:
+        with contextlib.suppress(OSError):
+            Path(tmp_path).unlink()
+
+    return PictureDescription(
+        page_number=page_number,
+        ordinal_in_page=ordinal,
+        name=name,
+        sha256=sha256,
+        description=description,
+        ocr_text=ocr_text,
+    )
+
+
+async def describe_pictures(
+    file_path: str,
+    filename: str,
+    vision_llm: Any,
+    *,
+    ocr_runner: OcrRunner | None = None,
+) -> PictureExtractionResult:
+    """Extract embedded images from a document and describe each via vision LLM.
+
+    When ``ocr_runner`` is provided, each image is also passed to it
+    (in parallel with the vision LLM) and the returned text is recorded
+    in :attr:`PictureDescription.ocr_text`. The runner is typically a
+    closure over a vision-LLM-less ``EtlPipelineService`` -- this lets
+    the same OCR engine that processes standalone image uploads
+    (Docling/Azure DI/LlamaCloud) also process embedded-in-PDF images,
+    giving per-image OCR attribution alongside the page-level OCR that
+    the parser already does.
+
+    Currently PDF-only. For non-PDF documents this returns an empty
+    result and the caller should leave the parser's markdown untouched.
+    """
+
+    result = PictureExtractionResult()
+    if not _is_pdf(filename) or vision_llm is None:
+        return result
+
+    raw_images = _extract_pdf_images(file_path)
+    if not raw_images:
+        return result
+
+    seen_hashes: set[str] = set()
+    eligible: list[tuple[int, int, str, str, bytes]] = []
+    for page_number, ordinal, name, data in raw_images:
+        if len(data) > _MAX_IMAGE_BYTES:
+            result.skipped_too_large += 1
+            continue
+        if len(data) < _MIN_IMAGE_BYTES:
+            result.skipped_too_small += 1
+            continue
+        sha = hashlib.sha256(data).hexdigest()
+        if sha in seen_hashes:
+            result.skipped_duplicate += 1
+            continue
+        seen_hashes.add(sha)
+        eligible.append((page_number, ordinal, name, sha, data))
+
+    if not eligible:
+        return result
+
+    semaphore = asyncio.Semaphore(_VISION_CONCURRENCY)
+    tasks = [
+        _describe_one(p, o, n, sha, d, vision_llm, semaphore, ocr_runner)
+        for (p, o, n, sha, d) in eligible
+    ]
+    descriptions = await asyncio.gather(*tasks)
+    for desc in descriptions:
+        if desc is None:
+            result.failed += 1
+        else:
+            result.descriptions.append(desc)
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Rendering: build the per-image markdown block + inject inline.
+# ---------------------------------------------------------------------------
+
+
+def _format_image_block(
+    name: str,
+    description: str,
+    ocr_text: str | None = None,
+) -> str:
+    """Render the per-image block as a horizontal-rule-delimited section.
+
+    Why no blockquote / no raw HTML / no XML?
+    -----------------------------------------
+    We tried each in turn and each failed in the document viewer:
+
+    - **Raw HTML / XML** (``<image>...</image>``): unknown elements
+      have no render rules in Streamdown or PlateJS, so the content
+      survives in the markdown source but is invisible to humans.
+    - **Blockquote with nested blocks**: nested fenced code blocks,
+      bullet lists, numbered lists, tables -- any *block* element
+      inside a ``>``-prefixed blockquote -- gets evicted by Streamdown
+      / remark, dropping everything after it onto the document level.
+      The vision LLM happily produces bulleted descriptions, so this
+      hit the viewer in practice.
+
+    A horizontal-rule-delimited section, by contrast, contains only
+    standard top-level markdown -- bold labels and free-form body --
+    so the description's native markdown (lists, prose, tables) all
+    renders natively in every renderer.
+
+    Layout (OCR section omitted when ``ocr_text`` is None/empty):
+
+        ---
+
+        **Embedded image:** `MM-130-a.jpeg`
+
+        **OCR text:**
+        Slice 24 / 60
+        L
+        R
+
+        **Visual description:**
+
+        - Axial contrast-enhanced CT showing a large cystic mass...
+        - Mass effect on the adjacent stomach.
+
+        ---
+
+    Still LLM-friendly: the ``**Embedded image:** `<filename>``` prefix
+    is unique and trivially regex-able (``^\\*\\*Embedded image:\\*\\* `(.+?)`$``).
+
+    Returned with leading and trailing blank-line padding so the rules
+    never merge with adjacent paragraphs after splicing.
+    """
+
+    parts: list[str] = [f"**Embedded image:** `{name}`"]
+
+    if ocr_text and ocr_text.strip():
+        # Bold "OCR text:" label with trailing two spaces (=> <br>) so
+        # the first OCR line sits directly under the label rather than
+        # forcing a paragraph break that some renderers would style
+        # differently. Subsequent OCR lines also use trailing two spaces
+        # for hard breaks, so multi-line OCR renders line-by-line
+        # without needing a (fragile) fenced code block.
+        ocr_clean_lines = [
+            ln.rstrip() for ln in ocr_text.strip().splitlines() if ln.strip()
+        ]
+        parts.append("")
+        parts.append("**OCR text:**  ")
+        for i, raw in enumerate(ocr_clean_lines):
+            suffix = "" if i == len(ocr_clean_lines) - 1 else "  "
+            parts.append(f"{raw}{suffix}")
+
+    parts.append("")
+    parts.append("**Visual description:**")
+    parts.append("")
+    parts.append(description.strip())
+
+    body = "\n".join(parts)
+    # Wrap with blank lines + horizontal rules so the block is clearly
+    # delimited from surrounding paragraphs and survives splicing into
+    # the middle of any markdown stream.
+    return "\n\n---\n\n" + body + "\n\n---\n\n"
+
+
+# Patterns we'll try to splice into. Each pattern captures the
+# original-PDF filename when one is available (group 1).
+#
+# Replace-style markers (the matched span is substituted with our block
+# because it carries no useful content of its own):
+#
+# 1. Docling's image placeholder followed by an "Image: <filename>"
+#    caption line. This is what our medxpertqa renderer produces:
+#    reportlab places the JPEG, then a caption, and Docling outputs
+#    the placeholder + caption.
+# 2. Docling's image placeholder alone (filename unknown -- we fall
+#    back to pypdf's name).
+# 3. A bare "Image: <filename>" caption line with no preceding
+#    placeholder. Rare in practice, but covers parsers that drop the
+#    placeholder entirely.
+_PLACEHOLDER_WITH_CAPTION = re.compile(
+    r"<!--\s*image\s*-->\s*\n\s*Image:\s*(\S+)\s*(?:\n|$)",
+    re.IGNORECASE,
+)
+_PLACEHOLDER_ONLY = re.compile(
+    r"<!--\s*image\s*-->",
+    re.IGNORECASE,
+)
+_CAPTION_ONLY = re.compile(
+    r"^[ \t]*Image:\s*(\S+)\s*$",
+    re.IGNORECASE | re.MULTILINE,
+)
+
+# Append-after marker (the matched span is preserved verbatim and our
+# block is inserted immediately after it):
+#
+# 4. ``<figure>...</figure>`` as emitted by layout-aware parsers (Azure
+#    Document Intelligence ``prebuilt-layout``, LlamaCloud premium).
+#    The figure's own contents -- chart bar values, axis labels,
+#    inline ``<figcaption>``, embedded ``<table>`` for tabular figures
+#    -- are themselves specialist OCR output, so we keep them and add
+#    our vision-LLM block alongside. ``[^>]*`` in the open tag tolerates
+#    optional attributes like ``<figure id="...">``; ``re.DOTALL``
+#    lets ``.`` cross the newlines inside the block.
+_FIGURE_BLOCK = re.compile(
+    r"<figure\b[^>]*>.*?</figure>",
+    re.DOTALL | re.IGNORECASE,
+)
+
+
+def _replace_one_match(
+    markdown: str,
+    pattern: re.Pattern[str],
+    descriptions: list[PictureDescription],
+    desc_idx: int,
+) -> tuple[str, int]:
+    """Replace the first occurrence of ``pattern`` with the next image block.
+
+    Returns the new markdown and the new ``desc_idx`` (advanced if a
+    replacement happened, unchanged otherwise).
+    """
+
+    if desc_idx >= len(descriptions):
+        return markdown, desc_idx
+
+    match = pattern.search(markdown)
+    if not match:
+        return markdown, desc_idx
+
+    desc = descriptions[desc_idx]
+    captured_name: str | None = None
+    if match.groups():
+        captured_name = match.group(1)
+    name = captured_name or desc.name
+    block = _format_image_block(name, desc.description, desc.ocr_text)
+
+    new_markdown = markdown[: match.start()] + block + markdown[match.end():]
+    return new_markdown, desc_idx + 1
+
+
+def _splice_after_figures(
+    markdown: str,
+    descriptions: list[PictureDescription],
+    desc_idx: int,
+) -> tuple[str, int]:
+    """Append vision-LLM blocks immediately after each ``<figure>...</figure>``.
+
+    Layout-aware parsers (Azure DI ``prebuilt-layout``, LlamaCloud
+    premium) wrap each figure / chart / inline table in this tag and
+    carry their own OCR of the figure's text content inside it. That
+    content is useful on its own, so we keep the original block
+    verbatim and add our vision-LLM block right after it -- giving
+    retrieval both signals in the same chunk.
+
+    Descriptions are matched to figures in document order (first
+    description -> first figure, etc.). All splice points are computed
+    upfront with :func:`re.finditer` and applied in REVERSE order so
+    earlier offsets stay valid as the markdown grows. Returns the
+    advanced ``desc_idx`` for the caller's leftover-handling.
+    """
+
+    if desc_idx >= len(descriptions):
+        return markdown, desc_idx
+
+    matches = list(_FIGURE_BLOCK.finditer(markdown))
+    if not matches:
+        return markdown, desc_idx
+
+    n_to_splice = min(len(matches), len(descriptions) - desc_idx)
+    if n_to_splice <= 0:
+        return markdown, desc_idx
+
+    out = markdown
+    # Walk in reverse so each splice's end-offset still points at the
+    # right place in the (still-mutating) string.
+    for i in range(n_to_splice - 1, -1, -1):
+        match = matches[i]
+        desc = descriptions[desc_idx + i]
+        block = _format_image_block(desc.name, desc.description, desc.ocr_text)
+        out = out[: match.end()] + block + out[match.end():]
+
+    return out, desc_idx + n_to_splice
+
+
+def inject_descriptions_inline(
+    markdown: str,
+    result: PictureExtractionResult,
+) -> tuple[str, int]:
+    """Splice per-image markdown blocks into the document at image positions.
+
+    Walks the markdown left-to-right, consuming descriptions in order.
+    Tries two splicing strategies, in this order:
+
+    1. **Append-after** for ``<figure>...</figure>`` blocks emitted by
+       layout-aware parsers (Azure DI ``prebuilt-layout``, LlamaCloud
+       premium). The figure block carries the parser's own OCR of the
+       figure -- we preserve it and add our vision-LLM block right
+       after.
+    2. **Replace** for Docling-style markers, in priority order:
+
+       - ``<!-- image -->`` followed by ``Image: <filename>`` caption,
+       - ``<!-- image -->`` placeholder alone,
+       - bare ``Image: <filename>`` caption.
+
+    A document typically uses one style or the other (depending on
+    which parser produced its markdown), so the two paths don't fight
+    each other in practice. When they do co-occur, figures are
+    consumed first.
+
+    Returns ``(new_markdown, n_inlined)`` -- the count of descriptions
+    that were placed inline. The caller decides what to do with any
+    leftover descriptions (typically: append them at the end).
+    """
+
+    if not result.descriptions:
+        return markdown, 0
+
+    descriptions = result.descriptions
+    desc_idx = 0
+    out = markdown
+
+    # Step 1: layout-aware figures. One-shot batch -- finds ALL
+    # <figure> blocks, splices in document order until we exhaust
+    # either side.
+    out, desc_idx = _splice_after_figures(out, descriptions, desc_idx)
+
+    # Step 2: Docling-style replacement markers. One match per
+    # iteration, so a doc that has both a figure (consumed above) and
+    # a Docling placeholder (consumed below) still works.
+    while desc_idx < len(descriptions):
+        before_idx = desc_idx
+        out, desc_idx = _replace_one_match(
+            out, _PLACEHOLDER_WITH_CAPTION, descriptions, desc_idx
+        )
+        if desc_idx > before_idx:
+            continue
+        out, desc_idx = _replace_one_match(
+            out, _PLACEHOLDER_ONLY, descriptions, desc_idx
+        )
+        if desc_idx > before_idx:
+            continue
+        out, desc_idx = _replace_one_match(
+            out, _CAPTION_ONLY, descriptions, desc_idx
+        )
+        if desc_idx > before_idx:
+            continue
+        # No more positions to splice into.
+        break
+
+    return out, desc_idx
+
+
+def render_appended_section(
+    descriptions: list[PictureDescription],
+    *,
+    skip_notes: PictureExtractionResult | None = None,
+    heading: str = "## Image Content (vision-LLM extracted)",
+) -> str:
+    """Render leftover descriptions as an appended section.
+
+    Used as a fallback when not every description could be inlined
+    (either because the parser produced no detectable image markers,
+    or because there were more extracted images than markers).
+    """
+
+    if not descriptions and not skip_notes:
+        return ""
+
+    parts: list[str] = ["", heading, ""]
+    for desc in descriptions:
+        parts.append(
+            _format_image_block(desc.name, desc.description, desc.ocr_text)
+        )
+        parts.append("")
+
+    if skip_notes is not None:
+        notes: list[str] = []
+        if skip_notes.skipped_too_large:
+            notes.append(f"{skip_notes.skipped_too_large} too large (> 5 MB)")
+        if skip_notes.skipped_too_small:
+            notes.append(f"{skip_notes.skipped_too_small} too small (< 1 KB)")
+        if skip_notes.skipped_duplicate:
+            notes.append(f"{skip_notes.skipped_duplicate} duplicate")
+        if skip_notes.failed:
+            notes.append(f"{skip_notes.failed} failed")
+        if notes:
+            parts.append(f"_Note: {', '.join(notes)} image(s) skipped._")
+
+    return "\n".join(parts)
+
+
+def merge_descriptions_into_markdown(
+    markdown: str,
+    result: PictureExtractionResult,
+) -> str:
+    """Top-level: inline what we can, append what's left over.
+
+    This is the function the ETL pipeline actually calls. It guarantees
+    that no successfully-described image is silently dropped: anything
+    we can't splice inline gets appended at the end with a heading
+    that makes it clear those came from the document but weren't
+    location-matched.
+    """
+
+    if not result.descriptions:
+        return markdown
+
+    new_markdown, n_inlined = inject_descriptions_inline(markdown, result)
+    leftover = result.descriptions[n_inlined:]
+
+    if not leftover:
+        return new_markdown
+
+    # Distinguish in the heading whether NONE were inlined (parser
+    # produced no markers at all) vs SOME (mismatched count).
+    heading = (
+        "## Image Content (vision-LLM extracted)"
+        if n_inlined == 0
+        else "## Image Content (additional, no inline marker found)"
+    )
+    section = render_appended_section(leftover, heading=heading)
+    if not section:
+        return new_markdown
+    return f"{new_markdown.rstrip()}\n\n{section.lstrip()}\n"
+
+
+__all__ = [
+    "PictureDescription",
+    "PictureExtractionResult",
+    "describe_pictures",
+    "inject_descriptions_inline",
+    "merge_descriptions_into_markdown",
+    "render_appended_section",
+]
diff --git a/surfsense_backend/app/services/docling_service.py b/surfsense_backend/app/services/docling_service.py
index af9a7d2d5..cf51efb4a 100644
--- a/surfsense_backend/app/services/docling_service.py
+++ b/surfsense_backend/app/services/docling_service.py
@@ -77,10 +77,16 @@ class DoclingService:
             # Create pipeline options with version-safe attribute checking
             pipeline_options = PdfPipelineOptions()
 
-            # Disable OCR (user request)
+            # Enable OCR so text-in-image (chart axes, ECG annotations,
+            # lab tables embedded as images, scanned pages, etc.) is
+            # lifted into the main markdown stream. This pairs with the
+            # vision-LLM picture-description pass downstream — OCR
+            # captures literal text; vision LLM captures the visual
+            # content. Together they give a faithful representation of
+            # PDFs that mix text and images.
             if hasattr(pipeline_options, "do_ocr"):
-                pipeline_options.do_ocr = False
-                logger.info("⚠️ OCR disabled by user request")
+                pipeline_options.do_ocr = True
+                logger.info("✅ OCR enabled for embedded text-in-image extraction")
             else:
                 logger.warning("⚠️ OCR attribute not available in this Docling version")
 
diff --git a/surfsense_backend/app/tasks/document_processors/file_processors.py b/surfsense_backend/app/tasks/document_processors/file_processors.py
index 1271550df..137c27cda 100644
--- a/surfsense_backend/app/tasks/document_processors/file_processors.py
+++ b/surfsense_backend/app/tasks/document_processors/file_processors.py
@@ -123,10 +123,6 @@ async def _process_non_document_upload(ctx: _ProcessingContext) -> Document | No
     """Extract content from a non-document file (plaintext/direct_convert/audio/image) via the unified ETL pipeline."""
     from app.etl_pipeline.etl_document import EtlRequest
     from app.etl_pipeline.etl_pipeline_service import EtlPipelineService
-    from app.etl_pipeline.file_classifier import (
-        FileCategory,
-        classify_file as etl_classify,
-    )
 
     await _notify(ctx, "parsing", "Processing file")
     await ctx.task_logger.log_task_progress(
@@ -135,8 +131,12 @@ async def _process_non_document_upload(ctx: _ProcessingContext) -> Document | No
         {"processing_stage": "extracting"},
     )
 
+    # Fetch the vision LLM whenever the operator opts in. The ETL
+    # pipeline decides what to do with it: image files run through the
+    # vision LLM directly; document files (PDFs) get per-image
+    # descriptions appended via picture_describer.
     vision_llm = None
-    if ctx.use_vision_llm and etl_classify(ctx.filename) == FileCategory.IMAGE:
+    if ctx.use_vision_llm:
         from app.services.llm_service import get_vision_llm
 
         vision_llm = await get_vision_llm(ctx.session, ctx.search_space_id)
@@ -230,7 +230,16 @@ async def _process_document_upload(ctx: _ProcessingContext) -> Document | None:
 
     await _notify(ctx, "parsing", "Extracting content")
 
-    etl_result = await EtlPipelineService().extract(
+    # Document files (PDF, docx, etc.) get vision LLM treatment too:
+    # the ETL pipeline appends a per-image description section when
+    # vision_llm is provided. See picture_describer.describe_pictures.
+    vision_llm = None
+    if ctx.use_vision_llm:
+        from app.services.llm_service import get_vision_llm
+
+        vision_llm = await get_vision_llm(ctx.session, ctx.search_space_id)
+
+    etl_result = await EtlPipelineService(vision_llm=vision_llm).extract(
         EtlRequest(
             file_path=ctx.file_path,
             filename=ctx.filename,
@@ -418,8 +427,12 @@ async def _extract_file_content(
         billable_pages = estimated_pages * mode.page_multiplier
         await page_limit_service.check_page_limit(user_id, billable_pages)
 
+    # Vision LLM is provided to the ETL pipeline for any file category
+    # when the operator opts in. Image files run through it directly;
+    # document files (PDFs) get per-image descriptions appended via
+    # picture_describer.
     vision_llm = None
-    if use_vision_llm and category == FileCategory.IMAGE:
+    if use_vision_llm:
         from app.services.llm_service import get_vision_llm
 
         vision_llm = await get_vision_llm(session, search_space_id)
diff --git a/surfsense_backend/tests/unit/etl_pipeline/test_etl_pipeline_service.py b/surfsense_backend/tests/unit/etl_pipeline/test_etl_pipeline_service.py
index 8571136c3..edfe94406 100644
--- a/surfsense_backend/tests/unit/etl_pipeline/test_etl_pipeline_service.py
+++ b/surfsense_backend/tests/unit/etl_pipeline/test_etl_pipeline_service.py
@@ -741,6 +741,372 @@ async def test_extract_image_falls_back_to_document_without_vision_llm(
     assert result.content_type == "document"
 
 
+# ---------------------------------------------------------------------------
+# Document path with vision LLM: per-image descriptions are appended
+# ---------------------------------------------------------------------------
+
+
+def _fake_extraction_result(*descriptions):
+    from app.etl_pipeline.picture_describer import (
+        PictureDescription,
+        PictureExtractionResult,
+    )
+
+    return PictureExtractionResult(
+        descriptions=[
+            PictureDescription(
+                page_number=d["page"],
+                ordinal_in_page=d.get("ordinal", 0),
+                name=d["name"],
+                sha256=d.get("sha", "deadbeef"),
+                description=d["desc"],
+            )
+            for d in descriptions
+        ]
+    )
+
+
+async def test_extract_pdf_with_vision_llm_inlines_image_blocks(tmp_path, mocker):
+    """A PDF with an `<!-- image -->` placeholder + caption gets the
+    block spliced inline (no orphaned ``## Image Content`` section).
+
+    This is the headline scenario for the medxpertqa benchmark: the
+    image content lives in the same chunk as the surrounding case text
+    so retrieval pulls the question, image, and answer options together.
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {
+        "content": (
+            "# MedXpertQA-MM MM-130\n\n"
+            "## Clinical case\n\nA 44-year-old man...\n\n"
+            "<!-- image -->\nImage: MM-130-a.jpeg\n\n"
+            "## Answer choices\n\nA) ...\n"
+        )
+    }
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    extraction = _fake_extraction_result(
+        {
+            "page": 1,
+            "name": "Im0",
+            "desc": "Axial CT showing a large cystic mass.",
+        }
+    )
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(return_value=extraction),
+    )
+
+    fake_llm = mocker.MagicMock()
+    result = await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    md = result.markdown_content
+    # The placeholder + caption are gone, replaced by a horizontal-
+    # rule-delimited section with the captioned filename.
+    assert "<!-- image -->" not in md
+    assert "Image: MM-130-a.jpeg" not in md
+    assert "**Embedded image:** `MM-130-a.jpeg`" in md
+    assert "**Visual description:**" in md
+    assert "Axial CT showing a large cystic mass." in md
+    # No OCR section -- our fake_extraction_result has no ocr_text,
+    # and the format omits the section when there's no text to show.
+    assert "**OCR text:**" not in md
+    # No raw HTML / XML tags or blockquote wrapping leak.
+    assert "<image" not in md
+    assert "> **Embedded image:**" not in md
+    # No appended section -- everything went inline.
+    assert "## Image Content" not in md
+    # Surrounding case text + answer options are preserved.
+    assert "A 44-year-old man..." in md
+    assert "## Answer choices" in md
+    assert "A) ..." in md
+
+
+async def test_extract_pdf_with_vision_llm_appends_when_no_marker(tmp_path, mocker):
+    """When parser markdown has no image markers, descriptions get appended.
+
+    This is the fallback path for parsers that drop image placeholders
+    entirely. The image content still ends up in the markdown -- just
+    in a clearly-labeled section rather than inline.
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {
+        "content": "# Parsed PDF text\n\nNo image markers anywhere.\n"
+    }
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    extraction = _fake_extraction_result(
+        {"page": 1, "name": "Im0", "desc": "An image description."}
+    )
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(return_value=extraction),
+    )
+
+    fake_llm = mocker.MagicMock()
+    result = await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    md = result.markdown_content
+    assert "# Parsed PDF text" in md
+    assert "## Image Content (vision-LLM extracted)" in md
+    assert "**Embedded image:** `Im0`" in md
+    assert "An image description." in md
+
+
+async def test_extract_pdf_without_vision_llm_skips_picture_descriptions(
+    tmp_path, mocker
+):
+    """No vision LLM -> parser markdown returned as-is."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {"content": "# Parsed PDF text"}
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    describe_mock = mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(),
+    )
+
+    result = await EtlPipelineService().extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    assert result.markdown_content == "# Parsed PDF text"
+    assert "<image" not in result.markdown_content
+    describe_mock.assert_not_called()
+
+
+async def test_extract_pdf_with_vision_llm_swallows_describe_failure(
+    tmp_path, mocker
+):
+    """A pypdf or vision LLM blow-up never fails the document upload."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {"content": "# Parsed PDF text"}
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(side_effect=RuntimeError("pypdf exploded")),
+    )
+
+    fake_llm = mocker.MagicMock()
+    result = await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    assert result.markdown_content == "# Parsed PDF text"
+    assert result.etl_service == "DOCLING"
+
+
+async def test_extract_pdf_with_vision_llm_no_images_returns_parser_text(
+    tmp_path, mocker
+):
+    """Vision-LLM-enabled PDF with zero extracted images is unchanged."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {"content": "# Just text, no images"}
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    empty = _fake_extraction_result()
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(return_value=empty),
+    )
+
+    fake_llm = mocker.MagicMock()
+    result = await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    assert result.markdown_content == "# Just text, no images"
+    assert "<image" not in result.markdown_content
+
+
+# ---------------------------------------------------------------------------
+# Per-image OCR runner: wiring + behaviour
+#
+# When extracting a PDF with a vision LLM, the ETL service must ALSO
+# pass an ``ocr_runner`` to picture_describer. The runner is a closure
+# that re-feeds each extracted image through a vision-LLM-less
+# EtlPipelineService -- i.e. the same OCR engine that handles
+# standalone image uploads (Docling/Azure DI/LlamaCloud) gets a crack
+# at each embedded image, with the text attached to the inline block.
+# ---------------------------------------------------------------------------
+
+
+async def test_extract_pdf_passes_ocr_runner_to_describe_pictures(
+    tmp_path, mocker
+):
+    """The ETL service must wire an ocr_runner kwarg to describe_pictures."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {"content": "# Parsed PDF text"}
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    describe_mock = mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=mocker.AsyncMock(return_value=_fake_extraction_result()),
+    )
+
+    fake_llm = mocker.MagicMock()
+    await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    describe_mock.assert_awaited_once()
+    _, kwargs = describe_mock.await_args
+    assert "ocr_runner" in kwargs
+    assert callable(kwargs["ocr_runner"])
+
+
+async def test_extract_pdf_ocr_runner_invokes_document_parser_on_image(
+    tmp_path, mocker
+):
+    """The OCR runner closure should re-extract each image via the parser.
+
+    We capture the runner that the ETL service passes to
+    describe_pictures, invoke it with a fake image path, and assert
+    that Docling was called with that image. This proves the closure
+    is wired to a vision-LLM-less sub-pipeline (otherwise it would
+    recurse into the vision LLM and never hit the OCR engine).
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+    image_file = tmp_path / "Im0.png"
+    image_file.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 100)
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {
+        "content": "Slice 24 / 60   L   R"
+    }
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    captured: dict = {}
+
+    async def capture_runner(*args, **kwargs):
+        captured["runner"] = kwargs["ocr_runner"]
+        return _fake_extraction_result()
+
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=capture_runner,
+    )
+
+    fake_llm = mocker.MagicMock()
+    await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    runner = captured["runner"]
+    ocr_text = await runner(str(image_file), "Im0.png")
+
+    assert ocr_text == "Slice 24 / 60   L   R"
+    # Docling was invoked twice in total: once for the PDF, once for
+    # the image we re-fed via the runner.
+    assert fake_docling.process_document.await_count == 2
+
+
+async def test_extract_pdf_ocr_runner_returns_empty_on_unsupported_image(
+    tmp_path, mocker
+):
+    """Unsupported image format → runner returns empty string, doesn't raise.
+
+    Common case: a PDF embeds a JPEG2000 or CCITT-TIFF image that
+    Docling can't load. We don't want an unsupported format on ONE
+    embedded image to spoil the whole PDF extraction; the runner
+    should swallow the EtlUnsupportedFileError and return "" so the
+    image gets a description but no OCR tag.
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake content")
+    weird_image = tmp_path / "Im0.jp2"  # JPEG2000, unlikely to be supported
+    weird_image.write_bytes(b"\x00\x00\x00\x0CjP" + b"\x00" * 50)
+
+    mocker.patch("app.config.config.ETL_SERVICE", "DOCLING")
+
+    fake_docling = mocker.AsyncMock()
+    fake_docling.process_document.return_value = {"content": "# Parsed PDF text"}
+    mocker.patch(
+        "app.services.docling_service.create_docling_service",
+        return_value=fake_docling,
+    )
+
+    captured: dict = {}
+
+    async def capture_runner(*args, **kwargs):
+        captured["runner"] = kwargs["ocr_runner"]
+        return _fake_extraction_result()
+
+    mocker.patch(
+        "app.etl_pipeline.picture_describer.describe_pictures",
+        new=capture_runner,
+    )
+
+    fake_llm = mocker.MagicMock()
+    await EtlPipelineService(vision_llm=fake_llm).extract(
+        EtlRequest(file_path=str(pdf_file), filename="report.pdf")
+    )
+
+    runner = captured["runner"]
+    ocr_text = await runner(str(weird_image), "Im0.jp2")
+
+    assert ocr_text == ""
+
+
 # ---------------------------------------------------------------------------
 # Processing Mode enum tests
 # ---------------------------------------------------------------------------
diff --git a/surfsense_backend/tests/unit/etl_pipeline/test_picture_describer.py b/surfsense_backend/tests/unit/etl_pipeline/test_picture_describer.py
new file mode 100644
index 000000000..407bc97a2
--- /dev/null
+++ b/surfsense_backend/tests/unit/etl_pipeline/test_picture_describer.py
@@ -0,0 +1,967 @@
+"""Unit tests for the picture_describer module.
+
+Covers:
+
+- :func:`describe_pictures` -- the PDF image walker + per-image vision
+  LLM call (structured output split into ``ocr_text`` and
+  ``description``);
+- :func:`inject_descriptions_inline` -- in-place replacement of image
+  placeholders / captions in the parser markdown;
+- :func:`merge_descriptions_into_markdown` -- the top-level helper
+  that inlines what it can and appends what it can't;
+- :func:`render_appended_section` -- the appended-fallback renderer.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from app.etl_pipeline.picture_describer import (
+    PictureDescription,
+    PictureExtractionResult,
+    describe_pictures,
+    inject_descriptions_inline,
+    merge_descriptions_into_markdown,
+    render_appended_section,
+)
+
+pytestmark = pytest.mark.unit
+
+
+def _make_image_obj(name: str, data: bytes):
+    """Mimic pypdf's ImageFile object shape for the bits we use."""
+    img = MagicMock()
+    img.name = name
+    img.data = data
+    return img
+
+
+# ---------------------------------------------------------------------------
+# describe_pictures: short-circuits
+# ---------------------------------------------------------------------------
+
+
+async def test_describe_pictures_no_op_for_non_pdf(tmp_path):
+    """Non-PDF files are silently no-op'd; we don't try to extract images."""
+    docx_file = tmp_path / "report.docx"
+    docx_file.write_bytes(b"PK fake docx")
+
+    fake_llm = AsyncMock()
+    result = await describe_pictures(str(docx_file), "report.docx", fake_llm)
+
+    assert result.descriptions == []
+    assert result.skipped_too_large == 0
+    fake_llm.ainvoke.assert_not_called()
+
+
+async def test_describe_pictures_no_op_when_vision_llm_is_none(tmp_path):
+    """If the caller didn't provide a vision LLM, we no-op even for PDFs."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    result = await describe_pictures(str(pdf_file), "report.pdf", None)
+    assert result.descriptions == []
+
+
+async def test_describe_pictures_no_op_for_pdf_with_no_images(tmp_path, mocker):
+    """A PDF that pypdf can open but contains zero images returns empty."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[]), MagicMock(images=[])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    fake_llm = AsyncMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert result.descriptions == []
+    fake_llm.ainvoke.assert_not_called()
+
+
+# ---------------------------------------------------------------------------
+# describe_pictures: happy paths
+# ---------------------------------------------------------------------------
+
+
+async def test_describe_pictures_runs_vision_llm_per_image(tmp_path, mocker):
+    """Every eligible image gets exactly one description-only vision call."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img_a = _make_image_obj("Im0.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    img_b = _make_image_obj("Im1.png", b"\x89PNG\r\n\x1a\n" + b"\xcd" * 2000)
+    page1 = MagicMock(images=[img_a])
+    page2 = MagicMock(images=[img_b])
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [page1, page2]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    parse_mock = mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(side_effect=["Description A", "Description B"]),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 2
+    by_name = {d.name: d.description for d in result.descriptions}
+    assert by_name == {"Im0.jpeg": "Description A", "Im1.png": "Description B"}
+    assert all(d.page_number in (1, 2) for d in result.descriptions)
+    assert parse_mock.await_count == 2
+
+
+async def test_describe_pictures_dedups_by_hash(tmp_path, mocker):
+    """An image that appears N times in the PDF is described once."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    payload = b"\x89PNG\r\n\x1a\n" + b"\x42" * 2000
+    img = _make_image_obj("logo.png", payload)
+    page1 = MagicMock(images=[img])
+    page2 = MagicMock(images=[_make_image_obj("logo.png", payload)])
+    page3 = MagicMock(images=[_make_image_obj("logo.png", payload)])
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [page1, page2, page3]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    parse_mock = mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="Logo desc"),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 1
+    assert result.skipped_duplicate == 2
+    assert parse_mock.await_count == 1
+
+
+async def test_describe_pictures_skips_too_small_images(tmp_path, mocker):
+    """Sub-1KB images (tracking pixels, dots, etc.) are skipped."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    tiny = _make_image_obj("dot.png", b"\x89PNG\r\n\x1a\n")
+    big = _make_image_obj("ct.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 3000)
+    page = MagicMock(images=[tiny, big])
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [page]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    parse_mock = mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="CT scan"),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].name == "ct.jpeg"
+    assert result.skipped_too_small == 1
+    assert parse_mock.await_count == 1
+
+
+async def test_describe_pictures_skips_too_large_images(tmp_path, mocker):
+    """Images larger than the vision LLM's per-image cap are skipped."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    huge = _make_image_obj("huge.jpeg", b"\xff" * (6 * 1024 * 1024))
+    ok = _make_image_obj("ok.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    page = MagicMock(images=[huge, ok])
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [page]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    parse_mock = mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="OK image"),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].name == "ok.jpeg"
+    assert result.skipped_too_large == 1
+    assert parse_mock.await_count == 1
+
+
+async def test_describe_pictures_swallows_per_image_failure(tmp_path, mocker):
+    """A vision LLM failure on one image must not kill the whole document."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img_a = _make_image_obj("a.jpeg", b"\xff\xd8" + b"\xab" * 2000)
+    img_b = _make_image_obj("b.jpeg", b"\xff\xd8" + b"\xcd" * 2000)
+    page = MagicMock(images=[img_a, img_b])
+
+    fake_reader = MagicMock()
+    fake_reader.pages = [page]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(side_effect=[RuntimeError("vision blew up"), "Success"]),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].description == "Success"
+    assert result.failed == 1
+
+
+async def test_describe_pictures_handles_pypdf_open_failure(tmp_path, mocker):
+    """A malformed PDF that pypdf can't open returns an empty result."""
+    pdf_file = tmp_path / "broken.pdf"
+    pdf_file.write_bytes(b"not a pdf")
+
+    mocker.patch("pypdf.PdfReader", side_effect=ValueError("EOF marker not found"))
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "broken.pdf", fake_llm)
+    assert result.descriptions == []
+
+
+# ---------------------------------------------------------------------------
+# inject_descriptions_inline: replacement patterns
+# ---------------------------------------------------------------------------
+
+
+def _desc(name="Im0", description="A CT scan."):
+    return PictureDescription(
+        page_number=1,
+        ordinal_in_page=0,
+        name=name,
+        sha256="aa",
+        description=description,
+    )
+
+
+def test_inject_no_op_when_no_descriptions():
+    markdown = "# Title\n\nbody text\n"
+    result = PictureExtractionResult()
+    out, n = inject_descriptions_inline(markdown, result)
+    assert out == markdown
+    assert n == 0
+
+
+def test_inject_replaces_placeholder_with_caption():
+    """`<!-- image -->` + `Image: <name>` together becomes one block.
+
+    This is the most common medxpertqa case: our renderer puts a caption
+    line right below the embedded JPEG, and Docling preserves both.
+    """
+    markdown = (
+        "# Case\n\n"
+        "Clinical text...\n\n"
+        "<!-- image -->\nImage: MM-130-a.jpeg\n\n"
+        "Answer choices: A) ...\n"
+    )
+    result = PictureExtractionResult(descriptions=[_desc(name="Im0")])
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "<!-- image -->" not in out
+    assert "Image: MM-130-a.jpeg" not in out  # caption consumed
+    # New format: horizontal-rule-delimited section with "Embedded
+    # image:" anchor and named "Visual description:" section. No
+    # blockquote wrapping -- nested blocks (lists, code, tables) inside
+    # a blockquote are silently dropped by Streamdown / remark.
+    assert "**Embedded image:** `MM-130-a.jpeg`" in out
+    assert "**Visual description:**" in out
+    assert "A CT scan." in out
+    # Block is delimited by horizontal rules so it stands out from
+    # surrounding paragraphs.
+    assert "\n---\n" in out
+    # No OCR section -- this fixture has no ocr_text on its descriptions.
+    assert "**OCR text:**" not in out
+    # No raw HTML tags / blockquote prefixes leak.
+    assert "<image" not in out
+    assert "</image>" not in out
+    assert "> **Embedded image:**" not in out  # we no longer wrap in `>`
+    # Surrounding context is preserved.
+    assert "Clinical text..." in out
+    assert "Answer choices: A) ..." in out
+
+
+def test_inject_uses_pypdf_name_when_no_caption():
+    """`<!-- image -->` alone uses the pypdf-given name as the attribute."""
+    markdown = "# Case\n\n<!-- image -->\n\nMore text\n"
+    result = PictureExtractionResult(descriptions=[_desc(name="Im0")])
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "**Embedded image:** `Im0`" in out
+
+
+def test_inject_replaces_bare_caption():
+    """A bare `Image: <name>` line (no placeholder) still gets replaced."""
+    markdown = "# Case\n\nText...\nImage: scan.jpeg\nMore text\n"
+    result = PictureExtractionResult(descriptions=[_desc(name="Im0")])
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "**Embedded image:** `scan.jpeg`" in out
+    assert "Image: scan.jpeg" not in out
+
+
+def test_inject_handles_multiple_images_in_order():
+    """Two placeholders + two descriptions: each consumed in document order."""
+    markdown = (
+        "Page 1\n\n<!-- image -->\nImage: a.jpeg\n\n"
+        "Between\n\n<!-- image -->\nImage: b.jpeg\n\nEnd\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[
+            PictureDescription(
+                page_number=1, ordinal_in_page=0, name="Im0", sha256="aa",
+                description="Desc A",
+            ),
+            PictureDescription(
+                page_number=2, ordinal_in_page=0, name="Im1", sha256="bb",
+                description="Desc B",
+            ),
+        ]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 2
+    assert "**Embedded image:** `a.jpeg`" in out
+    assert "**Embedded image:** `b.jpeg`" in out
+    assert out.index("a.jpeg") < out.index("b.jpeg")
+    assert "Desc A" in out and "Desc B" in out
+
+
+def test_inject_returns_remaining_count_when_more_descriptions_than_markers():
+    """Three descriptions, one marker -> only one inlined, two leftover."""
+    markdown = "Just one <!-- image --> here.\n"
+    result = PictureExtractionResult(
+        descriptions=[
+            _desc(name="Im0", description="First"),
+            _desc(name="Im1", description="Second"),
+            _desc(name="Im2", description="Third"),
+        ]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "**Embedded image:** `Im0`" in out
+    assert "**Embedded image:** `Im1`" not in out
+
+
+def test_inject_returns_zero_when_no_markers_present():
+    """Markdown with no image markers at all returns the input unchanged."""
+    markdown = "# Title\n\nJust text. No images mentioned at all.\n"
+    result = PictureExtractionResult(descriptions=[_desc(name="Im0")])
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 0
+    assert out == markdown
+
+
+# ---------------------------------------------------------------------------
+# render_appended_section
+# ---------------------------------------------------------------------------
+
+
+def test_render_appended_empty_when_nothing_passed():
+    assert render_appended_section([]) == ""
+
+
+def test_render_appended_renders_each_image_as_block():
+    descriptions = [
+        _desc(name="MM-130-a.jpeg", description="CT scan"),
+        _desc(name="MM-130-b.jpeg", description="Bar chart"),
+    ]
+    rendered = render_appended_section(descriptions)
+    assert "## Image Content (vision-LLM extracted)" in rendered
+    assert "**Embedded image:** `MM-130-a.jpeg`" in rendered
+    assert "CT scan" in rendered
+    assert "**Embedded image:** `MM-130-b.jpeg`" in rendered
+    assert "Bar chart" in rendered
+    # Each image block is delimited by horizontal rules.
+    assert rendered.count("\n---\n") >= 2
+    # No raw HTML / XML / blockquote prefixes.
+    assert "<image" not in rendered
+    assert "> **Embedded image:**" not in rendered
+    assert "**OCR text:**" not in rendered
+
+
+def test_render_appended_includes_skip_notes():
+    descriptions = [_desc()]
+    skip_result = PictureExtractionResult(
+        descriptions=descriptions,
+        skipped_too_small=2,
+        skipped_too_large=1,
+        skipped_duplicate=3,
+        failed=1,
+    )
+    rendered = render_appended_section(descriptions, skip_notes=skip_result)
+    assert "_Note:" in rendered
+    assert "2 too small" in rendered
+    assert "1 too large" in rendered
+    assert "3 duplicate" in rendered
+    assert "1 failed" in rendered
+
+
+# ---------------------------------------------------------------------------
+# merge_descriptions_into_markdown: top-level
+# ---------------------------------------------------------------------------
+
+
+def test_merge_inlines_when_marker_present():
+    markdown = "Text...\n\n<!-- image -->\nImage: scan.jpeg\n\nMore text\n"
+    result = PictureExtractionResult(descriptions=[_desc(name="Im0")])
+
+    out = merge_descriptions_into_markdown(markdown, result)
+
+    assert "**Embedded image:** `scan.jpeg`" in out
+    # Nothing leaked into an appended section -- we should NOT see the
+    # appended-section heading because everything went inline.
+    assert "## Image Content" not in out
+
+
+def test_merge_appends_when_no_marker_present():
+    """Zero markers means everything goes into an appended section."""
+    markdown = "Pure text doc, no image markers.\n"
+    result = PictureExtractionResult(
+        descriptions=[_desc(name="Im0", description="An image desc.")]
+    )
+
+    out = merge_descriptions_into_markdown(markdown, result)
+
+    assert "Pure text doc" in out
+    assert "## Image Content (vision-LLM extracted)" in out
+    assert "**Embedded image:** `Im0`" in out
+
+
+def test_merge_appends_leftovers_with_distinct_heading():
+    """One marker, two descriptions -> one inline, second appended under
+    a heading that signals it's a leftover.
+    """
+    markdown = "Text\n\n<!-- image -->\nImage: a.jpeg\n\nEnd\n"
+    result = PictureExtractionResult(
+        descriptions=[
+            _desc(name="Im0", description="First"),
+            _desc(name="Im1", description="Second"),
+        ]
+    )
+
+    out = merge_descriptions_into_markdown(markdown, result)
+
+    assert "**Embedded image:** `a.jpeg`" in out  # inlined
+    assert "## Image Content (additional, no inline marker found)" in out
+    assert "**Embedded image:** `Im1`" in out  # appended
+
+
+# ---------------------------------------------------------------------------
+# describe_pictures: ocr_runner integration
+#
+# These tests cover the per-image OCR side-channel: when the caller
+# supplies an ``ocr_runner`` callable, each extracted image is sent
+# both to the vision LLM (visual description) and to the OCR runner
+# (text-in-image), in parallel. The OCR text -- if any -- is recorded
+# on the PictureDescription and rendered in the inline block.
+# ---------------------------------------------------------------------------
+
+
+async def test_describe_pictures_calls_ocr_runner_per_image(tmp_path, mocker):
+    """When an ocr_runner is provided, it's invoked once per eligible image."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img_a = _make_image_obj("Im0.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    img_b = _make_image_obj("Im1.png", b"\x89PNG\r\n\x1a\n" + b"\xcd" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img_a, img_b])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(side_effect=["Visual A", "Visual B"]),
+    )
+    ocr_runner = AsyncMock(side_effect=["OCR text A", "OCR text B"])
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(
+        str(pdf_file), "report.pdf", fake_llm, ocr_runner=ocr_runner
+    )
+
+    assert ocr_runner.await_count == 2
+    by_name = {d.name: d.ocr_text for d in result.descriptions}
+    assert by_name == {"Im0.jpeg": "OCR text A", "Im1.png": "OCR text B"}
+
+
+async def test_describe_pictures_runs_vision_and_ocr_in_parallel(
+    tmp_path, mocker
+):
+    """Vision LLM and OCR run concurrently per image, not sequentially.
+
+    We verify this by recording call timestamps: if both finish within
+    a small window relative to the per-call sleep, they ran in parallel.
+    """
+    import asyncio
+    import time
+
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img = _make_image_obj("Im0.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    sleep_each = 0.05  # 50ms per call
+
+    async def slow_vision(*args, **kwargs):
+        await asyncio.sleep(sleep_each)
+        return "Visual"
+
+    async def slow_ocr(*args, **kwargs):
+        await asyncio.sleep(sleep_each)
+        return "OCR"
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=slow_vision,
+    )
+
+    fake_llm = MagicMock()
+    started = time.perf_counter()
+    result = await describe_pictures(
+        str(pdf_file), "report.pdf", fake_llm, ocr_runner=slow_ocr
+    )
+    elapsed = time.perf_counter() - started
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].ocr_text == "OCR"
+    # Sequential would be ~2*sleep_each. Parallel is ~1*sleep_each + overhead.
+    # Be generous with the bound so we're not flaky on slow CI.
+    assert elapsed < 1.5 * sleep_each, (
+        f"vision+OCR appear to be sequential (took {elapsed:.3f}s)"
+    )
+
+
+async def test_describe_pictures_treats_empty_ocr_as_none(tmp_path, mocker):
+    """Empty / whitespace-only OCR result is normalised to None.
+
+    This means the rendered image block won't carry an empty
+    "OCR text" section for images that contain no text at all
+    (e.g. a clean radiograph).
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img = _make_image_obj("scan.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="A radiograph."),
+    )
+    ocr_runner = AsyncMock(return_value="   \n  \n")
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(
+        str(pdf_file), "report.pdf", fake_llm, ocr_runner=ocr_runner
+    )
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].ocr_text is None
+
+
+async def test_describe_pictures_swallows_ocr_runner_failure(tmp_path, mocker):
+    """An OCR runner exception must not kill the description for that image.
+
+    OCR is supplementary; the vision LLM's description is the primary
+    payload. If OCR blows up we drop the OCR field for that image and
+    keep the description.
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img = _make_image_obj("scan.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="A radiograph."),
+    )
+    ocr_runner = AsyncMock(side_effect=RuntimeError("OCR backend down"))
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(
+        str(pdf_file), "report.pdf", fake_llm, ocr_runner=ocr_runner
+    )
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].description == "A radiograph."
+    assert result.descriptions[0].ocr_text is None
+    assert result.failed == 0  # the IMAGE didn't fail; only its OCR did
+
+
+async def test_describe_pictures_vision_failure_with_ocr_runner_skips_image(
+    tmp_path, mocker
+):
+    """If the vision LLM fails, the image is skipped even if OCR succeeded.
+
+    The inline block's primary purpose is the visual description; an
+    OCR-only block would be misleading (it'd look like the vision
+    pipeline ran when it didn't), so we treat vision failure as image
+    failure regardless of OCR outcome.
+    """
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img = _make_image_obj("scan.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(side_effect=RuntimeError("vision blew up")),
+    )
+    ocr_runner = AsyncMock(return_value="OCR text")
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(
+        str(pdf_file), "report.pdf", fake_llm, ocr_runner=ocr_runner
+    )
+
+    assert result.descriptions == []
+    assert result.failed == 1
+
+
+async def test_describe_pictures_no_ocr_runner_keeps_ocr_text_none(
+    tmp_path, mocker
+):
+    """Backward compat: omitting ocr_runner produces description-only blocks."""
+    pdf_file = tmp_path / "report.pdf"
+    pdf_file.write_bytes(b"%PDF-1.4 fake")
+
+    img = _make_image_obj("Im0.jpeg", b"\xff\xd8\xff\xe0" + b"\xab" * 2000)
+    fake_reader = MagicMock()
+    fake_reader.pages = [MagicMock(images=[img])]
+    mocker.patch("pypdf.PdfReader", return_value=fake_reader)
+
+    mocker.patch(
+        "app.etl_pipeline.parsers.vision_llm.parse_image_for_description",
+        new=AsyncMock(return_value="Visual"),
+    )
+
+    fake_llm = MagicMock()
+    result = await describe_pictures(str(pdf_file), "report.pdf", fake_llm)
+
+    assert len(result.descriptions) == 1
+    assert result.descriptions[0].ocr_text is None
+
+
+# ---------------------------------------------------------------------------
+# Rendering: "OCR text" section appears iff PictureDescription.ocr_text is set
+# ---------------------------------------------------------------------------
+
+
+def _desc_with_ocr(name="Im0", description="A CT scan.", ocr_text="L  R  10mm"):
+    return PictureDescription(
+        page_number=1,
+        ordinal_in_page=0,
+        name=name,
+        sha256="aa",
+        description=description,
+        ocr_text=ocr_text,
+    )
+
+
+def test_inject_renders_ocr_section_when_ocr_text_present():
+    markdown = "Text\n\n<!-- image -->\nImage: scan.jpeg\n\nMore\n"
+    result = PictureExtractionResult(
+        descriptions=[_desc_with_ocr(name="Im0", ocr_text="L  R  10mm")]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "**Embedded image:** `scan.jpeg`" in out
+    assert "**OCR text:**" in out
+    assert "L  R  10mm" in out
+    # OCR section comes before the visual description (literal text
+    # first, interpretation second).
+    assert out.index("**OCR text:**") < out.index("**Visual description:**")
+    # Critical: no nested-block constructs (fenced code, blockquote)
+    # that previous formats relied on -- both broke in Streamdown /
+    # PlateJS by escaping their container and dropping content.
+    assert "```" not in out
+    assert "> **" not in out
+
+
+def test_inject_renders_multiline_ocr_with_hard_breaks():
+    """Multi-line OCR uses trailing-two-spaces hard breaks so each
+    line renders on its own row, without needing a fragile fenced
+    code block or blockquote wrapper."""
+    markdown = "Text\n\n<!-- image -->\nImage: scan.jpeg\n\nMore\n"
+    ocr_multi = "Slice 24 / 60\nL\nR\n10 mm"
+    result = PictureExtractionResult(
+        descriptions=[_desc_with_ocr(name="Im0", ocr_text=ocr_multi)]
+    )
+
+    out, _ = inject_descriptions_inline(markdown, result)
+
+    # Every OCR line is present.
+    for line in ("Slice 24 / 60", "L", "R", "10 mm"):
+        assert line in out
+    # Non-last OCR lines get the trailing two-space hard break.
+    assert "Slice 24 / 60  \n" in out
+    assert "\nL  \n" in out
+    assert "\nR  \n" in out
+    # Last OCR line must NOT carry the two-space hard break (no stray <br>).
+    assert "10 mm  \n" not in out
+    assert "10 mm\n" in out
+
+
+def test_render_appended_renders_ocr_section_when_ocr_text_present():
+    descriptions = [
+        _desc_with_ocr(
+            name="MM-130-a.jpeg",
+            description="Axial CT.",
+            ocr_text="Slice 24 / 60",
+        ),
+    ]
+    rendered = render_appended_section(descriptions)
+
+    assert "**OCR text:**" in rendered
+    assert "Slice 24 / 60" in rendered
+    assert "Axial CT." in rendered
+
+
+def test_render_omits_ocr_section_when_ocr_text_is_none():
+    descriptions = [_desc(name="Im0", description="A clean radiograph.")]
+    rendered = render_appended_section(descriptions)
+
+    assert "**Embedded image:** `Im0`" in rendered
+    assert "**OCR text:**" not in rendered
+    assert "**Visual description:**" in rendered
+    # No raw HTML / blockquote prefixes.
+    assert "<image" not in rendered
+    assert "> **" not in rendered
+
+
+# ---------------------------------------------------------------------------
+# inject_descriptions_inline: <figure> blocks (layout-aware parsers)
+#
+# Azure Document Intelligence's ``prebuilt-layout`` and LlamaCloud
+# premium both emit ``<figure>...</figure>`` blocks that already contain
+# the parser's own OCR of the figure (chart bar values, axis labels,
+# inline ``<figcaption>``, embedded ``<table>`` for tabular figures).
+# That parser-side content is useful for retrieval on its own, so we
+# PRESERVE the figure verbatim and append our vision-LLM block
+# immediately after rather than substituting for it.
+# ---------------------------------------------------------------------------
+
+
+def test_inject_appends_block_after_figure_preserving_parser_content():
+    """Figure block stays intact; vision-LLM block goes right after it."""
+    markdown = (
+        "Some narrative text.\n\n"
+        "<figure>\n\n"
+        "Republican\n68\nDemocrat\n30\n"
+        "\n</figure>\n\n"
+        "Following paragraph.\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[_desc(name="Im0", description="Bar chart of party ID.")]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    # Original figure is preserved verbatim -- the parser's OCR'd
+    # numbers must still be searchable.
+    assert "<figure>" in out
+    assert "</figure>" in out
+    assert "Republican" in out and "68" in out
+    # Our vision-LLM block follows the figure, not before / inside it.
+    assert "**Embedded image:** `Im0`" in out
+    assert "Bar chart of party ID." in out
+    figure_close = out.index("</figure>")
+    embedded_at = out.index("**Embedded image:** `Im0`")
+    assert figure_close < embedded_at, "block must be appended AFTER </figure>"
+    # Surrounding narrative is preserved.
+    assert "Some narrative text." in out
+    assert "Following paragraph." in out
+
+
+def test_inject_handles_multiple_figures_in_document_order():
+    """N figures + N descriptions: each pair lands in the right place."""
+    markdown = (
+        "Page 1\n\n<figure>\nChart A bars\n</figure>\n\n"
+        "Between\n\n<figure>\nChart B bars\n</figure>\n\n"
+        "End.\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[
+            PictureDescription(
+                page_number=1, ordinal_in_page=0, name="Im0", sha256="aa",
+                description="Description of chart A.",
+            ),
+            PictureDescription(
+                page_number=2, ordinal_in_page=0, name="Im1", sha256="bb",
+                description="Description of chart B.",
+            ),
+        ]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 2
+    # Both figures preserved; both descriptions inlined; order matches.
+    assert out.count("<figure>") == 2
+    assert out.count("</figure>") == 2
+    assert "Description of chart A." in out
+    assert "Description of chart B." in out
+    assert out.index("Description of chart A.") < out.index(
+        "Description of chart B."
+    )
+    # Each description appears AFTER its corresponding </figure>.
+    first_close = out.index("</figure>")
+    assert first_close < out.index("Description of chart A.")
+    second_close = out.index("</figure>", first_close + 1)
+    assert second_close < out.index("Description of chart B.")
+
+
+def test_inject_figures_with_attributes_and_nested_tags():
+    """``<figure>`` with attributes and nested tags is matched and preserved."""
+    markdown = (
+        '<figure id="fig-3" class="chart">\n'
+        '<figcaption>Source: Pew Research</figcaption>\n'
+        "<table><tr><td>Republican</td><td>57</td></tr></table>\n"
+        "</figure>\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[_desc(name="Im0", description="Survey table.")]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    # All nested HTML is preserved (chunking will pick it up).
+    assert 'id="fig-3"' in out
+    assert "<figcaption>Source: Pew Research</figcaption>" in out
+    assert "<table>" in out and "Republican" in out and "57" in out
+    # Our block sits after the closing tag.
+    assert out.index("</figure>") < out.index("**Embedded image:** `Im0`")
+
+
+def test_inject_figures_more_descriptions_than_figures_returns_remaining():
+    """Three descriptions, one figure -> one inlined, two left for caller."""
+    markdown = "Text.\n<figure>\nbar values\n</figure>\nMore.\n"
+    result = PictureExtractionResult(
+        descriptions=[
+            _desc(name="Im0", description="First desc."),
+            _desc(name="Im1", description="Second desc."),
+            _desc(name="Im2", description="Third desc."),
+        ]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    assert "First desc." in out
+    # Leftovers are the caller's job; inject_descriptions_inline does
+    # not append them on its own.
+    assert "Second desc." not in out
+    assert "Third desc." not in out
+
+
+def test_inject_figures_more_figures_than_descriptions_leaves_extras_untouched():
+    """Two figures, one description -> first figure enriched, second left raw."""
+    markdown = (
+        "<figure>\nfigure 1 content\n</figure>\n"
+        "<figure>\nfigure 2 content\n</figure>\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[_desc(name="Im0", description="Only description.")]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 1
+    # Both figures still present; only the first one was enriched.
+    assert out.count("<figure>") == 2
+    assert "Only description." in out
+    # Second figure has no embedded-image block immediately after it.
+    second_open = out.index("<figure>", out.index("<figure>") + 1)
+    second_close = out.index("</figure>", second_open)
+    after_second = out[second_close:]
+    assert "**Embedded image:**" not in after_second
+
+
+def test_merge_inlines_at_figure_boundary():
+    """Top-level helper does the right thing with figures (no leftover section)."""
+    markdown = "Lead.\n<figure>\nbars\n</figure>\nTrailer.\n"
+    result = PictureExtractionResult(
+        descriptions=[_desc(name="Im0", description="Bar chart.")]
+    )
+
+    out = merge_descriptions_into_markdown(markdown, result)
+
+    # Inline succeeded -> no appended-section heading.
+    assert "## Image Content" not in out
+    assert "Bar chart." in out
+    assert "<figure>" in out and "</figure>" in out
+
+
+def test_inject_figures_then_falls_through_to_docling_marker():
+    """Mixed-marker doc: figure consumed first, then Docling placeholder.
+
+    Defensive -- single docs are usually one parser's output, but if a
+    pipeline ever stitches two parsers' markdowns together the inliner
+    should still place each description.
+    """
+    markdown = (
+        "<figure>\nChart bars: 50, 40, 30\n</figure>\n\n"
+        "Later in the doc:\n\n"
+        "<!-- image -->\nImage: scan.jpeg\n\n"
+        "End.\n"
+    )
+    result = PictureExtractionResult(
+        descriptions=[
+            _desc(name="Im0", description="Chart description."),
+            _desc(name="Im1", description="Scan description."),
+        ]
+    )
+
+    out, n = inject_descriptions_inline(markdown, result)
+
+    assert n == 2
+    # Figure preserved + augmented.
+    assert "<figure>" in out and "Chart bars: 50, 40, 30" in out
+    assert "Chart description." in out
+    # Docling placeholder + caption replaced.
+    assert "<!-- image -->" not in out
+    assert "Image: scan.jpeg" not in out
+    assert "**Embedded image:** `scan.jpeg`" in out
+    assert "Scan description." in out
diff --git a/surfsense_backend/tests/unit/etl_pipeline/test_vision_llm.py b/surfsense_backend/tests/unit/etl_pipeline/test_vision_llm.py
new file mode 100644
index 000000000..1293ff757
--- /dev/null
+++ b/surfsense_backend/tests/unit/etl_pipeline/test_vision_llm.py
@@ -0,0 +1,146 @@
+"""Unit tests for the vision_llm parser helpers.
+
+Two helpers exist:
+
+- :func:`parse_with_vision_llm` -- single-shot for standalone image
+  uploads (.png/.jpg/etc). Returns combined markdown (description +
+  verbatim OCR mixed) since the image *is* the document.
+- :func:`parse_image_for_description` -- per-image-in-PDF call. Returns
+  visual description only; OCR is the ETL service's job.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+pytestmark = pytest.mark.unit
+
+
+# ---------------------------------------------------------------------------
+# parse_with_vision_llm: legacy single-shot path
+# ---------------------------------------------------------------------------
+
+
+async def test_parse_with_vision_llm_returns_combined_markdown(tmp_path):
+    """Standalone image uploads still go through the combined-markdown path."""
+    from app.etl_pipeline.parsers.vision_llm import parse_with_vision_llm
+
+    img = tmp_path / "scan.png"
+    img.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 200)
+
+    fake_response = MagicMock()
+    fake_response.content = "# A scan of something."
+    fake_llm = AsyncMock()
+    fake_llm.ainvoke.return_value = fake_response
+
+    out = await parse_with_vision_llm(str(img), "scan.png", fake_llm)
+    assert out == "# A scan of something."
+    fake_llm.ainvoke.assert_awaited_once()
+
+
+async def test_parse_with_vision_llm_rejects_empty_response(tmp_path):
+    """An empty model response raises rather than silently returning blanks."""
+    from app.etl_pipeline.parsers.vision_llm import parse_with_vision_llm
+
+    img = tmp_path / "scan.png"
+    img.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 200)
+
+    fake_response = MagicMock()
+    fake_response.content = ""
+    fake_llm = AsyncMock()
+    fake_llm.ainvoke.return_value = fake_response
+
+    with pytest.raises(ValueError, match="empty content"):
+        await parse_with_vision_llm(str(img), "scan.png", fake_llm)
+
+
+# ---------------------------------------------------------------------------
+# parse_image_for_description: per-image-in-PDF, description only
+# ---------------------------------------------------------------------------
+
+
+async def test_parse_image_for_description_returns_description(tmp_path):
+    """Description-only path returns the model's markdown unchanged."""
+    from app.etl_pipeline.parsers.vision_llm import parse_image_for_description
+
+    img = tmp_path / "scan.png"
+    img.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 200)
+
+    fake_response = MagicMock()
+    fake_response.content = "Axial CT showing a large cystic mass."
+    fake_llm = AsyncMock()
+    fake_llm.ainvoke.return_value = fake_response
+
+    out = await parse_image_for_description(str(img), "scan.png", fake_llm)
+    assert out == "Axial CT showing a large cystic mass."
+
+
+async def test_parse_image_for_description_uses_description_only_prompt(tmp_path):
+    """The prompt explicitly tells the model NOT to transcribe text.
+
+    This is the contract that lets us drop OCR from the response: the
+    ETL pipeline already has the text (from page-level OCR), so asking
+    the vision LLM for it would be redundant cost.
+    """
+    from app.etl_pipeline.parsers.vision_llm import parse_image_for_description
+
+    img = tmp_path / "scan.png"
+    img.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 200)
+
+    fake_response = MagicMock()
+    fake_response.content = "A description"
+    fake_llm = AsyncMock()
+    fake_llm.ainvoke.return_value = fake_response
+
+    await parse_image_for_description(str(img), "scan.png", fake_llm)
+
+    # The prompt is the first text part of the message we sent.
+    sent_messages = fake_llm.ainvoke.call_args.args[0]
+    prompt_text = sent_messages[0].content[0]["text"].lower()
+    assert "describe what this image visually depicts" in prompt_text
+    assert "do not transcribe text" in prompt_text
+
+
+async def test_parse_image_for_description_rejects_empty(tmp_path):
+    """Empty response surfaces as ValueError so the caller can skip the image."""
+    from app.etl_pipeline.parsers.vision_llm import parse_image_for_description
+
+    img = tmp_path / "scan.png"
+    img.write_bytes(b"\x89PNG\r\n\x1a\n" + b"\x00" * 200)
+
+    fake_response = MagicMock()
+    fake_response.content = "   "  # whitespace-only counts as empty
+    fake_llm = AsyncMock()
+    fake_llm.ainvoke.return_value = fake_response
+
+    with pytest.raises(ValueError, match="empty content"):
+        await parse_image_for_description(str(img), "scan.png", fake_llm)
+
+
+# ---------------------------------------------------------------------------
+# Image size + extension validation (shared by both paths)
+# ---------------------------------------------------------------------------
+
+
+def test_image_to_data_url_rejects_oversized(tmp_path):
+    """Images larger than 5 MB raise before any LLM call is made."""
+    from app.etl_pipeline.parsers.vision_llm import _image_to_data_url
+
+    big = tmp_path / "huge.png"
+    big.write_bytes(b"\x89PNG" + b"\x00" * (6 * 1024 * 1024))
+
+    with pytest.raises(ValueError, match="Image too large"):
+        _image_to_data_url(str(big))
+
+
+def test_image_to_data_url_rejects_unsupported_extension(tmp_path):
+    """Unknown extensions raise rather than guessing a MIME type."""
+    from app.etl_pipeline.parsers.vision_llm import _image_to_data_url
+
+    weird = tmp_path / "scan.xyz"
+    weird.write_bytes(b"\x00" * 100)
+
+    with pytest.raises(ValueError, match="Unsupported image extension"):
+        _image_to_data_url(str(weird))
diff --git a/surfsense_evals/.env.example b/surfsense_evals/.env.example
new file mode 100644
index 000000000..632e77d8a
--- /dev/null
+++ b/surfsense_evals/.env.example
@@ -0,0 +1,65 @@
+# surfsense_evals — environment template.
+#
+# Copy this file to `.env` (in the surfsense_evals/ project root or your
+# CWD) and fill in the values. `python-dotenv` loads it automatically
+# the first time `core.config` is imported, so every CLI subcommand
+# (`setup`, `ingest`, `run`, `report`, `teardown`, `models list`, …)
+# will pick the values up.
+#
+#   cp .env.example .env
+#   # then edit .env with your values
+#
+# `.env` is gitignored — never commit real secrets.
+
+# ---------------------------------------------------------------------------
+# 1. Backend target — REQUIRED (default works for a local dev backend)
+# ---------------------------------------------------------------------------
+SURFSENSE_API_BASE=http://localhost:8000
+
+# ---------------------------------------------------------------------------
+# 2. OpenRouter — REQUIRED for any `run` invocation
+# ---------------------------------------------------------------------------
+# The `native_pdf` arm calls OpenRouter directly; the `surfsense` arm
+# routes through SurfSense which uses the same key under the hood.
+OPENROUTER_API_KEY=sk-or-...
+
+# Override only if you proxy OpenRouter through a private gateway:
+# OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
+
+# Multimodal benchmarks (medxpertqa, mmlongbench) require a vision-capable
+# slug. Recommended (verify in your catalog with `models list --grep ...`):
+#   anthropic/claude-sonnet-4.5    (default recommendation)
+#   anthropic/claude-opus-4.7      (strongest)
+#   openai/gpt-5                   (top-tier vision)
+#   google/gemini-2.5-pro          (1M-token context, best for long PDFs)
+# DO NOT use openai/gpt-5.4-mini for image-bearing benchmarks — it's
+# text-only on PDF content and the runner emits a warning if pinned.
+
+# ---------------------------------------------------------------------------
+# 3. Auth — pick EXACTLY ONE of the two modes below
+# ---------------------------------------------------------------------------
+
+# --- Mode A: LOCAL (backend started with AUTH_TYPE=LOCAL)
+# The harness POSTs these to /auth/jwt/login automatically.
+# SURFSENSE_USER_EMAIL=you@example.com
+# SURFSENSE_USER_PASSWORD=...
+
+# --- Mode B: GOOGLE OAuth (or any pre-issued JWT)
+# Open the SurfSense web UI in your browser, log in via Google, then in
+# DevTools → Application → Local Storage copy:
+#   surfsense_bearer_token  → SURFSENSE_JWT
+#   surfsense_refresh_token → SURFSENSE_REFRESH_TOKEN  (optional, enables
+#                                                       auto-refresh on 401)
+# SURFSENSE_JWT=eyJhbGciOi...
+# SURFSENSE_REFRESH_TOKEN=eyJhbGciOi...
+
+# ---------------------------------------------------------------------------
+# 4. Filesystem paths — OPTIONAL (defaults below)
+# ---------------------------------------------------------------------------
+# Where datasets, rendered PDFs, ingestion id maps, run outputs, and
+# state.json live. Default: <surfsense_evals>/data/
+# EVAL_DATA_DIR=./data
+
+# Where generated reports (summary.md / summary.json) get written.
+# Default: <surfsense_evals>/reports/
+# EVAL_REPORTS_DIR=./reports
diff --git a/surfsense_evals/.gitignore b/surfsense_evals/.gitignore
new file mode 100644
index 000000000..0f71d2635
--- /dev/null
+++ b/surfsense_evals/.gitignore
@@ -0,0 +1,29 @@
+# Python bytecode + caches
+__pycache__/
+*.py[cod]
+*.pyo
+
+# Editable-install / build artifacts
+*.egg-info/
+build/
+dist/
+.eggs/
+
+# Virtual envs (uv venv default + common alternates)
+.venv/
+venv/
+env/
+
+# Tooling caches
+.pytest_cache/
+.ruff_cache/
+.mypy_cache/
+.coverage
+.coverage.*
+htmlcov/
+
+# Local secrets — keep `.env.example` tracked, never the real `.env`.
+.env
+.env.local
+.env.*.local
+!.env.example
diff --git a/surfsense_evals/README.md b/surfsense_evals/README.md
new file mode 100644
index 000000000..c6314af80
--- /dev/null
+++ b/surfsense_evals/README.md
@@ -0,0 +1,228 @@
+# SurfSense Evals
+
+Domain-agnostic eval harness for SurfSense. Each benchmark is a Python subpackage under `suites/<domain>/<benchmark>/` that self-registers with the CLI; `core/` is the shared infrastructure (HTTP clients, arms, parsers, metrics, report writer, registry). The harness talks to SurfSense over HTTP only — it does **not** import any backend Python module — so it ships in its own venv and never bloats the FastAPI runtime image.
+
+## Benchmarks
+
+| Benchmark                       | Shape                                            | Vision required? | Default ingest             |
+|---------------------------------|--------------------------------------------------|------------------|----------------------------|
+| `medical/medxpertqa` (headline) | Native PDF vs SurfSense head-to-head, MCQ        | yes              | `vision=on, mode=basic`    |
+| `medical/mirage`                | SurfSense single-arm, MCQ                        | no               | `vision=off, mode=basic`   |
+| `medical/cure`                  | SurfSense single-arm retrieval (Recall/MRR/nDCG) | no               | `vision=off, mode=basic`   |
+| `multimodal_doc/mmlongbench`    | Native PDF vs SurfSense head-to-head, open-ended | yes              | `vision=on, mode=basic`    |
+
+Future domains (`legal/`, `finance/`, `code/`, `scientific/`) drop into `suites/` without touching `core/` or the CLI.
+
+## Install + auth
+
+```bash
+uv pip install -e ./surfsense_evals
+cp surfsense_evals/.env.example surfsense_evals/.env
+# Edit .env: SURFSENSE_API_BASE, OPENROUTER_API_KEY, and ONE of:
+#   LOCAL  → SURFSENSE_USER_EMAIL + SURFSENSE_USER_PASSWORD
+#   GOOGLE → SURFSENSE_JWT (+ optional SURFSENSE_REFRESH_TOKEN)
+#            (lift both from browser localStorage after a normal Google login)
+```
+
+## Step-by-step: run all four benchmarks
+
+The medical and multimodal_doc suites each get their own SearchSpace and pinned model, so they're independent — run them in any order. Both head-to-head benchmarks (`medxpertqa`, `mmlongbench`) require a **vision-capable** OpenRouter slug; pinning a text-only one (e.g. `openai/gpt-5.4-mini`) silently drops images and the runner emits a warning.
+
+Recommended vision slugs (use `models list --grep <name>` to confirm one): `anthropic/claude-sonnet-4.5` (balanced cost), `anthropic/claude-opus-4.7` (strongest reasoning), `openai/gpt-5` (top-tier vision), `google/gemini-2.5-pro` (best for long PDFs, 1M-token context).
+
+```bash
+# 0. (optional) discover what's registered
+python -m surfsense_evals suites list
+python -m surfsense_evals benchmarks list
+
+# 1. MEDICAL SUITE — one SearchSpace, three benchmarks
+python -m surfsense_evals setup --suite medical --provider-model anthropic/claude-sonnet-4.5
+
+#  1a. headline head-to-head: Native PDF (vision) vs SurfSense (vision RAG)
+#      Downloads dev+test JSONL + images.zip, renders one PDF per question
+#      (case + table + images + 5 options), uploads with use_vision_llm=True.
+python -m surfsense_evals ingest medical medxpertqa --split test
+python -m surfsense_evals run    medical medxpertqa --concurrency 4
+
+#  1b. MIRAGE — single-arm SurfSense MCQ accuracy
+#      (MMLU-Med / MedQA-US / MedMCQA / PubMedQA / BioASQ)
+python -m surfsense_evals ingest medical mirage
+python -m surfsense_evals run    medical mirage
+
+#  1c. CUREv1 — single-arm SurfSense retrieval (Recall@k / MRR / nDCG@10)
+python -m surfsense_evals ingest medical cure --lang en
+python -m surfsense_evals run    medical cure --lang en
+
+#  1d. write reports/medical/<UTC-ts>/summary.{md,json}
+python -m surfsense_evals report --suite medical
+
+# 2. MULTIMODAL_DOC SUITE — long PDFs with embedded images, charts, tables
+python -m surfsense_evals setup  --suite multimodal_doc --provider-model google/gemini-2.5-pro
+python -m surfsense_evals ingest multimodal_doc mmlongbench           # ~660MB, resumable
+python -m surfsense_evals run    multimodal_doc mmlongbench --concurrency 4
+python -m surfsense_evals report --suite multimodal_doc
+
+# 3. CLEANUP — soft-deletes the SearchSpaces; rendered PDFs stay cached
+python -m surfsense_evals teardown --suite medical
+python -m surfsense_evals teardown --suite multimodal_doc
+```
+
+## Asymmetric scenarios — the "vision-extract once, answer cheap" play
+
+The walkthrough above is `--scenario head-to-head` (default): both arms answer with the same vision-capable slug. SurfSense's actual architectural value-prop is that the **ingestion-time vision LLM and the runtime LLM are completely independent** — you can pay a vision LLM *once*, at ingest, to convert every embedded image into text (per-image OCR **and** semantic description, inlined where the image actually appears in the document — see [What `--use-vision-llm` produces](#what---use-vision-llm-produces) below). Then every query is served by a cheap text-only model that sees that extracted text natively. Two extra scenarios make this explicit:
+
+| `--scenario`       | Native arm answers with                | SurfSense arm answers with     | Question being measured                                                                  |
+|--------------------|----------------------------------------|--------------------------------|------------------------------------------------------------------------------------------|
+| `head-to-head`     | `--provider-model` (vision)            | `--provider-model` (vision)    | Pure RAG quality at parity. (Default.)                                                   |
+| `symmetric-cheap`  | `--provider-model` (cheap, text-only)  | `--provider-model` (same)      | Does pre-extracted image context let a non-vision LLM reason over image-heavy docs?      |
+| `cost-arbitrage`   | `--native-arm-model` (vision)          | `--provider-model` (cheap)     | How close does SurfSense get to a vision-native baseline at a fraction of per-query cost?|
+
+In all three modes the **ingest-time** vision LLM is set on the SearchSpace's `vision_llm_config_id` (auto-picked from the strongest registered global OpenRouter vision config — `claude-sonnet-4.5` > `claude-opus-4.7` > `gpt-5` > `gemini-2.5-pro`, override with `--vision-llm <slug>`). What changes is which slug the *answering* models hit per arm.
+
+### Ingest with vision, evaluate with a non-vision LLM (`symmetric-cheap`)
+
+This is the answer to *"does SurfSense give a non-vision LLM enough context to reason over image-heavy docs?"*. Both arms hit the same cheap text-only slug. The native arm is structurally blind to images (text-only LLM + raw PDFs). The SurfSense arm reads chunks that already contain the per-image OCR and visual descriptions, written there by the vision LLM at ingest time.
+
+```bash
+python -m surfsense_evals setup --suite medical \
+  --scenario symmetric-cheap \
+  --provider-model openai/gpt-5.4-mini
+  # vision LLM at ingest = auto-picked (claude-sonnet-4.5 by default)
+  # answer LLM for BOTH arms = openai/gpt-5.4-mini (text-only)
+
+python -m surfsense_evals ingest medical medxpertqa --split test  # vision=on by default
+python -m surfsense_evals run    medical medxpertqa --concurrency 4
+python -m surfsense_evals report --suite medical
+# Δ accuracy on image-required MCQs is the headline number; native arm
+# baseline is "what a text-only LLM gets without seeing the images".
+```
+
+### Cheap SurfSense vs vision-native baseline (`cost-arbitrage`)
+
+```bash
+python -m surfsense_evals setup --suite medical \
+  --scenario cost-arbitrage \
+  --provider-model openai/gpt-5.4-mini \
+  --native-arm-model anthropic/claude-sonnet-4.5
+  # vision LLM at ingest = auto-picked claude-sonnet-4.5
+  # native arm = sonnet (vision); SurfSense arm = gpt-5.4-mini (text-only)
+
+python -m surfsense_evals ingest medical medxpertqa --split test
+python -m surfsense_evals run    medical medxpertqa --concurrency 4
+python -m surfsense_evals report --suite medical
+# Report header reads:
+#   Scenario: cost-arbitrage — native arm answers with `anthropic/claude-sonnet-4.5`
+#   (vision); SurfSense answers with `openai/gpt-5.4-mini` over chunks vision-extracted
+#   at ingest by `anthropic/claude-sonnet-4.5`.
+```
+
+Notes:
+- `cost-arbitrage` requires both `--provider-model` (the cheap SurfSense slug) AND `--native-arm-model <vision slug>`.
+- `--vision-llm <slug>` is optional; if omitted the harness queries `GET /api/v1/global-vision-llm-configs` and auto-picks the strongest registered one. Pass `--no-vision-llm-setup` if you want to keep whatever vision config is already attached to the SearchSpace.
+- The runner's "looks text-only" warning is suppressed (or relabelled as informational) for `symmetric-cheap` so intentional asymmetry doesn't read as a misconfiguration.
+- All three scenario fields (`scenario`, `provider_model`, `native_arm_model`, `vision_provider_model`) are persisted to `state.json` and recorded in `run_artifact.extra` + the report header — no need to retrace what was set.
+
+## Per-benchmark useful flags
+
+`medical/medxpertqa` (`run`):
+- `--split {test,dev,all}` — pick a subset (default `test`)
+- `--task "Diagnosis"` / `--body-system "Cardiovascular"` — slice the report
+- `--require-images` — drop rare rows where every image filename failed to resolve
+- `--n 100` — quick smoke run
+- `--no-mentions` — let SurfSense retrieve unscoped ("did the @-mention matter?")
+
+`multimodal_doc/mmlongbench`:
+- `--max-docs N` (ingest) — cap downloads at the first N unique PDFs
+- `--format {str,int,float,list,none}` (run) — slice by answer format; `none` = the ~22% intentionally unanswerable hallucination probes
+- `--skip-unanswerable` (run) — drop unanswerable questions
+- `--docs <a.pdf>,<b.pdf>` (run) — scope to specific docs
+
+## Ingestion knobs (vision LLM, processing mode, summarize)
+
+The harness exposes `POST /api/v1/documents/fileupload`'s three knobs on every `ingest` subcommand:
+
+| Flag pair                                  | Effect                                                                                  |
+|--------------------------------------------|-----------------------------------------------------------------------------------------|
+| `--use-vision-llm` / `--no-vision-llm`     | Walk every embedded image in the PDF and inline image-derived text at the image's position (see below). |
+| `--processing-mode {basic,premium}`        | `premium` carries a 10× page multiplier and routes to a stronger ETL (e.g. LlamaCloud). |
+| `--should-summarize` / `--no-summarize`    | Generate a per-document summary at ingest.                                              |
+
+The "Default ingest" column in the benchmarks table is what runs if you don't pass any flag. Whatever was actually used is recorded as a `__settings__` header in the doc map (`data/<suite>/maps/<benchmark>_*_map.jsonl`) and as `extra.ingest_settings` in `run_artifact.json`, then surfaced in the report — no need to hunt through CLI history.
+
+> The backend's `ETL_SERVICE` env var (`DOCLING` | `UNSTRUCTURED` | `LLAMACLOUD`) is **not** per-upload. Restart the backend with a different `ETL_SERVICE` and re-ingest to compare ETLs (route through `--processing-mode premium` if your backend uses that mode for the stronger ETL).
+
+### What `--use-vision-llm` produces
+
+When vision is on, the backend's ETL pipeline (`app/etl_pipeline/picture_describer.py`) does, **per embedded image** in the PDF:
+
+1. Extract the raw image bytes via `pypdf` (deduped by sha256, size-capped to match the vision LLM's per-image limit).
+2. **Per-image OCR** — re-feed the image as a standalone upload through the configured ETL service (Docling / Azure DI / LlamaCloud) with `vision_llm=None`, so the ETL's OCR engine extracts the literal text-in-image.
+3. **Visual description** — call the vision LLM on the image with a description-only prompt (it's explicitly told *not* to transcribe text — that's OCR's job). Steps 2 and 3 run in parallel per image.
+4. Splice a horizontal-rule-delimited section **at the image's original position** in the parser markdown (replacing Docling's `<!-- image -->` placeholder + caption, or the bare `Image: <name>` caption a stripped-image parser leaves behind):
+
+   ```markdown
+   ---
+
+   **Embedded image:** `MM-130-a.jpeg`
+
+   **OCR text:**
+   Slice 24 / 60
+   L  R
+
+   **Visual description:**
+
+   - Axial contrast-enhanced CT showing a large cystic mass in the left upper quadrant.
+   - Mass effect on the adjacent stomach; left kidney displaced inferiorly.
+
+   ---
+   ```
+
+This is what makes `--scenario symmetric-cheap` and `--scenario cost-arbitrage` work: a non-vision LLM reading SurfSense's chunks sees the image's text and semantic content as plain markdown, alongside the surrounding case text, in the same retrieved chunk. Without it the cheap LLM would have nothing extra to read.
+
+### A/B testing the same corpus with different settings
+
+SurfSense dedupes uploads by `(filename, search_space_id)` — **not** by content hash and **not** by ingestion settings. Re-uploading the same filename to the same SearchSpace with a different `--use-vision-llm` flag silently skips re-processing. Give each variant its own SearchSpace:
+
+```bash
+# Baseline arm (vision off)
+python -m surfsense_evals setup    --suite medical --provider-model anthropic/claude-sonnet-4.5
+python -m surfsense_evals ingest   medical medxpertqa --no-vision-llm
+python -m surfsense_evals run      medical medxpertqa --n 100
+python -m surfsense_evals teardown --suite medical
+
+# Vision arm (the benchmark default)
+python -m surfsense_evals setup    --suite medical --provider-model anthropic/claude-sonnet-4.5
+python -m surfsense_evals ingest   medical medxpertqa
+python -m surfsense_evals run      medical medxpertqa --n 100
+python -m surfsense_evals report   --suite medical
+```
+
+Both runs land in `data/medical/runs/<ts>/medxpertqa/` with their settings recorded; rendered PDFs stay cached under `data/medical/medxpertqa/pdfs/` so the second `ingest` is upload-only.
+
+## Environment variables
+
+- `SURFSENSE_API_BASE` (default `http://localhost:8000`)
+- `OPENROUTER_API_KEY` — required for the `native_pdf` arm and for `models list`
+- One of `SURFSENSE_USER_EMAIL` + `SURFSENSE_USER_PASSWORD` (LOCAL), **or** `SURFSENSE_JWT` (+ optional `SURFSENSE_REFRESH_TOKEN`) for GOOGLE/pre-issued JWT
+- `EVAL_DATA_DIR` (default `<project>/data`) — datasets, rendered PDFs, ingestion id maps, run outputs, `state.json`
+- `EVAL_REPORTS_DIR` (default `<project>/reports`)
+- `OPENROUTER_BASE_URL` (default `https://openrouter.ai/api/v1`) — only if you proxy OpenRouter
+
+## Adding a new domain suite
+
+1. Create `surfsense_evals/src/surfsense_evals/suites/<domain>/<benchmark>/` with `__init__.py`, `ingest.py`, `runner.py`, optional `prompt.py`.
+2. Implement a `Benchmark` subclass (see `core/registry.py`); compose with `core.clients.*`, `core.arms.*`, `core.parse.*`, `core.metrics.*`.
+3. Call `register(MyBenchmark())` at the bottom of `<benchmark>/__init__.py`. Auto-discovery picks it up; `setup --suite <domain>` and `ingest/run <domain> <benchmark>` work immediately.
+
+Each suite gets its own SearchSpace (`eval-<suite>-<UTC-ts>`), `state.json` slot, data dir, reports dir, and pinned LLM. Suites never share a SearchSpace.
+
+## Out of scope (follow-up PRs)
+
+- Docker service for `docker compose run evals run medical medxpertqa`.
+- Multi-model sweeps (one slug per `setup` for now; aggregate reports come later).
+- A long-context-stuffing arm (give the model the same retrieved chunks SurfSense saw).
+- LLM-judge grader for MMLongBench-Doc (paper uses GPT-4 as judge; we ship a deterministic rule-based grader).
+- MedXpertQA-MM accuracy by image modality — dataset doesn't tag modality directly; we slice by `medical_task` and `body_system`.
+- A `--slot <name>` flag that decouples the state-slot key from the benchmark registry's `suite` attribute, so parallel SearchSpaces with different ingestion settings can coexist on the same benchmark without `teardown` between A/B arms.
+
+See `c:/Users/91882/.cursor/plans/medical_rag_evals_(mirage_+_curev1)_e797a324.plan.md` for the full design rationale.
diff --git a/surfsense_evals/data/.gitignore b/surfsense_evals/data/.gitignore
new file mode 100644
index 000000000..d6b7ef32c
--- /dev/null
+++ b/surfsense_evals/data/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
diff --git a/surfsense_evals/pyproject.toml b/surfsense_evals/pyproject.toml
new file mode 100644
index 000000000..a23e8a8be
--- /dev/null
+++ b/surfsense_evals/pyproject.toml
@@ -0,0 +1,60 @@
+[project]
+name = "surfsense-evals"
+version = "0.1.0"
+description = "Domain-agnostic evaluation harness for SurfSense (medical RAG suite ships first; legal/finance/code suites slot in under suites/)."
+readme = "README.md"
+requires-python = ">=3.12"
+license = { text = "Apache-2.0" }
+authors = [{ name = "SurfSense" }]
+
+dependencies = [
+    "httpx>=0.27.0",
+    "httpx-sse>=0.4.0",
+    "datasets>=2.21.0",
+    "huggingface_hub>=0.24.0",
+    "reportlab>=4.0.0",
+    "Pillow>=10.0.0",
+    "pyarrow>=15.0.0",
+    "pydantic>=2.6.0",
+    "tqdm>=4.66.0",
+    "numpy>=1.26.0",
+    "scikit-learn>=1.4.0",
+    "scipy>=1.12.0",
+    "python-dotenv>=1.0.0",
+    "rich>=13.7.0",
+    "trafilatura>=1.12.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0.0",
+    "pytest-asyncio>=0.23.0",
+    "respx>=0.21.0",
+    "ruff>=0.5.0",
+]
+
+[project.scripts]
+surfsense-evals = "surfsense_evals.core.cli:main"
+
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["surfsense_evals*"]
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+testpaths = ["tests"]
+markers = [
+    "integration: opt-in tests that hit a live SurfSense instance (run with `-m integration`)",
+]
+
+[tool.ruff]
+line-length = 100
+target-version = "py312"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "B", "UP", "SIM", "ASYNC"]
+ignore = ["E501"]
diff --git a/surfsense_evals/reports/.gitignore b/surfsense_evals/reports/.gitignore
new file mode 100644
index 000000000..bd8c8feaa
--- /dev/null
+++ b/surfsense_evals/reports/.gitignore
@@ -0,0 +1,4 @@
+*
+!.gitignore
+!medical/
+!medical/sample_summary.md
diff --git a/surfsense_evals/scripts/download_crag_task3.py b/surfsense_evals/scripts/download_crag_task3.py
new file mode 100644
index 000000000..a646838fe
--- /dev/null
+++ b/surfsense_evals/scripts/download_crag_task3.py
@@ -0,0 +1,97 @@
+"""Download CRAG Task 3's 4 .tar.bz2 parts in parallel.
+
+Run once before ``ingest research crag_t3`` to avoid the ingest
+synchronously blocking on a 7 GB download. Skips parts already
+present and complete on disk.
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+import time
+import urllib.request
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+)
+log = logging.getLogger("download_task3")
+
+
+_BASE = (
+    "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/"
+    "crag_task_3_dev_v4.tar.bz2.part"
+)
+_USER_AGENT = "SurfSense-Evals/0.1 (CRAG Task 3 fetch)"
+
+
+def _expected_size(url: str) -> int:
+    req = urllib.request.Request(url, method="HEAD", headers={"User-Agent": _USER_AGENT})
+    with urllib.request.urlopen(req, timeout=30) as resp:
+        return int(resp.headers.get("content-length", 0))
+
+
+def download_one(part: int, dest_dir: Path) -> Path:
+    url = f"{_BASE}{part}"
+    dest = dest_dir / f"crag_task_3_dev_v4.tar.bz2.part{part}"
+    expected = _expected_size(url)
+    if dest.exists() and dest.stat().st_size == expected:
+        log.info("part%d: cached (%d bytes)", part, expected)
+        return dest
+    log.info("part%d: downloading %d bytes ...", part, expected)
+    tmp = dest.with_suffix(dest.suffix + ".part_dl")
+    started = time.monotonic()
+    last_log = started
+    with urllib.request.urlopen(
+        urllib.request.Request(url, headers={"User-Agent": _USER_AGENT}),
+        timeout=900,
+    ) as resp, tmp.open("wb") as fh:
+        downloaded = 0
+        chunk = resp.read(1 << 20)
+        while chunk:
+            fh.write(chunk)
+            downloaded += len(chunk)
+            now = time.monotonic()
+            if now - last_log > 5.0:
+                pct = 100 * downloaded / expected if expected else 0
+                rate_mb = (downloaded / (now - started)) / (1 << 20)
+                log.info(
+                    "part%d: %5.1f%% (%.1f / %.1f MiB at %.1f MiB/s)",
+                    part, pct, downloaded / (1 << 20), expected / (1 << 20), rate_mb,
+                )
+                last_log = now
+            chunk = resp.read(1 << 20)
+    tmp.replace(dest)
+    elapsed = time.monotonic() - started
+    log.info(
+        "part%d: done in %.1fs (%.1f MiB/s avg)",
+        part, elapsed, (expected / (1 << 20)) / max(elapsed, 0.001),
+    )
+    return dest
+
+
+def main() -> int:
+    dest_dir = Path("data/research/crag_t3/.raw_cache")
+    dest_dir.mkdir(parents=True, exist_ok=True)
+
+    # 4 parts in parallel — typical residential connection saturates around
+    # 2 streams; GitHub raw serves these fine in parallel.
+    started = time.monotonic()
+    with ThreadPoolExecutor(max_workers=4) as ex:
+        futures = {ex.submit(download_one, i, dest_dir): i for i in range(1, 5)}
+        for fut in as_completed(futures):
+            part = futures[fut]
+            try:
+                fut.result()
+            except Exception as exc:  # noqa: BLE001
+                log.error("part%d failed: %s", part, exc)
+                return 1
+    log.info("All 4 parts downloaded in %.1fs", time.monotonic() - started)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/surfsense_evals/scripts/peek_crag_run.py b/surfsense_evals/scripts/peek_crag_run.py
new file mode 100644
index 000000000..225e5ec98
--- /dev/null
+++ b/surfsense_evals/scripts/peek_crag_run.py
@@ -0,0 +1,37 @@
+"""Tiny helper to inspect the latest CRAG run's per-question outputs."""
+
+from __future__ import annotations
+
+import glob
+import json
+from collections import defaultdict
+
+
+def main() -> None:
+    raw_path = sorted(glob.glob("data/research/runs/*/crag/raw.jsonl"))[-1]
+    print(f"Reading: {raw_path}")
+    rows = [json.loads(line) for line in open(raw_path, encoding="utf-8") if line.strip()]
+    by_q: dict[str, dict[str, dict]] = defaultdict(dict)
+    for r in rows:
+        by_q[r["qid"]][r["arm"]] = r
+
+    for qid, arms in list(by_q.items()):
+        b = arms.get("bare_llm", {})
+        l = arms.get("long_context", {})
+        s = arms.get("surfsense", {})
+        print(f"\n=== {qid} ({b.get('domain')}/{b.get('question_type')}) ===")
+        print(f"  question: {b.get('extra', {}).get('question', '?')!r}")
+        print(f"  gold: {b.get('gold')!r}")
+        for arm_name, a in (("bare_llm", b), ("long_context", l), ("surfsense", s)):
+            grade = a.get("graded", {})
+            text = (a.get("raw_text") or "").strip()
+            tail = text[-200:] if text else ""
+            print(
+                f"  [{arm_name}] grade={grade.get('grade')} "
+                f"method={grade.get('method')}"
+            )
+            print(f"    -> {tail!r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/peek_disagreements.py b/surfsense_evals/scripts/peek_disagreements.py
new file mode 100644
index 000000000..c0fe0acd9
--- /dev/null
+++ b/surfsense_evals/scripts/peek_disagreements.py
@@ -0,0 +1,64 @@
+"""Show questions where SurfSense was wrong but long-context was right (and vice versa)."""
+
+from __future__ import annotations
+
+import glob
+import json
+from collections import defaultdict
+
+
+def main() -> None:
+    raw_path = sorted(glob.glob("data/research/runs/*/crag/raw.jsonl"))[-1]
+    print(f"Reading: {raw_path}")
+    rows = [json.loads(line) for line in open(raw_path, encoding="utf-8") if line.strip()]
+    by_q: dict[str, dict[str, dict]] = defaultdict(dict)
+    for r in rows:
+        by_q[r["qid"]][r["arm"]] = r
+
+    surf_wrong_lc_right = []
+    lc_wrong_surf_right = []
+    surf_wrong_bare_right = []
+    for qid, arms in by_q.items():
+        b = arms.get("bare_llm", {}).get("graded", {}).get("grade")
+        lc = arms.get("long_context", {}).get("graded", {}).get("grade")
+        s = arms.get("surfsense", {}).get("graded", {}).get("grade")
+        if s == "incorrect" and lc == "correct":
+            surf_wrong_lc_right.append(qid)
+        if lc == "incorrect" and s == "correct":
+            lc_wrong_surf_right.append(qid)
+        if s == "incorrect" and b == "correct":
+            surf_wrong_bare_right.append(qid)
+
+    print(f"\nSurfSense INCORRECT but Long-Context CORRECT: {len(surf_wrong_lc_right)}")
+    print(f"Long-Context INCORRECT but SurfSense CORRECT: {len(lc_wrong_surf_right)}")
+    print(f"SurfSense INCORRECT but Bare CORRECT: {len(surf_wrong_bare_right)}")
+
+    print("\n=== Where SurfSense is wrong but long-context is right (top 5) ===")
+    for qid in surf_wrong_lc_right[:5]:
+        arms = by_q[qid]
+        b = arms.get("bare_llm", {})
+        print(f"\n[{qid}] domain={b.get('domain')} qtype={b.get('question_type')}")
+        print(f"  GOLD: {b.get('gold')!r}")
+        for arm_name in ("bare_llm", "long_context", "surfsense"):
+            a = arms.get(arm_name, {})
+            t = (a.get("raw_text") or "").strip()
+            tail = t[-180:] if t else ""
+            grade = a.get("graded", {})
+            print(f"  [{arm_name}] {grade.get('grade')} ({grade.get('method')}): {tail!r}")
+
+    print("\n=== Where Long-Context is wrong but SurfSense is right (top 5) ===")
+    for qid in lc_wrong_surf_right[:5]:
+        arms = by_q[qid]
+        b = arms.get("bare_llm", {})
+        print(f"\n[{qid}] domain={b.get('domain')} qtype={b.get('question_type')}")
+        print(f"  GOLD: {b.get('gold')!r}")
+        for arm_name in ("bare_llm", "long_context", "surfsense"):
+            a = arms.get(arm_name, {})
+            t = (a.get("raw_text") or "").strip()
+            tail = t[-180:] if t else ""
+            grade = a.get("graded", {})
+            print(f"  [{arm_name}] {grade.get('grade')} ({grade.get('method')}): {tail!r}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/peek_t3_doc_map.py b/surfsense_evals/scripts/peek_t3_doc_map.py
new file mode 100644
index 000000000..6954cdcad
--- /dev/null
+++ b/surfsense_evals/scripts/peek_t3_doc_map.py
@@ -0,0 +1,40 @@
+"""Quick sanity-check for the CRAG Task 3 doc map after ingest."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+
+def main() -> int:
+    p = Path("data/research/maps/crag_t3_doc_map.jsonl")
+    if not p.exists():
+        print(f"Doc map missing: {p}")
+        return 1
+    rows = []
+    settings = {}
+    for line in p.read_text(encoding="utf-8").splitlines():
+        if not line.strip():
+            continue
+        row = json.loads(line)
+        if "__settings__" in row:
+            settings = row
+            continue
+        rows.append(row)
+    print(f"Settings header: {settings}")
+    print(f"Doc map rows:   {len(rows)}")
+    for r in rows:
+        print(f"  qid={r['qid']:<10} domain={r['domain']:<8} qtype={r['question_type']}")
+        print(f"    question: {r['question'][:90]}")
+        print(f"    gold:     {r['gold_answer'][:90]}")
+        print(
+            f"    pages:    {len(r['page_filenames'])} extracted, "
+            f"{len(r['document_ids'])} doc_ids, "
+            f"{len(r['missing_pages'])} missing"
+        )
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/surfsense_evals/scripts/summarise_crag_run.py b/surfsense_evals/scripts/summarise_crag_run.py
new file mode 100644
index 000000000..646fb6a97
--- /dev/null
+++ b/surfsense_evals/scripts/summarise_crag_run.py
@@ -0,0 +1,65 @@
+"""Render a quick textual summary of the latest CRAG run."""
+
+from __future__ import annotations
+
+import glob
+import json
+
+
+def main() -> None:
+    runs = sorted(glob.glob("data/research/runs/*/crag/run_artifact.json"))
+    if not runs:
+        print("(no CRAG runs found)")
+        return
+    m = json.load(open(runs[-1], encoding="utf-8"))
+    metrics = m["metrics"]
+
+    print(f"Reading: {runs[-1]}")
+    print(f"n_questions: {m['extra']['n_questions']}")
+    print()
+    print("=== ARMS ===")
+    for arm in ("bare_llm", "long_context", "surfsense"):
+        d = metrics[arm]
+        print(
+            f"{arm:14s}: "
+            f"acc={d['accuracy']*100:5.1f}% (Wilson 95% CI "
+            f"{d['ci_low']*100:.1f}-{d['ci_high']*100:.1f}) | "
+            f"correct={d['correct_rate']*100:5.1f}% "
+            f"missing={d['missing_rate']*100:5.1f}% "
+            f"incorrect={d['incorrect_rate']*100:5.1f}% | "
+            f"truth={d['truthfulness_score']*100:+5.1f}%"
+        )
+
+    print()
+    print("=== DELTAS ===")
+    for key, d in metrics["deltas"].items():
+        print(
+            f"{key:30s}: acc={d['accuracy_pp']:+5.1f}pp "
+            f"truth={d['truthfulness_score_pp']:+5.1f}pp "
+            f"McNemar p={d['mcnemar_p_value']:.4f} ({d['mcnemar_method']}) "
+            f"bootstrap CI [{d['bootstrap_ci_low']:+.1f}, {d['bootstrap_ci_high']:+.1f}]"
+        )
+
+    print()
+    print("=== PER-QUESTION-TYPE TRUTHFULNESS ===")
+    for qt, row in sorted(metrics["per_question_type"].items()):
+        n = row["n"]
+        pieces = [f"{qt:20s} (n={n:3d}):"]
+        for arm in ("bare_llm", "long_context", "surfsense"):
+            if arm in row:
+                pieces.append(f"{arm}={row[arm]['truthfulness_score']*100:+7.1f}%")
+        print(" ".join(pieces))
+
+    print()
+    print("=== PER-DOMAIN TRUTHFULNESS ===")
+    for dom, row in sorted(metrics["per_domain"].items()):
+        n = row["n"]
+        pieces = [f"{dom:10s} (n={n:3d}):"]
+        for arm in ("bare_llm", "long_context", "surfsense"):
+            if arm in row:
+                pieces.append(f"{arm}={row[arm]['truthfulness_score']*100:+7.1f}%")
+        print(" ".join(pieces))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/src/surfsense_evals/__init__.py b/surfsense_evals/src/surfsense_evals/__init__.py
new file mode 100644
index 000000000..fc8a81482
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/__init__.py
@@ -0,0 +1,10 @@
+"""SurfSense Evals — domain-agnostic eval harness.
+
+Public entry-point is the ``surfsense_evals`` CLI (``python -m surfsense_evals``).
+Programmatic embedding is a non-goal for now; everything goes through the CLI
++ filesystem outputs (state.json, raw run JSONL, summary.md/json reports).
+"""
+
+from __future__ import annotations
+
+__version__ = "0.1.0"
diff --git a/surfsense_evals/src/surfsense_evals/__main__.py b/surfsense_evals/src/surfsense_evals/__main__.py
new file mode 100644
index 000000000..0efb932dd
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/__main__.py
@@ -0,0 +1,13 @@
+"""Module entry point: ``python -m surfsense_evals ...``.
+
+Delegates to ``core.cli.main``. ``core.cli`` lazily imports
+``surfsense_evals.suites`` so every benchmark gets a chance to register
+before argparse builds its subcommand groups.
+"""
+
+from __future__ import annotations
+
+from surfsense_evals.core.cli import main
+
+if __name__ == "__main__":  # pragma: no cover
+    raise SystemExit(main())
diff --git a/surfsense_evals/src/surfsense_evals/core/__init__.py b/surfsense_evals/src/surfsense_evals/core/__init__.py
new file mode 100644
index 000000000..b5cc64a56
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/__init__.py
@@ -0,0 +1,8 @@
+"""Domain-agnostic infrastructure shared by every suite.
+
+Nothing under ``core/`` knows or cares about a specific evaluation domain.
+Suites live under ``surfsense_evals.suites.<domain>.<benchmark>`` and
+register themselves with ``core.registry`` on import.
+"""
+
+from __future__ import annotations
diff --git a/surfsense_evals/src/surfsense_evals/core/arms/__init__.py b/surfsense_evals/src/surfsense_evals/core/arms/__init__.py
new file mode 100644
index 000000000..0e7ce46e4
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/arms/__init__.py
@@ -0,0 +1,44 @@
+"""Arm protocol + concrete arms shared across suites.
+
+Concrete arms (``NativePdfArm``, ``SurfSenseArm``, ``BareLlmArm``) are
+imported lazily via ``__getattr__`` so consumers that only need the
+protocol — e.g. the registry's ``Arm`` re-export — don't transitively
+pull in ``httpx`` providers or the SurfSense client unless they
+actually use those arms.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .base import Arm, ArmRequest, ArmResult
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .bare_llm import BareLlmArm
+    from .native_pdf import NativePdfArm
+    from .surfsense import SurfSenseArm
+
+__all__ = [
+    "Arm",
+    "ArmRequest",
+    "ArmResult",
+    "BareLlmArm",
+    "NativePdfArm",
+    "SurfSenseArm",
+]
+
+
+def __getattr__(name: str):  # PEP 562
+    if name == "NativePdfArm":
+        from .native_pdf import NativePdfArm
+
+        return NativePdfArm
+    if name == "SurfSenseArm":
+        from .surfsense import SurfSenseArm
+
+        return SurfSenseArm
+    if name == "BareLlmArm":
+        from .bare_llm import BareLlmArm
+
+        return BareLlmArm
+    raise AttributeError(f"module 'surfsense_evals.core.arms' has no attribute {name!r}")
diff --git a/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py b/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py
new file mode 100644
index 000000000..1e3215415
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py
@@ -0,0 +1,100 @@
+"""Bare-LLM arm: chat completion with prompt-only input, no retrieval.
+
+Pairs with ``SurfSenseArm`` for any benchmark that wants to measure
+"how much does the model already know without RAG?". For factuality /
+multi-hop benchmarks (FRAMES, MuSiQue, …) this produces the published
+"naive prompting" baseline — e.g. FRAMES's 40.8% on Gemini-Pro-1.5.
+
+Symmetric with ``NativePdfArm`` in shape, but the request carries no
+``pdf_paths``: the prompt itself is the only input the model gets.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from ..providers.openrouter_chat import OpenRouterChatProvider
+from .base import Arm, ArmRequest, ArmResult
+
+logger = logging.getLogger(__name__)
+
+
+class BareLlmArm(Arm):
+    """``Arm`` implementation backed by ``OpenRouterChatProvider``.
+
+    ``name`` defaults to ``"bare_llm"`` but is overridable per-instance.
+    Suites that want two distinct OpenRouter chat arms (e.g. CRAG's
+    ``bare_llm`` vs ``long_context`` — both backed by chat-completions
+    but exercising different prompt strategies) instantiate twice with
+    different names so the metrics aggregator can keep them separate.
+    """
+
+    name: str = "bare_llm"
+
+    def __init__(
+        self,
+        *,
+        provider: OpenRouterChatProvider,
+        max_output_tokens: int | None = 1024,
+        system_prompt: str | None = None,
+        name: str | None = None,
+    ) -> None:
+        self._provider = provider
+        self._max_output = max_output_tokens
+        self._system_prompt = system_prompt
+        if name:
+            self.name = name
+
+    @classmethod
+    def from_env(
+        cls,
+        *,
+        api_key: str,
+        model: str,
+        base_url: str = "https://openrouter.ai/api/v1",
+        max_output_tokens: int | None = 1024,
+        system_prompt: str | None = None,
+        name: str | None = None,
+    ) -> BareLlmArm:
+        provider = OpenRouterChatProvider(
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+        )
+        return cls(
+            provider=provider,
+            max_output_tokens=max_output_tokens,
+            system_prompt=system_prompt,
+            name=name,
+        )
+
+    async def answer(self, request: ArmRequest) -> ArmResult:
+        try:
+            response = await self._provider.complete(
+                prompt=request.prompt,
+                system_prompt=self._system_prompt,
+                max_tokens=self._max_output,
+            )
+        except Exception as exc:  # noqa: BLE001
+            return ArmResult(
+                arm=self.name,
+                question_id=request.question_id,
+                raw_text="",
+                error=f"{type(exc).__name__}: {exc}",
+            )
+        return ArmResult(
+            arm=self.name,
+            question_id=request.question_id,
+            raw_text=response.text,
+            input_tokens=response.input_tokens,
+            output_tokens=response.output_tokens,
+            cost_micros=response.cost_micros,
+            latency_ms=response.latency_ms,
+            extra={
+                "model": self._provider.model,
+                "finish_reason": response.finish_reason,
+            },
+        )
+
+
+__all__ = ["BareLlmArm"]
diff --git a/surfsense_evals/src/surfsense_evals/core/arms/base.py b/surfsense_evals/src/surfsense_evals/core/arms/base.py
new file mode 100644
index 000000000..3e327fef2
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/arms/base.py
@@ -0,0 +1,93 @@
+"""Arm protocol + the value types every arm exchanges with a runner.
+
+An ``Arm`` is "one way to answer one question". Two ship in this PR:
+
+* ``NativePdfArm`` — drop the PDF straight into an OpenRouter
+  chat-completions request with ``plugins=[{file-parser, engine:
+  native}]``. Used for the head-to-head "is the model good enough on
+  its own?" measurement.
+* ``SurfSenseArm`` — POST ``/api/v1/new_chat`` with the question
+  scoped to the relevant ``mentioned_document_ids``; consume the SSE
+  stream and parse citations.
+
+Both implement the same protocol so a benchmark runner only sees
+``Arm.answer(request) -> ArmResult``.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Protocol
+
+
+@dataclass
+class ArmRequest:
+    """One arm-call worth of input.
+
+    * ``question_id`` is opaque — used for logging and joining results.
+    * ``prompt`` is the fully-formatted text the arm should send. The
+      runner is responsible for prompt construction so head-to-head
+      comparisons use byte-identical text.
+    * ``pdf_paths`` is the per-question source PDFs (used by
+      ``NativePdfArm``). Empty for retrieval-only / corpus-wide
+      benchmarks.
+    * ``mentioned_document_ids`` is the SurfSense document scoping list
+      (used by ``SurfSenseArm``). When ``None`` SurfSense retrieves
+      across the whole search space.
+    * ``options`` is a free-form bag of arm-specific overrides
+      (e.g. SurfSense's ``disabled_tools``).
+    """
+
+    question_id: str
+    prompt: str
+    pdf_paths: list[Path] = field(default_factory=list)
+    mentioned_document_ids: list[int] | None = None
+    options: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ArmResult:
+    """Outcome of one ``Arm.answer`` invocation."""
+
+    arm: str
+    question_id: str
+    raw_text: str
+    answer_letter: str | None = None
+    citations: list[dict[str, Any]] = field(default_factory=list)
+    input_tokens: int = 0
+    output_tokens: int = 0
+    cost_micros: int = 0
+    latency_ms: int = 0
+    error: str | None = None
+    extra: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def ok(self) -> bool:
+        return self.error is None
+
+    def to_jsonl(self) -> dict[str, Any]:
+        """Stable dict shape for ``data/<suite>/runs/<ts>/<bench>_raw.jsonl``."""
+
+        return {
+            "arm": self.arm,
+            "question_id": self.question_id,
+            "answer_letter": self.answer_letter,
+            "raw_text": self.raw_text,
+            "citations": self.citations,
+            "input_tokens": self.input_tokens,
+            "output_tokens": self.output_tokens,
+            "cost_micros": self.cost_micros,
+            "latency_ms": self.latency_ms,
+            "error": self.error,
+            "extra": self.extra,
+        }
+
+
+class Arm(Protocol):
+    """One concrete way to answer questions for a given run."""
+
+    name: str
+
+    async def answer(self, request: ArmRequest) -> ArmResult:  # pragma: no cover - protocol
+        ...
diff --git a/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py b/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py
new file mode 100644
index 000000000..9294ed032
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py
@@ -0,0 +1,104 @@
+"""Native-PDF arm: drop the PDF straight into OpenRouter chat-completions.
+
+Generic across suites — a benchmark just supplies the prompt and the
+single PDF path. Multi-PDF questions concatenate in the runner before
+calling this arm so each ``answer`` invocation feeds the model exactly
+one ``data:application/pdf;base64,...`` block (matches the human
+"drag-and-drop one PDF into Claude" intent).
+"""
+
+from __future__ import annotations
+
+import logging
+
+from ..parse.answer_letter import extract_answer_letter
+from ..providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine
+from .base import Arm, ArmRequest, ArmResult
+
+logger = logging.getLogger(__name__)
+
+
+class NativePdfArm(Arm):
+    """``Arm`` implementation backed by ``OpenRouterPdfProvider``."""
+
+    name: str = "native_pdf"
+
+    def __init__(
+        self,
+        *,
+        provider: OpenRouterPdfProvider,
+        max_output_tokens: int | None = 1024,
+    ) -> None:
+        self._provider = provider
+        self._max_output = max_output_tokens
+
+    @classmethod
+    def from_env(
+        cls,
+        *,
+        api_key: str,
+        model: str,
+        engine: PdfEngine = PdfEngine.NATIVE,
+        base_url: str = "https://openrouter.ai/api/v1",
+        max_output_tokens: int | None = 1024,
+    ) -> NativePdfArm:
+        provider = OpenRouterPdfProvider(
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            engine=engine,
+        )
+        return cls(provider=provider, max_output_tokens=max_output_tokens)
+
+    async def answer(self, request: ArmRequest) -> ArmResult:
+        if not request.pdf_paths:
+            return ArmResult(
+                arm=self.name,
+                question_id=request.question_id,
+                raw_text="",
+                error="native_pdf arm requires at least one pdf_path",
+            )
+        if len(request.pdf_paths) > 1:
+            # The plan calls out one-PDF-per-question so the head-to-head
+            # is fair; runners are responsible for upstream concatenation.
+            logger.debug(
+                "qid=%s native_pdf got %d pdfs; using first only",
+                request.question_id,
+                len(request.pdf_paths),
+            )
+        pdf = request.pdf_paths[0]
+        try:
+            response = await self._provider.complete(
+                prompt=request.prompt,
+                pdf_path=pdf,
+                max_tokens=self._max_output,
+            )
+        except Exception as exc:  # noqa: BLE001
+            return ArmResult(
+                arm=self.name,
+                question_id=request.question_id,
+                raw_text="",
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+        letter = extract_answer_letter(response.text)
+        return ArmResult(
+            arm=self.name,
+            question_id=request.question_id,
+            raw_text=response.text,
+            answer_letter=letter.letter,
+            input_tokens=response.input_tokens,
+            output_tokens=response.output_tokens,
+            cost_micros=response.cost_micros,
+            latency_ms=response.latency_ms,
+            extra={
+                "model": self._provider.model,
+                "engine": self._provider.engine.value,
+                "answer_letter_strategy": letter.strategy,
+                "finish_reason": response.finish_reason,
+                "pdf_filename": pdf.name,
+            },
+        )
+
+
+__all__ = ["NativePdfArm"]
diff --git a/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py b/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py
new file mode 100644
index 000000000..a84350dfd
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py
@@ -0,0 +1,104 @@
+"""SurfSense arm: per-question fresh thread + ``/api/v1/new_chat`` stream.
+
+For every question:
+
+* Create a fresh ``NewChatThread`` on the suite's pinned SearchSpace.
+  This sidesteps the per-thread ``THREAD_BUSY`` 409 (a single thread
+  serialises turns, see ``surfsense_backend/app/routes/new_chat_routes.py:191-220``).
+* POST ``/api/v1/new_chat`` with the prompt and the per-question
+  ``mentioned_document_ids`` (``surfsense_backend/app/schemas/new_chat.py:241-243``).
+* Consume the SSE stream via ``NewChatClient.ask`` which accumulates
+  text deltas and returns ``StreamedAnswer``.
+* Optionally delete the thread (default ON for ephemeral runs).
+
+Citations are parsed from the streamed assistant text via the
+canonical regex port; chunk ids are returned in ``ArmResult.citations``
+for the runner to map back to corpus ids.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from ..clients import NewChatClient
+from ..parse.answer_letter import extract_answer_letter
+from .base import Arm, ArmRequest, ArmResult
+
+logger = logging.getLogger(__name__)
+
+
+class SurfSenseArm(Arm):
+    """``Arm`` implementation backed by ``NewChatClient``."""
+
+    name: str = "surfsense"
+
+    def __init__(
+        self,
+        *,
+        client: NewChatClient,
+        search_space_id: int,
+        ephemeral_threads: bool = True,
+        thread_title_prefix: str = "eval",
+    ) -> None:
+        self._client = client
+        self._search_space_id = search_space_id
+        self._ephemeral = ephemeral_threads
+        self._title_prefix = thread_title_prefix
+
+    async def answer(self, request: ArmRequest) -> ArmResult:
+        thread_id: int | None = None
+        try:
+            thread_id = await self._client.create_thread(
+                search_space_id=self._search_space_id,
+                title=f"{self._title_prefix}:{request.question_id}",
+            )
+            answer = await self._client.ask(
+                thread_id=thread_id,
+                search_space_id=self._search_space_id,
+                user_query=request.prompt,
+                mentioned_document_ids=request.mentioned_document_ids,
+                disabled_tools=request.options.get("disabled_tools"),
+            )
+        except Exception as exc:  # noqa: BLE001
+            return ArmResult(
+                arm=self.name,
+                question_id=request.question_id,
+                raw_text="",
+                error=f"{type(exc).__name__}: {exc}",
+                extra={"thread_id": thread_id},
+            )
+        finally:
+            if self._ephemeral and thread_id is not None:
+                try:
+                    await self._client.delete_thread(thread_id)
+                except Exception as exc:  # noqa: BLE001
+                    logger.debug(
+                        "Failed to delete thread %s: %s", thread_id, exc
+                    )
+
+        letter = extract_answer_letter(answer.text)
+        return ArmResult(
+            arm=self.name,
+            question_id=request.question_id,
+            raw_text=answer.text,
+            answer_letter=letter.letter,
+            citations=answer.citations,
+            latency_ms=answer.latency_ms,
+            # SurfSense doesn't surface input/output token counts in the
+            # SSE stream today; leaving the cost / token fields at 0
+            # documents that gap. Estimating from the raw text would
+            # bias the comparison against the SurfSense arm.
+            extra={
+                "thread_id": thread_id,
+                "search_space_id": self._search_space_id,
+                "answer_letter_strategy": letter.strategy,
+                "user_message_id": answer.user_message_id,
+                "assistant_message_id": answer.assistant_message_id,
+                "finished_normally": answer.finished_normally,
+                "n_raw_events": len(answer.raw_events),
+                "n_mentioned_documents": len(request.mentioned_document_ids or []),
+            },
+        )
+
+
+__all__ = ["SurfSenseArm"]
diff --git a/surfsense_evals/src/surfsense_evals/core/auth.py b/surfsense_evals/src/surfsense_evals/core/auth.py
new file mode 100644
index 000000000..1e7cc5b3e
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/auth.py
@@ -0,0 +1,273 @@
+"""Dual-mode credential resolver + httpx client factory with 401 auto-refresh.
+
+SurfSense supports ``AUTH_TYPE=LOCAL`` (email + password) and
+``AUTH_TYPE=GOOGLE`` (Google OAuth → frontend stores JWT in ``localStorage``).
+There is no headless equivalent of the Google flow, so the harness handles
+both modes by treating the JWT as the universal credential:
+
+* **LOCAL**: harness POSTs form-encoded ``username`` + ``password`` to
+  ``/auth/jwt/login``, reads ``{access_token, refresh_token}``.
+* **GOOGLE / pre-issued JWT**: operator pastes their existing JWT (and
+  optionally refresh token) into ``SURFSENSE_JWT`` /
+  ``SURFSENSE_REFRESH_TOKEN``; harness skips login.
+
+Either way ``client_with_auth`` returns one shared
+``httpx.AsyncClient`` with ``Authorization: Bearer <jwt>`` set and an
+event hook that, on a 401 with a refresh token in scope, calls
+``POST /auth/jwt/refresh`` and retries the original request once. JWT
+lifetime defaults to one day backend-side, so this matters for long
+MIRAGE runs.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+import httpx
+
+from .config import Config
+
+logger = logging.getLogger(__name__)
+
+
+class CredentialError(RuntimeError):
+    """Raised when no credential mode is configured."""
+
+
+_NO_CREDENTIALS_MESSAGE = (
+    "No SurfSense credentials configured. Set ONE of:\n"
+    "  (LOCAL)  SURFSENSE_USER_EMAIL + SURFSENSE_USER_PASSWORD\n"
+    "  (GOOGLE) SURFSENSE_JWT (and optionally SURFSENSE_REFRESH_TOKEN)\n"
+    "For GOOGLE: log in to SurfSense in your browser, open DevTools → "
+    "Application → Local Storage → copy `surfsense_bearer_token` and "
+    "`surfsense_refresh_token` into those env vars."
+)
+
+
+@dataclass
+class TokenBundle:
+    """Mutable token state — refresh hook updates ``access_token`` in place."""
+
+    access_token: str
+    refresh_token: str | None = None
+    # ``mode`` is informational only ("local" or "jwt"); used in error messages.
+    mode: str = "jwt"
+
+
+# ---------------------------------------------------------------------------
+# Token acquisition
+# ---------------------------------------------------------------------------
+
+
+async def acquire_token(config: Config, *, http: httpx.AsyncClient | None = None) -> TokenBundle:
+    """Resolve credentials → ``TokenBundle``.
+
+    Precedence:
+
+    1. ``SURFSENSE_JWT`` set → use it directly. Refresh token captured if
+       supplied.
+    2. ``SURFSENSE_USER_EMAIL`` + ``SURFSENSE_USER_PASSWORD`` set →
+       form-encoded POST to ``/auth/jwt/login``.
+    3. Neither → raise ``CredentialError``.
+
+    The optional ``http`` argument lets tests inject a mocked client; if
+    omitted a one-shot client is created for the login call only.
+    """
+
+    if config.has_jwt_mode():
+        return TokenBundle(
+            access_token=config.surfsense_jwt or "",
+            refresh_token=config.surfsense_refresh_token,
+            mode="jwt",
+        )
+
+    if config.has_local_mode():
+        async def _login(client: httpx.AsyncClient) -> TokenBundle:
+            response = await client.post(
+                f"{config.surfsense_api_base}/auth/jwt/login",
+                data={
+                    "username": config.surfsense_user_email,
+                    "password": config.surfsense_user_password,
+                },
+                headers={"Accept": "application/json"},
+            )
+            if response.status_code != 200:
+                raise CredentialError(
+                    f"LOCAL login failed (HTTP {response.status_code}): "
+                    f"{_safe_text(response)}"
+                )
+            payload = response.json()
+            access = payload.get("access_token")
+            if not access:
+                raise CredentialError(
+                    f"LOCAL login response missing access_token: {payload!r}"
+                )
+            return TokenBundle(
+                access_token=access,
+                refresh_token=payload.get("refresh_token") or None,
+                mode="local",
+            )
+
+        if http is not None:
+            return await _login(http)
+        async with httpx.AsyncClient(timeout=httpx.Timeout(30.0, connect=10.0)) as client:
+            return await _login(client)
+
+    raise CredentialError(_NO_CREDENTIALS_MESSAGE)
+
+
+def _safe_text(response: httpx.Response, *, limit: int = 200) -> str:
+    body = response.text or ""
+    if len(body) > limit:
+        return body[:limit] + "…"
+    return body
+
+
+# ---------------------------------------------------------------------------
+# httpx client + 401 auto-refresh
+# ---------------------------------------------------------------------------
+
+
+class _AuthState:
+    """Shared mutable holder closed over by the auth event hook.
+
+    Kept private so callers can't accidentally mutate the access token
+    out-of-band; ``client_with_auth`` returns the client directly.
+    """
+
+    def __init__(self, config: Config, tokens: TokenBundle) -> None:
+        self.config = config
+        self.tokens = tokens
+        self._refresh_in_flight: bool = False
+
+
+def _build_auth_request(state: _AuthState, request: httpx.Request) -> None:
+    """Stamp the current bearer onto ``request`` (request-event hook)."""
+
+    request.headers["Authorization"] = f"Bearer {state.tokens.access_token}"
+
+
+async def _refresh_access_token(
+    state: _AuthState, transport: httpx.AsyncBaseTransport | None = None
+) -> bool:
+    """POST ``/auth/jwt/refresh`` with the current refresh token.
+
+    Returns ``True`` on success and updates ``state.tokens`` in place.
+    Returns ``False`` if no refresh token is configured or the call fails.
+    Recursive 401s are avoided by using a *new* client without the auth
+    hook.
+    """
+
+    refresh = state.tokens.refresh_token
+    if not refresh:
+        return False
+    try:
+        async with httpx.AsyncClient(
+            timeout=httpx.Timeout(15.0, connect=5.0),
+            transport=transport,
+        ) as inner:
+            response = await inner.post(
+                f"{state.config.surfsense_api_base}/auth/jwt/refresh",
+                json={"refresh_token": refresh},
+                headers={"Accept": "application/json"},
+            )
+    except httpx.HTTPError as exc:
+        logger.warning("Token refresh transport error: %s", exc)
+        return False
+    if response.status_code != 200:
+        logger.warning(
+            "Token refresh rejected (HTTP %s): %s",
+            response.status_code,
+            _safe_text(response),
+        )
+        return False
+    payload = response.json()
+    new_access = payload.get("access_token")
+    if not new_access:
+        logger.warning("Refresh response missing access_token: %r", payload)
+        return False
+    state.tokens.access_token = new_access
+    new_refresh = payload.get("refresh_token")
+    if new_refresh:
+        state.tokens.refresh_token = new_refresh
+    return True
+
+
+def client_with_auth(
+    config: Config,
+    tokens: TokenBundle,
+    *,
+    timeout: float = 60.0,
+    transport: httpx.AsyncBaseTransport | None = None,
+    base_url: str | None = None,
+) -> httpx.AsyncClient:
+    """Build a single shared ``httpx.AsyncClient`` for the SurfSense API.
+
+    * Stamps ``Authorization: Bearer <jwt>`` on every outgoing request.
+    * On any 401 response, attempts a single refresh (if a refresh token
+      is configured) and retries the original request once. The retry
+      uses a fresh stamping of the bearer header, so a successful
+      refresh transparently unblocks long runs.
+    * The retry is best-effort — repeated 401s after a refresh attempt
+      are surfaced to the caller so they can re-auth manually.
+
+    Pass ``base_url`` to scope a sub-client (e.g. tests). The default
+    keeps full URLs in calling code, which makes route-spec citations in
+    the codebase easier to grep.
+    """
+
+    state = _AuthState(config, tokens)
+
+    async def _request_hook(request: httpx.Request) -> None:
+        _build_auth_request(state, request)
+
+    # ``send`` is overridden in ``_AuthAwareClient`` to retry once on 401
+    # after refreshing the bearer. httpx's response event-hook can't
+    # *replace* a response, so we need a subclass to do the replay.
+    client = _AuthAwareClient(
+        state=state,
+        transport=transport,
+        timeout=httpx.Timeout(timeout, connect=10.0),
+        base_url=base_url or "",
+        event_hooks={"request": [_request_hook]},
+    )
+    return client
+
+
+class _AuthAwareClient(httpx.AsyncClient):
+    """``AsyncClient`` that retries once on 401 after refreshing the token."""
+
+    def __init__(self, *, state: _AuthState, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self._auth_state = state
+
+    async def send(  # type: ignore[override]
+        self, request: httpx.Request, **kwargs: Any
+    ) -> httpx.Response:
+        response = await super().send(request, **kwargs)
+        if response.status_code != 401:
+            return response
+        # Don't refresh while a refresh is itself in flight.
+        if self._auth_state._refresh_in_flight:
+            return response
+        self._auth_state._refresh_in_flight = True
+        try:
+            refreshed = await _refresh_access_token(self._auth_state)
+        finally:
+            self._auth_state._refresh_in_flight = False
+        if not refreshed:
+            return response
+        # Re-stamp and replay once. ``request`` is reusable.
+        await response.aclose()
+        request.headers["Authorization"] = f"Bearer {self._auth_state.tokens.access_token}"
+        return await super().send(request, **kwargs)
+
+
+__all__ = [
+    "CredentialError",
+    "TokenBundle",
+    "acquire_token",
+    "client_with_auth",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/cli.py b/surfsense_evals/src/surfsense_evals/core/cli.py
new file mode 100644
index 000000000..3d4d0fd24
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/cli.py
@@ -0,0 +1,790 @@
+"""Argparse CLI for ``python -m surfsense_evals``.
+
+Subcommands:
+
+* ``setup    --suite <name> --provider-model <slug> [--agent-llm-id <int>]``
+* ``teardown --suite <name>``
+* ``models  list [--provider openrouter] [--grep <s>]``
+* ``suites  list``
+* ``benchmarks list [--suite <name>]``
+* ``ingest <suite> <benchmark> [benchmark flags]``
+* ``run    <suite> <benchmark> [benchmark flags]``
+* ``report --suite <name> [--benchmark <name>]``
+
+The ``ingest`` / ``run`` subparsers are built dynamically from the
+registry — adding a new benchmark only requires registering it; the
+CLI surface comes for free. ``add_run_args`` lets each benchmark
+publish its own flags.
+
+Design choices worth flagging:
+
+* ``setup`` rejects ``agent_llm_id == 0`` (Auto / LiteLLM router) so
+  per-question accuracy is reproducible.
+* ``setup`` validates that the picked LLM config has
+  ``provider == "OPENROUTER"`` and ``model_name == --provider-model``
+  before declaring success — both arms of the head-to-head must hit
+  the same OpenRouter slug.
+* Lifecycle state is keyed by suite, so ``setup --suite legal`` does
+  not touch ``medical``'s SearchSpace, and vice versa.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import sys
+from dataclasses import dataclass
+from typing import Any
+
+import sys
+
+import httpx
+from rich.console import Console
+from rich.table import Table
+
+# Windows' legacy console (cp1252) crashes when Rich tries to write characters
+# outside the active codepage (e.g. '->', em-dashes, box-drawing). Force UTF-8
+# on stdout/stderr and disable Rich's legacy_windows render path so the file
+# stream is used directly. Modern Windows (>=10, VS Code terminal, Windows
+# Terminal, PowerShell, cmd) all interpret ANSI escapes natively.
+if sys.platform == "win32":
+    for _stream in (sys.stdout, sys.stderr):
+        try:
+            _stream.reconfigure(encoding="utf-8", errors="replace")
+        except (AttributeError, ValueError):
+            pass
+
+from . import registry
+from .auth import CredentialError, acquire_token, client_with_auth
+from .clients import SearchSpaceClient
+from .clients.search_space import LlmPreferences
+from .config import (
+    DEFAULT_SCENARIO,
+    SCENARIOS,
+    Config,
+    SuiteState,
+    clear_suite_state,
+    get_suite_state,
+    load_config,
+    set_suite_state,
+    utc_iso_timestamp,
+)
+from .vision_llm import VisionConfigError, resolve_vision_llm
+
+logger = logging.getLogger("surfsense_evals")
+console = Console(legacy_windows=False)
+
+
+# ---------------------------------------------------------------------------
+# Discovery
+# ---------------------------------------------------------------------------
+
+
+def _discover_suites() -> list[str]:
+    """Trigger ``register(...)`` for every benchmark.
+
+    Imported lazily so ``models list`` (which doesn't need any
+    benchmark) still runs fast.
+    """
+
+    from surfsense_evals.suites import discover_suites
+
+    return discover_suites()
+
+
+# ---------------------------------------------------------------------------
+# Global LLM config fetcher (used by setup + models list)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class LlmConfigEntry:
+    id: int
+    name: str
+    provider: str
+    model_name: str
+    raw: dict[str, Any]
+
+    @classmethod
+    def from_payload(cls, payload: dict[str, Any]) -> LlmConfigEntry:
+        return cls(
+            id=int(payload["id"]),
+            name=str(payload.get("name", "")),
+            provider=str(payload.get("provider", "")).upper(),
+            model_name=str(payload.get("model_name", "")),
+            raw=payload,
+        )
+
+
+async def _list_global_llm_configs(http: httpx.AsyncClient, base: str) -> list[LlmConfigEntry]:
+    response = await http.get(
+        f"{base}/api/v1/global-new-llm-configs",
+        headers={"Accept": "application/json"},
+    )
+    response.raise_for_status()
+    payload = response.json()
+    if not isinstance(payload, list):
+        raise RuntimeError(f"Unexpected /global-new-llm-configs payload: {payload!r}")
+    return [LlmConfigEntry.from_payload(item) for item in payload]
+
+
+def _resolve_openrouter_id(
+    candidates: list[LlmConfigEntry],
+    provider_model: str,
+    *,
+    explicit_id: int | None,
+) -> int:
+    """Resolve the SurfSense LLM id for ``provider_model``.
+
+    Behaviour:
+
+    * If ``explicit_id`` is given: return it directly. The caller is
+      then expected to GET-validate that the row's
+      ``provider == "OPENROUTER"`` and ``model_name`` matches the slug.
+      That branch supports positive BYOK ``NewLLMConfig`` rows whose
+      slugs may overlap with global OpenRouter virtuals.
+    * Otherwise: filter to ``provider == "OPENROUTER"`` and
+      ``model_name == provider_model``. Expect exactly one match —
+      raise with a friendly message otherwise.
+    """
+
+    if explicit_id is not None:
+        return explicit_id
+
+    matches = [
+        c for c in candidates if c.provider == "OPENROUTER" and c.model_name == provider_model
+    ]
+    if not matches:
+        sample = ", ".join(
+            f"{c.model_name} (id={c.id})" for c in candidates if c.provider == "OPENROUTER"
+        )[:600]
+        raise RuntimeError(
+            f"No OpenRouter config found for slug '{provider_model}'. "
+            "Make sure `openrouter_integration.enabled: true` in "
+            "global_llm_config.yaml and that the Celery worker has "
+            "finished its first refresh (the catalogue is fetched at "
+            "Celery startup per `app/celery_app.py`). "
+            f"Available OpenRouter slugs (sample): {sample or '<none>'}.\n"
+            "Browse with: python -m surfsense_evals models list --grep <substring>"
+        )
+    if len(matches) > 1:
+        listing = "\n".join(f"  id={c.id}  name={c.name!r}" for c in matches)
+        raise RuntimeError(
+            f"Multiple OpenRouter configs for slug '{provider_model}':\n{listing}\n"
+            "Pass --agent-llm-id <id> to disambiguate."
+        )
+    return matches[0].id
+
+
+# ---------------------------------------------------------------------------
+# Subcommand implementations
+# ---------------------------------------------------------------------------
+
+
+async def _cmd_setup(args: argparse.Namespace) -> int:
+    suite = args.suite
+    provider_model: str = args.provider_model
+    explicit_id: int | None = args.agent_llm_id
+    scenario: str = args.scenario
+    vision_llm_slug: str | None = args.vision_llm
+    native_arm_model: str | None = args.native_arm_model
+    skip_vision_setup: bool = args.no_vision_llm_setup
+
+    if explicit_id == 0:
+        console.print(
+            "[red]agent_llm_id == 0 (Auto / LiteLLM router) is not allowed — "
+            "results would not be reproducible.[/red]"
+        )
+        return 2
+
+    if scenario not in SCENARIOS:
+        console.print(
+            f"[red]Unknown scenario {scenario!r}. Pick one of: "
+            f"{', '.join(SCENARIOS)}[/red]"
+        )
+        return 2
+
+    # Scenario-specific validation. Each branch documents WHY the rule
+    # exists so the operator's mental model matches what the runner does.
+    if scenario == "cost-arbitrage":
+        if not native_arm_model:
+            console.print(
+                "[red]--scenario cost-arbitrage requires --native-arm-model "
+                "<vision-capable slug>.[/red] The native arm needs a vision "
+                "model to fairly answer image-bearing questions; SurfSense "
+                "answers from already-extracted text via --provider-model."
+            )
+            return 2
+        if native_arm_model == provider_model:
+            console.print(
+                "[yellow]--native-arm-model equals --provider-model in "
+                "cost-arbitrage; that's degenerate (same as head-to-head). "
+                "Pick a different slug or switch to --scenario head-to-head.[/yellow]"
+            )
+    elif scenario in ("head-to-head", "symmetric-cheap"):
+        if native_arm_model:
+            console.print(
+                f"[yellow]--native-arm-model is ignored for --scenario {scenario} "
+                f"(both arms answer with --provider-model={provider_model!r}).[/yellow]"
+            )
+            native_arm_model = None  # don't persist a stale value
+
+    config = load_config()
+    try:
+        token = await acquire_token(config)
+    except CredentialError as exc:
+        console.print(f"[red]{exc}[/red]")
+        return 2
+
+    async with client_with_auth(config, token) as http:
+        candidates = await _list_global_llm_configs(http, config.surfsense_api_base)
+
+        try:
+            agent_llm_id = _resolve_openrouter_id(
+                candidates, provider_model, explicit_id=explicit_id
+            )
+        except RuntimeError as exc:
+            console.print(f"[red]{exc}[/red]")
+            return 2
+
+        ss_client = SearchSpaceClient(http, config.surfsense_api_base)
+        existing = get_suite_state(config, suite)
+        if existing is not None:
+            try:
+                row = await ss_client.get(existing.search_space_id)
+                console.print(
+                    f"Reusing existing SearchSpace [cyan]{row.name}[/cyan] "
+                    f"(id={row.id}) for suite [bold]{suite}[/bold]."
+                )
+                search_space_id = row.id
+            except httpx.HTTPStatusError as exc:
+                if exc.response.status_code == 404:
+                    console.print(
+                        f"[yellow]state.json pointed at SearchSpace id={existing.search_space_id} "
+                        f"but backend returned 404; creating a fresh one.[/yellow]"
+                    )
+                    existing = None
+                else:
+                    raise
+        if existing is None:
+            ss_name = f"eval-{suite}-{utc_iso_timestamp()}"
+            row = await ss_client.create(
+                ss_name, description=f"surfsense-evals lifecycle ({suite})"
+            )
+            console.print(
+                f"Created SearchSpace [cyan]{row.name}[/cyan] (id={row.id}) "
+                f"for suite [bold]{suite}[/bold]."
+            )
+            search_space_id = row.id
+
+        # Resolve + attach the vision LLM config (unless explicitly skipped).
+        # Asymmetric scenarios make the vision LLM at ingest a hard
+        # requirement — without it, SurfSense's chunks have no image
+        # content and the entire framing collapses.
+        vision_required = scenario in ("symmetric-cheap", "cost-arbitrage")
+        vision_config_id: int | None = None
+        vision_provider_model: str | None = None
+        if not skip_vision_setup and (vision_required or vision_llm_slug is not None):
+            try:
+                vision_candidates = await ss_client.list_global_vision_llm_configs()
+                resolved = resolve_vision_llm(
+                    vision_candidates, explicit_slug=vision_llm_slug
+                )
+            except VisionConfigError as exc:
+                console.print(f"[red]{exc}[/red]")
+                return 2
+            vision_config_id = resolved.config_id
+            vision_provider_model = resolved.provider_model
+            console.print(
+                f"Vision LLM at ingest: [cyan]{vision_provider_model}[/cyan] "
+                f"(id={vision_config_id}, selected_via={resolved.selected_via})."
+            )
+
+        pref_kwargs: dict[str, Any] = {"agent_llm_id": agent_llm_id}
+        if vision_config_id is not None:
+            pref_kwargs["vision_llm_config_id"] = vision_config_id
+
+        await ss_client.set_llm_preferences(search_space_id, **pref_kwargs)
+        prefs = await ss_client.get_llm_preferences(search_space_id)
+        if not _validate_pin(prefs, provider_model):
+            agent = prefs.agent_llm or {}
+            console.print(
+                f"[red]LLM pin validation FAILED.[/red] After PUT, "
+                f"agent_llm.provider={agent.get('provider')!r}, "
+                f"model_name={agent.get('model_name')!r}; expected "
+                f"provider=OPENROUTER, model_name={provider_model!r}."
+            )
+            return 2
+        if vision_config_id is not None and prefs.vision_llm_config_id != vision_config_id:
+            console.print(
+                f"[red]Vision LLM pin validation FAILED.[/red] After PUT, "
+                f"vision_llm_config_id={prefs.vision_llm_config_id!r}; "
+                f"expected {vision_config_id!r}."
+            )
+            return 2
+
+        suite_state = SuiteState(
+            search_space_id=search_space_id,
+            agent_llm_id=agent_llm_id,
+            provider_model=provider_model,
+            created_at=utc_iso_timestamp(),
+            ingestion_maps=existing.ingestion_maps if existing else {},
+            scenario=scenario,
+            vision_llm_config_id=vision_config_id,
+            vision_provider_model=vision_provider_model,
+            native_arm_model=native_arm_model,
+        )
+        set_suite_state(config, suite, suite_state)
+
+    summary_bits = [
+        f"suite={suite!r}",
+        f"scenario={scenario!r}",
+        f"search_space_id={suite_state.search_space_id}",
+        f"agent_llm_id={suite_state.agent_llm_id}",
+        f"provider_model={suite_state.provider_model!r}",
+    ]
+    if suite_state.vision_provider_model:
+        summary_bits.append(f"vision_provider_model={suite_state.vision_provider_model!r}")
+    if suite_state.native_arm_model:
+        summary_bits.append(f"native_arm_model={suite_state.native_arm_model!r}")
+    console.print(f"[green]setup OK[/green] {' '.join(summary_bits)}")
+    return 0
+
+
+def _validate_pin(prefs: LlmPreferences, provider_model: str) -> bool:
+    agent = prefs.agent_llm or {}
+    return (
+        str(agent.get("provider", "")).upper() == "OPENROUTER"
+        and str(agent.get("model_name", "")) == provider_model
+    )
+
+
+async def _cmd_teardown(args: argparse.Namespace) -> int:
+    suite = args.suite
+    config = load_config()
+    state = get_suite_state(config, suite)
+    if state is None:
+        console.print(f"[yellow]No state for suite {suite!r}; nothing to tear down.[/yellow]")
+        return 0
+    try:
+        token = await acquire_token(config)
+    except CredentialError as exc:
+        console.print(f"[red]{exc}[/red]")
+        return 2
+    async with client_with_auth(config, token) as http:
+        ss_client = SearchSpaceClient(http, config.surfsense_api_base)
+        try:
+            await ss_client.delete(state.search_space_id)
+        except httpx.HTTPStatusError as exc:
+            console.print(
+                f"[yellow]DELETE failed (HTTP {exc.response.status_code}); "
+                "clearing state.json anyway.[/yellow]"
+            )
+    clear_suite_state(config, suite)
+    console.print(
+        f"[green]teardown OK[/green] suite={suite!r} "
+        f"(SearchSpace soft-deleted, state.json slot cleared)."
+    )
+    return 0
+
+
+async def _cmd_models_list(args: argparse.Namespace) -> int:
+    config = load_config()
+    try:
+        token = await acquire_token(config)
+    except CredentialError as exc:
+        console.print(f"[red]{exc}[/red]")
+        return 2
+    async with client_with_auth(config, token) as http:
+        entries = await _list_global_llm_configs(http, config.surfsense_api_base)
+    grep = (args.grep or "").lower()
+    provider_filter = (args.provider or "").upper()
+    rows: list[LlmConfigEntry] = []
+    for e in entries:
+        if provider_filter and e.provider != provider_filter:
+            continue
+        if grep and grep not in e.model_name.lower() and grep not in e.name.lower():
+            continue
+        rows.append(e)
+    table = Table(
+        title=f"Global LLM configs ({len(rows)} of {len(entries)})",
+        show_lines=False,
+    )
+    table.add_column("id", justify="right", style="cyan")
+    table.add_column("provider", style="magenta")
+    table.add_column("model_name", style="green")
+    table.add_column("name")
+    for e in sorted(rows, key=lambda x: (x.provider, x.model_name)):
+        table.add_row(str(e.id), e.provider, e.model_name, e.name)
+    console.print(table)
+    return 0
+
+
+def _cmd_suites_list(_args: argparse.Namespace) -> int:
+    _discover_suites()
+    suites = registry.list_suites()
+    if not suites:
+        console.print(
+            "[yellow]No suites registered. Drop a benchmark under "
+            "src/surfsense_evals/suites/<domain>/<benchmark>/.[/yellow]"
+        )
+        return 0
+    table = Table(title=f"Registered suites ({len(suites)})")
+    table.add_column("suite", style="bold")
+    table.add_column("benchmarks", style="green")
+    for suite in suites:
+        names = [b.name for b in registry.list_benchmarks(suite)]
+        table.add_row(suite, ", ".join(names) or "<none>")
+    console.print(table)
+    return 0
+
+
+def _cmd_benchmarks_list(args: argparse.Namespace) -> int:
+    _discover_suites()
+    benchmarks = registry.list_benchmarks(args.suite)
+    if not benchmarks:
+        console.print("[yellow]No benchmarks registered.[/yellow]")
+        return 0
+    table = Table(title=f"Benchmarks ({len(benchmarks)})")
+    table.add_column("suite", style="bold")
+    table.add_column("name", style="cyan")
+    table.add_column("headline", justify="center")
+    table.add_column("description")
+    for b in benchmarks:
+        table.add_row(
+            b.suite,
+            b.name,
+            "yes" if b.headline else "no",
+            getattr(b, "description", ""),
+        )
+    console.print(table)
+    return 0
+
+
+async def _cmd_ingest(args: argparse.Namespace) -> int:
+    benchmark = registry.get(args.suite, args.benchmark)
+    config = load_config()
+    state = get_suite_state(config, args.suite)
+    if state is None:
+        console.print(
+            f"[red]No setup for suite {args.suite!r}. Run "
+            f"`python -m surfsense_evals setup --suite {args.suite} "
+            f"--provider-model <slug>` first.[/red]"
+        )
+        return 2
+    try:
+        token = await acquire_token(config)
+    except CredentialError as exc:
+        console.print(f"[red]{exc}[/red]")
+        return 2
+
+    # Forward parsed CLI flags into ingest() so a benchmark can honour
+    # its own flags (e.g. MIRAGE's --skip-snippet-filter / --corpus).
+    extra_kwargs = {
+        k: v
+        for k, v in vars(args).items()
+        if k not in {"_func", "_async", "command", "subcommand", "suite", "benchmark", "log_level"}
+    }
+    async with client_with_auth(config, token) as http:
+        ctx = registry.RunContext(
+            suite=args.suite,
+            benchmark=args.benchmark,
+            config=config,
+            suite_state=state,
+            http=http,
+        )
+        await benchmark.ingest(ctx, **extra_kwargs)
+    console.print(f"[green]ingest OK[/green] {args.suite}/{args.benchmark}")
+    return 0
+
+
+async def _cmd_run(args: argparse.Namespace) -> int:
+    benchmark = registry.get(args.suite, args.benchmark)
+    config = load_config()
+    state = get_suite_state(config, args.suite)
+    if state is None:
+        console.print(
+            f"[red]No setup for suite {args.suite!r}. Run "
+            f"`python -m surfsense_evals setup --suite {args.suite} "
+            f"--provider-model <slug>` first.[/red]"
+        )
+        return 2
+    try:
+        token = await acquire_token(config)
+    except CredentialError as exc:
+        console.print(f"[red]{exc}[/red]")
+        return 2
+
+    extra_kwargs = {
+        k: v
+        for k, v in vars(args).items()
+        if k not in {"_func", "_async", "command", "subcommand", "suite", "benchmark", "log_level"}
+    }
+    async with client_with_auth(config, token) as http:
+        ctx = registry.RunContext(
+            suite=args.suite,
+            benchmark=args.benchmark,
+            config=config,
+            suite_state=state,
+            http=http,
+        )
+        artifact = await benchmark.run(ctx, **extra_kwargs)
+
+    console.print(
+        f"[green]run OK[/green] {args.suite}/{args.benchmark} → "
+        f"{artifact.raw_path}"
+    )
+    return 0
+
+
+async def _cmd_report(args: argparse.Namespace) -> int:
+    from .report import write_report
+
+    benchmark_filter = args.benchmark
+    config = load_config()
+    state = get_suite_state(config, args.suite)
+    if state is None:
+        console.print(f"[red]No setup for suite {args.suite!r}.[/red]")
+        return 2
+    benchmarks = registry.list_benchmarks(args.suite)
+    if benchmark_filter:
+        benchmarks = [b for b in benchmarks if b.name == benchmark_filter]
+        if not benchmarks:
+            console.print(
+                f"[red]No registered benchmark named {benchmark_filter!r} in suite {args.suite!r}.[/red]"
+            )
+            return 2
+
+    artifacts = _collect_artifacts(config, args.suite, [b.name for b in benchmarks])
+    if not artifacts:
+        console.print(
+            "[yellow]No run artifacts found under "
+            f"{config.suite_runs_dir(args.suite)}. Run a benchmark first.[/yellow]"
+        )
+        return 1
+
+    grouped: dict[str, list[registry.RunArtifact]] = {}
+    for art in artifacts:
+        grouped.setdefault(art.benchmark, []).append(art)
+    sections: list[registry.ReportSection] = []
+    for benchmark in benchmarks:
+        if benchmark.name not in grouped:
+            continue
+        sections.append(benchmark.report_section(grouped[benchmark.name]))
+
+    summary_path = write_report(
+        config=config,
+        suite=args.suite,
+        sections=sections,
+        run_timestamp=utc_iso_timestamp(),
+    )
+    console.print(f"[green]report OK[/green] → {summary_path}")
+    return 0
+
+
+def _collect_artifacts(
+    config: Config, suite: str, benchmark_names: list[str]
+) -> list[registry.RunArtifact]:
+    """Walk ``data/<suite>/runs/*/<benchmark>/`` for the latest artifacts.
+
+    Reads any ``run_artifact.json`` written by a benchmark runner. The
+    runner is responsible for writing this manifest alongside its raw
+    JSONL so the report writer doesn't have to know benchmark-specific
+    metric shapes.
+    """
+
+    runs_dir = config.suite_runs_dir(suite)
+    if not runs_dir.exists():
+        return []
+    artifacts: list[registry.RunArtifact] = []
+    by_bench: dict[str, registry.RunArtifact] = {}
+    for ts_dir in sorted(runs_dir.iterdir()):
+        if not ts_dir.is_dir():
+            continue
+        for bench_name in benchmark_names:
+            bench_dir = ts_dir / bench_name
+            manifest = bench_dir / "run_artifact.json"
+            if not manifest.exists():
+                continue
+            try:
+                with manifest.open("r", encoding="utf-8") as fh:
+                    payload = json.load(fh)
+            except (OSError, json.JSONDecodeError):
+                continue
+            artifact = registry.RunArtifact(
+                suite=suite,
+                benchmark=bench_name,
+                run_timestamp=ts_dir.name,
+                raw_path=bench_dir / payload.get("raw_path", "raw.jsonl"),
+                metrics=payload.get("metrics", {}),
+                extra=payload.get("extra", {}),
+            )
+            # Latest run wins per benchmark.
+            by_bench[bench_name] = artifact
+    artifacts = list(by_bench.values())
+    return artifacts
+
+
+# ---------------------------------------------------------------------------
+# Argparse wiring
+# ---------------------------------------------------------------------------
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        prog="surfsense-evals",
+        description="SurfSense evaluation harness — domain-agnostic core + pluggable suites.",
+    )
+    parser.add_argument(
+        "--log-level", default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"]
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    p_setup = sub.add_parser("setup", help="Create per-suite SearchSpace + pin LLM.")
+    p_setup.add_argument("--suite", required=True)
+    p_setup.add_argument(
+        "--provider-model",
+        required=True,
+        help=(
+            "OpenRouter slug for the SurfSense answer LLM (and the native arm "
+            "too unless --native-arm-model is set), e.g. "
+            "'anthropic/claude-sonnet-4.5'."
+        ),
+    )
+    p_setup.add_argument(
+        "--agent-llm-id",
+        type=int,
+        default=None,
+        help="Optional override for BYOK NewLLMConfig rows.",
+    )
+    p_setup.add_argument(
+        "--scenario",
+        choices=SCENARIOS,
+        default=DEFAULT_SCENARIO,
+        help=(
+            "head-to-head (default): both arms answer with --provider-model; "
+            "symmetric-cheap: both arms use the same cheap text-only slug, "
+            "SurfSense pre-extracted images at ingest with a vision LLM; "
+            "cost-arbitrage: native arm uses --native-arm-model (vision), "
+            "SurfSense uses --provider-model (cheap, text-only) over chunks "
+            "the vision LLM already extracted at ingest."
+        ),
+    )
+    p_setup.add_argument(
+        "--vision-llm",
+        default=None,
+        metavar="SLUG",
+        help=(
+            "OpenRouter slug for the vision LLM SurfSense uses at ingest "
+            "when --use-vision-llm is on. If omitted in symmetric-cheap / "
+            "cost-arbitrage, the strongest registered vision config is "
+            "auto-picked (priority: claude-sonnet-4.5 > claude-opus-4.7 > "
+            "gpt-5 > gemini-2.5-pro)."
+        ),
+    )
+    p_setup.add_argument(
+        "--native-arm-model",
+        default=None,
+        metavar="SLUG",
+        help=(
+            "Required for --scenario cost-arbitrage. OpenRouter slug used "
+            "by the native_pdf arm only; SurfSense answers with "
+            "--provider-model. Ignored for head-to-head / symmetric-cheap."
+        ),
+    )
+    p_setup.add_argument(
+        "--no-vision-llm-setup",
+        action="store_true",
+        help=(
+            "Skip attaching a vision LLM config to the SearchSpace even if "
+            "the scenario would normally require one. Use when you want to "
+            "keep whatever is already attached (e.g. a per-user config)."
+        ),
+    )
+    p_setup.set_defaults(_func=_cmd_setup, _async=True)
+
+    p_teardown = sub.add_parser("teardown", help="Soft-delete the suite SearchSpace + clear state slot.")
+    p_teardown.add_argument("--suite", required=True)
+    p_teardown.set_defaults(_func=_cmd_teardown, _async=True)
+
+    p_models = sub.add_parser("models", help="LLM-config discovery helpers.")
+    models_sub = p_models.add_subparsers(dest="subcommand", required=True)
+    p_models_list = models_sub.add_parser("list", help="List global LLM configs.")
+    p_models_list.add_argument("--provider", default=None, help="Filter by provider, e.g. openrouter")
+    p_models_list.add_argument("--grep", default=None, help="Substring filter on name / model_name.")
+    p_models_list.set_defaults(_func=_cmd_models_list, _async=True)
+
+    p_suites = sub.add_parser("suites", help="List registered suites.")
+    suites_sub = p_suites.add_subparsers(dest="subcommand", required=True)
+    p_suites_list = suites_sub.add_parser("list", help="List suites.")
+    p_suites_list.set_defaults(_func=_cmd_suites_list, _async=False)
+
+    p_benchmarks = sub.add_parser("benchmarks", help="List registered benchmarks.")
+    bench_sub = p_benchmarks.add_subparsers(dest="subcommand", required=True)
+    p_bench_list = bench_sub.add_parser("list", help="List benchmarks.")
+    p_bench_list.add_argument("--suite", default=None)
+    p_bench_list.set_defaults(_func=_cmd_benchmarks_list, _async=False)
+
+    # Dynamic ingest / run subcommands need the registry populated, so
+    # discover up-front (cheap on import — modules just register).
+    _discover_suites()
+
+    p_ingest = sub.add_parser("ingest", help="Ingest a benchmark's corpus.")
+    ingest_sub = p_ingest.add_subparsers(dest="suite", required=True)
+    for suite in registry.list_suites():
+        suite_parser = ingest_sub.add_parser(suite, help=f"Ingest a {suite} benchmark.")
+        suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True)
+        for benchmark in registry.list_benchmarks(suite):
+            bp = suite_bench.add_parser(benchmark.name, help=getattr(benchmark, "description", benchmark.name))
+            if hasattr(benchmark, "add_run_args"):
+                benchmark.add_run_args(bp)
+            bp.set_defaults(_func=_cmd_ingest, _async=True)
+
+    p_run = sub.add_parser("run", help="Run a benchmark.")
+    run_sub = p_run.add_subparsers(dest="suite", required=True)
+    for suite in registry.list_suites():
+        suite_parser = run_sub.add_parser(suite, help=f"Run a {suite} benchmark.")
+        suite_bench = suite_parser.add_subparsers(dest="benchmark", required=True)
+        for benchmark in registry.list_benchmarks(suite):
+            bp = suite_bench.add_parser(benchmark.name, help=getattr(benchmark, "description", benchmark.name))
+            if hasattr(benchmark, "add_run_args"):
+                benchmark.add_run_args(bp)
+            bp.set_defaults(_func=_cmd_run, _async=True)
+
+    p_report = sub.add_parser("report", help="Aggregate latest run artifacts into a summary.")
+    p_report.add_argument("--suite", required=True)
+    p_report.add_argument("--benchmark", default=None, help="Optional: report only this benchmark.")
+    p_report.set_defaults(_func=_cmd_report, _async=True)
+
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = _build_parser()
+    args = parser.parse_args(argv)
+    logging.basicConfig(
+        level=getattr(logging, args.log_level),
+        format="%(asctime)s %(levelname)s %(name)s %(message)s",
+    )
+    func = getattr(args, "_func", None)
+    if func is None:
+        parser.print_help()
+        return 2
+    is_async = getattr(args, "_async", False)
+    try:
+        if is_async:
+            return asyncio.run(func(args))
+        return func(args)
+    except KeyboardInterrupt:
+        console.print("[yellow]Interrupted.[/yellow]")
+        return 130
+    except Exception as exc:  # noqa: BLE001
+        logger.exception("CLI command failed")
+        console.print(f"[red]Command failed: {exc}[/red]")
+        return 1
+
+
+if __name__ == "__main__":  # pragma: no cover
+    sys.exit(main())
diff --git a/surfsense_evals/src/surfsense_evals/core/clients/__init__.py b/surfsense_evals/src/surfsense_evals/core/clients/__init__.py
new file mode 100644
index 000000000..37246c221
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/clients/__init__.py
@@ -0,0 +1,14 @@
+"""HTTP clients for the SurfSense API. All share one ``httpx.AsyncClient``."""
+
+from __future__ import annotations
+
+from .documents import DocumentsClient
+from .new_chat import NewChatClient, StreamedAnswer
+from .search_space import SearchSpaceClient
+
+__all__ = [
+    "DocumentsClient",
+    "NewChatClient",
+    "SearchSpaceClient",
+    "StreamedAnswer",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/clients/documents.py b/surfsense_evals/src/surfsense_evals/core/clients/documents.py
new file mode 100644
index 000000000..02bcf74da
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/clients/documents.py
@@ -0,0 +1,277 @@
+"""Client for ``/api/v1/documents/{fileupload,status,{id}/chunks}``.
+
+Verified against:
+
+* ``surfsense_backend/app/routes/documents_routes.py:122-292`` (POST fileupload)
+* ``surfsense_backend/app/routes/documents_routes.py:806-871`` (GET status batch)
+* ``surfsense_backend/app/routes/documents_routes.py:1062-1128`` (GET {id}/chunks paginated)
+
+Document processing is asynchronous:
+* ``POST /documents/fileupload`` returns immediately with
+  ``document_ids`` in ``pending``;
+* a Celery worker moves each through ``processing → ready/failed``;
+* the harness polls ``GET /documents/status?document_ids=...`` until
+  every doc is ``ready`` (otherwise the retriever sees an empty corpus
+  and accuracy numbers are meaningless).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import mimetypes
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FileUploadResult:
+    """Mirrors the JSON returned by ``POST /documents/fileupload``."""
+
+    document_ids: list[int]
+    duplicate_document_ids: list[int]
+    total_files: int
+    pending_files: int
+    skipped_duplicates: int
+    message: str = ""
+
+    @classmethod
+    def from_payload(cls, payload: dict[str, Any]) -> FileUploadResult:
+        return cls(
+            document_ids=[int(x) for x in payload.get("document_ids", [])],
+            duplicate_document_ids=[int(x) for x in payload.get("duplicate_document_ids", [])],
+            total_files=int(payload.get("total_files", 0)),
+            pending_files=int(payload.get("pending_files", 0)),
+            skipped_duplicates=int(payload.get("skipped_duplicates", 0)),
+            message=str(payload.get("message", "")),
+        )
+
+
+@dataclass
+class DocumentStatus:
+    document_id: int
+    title: str
+    document_type: str
+    state: str
+    reason: str | None = None
+
+    @property
+    def is_ready(self) -> bool:
+        return self.state == "ready"
+
+    @property
+    def is_failed(self) -> bool:
+        return self.state == "failed"
+
+
+@dataclass
+class ChunkRow:
+    id: int
+    document_id: int
+    content: str = ""
+    raw: dict[str, Any] = field(default_factory=dict)
+
+
+class DocumentProcessingFailed(RuntimeError):
+    """Raised when a polled document lands in ``failed``."""
+
+    def __init__(self, statuses: Sequence[DocumentStatus]) -> None:
+        details = ", ".join(
+            f"id={s.document_id} ({s.title!r}): {s.reason or 'unknown'}"
+            for s in statuses
+        )
+        super().__init__(f"Document(s) failed to process: {details}")
+        self.statuses = list(statuses)
+
+
+class DocumentProcessingTimeout(RuntimeError):
+    """Raised when polling exceeds the per-doc timeout budget."""
+
+
+class DocumentsClient:
+    """Document upload + status polling + chunk listing."""
+
+    def __init__(self, http: httpx.AsyncClient, base_url: str) -> None:
+        self._http = http
+        self._base = base_url.rstrip("/")
+
+    # ------------------------------------------------------------------
+    # upload
+    # ------------------------------------------------------------------
+
+    async def upload(
+        self,
+        files: Iterable[Path],
+        *,
+        search_space_id: int,
+        should_summarize: bool = False,
+        use_vision_llm: bool = False,
+        processing_mode: str = "basic",
+    ) -> FileUploadResult:
+        """Upload files to ``/api/v1/documents/fileupload``.
+
+        ``files`` is materialised to a list because we may need to
+        re-read on retry. Caller is responsible for ensuring each path
+        exists and respects the per-file size cap (50 MB backend default).
+        """
+
+        materialised = [Path(p) for p in files]
+        if not materialised:
+            return FileUploadResult(
+                document_ids=[],
+                duplicate_document_ids=[],
+                total_files=0,
+                pending_files=0,
+                skipped_duplicates=0,
+                message="No files supplied",
+            )
+
+        opened: list[tuple[str, Any]] = []
+        try:
+            for path in materialised:
+                # ``open`` directly — httpx wraps it in MultipartStream.
+                file_obj = path.open("rb")
+                mime, _ = mimetypes.guess_type(path.name)
+                opened.append(
+                    (
+                        "files",
+                        (path.name, file_obj, mime or "application/octet-stream"),
+                    )
+                )
+
+            response = await self._http.post(
+                f"{self._base}/api/v1/documents/fileupload",
+                data={
+                    "search_space_id": str(search_space_id),
+                    "should_summarize": "true" if should_summarize else "false",
+                    "use_vision_llm": "true" if use_vision_llm else "false",
+                    "processing_mode": processing_mode,
+                },
+                files=opened,
+                # Multipart uploads can be slow for big PDFs; bump per-call.
+                timeout=httpx.Timeout(120.0, connect=10.0),
+            )
+        finally:
+            for _, (_, file_obj, _) in opened:
+                try:
+                    file_obj.close()
+                except Exception:  # noqa: BLE001
+                    pass
+
+        response.raise_for_status()
+        return FileUploadResult.from_payload(response.json())
+
+    # ------------------------------------------------------------------
+    # status polling
+    # ------------------------------------------------------------------
+
+    async def get_status(
+        self, *, search_space_id: int, document_ids: Sequence[int]
+    ) -> list[DocumentStatus]:
+        if not document_ids:
+            return []
+        response = await self._http.get(
+            f"{self._base}/api/v1/documents/status",
+            params={
+                "search_space_id": search_space_id,
+                "document_ids": ",".join(str(d) for d in document_ids),
+            },
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        payload = response.json()
+        return [
+            DocumentStatus(
+                document_id=int(item["id"]),
+                title=str(item.get("title", "")),
+                document_type=str(item.get("document_type", "")),
+                state=str((item.get("status") or {}).get("state", "ready")),
+                reason=(item.get("status") or {}).get("reason"),
+            )
+            for item in payload.get("items", [])
+        ]
+
+    async def wait_until_ready(
+        self,
+        *,
+        search_space_id: int,
+        document_ids: Sequence[int],
+        timeout_s: float = 300.0,
+        initial_poll_s: float = 1.0,
+        max_poll_s: float = 10.0,
+    ) -> list[DocumentStatus]:
+        """Poll ``GET /documents/status`` until every doc is ``ready``.
+
+        Exponential backoff from ``initial_poll_s`` up to ``max_poll_s``.
+        Raises ``DocumentProcessingFailed`` if any doc lands in
+        ``failed`` (with the offending document ids), or
+        ``DocumentProcessingTimeout`` if the budget is exhausted.
+        """
+
+        if not document_ids:
+            return []
+        deadline = asyncio.get_event_loop().time() + timeout_s
+        poll = initial_poll_s
+        while True:
+            statuses = await self.get_status(
+                search_space_id=search_space_id, document_ids=document_ids
+            )
+            failed = [s for s in statuses if s.is_failed]
+            if failed:
+                raise DocumentProcessingFailed(failed)
+            ready = [s for s in statuses if s.is_ready]
+            if len(ready) == len(document_ids):
+                return statuses
+            now = asyncio.get_event_loop().time()
+            if now >= deadline:
+                pending = [s for s in statuses if not s.is_ready and not s.is_failed]
+                pending_ids = [s.document_id for s in pending]
+                raise DocumentProcessingTimeout(
+                    f"Timed out after {timeout_s:.0f}s waiting for documents "
+                    f"(still pending/processing: {pending_ids})"
+                )
+            await asyncio.sleep(min(poll, max(0.1, deadline - now)))
+            poll = min(poll * 1.5, max_poll_s)
+
+    # ------------------------------------------------------------------
+    # chunks (chunk_id -> document_id map)
+    # ------------------------------------------------------------------
+
+    async def list_chunks(
+        self, document_id: int, *, page_size: int = 100
+    ) -> list[ChunkRow]:
+        """Walk ``GET /documents/{id}/chunks`` until ``has_more=False``.
+
+        Used by ingestion to materialise the ``chunk_id -> document_id``
+        map needed for retrieval scoring (CUREv1).
+        """
+
+        rows: list[ChunkRow] = []
+        page = 0
+        while True:
+            response = await self._http.get(
+                f"{self._base}/api/v1/documents/{document_id}/chunks",
+                params={"page": page, "page_size": page_size},
+                headers={"Accept": "application/json"},
+            )
+            response.raise_for_status()
+            payload = response.json()
+            for item in payload.get("items", []):
+                rows.append(
+                    ChunkRow(
+                        id=int(item["id"]),
+                        document_id=document_id,
+                        content=str(item.get("content", "")),
+                        raw=item,
+                    )
+                )
+            if not payload.get("has_more"):
+                break
+            page += 1
+        return rows
diff --git a/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py b/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py
new file mode 100644
index 000000000..a4c23d010
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/clients/new_chat.py
@@ -0,0 +1,280 @@
+"""Client for ``/api/v1/threads`` and ``/api/v1/new_chat`` (SSE).
+
+Verified against:
+
+* ``surfsense_backend/app/routes/new_chat_routes.py:793-848`` (POST /threads)
+* ``surfsense_backend/app/routes/new_chat_routes.py:1073-1142`` (DELETE /threads/{id})
+* ``surfsense_backend/app/routes/new_chat_routes.py:1689-1800`` (POST /new_chat SSE)
+* ``surfsense_backend/app/routes/new_chat_routes.py:191-220`` (THREAD_BUSY / TURN_CANCELLING 409)
+* ``surfsense_backend/app/services/streaming/envelope/sse.py`` (wire framing)
+* ``surfsense_backend/app/services/streaming/events/text.py`` (text-delta events)
+* ``surfsense_backend/app/schemas/new_chat.py:234-288`` (NewChatRequest body)
+
+The wire format is "Vercel AI SDK"-flavoured SSE with one event per
+``data: <json>\n\n`` block (or the literal ``data: [DONE]\n\n``
+terminator). Text deltas arrive as ``{"type":"text-delta","id":...,"delta":...}``
+events; we accumulate them per ``id`` and emit the final concatenated
+text plus parsed citations.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+from collections.abc import AsyncIterator, Sequence
+from dataclasses import dataclass, field
+from typing import Any
+
+import httpx
+
+from ..parse import iter_sse_events, parse_citations
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class StreamedAnswer:
+    """Result of a single ``/new_chat`` turn."""
+
+    text: str
+    raw_events: list[dict[str, Any]] = field(default_factory=list)
+    latency_ms: int = 0
+    user_message_id: str | None = None
+    assistant_message_id: str | None = None
+    finished_normally: bool = False
+
+    @property
+    def citations(self) -> list[dict[str, Any]]:
+        """Parsed citation tokens (lazy; small enough to recompute)."""
+
+        return [token.to_dict() for token in parse_citations(self.text)]
+
+
+class ThreadBusyError(RuntimeError):
+    """Raised after exhausting retries on a 409 ``THREAD_BUSY`` / ``TURN_CANCELLING``."""
+
+    def __init__(self, error_code: str, message: str) -> None:
+        super().__init__(f"{error_code}: {message}")
+        self.error_code = error_code
+
+
+class NewChatClient:
+    """Thread create / delete / SSE ask."""
+
+    def __init__(self, http: httpx.AsyncClient, base_url: str) -> None:
+        self._http = http
+        self._base = base_url.rstrip("/")
+
+    # ------------------------------------------------------------------
+    # threads
+    # ------------------------------------------------------------------
+
+    async def create_thread(
+        self,
+        *,
+        search_space_id: int,
+        title: str = "eval",
+        archived: bool = False,
+        visibility: str = "PRIVATE",
+    ) -> int:
+        response = await self._http.post(
+            f"{self._base}/api/v1/threads",
+            json={
+                "search_space_id": search_space_id,
+                "title": title,
+                "archived": archived,
+                "visibility": visibility,
+            },
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        payload = response.json()
+        return int(payload["id"])
+
+    async def delete_thread(self, thread_id: int) -> None:
+        response = await self._http.delete(
+            f"{self._base}/api/v1/threads/{thread_id}",
+            headers={"Accept": "application/json"},
+        )
+        if response.status_code == 404:
+            return  # idempotent
+        response.raise_for_status()
+
+    # ------------------------------------------------------------------
+    # /new_chat SSE
+    # ------------------------------------------------------------------
+
+    async def ask(
+        self,
+        *,
+        thread_id: int,
+        search_space_id: int,
+        user_query: str,
+        mentioned_document_ids: Sequence[int] | None = None,
+        disabled_tools: Sequence[str] | None = None,
+        max_busy_retries: int = 4,
+        timeout_s: float = 600.0,
+    ) -> StreamedAnswer:
+        """Stream a single turn and return the accumulated answer.
+
+        Honours backend ``THREAD_BUSY`` / ``TURN_CANCELLING`` 409
+        responses by sleeping for the ``Retry-After`` header (or the
+        ``retry-after-ms`` header if present) and replaying. Bounded
+        by ``max_busy_retries`` so a stuck thread never blocks the
+        whole run.
+        """
+
+        body: dict[str, Any] = {
+            "chat_id": thread_id,
+            "search_space_id": search_space_id,
+            "user_query": user_query,
+        }
+        if mentioned_document_ids:
+            body["mentioned_document_ids"] = list(mentioned_document_ids)
+        if disabled_tools:
+            body["disabled_tools"] = list(disabled_tools)
+
+        attempt = 0
+        while True:
+            try:
+                return await self._stream_once(body=body, timeout_s=timeout_s)
+            except ThreadBusyError as exc:
+                attempt += 1
+                if attempt > max_busy_retries:
+                    raise
+                # Cap wait at 30s; backend retry hint is exponential anyway.
+                wait = min(30.0, 0.5 * (2 ** attempt))
+                logger.info(
+                    "thread_id=%s busy (%s); retry %d/%d after %.1fs",
+                    thread_id,
+                    exc.error_code,
+                    attempt,
+                    max_busy_retries,
+                    wait,
+                )
+                await asyncio.sleep(wait)
+
+    async def _stream_once(
+        self,
+        *,
+        body: dict[str, Any],
+        timeout_s: float,
+    ) -> StreamedAnswer:
+        # Per-call timeout — the connect should be quick, the read needs
+        # to outlive the longest LLM completion.
+        timeout = httpx.Timeout(timeout_s, connect=10.0)
+        started = time.monotonic()
+        async with self._http.stream(
+            "POST",
+            f"{self._base}/api/v1/new_chat",
+            json=body,
+            headers={"Accept": "text/event-stream"},
+            timeout=timeout,
+        ) as response:
+            if response.status_code == 409:
+                detail = await self._extract_busy_detail(response)
+                raise ThreadBusyError(
+                    error_code=detail.get("errorCode", "THREAD_BUSY"),
+                    message=detail.get("message", "Thread is busy"),
+                )
+            response.raise_for_status()
+            answer = await self._consume_sse(response)
+        answer.latency_ms = int((time.monotonic() - started) * 1000)
+        return answer
+
+    @staticmethod
+    async def _extract_busy_detail(response: httpx.Response) -> dict[str, Any]:
+        try:
+            payload = json.loads(await response.aread())
+        except (json.JSONDecodeError, ValueError):
+            return {"errorCode": "THREAD_BUSY", "message": response.text}
+        if isinstance(payload, dict) and isinstance(payload.get("detail"), dict):
+            return payload["detail"]
+        return payload if isinstance(payload, dict) else {}
+
+    @staticmethod
+    async def _consume_sse(response: httpx.Response) -> StreamedAnswer:
+        """Walk SSE events, accumulate text-delta payloads.
+
+        Backend events of interest:
+
+        * ``{"type": "text-start", "id": ...}``
+        * ``{"type": "text-delta", "id": ..., "delta": ...}``
+        * ``{"type": "text-end", "id": ...}``
+        * ``{"type": "start", "messageId": ...}``  (top-level message id)
+        * ``{"type": "finish"}``
+        * literal ``[DONE]`` sentinel
+
+        Multiple ``text-start`` blocks can interleave — each gets its
+        own ``id`` and we concatenate them in arrival order. That
+        mirrors the AI SDK client behaviour: one continuous assistant
+        message visible to the user.
+        """
+
+        ordered_text_ids: list[str] = []
+        text_buffers: dict[str, list[str]] = {}
+        raw_events: list[dict[str, Any]] = []
+        user_message_id: str | None = None
+        assistant_message_id: str | None = None
+        finished = False
+
+        async for event in iter_sse_events(_aiter_lines(response)):
+            data = event.data
+            if data == "[DONE]":
+                finished = True
+                continue
+            try:
+                payload = json.loads(data)
+            except (json.JSONDecodeError, ValueError):
+                logger.debug("Skipping non-JSON SSE payload: %r", data[:120])
+                continue
+            if not isinstance(payload, dict):
+                continue
+            raw_events.append(payload)
+            ev_type = payload.get("type")
+            if ev_type == "text-delta":
+                tid = str(payload.get("id", ""))
+                delta = payload.get("delta", "")
+                if not isinstance(delta, str):
+                    continue
+                if tid not in text_buffers:
+                    text_buffers[tid] = []
+                    ordered_text_ids.append(tid)
+                text_buffers[tid].append(delta)
+            elif ev_type == "text-start":
+                tid = str(payload.get("id", ""))
+                if tid and tid not in text_buffers:
+                    text_buffers[tid] = []
+                    ordered_text_ids.append(tid)
+            elif ev_type == "start":
+                msg_id = payload.get("messageId")
+                if isinstance(msg_id, str):
+                    user_message_id = user_message_id or msg_id
+            elif ev_type == "data-user-message-id":
+                msg_id = (payload.get("data") or {}).get("id") or payload.get("id")
+                if isinstance(msg_id, str):
+                    user_message_id = msg_id
+            elif ev_type == "data-assistant-message-id":
+                msg_id = (payload.get("data") or {}).get("id") or payload.get("id")
+                if isinstance(msg_id, str):
+                    assistant_message_id = msg_id
+            elif ev_type == "finish":
+                finished = True
+
+        text = "".join("".join(text_buffers.get(tid, [])) for tid in ordered_text_ids)
+        return StreamedAnswer(
+            text=text,
+            raw_events=raw_events,
+            user_message_id=user_message_id,
+            assistant_message_id=assistant_message_id,
+            finished_normally=finished,
+        )
+
+
+async def _aiter_lines(response: httpx.Response) -> AsyncIterator[str]:
+    """Adapter so the parser can consume any line iterator (mockable in tests)."""
+
+    async for line in response.aiter_lines():
+        yield line
diff --git a/surfsense_evals/src/surfsense_evals/core/clients/search_space.py b/surfsense_evals/src/surfsense_evals/core/clients/search_space.py
new file mode 100644
index 000000000..37fa69f80
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/clients/search_space.py
@@ -0,0 +1,207 @@
+"""Client for ``/api/v1/searchspaces`` and ``/api/v1/search-spaces/{id}/llm-preferences``.
+
+Verified against:
+
+* ``surfsense_backend/app/routes/search_spaces_routes.py:116`` (POST create)
+* ``surfsense_backend/app/routes/search_spaces_routes.py:234`` (GET by id)
+* ``surfsense_backend/app/routes/search_spaces_routes.py:422`` (DELETE soft-delete)
+* ``surfsense_backend/app/routes/search_spaces_routes.py:698-849`` (GET/PUT llm-preferences)
+* ``surfsense_backend/app/schemas/search_space.py:14`` (SearchSpaceCreate body)
+* ``surfsense_backend/app/routes/vision_llm_routes.py:60`` (GET global vision configs)
+
+Note the inconsistent pluralisation in the backend: ``/searchspaces``
+(no hyphen) for CRUD, but ``/search-spaces`` (hyphenated) for the
+``llm-preferences`` sub-resource. Both are mirrored verbatim here.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import httpx
+
+
+@dataclass
+class SearchSpaceRow:
+    """Subset of the SearchSpace row we care about."""
+
+    id: int
+    name: str
+    description: str | None
+    user_id: str
+    citations_enabled: bool
+    qna_custom_instructions: str | None
+
+    @classmethod
+    def from_payload(cls, payload: dict[str, Any]) -> SearchSpaceRow:
+        return cls(
+            id=int(payload["id"]),
+            name=str(payload["name"]),
+            description=payload.get("description"),
+            user_id=str(payload.get("user_id", "")),
+            citations_enabled=bool(payload.get("citations_enabled", True)),
+            qna_custom_instructions=payload.get("qna_custom_instructions"),
+        )
+
+
+@dataclass
+class VisionLlmConfigEntry:
+    """Subset of one ``GET /global-vision-llm-configs`` row.
+
+    The backend returns negative ids for global / OpenRouter-derived
+    vision configs and positive ids for per-user BYOK rows. Either is
+    accepted by ``set_llm_preferences(vision_llm_config_id=...)``.
+    """
+
+    id: int
+    name: str
+    provider: str
+    model_name: str
+    is_auto_mode: bool
+    raw: dict[str, Any]
+
+    @classmethod
+    def from_payload(cls, payload: dict[str, Any]) -> VisionLlmConfigEntry:
+        return cls(
+            id=int(payload.get("id", 0)),
+            name=str(payload.get("name", "")),
+            provider=str(payload.get("provider", "")).upper(),
+            model_name=str(payload.get("model_name", "")),
+            is_auto_mode=bool(payload.get("is_auto_mode", False)),
+            raw=payload,
+        )
+
+
+@dataclass
+class LlmPreferences:
+    """Resolved LLM preferences with the embedded full config row.
+
+    Mirrors ``LLMPreferencesRead`` from the backend so the lifecycle
+    command can introspect ``provider`` / ``model_name`` to validate the
+    OpenRouter pin.
+    """
+
+    agent_llm_id: int | None
+    document_summary_llm_id: int | None
+    image_generation_config_id: int | None
+    vision_llm_config_id: int | None
+    agent_llm: dict[str, Any] | None
+    raw: dict[str, Any]
+
+    @classmethod
+    def from_payload(cls, payload: dict[str, Any]) -> LlmPreferences:
+        return cls(
+            agent_llm_id=payload.get("agent_llm_id"),
+            document_summary_llm_id=payload.get("document_summary_llm_id"),
+            image_generation_config_id=payload.get("image_generation_config_id"),
+            vision_llm_config_id=payload.get("vision_llm_config_id"),
+            agent_llm=payload.get("agent_llm"),
+            raw=payload,
+        )
+
+
+class SearchSpaceClient:
+    """Thin wrapper around the SearchSpace + LLM preferences endpoints."""
+
+    def __init__(self, http: httpx.AsyncClient, base_url: str) -> None:
+        self._http = http
+        self._base = base_url.rstrip("/")
+
+    async def create(self, name: str, *, description: str | None = None) -> SearchSpaceRow:
+        body: dict[str, Any] = {"name": name}
+        if description is not None:
+            body["description"] = description
+        # citations_enabled defaults to True backend-side; keep that default.
+        response = await self._http.post(
+            f"{self._base}/api/v1/searchspaces",
+            json=body,
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        return SearchSpaceRow.from_payload(response.json())
+
+    async def get(self, search_space_id: int) -> SearchSpaceRow:
+        response = await self._http.get(
+            f"{self._base}/api/v1/searchspaces/{search_space_id}",
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        return SearchSpaceRow.from_payload(response.json())
+
+    async def delete(self, search_space_id: int) -> None:
+        """Soft-delete: backend prefixes name with ``[DELETING]`` and dispatches a Celery cascade."""
+
+        response = await self._http.delete(
+            f"{self._base}/api/v1/searchspaces/{search_space_id}",
+            headers={"Accept": "application/json"},
+        )
+        # 404 means it's already gone — treat as success (idempotent teardown).
+        if response.status_code == 404:
+            return
+        response.raise_for_status()
+
+    async def get_llm_preferences(self, search_space_id: int) -> LlmPreferences:
+        response = await self._http.get(
+            f"{self._base}/api/v1/search-spaces/{search_space_id}/llm-preferences",
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        return LlmPreferences.from_payload(response.json())
+
+    async def set_llm_preferences(
+        self,
+        search_space_id: int,
+        *,
+        agent_llm_id: int | None = None,
+        document_summary_llm_id: int | None = None,
+        image_generation_config_id: int | None = None,
+        vision_llm_config_id: int | None = None,
+    ) -> LlmPreferences:
+        """PUT a partial update to ``/search-spaces/{id}/llm-preferences``.
+
+        Backend uses ``model_dump(exclude_unset=True)`` so omitted fields
+        are left unchanged.
+        """
+
+        body: dict[str, Any] = {}
+        if agent_llm_id is not None:
+            body["agent_llm_id"] = agent_llm_id
+        if document_summary_llm_id is not None:
+            body["document_summary_llm_id"] = document_summary_llm_id
+        if image_generation_config_id is not None:
+            body["image_generation_config_id"] = image_generation_config_id
+        if vision_llm_config_id is not None:
+            body["vision_llm_config_id"] = vision_llm_config_id
+        response = await self._http.put(
+            f"{self._base}/api/v1/search-spaces/{search_space_id}/llm-preferences",
+            json=body,
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        return LlmPreferences.from_payload(response.json())
+
+    async def list_global_vision_llm_configs(self) -> list[VisionLlmConfigEntry]:
+        """List the registered global vision LLM configs.
+
+        Used by ``setup`` to (a) resolve an explicit ``--vision-llm <slug>``
+        to a config id and (b) auto-pick the strongest registered vision
+        config when the operator doesn't pass one. The ``Auto (Fastest)``
+        entry (``id=0``) is filtered out — accuracy must be reproducible.
+        """
+
+        response = await self._http.get(
+            f"{self._base}/api/v1/global-vision-llm-configs",
+            headers={"Accept": "application/json"},
+        )
+        response.raise_for_status()
+        payload = response.json()
+        if not isinstance(payload, list):
+            raise RuntimeError(
+                f"Unexpected /global-vision-llm-configs payload: {payload!r}"
+            )
+        return [
+            VisionLlmConfigEntry.from_payload(item)
+            for item in payload
+            if not bool(item.get("is_auto_mode", False))
+        ]
diff --git a/surfsense_evals/src/surfsense_evals/core/config.py b/surfsense_evals/src/surfsense_evals/core/config.py
new file mode 100644
index 000000000..164955914
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/config.py
@@ -0,0 +1,279 @@
+"""Environment + filesystem configuration for the harness.
+
+Two responsibilities:
+
+1. Load env vars (with sensible defaults) into a single immutable ``Config``
+   so that every other module reads it from one place.
+2. Read / write ``data/state.json``. State is keyed by suite name so multiple
+   suites can be set up in parallel and torn down independently.
+
+The pinned ``search_space_id`` lives in ``state.json`` (not env) so re-runs
+are idempotent without forcing the operator to remember an integer.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from dotenv import load_dotenv
+
+# Resolve once at import time. ``find_dotenv`` walks up; an explicit ``.env``
+# at the package root or in CWD wins. Silent-no-op if neither exists.
+load_dotenv()
+
+
+_PROJECT_ROOT = Path(__file__).resolve().parents[3]
+"""Resolves to ``surfsense_evals/`` (the package root, not ``src/``)."""
+
+
+def _project_root() -> Path:
+    """Return the ``surfsense_evals/`` project root.
+
+    Computed from this file's path: ``src/surfsense_evals/core/config.py`` →
+    walk up four levels. Kept as a function so tests can monkeypatch.
+    """
+
+    return _PROJECT_ROOT
+
+
+@dataclass(frozen=True)
+class Config:
+    """Immutable runtime configuration."""
+
+    surfsense_api_base: str
+    openrouter_api_key: str | None
+    openrouter_base_url: str
+
+    # Credentials — exactly ONE mode must be supplied.
+    surfsense_jwt: str | None
+    surfsense_refresh_token: str | None
+    surfsense_user_email: str | None
+    surfsense_user_password: str | None
+
+    # Filesystem paths.
+    data_dir: Path
+    reports_dir: Path
+
+    @property
+    def state_path(self) -> Path:
+        return self.data_dir / "state.json"
+
+    def has_jwt_mode(self) -> bool:
+        return bool(self.surfsense_jwt)
+
+    def has_local_mode(self) -> bool:
+        return bool(self.surfsense_user_email and self.surfsense_user_password)
+
+    def credential_mode(self) -> str:
+        """Return ``"jwt"``, ``"local"``, or ``"none"`` (no credentials supplied)."""
+
+        if self.has_jwt_mode():
+            return "jwt"
+        if self.has_local_mode():
+            return "local"
+        return "none"
+
+    def suite_data_dir(self, suite: str) -> Path:
+        return self.data_dir / suite
+
+    def suite_reports_dir(self, suite: str) -> Path:
+        return self.reports_dir / suite
+
+    def suite_runs_dir(self, suite: str) -> Path:
+        return self.suite_data_dir(suite) / "runs"
+
+    def suite_maps_dir(self, suite: str) -> Path:
+        return self.suite_data_dir(suite) / "maps"
+
+
+def load_config() -> Config:
+    """Read the current process env into a ``Config``.
+
+    No validation is performed here; callers (e.g. ``auth.acquire_token``,
+    ``cli`` subcommands) decide which fields they require. This keeps
+    ``models list`` and ``suites list`` runnable without OpenRouter creds.
+    """
+
+    project_root = _project_root()
+    data_dir = Path(os.environ.get("EVAL_DATA_DIR") or (project_root / "data")).resolve()
+    reports_dir = Path(os.environ.get("EVAL_REPORTS_DIR") or (project_root / "reports")).resolve()
+    return Config(
+        surfsense_api_base=os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip("/"),
+        openrouter_api_key=os.environ.get("OPENROUTER_API_KEY") or None,
+        openrouter_base_url=os.environ.get(
+            "OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"
+        ).rstrip("/"),
+        surfsense_jwt=os.environ.get("SURFSENSE_JWT") or None,
+        surfsense_refresh_token=os.environ.get("SURFSENSE_REFRESH_TOKEN") or None,
+        surfsense_user_email=os.environ.get("SURFSENSE_USER_EMAIL") or None,
+        surfsense_user_password=os.environ.get("SURFSENSE_USER_PASSWORD") or None,
+        data_dir=data_dir,
+        reports_dir=reports_dir,
+    )
+
+
+# ---------------------------------------------------------------------------
+# state.json — per-suite slots
+# ---------------------------------------------------------------------------
+
+
+# Scenario names — chosen at ``setup`` time, persisted in ``state.json``.
+#
+# * ``head-to-head`` (default, current behaviour): both arms answer with the
+#   SAME slug pinned via ``--provider-model``. Vision LLM at ingest is
+#   optional but recommended for image-bearing benchmarks.
+# * ``symmetric-cheap``: both arms answer with the SAME (cheap, text-only)
+#   slug; SurfSense pre-extracted images at ingest with a vision LLM.
+#   Measures whether vision-RAG ingestion lets a cheap downstream model
+#   match a vision one. Native arm structurally loses on image questions —
+#   that's the point, and the report labels it accordingly.
+# * ``cost-arbitrage``: native arm answers with an EXPENSIVE vision slug
+#   (``--native-arm-model``), SurfSense answers with a CHEAP text-only slug
+#   (``--provider-model``) over chunks the vision LLM already extracted at
+#   ingest. Measures how close SurfSense gets to native at a fraction of
+#   the per-query cost. The most compelling "shines" framing.
+SCENARIOS: tuple[str, ...] = ("head-to-head", "symmetric-cheap", "cost-arbitrage")
+DEFAULT_SCENARIO: str = "head-to-head"
+
+
+@dataclass
+class SuiteState:
+    """Per-suite persisted state.
+
+    ``provider_model`` is the slug pinned to the SearchSpace's
+    ``agent_llm`` — what answers SurfSense queries (and what the native
+    arm uses too, unless ``native_arm_model`` is set for cost-arbitrage).
+
+    ``vision_provider_model`` is the slug of the OpenRouter vision LLM
+    config attached to the SearchSpace's ``vision_llm_config_id`` — what
+    SurfSense uses to extract image content at ingest time when
+    ``use_vision_llm=True``. ``None`` means no vision config was attached
+    at setup (legacy or text-only suite).
+    """
+
+    search_space_id: int
+    agent_llm_id: int
+    provider_model: str
+    created_at: str
+    ingestion_maps: dict[str, str] = field(default_factory=dict)
+    scenario: str = DEFAULT_SCENARIO
+    vision_llm_config_id: int | None = None
+    vision_provider_model: str | None = None
+    native_arm_model: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "search_space_id": self.search_space_id,
+            "agent_llm_id": self.agent_llm_id,
+            "provider_model": self.provider_model,
+            "created_at": self.created_at,
+            "ingestion_maps": dict(self.ingestion_maps),
+            "scenario": self.scenario,
+            "vision_llm_config_id": self.vision_llm_config_id,
+            "vision_provider_model": self.vision_provider_model,
+            "native_arm_model": self.native_arm_model,
+        }
+
+    @classmethod
+    def from_dict(cls, payload: Mapping[str, Any]) -> SuiteState:
+        # ``scenario`` / vision / native fields default for back-compat with
+        # ``state.json`` written before scenarios shipped.
+        scenario = str(payload.get("scenario") or DEFAULT_SCENARIO)
+        if scenario not in SCENARIOS:
+            scenario = DEFAULT_SCENARIO
+        raw_vision_id = payload.get("vision_llm_config_id")
+        return cls(
+            search_space_id=int(payload["search_space_id"]),
+            agent_llm_id=int(payload["agent_llm_id"]),
+            provider_model=str(payload["provider_model"]),
+            created_at=str(payload.get("created_at") or ""),
+            ingestion_maps=dict(payload.get("ingestion_maps") or {}),
+            scenario=scenario,
+            vision_llm_config_id=int(raw_vision_id) if raw_vision_id is not None else None,
+            vision_provider_model=(
+                str(payload["vision_provider_model"])
+                if payload.get("vision_provider_model")
+                else None
+            ),
+            native_arm_model=(
+                str(payload["native_arm_model"])
+                if payload.get("native_arm_model")
+                else None
+            ),
+        )
+
+    @property
+    def effective_native_arm_model(self) -> str:
+        """Slug the native arm should use; falls back to ``provider_model``."""
+
+        return self.native_arm_model or self.provider_model
+
+
+def _load_state(config: Config) -> dict[str, Any]:
+    if not config.state_path.exists():
+        return {"suites": {}}
+    try:
+        with config.state_path.open("r", encoding="utf-8") as fh:
+            data = json.load(fh)
+    except (OSError, json.JSONDecodeError) as exc:
+        raise RuntimeError(
+            f"Failed to read state file {config.state_path}: {exc!s}. "
+            "Delete it if you want to start fresh."
+        ) from exc
+    if not isinstance(data, dict) or "suites" not in data:
+        return {"suites": {}}
+    return data
+
+
+def _write_state(config: Config, payload: Mapping[str, Any]) -> None:
+    config.data_dir.mkdir(parents=True, exist_ok=True)
+    tmp = config.state_path.with_suffix(".json.tmp")
+    with tmp.open("w", encoding="utf-8") as fh:
+        json.dump(dict(payload), fh, indent=2, sort_keys=True)
+        fh.write("\n")
+    tmp.replace(config.state_path)
+
+
+def get_suite_state(config: Config, suite: str) -> SuiteState | None:
+    """Return ``SuiteState`` for ``suite`` or ``None`` if not set up."""
+
+    state = _load_state(config)
+    raw = (state.get("suites") or {}).get(suite)
+    if not raw:
+        return None
+    return SuiteState.from_dict(raw)
+
+
+def set_suite_state(config: Config, suite: str, suite_state: SuiteState) -> None:
+    """Persist ``suite_state`` under the suite slot. Other suites are untouched."""
+
+    state = _load_state(config)
+    suites = dict(state.get("suites") or {})
+    suites[suite] = suite_state.to_dict()
+    state["suites"] = suites
+    _write_state(config, state)
+
+
+def clear_suite_state(config: Config, suite: str) -> bool:
+    """Remove the slot for ``suite``. Returns ``True`` if removal happened."""
+
+    state = _load_state(config)
+    suites = dict(state.get("suites") or {})
+    if suite not in suites:
+        return False
+    del suites[suite]
+    state["suites"] = suites
+    _write_state(config, state)
+    return True
+
+
+def utc_iso_timestamp() -> str:
+    """Filesystem-safe UTC ISO timestamp, e.g. ``2026-05-11T20-30-00Z``."""
+
+    return datetime.now(UTC).strftime("%Y-%m-%dT%H-%M-%SZ")
diff --git a/surfsense_evals/src/surfsense_evals/core/ingest_settings.py b/surfsense_evals/src/surfsense_evals/core/ingest_settings.py
new file mode 100644
index 000000000..5cdece577
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/ingest_settings.py
@@ -0,0 +1,311 @@
+"""Per-upload ingestion settings shared across every benchmark.
+
+The SurfSense ``POST /api/v1/documents/fileupload`` endpoint exposes
+exactly three knobs (verified at
+``surfsense_backend/app/routes/documents_routes.py`` and
+``surfsense_backend/app/etl_pipeline/etl_document.py``):
+
+* ``processing_mode``     — ``"basic"`` (default) | ``"premium"``
+* ``use_vision_llm``      — ``bool`` (run vision LLM during ingest to
+                            extract image content / captions / tables)
+* ``should_summarize``    — ``bool`` (generate document summary)
+
+This module gives every benchmark a uniform way to:
+
+1. Receive sensible per-benchmark defaults (text-only benchmarks
+   default vision off; image-bearing benchmarks default vision on).
+2. Accept CLI overrides (``--use-vision-llm`` / ``--no-vision-llm``,
+   ``--processing-mode {basic,premium}``,
+   ``--should-summarize`` / ``--no-summarize``).
+3. Persist the *actual* settings used into the doc-map manifest and
+   the run artifact so reports can show "vision=ON, mode=premium →
+   65% accuracy" head-to-head with "vision=OFF, mode=basic → 52%".
+
+A/B testing on the same corpus
+------------------------------
+
+SurfSense dedupes uploads by ``(filename, search_space_id)`` — NOT by
+content hash and NOT by ingestion settings. Re-uploading the same
+filename to the same SearchSpace with a different ``use_vision_llm``
+flag will hit the duplicate branch and *not* re-process. To compare
+two settings combos head-to-head on the same corpus you must give
+each combo its own SearchSpace, which today means:
+
+    teardown --suite <s>
+    setup    --suite <s> ...
+    ingest   <s> <bench>  --no-vision-llm   # baseline run
+    run      <s> <bench>
+    teardown --suite <s>
+    setup    --suite <s> ...
+    ingest   <s> <bench>  --use-vision-llm  # vision arm
+    run      <s> <bench>
+
+The runs land in different timestamped subdirectories under
+``data/<suite>/runs/`` and ``report --suite <s>`` aggregates whichever
+manifest is currently latest per benchmark.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections.abc import Mapping
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+# Keep the constant list of valid processing modes here so benchmarks
+# don't have to re-import from the backend (they don't have access to
+# the backend package anyway).
+PROCESSING_MODES: tuple[str, ...] = ("basic", "premium")
+
+
+@dataclass(frozen=True)
+class IngestSettings:
+    """Resolved per-upload knobs handed to ``DocumentsClient.upload``.
+
+    Use ``IngestSettings(...)`` directly to define benchmark defaults,
+    or ``IngestSettings.merge(defaults, opts)`` to apply CLI overrides
+    on top of those defaults.
+    """
+
+    use_vision_llm: bool = False
+    processing_mode: str = "basic"
+    should_summarize: bool = False
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "use_vision_llm": self.use_vision_llm,
+            "processing_mode": self.processing_mode,
+            "should_summarize": self.should_summarize,
+        }
+
+    @classmethod
+    def merge(cls, defaults: IngestSettings, opts: Mapping[str, Any]) -> IngestSettings:
+        """Apply CLI overrides on top of ``defaults``.
+
+        ``opts`` is the kwargs dict built by ``core.cli`` from the
+        argparse namespace (see ``_cmd_ingest`` / ``_cmd_run``). Keys
+        we look for: ``use_vision_llm`` (bool or None), ``processing_mode``
+        (str or None), ``should_summarize`` (bool or None). Anything
+        else is ignored so benchmarks can pass through their own opts.
+        """
+
+        return cls(
+            use_vision_llm=_coerce_bool(opts.get("use_vision_llm"), defaults.use_vision_llm),
+            processing_mode=_coerce_mode(opts.get("processing_mode"), defaults.processing_mode),
+            should_summarize=_coerce_bool(opts.get("should_summarize"), defaults.should_summarize),
+        )
+
+    def render_label(self) -> str:
+        """Human-readable single-line label for reports / log lines."""
+
+        return (
+            f"vision={'on' if self.use_vision_llm else 'off'}, "
+            f"mode={self.processing_mode}, "
+            f"summarize={'on' if self.should_summarize else 'off'}"
+        )
+
+
+def _coerce_bool(value: Any, default: bool) -> bool:
+    """Argparse with ``BooleanOptionalAction`` yields True/False/None.
+
+    ``None`` means the operator didn't pass the flag → fall back to
+    the benchmark default.
+    """
+
+    if value is None:
+        return default
+    if isinstance(value, bool):
+        return value
+    if isinstance(value, str):
+        return value.strip().lower() in {"1", "true", "yes", "on"}
+    return bool(value)
+
+
+def _coerce_mode(value: Any, default: str) -> str:
+    if value is None or value == "":
+        return default
+    val = str(value).strip().lower()
+    if val not in PROCESSING_MODES:
+        raise ValueError(
+            f"Invalid processing_mode {val!r}; must be one of {PROCESSING_MODES}"
+        )
+    return val
+
+
+# ---------------------------------------------------------------------------
+# Argparse helper
+# ---------------------------------------------------------------------------
+
+
+def _add_bool_pair(
+    parser: argparse.ArgumentParser,
+    *,
+    dest: str,
+    on_flag: str,
+    off_flag: str,
+    on_help: str,
+    off_help: str,
+) -> None:
+    """Add a mutually exclusive ``--foo`` / ``--no-foo`` pair.
+
+    We don't use ``argparse.BooleanOptionalAction`` because it would
+    auto-generate ``--no-use-vision-llm`` rather than the friendlier
+    ``--no-vision-llm`` that operators reach for. Default is ``None``
+    so ``IngestSettings.merge`` can distinguish "silent" from
+    "explicit false".
+    """
+
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument(
+        on_flag,
+        dest=dest,
+        action="store_true",
+        default=None,
+        help=on_help,
+    )
+    group.add_argument(
+        off_flag,
+        dest=dest,
+        action="store_false",
+        default=None,
+        help=off_help,
+    )
+
+
+def add_ingest_settings_args(
+    parser: argparse.ArgumentParser,
+    *,
+    defaults: IngestSettings,
+) -> None:
+    """Attach the three ingest-settings flag pairs to ``parser``.
+
+    Each bool exposes a mutually exclusive ``--foo`` / ``--no-foo``
+    pair so an operator can flip either direction without restating
+    every flag. Default is ``None`` so that "operator didn't pass the
+    flag" is distinguishable from "operator explicitly passed false"
+    — ``IngestSettings.merge`` then folds in the benchmark default
+    only when the operator was silent.
+    """
+
+    settings_group = parser.add_argument_group(
+        "ingest settings",
+        f"Per-upload knobs (forwarded to /documents/fileupload). "
+        f"Defaults for this benchmark: {defaults.render_label()}.",
+    )
+    _add_bool_pair(
+        settings_group,
+        dest="use_vision_llm",
+        on_flag="--use-vision-llm",
+        off_flag="--no-vision-llm",
+        on_help=(
+            "Run vision LLM during ingest to extract image content "
+            f"(default for this benchmark: "
+            f"{'on' if defaults.use_vision_llm else 'off'})."
+        ),
+        off_help="Skip vision LLM during ingest (text-only ETL).",
+    )
+    settings_group.add_argument(
+        "--processing-mode",
+        dest="processing_mode",
+        choices=PROCESSING_MODES,
+        default=None,
+        help=(
+            "SurfSense ETL processing mode (premium uses a 10x page "
+            f"multiplier and typically routes to a stronger ETL). "
+            f"Default for this benchmark: {defaults.processing_mode!r}."
+        ),
+    )
+    _add_bool_pair(
+        settings_group,
+        dest="should_summarize",
+        on_flag="--should-summarize",
+        off_flag="--no-summarize",
+        on_help=(
+            "Have SurfSense generate a document summary at ingest "
+            f"(default for this benchmark: "
+            f"{'on' if defaults.should_summarize else 'off'})."
+        ),
+        off_help="Skip per-document summary generation.",
+    )
+
+
+# ---------------------------------------------------------------------------
+# Doc-map manifest helpers
+# ---------------------------------------------------------------------------
+#
+# Every benchmark writes a doc-map JSONL under ``data/<suite>/maps/`` that
+# pairs source identifiers (case_id, snippet_id, doc_path, …) to the
+# SurfSense document_ids returned by the upload. To make the report
+# self-describing we also write a header line:
+#
+#     {"__settings__": {"use_vision_llm": ..., "processing_mode": ..., ...}}
+#
+# These two helpers centralise that protocol so each benchmark only has to
+# call ``write_settings_header`` and ``read_settings_header``.
+
+SETTINGS_HEADER_KEY = "__settings__"
+
+
+def settings_header_line(settings: IngestSettings) -> str:
+    """Return the JSON-serialised header line (no trailing newline)."""
+
+    return json.dumps({SETTINGS_HEADER_KEY: settings.to_dict()})
+
+
+def is_settings_header(row: Mapping[str, Any]) -> bool:
+    return SETTINGS_HEADER_KEY in row
+
+
+def read_settings_header(map_path: Path) -> dict[str, Any]:
+    """Read the ``__settings__`` header out of a doc-map JSONL.
+
+    Returns ``{}`` on a missing file, an empty file, an unreadable
+    file, or a file whose first non-blank line is not a settings
+    header (e.g. a corpus ingested before this feature existed).
+    Callers use this purely to surface settings in the report; it
+    must never fail the run.
+    """
+
+    if not map_path.exists():
+        return {}
+    try:
+        with map_path.open("r", encoding="utf-8") as fh:
+            for line in fh:
+                line = line.strip()
+                if not line:
+                    continue
+                row = json.loads(line)
+                if isinstance(row, dict) and SETTINGS_HEADER_KEY in row:
+                    return dict(row[SETTINGS_HEADER_KEY])
+                return {}
+    except (OSError, json.JSONDecodeError):
+        return {}
+    return {}
+
+
+def format_ingest_settings_md(settings: Any) -> str:
+    """Render the resolved settings as a single Markdown bullet line."""
+
+    if not isinstance(settings, Mapping) or not settings:
+        return "- SurfSense ingest settings: (not recorded — re-ingest to capture)"
+    vision = "on" if settings.get("use_vision_llm") else "off"
+    mode = settings.get("processing_mode") or "basic"
+    summarize = "on" if settings.get("should_summarize") else "off"
+    return (
+        f"- SurfSense ingest settings: vision_llm=`{vision}`, "
+        f"processing_mode=`{mode}`, summarize=`{summarize}`"
+    )
+
+
+__all__ = [
+    "PROCESSING_MODES",
+    "SETTINGS_HEADER_KEY",
+    "IngestSettings",
+    "add_ingest_settings_args",
+    "format_ingest_settings_md",
+    "is_settings_header",
+    "read_settings_header",
+    "settings_header_line",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py b/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py
new file mode 100644
index 000000000..bd0e6aafb
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/metrics/__init__.py
@@ -0,0 +1,50 @@
+"""Pure-function metric primitives. Lazy imports."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .comparison import McnemarResult, bootstrap_delta_ci, mcnemar_test, paired_aggregate
+    from .mc_accuracy import AccuracyResult, accuracy_with_wilson_ci, wilson_ci
+    from .retrieval import RetrievalScores, mrr, ndcg_at_k, recall_at_k, score_run
+
+__all__ = [
+    "AccuracyResult",
+    "McnemarResult",
+    "RetrievalScores",
+    "accuracy_with_wilson_ci",
+    "bootstrap_delta_ci",
+    "mcnemar_test",
+    "mrr",
+    "ndcg_at_k",
+    "paired_aggregate",
+    "recall_at_k",
+    "score_run",
+    "wilson_ci",
+]
+
+
+_MODULE_FOR = {
+    "AccuracyResult": "mc_accuracy",
+    "accuracy_with_wilson_ci": "mc_accuracy",
+    "wilson_ci": "mc_accuracy",
+    "RetrievalScores": "retrieval",
+    "mrr": "retrieval",
+    "ndcg_at_k": "retrieval",
+    "recall_at_k": "retrieval",
+    "score_run": "retrieval",
+    "McnemarResult": "comparison",
+    "bootstrap_delta_ci": "comparison",
+    "mcnemar_test": "comparison",
+    "paired_aggregate": "comparison",
+}
+
+
+def __getattr__(name: str):
+    if name in _MODULE_FOR:
+        from importlib import import_module
+
+        mod = import_module(f".{_MODULE_FOR[name]}", __name__)
+        return getattr(mod, name)
+    raise AttributeError(f"module 'surfsense_evals.core.metrics' has no attribute {name!r}")
diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py b/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py
new file mode 100644
index 000000000..579576f4f
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/metrics/comparison.py
@@ -0,0 +1,258 @@
+"""Paired comparison statistics for head-to-head benchmarks.
+
+In every head-to-head benchmark (currently MedXpertQA-MM and
+MMLongBench-Doc) each question is answered by both arms (Native PDF
+and SurfSense). That makes per-question outcomes paired, so
+``McNemar's test`` on the discordant pairs is the right significance
+test for "are the two arms different?". We also expose a bootstrap
+delta CI for visualising effect size.
+
+Aggregate cost / latency / token deltas are mean-based; the runner
+slices them by arm before passing them in.
+"""
+
+from __future__ import annotations
+
+import math
+import statistics
+from collections.abc import Sequence
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class McnemarResult:
+    """Discordant pair counts + the test statistics."""
+
+    n_total: int
+    b: int  # native correct, surfsense wrong
+    c: int  # native wrong,   surfsense correct
+    statistic: float
+    p_value: float
+    method: str
+
+    def to_dict(self) -> dict[str, float | int | str]:
+        return {
+            "n_total": self.n_total,
+            "b_native_correct_only": self.b,
+            "c_surfsense_correct_only": self.c,
+            "statistic": self.statistic,
+            "p_value": self.p_value,
+            "method": self.method,
+        }
+
+
+def mcnemar_test(
+    arm_a_correct: Sequence[bool],
+    arm_b_correct: Sequence[bool],
+    *,
+    use_exact_below: int = 11,
+) -> McnemarResult:
+    """Paired McNemar's test on per-question correctness.
+
+    ``arm_a_correct`` is treated as the reference arm (typically the
+    "native" arm); ``arm_b_correct`` is the challenger (typically
+    "surfsense"). The test statistic only depends on discordant pairs.
+
+    Default switch-over (``b + c < 11``): for very small discordant
+    samples the exact binomial test is preferred; above that the
+    continuity-corrected chi-square is well-behaved (Edwards 1948).
+    Callers can raise ``use_exact_below`` if they prefer the more
+    conservative ``b + c < 25`` rule.
+
+    No external statistical package is required: scipy is a heavy dep
+    and we only need binomial CDFs / chi-square sf, both implementable
+    in stdlib + numpy without surprises.
+    """
+
+    if len(arm_a_correct) != len(arm_b_correct):
+        raise ValueError(
+            f"Length mismatch: arm_a={len(arm_a_correct)}, arm_b={len(arm_b_correct)}"
+        )
+    n = len(arm_a_correct)
+    b = sum(1 for a, c in zip(arm_a_correct, arm_b_correct) if a and not c)
+    c = sum(1 for a, cc in zip(arm_a_correct, arm_b_correct) if (not a) and cc)
+    discordant = b + c
+    if discordant == 0:
+        return McnemarResult(
+            n_total=n, b=b, c=c, statistic=0.0, p_value=1.0, method="degenerate"
+        )
+
+    if discordant < use_exact_below:
+        # Exact binomial: under H0 each discordant pair is a Bernoulli(0.5).
+        # p-value = 2 * P(X <= min(b,c) | n=discordant, p=0.5), capped at 1.
+        k = min(b, c)
+        cdf = sum(_binom_pmf(discordant, i) for i in range(k + 1))
+        p_value = min(1.0, 2.0 * cdf)
+        return McnemarResult(
+            n_total=n, b=b, c=c, statistic=float(k), p_value=p_value, method="exact"
+        )
+
+    # Chi-square with continuity correction (McNemar-Edwards).
+    chi = ((abs(b - c) - 1) ** 2) / discordant
+    p_value = _chi2_sf(chi, df=1)
+    return McnemarResult(
+        n_total=n, b=b, c=c, statistic=chi, p_value=p_value, method="chi2_cc"
+    )
+
+
+def _binom_pmf(n: int, k: int) -> float:
+    return math.comb(n, k) * (0.5 ** n)
+
+
+def _chi2_sf(x: float, *, df: int) -> float:
+    """Survival function (1 - CDF) of chi-square; df=1 closed form."""
+
+    if x <= 0:
+        return 1.0
+    if df == 1:
+        # Chi^2(1) = N(0,1)^2; sf(x) = 2 * Phi_complement(sqrt(x))
+        return math.erfc(math.sqrt(x / 2.0))
+    # General fallback via regularized upper incomplete gamma.
+    a = df / 2.0
+    z = x / 2.0
+    return _gammaincc(a, z)
+
+
+def _gammaincc(a: float, x: float, *, max_iter: int = 200, tol: float = 1e-12) -> float:
+    """Regularised upper incomplete gamma Q(a, x). Series + continued fraction."""
+
+    if x < 0 or a <= 0:
+        return float("nan")
+    if x == 0:
+        return 1.0
+    if x < a + 1.0:
+        # Series for P(a, x); subtract from 1.
+        p_series = _gammainc_series(a, x, max_iter=max_iter, tol=tol)
+        return 1.0 - p_series
+    return _gammaincc_cf(a, x, max_iter=max_iter, tol=tol)
+
+
+def _gammainc_series(a: float, x: float, *, max_iter: int, tol: float) -> float:
+    term = 1.0 / a
+    summation = term
+    for n in range(1, max_iter):
+        term *= x / (a + n)
+        summation += term
+        if abs(term) < abs(summation) * tol:
+            break
+    log_pre = -x + a * math.log(x) - math.lgamma(a)
+    return summation * math.exp(log_pre)
+
+
+def _gammaincc_cf(a: float, x: float, *, max_iter: int, tol: float) -> float:
+    b = x + 1.0 - a
+    c_val = 1.0 / 1e-300
+    d = 1.0 / b
+    h = d
+    for i in range(1, max_iter):
+        an = -i * (i - a)
+        b += 2.0
+        d = an * d + b
+        if abs(d) < 1e-300:
+            d = 1e-300
+        c_val = b + an / c_val
+        if abs(c_val) < 1e-300:
+            c_val = 1e-300
+        d = 1.0 / d
+        delta = d * c_val
+        h *= delta
+        if abs(delta - 1.0) < tol:
+            break
+    log_pre = -x + a * math.log(x) - math.lgamma(a)
+    return h * math.exp(log_pre)
+
+
+# ---------------------------------------------------------------------------
+# Bootstrap delta CI
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class BootstrapDelta:
+    delta: float
+    ci_low: float
+    ci_high: float
+    n_resamples: int
+
+    def to_dict(self) -> dict[str, float | int]:
+        return {
+            "delta": self.delta,
+            "ci_low": self.ci_low,
+            "ci_high": self.ci_high,
+            "n_resamples": self.n_resamples,
+        }
+
+
+def bootstrap_delta_ci(
+    arm_a_correct: Sequence[bool],
+    arm_b_correct: Sequence[bool],
+    *,
+    n_resamples: int = 5000,
+    level: float = 0.95,
+    random_state: int | None = 0,
+) -> BootstrapDelta:
+    """Paired-sample bootstrap CI for ``mean(arm_b) - mean(arm_a)``.
+
+    Resamples *paired indices* with replacement so the dependency
+    between arms is preserved.
+    """
+
+    if len(arm_a_correct) != len(arm_b_correct):
+        raise ValueError("paired arms must have the same length")
+    n = len(arm_a_correct)
+    if n == 0:
+        return BootstrapDelta(0.0, 0.0, 0.0, 0)
+    a = np.asarray(arm_a_correct, dtype=np.int8)
+    b = np.asarray(arm_b_correct, dtype=np.int8)
+    delta = float(b.mean() - a.mean())
+
+    rng = np.random.default_rng(random_state)
+    deltas = np.empty(n_resamples, dtype=np.float64)
+    for i in range(n_resamples):
+        idx = rng.integers(0, n, size=n)
+        deltas[i] = b[idx].mean() - a[idx].mean()
+    alpha = (1.0 - level) / 2.0
+    ci_low, ci_high = float(np.quantile(deltas, alpha)), float(np.quantile(deltas, 1 - alpha))
+    return BootstrapDelta(delta=delta, ci_low=ci_low, ci_high=ci_high, n_resamples=n_resamples)
+
+
+# ---------------------------------------------------------------------------
+# Simple aggregate helpers (cost / latency / tokens)
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class Aggregate:
+    mean: float
+    median: float
+    p95: float
+    n: int
+
+    def to_dict(self) -> dict[str, float | int]:
+        return {"mean": self.mean, "median": self.median, "p95": self.p95, "n": self.n}
+
+
+def paired_aggregate(values: Sequence[float]) -> Aggregate:
+    """Mean / median / p95 of a list of numbers (e.g. cost-per-question)."""
+
+    if not values:
+        return Aggregate(0.0, 0.0, 0.0, 0)
+    arr = np.asarray(values, dtype=np.float64)
+    return Aggregate(
+        mean=float(arr.mean()),
+        median=float(statistics.median(values)),
+        p95=float(np.quantile(arr, 0.95)),
+        n=len(values),
+    )
+
+
+__all__ = [
+    "Aggregate",
+    "BootstrapDelta",
+    "McnemarResult",
+    "bootstrap_delta_ci",
+    "mcnemar_test",
+    "paired_aggregate",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py b/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py
new file mode 100644
index 000000000..8b0188ca4
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py
@@ -0,0 +1,130 @@
+"""Multiple-choice accuracy + Wilson 95% confidence intervals.
+
+Wilson CI is preferred over normal-approximation because MIRAGE's
+per-task subsets can be small (PubMedQA* and BioASQ-Y/N have a few
+hundred questions each) and Wilson handles n→0 / p→{0,1} edges
+gracefully.
+
+Reference for the closed form: Wilson (1927); identical to the
+``statsmodels.stats.proportion.proportion_confint(method='wilson')``
+output and what scikit-learn implements internally for its bounded
+estimators.
+"""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class AccuracyResult:
+    """Per-task accuracy with Wilson CI."""
+
+    n_correct: int
+    n_total: int
+    accuracy: float
+    ci_low: float
+    ci_high: float
+
+    def to_dict(self) -> dict[str, float | int]:
+        return {
+            "n_correct": self.n_correct,
+            "n_total": self.n_total,
+            "accuracy": self.accuracy,
+            "ci_low": self.ci_low,
+            "ci_high": self.ci_high,
+        }
+
+
+# Two-sided Wilson z values. 1.959964 ≈ z_{0.975}.
+_Z_FOR_LEVEL: dict[float, float] = {
+    0.90: 1.6448536269514722,
+    0.95: 1.959963984540054,
+    0.99: 2.5758293035489004,
+}
+
+
+def wilson_ci(
+    n_correct: int, n_total: int, *, level: float = 0.95
+) -> tuple[float, float]:
+    """Two-sided Wilson score confidence interval for a proportion.
+
+    Returns ``(low, high)``. ``n_total == 0`` returns ``(0.0, 1.0)`` —
+    the maximally uncertain interval.
+    """
+
+    if n_total <= 0:
+        return 0.0, 1.0
+    if level not in _Z_FOR_LEVEL:
+        raise ValueError(f"Unsupported confidence level {level!r}")
+    z = _Z_FOR_LEVEL[level]
+    p = n_correct / n_total
+    n = n_total
+    denom = 1.0 + (z * z) / n
+    centre = (p + (z * z) / (2 * n)) / denom
+    half = (z / denom) * math.sqrt((p * (1 - p) / n) + (z * z) / (4 * n * n))
+    low = max(0.0, centre - half)
+    high = min(1.0, centre + half)
+    return low, high
+
+
+def accuracy_with_wilson_ci(
+    n_correct: int, n_total: int, *, level: float = 0.95
+) -> AccuracyResult:
+    if n_total < 0:
+        raise ValueError(f"n_total must be >= 0, got {n_total}")
+    if n_correct < 0 or n_correct > n_total:
+        raise ValueError(
+            f"n_correct must be in [0, n_total]; got n_correct={n_correct}, n_total={n_total}"
+        )
+    accuracy = (n_correct / n_total) if n_total > 0 else 0.0
+    low, high = wilson_ci(n_correct, n_total, level=level)
+    return AccuracyResult(
+        n_correct=n_correct,
+        n_total=n_total,
+        accuracy=accuracy,
+        ci_low=low,
+        ci_high=high,
+    )
+
+
+def per_task_accuracy(
+    rows: Sequence[Mapping[str, object]],
+    *,
+    task_key: str = "task",
+    correct_key: str = "is_correct",
+    level: float = 0.95,
+) -> dict[str, AccuracyResult]:
+    """Group ``rows`` by ``task_key`` and compute per-task ``AccuracyResult``.
+
+    ``rows[i][correct_key]`` must be truthy iff the answer was correct.
+    """
+
+    counts: dict[str, list[int]] = {}
+    for row in rows:
+        task = str(row.get(task_key, ""))
+        bucket = counts.setdefault(task, [0, 0])
+        bucket[1] += 1
+        if row.get(correct_key):
+            bucket[0] += 1
+    return {
+        task: accuracy_with_wilson_ci(c[0], c[1], level=level)
+        for task, c in counts.items()
+    }
+
+
+def macro_accuracy(per_task: Mapping[str, AccuracyResult]) -> float:
+    if not per_task:
+        return 0.0
+    return sum(r.accuracy for r in per_task.values()) / len(per_task)
+
+
+__all__ = [
+    "AccuracyResult",
+    "accuracy_with_wilson_ci",
+    "macro_accuracy",
+    "per_task_accuracy",
+    "wilson_ci",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py b/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py
new file mode 100644
index 000000000..d4cfe10ae
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/metrics/retrieval.py
@@ -0,0 +1,132 @@
+"""Retrieval metrics: Recall@k, MRR, nDCG@k.
+
+Used by CUREv1's runner to score the SurfSense arm against the
+benchmark's qrels. ``corpus_id`` is the canonical CUREv1 passage id
+(string); the runner maps SurfSense ``chunk_id`` → ``document_id`` →
+``corpus_id`` before calling these.
+
+Graded relevance (CUREv1 uses 0/1/2 grades) is honoured by ``ndcg_at_k``;
+``recall_at_k`` and ``mrr`` flatten anything > 0 to "relevant".
+"""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class RetrievalScores:
+    """Aggregated retrieval scores."""
+
+    recall_at_k: dict[int, float]
+    mrr: float
+    ndcg_at_10: float
+    n_queries: int
+
+    def to_dict(self) -> dict:
+        return {
+            "recall_at_k": dict(self.recall_at_k),
+            "mrr": self.mrr,
+            "ndcg_at_10": self.ndcg_at_10,
+            "n_queries": self.n_queries,
+        }
+
+
+def recall_at_k(retrieved: Sequence[str], relevant: Iterable[str], k: int) -> float:
+    """Fraction of ``relevant`` documents found in ``retrieved[:k]``."""
+
+    if not relevant:
+        return 0.0
+    relevant_set = set(relevant)
+    if not relevant_set:
+        return 0.0
+    top_k = list(retrieved)[:k]
+    hits = sum(1 for doc in top_k if doc in relevant_set)
+    return hits / len(relevant_set)
+
+
+def mrr(retrieved: Sequence[str], relevant: Iterable[str]) -> float:
+    """Reciprocal rank of the first relevant doc, 0 if none found."""
+
+    relevant_set = set(relevant)
+    for rank, doc in enumerate(retrieved, start=1):
+        if doc in relevant_set:
+            return 1.0 / rank
+    return 0.0
+
+
+def _dcg_at_k(grades: Sequence[float], k: int) -> float:
+    s = 0.0
+    for i, grade in enumerate(grades[:k], start=1):
+        # Standard log-base-2 discount; gain = 2^grade - 1 for graded relevance.
+        s += (2.0 ** grade - 1.0) / math.log2(i + 1)
+    return s
+
+
+def ndcg_at_k(
+    retrieved: Sequence[str],
+    qrels: Mapping[str, float],
+    k: int,
+) -> float:
+    """nDCG@k against graded ``qrels`` (``{doc_id: grade}``).
+
+    Unjudged documents in ``retrieved`` contribute zero gain. The
+    ideal ordering is ``qrels`` sorted by grade descending.
+    """
+
+    if not qrels:
+        return 0.0
+    grades = [float(qrels.get(doc, 0.0)) for doc in retrieved]
+    dcg = _dcg_at_k(grades, k)
+    ideal = sorted(qrels.values(), reverse=True)
+    idcg = _dcg_at_k([float(g) for g in ideal], k)
+    if idcg == 0.0:
+        return 0.0
+    return dcg / idcg
+
+
+def score_run(
+    *,
+    per_query_retrieved: Mapping[str, Sequence[str]],
+    per_query_qrels: Mapping[str, Mapping[str, float]],
+    ks: Sequence[int] = (1, 5, 10, 32),
+    ndcg_k: int = 10,
+) -> RetrievalScores:
+    """Aggregate Recall@k, MRR, nDCG@k across a run.
+
+    ``per_query_retrieved`` maps ``query_id -> ordered list of doc ids``.
+    ``per_query_qrels`` maps ``query_id -> {doc_id: grade}`` (grade > 0
+    is relevant).
+
+    Queries present in retrieved but not in qrels are skipped. Queries
+    in qrels but missing from retrieved contribute zeros.
+    """
+
+    qids = set(per_query_qrels.keys()) & set(per_query_retrieved.keys())
+    if not qids:
+        return RetrievalScores(recall_at_k={k: 0.0 for k in ks}, mrr=0.0, ndcg_at_10=0.0, n_queries=0)
+
+    recall_totals = {k: 0.0 for k in ks}
+    mrr_total = 0.0
+    ndcg_total = 0.0
+    for qid in qids:
+        retrieved = list(per_query_retrieved[qid])
+        qrels = per_query_qrels[qid]
+        relevant_docs = [d for d, g in qrels.items() if g > 0]
+        for k in ks:
+            recall_totals[k] += recall_at_k(retrieved, relevant_docs, k)
+        mrr_total += mrr(retrieved, relevant_docs)
+        ndcg_total += ndcg_at_k(retrieved, qrels, ndcg_k)
+
+    n = len(qids)
+    return RetrievalScores(
+        recall_at_k={k: v / n for k, v in recall_totals.items()},
+        mrr=mrr_total / n,
+        ndcg_at_10=ndcg_total / n,
+        n_queries=n,
+    )
+
+
+__all__ = ["RetrievalScores", "mrr", "ndcg_at_k", "recall_at_k", "score_run"]
diff --git a/surfsense_evals/src/surfsense_evals/core/parse/__init__.py b/surfsense_evals/src/surfsense_evals/core/parse/__init__.py
new file mode 100644
index 000000000..208c2d374
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parse/__init__.py
@@ -0,0 +1,21 @@
+"""Parsers shared across suites: citations, MCQ envelopes, AI-SDK SSE."""
+
+from __future__ import annotations
+
+from .answer_letter import AnswerLetterResult, extract_answer_letter
+from .citations import CITATION_REGEX, CitationToken, ChunkCitation, UrlCitation, parse_citations
+from .freeform_answer import extract_freeform_answer
+from .sse import SseEvent, iter_sse_events
+
+__all__ = [
+    "CITATION_REGEX",
+    "CitationToken",
+    "ChunkCitation",
+    "UrlCitation",
+    "parse_citations",
+    "AnswerLetterResult",
+    "extract_answer_letter",
+    "extract_freeform_answer",
+    "SseEvent",
+    "iter_sse_events",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py b/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py
new file mode 100644
index 000000000..8cf23869b
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parse/answer_letter.py
@@ -0,0 +1,122 @@
+"""Robust extractor for MCQ answer letters.
+
+Handles three answer shapes seen in the wild:
+
+1. **MedRAG envelope** — ``{"step_by_step_thinking": "...", "answer_choice": "A"}``
+   embedded somewhere in the assistant message (often inside ```` ```json ```` /
+   ``` ``` ``` fences). The regex grabs the JSON object and reads the
+   ``answer_choice`` field.
+
+2. **Final-line letter** — e.g. ``Answer: B`` or ``The correct answer is (C).``.
+   Falls back to a permissive regex over the last few lines.
+
+3. **Bare letter** — single uppercase letter at the end of the message.
+
+The function returns the parsed letter (uppercased) plus a discriminator
+of which strategy fired so the runner / report can flag suspicious
+parses (typically zero-confidence parses indicate the model didn't
+follow the prompt).
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import dataclass
+from typing import Literal
+
+ParserStrategy = Literal["json_envelope", "answer_line", "bare_letter", "none"]
+
+
+@dataclass(frozen=True)
+class AnswerLetterResult:
+    letter: str | None
+    strategy: ParserStrategy
+
+    @property
+    def found(self) -> bool:
+        return self.letter is not None
+
+
+# ---------------------------------------------------------------------------
+# Strategies
+# ---------------------------------------------------------------------------
+
+
+_JSON_BLOCK = re.compile(r"\{[^{}]*\"answer_choice\"\s*:\s*\"([A-Za-z])\"[^{}]*\}", re.DOTALL)
+_FENCED_JSON = re.compile(r"```(?:json)?\s*(\{.*?\})\s*```", re.DOTALL | re.IGNORECASE)
+_ANSWER_LINE = re.compile(
+    r"(?:final\s*answer|answer\s*choice|the\s+correct\s+answer\s+is|answer)\s*[:=\-]?\s*"
+    r"\(?\s*([A-Za-z])\s*[\)\.]*\s*$",
+    re.IGNORECASE | re.MULTILINE,
+)
+_BARE_LETTER = re.compile(r"^\s*\(?\s*([A-Za-z])\s*[\)\.]*\s*$", re.MULTILINE)
+
+
+def _from_json_envelope(text: str) -> str | None:
+    # Try fenced code blocks first (most likely to contain the JSON).
+    for fence in _FENCED_JSON.finditer(text):
+        try:
+            obj = json.loads(fence.group(1))
+        except (json.JSONDecodeError, ValueError):
+            continue
+        if isinstance(obj, dict):
+            choice = obj.get("answer_choice")
+            if isinstance(choice, str) and choice.strip():
+                return choice.strip()[:1].upper()
+
+    # Fall back to a tolerant regex over the whole text (handles
+    # responses that drop the fences).
+    match = _JSON_BLOCK.search(text)
+    if match:
+        return match.group(1).upper()
+    return None
+
+
+def _from_answer_line(text: str) -> str | None:
+    # Walk lines bottom-up; the answer is almost always near the end.
+    for match in reversed(list(_ANSWER_LINE.finditer(text))):
+        letter = match.group(1).upper()
+        if letter.isalpha():
+            return letter
+    return None
+
+
+def _from_bare_letter(text: str) -> str | None:
+    # Inspect only the final non-empty lines (avoid grabbing in-prose
+    # mentions of "A" or "I").
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    for ln in reversed(lines[-3:]):
+        match = _BARE_LETTER.match(ln)
+        if match:
+            return match.group(1).upper()
+    return None
+
+
+def extract_answer_letter(text: str) -> AnswerLetterResult:
+    """Run strategies in order and return the first hit.
+
+    Order: JSON envelope → final-answer-line regex → bare-letter
+    fallback. Empty / whitespace-only text returns
+    ``AnswerLetterResult(None, "none")``.
+    """
+
+    if not text or not text.strip():
+        return AnswerLetterResult(None, "none")
+
+    letter = _from_json_envelope(text)
+    if letter:
+        return AnswerLetterResult(letter, "json_envelope")
+
+    letter = _from_answer_line(text)
+    if letter:
+        return AnswerLetterResult(letter, "answer_line")
+
+    letter = _from_bare_letter(text)
+    if letter:
+        return AnswerLetterResult(letter, "bare_letter")
+
+    return AnswerLetterResult(None, "none")
+
+
+__all__ = ["AnswerLetterResult", "ParserStrategy", "extract_answer_letter"]
diff --git a/surfsense_evals/src/surfsense_evals/core/parse/citations.py b/surfsense_evals/src/surfsense_evals/core/parse/citations.py
new file mode 100644
index 000000000..1fcd35434
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parse/citations.py
@@ -0,0 +1,110 @@
+"""Python port of the canonical citation parser.
+
+Source of truth: ``surfsense_web/lib/citations/citation-parser.ts:20-21``.
+The pattern is byte-for-byte identical to the TS export ``CITATION_REGEX``
+so a SurfSense user reading the web client and a CUREv1 retrieval scorer
+running here see the same chunk_ids extracted from the same answer.
+
+The TS reference also handles a ``urlcite{N}`` placeholder produced by
+``preprocessCitationMarkdown`` — that pre-processing step is web-only
+(GFM autolink workaround), so the harness sees raw ``[citation:URL]``
+tokens and ``parse_citations`` returns them as ``UrlCitation`` directly.
+"""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from typing import Any, Union
+
+# Pattern preserves the TS source verbatim:
+#   /[\[【]\u200B?citation:\s*(https?:\/\/[^\]】\u200B]+|urlcite\d+|(?:doc-)?-?\d+(?:\s*,\s*(?:doc-)?-?\d+)*)\s*\u200B?[\]】]/g
+#
+# Notes:
+# * Matches both ASCII ``[]`` and Chinese fullwidth ``【】`` brackets.
+# * Allows an optional ZWSP (``\u200B``) just inside each bracket.
+# * ``citation:`` then EITHER a URL (anything not ``]``, ``】``, or ZWSP),
+#   OR a ``urlcite\d+`` placeholder, OR one or more comma-separated
+#   chunk ids (each optionally prefixed with ``doc-`` and optionally
+#   negative).
+# * URL char class deliberately excludes the closing brackets so a
+#   ``[citation:https://x.com]`` doesn't swallow the ``]``.
+# The ZWSP must be the actual code-point — the original TS source uses
+# the regex literal ``\u200B`` which the JS engine interprets as the
+# character. Python's ``re`` doesn't process the ``\u`` escape inside
+# the pattern source, so we splice the literal character in via an
+# f-string. This keeps our pattern functionally identical to the TS
+# reference and lets ``"\u200B" in CITATION_REGEX.pattern`` succeed.
+_ZWSP = "\u200B"
+CITATION_REGEX = re.compile(
+    rf"[\[【]{_ZWSP}?citation:\s*("
+    rf"https?://[^\]】{_ZWSP}]+|urlcite\d+|(?:doc-)?-?\d+(?:\s*,\s*(?:doc-)?-?\d+)*"
+    rf")\s*{_ZWSP}?[\]】]"
+)
+
+
+@dataclass(frozen=True)
+class ChunkCitation:
+    chunk_id: int
+    is_docs_chunk: bool
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "kind": "chunk",
+            "chunk_id": self.chunk_id,
+            "is_docs_chunk": self.is_docs_chunk,
+        }
+
+
+@dataclass(frozen=True)
+class UrlCitation:
+    url: str
+
+    def to_dict(self) -> dict[str, Any]:
+        return {"kind": "url", "url": self.url}
+
+
+CitationToken = Union[ChunkCitation, UrlCitation]
+
+
+def parse_citations(text: str, *, url_map: dict[str, str] | None = None) -> list[CitationToken]:
+    """Return the citation tokens found in ``text`` in document order.
+
+    ``url_map`` is the optional ``urlciteN -> URL`` lookup that the web
+    client builds in its preprocessing step. The harness ordinarily
+    doesn't preprocess (we don't render the markdown, we score it), so
+    the default empty map means ``urlciteN`` placeholders are dropped
+    rather than mis-resolved to a missing URL.
+
+    Multi-id payloads like ``[citation:1, doc-2, -3]`` are flattened
+    into separate ``ChunkCitation`` entries — same as the TS reference.
+    """
+
+    out: list[CitationToken] = []
+    for match in CITATION_REGEX.finditer(text):
+        captured = match.group(1)
+        if captured.startswith("http://") or captured.startswith("https://"):
+            out.append(UrlCitation(url=captured.strip()))
+            continue
+        if captured.startswith("urlcite"):
+            if url_map and captured in url_map:
+                out.append(UrlCitation(url=url_map[captured]))
+            continue
+        for raw_id in (s.strip() for s in captured.split(",")):
+            is_docs_chunk = raw_id.startswith("doc-")
+            number_part = raw_id[4:] if is_docs_chunk else raw_id
+            try:
+                chunk_id = int(number_part)
+            except ValueError:
+                continue
+            out.append(ChunkCitation(chunk_id=chunk_id, is_docs_chunk=is_docs_chunk))
+    return out
+
+
+__all__ = [
+    "CITATION_REGEX",
+    "ChunkCitation",
+    "UrlCitation",
+    "CitationToken",
+    "parse_citations",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py b/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py
new file mode 100644
index 000000000..959b045a5
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parse/freeform_answer.py
@@ -0,0 +1,85 @@
+"""Extract free-form answers from open-ended LLM responses.
+
+Used by benchmarks that don't have a fixed letter set (MMLongBench-Doc,
+DocVQA-style benchmarks, future legal/finance suites). The contract:
+
+* Strip leading "Answer:" / "Final answer:" markers if present.
+* Drop fenced code blocks if the model wrapped its answer in one.
+* Trim leading/trailing whitespace.
+* Return the *last* meaningful chunk — models often think out loud
+  before stating the answer.
+
+If the message is empty or only contains a fence, return ``""``.
+"""
+
+from __future__ import annotations
+
+import re
+
+_ANSWER_PREFIX = re.compile(
+    r"^\s*(?:final\s*answer|the\s+answer\s+is|answer)\s*[:=\-]\s*",
+    re.IGNORECASE,
+)
+# Marker-only regex (no capture group) used to find every "Answer:"
+# token position. We then slice from the LAST marker's end to the
+# next newline ourselves — robust to multiple inline answers because
+# we never let the engine greedy-capture across markers.
+_ANSWER_MARKER = re.compile(
+    r"(?:final\s*answer|the\s+answer\s+is|answer)\s*[:=\-]\s*",
+    re.IGNORECASE,
+)
+_FENCED_BLOCK = re.compile(r"```[a-zA-Z0-9]*\s*([\s\S]*?)\s*```")
+
+
+def extract_freeform_answer(text: str) -> str:
+    """Pull the model's final answer out of a possibly-verbose response."""
+
+    if not text or not text.strip():
+        return ""
+
+    # 1. Find the last line that starts with an Answer: marker. If
+    #    nothing matches, walk back to the last non-empty line.
+    lines = [ln.rstrip() for ln in text.strip().splitlines()]
+    candidate = ""
+    for ln in reversed(lines):
+        if not ln.strip():
+            continue
+        if _ANSWER_PREFIX.search(ln):
+            candidate = _ANSWER_PREFIX.sub("", ln, count=1).strip()
+            break
+
+    if not candidate:
+        # 2. Inline match: find every "Answer:" marker position and
+        # slice from the LAST marker's end to the next newline. Robust
+        # to "preamble.Answer: 42" one-liners and multiple inline
+        # markers (we always pick the final, freshest one).
+        marker_matches = list(_ANSWER_MARKER.finditer(text))
+        if marker_matches:
+            last = marker_matches[-1]
+            tail = text[last.end():]
+            nl = tail.find("\n")
+            if nl >= 0:
+                tail = tail[:nl]
+            candidate = tail.strip()
+
+    if not candidate:
+        # 3. No "Answer:" marker — try fenced blocks.
+        fences = _FENCED_BLOCK.findall(text)
+        if fences:
+            candidate = fences[-1].strip()
+        else:
+            # Last non-empty line as a fallback.
+            for ln in reversed(lines):
+                if ln.strip():
+                    candidate = ln.strip()
+                    break
+
+    # 2. Strip wrapping quotes / parens / trailing punctuation that
+    #    confuse the grader without changing meaning.
+    candidate = candidate.strip().strip("`").strip()
+    if candidate.startswith(("\"", "'")) and candidate.endswith(("\"", "'")):
+        candidate = candidate[1:-1].strip()
+    return candidate
+
+
+__all__ = ["extract_freeform_answer"]
diff --git a/surfsense_evals/src/surfsense_evals/core/parse/sse.py b/surfsense_evals/src/surfsense_evals/core/parse/sse.py
new file mode 100644
index 000000000..76ded2d13
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parse/sse.py
@@ -0,0 +1,72 @@
+"""Minimal SSE consumer compatible with SurfSense's wire format.
+
+SurfSense uses ``app/services/streaming/envelope/sse.py`` to frame events:
+
+* ``data: <single-line-string>\\n\\n``
+* ``data: <json-string>\\n\\n``  (most events)
+* ``data: [DONE]\\n\\n``  (terminator)
+
+There is no ``event:``, ``id:``, or ``retry:`` framing in production —
+``format_sse(payload)`` only emits the ``data:`` line. This implementation
+is therefore intentionally smaller than ``httpx-sse`` (which we still
+list as a dep so callers who want richer parsing can opt in): one event
+per ``data:`` line, separated by blank lines.
+
+We accept any line iterator (an ``httpx.Response.aiter_lines`` adapter
+in production, a list in tests) so this is unit-testable without a
+network mock.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class SseEvent:
+    """A parsed SSE event. Only the ``data`` field is populated.
+
+    Multi-line payloads (``data: a\\ndata: b``) are joined with ``\\n``
+    per the SSE spec, even though SurfSense doesn't currently emit them.
+    """
+
+    data: str
+
+
+async def iter_sse_events(lines: AsyncIterator[str]) -> AsyncIterator[SseEvent]:
+    """Yield one ``SseEvent`` per blank-line-terminated frame.
+
+    Lines that are empty or whitespace flush the buffer. ``data:`` lines
+    are accumulated into the buffer; everything else is ignored
+    (matches the lenient browser EventSource behaviour).
+    """
+
+    buffer: list[str] = []
+    async for raw in lines:
+        if raw is None:
+            continue
+        line = raw.rstrip("\r")
+        if line == "":
+            if buffer:
+                yield SseEvent(data="\n".join(buffer))
+                buffer.clear()
+            continue
+        if line.startswith(":"):
+            # comment / heartbeat
+            continue
+        if line.startswith("data:"):
+            # spec: optional single space after the colon.
+            payload = line[5:]
+            if payload.startswith(" "):
+                payload = payload[1:]
+            buffer.append(payload)
+            continue
+        # Any other field (event:, id:, retry:) is currently unused.
+        continue
+
+    if buffer:
+        yield SseEvent(data="\n".join(buffer))
+
+
+__all__ = ["SseEvent", "iter_sse_events"]
diff --git a/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py b/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py
new file mode 100644
index 000000000..e03fa34c9
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/pdf/__init__.py
@@ -0,0 +1,31 @@
+"""Domain-agnostic PDF rendering helper. Lazy import."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .render import (
+        PdfImage,
+        render_pdf,
+        render_pdf_with_images,
+        render_text_files_to_pdf,
+    )
+
+__all__ = [
+    "PdfImage",
+    "render_pdf",
+    "render_pdf_with_images",
+    "render_text_files_to_pdf",
+]
+
+
+_LAZY = {"PdfImage", "render_pdf", "render_pdf_with_images", "render_text_files_to_pdf"}
+
+
+def __getattr__(name: str):
+    if name in _LAZY:
+        from . import render as _mod
+
+        return getattr(_mod, name)
+    raise AttributeError(f"module 'surfsense_evals.core.pdf' has no attribute {name!r}")
diff --git a/surfsense_evals/src/surfsense_evals/core/pdf/render.py b/surfsense_evals/src/surfsense_evals/core/pdf/render.py
new file mode 100644
index 000000000..624136d7c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/pdf/render.py
@@ -0,0 +1,351 @@
+"""Deterministic ``.txt`` / ``.md`` → single PDF via reportlab.
+
+Used wherever a benchmark needs the same source bytes fed to both the
+native-PDF arm and the SurfSense ingestion arm. The head-to-head
+comparison is fair only if the *same* PDF is the input to both arms,
+which is why we go to lengths to make the rendering deterministic.
+
+Determinism notes:
+
+* We pin the PDF metadata to a fixed creation date and producer
+  (``reportlab`` accepts neither directly, but ``Canvas.setAuthor`` and
+  the absence of an ``info`` mutator means the bytes only differ by
+  ``CreationDate`` / ``ModDate``). We post-process the PDF to scrub
+  those if ``deterministic=True`` is passed.
+* Page size, font, margins, and tab handling are fixed in code so the
+  same input yields the same byte output across machines.
+* PDF/A is overkill for our use; basic PDF 1.4 is what every model
+  expects.
+"""
+
+from __future__ import annotations
+
+import io
+import re
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+
+from reportlab.lib.pagesizes import LETTER
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.lib.units import inch
+from reportlab.lib.utils import ImageReader
+from reportlab.platypus import (
+    Image,
+    KeepTogether,
+    PageBreak,
+    Paragraph,
+    SimpleDocTemplate,
+    Spacer,
+)
+
+
+@dataclass
+class RenderedPdf:
+    path: Path
+    n_pages_estimate: int
+    n_chars: int
+
+
+_PDF_DATE_KEY = re.compile(rb"/(?:CreationDate|ModDate)\s*\(D:[^)]*\)")
+# reportlab also writes a `/ID [<hex1><hex2>]` trailer entry that
+# embeds a per-run hash. Scrub it so two renders of the same input
+# produce the same bytes.
+_PDF_ID_ARRAY = re.compile(rb"/ID\s*\[\s*<[^>]*>\s*<[^>]*>\s*\]")
+
+
+def _scrub_dates(pdf_bytes: bytes) -> bytes:
+    """Remove ``CreationDate`` / ``ModDate`` / trailer ``/ID`` so the
+    file is byte-deterministic across runs."""
+
+    pdf_bytes = _PDF_DATE_KEY.sub(b"/CreationDate (D:19700101000000Z)", pdf_bytes)
+    pdf_bytes = _PDF_ID_ARRAY.sub(b"/ID [<00><00>]", pdf_bytes)
+    return pdf_bytes
+
+
+_DEFAULT_STYLES = getSampleStyleSheet()
+
+
+def _build_body_style() -> ParagraphStyle:
+    base = _DEFAULT_STYLES["BodyText"]
+    style = ParagraphStyle(
+        "EvalBody",
+        parent=base,
+        fontName="Helvetica",
+        fontSize=10.5,
+        leading=14,
+        spaceAfter=6,
+        spaceBefore=0,
+    )
+    return style
+
+
+def _build_heading_style() -> ParagraphStyle:
+    base = _DEFAULT_STYLES["Heading2"]
+    style = ParagraphStyle(
+        "EvalHeading",
+        parent=base,
+        fontName="Helvetica-Bold",
+        fontSize=14,
+        leading=18,
+        spaceAfter=10,
+        spaceBefore=8,
+    )
+    return style
+
+
+def _normalise_paragraphs(text: str) -> list[str]:
+    """Split a text blob into paragraphs while preserving blank-line structure."""
+
+    blocks: list[list[str]] = [[]]
+    for line in text.splitlines():
+        stripped = line.rstrip()
+        if stripped == "":
+            if blocks[-1]:
+                blocks.append([])
+            continue
+        blocks[-1].append(stripped)
+    paragraphs: list[str] = []
+    for block in blocks:
+        if not block:
+            continue
+        # Join lines within a paragraph with spaces (text-from-PDF style).
+        paragraphs.append(" ".join(block))
+    return paragraphs
+
+
+def _escape_html(text: str) -> str:
+    return (
+        text.replace("&", "&amp;")
+        .replace("<", "&lt;")
+        .replace(">", "&gt;")
+    )
+
+
+def render_pdf(
+    *,
+    title: str,
+    sections: Sequence[tuple[str | None, str]],
+    output_path: Path,
+    deterministic: bool = True,
+) -> RenderedPdf:
+    """Render one PDF from a list of ``(section_heading, section_text)`` tuples.
+
+    ``section_heading`` may be ``None`` for an unnamed section. Each
+    section is followed by a page break so the model's PDF parser sees
+    a clean structural boundary between source files.
+    """
+
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    buffer = io.BytesIO()
+    doc = SimpleDocTemplate(
+        buffer,
+        pagesize=LETTER,
+        leftMargin=0.75 * inch,
+        rightMargin=0.75 * inch,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+        title=title,
+        author="surfsense-evals",
+        subject="Eval input",
+        creator="surfsense-evals",
+    )
+
+    body_style = _build_body_style()
+    heading_style = _build_heading_style()
+    title_style = ParagraphStyle(
+        "EvalTitle",
+        parent=_DEFAULT_STYLES["Title"],
+        fontName="Helvetica-Bold",
+        fontSize=18,
+        leading=22,
+        spaceAfter=14,
+    )
+
+    flow: list = [Paragraph(_escape_html(title), title_style)]
+    total_chars = 0
+    for index, (heading, text) in enumerate(sections):
+        if index > 0:
+            flow.append(PageBreak())
+        if heading:
+            flow.append(Paragraph(_escape_html(heading), heading_style))
+        for paragraph in _normalise_paragraphs(text):
+            total_chars += len(paragraph)
+            flow.append(Paragraph(_escape_html(paragraph), body_style))
+            flow.append(Spacer(1, 4))
+
+    doc.build(flow)
+    pdf_bytes = buffer.getvalue()
+    if deterministic:
+        pdf_bytes = _scrub_dates(pdf_bytes)
+    output_path.write_bytes(pdf_bytes)
+
+    # Conservative page estimate: ~3000 chars per LETTER page at 10.5pt.
+    n_pages = max(1, total_chars // 3000 + len(sections))
+    return RenderedPdf(path=output_path, n_pages_estimate=n_pages, n_chars=total_chars)
+
+
+@dataclass
+class PdfImage:
+    """One image to embed inside a section.
+
+    ``caption`` is rendered below the image (italic). ``max_width_in``
+    caps the rendered width in inches; height auto-scales to preserve
+    aspect ratio (read with PIL).
+    """
+
+    path: Path
+    caption: str = ""
+    max_width_in: float = 5.5  # default leaves margin for LETTER 8.5"
+
+
+def _make_image_flowable(image: PdfImage) -> Image:
+    """Build a reportlab Image flowable scaled to fit page width."""
+
+    reader = ImageReader(str(image.path))
+    iw, ih = reader.getSize()
+    if iw <= 0 or ih <= 0:
+        raise ValueError(f"Invalid image dimensions for {image.path}: {iw}x{ih}")
+    target_w = image.max_width_in * inch
+    target_h = target_w * (ih / iw)
+    # Cap height too — some medical images are extreme portrait.
+    max_h = 7.0 * inch
+    if target_h > max_h:
+        target_h = max_h
+        target_w = target_h * (iw / ih)
+    return Image(str(image.path), width=target_w, height=target_h)
+
+
+def render_pdf_with_images(
+    *,
+    title: str,
+    sections: Sequence[tuple[str | None, str, Sequence[PdfImage] | None]],
+    output_path: Path,
+    deterministic: bool = True,
+    page_break_between_sections: bool = False,
+) -> RenderedPdf:
+    """Render a PDF that mixes text and embedded images.
+
+    Each section is ``(heading, body_text, images)``. Images render
+    inline after the body text, each followed by an italic caption.
+    Set ``page_break_between_sections=True`` if you want explicit
+    structural boundaries (mostly useful for multi-case PDFs); the
+    default keeps everything on one page when possible (so a single
+    MedXpertQA case is one PDF page with case + images + options).
+    """
+
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    buffer = io.BytesIO()
+    doc = SimpleDocTemplate(
+        buffer,
+        pagesize=LETTER,
+        leftMargin=0.75 * inch,
+        rightMargin=0.75 * inch,
+        topMargin=0.75 * inch,
+        bottomMargin=0.75 * inch,
+        title=title,
+        author="surfsense-evals",
+        subject="Eval input",
+        creator="surfsense-evals",
+    )
+
+    body_style = _build_body_style()
+    heading_style = _build_heading_style()
+    caption_style = ParagraphStyle(
+        "EvalCaption",
+        parent=body_style,
+        fontSize=9,
+        leading=11,
+        textColor="#444",
+        spaceBefore=2,
+        spaceAfter=10,
+    )
+    title_style = ParagraphStyle(
+        "EvalTitle",
+        parent=_DEFAULT_STYLES["Title"],
+        fontName="Helvetica-Bold",
+        fontSize=18,
+        leading=22,
+        spaceAfter=14,
+    )
+
+    flow: list = [Paragraph(_escape_html(title), title_style)]
+    total_chars = 0
+    for index, (heading, text, images) in enumerate(sections):
+        if index > 0 and page_break_between_sections:
+            flow.append(PageBreak())
+        if heading:
+            flow.append(Paragraph(_escape_html(heading), heading_style))
+        for paragraph in _normalise_paragraphs(text):
+            total_chars += len(paragraph)
+            flow.append(Paragraph(_escape_html(paragraph), body_style))
+            flow.append(Spacer(1, 4))
+        for image in images or []:
+            try:
+                img_flow = _make_image_flowable(image)
+            except Exception:  # noqa: BLE001 — bad image shouldn't kill PDF
+                continue
+            grouped = [img_flow]
+            if image.caption:
+                grouped.append(Paragraph(_escape_html(image.caption), caption_style))
+            else:
+                grouped.append(Spacer(1, 8))
+            flow.append(KeepTogether(grouped))
+
+    doc.build(flow)
+    pdf_bytes = buffer.getvalue()
+    if deterministic:
+        pdf_bytes = _scrub_dates(pdf_bytes)
+    output_path.write_bytes(pdf_bytes)
+
+    n_pages = max(1, total_chars // 3000 + len(sections))
+    return RenderedPdf(path=output_path, n_pages_estimate=n_pages, n_chars=total_chars)
+
+
+def render_text_files_to_pdf(
+    *,
+    title: str,
+    files: Iterable[Path],
+    output_path: Path,
+    deterministic: bool = True,
+) -> RenderedPdf:
+    """Convenience wrapper: read a list of text files, render to one PDF.
+
+    The heading of each section is the file's name (no extension), so
+    e.g. ``admission_note.txt`` becomes a section header ``admission_note``
+    in the rendered PDF. Useful for any text-only benchmark that ships
+    a corpus as separate ``.txt`` / ``.md`` shards per logical document.
+    """
+
+    sections: list[tuple[str | None, str]] = []
+    for path in files:
+        path = Path(path)
+        text = path.read_text(encoding="utf-8")
+        sections.append((path.stem, text))
+    return render_pdf(
+        title=title,
+        sections=sections,
+        output_path=output_path,
+        deterministic=deterministic,
+    )
+
+
+# Tiny self-check — handy when debugging.
+def _self_test() -> None:  # pragma: no cover
+    out = Path("./_render_self_test.pdf")
+    sections = [
+        ("intro", "Hello world.\n\nThis is a test."),
+        ("body", "Line one.\nLine two."),
+    ]
+    rendered = render_pdf(title="Self test", sections=sections, output_path=out)
+    print(f"wrote {rendered.path} ({rendered.n_chars} chars)")
+
+
+# Importing ``datetime`` keeps the timezone helper handy if a future
+# benchmark wants to embed a real timestamp without losing determinism.
+_NOW_FROZEN = datetime(2026, 5, 11, tzinfo=UTC)
diff --git a/surfsense_evals/src/surfsense_evals/core/providers/__init__.py b/surfsense_evals/src/surfsense_evals/core/providers/__init__.py
new file mode 100644
index 000000000..fa82bcbf2
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/providers/__init__.py
@@ -0,0 +1,22 @@
+"""External LLM providers (used by the native arm).
+
+Lazy imports so the SurfSense-only path doesn't transitively load the
+OpenRouter client until something actually constructs ``OpenRouterPdfProvider``.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .openrouter_pdf import OpenRouterPdfProvider, OpenRouterResponse
+
+__all__ = ["OpenRouterPdfProvider", "OpenRouterResponse"]
+
+
+def __getattr__(name: str):
+    if name in {"OpenRouterPdfProvider", "OpenRouterResponse"}:
+        from . import openrouter_pdf as _mod
+
+        return getattr(_mod, name)
+    raise AttributeError(f"module 'surfsense_evals.core.providers' has no attribute {name!r}")
diff --git a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py
new file mode 100644
index 000000000..2494434be
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py
@@ -0,0 +1,118 @@
+"""Bare OpenRouter ``chat/completions`` provider — no PDF, no plugins.
+
+Used by ``BareLlmArm`` to measure "what does the model answer with
+zero retrieval context?". Same wire shape as ``OpenRouterPdfProvider``
+minus the file-parser plugin and the ``file`` content part:
+
+```json
+{
+  "model": "openai/gpt-5.4-mini",
+  "messages": [
+    {"role": "system", "content": "<optional>"},
+    {"role": "user",   "content": "<prompt>"}
+  ]
+}
+```
+
+The response shape is identical to the PDF provider's, so we re-use
+``_parse_chat_completion`` from ``openrouter_pdf`` and only specialise
+the request builder. That keeps cost-extraction, token-counting, and
+content-array handling in one place.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Any
+
+import httpx
+
+from .openrouter_pdf import (
+    OpenRouterResponse,
+    _DEFAULT_HEADERS,
+    _parse_chat_completion,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class OpenRouterChatProvider:
+    """Stateless bare-chat client. No PDF, no file-parser plugin."""
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str = "https://openrouter.ai/api/v1",
+        model: str,
+        timeout_s: float = 600.0,
+    ) -> None:
+        if not api_key:
+            raise ValueError("OPENROUTER_API_KEY is required for the bare-LLM arm.")
+        self._api_key = api_key
+        self._base = base_url.rstrip("/")
+        self._model = model
+        self._timeout = httpx.Timeout(timeout_s, connect=15.0)
+
+    @property
+    def model(self) -> str:
+        return self._model
+
+    def _build_payload(
+        self,
+        *,
+        prompt: str,
+        system_prompt: str | None,
+        max_tokens: int | None,
+    ) -> dict[str, Any]:
+        messages: list[dict[str, Any]] = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": prompt})
+        body: dict[str, Any] = {"model": self._model, "messages": messages}
+        if max_tokens:
+            body["max_tokens"] = max_tokens
+        return body
+
+    async def complete(
+        self,
+        *,
+        prompt: str,
+        system_prompt: str | None = None,
+        max_tokens: int | None = None,
+        http: httpx.AsyncClient | None = None,
+    ) -> OpenRouterResponse:
+        """Single chat completion. Errors are raised verbatim — caller decides retries."""
+
+        payload = self._build_payload(
+            prompt=prompt,
+            system_prompt=system_prompt,
+            max_tokens=max_tokens,
+        )
+        headers = {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            **_DEFAULT_HEADERS,
+        }
+        url = f"{self._base}/chat/completions"
+        started = time.monotonic()
+        if http is not None:
+            response = await http.post(url, json=payload, headers=headers, timeout=self._timeout)
+        else:
+            async with httpx.AsyncClient(timeout=self._timeout) as client:
+                response = await client.post(
+                    url, json=payload, headers=headers, timeout=self._timeout
+                )
+        latency_ms = int((time.monotonic() - started) * 1000)
+        if response.status_code >= 400:
+            raise httpx.HTTPStatusError(
+                f"OpenRouter HTTP {response.status_code}: {response.text[:300]}",
+                request=response.request,
+                response=response,
+            )
+        return _parse_chat_completion(response.json(), latency_ms=latency_ms)
+
+
+__all__ = ["OpenRouterChatProvider"]
diff --git a/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py
new file mode 100644
index 000000000..e98590cbf
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py
@@ -0,0 +1,231 @@
+"""Native-PDF arm provider: OpenRouter ``chat/completions`` with PDF input.
+
+Per `<https://openrouter.ai/docs/features/multimodal/pdfs>`__ the wire
+shape is OpenAI-compatible with one PDF-specific extra:
+
+```json
+{
+  "model": "anthropic/claude-sonnet-4.5",
+  "messages": [{
+    "role": "user",
+    "content": [
+      {"type": "file", "file": {"filename": "case.pdf",
+        "file_data": "data:application/pdf;base64,<b64>"}},
+      {"type": "text", "text": "<prompt>"}
+    ]
+  }],
+  "plugins": [{"id": "file-parser", "pdf": {"engine": "native"}}]
+}
+```
+
+``engine: "native"`` is the only engine that doesn't pre-OCR the
+PDF — it forwards raw bytes to PDF-native models (Claude, Gemini),
+matching what a human user does when "dropping the PDF into Claude".
+``mistral-ocr`` and ``cloudflare-ai`` are exposed as enum options for
+non-native models.
+
+Headers ``HTTP-Referer`` and ``X-Title`` make spend show up cleanly on
+the OpenRouter dashboard.
+"""
+
+from __future__ import annotations
+
+import base64
+import logging
+import time
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from typing import Any
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+class PdfEngine(str, Enum):
+    NATIVE = "native"
+    MISTRAL_OCR = "mistral-ocr"
+    CLOUDFLARE_AI = "cloudflare-ai"
+
+
+@dataclass
+class OpenRouterResponse:
+    """Subset of the OpenRouter response we care about for scoring."""
+
+    text: str
+    input_tokens: int
+    output_tokens: int
+    total_tokens: int
+    cost_micros: int
+    latency_ms: int
+    finish_reason: str | None
+    raw: dict[str, Any]
+
+
+_DEFAULT_HEADERS = {
+    "HTTP-Referer": "https://github.com/MODSetter/SurfSense",
+    "X-Title": "SurfSense-evals",
+}
+
+
+class OpenRouterPdfProvider:
+    """Thin httpx-based client. Stateless; safe to reuse per arm instance."""
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str = "https://openrouter.ai/api/v1",
+        model: str,
+        engine: PdfEngine = PdfEngine.NATIVE,
+        timeout_s: float = 600.0,
+    ) -> None:
+        if not api_key:
+            raise ValueError("OPENROUTER_API_KEY is required for the native arm.")
+        self._api_key = api_key
+        self._base = base_url.rstrip("/")
+        self._model = model
+        self._engine = engine
+        self._timeout = httpx.Timeout(timeout_s, connect=15.0)
+
+    @property
+    def model(self) -> str:
+        return self._model
+
+    @property
+    def engine(self) -> PdfEngine:
+        return self._engine
+
+    def _build_payload(
+        self,
+        *,
+        prompt: str,
+        pdf_path: Path,
+        max_tokens: int | None,
+        extra_messages: list[dict[str, Any]] | None,
+    ) -> dict[str, Any]:
+        b64 = base64.b64encode(pdf_path.read_bytes()).decode("ascii")
+        user_content: list[dict[str, Any]] = [
+            {
+                "type": "file",
+                "file": {
+                    "filename": pdf_path.name,
+                    "file_data": f"data:application/pdf;base64,{b64}",
+                },
+            },
+            {"type": "text", "text": prompt},
+        ]
+        messages: list[dict[str, Any]] = list(extra_messages or [])
+        messages.append({"role": "user", "content": user_content})
+        body: dict[str, Any] = {
+            "model": self._model,
+            "messages": messages,
+            "plugins": [
+                {"id": "file-parser", "pdf": {"engine": self._engine.value}}
+            ],
+        }
+        if max_tokens:
+            body["max_tokens"] = max_tokens
+        return body
+
+    async def complete(
+        self,
+        *,
+        prompt: str,
+        pdf_path: Path,
+        max_tokens: int | None = None,
+        extra_messages: list[dict[str, Any]] | None = None,
+        http: httpx.AsyncClient | None = None,
+    ) -> OpenRouterResponse:
+        """Single chat completion. Errors are raised verbatim — runner decides retries."""
+
+        payload = self._build_payload(
+            prompt=prompt,
+            pdf_path=pdf_path,
+            max_tokens=max_tokens,
+            extra_messages=extra_messages,
+        )
+        headers = {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            **_DEFAULT_HEADERS,
+        }
+        url = f"{self._base}/chat/completions"
+        started = time.monotonic()
+        if http is not None:
+            response = await http.post(url, json=payload, headers=headers, timeout=self._timeout)
+        else:
+            async with httpx.AsyncClient(timeout=self._timeout) as client:
+                response = await client.post(
+                    url, json=payload, headers=headers, timeout=self._timeout
+                )
+        latency_ms = int((time.monotonic() - started) * 1000)
+        if response.status_code >= 400:
+            raise httpx.HTTPStatusError(
+                f"OpenRouter HTTP {response.status_code}: {response.text[:300]}",
+                request=response.request,
+                response=response,
+            )
+        data = response.json()
+        return _parse_chat_completion(data, latency_ms=latency_ms)
+
+
+def _parse_chat_completion(payload: dict[str, Any], *, latency_ms: int) -> OpenRouterResponse:
+    """Tolerant parser for OpenRouter / OpenAI chat-completions JSON.
+
+    OpenRouter passes through any provider-specific extras, but the
+    canonical shape is ``choices[0].message.content`` (string OR array
+    of content parts) and ``usage.prompt_tokens / completion_tokens / total_tokens``.
+    Cost lives at the top level (``payload["usage"]["cost"]`` or
+    ``payload["x-or-cost"]``) depending on routing.
+    """
+
+    text = ""
+    finish_reason: str | None = None
+    choices = payload.get("choices") or []
+    if choices:
+        message = (choices[0] or {}).get("message") or {}
+        content = message.get("content")
+        if isinstance(content, str):
+            text = content
+        elif isinstance(content, list):
+            chunks: list[str] = []
+            for part in content:
+                if isinstance(part, dict) and part.get("type") in {"text", "output_text"}:
+                    chunks.append(str(part.get("text", "")))
+            text = "".join(chunks)
+        finish_reason = (choices[0] or {}).get("finish_reason") or None
+
+    usage = payload.get("usage") or {}
+    input_tokens = int(usage.get("prompt_tokens") or 0)
+    output_tokens = int(usage.get("completion_tokens") or 0)
+    total_tokens = int(usage.get("total_tokens") or (input_tokens + output_tokens))
+
+    # OpenRouter exposes cost in dollars on `usage.cost` or `cost`. We
+    # convert to integer micros to avoid float-summing surprises across
+    # 7,663 MIRAGE questions.
+    raw_cost = usage.get("cost")
+    if raw_cost is None:
+        raw_cost = payload.get("cost")
+    cost_micros = 0
+    if raw_cost is not None:
+        try:
+            cost_micros = int(round(float(raw_cost) * 1_000_000))
+        except (TypeError, ValueError):
+            cost_micros = 0
+
+    return OpenRouterResponse(
+        text=text,
+        input_tokens=input_tokens,
+        output_tokens=output_tokens,
+        total_tokens=total_tokens,
+        cost_micros=cost_micros,
+        latency_ms=latency_ms,
+        finish_reason=finish_reason,
+        raw=payload,
+    )
+
+
+__all__ = ["OpenRouterPdfProvider", "OpenRouterResponse", "PdfEngine"]
diff --git a/surfsense_evals/src/surfsense_evals/core/registry.py b/surfsense_evals/src/surfsense_evals/core/registry.py
new file mode 100644
index 000000000..cc8b725e0
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/registry.py
@@ -0,0 +1,265 @@
+"""Suite + Benchmark protocols and the global registry.
+
+The extensibility seam: ``core.cli`` walks ``surfsense_evals.suites`` on
+import, which auto-imports every benchmark subpackage, which calls
+``register(<benchmark>)`` at module bottom. The CLI then iterates the
+populated registry to build subcommand groups dynamically.
+
+Adding a new domain = drop a folder under ``suites/<domain>/<bench>/``
+that ends in ``register(MyBenchmark())``. No edits anywhere in
+``core/`` are required.
+"""
+
+from __future__ import annotations
+
+import argparse
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Protocol, runtime_checkable
+
+import httpx
+
+from .clients import DocumentsClient, NewChatClient, SearchSpaceClient
+from .config import Config, SuiteState
+
+# ---------------------------------------------------------------------------
+# Run context — what every benchmark.ingest/run receives
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RunContext:
+    """Per-invocation environment threaded into ``ingest`` and ``run``.
+
+    A benchmark uses this to read pinned suite state, build new HTTP
+    clients on the shared ``http`` session, find the right data /
+    reports paths, and discover the active OpenRouter model + key.
+
+    ``http`` is the authenticated SurfSense client (auth event hook
+    attached). It is **not** an OpenRouter client — providers create
+    their own short-lived clients because OpenRouter doesn't share the
+    SurfSense bearer.
+    """
+
+    suite: str
+    benchmark: str
+    config: Config
+    suite_state: SuiteState
+    http: httpx.AsyncClient
+
+    @property
+    def search_space_id(self) -> int:
+        return self.suite_state.search_space_id
+
+    @property
+    def agent_llm_id(self) -> int:
+        return self.suite_state.agent_llm_id
+
+    @property
+    def provider_model(self) -> str:
+        """Slug used by the SurfSense agent (and the native arm by default).
+
+        For ``cost-arbitrage`` scenarios this is the *cheap, text-only*
+        slug — SurfSense answers from the chunks the vision LLM already
+        extracted at ingest. The native arm should use
+        ``native_arm_model`` instead in that scenario.
+        """
+
+        return self.suite_state.provider_model
+
+    @property
+    def native_arm_model(self) -> str:
+        """Slug the native_pdf arm should use.
+
+        Defaults to ``provider_model`` (head-to-head / symmetric-cheap);
+        for ``cost-arbitrage`` it returns the explicit
+        ``--native-arm-model`` so the native arm can fairly answer
+        image-bearing questions.
+        """
+
+        return self.suite_state.effective_native_arm_model
+
+    @property
+    def vision_provider_model(self) -> str | None:
+        """Slug of the OpenRouter vision LLM SurfSense used at ingest.
+
+        ``None`` if no vision config was attached at setup (legacy or
+        text-only suite). Used by runners purely to record what was
+        actually used in ``RunArtifact.extra`` and to label reports.
+        """
+
+        return self.suite_state.vision_provider_model
+
+    @property
+    def scenario(self) -> str:
+        """Scenario name pinned at setup time (see ``config.SCENARIOS``)."""
+
+        return self.suite_state.scenario
+
+    def search_space_client(self) -> SearchSpaceClient:
+        return SearchSpaceClient(self.http, self.config.surfsense_api_base)
+
+    def documents_client(self) -> DocumentsClient:
+        return DocumentsClient(self.http, self.config.surfsense_api_base)
+
+    def new_chat_client(self) -> NewChatClient:
+        return NewChatClient(self.http, self.config.surfsense_api_base)
+
+    def maps_dir(self) -> Path:
+        path = self.config.suite_maps_dir(self.suite)
+        path.mkdir(parents=True, exist_ok=True)
+        return path
+
+    def runs_dir(self, *, run_timestamp: str) -> Path:
+        path = self.config.suite_runs_dir(self.suite) / run_timestamp / self.benchmark
+        path.mkdir(parents=True, exist_ok=True)
+        return path
+
+    def benchmark_data_dir(self) -> Path:
+        path = self.config.suite_data_dir(self.suite) / self.benchmark
+        path.mkdir(parents=True, exist_ok=True)
+        return path
+
+
+# ---------------------------------------------------------------------------
+# Run artifact + report section
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class RunArtifact:
+    """Everything a runner persists for the report writer to consume.
+
+    ``raw_path`` points at the JSONL of per-question ``ArmResult``
+    rows. ``metrics`` is a free-form dict the benchmark fills in (e.g.
+    ``{"native": {...}, "surfsense": {...}, "delta": {...}}``).
+    """
+
+    suite: str
+    benchmark: str
+    run_timestamp: str
+    raw_path: Path
+    metrics: dict[str, Any] = field(default_factory=dict)
+    extra: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ReportSection:
+    """One benchmark's slice of the final summary."""
+
+    title: str
+    headline: bool
+    body_md: str
+    body_json: dict[str, Any] = field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# Benchmark protocol + registry
+# ---------------------------------------------------------------------------
+
+
+@runtime_checkable
+class Benchmark(Protocol):
+    """The contract every benchmark module ends with ``register(<x>)``."""
+
+    suite: str
+    name: str
+    headline: bool
+    description: str
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:  # pragma: no cover - protocol
+        ...
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:  # pragma: no cover - protocol
+        ...
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:  # pragma: no cover - protocol
+        """Add benchmark-specific flags to ``run <suite> <benchmark>``."""
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:  # pragma: no cover - protocol
+        ...
+
+
+# ---------------------------------------------------------------------------
+# Registry storage
+# ---------------------------------------------------------------------------
+
+
+_REGISTRY: dict[tuple[str, str], Benchmark] = {}
+
+
+def register(benchmark: Benchmark) -> None:
+    """Add ``benchmark`` to the registry. Last-wins on duplicate keys.
+
+    Duplicate registrations log a warning rather than raising so a
+    benchmark module imported twice (once via auto-discovery, once via
+    a test directly importing it) doesn't blow up the CLI.
+    """
+
+    key = (benchmark.suite, benchmark.name)
+    if key in _REGISTRY:
+        import logging
+
+        logging.getLogger(__name__).warning(
+            "Benchmark %s/%s re-registered (overwriting prior)", *key
+        )
+    _REGISTRY[key] = benchmark
+
+
+def unregister(suite: str, name: str) -> None:
+    """Test helper: drop a single benchmark from the registry."""
+
+    _REGISTRY.pop((suite, name), None)
+
+
+def reset() -> None:
+    """Test helper: wipe the registry (use with monkeypatched discovery)."""
+
+    _REGISTRY.clear()
+
+
+def get(suite: str, name: str) -> Benchmark:
+    try:
+        return _REGISTRY[(suite, name)]
+    except KeyError as exc:
+        available = ", ".join(f"{s}/{n}" for s, n in sorted(_REGISTRY)) or "<none>"
+        raise KeyError(
+            f"Unknown benchmark '{suite}/{name}'. Registered: {available}"
+        ) from exc
+
+
+def list_suites() -> list[str]:
+    return sorted({s for s, _ in _REGISTRY})
+
+
+def list_benchmarks(suite: str | None = None) -> list[Benchmark]:
+    if suite is None:
+        return [_REGISTRY[k] for k in sorted(_REGISTRY)]
+    return [_REGISTRY[k] for k in sorted(_REGISTRY) if k[0] == suite]
+
+
+def snapshot() -> Mapping[tuple[str, str], Benchmark]:
+    """Read-only view for diagnostics (e.g. ``benchmarks list`` rendering)."""
+
+    return dict(_REGISTRY)
+
+
+__all__ = [
+    "Arm",
+    "Benchmark",
+    "ReportSection",
+    "RunArtifact",
+    "RunContext",
+    "get",
+    "list_benchmarks",
+    "list_suites",
+    "register",
+    "reset",
+    "snapshot",
+    "unregister",
+]
+
+
+# Re-export Arm from arms.base so suites can `from core.registry import Arm`.
+from .arms.base import Arm  # noqa: E402, F401  (deliberate re-export at bottom)
diff --git a/surfsense_evals/src/surfsense_evals/core/report/__init__.py b/surfsense_evals/src/surfsense_evals/core/report/__init__.py
new file mode 100644
index 000000000..c5ccbc64c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/report/__init__.py
@@ -0,0 +1,18 @@
+"""Report writer + section composition primitives. Lazy import."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .writer import write_report
+
+__all__ = ["write_report"]
+
+
+def __getattr__(name: str):
+    if name == "write_report":
+        from .writer import write_report
+
+        return write_report
+    raise AttributeError(f"module 'surfsense_evals.core.report' has no attribute {name!r}")
diff --git a/surfsense_evals/src/surfsense_evals/core/report/writer.py b/surfsense_evals/src/surfsense_evals/core/report/writer.py
new file mode 100644
index 000000000..8d1ffa07a
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/report/writer.py
@@ -0,0 +1,89 @@
+"""Report writer — composes per-benchmark sections into one summary.
+
+Output:
+
+* ``reports/<suite>/<run-timestamp>/summary.md`` — human-readable.
+  Bullet lists only (no tables) per project's coding-standards.
+* ``reports/<suite>/<run-timestamp>/summary.json`` — same content as
+  structured JSON for downstream tooling (CI dashboards, regressions).
+
+Headline benchmarks come first in both outputs.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Iterable
+from pathlib import Path
+
+from ..config import Config
+from ..registry import ReportSection
+
+
+def write_report(
+    *,
+    config: Config,
+    suite: str,
+    sections: Iterable[ReportSection],
+    run_timestamp: str,
+) -> Path:
+    """Write ``summary.md`` + ``summary.json``. Returns the path of the .md file."""
+
+    sections_list = list(sections)
+    sections_list.sort(key=lambda s: (not s.headline, s.title.lower()))
+
+    out_dir = config.suite_reports_dir(suite) / run_timestamp
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    md_path = out_dir / "summary.md"
+    json_path = out_dir / "summary.json"
+
+    md_lines: list[str] = [
+        f"# SurfSense evals — suite `{suite}`",
+        "",
+        f"- Run timestamp: `{run_timestamp}`",
+        f"- Sections: {len(sections_list)}",
+        "",
+    ]
+    headline = [s for s in sections_list if s.headline]
+    secondary = [s for s in sections_list if not s.headline]
+    if headline:
+        md_lines.append("## Headline")
+        md_lines.append("")
+        for section in headline:
+            md_lines.append(f"### {section.title}")
+            md_lines.append("")
+            md_lines.append(section.body_md.rstrip())
+            md_lines.append("")
+    if secondary:
+        md_lines.append("## Secondary measurements")
+        md_lines.append("")
+        for section in secondary:
+            md_lines.append(f"### {section.title}")
+            md_lines.append("")
+            md_lines.append(section.body_md.rstrip())
+            md_lines.append("")
+
+    md_path.write_text("\n".join(md_lines).rstrip() + "\n", encoding="utf-8")
+
+    json_payload = {
+        "suite": suite,
+        "run_timestamp": run_timestamp,
+        "sections": [
+            {
+                "title": s.title,
+                "headline": s.headline,
+                "body_md": s.body_md,
+                "body_json": s.body_json,
+            }
+            for s in sections_list
+        ],
+    }
+    json_path.write_text(
+        json.dumps(json_payload, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    return md_path
+
+
+__all__ = ["ReportSection", "write_report"]
diff --git a/surfsense_evals/src/surfsense_evals/core/scenarios.py b/surfsense_evals/src/surfsense_evals/core/scenarios.py
new file mode 100644
index 000000000..16874a069
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/scenarios.py
@@ -0,0 +1,58 @@
+"""Shared scenario formatting helpers for head-to-head benchmark reports.
+
+The scenario chosen at ``setup`` time (``head-to-head``, ``symmetric-cheap``,
+``cost-arbitrage``) materially changes how a head-to-head report should be
+read. This module produces the one-bullet summary every head-to-head
+runner stamps near the top of its ``report_section`` body so reviewers
+immediately see the framing — no need to dig into ``run_artifact.json``.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+
+def format_scenario_md(extra: Mapping[str, Any] | None) -> str:
+    """Render a scenario-aware bullet for a benchmark report.
+
+    Reads ``extra["scenario"]`` plus the runtime LLM slugs the runner
+    recorded. Falls back to a sensible "head-to-head" line if the artifact
+    pre-dates scenarios so old runs still render cleanly.
+    """
+
+    extra = dict(extra or {})
+    scenario = str(extra.get("scenario") or "head-to-head")
+    surf_slug = str(extra.get("provider_model") or "?")
+    native_slug = str(extra.get("native_arm_model") or surf_slug)
+    vision_slug = extra.get("vision_provider_model")
+
+    if scenario == "cost-arbitrage":
+        body = (
+            f"- Scenario: **cost-arbitrage** — native arm answers with "
+            f"`{native_slug}` (vision); SurfSense answers with `{surf_slug}` "
+            f"over chunks vision-extracted at ingest"
+            f"{f' by `{vision_slug}`' if vision_slug else ''}. "
+            "Measures how close SurfSense gets to native at a fraction of "
+            "the per-query cost."
+        )
+    elif scenario == "symmetric-cheap":
+        body = (
+            f"- Scenario: **symmetric-cheap** — both arms answer with "
+            f"`{surf_slug}`; SurfSense pre-extracted images at ingest"
+            f"{f' via `{vision_slug}`' if vision_slug else ''}. "
+            "Native arm structurally loses on image-bearing questions "
+            "(text-only model can't see images) — that's the point."
+        )
+    else:
+        body = (
+            f"- Scenario: head-to-head — both arms answer with `{surf_slug}` "
+            "via OpenRouter."
+        )
+        if vision_slug:
+            body += f" SurfSense ingest VLM: `{vision_slug}`."
+
+    return body
+
+
+__all__ = ["format_scenario_md"]
diff --git a/surfsense_evals/src/surfsense_evals/core/vision_llm.py b/surfsense_evals/src/surfsense_evals/core/vision_llm.py
new file mode 100644
index 000000000..ae96f1285
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/vision_llm.py
@@ -0,0 +1,127 @@
+"""Vision LLM resolution + auto-pick logic for the harness's ``setup`` command.
+
+Two responsibilities:
+
+1. Resolve an explicit ``--vision-llm <slug>`` to a global OpenRouter
+   vision LLM config id that ``set_llm_preferences(vision_llm_config_id=...)``
+   can accept.
+2. Auto-pick the strongest registered vision config when the operator
+   doesn't pass ``--vision-llm`` but the scenario / benchmark needs one.
+
+The priority list mirrors the recommended slugs in the README so the
+auto-pick is deterministic and reviewable.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+from .clients.search_space import VisionLlmConfigEntry
+
+# Order matters — first match wins when auto-picking. Keep these in sync
+# with the "Recommended vision slugs" table in the README so the
+# auto-pick story is the same one users read about.
+RECOMMENDED_VISION_PRIORITY: tuple[str, ...] = (
+    "anthropic/claude-sonnet-4.5",
+    "anthropic/claude-opus-4.7",
+    "openai/gpt-5",
+    "google/gemini-2.5-pro",
+)
+
+
+class VisionConfigError(RuntimeError):
+    """Raised when no vision config can be resolved (explicit or auto)."""
+
+
+@dataclass(frozen=True)
+class ResolvedVisionConfig:
+    """Result of ``resolve_vision_llm`` — what to attach + a label for logs."""
+
+    config_id: int
+    provider_model: str
+    selected_via: str  # "explicit" | "auto-priority" | "auto-fallback"
+
+
+def _openrouter_only(entries: Iterable[VisionLlmConfigEntry]) -> list[VisionLlmConfigEntry]:
+    return [e for e in entries if e.provider == "OPENROUTER" and not e.is_auto_mode]
+
+
+def resolve_vision_llm(
+    candidates: list[VisionLlmConfigEntry],
+    *,
+    explicit_slug: str | None,
+) -> ResolvedVisionConfig:
+    """Resolve a vision LLM config id from a slug or by auto-picking.
+
+    * If ``explicit_slug`` is given: must match exactly one OpenRouter
+      vision config's ``model_name``. Raises ``VisionConfigError`` with a
+      friendly listing if zero / many match.
+    * Otherwise: walk ``RECOMMENDED_VISION_PRIORITY`` in order and return
+      the first registered one. If none of the recommended slugs are
+      registered, fall back to the first OpenRouter vision config in the
+      list (deterministic by listing order). Raises ``VisionConfigError``
+      if zero are registered at all.
+    """
+
+    or_vision = _openrouter_only(candidates)
+
+    if explicit_slug is not None:
+        matches = [e for e in or_vision if e.model_name == explicit_slug]
+        if not matches:
+            sample = ", ".join(e.model_name for e in or_vision[:8]) or "<none>"
+            raise VisionConfigError(
+                f"No OpenRouter vision config found for slug '{explicit_slug}'. "
+                "Make sure `openrouter_integration.vision_enabled: true` in "
+                "global_llm_config.yaml and that the Celery worker has finished "
+                "its first refresh. "
+                f"Available OpenRouter vision slugs (sample): {sample}."
+            )
+        if len(matches) > 1:
+            listing = "\n".join(f"  id={e.id}  name={e.name!r}" for e in matches)
+            raise VisionConfigError(
+                f"Multiple OpenRouter vision configs match '{explicit_slug}':\n{listing}"
+            )
+        only = matches[0]
+        return ResolvedVisionConfig(
+            config_id=only.id,
+            provider_model=only.model_name,
+            selected_via="explicit",
+        )
+
+    if not or_vision:
+        raise VisionConfigError(
+            "No OpenRouter vision LLM configs are registered with this "
+            "SurfSense backend. Either pass `--no-vision-llm` to the ingest "
+            "step (text-only ingestion), or enable "
+            "`openrouter_integration.vision_enabled: true` in "
+            "global_llm_config.yaml so the Celery worker syncs vision-capable "
+            "OpenRouter models on next refresh."
+        )
+
+    by_slug = {e.model_name: e for e in or_vision}
+    for preferred in RECOMMENDED_VISION_PRIORITY:
+        match = by_slug.get(preferred)
+        if match is not None:
+            return ResolvedVisionConfig(
+                config_id=match.id,
+                provider_model=match.model_name,
+                selected_via="auto-priority",
+            )
+
+    # Fallback: first registered OpenRouter vision config. Deterministic
+    # because the backend returns them in a stable order.
+    fallback = or_vision[0]
+    return ResolvedVisionConfig(
+        config_id=fallback.id,
+        provider_model=fallback.model_name,
+        selected_via="auto-fallback",
+    )
+
+
+__all__ = [
+    "RECOMMENDED_VISION_PRIORITY",
+    "ResolvedVisionConfig",
+    "VisionConfigError",
+    "resolve_vision_llm",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/__init__.py b/surfsense_evals/src/surfsense_evals/suites/__init__.py
new file mode 100644
index 000000000..95ed958ca
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/__init__.py
@@ -0,0 +1,66 @@
+"""Suite registry auto-discovery.
+
+Importing ``surfsense_evals.suites`` walks every subpackage one level deep
+(domain like ``medical``) AND its benchmark subpackages
+(``medical/medxpertqa``, ``medical/mirage``, ``medical/cure``). Each
+benchmark's ``__init__.py`` is expected to call
+``core.registry.register(<Benchmark>)`` at module bottom; merely importing
+the module is enough to populate the registry.
+
+Adding a new domain is therefore: drop a folder under ``suites/`` with the
+right structure. No edits anywhere else.
+
+Subpackages whose name starts with ``_`` are skipped — that's reserved for
+test fixtures (e.g. ``suites/_demo/``) so they don't accidentally show up
+in ``benchmarks list``.
+"""
+
+from __future__ import annotations
+
+import importlib
+import logging
+import pkgutil
+from typing import Iterable
+
+logger = logging.getLogger(__name__)
+
+
+def _iter_subpackages(package) -> Iterable[str]:
+    """Yield fully-qualified subpackage names one level deep, skipping ``_*``."""
+
+    for module_info in pkgutil.iter_modules(package.__path__, prefix=f"{package.__name__}."):
+        if not module_info.ispkg:
+            continue
+        leaf = module_info.name.rsplit(".", 1)[-1]
+        if leaf.startswith("_"):
+            continue
+        yield module_info.name
+
+
+def discover_suites() -> list[str]:
+    """Import every domain + benchmark subpackage so registrations fire.
+
+    Returns the list of fully-qualified benchmark module names that were
+    successfully imported. Failures are logged (not raised) so a single
+    broken benchmark doesn't take down the whole CLI — the operator still
+    sees the working benchmarks via ``benchmarks list``.
+    """
+
+    import surfsense_evals.suites as _suites  # self-import for __path__
+
+    imported: list[str] = []
+    for domain_name in _iter_subpackages(_suites):
+        try:
+            domain_pkg = importlib.import_module(domain_name)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("Failed to import suite domain %s: %s", domain_name, exc)
+            continue
+        for benchmark_name in _iter_subpackages(domain_pkg):
+            try:
+                importlib.import_module(benchmark_name)
+                imported.append(benchmark_name)
+            except Exception as exc:  # noqa: BLE001
+                logger.warning(
+                    "Failed to import benchmark %s: %s", benchmark_name, exc
+                )
+    return imported
diff --git a/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py b/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py
new file mode 100644
index 000000000..9a8cd447e
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/_demo/__init__.py
@@ -0,0 +1,8 @@
+"""Test fixture suite — skipped by the auto-discovery walker (name starts with ``_``).
+
+Imported explicitly by ``tests/core/test_registry.py`` to prove the
+register-on-import contract works without polluting the production
+benchmark list.
+"""
+
+from __future__ import annotations
diff --git a/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py b/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py
new file mode 100644
index 000000000..1da33926c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/_demo/hello/__init__.py
@@ -0,0 +1,46 @@
+"""Demo benchmark — registers on import, used only by the registry tests."""
+
+from __future__ import annotations
+
+import argparse
+from typing import Any
+
+from ....core.registry import (
+    Benchmark,
+    ReportSection,
+    RunArtifact,
+    RunContext,
+    register,
+)
+
+
+class HelloBenchmark:
+    suite: str = "_demo"
+    name: str = "hello"
+    headline: bool = False
+    description: str = "Demo benchmark used by the registry test."
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument("--echo", default="hi")
+
+    async def ingest(self, ctx: RunContext, **_opts: Any) -> None:  # pragma: no cover
+        return None
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:  # pragma: no cover
+        return RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp="0",
+            raw_path=ctx.benchmark_data_dir() / "raw.jsonl",
+            metrics={"echo": opts.get("echo")},
+        )
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        return ReportSection(
+            title="Hello demo",
+            headline=False,
+            body_md="- runs: " + str(len(artifacts)),
+        )
+
+
+register(HelloBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py
new file mode 100644
index 000000000..9c0067e25
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/__init__.py
@@ -0,0 +1,7 @@
+"""Medical RAG benchmarks (MedXpertQA-MM headline + MIRAGE/CUREv1 secondary).
+
+Subpackages register themselves with ``core.registry`` on import. The
+``suites/__init__.py`` discovery walker imports them automatically.
+"""
+
+from __future__ import annotations
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py
new file mode 100644
index 000000000..e13224be7
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/cure/__init__.py
@@ -0,0 +1,18 @@
+"""CUREv1 — secondary single-arm SurfSense retrieval measurement.
+
+Source: https://huggingface.co/datasets/clinia/CUREv1
+Paper: https://arxiv.org/html/2412.06954v4
+
+Pure retrieval benchmark — 10 medical disciplines, English/French/Spanish
+queries, expert-curated qrels (graded 0/1/2). The harness ingests the
+corpus, runs each query via SurfSense's ``/api/v1/new_chat``, parses
+chunk citations, maps them back to CUREv1 ``corpus-id``, and scores
+Recall@k / MRR / nDCG@10 against qrels.
+"""
+
+from __future__ import annotations
+
+from .runner import CureBenchmark
+from ....core import registry as _registry
+
+_registry.register(CureBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py
new file mode 100644
index 000000000..6eca8810c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/cure/ingest.py
@@ -0,0 +1,239 @@
+"""CUREv1 ingestion.
+
+For each (lang, discipline) requested, downloads the corpus split via
+``datasets.load_dataset(path="clinia/CUREv1", name="corpus", split=<discipline>)``,
+batches passages into ~5 MB markdown bundles, uploads them to
+SurfSense, polls until ``ready``, and persists the
+``corpus_id -> document_id`` map under
+``data/medical/maps/cure_corpus_map_<discipline>.jsonl``. A union map
+``cure_corpus_map.jsonl`` is also written so the runner can resolve
+citations across disciplines without juggling per-file paths.
+"""
+
+from __future__ import annotations
+
+import io
+import json
+import logging
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+
+logger = logging.getLogger(__name__)
+
+
+_BATCH_SIZE_BYTES = 5 * 1024 * 1024
+
+# 10 disciplines covered by the dataset card. We exhaustively list
+# them so a smoke test can default to one.
+DISCIPLINES = (
+    "anesthesiology",
+    "cardiology",
+    "dermatology",
+    "endocrinology",
+    "gastroenterology",
+    "hematology",
+    "nephrology",
+    "neurology",
+    "obstetrics_gynecology",
+    "psychiatry",
+)
+
+
+@dataclass
+class CorpusPassage:
+    corpus_id: str
+    title: str
+    text: str
+
+    def to_markdown(self) -> str:
+        title = (self.title or "").strip() or "Untitled"
+        body = (self.text or "").strip()
+        return f"# {title}\n\n_id: `{self.corpus_id}`_\n\n{body}\n"
+
+
+@dataclass
+class PassageBatch:
+    path: Path
+    corpus_ids: list[str]
+
+
+def _stream_corpus(discipline: str) -> Iterable[CorpusPassage]:
+    """Stream corpus rows for one discipline via the ``datasets`` library."""
+
+    from datasets import load_dataset  # noqa: PLC0415
+
+    logger.info("Loading CUREv1 corpus for discipline=%s", discipline)
+    ds = load_dataset(path="clinia/CUREv1", name="corpus", split=discipline)
+    for row in ds:
+        cid = str(row.get("_id") or "")
+        if not cid:
+            continue
+        yield CorpusPassage(
+            corpus_id=cid,
+            title=str(row.get("title") or ""),
+            text=str(row.get("text") or ""),
+        )
+
+
+def _write_batches(
+    passages: Iterable[CorpusPassage],
+    *,
+    out_dir: Path,
+    discipline: str,
+    batch_bytes: int = _BATCH_SIZE_BYTES,
+) -> list[PassageBatch]:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    batches: list[PassageBatch] = []
+    current_buffer = io.StringIO()
+    current_ids: list[str] = []
+    current_bytes = 0
+    batch_idx = 0
+
+    def _flush() -> None:
+        nonlocal current_buffer, current_ids, current_bytes, batch_idx
+        if not current_ids:
+            return
+        path = out_dir / f"cure_{discipline}_{batch_idx:04d}.md"
+        path.write_text(current_buffer.getvalue(), encoding="utf-8")
+        batches.append(PassageBatch(path=path, corpus_ids=current_ids))
+        batch_idx += 1
+        current_buffer = io.StringIO()
+        current_ids = []
+        current_bytes = 0
+
+    for passage in passages:
+        chunk = passage.to_markdown() + "\n---\n\n"
+        chunk_bytes = len(chunk.encode("utf-8"))
+        if current_bytes + chunk_bytes > batch_bytes and current_ids:
+            _flush()
+        current_buffer.write(chunk)
+        current_ids.append(passage.corpus_id)
+        current_bytes += chunk_bytes
+    _flush()
+    return batches
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    disciplines: list[str] | None = None,
+    max_per_discipline: int | None = None,
+    settings: IngestSettings | None = None,
+) -> None:
+    disciplines = disciplines or list(DISCIPLINES)
+    settings = settings or IngestSettings(use_vision_llm=False, processing_mode="basic")
+    bench_dir = ctx.benchmark_data_dir()
+    batches_root = bench_dir / "batches"
+    batches_root.mkdir(parents=True, exist_ok=True)
+
+    docs_client = ctx.documents_client()
+    union_map_path = ctx.maps_dir() / "cure_corpus_map.jsonl"
+    union_map_fh = union_map_path.open("w", encoding="utf-8")
+    # Header row records the ingest-time settings so the runner can
+    # surface them in the report (see core/ingest_settings.py).
+    union_map_fh.write(settings_header_line(settings) + "\n")
+    try:
+        for discipline in disciplines:
+            try:
+                passages_iter = _stream_corpus(discipline)
+                if max_per_discipline is not None:
+                    passages_iter = _take(passages_iter, max_per_discipline)
+                batches = _write_batches(
+                    passages_iter,
+                    out_dir=batches_root / discipline,
+                    discipline=discipline,
+                )
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("Skipping discipline %s: %s", discipline, exc)
+                continue
+            if not batches:
+                logger.warning("Discipline %s produced 0 batches; skipping upload", discipline)
+                continue
+            logger.info(
+                "Uploading %d batches for discipline %s", len(batches), discipline
+            )
+            upload_result = await docs_client.upload(
+                files=[b.path for b in batches],
+                search_space_id=ctx.search_space_id,
+                should_summarize=settings.should_summarize,
+                use_vision_llm=settings.use_vision_llm,
+                processing_mode=settings.processing_mode,
+            )
+            new_doc_ids = list(upload_result.document_ids)
+            if new_doc_ids:
+                await docs_client.wait_until_ready(
+                    search_space_id=ctx.search_space_id,
+                    document_ids=new_doc_ids,
+                    timeout_s=3600.0,
+                    max_poll_s=15.0,
+                )
+            statuses = await docs_client.get_status(
+                search_space_id=ctx.search_space_id,
+                document_ids=new_doc_ids + upload_result.duplicate_document_ids,
+            )
+            title_to_doc = {s.title: s.document_id for s in statuses}
+
+            per_discipline_path = (
+                ctx.maps_dir() / f"cure_corpus_map_{discipline}.jsonl"
+            )
+            with per_discipline_path.open("w", encoding="utf-8") as fh:
+                fh.write(settings_header_line(settings) + "\n")
+                for batch in batches:
+                    doc_id = title_to_doc.get(batch.path.name)
+                    if doc_id is None:
+                        logger.warning("No document_id for batch %s", batch.path.name)
+                        continue
+                    for cid in batch.corpus_ids:
+                        record = {
+                            "corpus_id": cid,
+                            "document_id": doc_id,
+                            "discipline": discipline,
+                        }
+                        fh.write(json.dumps(record) + "\n")
+                        union_map_fh.write(json.dumps(record) + "\n")
+
+            chunks_map_path = ctx.maps_dir() / f"cure_chunk_map_{discipline}.jsonl"
+            with chunks_map_path.open("w", encoding="utf-8") as fh:
+                for doc_id in {title_to_doc.get(b.path.name) for b in batches} - {None}:
+                    try:
+                        chunks = await docs_client.list_chunks(int(doc_id))
+                    except Exception as exc:  # noqa: BLE001
+                        logger.warning(
+                            "Failed to list chunks for doc_id=%s: %s", doc_id, exc
+                        )
+                        continue
+                    for chunk in chunks:
+                        fh.write(
+                            json.dumps(
+                                {
+                                    "chunk_id": chunk.id,
+                                    "document_id": doc_id,
+                                    "discipline": discipline,
+                                }
+                            )
+                            + "\n"
+                        )
+    finally:
+        union_map_fh.close()
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["cure"] = str(union_map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+    logger.info("CUREv1 ingestion complete; union map at %s", union_map_path)
+
+
+def _take(it: Iterable, n: int) -> Iterable:
+    yielded = 0
+    for x in it:
+        if yielded >= n:
+            return
+        yield x
+        yielded += 1
+
+
+__all__ = ["DISCIPLINES", "CorpusPassage", "PassageBatch", "run_ingest"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py
new file mode 100644
index 000000000..416912b14
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/cure/runner.py
@@ -0,0 +1,397 @@
+"""CUREv1 runner — single-arm SurfSense retrieval scoring.
+
+For each query we ask SurfSense via ``/api/v1/new_chat`` (no
+``mentioned_document_ids``) and parse chunk citations from the
+streamed answer. Cited ``chunk_id`` → ``document_id`` (chunk map) →
+``corpus_id`` (corpus map). The resulting ranked list is scored
+against the dataset's qrels.
+
+The prompt nudges the model to surface its supporting passages via
+SurfSense's standard ``[citation:CHUNK_ID]`` format (already required
+by the agent system prompt), so we recover retrieval ordering from
+the answer text without needing a separate retrieval API.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+from collections import defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+    read_settings_header,
+)
+from ....core.metrics.retrieval import score_run
+from ....core.registry import (
+    Benchmark,
+    ReportSection,
+    RunArtifact,
+    RunContext,
+)
+
+logger = logging.getLogger(__name__)
+
+
+_PROMPT = """\
+You are a medical literature retrieval assistant for the question
+below. Identify the top passages from the knowledge base that best
+answer it and cite each one in the standard format
+[citation:CHUNK_ID]. List as many citations as are useful, ordered
+from most to least relevant. Provide a one-sentence justification
+for each citation.
+
+Query: {query}
+"""
+
+
+_DESCRIPTION = "CUREv1 retrieval (single-arm SurfSense): Recall@k / MRR / nDCG@10."
+
+# CUREv1 corpus is text-only markdown bundles; vision LLM at ingest
+# is wasted by default but the operator can flip it via CLI for an
+# A/B comparison.
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=False,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+@dataclass
+class CureQuery:
+    qid: str
+    text: str
+    discipline: str
+
+
+def _load_chunk_map(maps_dir: Path) -> dict[int, int]:
+    """Union all ``cure_chunk_map_<discipline>.jsonl`` into one dict."""
+
+    out: dict[int, int] = {}
+    for path in sorted(maps_dir.glob("cure_chunk_map_*.jsonl")):
+        with path.open("r", encoding="utf-8") as fh:
+            for line in fh:
+                if not line.strip():
+                    continue
+                row = json.loads(line)
+                if is_settings_header(row):
+                    continue
+                try:
+                    out[int(row["chunk_id"])] = int(row["document_id"])
+                except (KeyError, TypeError, ValueError):
+                    continue
+    return out
+
+
+def _load_doc_to_corpus(maps_dir: Path) -> dict[int, list[str]]:
+    """Map ``document_id -> [corpus_id, ...]`` from the union map.
+
+    Multiple corpus passages may live in one batched markdown
+    document, so each doc_id maps to a list. Citation ordering of the
+    first occurrence is preserved.
+    """
+
+    out: dict[int, list[str]] = defaultdict(list)
+    union_path = maps_dir / "cure_corpus_map.jsonl"
+    if not union_path.exists():
+        return out
+    with union_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            if not line.strip():
+                continue
+            row = json.loads(line)
+            if is_settings_header(row):
+                continue
+            try:
+                out[int(row["document_id"])].append(str(row["corpus_id"]))
+            except (KeyError, TypeError, ValueError):
+                continue
+    return out
+
+
+def _load_queries(*, lang: str, disciplines: list[str], sample_n: int | None) -> list[CureQuery]:
+    from datasets import load_dataset  # noqa: PLC0415
+
+    out: list[CureQuery] = []
+    for discipline in disciplines:
+        try:
+            ds = load_dataset(path="clinia/CUREv1", name=f"queries-{lang}", split=discipline)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("Skipping queries for %s/%s: %s", lang, discipline, exc)
+            continue
+        for row in ds:
+            qid = str(row.get("_id") or "")
+            text = str(row.get("text") or "")
+            if not qid or not text:
+                continue
+            out.append(CureQuery(qid=qid, text=text, discipline=discipline))
+    out.sort(key=lambda q: (q.discipline, q.qid))
+    if sample_n is not None and sample_n > 0:
+        # Stratified-by-discipline slice.
+        per_d = max(1, sample_n // max(1, len(disciplines)))
+        sliced: list[CureQuery] = []
+        counter: dict[str, int] = defaultdict(int)
+        for q in out:
+            if counter[q.discipline] >= per_d:
+                continue
+            sliced.append(q)
+            counter[q.discipline] += 1
+            if len(sliced) >= sample_n:
+                break
+        out = sliced
+    return out
+
+
+def _load_qrels(*, disciplines: list[str]) -> dict[str, dict[str, float]]:
+    from datasets import load_dataset  # noqa: PLC0415
+
+    out: dict[str, dict[str, float]] = defaultdict(dict)
+    for discipline in disciplines:
+        try:
+            ds = load_dataset(path="clinia/CUREv1", name="qrels", split=discipline)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("Skipping qrels for %s: %s", discipline, exc)
+            continue
+        for row in ds:
+            qid = str(row.get("query-id") or row.get("query_id") or "")
+            cid = str(row.get("corpus-id") or row.get("corpus_id") or "")
+            score = row.get("score")
+            if not qid or not cid or score is None:
+                continue
+            try:
+                out[qid][cid] = float(score)
+            except (TypeError, ValueError):
+                continue
+    return out
+
+
+async def _gather_with_limit(coros, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(c):
+        async with sem:
+            return await c
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+class CureBenchmark:
+    suite: str = "medical"
+    name: str = "cure"
+    headline: bool = False
+    description: str = _DESCRIPTION
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument("--lang", default="en", choices=("en", "es", "fr"))
+        parser.add_argument("--discipline", default=None,
+                            help="Restrict to one discipline (default: all ingested).")
+        parser.add_argument("--n", dest="sample_n", type=int, default=None)
+        parser.add_argument("--concurrency", type=int, default=4)
+        parser.add_argument(
+            "--max-passages-per-discipline", type=int, default=None,
+            help="(ingest only) cap corpus rows per discipline for smoke testing.",
+        )
+        # Per-upload knobs forwarded to /documents/fileupload at ingest;
+        # ignored at run-time (runner reads resolved settings from the
+        # union-map header).
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import DISCIPLINES, run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            disciplines=list(DISCIPLINES),
+            max_per_discipline=opts.get("max_passages_per_discipline"),
+            settings=settings,
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        lang = opts.get("lang") or "en"
+        discipline_filter = opts.get("discipline")
+        sample_n = opts.get("sample_n")
+        concurrency = int(opts.get("concurrency") or 4)
+
+        maps_dir = ctx.maps_dir()
+        chunk_to_doc = _load_chunk_map(maps_dir)
+        doc_to_corpus = _load_doc_to_corpus(maps_dir)
+        ingest_settings = read_settings_header(maps_dir / "cure_corpus_map.jsonl")
+        if not chunk_to_doc or not doc_to_corpus:
+            raise RuntimeError(
+                "CUREv1 not ingested for this suite. Run "
+                "`python -m surfsense_evals ingest medical cure` first."
+            )
+
+        # Disciplines to query are determined by the per-discipline maps
+        # actually present (either user-filtered or whatever was ingested).
+        ingested_disciplines = sorted({
+            row_disc
+            for path in maps_dir.glob("cure_corpus_map_*.jsonl")
+            for row_disc in [path.stem[len("cure_corpus_map_"):]]
+        })
+        if discipline_filter:
+            disciplines = [discipline_filter]
+        else:
+            disciplines = ingested_disciplines or ["dermatology"]
+
+        queries = _load_queries(lang=lang, disciplines=disciplines, sample_n=sample_n)
+        if not queries:
+            raise RuntimeError(
+                f"No CUREv1 queries matched lang={lang!r} disciplines={disciplines!r}."
+            )
+        qrels = _load_qrels(disciplines=disciplines)
+        logger.info(
+            "CUREv1: %d queries / %d qrels across disciplines %s",
+            len(queries),
+            len(qrels),
+            disciplines,
+        )
+
+        arm = SurfSenseArm(
+            client=ctx.new_chat_client(),
+            search_space_id=ctx.search_space_id,
+            ephemeral_threads=True,
+        )
+
+        async def _ask(q: CureQuery) -> ArmResult:
+            return await arm.answer(
+                ArmRequest(
+                    question_id=f"{q.discipline}::{q.qid}",
+                    prompt=_PROMPT.format(query=q.text.strip()),
+                )
+            )
+
+        results: list[ArmResult] = await _gather_with_limit(
+            (_ask(q) for q in queries), concurrency=concurrency
+        )
+
+        per_query_retrieved: dict[str, list[str]] = {}
+        for q, res in zip(queries, results):
+            chunk_ids: list[int] = []
+            seen: set[int] = set()
+            for citation in res.citations:
+                if citation.get("kind") != "chunk":
+                    continue
+                cid = int(citation.get("chunk_id"))
+                if cid in seen:
+                    continue
+                chunk_ids.append(cid)
+                seen.add(cid)
+            corpus_ids: list[str] = []
+            seen_corpus: set[str] = set()
+            for cid in chunk_ids:
+                doc_id = chunk_to_doc.get(cid)
+                if doc_id is None:
+                    continue
+                for corpus_id in doc_to_corpus.get(doc_id, []):
+                    if corpus_id in seen_corpus:
+                        continue
+                    corpus_ids.append(corpus_id)
+                    seen_corpus.add(corpus_id)
+            per_query_retrieved[q.qid] = corpus_ids
+
+        scores = score_run(
+            per_query_retrieved=per_query_retrieved,
+            per_query_qrels=qrels,
+            ks=(1, 5, 10, 32),
+            ndcg_k=10,
+        )
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, res in zip(queries, results):
+                fh.write(
+                    json.dumps(
+                        {
+                            "discipline": q.discipline,
+                            "qid": q.qid,
+                            "lang": lang,
+                            "retrieved_corpus_ids": per_query_retrieved.get(q.qid, []),
+                            **res.to_jsonl(),
+                        }
+                    )
+                    + "\n"
+                )
+
+        metrics = scores.to_dict()
+        metrics["lang"] = lang
+        metrics["disciplines"] = disciplines
+
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_queries": len(queries),
+                "lang": lang,
+                "disciplines": disciplines,
+                "concurrency": concurrency,
+                "provider_model": ctx.provider_model,
+                "ingest_settings": ingest_settings,
+            },
+        )
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="CUREv1 — single-arm SurfSense retrieval",
+                headline=False,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        recall = m.get("recall_at_k", {})
+        lines: list[str] = [
+            format_ingest_settings_md(latest.extra.get("ingest_settings")),
+            f"- Language: {m.get('lang', '?')}",
+            f"- Disciplines: {', '.join(m.get('disciplines', []) or ['?'])}",
+            f"- n_queries (after qrels intersection): {m.get('n_queries', 0)}",
+        ]
+        for k in (1, 5, 10, 32):
+            v = recall.get(str(k), recall.get(k))
+            if v is not None:
+                lines.append(f"- Recall@{k}: {float(v):.3f}")
+        lines.append(f"- MRR: {float(m.get('mrr', 0.0)):.3f}")
+        lines.append(f"- nDCG@10: {float(m.get('ndcg_at_10', 0.0)):.3f}")
+        return ReportSection(
+            title="CUREv1 — single-arm SurfSense retrieval",
+            headline=False,
+            body_md="\n".join(lines),
+            body_json=m,
+        )
+
+
+__all__ = ["CureBenchmark", "CureQuery"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py
new file mode 100644
index 000000000..3e803398d
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/__init__.py
@@ -0,0 +1,25 @@
+"""MedXpertQA-MM — multimodal medical exam head-to-head (medical suite headline).
+
+Source: https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA
+Paper:  https://arxiv.org/abs/2501.18362 (ICML 2025)
+
+* MM subset: ~2,000 expert-level exam questions with diverse medical
+  images (radiology, dermatology, pathology, ECGs, gross specimens,
+  fundus photos) and structured patient information embedded in the
+  question stem.
+* 5 answer choices per MM question (A–E).
+* USMLE / COMLEX / 17 specialty board sources; rigorously filtered
+  and reviewed by physicians.
+
+Real diagnostic images carry signal that text-only patient charts
+cannot (e.g. CT scans, dermoscopy), so this benchmark exercises the
+full vision RAG pipeline end-to-end against a vision-capable model
+fed the same PDF natively.
+"""
+
+from __future__ import annotations
+
+from ....core import registry as _registry
+from .runner import MedXpertQAMMBenchmark
+
+_registry.register(MedXpertQAMMBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py
new file mode 100644
index 000000000..5293e116f
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/ingest.py
@@ -0,0 +1,394 @@
+"""MedXpertQA-MM ingestion.
+
+Steps:
+
+1. Pull ``MM/test.jsonl`` (and optionally ``MM/dev.jsonl``) plus
+   ``images.zip`` from
+   ``hf://datasets/TsinghuaC3I/MedXpertQA``. Cache under
+   ``<data_dir>/medical/medxpertqa/``.
+2. Extract ``images.zip`` once into ``<data_dir>/medical/medxpertqa/images/``.
+3. Render one PDF per MM question (text question + structured patient
+   info embedded in the question stem + each image flowable + answer
+   options). Output: ``<data_dir>/medical/medxpertqa/pdfs/<id>.pdf``.
+4. Upload each PDF to SurfSense with ``use_vision_llm=True``; persist
+   ``id -> document_id`` in
+   ``<data_dir>/medical/maps/medxpertqa_doc_map.jsonl``.
+
+Both arms then receive byte-identical PDFs. The native arm sends the
+PDF directly to OpenRouter; SurfSense ingests via its own vision
+pipeline and the runner queries with ``mentioned_document_ids=[...]``
+to scope retrieval to the question's PDF.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import zipfile
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.pdf import PdfImage, render_pdf_with_images
+from ....core.registry import RunContext
+from .prompt import format_options
+
+logger = logging.getLogger(__name__)
+
+
+HF_REPO_ID = "TsinghuaC3I/MedXpertQA"
+HF_REPO_TYPE = "dataset"
+
+
+def _hf_hub_download(*args, **kwargs):
+    from huggingface_hub import hf_hub_download
+
+    return hf_hub_download(*args, **kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Question shape
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class MedXpertQuestion:
+    qid: str                         # e.g. "MM-26"
+    question: str                    # full question text (case + ask)
+    options: dict[str, str]          # A-E
+    label: str                       # "A".."E"
+    image_files: list[str]           # filenames inside images.zip
+    medical_task: str
+    body_system: str
+    question_type: str
+    split: str                       # "test" or "dev"
+
+
+def _load_jsonl(path: Path, *, split: str) -> list[MedXpertQuestion]:
+    out: list[MedXpertQuestion] = []
+    with path.open("r", encoding="utf-8") as fh:
+        for raw_line in fh:
+            line = raw_line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            qid = str(row.get("id") or "").strip()
+            question = str(row.get("question") or "").strip()
+            options = row.get("options") or {}
+            label = str(row.get("label") or "").strip().upper()
+            if not qid or not question or not isinstance(options, dict) or not label:
+                continue
+            opts = {str(k).strip().upper(): str(v).strip() for k, v in options.items()}
+            images = row.get("images") or []
+            if not isinstance(images, list):
+                images = []
+            out.append(MedXpertQuestion(
+                qid=qid,
+                question=question,
+                options=opts,
+                label=label,
+                image_files=[str(x).strip() for x in images if str(x).strip()],
+                medical_task=str(row.get("medical_task") or "").strip(),
+                body_system=str(row.get("body_system") or "").strip(),
+                question_type=str(row.get("question_type") or "").strip(),
+                split=split,
+            ))
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Image archive helpers
+# ---------------------------------------------------------------------------
+
+
+def _ensure_images_extracted(images_zip: Path, images_dir: Path) -> None:
+    """Extract images.zip once, tolerantly handle re-runs."""
+
+    marker = images_dir / ".extracted_ok"
+    if marker.exists():
+        return
+    images_dir.mkdir(parents=True, exist_ok=True)
+    logger.info("Extracting MedXpertQA images.zip -> %s", images_dir)
+    with zipfile.ZipFile(images_zip) as zf:
+        zf.extractall(images_dir)
+    marker.write_text("ok\n", encoding="utf-8")
+
+
+def _resolve_image_path(image_filename: str, images_dir: Path) -> Path | None:
+    """Find a question's image in the (possibly nested) extract directory.
+
+    The zip layout sometimes nests under ``images/`` and sometimes
+    flat — handle both.
+    """
+
+    direct = images_dir / image_filename
+    if direct.exists():
+        return direct
+    nested = images_dir / "images" / image_filename
+    if nested.exists():
+        return nested
+    # Last-ditch: glob recursively (slow but correct for unusual layouts).
+    matches = list(images_dir.rglob(image_filename))
+    return matches[0] if matches else None
+
+
+# ---------------------------------------------------------------------------
+# PDF rendering
+# ---------------------------------------------------------------------------
+
+
+def _render_question_pdf(
+    q: MedXpertQuestion,
+    *,
+    images_dir: Path,
+    pdfs_dir: Path,
+) -> tuple[Path, list[str]]:
+    """Render one MedXpertQA question into a PDF.
+
+    Layout:
+      Title:    MedXpertQA — <qid>  (medical_task / body_system)
+      Section 1 (case):       full question text
+      Section 1 images:       each image flowable + caption
+      Section 2 (options):    A) ... B) ... C) ... D) ... E) ...
+
+    Returns (pdf_path, missing_images) so the caller can warn on
+    questions where some image files weren't found.
+    """
+
+    out_path = pdfs_dir / f"{q.qid}.pdf"
+    images: list[PdfImage] = []
+    missing: list[str] = []
+    for fname in q.image_files:
+        resolved = _resolve_image_path(fname, images_dir)
+        if resolved is None:
+            missing.append(fname)
+            continue
+        images.append(PdfImage(path=resolved, caption=f"Image: {fname}", max_width_in=5.5))
+
+    title_meta_parts = []
+    if q.medical_task:
+        title_meta_parts.append(q.medical_task)
+    if q.body_system:
+        title_meta_parts.append(q.body_system)
+    if q.question_type:
+        title_meta_parts.append(q.question_type)
+    title_suffix = f" ({' / '.join(title_meta_parts)})" if title_meta_parts else ""
+
+    sections = [
+        ("Clinical case", q.question, images),
+        ("Answer choices", format_options(q.options), None),
+    ]
+    render_pdf_with_images(
+        title=f"MedXpertQA-MM {q.qid}{title_suffix}",
+        sections=sections,
+        output_path=out_path,
+    )
+    return out_path, missing
+
+
+# ---------------------------------------------------------------------------
+# Upload helper
+# ---------------------------------------------------------------------------
+
+
+async def _upload_pdfs(
+    ctx: RunContext,
+    pdf_paths: Iterable[Path],
+    *,
+    batch_size: int,
+    settings: IngestSettings,
+) -> dict[str, int]:
+    docs_client = ctx.documents_client()
+    name_to_id: dict[str, int] = {}
+    pdf_list = list(pdf_paths)
+    for batch_start in range(0, len(pdf_list), batch_size):
+        batch = pdf_list[batch_start:batch_start + batch_size]
+        result = await docs_client.upload(
+            files=batch,
+            search_space_id=ctx.search_space_id,
+            should_summarize=settings.should_summarize,
+            use_vision_llm=settings.use_vision_llm,
+            processing_mode=settings.processing_mode,
+        )
+        all_ids = list(result.document_ids) + list(result.duplicate_document_ids)
+        if all_ids:
+            await docs_client.wait_until_ready(
+                search_space_id=ctx.search_space_id,
+                document_ids=result.document_ids,
+                timeout_s=1800.0,
+            )
+            statuses = await docs_client.get_status(
+                search_space_id=ctx.search_space_id,
+                document_ids=all_ids,
+            )
+            for s in statuses:
+                name_to_id[s.title] = s.document_id
+        logger.info(
+            "Uploaded MedXpertQA batch %d-%d: %d new, %d duplicate",
+            batch_start, batch_start + len(batch),
+            len(result.document_ids), len(result.duplicate_document_ids),
+        )
+    return name_to_id
+
+
+# ---------------------------------------------------------------------------
+# Public entry point
+# ---------------------------------------------------------------------------
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    split: str = "test",
+    max_questions: int | None = None,
+    upload_batch_size: int = 8,
+    skip_upload: bool = False,
+    include_dev: bool = False,
+    settings: IngestSettings | None = None,
+) -> None:
+    """Ingest MedXpertQA-MM into the medical suite.
+
+    Parameters
+    ----------
+    split : 'test' (default), 'dev', or 'both'
+        Which subset to render + upload.
+    max_questions : int | None
+        Cap on number of questions ingested (handy for fast iteration).
+    upload_batch_size : int
+        PDFs per ``fileupload`` call.
+    skip_upload : bool
+        Render PDFs locally but don't push to SurfSense.
+    include_dev : bool
+        Convenience: equivalent to ``split='both'``.
+    """
+
+    settings = settings or IngestSettings(use_vision_llm=True, processing_mode="basic")
+    bench_dir = ctx.benchmark_data_dir()
+    images_zip_local = bench_dir / "images.zip"
+    images_dir = bench_dir / "images"
+    pdfs_dir = bench_dir / "pdfs"
+    pdfs_dir.mkdir(parents=True, exist_ok=True)
+    hf_cache = bench_dir / ".hf_cache"
+    hf_cache.mkdir(parents=True, exist_ok=True)
+
+    # Step 1: download jsonl(s)
+    splits_to_load: list[str] = []
+    if split == "both" or include_dev:
+        splits_to_load = ["dev", "test"]
+    elif split in {"dev", "test"}:
+        splits_to_load = [split]
+    else:
+        raise ValueError(f"Unknown split {split!r}; use 'test' / 'dev' / 'both'")
+
+    questions: list[MedXpertQuestion] = []
+    for sp in splits_to_load:
+        rel = f"MM/{sp}.jsonl"
+        local = _hf_hub_download(
+            repo_id=HF_REPO_ID,
+            filename=rel,
+            repo_type=HF_REPO_TYPE,
+            cache_dir=str(hf_cache),
+        )
+        loaded = _load_jsonl(Path(local), split=sp)
+        questions.extend(loaded)
+        logger.info("Loaded %d MedXpertQA-MM questions from %s split", len(loaded), sp)
+
+    if max_questions is not None and max_questions > 0:
+        questions = questions[:max_questions]
+    if not questions:
+        raise RuntimeError("No MedXpertQA-MM questions loaded; check the split argument.")
+
+    # Step 2: download images.zip + extract once
+    if not images_zip_local.exists():
+        local_zip = _hf_hub_download(
+            repo_id=HF_REPO_ID,
+            filename="images.zip",
+            repo_type=HF_REPO_TYPE,
+            cache_dir=str(hf_cache),
+        )
+        # Materialise into bench_dir so the path is stable.
+        try:
+            from os import link as _link
+            _link(local_zip, images_zip_local)
+        except OSError:
+            from shutil import copy2
+            copy2(local_zip, images_zip_local)
+    _ensure_images_extracted(images_zip_local, images_dir)
+
+    # Step 3: render PDFs
+    pdf_paths: dict[str, Path] = {}
+    missing_image_count = 0
+    for i, q in enumerate(questions, start=1):
+        try:
+            pdf, missing = _render_question_pdf(q, images_dir=images_dir, pdfs_dir=pdfs_dir)
+            pdf_paths[q.qid] = pdf
+            if missing:
+                missing_image_count += len(missing)
+                logger.debug("qid=%s missing %d images: %s", q.qid, len(missing), missing)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("Failed to render MedXpertQA PDF for %s: %s", q.qid, exc)
+        if i % 50 == 0:
+            logger.info("  ... rendered %d / %d PDFs", i, len(questions))
+    if missing_image_count:
+        logger.warning(
+            "MedXpertQA: %d image references could not be resolved on disk "
+            "(rendered PDFs may be missing some images).",
+            missing_image_count,
+        )
+
+    # Step 4: upload
+    name_to_id: dict[str, int] = {}
+    if skip_upload:
+        logger.info("MedXpertQA: --skip-upload set; skipping SurfSense ingestion")
+    else:
+        logger.info("MedXpertQA upload settings: %s", settings.render_label())
+        name_to_id = await _upload_pdfs(
+            ctx,
+            pdf_paths.values(),
+            batch_size=upload_batch_size,
+            settings=settings,
+        )
+
+    # Step 5: persist manifest + questions
+    questions_jsonl = bench_dir / "questions.jsonl"
+    with questions_jsonl.open("w", encoding="utf-8") as fh:
+        for q in questions:
+            fh.write(json.dumps({
+                "qid": q.qid,
+                "question": q.question,
+                "options": q.options,
+                "label": q.label,
+                "image_files": q.image_files,
+                "medical_task": q.medical_task,
+                "body_system": q.body_system,
+                "question_type": q.question_type,
+                "split": q.split,
+            }) + "\n")
+    logger.info("Wrote %d MedXpertQA questions to %s", len(questions), questions_jsonl)
+
+    map_path = ctx.maps_dir() / "medxpertqa_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        # Header line records the resolved ingest settings
+        # (see core/ingest_settings.py).
+        fh.write(settings_header_line(settings) + "\n")
+        for q in questions:
+            local = pdf_paths.get(q.qid)
+            if local is None:
+                continue
+            fh.write(json.dumps({
+                "qid": q.qid,
+                "document_id": name_to_id.get(local.name),
+                "pdf_path": str(local),
+                "n_images": len(q.image_files),
+                "split": q.split,
+            }) + "\n")
+    logger.info("Wrote MedXpertQA doc map to %s", map_path)
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["medxpertqa"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+
+__all__ = ["MedXpertQuestion", "run_ingest"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py
new file mode 100644
index 000000000..5c4a69916
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/prompt.py
@@ -0,0 +1,54 @@
+"""MedXpertQA-MM prompt.
+
+Mirrors the upstream paper's evaluation prompt (Zuo et al., ICML 2025
+§3.4): present case + 5 options A-E, ask for a single letter answer.
+We also instruct the model to use the embedded images explicitly,
+since the whole point of the MM subset is that the answer depends on
+visual evidence (radiology / dermoscopy / pathology / ECG, etc.).
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+
+ANSWER_LETTERS = ("A", "B", "C", "D", "E")
+
+
+_PROMPT = """\
+You are a board-certified physician. The following exam question
+includes a clinical case and one or more medical images (radiology,
+dermatology, pathology, ECG, etc.). Use BOTH the text and the images
+to choose the best answer. Do not rely on memorisation of the case;
+read the images carefully — they often determine the correct answer.
+
+Case + question:
+{question}
+
+Answer choices:
+{options_block}
+
+Respond on a single line in the format `Answer: X` where X is one of
+A, B, C, D, or E.
+"""
+
+
+def format_options(options: Mapping[str, str]) -> str:
+    """Render the ``A) ... E) ...`` options block."""
+
+    parts: list[str] = []
+    for letter in ANSWER_LETTERS:
+        text = options.get(letter)
+        if text is None or str(text).strip() == "":
+            continue
+        parts.append(f"{letter}) {str(text).strip()}")
+    return "\n".join(parts)
+
+
+def build_prompt(question: str, options: Mapping[str, str]) -> str:
+    return _PROMPT.format(
+        question=question.strip(),
+        options_block=format_options(options),
+    )
+
+
+__all__ = ["ANSWER_LETTERS", "build_prompt", "format_options"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py
new file mode 100644
index 000000000..75646ef32
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/medxpertqa/runner.py
@@ -0,0 +1,681 @@
+"""MedXpertQA-MM runner — Native PDF (vision) vs SurfSense (vision RAG).
+
+Headline benchmark for the medical suite.
+
+* Native arm reads the rendered PDF (case + images + options) via
+  OpenRouter ``chat/completions`` + the file-parser plugin.
+* SurfSense arm queries ``POST /api/v1/new_chat`` scoped via
+  ``mentioned_document_ids=[doc_id]`` to the same per-question PDF.
+
+Operational notes:
+
+* PDFs contain real images (radiology, dermoscopy, pathology, ECGs).
+  Operator must pin a vision-capable model via
+  ``setup --provider-model anthropic/claude-sonnet-4.5`` (or similar);
+  the runner emits a warning if a known text-only slug is pinned.
+* MedXpertQA tags ``medical_task`` (Diagnosis / Treatment / Basic
+  Medicine) and ``body_system`` (Cardiovascular / Lymphatic / …)
+  directly on every row; we slice the report by both.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, NativePdfArm, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+)
+from ....core.metrics.comparison import (
+    bootstrap_delta_ci,
+    mcnemar_test,
+    paired_aggregate,
+)
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci
+from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine
+from ....core.registry import (
+    ReportSection,
+    RunArtifact,
+    RunContext,
+)
+from ....core.scenarios import format_scenario_md
+from .prompt import ANSWER_LETTERS, build_prompt
+
+logger = logging.getLogger(__name__)
+
+
+_TEXT_ONLY_HINTS = ("gpt-5.4-mini", "gpt-3.5", "text-only", "instruct-")
+
+
+@dataclass
+class MXQuestion:
+    qid: str
+    question: str
+    options: dict[str, str]
+    label: str
+    medical_task: str
+    body_system: str
+    question_type: str
+    split: str
+    n_images: int
+    pdf_path: Path
+    document_id: int | None
+
+
+def _load_doc_map(map_path: Path) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]:
+    """Read the doc map JSONL.
+
+    Returns ``(rows, settings)`` where ``settings`` is the
+    ``__settings__`` header blob (or ``{}`` for legacy maps).
+    """
+
+    rows: dict[str, dict[str, Any]] = {}
+    settings: dict[str, Any] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if is_settings_header(row):
+                settings = dict(row["__settings__"])
+                continue
+            rows[str(row["qid"])] = row
+    return rows, settings
+
+
+def _load_questions(
+    questions_jsonl: Path,
+    doc_map: dict[str, dict[str, Any]],
+    *,
+    split_filter: str | None,
+    task_filter: str | None,
+    body_filter: str | None,
+    require_images: bool,
+    sample_n: int | None,
+) -> list[MXQuestion]:
+    out: list[MXQuestion] = []
+    with questions_jsonl.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            qid = str(row.get("qid") or "").strip()
+            if not qid:
+                continue
+            if split_filter and split_filter != "all" and row.get("split") != split_filter:
+                continue
+            if task_filter and task_filter != "all" and row.get("medical_task") != task_filter:
+                continue
+            if body_filter and body_filter != "all" and row.get("body_system") != body_filter:
+                continue
+            map_row = doc_map.get(qid)
+            if map_row is None:
+                logger.debug("No doc-map entry for %s; skipping", qid)
+                continue
+            n_images = int(map_row.get("n_images", 0))
+            if require_images and n_images <= 0:
+                continue
+            out.append(MXQuestion(
+                qid=qid,
+                question=str(row.get("question") or ""),
+                options={str(k).upper(): str(v) for k, v in (row.get("options") or {}).items()},
+                label=str(row.get("label") or "").strip().upper(),
+                medical_task=str(row.get("medical_task") or "").strip(),
+                body_system=str(row.get("body_system") or "").strip(),
+                question_type=str(row.get("question_type") or "").strip(),
+                split=str(row.get("split") or ""),
+                n_images=n_images,
+                pdf_path=Path(map_row["pdf_path"]),
+                document_id=map_row.get("document_id"),
+            ))
+    out.sort(key=lambda q: (q.split, q.qid))
+    if sample_n is not None and sample_n > 0:
+        out = out[:sample_n]
+    return out
+
+
+async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+_DESCRIPTION = (
+    "MedXpertQA-MM (~2,000 multimodal medical exam questions, 5 options, with images) — "
+    "Native PDF (vision) vs SurfSense (vision RAG) head-to-head."
+)
+
+# MedXpertQA-MM PDFs embed clinical images; vision LLM at ingest is
+# the whole point. Operators can flip ``--no-vision-llm`` to measure
+# how much we degrade without it (likely material).
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=True,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+class MedXpertQAMMBenchmark:
+    """Multimodal medical exam head-to-head."""
+
+    suite: str = "medical"
+    name: str = "medxpertqa"
+    headline: bool = True  # The medical suite headline.
+    description: str = _DESCRIPTION
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--split", default="test", choices=["test", "dev", "all"],
+            help="Which MedXpertQA-MM split to run (default: test).",
+        )
+        parser.add_argument(
+            "--task", default="all",
+            help="Filter by medical_task value (e.g. Diagnosis, Treatment, Basic Medicine).",
+        )
+        parser.add_argument(
+            "--body-system", dest="body_filter", default="all",
+            help="Filter by body_system value (e.g. Cardiovascular, Lymphatic).",
+        )
+        parser.add_argument(
+            "--require-images", dest="require_images", action="store_true",
+            help="Skip rare MM rows that ended up with zero resolvable images.",
+        )
+        parser.add_argument("--n", dest="sample_n", type=int, default=None,
+                            help="Run only the first N questions after filters apply.")
+        parser.add_argument("--concurrency", type=int, default=4,
+                            help="Parallel question workers per arm.")
+        parser.add_argument("--no-mentions", dest="no_mentions", action="store_true",
+                            help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).")
+        parser.add_argument(
+            "--pdf-engine", default="native",
+            choices=[e.value for e in PdfEngine],
+            help="OpenRouter file-parser engine for the native arm.",
+        )
+        parser.add_argument(
+            "--max-output-tokens", type=int, default=512,
+            help="Cap on completion length for both arms.",
+        )
+        # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest).
+        parser.add_argument(
+            "--max-questions", dest="max_questions", type=int, default=None,
+            help="(ingest only) cap on number of MM questions to render + upload.",
+        )
+        parser.add_argument(
+            "--upload-batch-size", dest="upload_batch_size", type=int, default=8,
+            help="(ingest only) PDFs per fileupload call.",
+        )
+        parser.add_argument(
+            "--skip-upload", dest="skip_upload", action="store_true",
+            help="(ingest only) render PDFs locally but don't push to SurfSense.",
+        )
+        parser.add_argument(
+            "--include-dev", dest="include_dev", action="store_true",
+            help="(ingest only) shorthand for --split all.",
+        )
+        # Per-upload knobs forwarded to /documents/fileupload at ingest;
+        # ignored at run-time (runner reads the resolved settings out of
+        # the doc-map manifest header).
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            split=opts.get("split") or "test",
+            max_questions=opts.get("max_questions"),
+            upload_batch_size=int(opts.get("upload_batch_size") or 8),
+            skip_upload=bool(opts.get("skip_upload", False)),
+            include_dev=bool(opts.get("include_dev", False)),
+            settings=settings,
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        split_filter = opts.get("split") or "test"
+        task_filter = opts.get("task") or "all"
+        body_filter = opts.get("body_filter") or "all"
+        require_images = bool(opts.get("require_images"))
+        sample_n = opts.get("sample_n")
+        concurrency = int(opts.get("concurrency") or 4)
+        no_mentions = bool(opts.get("no_mentions"))
+        pdf_engine_name = opts.get("pdf_engine") or "native"
+        max_output_tokens = int(opts.get("max_output_tokens") or 512)
+
+        bench_dir = ctx.benchmark_data_dir()
+        questions_jsonl = bench_dir / "questions.jsonl"
+        map_path = ctx.maps_dir() / "medxpertqa_doc_map.jsonl"
+        if not questions_jsonl.exists() or not map_path.exists():
+            raise RuntimeError(
+                "MedXpertQA-MM not ingested for this suite. Run "
+                "`python -m surfsense_evals ingest medical medxpertqa` first."
+            )
+
+        doc_map, ingest_settings = _load_doc_map(map_path)
+        questions = _load_questions(
+            questions_jsonl, doc_map,
+            split_filter=split_filter,
+            task_filter=task_filter if task_filter != "all" else None,
+            body_filter=body_filter if body_filter != "all" else None,
+            require_images=require_images,
+            sample_n=sample_n,
+        )
+        if not questions:
+            raise RuntimeError(
+                "No MedXpertQA-MM questions matched the filters; broaden --split/--task/--body-system/--n."
+            )
+        logger.info("MedXpertQA-MM: scheduled %d questions", len(questions))
+
+        api_key = os.environ.get("OPENROUTER_API_KEY")
+        if not api_key:
+            raise RuntimeError("OPENROUTER_API_KEY env var is required for the native arm.")
+
+        # Native arm slug differs from SurfSense slug only in cost-arbitrage
+        # scenario; otherwise both arms answer with provider_model.
+        native_arm_model = ctx.native_arm_model
+        if any(hint in native_arm_model.lower() for hint in _TEXT_ONLY_HINTS):
+            if ctx.scenario == "symmetric-cheap":
+                logger.info(
+                    "symmetric-cheap: native arm pinned to text-only %r as "
+                    "intended; expect it to lose on image-bearing questions "
+                    "(SurfSense answers from vision-extracted chunks).",
+                    native_arm_model,
+                )
+            else:
+                logger.warning(
+                    "Native arm slug %r looks text-only; image content in "
+                    "MedXpertQA PDFs will be ignored. Re-pin via "
+                    "`setup --provider-model anthropic/claude-sonnet-4.5` "
+                    "(or pass --native-arm-model and --scenario cost-arbitrage "
+                    "to make this asymmetry explicit).",
+                    native_arm_model,
+                )
+
+        provider = OpenRouterPdfProvider(
+            api_key=api_key,
+            base_url=ctx.config.openrouter_base_url,
+            model=native_arm_model,
+            engine=PdfEngine(pdf_engine_name),
+        )
+        native_arm = NativePdfArm(provider=provider, max_output_tokens=max_output_tokens)
+        surf_arm = SurfSenseArm(
+            client=ctx.new_chat_client(),
+            search_space_id=ctx.search_space_id,
+            ephemeral_threads=True,
+        )
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+
+        async def _native_one(q: MXQuestion) -> ArmResult:
+            return await native_arm.answer(_make_native_request(q, max_output_tokens))
+
+        async def _surf_one(q: MXQuestion) -> ArmResult:
+            return await surf_arm.answer(_make_surfsense_request(q, no_mentions=no_mentions))
+
+        native_results, surf_results = await asyncio.gather(
+            _gather_with_limit((_native_one(q) for q in questions), concurrency=concurrency),
+            _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency),
+        )
+
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, n_res, s_res in zip(questions, native_results, surf_results, strict=False):
+                meta = {
+                    "qid": q.qid,
+                    "split": q.split,
+                    "medical_task": q.medical_task,
+                    "body_system": q.body_system,
+                    "question_type": q.question_type,
+                    "n_images": q.n_images,
+                    "correct": q.label,
+                    "document_id": q.document_id,
+                }
+                fh.write(json.dumps({**meta, **n_res.to_jsonl()}) + "\n")
+                fh.write(json.dumps({**meta, **s_res.to_jsonl()}) + "\n")
+
+        metrics = _compute_metrics(questions, native_results, surf_results)
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "concurrency": concurrency,
+                "split_filter": split_filter,
+                "task_filter": task_filter,
+                "body_filter": body_filter,
+                "require_images": require_images,
+                "no_mentions": no_mentions,
+                "pdf_engine": pdf_engine_name,
+                "scenario": ctx.scenario,
+                "provider_model": ctx.provider_model,
+                "native_arm_model": native_arm_model,
+                "vision_provider_model": ctx.vision_provider_model,
+                "agent_llm_id": ctx.agent_llm_id,
+                "ingest_settings": ingest_settings,
+            },
+        )
+
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps({
+                "suite": self.suite,
+                "benchmark": self.name,
+                "raw_path": "raw.jsonl",
+                "metrics": metrics,
+                "extra": artifact.extra,
+            }, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="MedXpertQA-MM — Native PDF (vision) vs SurfSense (vision RAG)",
+                headline=False,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        native = m.get("native", {})
+        surf = m.get("surfsense", {})
+        delta = m.get("delta", {})
+        per_task = m.get("per_task", {})
+        per_body = m.get("per_body_system", {})
+        extra = latest.extra
+
+        body_lines: list[str] = []
+        body_lines.append(
+            f"- Sample size: {extra.get('n_questions', '?')} questions "
+            f"(split: `{extra.get('split_filter', 'test')}`, "
+            f"task: `{extra.get('task_filter', 'all')}`, "
+            f"body: `{extra.get('body_filter', 'all')}`, "
+            f"engine: `{extra.get('pdf_engine', 'native')}`)."
+        )
+        body_lines.append(format_scenario_md(extra))
+        body_lines.append(format_ingest_settings_md(extra.get("ingest_settings")))
+        body_lines.append(
+            "- Native arm (OpenRouter `chat/completions` + file plugin, "
+            f"`{extra.get('native_arm_model') or extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(native, indent="  "))
+        body_lines.append(
+            "- SurfSense arm (`POST /api/v1/new_chat`, vision RAG over chunks, "
+            f"`{extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(surf, indent="  "))
+        body_lines.append("- Delta (paired):")
+        body_lines.append(
+            f"  - Accuracy: SurfSense {_pp(delta.get('accuracy_pp'))} pp "
+            f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, "
+            f"method={delta.get('mcnemar_method')})"
+        )
+        body_lines.append(
+            f"  - Bootstrap 95% CI on delta: "
+            f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]"
+        )
+        body_lines.append(
+            f"  - Cost / question: native ${_dollars(native.get('cost_micros_mean'))}, "
+            f"surfsense ${_dollars(surf.get('cost_micros_mean'))} "
+            f"(SurfSense delta {_pct_change(delta.get('cost_micros_pct'))})"
+        )
+        body_lines.append(
+            f"  - Latency p50: native {_ms_to_s(native.get('latency_ms_median'))}, "
+            f"surfsense {_ms_to_s(surf.get('latency_ms_median'))} "
+            f"(SurfSense delta {_pct_change(delta.get('latency_ms_pct'))})"
+        )
+        if per_task:
+            body_lines.append("- Per-medical_task split:")
+            for task_name, vals in sorted(per_task.items()):
+                body_lines.append(
+                    f"  - {task_name}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
+                    f"(n={vals.get('n')})"
+                )
+        if per_body:
+            body_lines.append("- Per-body_system split (top 5 by sample size):")
+            top = sorted(per_body.items(), key=lambda kv: -kv[1].get("n", 0))[:5]
+            for body_name, vals in top:
+                body_lines.append(
+                    f"  - {body_name}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
+                    f"(n={vals.get('n')})"
+                )
+
+        return ReportSection(
+            title="MedXpertQA-MM — Native PDF (vision) vs SurfSense (vision RAG)",
+            headline=False,
+            body_md="\n".join(body_lines),
+            body_json=m,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Per-question helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_native_request(q: MXQuestion, max_tokens: int) -> ArmRequest:
+    prompt = build_prompt(q.question, q.options)
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=prompt,
+        pdf_paths=[q.pdf_path],
+        options={"max_tokens": max_tokens},
+    )
+
+
+def _make_surfsense_request(q: MXQuestion, *, no_mentions: bool) -> ArmRequest:
+    prompt = build_prompt(q.question, q.options)
+    mentions: list[int] | None = None
+    if not no_mentions and q.document_id is not None:
+        mentions = [int(q.document_id)]
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=prompt,
+        mentioned_document_ids=mentions,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Metrics
+# ---------------------------------------------------------------------------
+
+
+def _compute_metrics(
+    questions: list[MXQuestion],
+    native_results: list[ArmResult],
+    surf_results: list[ArmResult],
+) -> dict[str, Any]:
+    native_correct: list[bool] = []
+    surf_correct: list[bool] = []
+    for q, n_res, s_res in zip(questions, native_results, surf_results, strict=False):
+        gold = q.label
+        n_ok = (n_res.answer_letter or "").upper() == gold and gold in ANSWER_LETTERS
+        s_ok = (s_res.answer_letter or "").upper() == gold and gold in ANSWER_LETTERS
+        native_correct.append(n_ok)
+        surf_correct.append(s_ok)
+
+    native_costs = [float(r.cost_micros) for r in native_results]
+    surf_costs = [float(r.cost_micros) for r in surf_results]
+    native_lats = [float(r.latency_ms) for r in native_results]
+    surf_lats = [float(r.latency_ms) for r in surf_results]
+    native_in = [float(r.input_tokens) for r in native_results]
+    native_out = [float(r.output_tokens) for r in native_results]
+
+    native_acc = accuracy_with_wilson_ci(sum(native_correct), len(native_correct))
+    surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct))
+    mc = mcnemar_test(native_correct, surf_correct)
+    boot = bootstrap_delta_ci(native_correct, surf_correct, n_resamples=2000)
+
+    native_cost_agg = paired_aggregate(native_costs)
+    surf_cost_agg = paired_aggregate(surf_costs)
+    native_lat_agg = paired_aggregate(native_lats)
+    surf_lat_agg = paired_aggregate(surf_lats)
+
+    cost_pct = _safe_pct(surf_cost_agg.mean, native_cost_agg.mean)
+    lat_pct = _safe_pct(surf_lat_agg.median, native_lat_agg.median)
+
+    per_task = _per_field(questions, native_correct, surf_correct, key=lambda q: q.medical_task or "unknown")
+    per_body = _per_field(questions, native_correct, surf_correct, key=lambda q: q.body_system or "unknown")
+
+    return {
+        "native": {
+            **native_acc.to_dict(),
+            "cost_micros_mean": native_cost_agg.mean,
+            "cost_micros_median": native_cost_agg.median,
+            "latency_ms_mean": native_lat_agg.mean,
+            "latency_ms_median": native_lat_agg.median,
+            "latency_ms_p95": native_lat_agg.p95,
+            "input_tokens_mean": (sum(native_in) / len(native_in)) if native_in else 0.0,
+            "output_tokens_mean": (sum(native_out) / len(native_out)) if native_out else 0.0,
+        },
+        "surfsense": {
+            **surf_acc.to_dict(),
+            "cost_micros_mean": surf_cost_agg.mean,
+            "cost_micros_median": surf_cost_agg.median,
+            "latency_ms_mean": surf_lat_agg.mean,
+            "latency_ms_median": surf_lat_agg.median,
+            "latency_ms_p95": surf_lat_agg.p95,
+        },
+        "delta": {
+            "accuracy_pp": 100.0 * (surf_acc.accuracy - native_acc.accuracy),
+            "mcnemar_p_value": mc.p_value,
+            "mcnemar_method": mc.method,
+            "mcnemar_b_native_only": mc.b,
+            "mcnemar_c_surfsense_only": mc.c,
+            "bootstrap_ci_low": 100.0 * boot.ci_low,
+            "bootstrap_ci_high": 100.0 * boot.ci_high,
+            "cost_micros_pct": cost_pct,
+            "latency_ms_pct": lat_pct,
+        },
+        "per_task": per_task,
+        "per_body_system": per_body,
+    }
+
+
+def _per_field(
+    questions: list[MXQuestion],
+    native_correct: list[bool],
+    surf_correct: list[bool],
+    *,
+    key,
+) -> dict[str, dict[str, Any]]:
+    bucket: dict[str, list[tuple[bool, bool]]] = {}
+    for q, n_ok, s_ok in zip(questions, native_correct, surf_correct, strict=False):
+        bucket.setdefault(key(q), []).append((n_ok, s_ok))
+    out: dict[str, dict[str, Any]] = {}
+    for k, pairs in bucket.items():
+        n_correct = [a for a, _ in pairs]
+        s_correct = [b for _, b in pairs]
+        out[k] = {
+            "n": len(pairs),
+            "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0,
+            "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
+            "delta_accuracy_pp": (
+                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs)
+                if pairs else 0.0
+            ),
+        }
+    return out
+
+
+def _safe_pct(numerator: float, denominator: float) -> float | None:
+    if denominator == 0:
+        return None
+    return 100.0 * (numerator - denominator) / denominator
+
+
+# ---------------------------------------------------------------------------
+# Formatters
+# ---------------------------------------------------------------------------
+
+
+def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str:
+    if not d:
+        return f"{indent}(no data)"
+    acc = d.get("accuracy", 0.0)
+    low = d.get("ci_low", 0.0)
+    high = d.get("ci_high", 0.0)
+    lines = [
+        f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)",
+        f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), "
+        f"${_dollars(d.get('cost_micros_median'))} (median)",
+        f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, "
+        f"p95 {_ms_to_s(d.get('latency_ms_p95'))}",
+    ]
+    if "input_tokens_mean" in d:
+        lines.append(
+            f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, "
+            f"out {d.get('output_tokens_mean', 0):.0f}"
+        )
+    return "\n".join(lines)
+
+
+def _dollars(micros: Any) -> str:
+    if micros is None:
+        return "?"
+    try:
+        return f"{(float(micros) / 1_000_000):.4f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _ms_to_s(ms: Any) -> str:
+    if ms is None:
+        return "?"
+    try:
+        return f"{float(ms) / 1000:.1f}s"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pp(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.1f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pct_change(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.0f}%"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _fmt(value: Any, ndigits: int) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):.{ndigits}f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+__all__ = ["MedXpertQAMMBenchmark", "MXQuestion"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py
new file mode 100644
index 000000000..e527b37f4
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/__init__.py
@@ -0,0 +1,17 @@
+"""MIRAGE — secondary single-arm SurfSense MCQ measurement.
+
+Source: https://github.com/Teddy-XiongGZ/MIRAGE, paper
+https://aclanthology.org/2024.findings-acl.372/. 7,663 questions
+across MMLU-Med, MedQA-US, MedMCQA, PubMedQA*, BioASQ-Y/N.
+
+This is a SurfSense-only measurement (not a head-to-head); native
+PDF-in-LLM doesn't apply because there is no per-question discrete
+document — the corpus is millions of biomedical snippets.
+"""
+
+from __future__ import annotations
+
+from .runner import MirageBenchmark
+from ....core import registry as _registry
+
+_registry.register(MirageBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py
new file mode 100644
index 000000000..9769d078b
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/ingest.py
@@ -0,0 +1,548 @@
+"""MIRAGE ingestion.
+
+Downloads:
+
+* ``benchmark.json`` (≈ 4 MB; questions for the 5 sub-tasks).
+* ``retrieved_snippets_10k.zip`` (the union of top-10k snippet ids
+  retrieved by every retriever in the MedRAG paper, per task — a
+  recall ceiling that avoids needing the full 23.9M-doc PubMed mirror).
+
+Snippet *content* lives in the MedRAG HF mirrors
+(``MedRAG/textbooks``, ``MedRAG/pubmed``, ``MedRAG/statpearls``,
+``MedRAG/wikipedia``). We default to ``MedRAG/textbooks`` (212 MB,
+125k snippets) which is the smallest and covers the majority of
+``MedQA-US`` and the medical examination subsets. Operators can
+opt into larger corpora with ``--corpus``.
+
+Each snippet is written as one markdown file then batched into
+``~5 MB`` markdown bundles for SurfSense's file upload (smaller
+than backend default ``MAX_FILE_SIZE_BYTES`` and avoids the per-call
+overhead of one HTTP request per snippet).
+
+The ingestion produces two maps under ``data/medical/maps/``:
+
+* ``mirage_snippet_map.jsonl`` — ``{snippet_id, document_id, batch_path}``
+* ``mirage_chunk_map.jsonl`` — ``{chunk_id, document_id, snippet_id?}``
+  (best-effort; chunk text is heuristically attributed to the
+  snippet it overlaps when the SurfSense chunker splits a batched
+  markdown).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import io
+import json
+import logging
+import zipfile
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+
+import httpx
+
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+
+logger = logging.getLogger(__name__)
+
+
+MIRAGE_BENCHMARK_URL = (
+    "https://raw.githubusercontent.com/Teddy-XiongGZ/MIRAGE/main/benchmark.json"
+)
+# Upstream only ships ONE zip — top-10k retrievals across 5 retrievers,
+# ~16 GB. We default to skipping it (see `--skip-snippet-filter`) and
+# ingesting the chosen corpus in full; this URL is only fetched when
+# the operator explicitly opts in.
+MIRAGE_SNIPPETS_ZIP_URL = (
+    "https://virginia.box.com/shared/static/cxq17th6eisl2pn04vp0x723zczlvlzc.zip"
+)
+
+
+_DEFAULT_CORPUS = "MedRAG/textbooks"
+_BATCH_SIZE_BYTES = 5 * 1024 * 1024
+# 2 GB safety cap. Anything larger requires --allow-large-download.
+# Set high enough that ``benchmark.json`` and small zips pass through
+# untouched but the 16 GB MIRAGE retrievals zip trips the guard.
+_LARGE_DOWNLOAD_BYTES = 2 * 1024 * 1024 * 1024
+_DOWNLOAD_RETRIES = 5
+_RETRYABLE_NET_EXC: tuple[type[BaseException], ...] = (
+    httpx.RemoteProtocolError,
+    httpx.ReadError,
+    httpx.ReadTimeout,
+    httpx.ConnectError,
+    httpx.ConnectTimeout,
+)
+
+
+@dataclass
+class SnippetRow:
+    snippet_id: str
+    title: str
+    content: str
+
+    def to_markdown(self) -> str:
+        title = (self.title or "").strip() or "Untitled"
+        body = (self.content or "").strip()
+        return f"# {title}\n\n_id: `{self.snippet_id}`_\n\n{body}\n"
+
+
+# ---------------------------------------------------------------------------
+# Download helpers
+# ---------------------------------------------------------------------------
+
+
+async def _fetch_to_path(
+    url: str,
+    *,
+    dest: Path,
+    label: str,
+    timeout_s: float = 600.0,
+    allow_large_download: bool = False,
+    expect_zip: bool = False,
+) -> Path:
+    """Download ``url`` to ``dest`` with retry, atomic-rename, and
+    HTTP ``Range`` resume.
+
+    Operational properties:
+
+    * If ``dest`` already exists *and* (when ``expect_zip`` is True) the
+      cached file is a valid ZIP, returns it immediately. A corrupt ZIP
+      is removed and re-downloaded — this is the safety net for the
+      `box.com truncated 16 GB zip` failure mode where the previous
+      run wrote a half-completed file then exited with an exception.
+    * Bytes are written to ``<dest>.partial`` and renamed only after the
+      stream completes cleanly (and, for zips, only after a quick
+      central-directory check). A failure mid-download leaves the
+      ``.partial`` file in place so the next attempt can resume from
+      where it stopped via an HTTP ``Range`` header.
+    * Retries on transient network errors (``RemoteProtocolError``,
+      ``ReadError``, ``ReadTimeout``, ``ConnectError``,
+      ``ConnectTimeout``) with exponential backoff, up to
+      ``_DOWNLOAD_RETRIES``.
+    * Aborts before downloading if the ``Content-Length`` (or already-
+      downloaded ``.partial`` size) is over ``_LARGE_DOWNLOAD_BYTES``
+      and ``allow_large_download`` is False, to keep an operator from
+      surprise-grabbing 16 GB on a slow link.
+    """
+
+    if dest.exists():
+        if expect_zip and not _is_valid_zip(dest):
+            logger.warning(
+                "Cached %s at %s failed ZIP validation (size=%d B); deleting "
+                "and re-downloading.",
+                label,
+                dest,
+                dest.stat().st_size,
+            )
+            dest.unlink(missing_ok=True)
+        else:
+            logger.info("Using cached %s at %s", label, dest)
+            return dest
+
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    partial = dest.with_suffix(dest.suffix + ".partial")
+    last_exc: BaseException | None = None
+
+    for attempt in range(1, _DOWNLOAD_RETRIES + 1):
+        existing_bytes = partial.stat().st_size if partial.exists() else 0
+        headers: dict[str, str] = {}
+        if existing_bytes:
+            headers["Range"] = f"bytes={existing_bytes}-"
+            logger.info(
+                "Resuming %s from byte %d (attempt %d/%d)",
+                label,
+                existing_bytes,
+                attempt,
+                _DOWNLOAD_RETRIES,
+            )
+        else:
+            logger.info(
+                "Downloading %s from %s (attempt %d/%d)",
+                label,
+                url,
+                attempt,
+                _DOWNLOAD_RETRIES,
+            )
+
+        try:
+            async with httpx.AsyncClient(
+                timeout=httpx.Timeout(timeout_s, connect=20.0),
+                follow_redirects=True,
+            ) as client:
+                async with client.stream("GET", url, headers=headers) as response:
+                    if existing_bytes and response.status_code == 200:
+                        logger.warning(
+                            "Server ignored Range header for %s; restarting from 0.",
+                            label,
+                        )
+                        partial.unlink(missing_ok=True)
+                        existing_bytes = 0
+                    elif response.status_code == 416:
+                        # Range not satisfiable — the .partial is at or
+                        # past the end. Treat as "already downloaded";
+                        # validate by closing and re-opening for atomic
+                        # rename below.
+                        logger.info(
+                            "Server reports %s already complete (HTTP 416).",
+                            label,
+                        )
+                    elif response.status_code not in (200, 206):
+                        response.raise_for_status()
+
+                    total_size = _planned_total_size(response, existing_bytes)
+                    if (
+                        total_size is not None
+                        and total_size > _LARGE_DOWNLOAD_BYTES
+                        and not allow_large_download
+                    ):
+                        raise _LargeDownloadAbort(label, total_size)
+
+                    mode = "ab" if existing_bytes else "wb"
+                    with partial.open(mode) as fh:
+                        async for chunk in response.aiter_bytes(chunk_size=1 << 18):
+                            fh.write(chunk)
+            # Optional content sanity check before promoting to dest.
+            if expect_zip and not _is_valid_zip(partial):
+                raise zipfile.BadZipFile(
+                    f"{label} downloaded to {partial} but failed central-"
+                    "directory check; will retry."
+                )
+            partial.replace(dest)
+            return dest
+        except _LargeDownloadAbort:
+            raise
+        except _RETRYABLE_NET_EXC as exc:
+            last_exc = exc
+            wait = min(60.0, 2.0 ** attempt)
+            logger.warning(
+                "Network error fetching %s (%s: %s); retrying in %.0fs.",
+                label,
+                type(exc).__name__,
+                exc,
+                wait,
+            )
+            await asyncio.sleep(wait)
+        except zipfile.BadZipFile as exc:
+            last_exc = exc
+            # Truncated body — drop the partial and retry from scratch.
+            partial.unlink(missing_ok=True)
+            wait = min(60.0, 2.0 ** attempt)
+            logger.warning(
+                "Truncated ZIP for %s; restarting from byte 0 in %.0fs.",
+                label,
+                wait,
+            )
+            await asyncio.sleep(wait)
+
+    raise RuntimeError(
+        f"Failed to download {label} after {_DOWNLOAD_RETRIES} attempts: {last_exc!s}"
+    )
+
+
+def _planned_total_size(response: httpx.Response, existing_bytes: int) -> int | None:
+    """Best-effort total size including any already-buffered .partial bytes."""
+
+    cl = response.headers.get("Content-Length")
+    if not cl:
+        return None
+    try:
+        remaining = int(cl)
+    except ValueError:
+        return None
+    return existing_bytes + remaining
+
+
+def _is_valid_zip(path: Path) -> bool:
+    """Cheap ZIP validity check via central-directory parse."""
+
+    try:
+        with zipfile.ZipFile(path) as zf:
+            # ``namelist`` forces the central directory to be parsed.
+            zf.namelist()
+        return True
+    except (zipfile.BadZipFile, OSError):
+        return False
+
+
+class _LargeDownloadAbort(RuntimeError):
+    """Raised when a download exceeds the safety threshold without opt-in."""
+
+    def __init__(self, label: str, size_bytes: int) -> None:
+        gb = size_bytes / (1024 ** 3)
+        super().__init__(
+            f"{label} would download ~{gb:.1f} GB, above the {_LARGE_DOWNLOAD_BYTES / (1024 ** 3):.0f} GB safety cap. "
+            "Re-run with `--allow-large-download` to acknowledge, or use "
+            "`--skip-snippet-filter` to bypass this download entirely and "
+            "ingest the full corpus instead."
+        )
+
+
+def _read_snippet_ids(zip_path: Path, *, tasks: list[str]) -> dict[str, set[str]]:
+    """Walk the ZIP for files whose path contains any task name.
+
+    Each MedRAG retriever produces one JSON file per task in the zip;
+    we union all retrievers' top-K ids. The exact directory layout has
+    historically been ``<retriever>/<task>.json`` mapping
+    ``question_id -> [snippet_id, ...]``.
+    """
+
+    out: dict[str, set[str]] = {t: set() for t in tasks}
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        for member in zf.namelist():
+            if not member.lower().endswith(".json"):
+                continue
+            stem = Path(member).stem.lower()
+            for task in tasks:
+                if task.lower() in stem:
+                    try:
+                        with zf.open(member) as fh:
+                            payload = json.loads(fh.read().decode("utf-8"))
+                    except (json.JSONDecodeError, KeyError):
+                        continue
+                    for ids in payload.values():
+                        if isinstance(ids, list):
+                            for sid in ids:
+                                if isinstance(sid, str):
+                                    out[task].add(sid)
+                                elif isinstance(sid, dict) and "id" in sid:
+                                    out[task].add(str(sid["id"]))
+                    break
+    return out
+
+
+def _load_corpus(
+    corpus_name: str, snippet_ids: set[str] | None
+) -> Iterable[SnippetRow]:
+    """Stream rows from a MedRAG HF corpus.
+
+    * ``snippet_ids=None`` → yield every row (full-corpus ingestion path).
+    * ``snippet_ids={...}`` → filter to the requested ids.
+
+    Imported lazily — ``datasets`` is a heavyweight dep.
+    """
+
+    if snippet_ids is not None and not snippet_ids:
+        return iter(())
+    from datasets import load_dataset  # noqa: PLC0415
+
+    logger.info("Loading corpus %s (this may take a while)", corpus_name)
+    ds = load_dataset(corpus_name, split="train", streaming=True)
+    for row in ds:
+        sid = str(row.get("id") or "")
+        if snippet_ids is not None and sid not in snippet_ids:
+            continue
+        yield SnippetRow(
+            snippet_id=sid,
+            title=str(row.get("title") or ""),
+            content=str(row.get("content") or row.get("contents") or ""),
+        )
+
+
+# ---------------------------------------------------------------------------
+# Batching + upload
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class SnippetBatch:
+    path: Path
+    snippet_ids: list[str]
+
+
+def _write_batches(
+    snippets: Iterable[SnippetRow],
+    *,
+    out_dir: Path,
+    batch_bytes: int = _BATCH_SIZE_BYTES,
+    prefix: str = "mirage",
+) -> list[SnippetBatch]:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    batches: list[SnippetBatch] = []
+    current_buffer = io.StringIO()
+    current_ids: list[str] = []
+    current_bytes = 0
+    batch_idx = 0
+
+    def _flush() -> None:
+        nonlocal current_buffer, current_ids, current_bytes, batch_idx
+        if not current_ids:
+            return
+        path = out_dir / f"{prefix}_{batch_idx:04d}.md"
+        path.write_text(current_buffer.getvalue(), encoding="utf-8")
+        batches.append(SnippetBatch(path=path, snippet_ids=current_ids))
+        batch_idx += 1
+        current_buffer = io.StringIO()
+        current_ids = []
+        current_bytes = 0
+
+    for snippet in snippets:
+        chunk = snippet.to_markdown() + "\n---\n\n"
+        chunk_bytes = len(chunk.encode("utf-8"))
+        if current_bytes + chunk_bytes > batch_bytes and current_ids:
+            _flush()
+        current_buffer.write(chunk)
+        current_ids.append(snippet.snippet_id)
+        current_bytes += chunk_bytes
+    _flush()
+    return batches
+
+
+# ---------------------------------------------------------------------------
+# Public entry point
+# ---------------------------------------------------------------------------
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    tasks: list[str] | None = None,
+    corpus: str = _DEFAULT_CORPUS,
+    max_snippets_per_task: int | None = None,
+    skip_snippet_filter: bool = True,
+    allow_large_download: bool = False,
+    settings: IngestSettings | None = None,
+) -> None:
+    """Ingest a MedRAG corpus into the suite SearchSpace.
+
+    By default (``skip_snippet_filter=True``) we ingest the **entire**
+    chosen corpus and let SurfSense's own retriever do the work. The
+    upstream MIRAGE retrieval zip is ~16 GB and only useful when you
+    want to pre-filter the corpus to the set of snippets some other
+    retriever surfaced; for ``MedRAG/textbooks`` (212 MB / 125k snippets)
+    that pre-filter is unnecessary overhead and routinely fails to
+    download (box.com truncates the stream). Set
+    ``skip_snippet_filter=False`` (CLI: ``--use-snippet-filter``) only
+    if you specifically want the upstream filter — and budget the
+    16 GB zip transfer.
+    """
+
+    tasks = tasks or ["mmlu", "medqa", "medmcqa", "pubmedqa", "bioasq"]
+    settings = settings or IngestSettings(use_vision_llm=False, processing_mode="basic")
+
+    bench_path = ctx.benchmark_data_dir() / "benchmark.json"
+    await _fetch_to_path(MIRAGE_BENCHMARK_URL, dest=bench_path, label="MIRAGE benchmark.json")
+
+    if skip_snippet_filter:
+        logger.info(
+            "Skipping retrieved_snippets_10k.zip (skip_snippet_filter=True); "
+            "ingesting entire corpus %s.",
+            corpus,
+        )
+        snippets = list(_load_corpus(corpus, snippet_ids=None))
+    else:
+        zip_path = ctx.benchmark_data_dir() / "retrieved_snippets_10k.zip"
+        await _fetch_to_path(
+            MIRAGE_SNIPPETS_ZIP_URL,
+            dest=zip_path,
+            label="MIRAGE retrieved_snippets_10k.zip",
+            allow_large_download=allow_large_download,
+            expect_zip=True,
+        )
+
+        by_task = _read_snippet_ids(zip_path, tasks=tasks)
+        if max_snippets_per_task is not None:
+            by_task = {k: set(list(v)[:max_snippets_per_task]) for k, v in by_task.items()}
+
+        union_ids = set().union(*by_task.values())
+        logger.info(
+            "MIRAGE: tasks=%s, snippet ids per task: %s, union=%d",
+            tasks,
+            {k: len(v) for k, v in by_task.items()},
+            len(union_ids),
+        )
+        if not union_ids:
+            raise RuntimeError(
+                f"No snippet ids parsed for tasks {tasks!r} from {zip_path}. "
+                "Check the zip layout (the upstream archive may have changed)."
+            )
+
+        snippets = list(_load_corpus(corpus, snippet_ids=union_ids))
+        logger.info(
+            "Loaded %d / %d requested snippets from corpus %s",
+            len(snippets),
+            len(union_ids),
+            corpus,
+        )
+    if not snippets:
+        raise RuntimeError(
+            f"Corpus {corpus} returned 0 matching rows. Either the snippet "
+            "ids reference a different corpus (e.g. PubMed) or the HF mirror "
+            "is unavailable. Pass --corpus to override."
+        )
+
+    batches_dir = ctx.benchmark_data_dir() / "batches"
+    batches = _write_batches(snippets, out_dir=batches_dir)
+    logger.info("Wrote %d snippet batches to %s", len(batches), batches_dir)
+
+    docs_client = ctx.documents_client()
+    upload_result = await docs_client.upload(
+        files=[b.path for b in batches],
+        search_space_id=ctx.search_space_id,
+        should_summarize=settings.should_summarize,
+        use_vision_llm=settings.use_vision_llm,
+        processing_mode=settings.processing_mode,
+    )
+    logger.info("MIRAGE upload settings: %s", settings.render_label())
+    new_doc_ids = list(upload_result.document_ids)
+    if new_doc_ids:
+        await docs_client.wait_until_ready(
+            search_space_id=ctx.search_space_id,
+            document_ids=new_doc_ids,
+            timeout_s=3600.0,
+            max_poll_s=15.0,
+        )
+
+    statuses = await docs_client.get_status(
+        search_space_id=ctx.search_space_id,
+        document_ids=new_doc_ids + upload_result.duplicate_document_ids,
+    )
+    title_to_doc = {s.title: s.document_id for s in statuses}
+
+    snippet_map_path = ctx.maps_dir() / "mirage_snippet_map.jsonl"
+    chunk_map_path = ctx.maps_dir() / "mirage_chunk_map.jsonl"
+    with snippet_map_path.open("w", encoding="utf-8") as fh:
+        # Header line records the ingest-time settings (see
+        # core/ingest_settings.py for the protocol).
+        fh.write(settings_header_line(settings) + "\n")
+        for batch in batches:
+            doc_id = title_to_doc.get(batch.path.name)
+            if doc_id is None:
+                logger.warning("No document_id for batch %s", batch.path.name)
+                continue
+            for sid in batch.snippet_ids:
+                fh.write(
+                    json.dumps(
+                        {
+                            "snippet_id": sid,
+                            "document_id": doc_id,
+                            "batch_path": str(batch.path),
+                        }
+                    )
+                    + "\n"
+                )
+
+    # Best-effort chunk map. SurfSense doesn't expose snippet attribution
+    # per chunk, so we just record (chunk_id -> document_id) here; the
+    # MIRAGE runner only needs document_id for accuracy scoring.
+    with chunk_map_path.open("w", encoding="utf-8") as fh:
+        for doc_id in {b.path.name and title_to_doc.get(b.path.name) for b in batches} - {None}:
+            try:
+                chunks = await docs_client.list_chunks(int(doc_id))
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("Failed to list chunks for doc_id=%s: %s", doc_id, exc)
+                continue
+            for chunk in chunks:
+                fh.write(
+                    json.dumps({"chunk_id": chunk.id, "document_id": doc_id})
+                    + "\n"
+                )
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["mirage"] = str(snippet_map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+    logger.info("Wrote MIRAGE maps to %s and %s", snippet_map_path, chunk_map_path)
+
+
+__all__ = ["run_ingest", "SnippetRow", "SnippetBatch"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py
new file mode 100644
index 000000000..9e5b1c618
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/prompt.py
@@ -0,0 +1,44 @@
+"""MedRAG ``{step_by_step_thinking, answer_choice}`` MCQ prompt.
+
+Mirrors the MedRAG paper's prompt format so accuracy numbers are
+comparable to the published MIRAGE leaderboard.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+
+
+_PROMPT_TEMPLATE = """\
+You are a helpful medical expert. Answer the following multiple-choice
+question using the relevant medical knowledge available to you (and any
+retrieved context, if provided).
+
+Respond with a JSON object on a single line:
+{{"step_by_step_thinking": "<your reasoning>", "answer_choice": "<letter>"}}
+
+Question: {question}
+
+Options:
+{options_block}
+"""
+
+
+def _options_block(options: Mapping[str, str]) -> str:
+    parts: list[str] = []
+    for letter in sorted(options.keys()):
+        text = options.get(letter)
+        if text is None or text == "":
+            continue
+        parts.append(f"{letter}) {text}")
+    return "\n".join(parts)
+
+
+def build_prompt(question: str, options: Mapping[str, str]) -> str:
+    return _PROMPT_TEMPLATE.format(
+        question=question.strip(),
+        options_block=_options_block(options),
+    )
+
+
+__all__ = ["build_prompt"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py
new file mode 100644
index 000000000..0f336c0d5
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/medical/mirage/runner.py
@@ -0,0 +1,332 @@
+"""MIRAGE runner: SurfSense-only per-task accuracy.
+
+The benchmark file format is one top-level dict per task (``mmlu``,
+``medqa``, ``medmcqa``, ``pubmedqa``, ``bioasq``); each task value is
+``{question_id: {question, options, answer}}``.
+
+We restrict retrieval to the suite SearchSpace's full corpus (no
+``mentioned_document_ids`` — MIRAGE has no per-question ground-truth
+document; retrieval *is* the test). Accuracy is paired against the
+``answer`` letter from the dataset.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    read_settings_header,
+)
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci, macro_accuracy
+from ....core.registry import (
+    Benchmark,
+    ReportSection,
+    RunArtifact,
+    RunContext,
+)
+from .prompt import build_prompt
+
+logger = logging.getLogger(__name__)
+
+
+_TASKS = ("mmlu", "medqa", "medmcqa", "pubmedqa", "bioasq")
+_DESCRIPTION = "MIRAGE (7,663 medical MCQs) — single-arm SurfSense per-task accuracy."
+
+# MIRAGE corpus is text-only (textbook + abstract markdown). Vision
+# LLM at ingest is wasted compute by default; flip ``--use-vision-llm``
+# to measure cost.
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=False,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+@dataclass
+class MirageQuestion:
+    task: str
+    qid: str
+    question: str
+    options: dict[str, str]
+    correct: str
+
+    @property
+    def question_id(self) -> str:
+        return f"{self.task}::{self.qid}"
+
+
+def _load_questions(
+    benchmark: dict[str, Any],
+    *,
+    tasks: list[str],
+    sample_n: int | None,
+) -> list[MirageQuestion]:
+    out: list[MirageQuestion] = []
+    for task in tasks:
+        rows = benchmark.get(task) or {}
+        if not isinstance(rows, dict):
+            continue
+        for qid, raw in rows.items():
+            if not isinstance(raw, dict):
+                continue
+            options = raw.get("options") or {}
+            if not isinstance(options, dict):
+                continue
+            answer_raw = str(raw.get("answer") or "").strip()
+            if not answer_raw:
+                continue
+            answer_letter = answer_raw[:1].upper()
+            out.append(
+                MirageQuestion(
+                    task=task,
+                    qid=str(qid),
+                    question=str(raw.get("question", "")),
+                    options={str(k): str(v) for k, v in options.items() if v},
+                    correct=answer_letter,
+                )
+            )
+    out.sort(key=lambda q: (q.task, q.qid))
+    if sample_n is not None and sample_n > 0:
+        # Stratified-by-task slice so smoke runs cover every task.
+        per_task = max(1, sample_n // max(1, len(tasks)))
+        sliced: list[MirageQuestion] = []
+        per_task_counter: dict[str, int] = {}
+        for q in out:
+            n = per_task_counter.get(q.task, 0)
+            if n >= per_task:
+                continue
+            sliced.append(q)
+            per_task_counter[q.task] = n + 1
+            if len(sliced) >= sample_n:
+                break
+        out = sliced
+    return out
+
+
+async def _gather_with_limit(coros, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(c):
+        async with sem:
+            return await c
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+class MirageBenchmark:
+    suite: str = "medical"
+    name: str = "mirage"
+    headline: bool = False
+    description: str = _DESCRIPTION
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--task",
+            default="all",
+            choices=("all", *_TASKS),
+            help="Run a single task or all (default: all).",
+        )
+        parser.add_argument("--n", dest="sample_n", type=int, default=None,
+                            help="Stratified sample size across tasks.")
+        parser.add_argument("--concurrency", type=int, default=4)
+        parser.add_argument(
+            "--corpus", default="MedRAG/textbooks",
+            help="HF MedRAG corpus to ingest from (default: MedRAG/textbooks).",
+        )
+        parser.add_argument(
+            "--max-snippets-per-task", type=int, default=None,
+            help="Cap the per-task ingestion to N snippets (smoke).",
+        )
+        # Mutually exclusive: by default we skip the upstream 16 GB
+        # retrievals zip and ingest the entire corpus. Operators who
+        # want the upstream pre-filter pass --use-snippet-filter (and,
+        # if their corpus mismatch warrants the 16 GB transfer,
+        # --allow-large-download).
+        snippet_group = parser.add_mutually_exclusive_group()
+        snippet_group.add_argument(
+            "--use-snippet-filter", dest="use_snippet_filter", action="store_true",
+            default=False,
+            help="Download retrieved_snippets_10k.zip (~16 GB) and "
+                 "filter the corpus to those ids before ingest. "
+                 "Default: skip and ingest entire corpus.",
+        )
+        snippet_group.add_argument(
+            "--skip-snippet-filter", dest="use_snippet_filter", action="store_false",
+            help="(Default) Skip the 16 GB upstream zip; ingest entire corpus.",
+        )
+        parser.add_argument(
+            "--allow-large-download", action="store_true", default=False,
+            help="Permit downloads larger than 2 GB (e.g. retrieved_snippets_10k.zip).",
+        )
+        # Per-upload knobs; ignored at run-time (runner reads the
+        # resolved settings out of the snippet-map manifest header).
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            corpus=str(opts.get("corpus") or "MedRAG/textbooks"),
+            max_snippets_per_task=opts.get("max_snippets_per_task"),
+            skip_snippet_filter=not bool(opts.get("use_snippet_filter")),
+            allow_large_download=bool(opts.get("allow_large_download")),
+            settings=settings,
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        task_filter = opts.get("task") or "all"
+        tasks = list(_TASKS) if task_filter == "all" else [task_filter]
+        sample_n = opts.get("sample_n")
+        concurrency = int(opts.get("concurrency") or 4)
+
+        bench_path = ctx.benchmark_data_dir() / "benchmark.json"
+        if not bench_path.exists():
+            raise RuntimeError(
+                "MIRAGE benchmark.json missing. Run "
+                "`python -m surfsense_evals ingest medical mirage` first."
+            )
+        benchmark = json.loads(bench_path.read_text(encoding="utf-8"))
+        ingest_settings = read_settings_header(
+            ctx.maps_dir() / "mirage_snippet_map.jsonl"
+        )
+        questions = _load_questions(benchmark, tasks=tasks, sample_n=sample_n)
+        if not questions:
+            raise RuntimeError(
+                f"No MIRAGE questions matched task={task_filter!r} sample_n={sample_n!r}."
+            )
+        logger.info("MIRAGE: scheduled %d questions across tasks %s",
+                    len(questions), tasks)
+
+        arm = SurfSenseArm(
+            client=ctx.new_chat_client(),
+            search_space_id=ctx.search_space_id,
+            ephemeral_threads=True,
+        )
+
+        async def _ask(q: MirageQuestion) -> ArmResult:
+            request = ArmRequest(
+                question_id=q.question_id,
+                prompt=build_prompt(q.question, q.options),
+            )
+            return await arm.answer(request)
+
+        results: list[ArmResult] = await _gather_with_limit(
+            (_ask(q) for q in questions), concurrency=concurrency
+        )
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, res in zip(questions, results):
+                fh.write(
+                    json.dumps(
+                        {
+                            "task": q.task,
+                            "qid": q.qid,
+                            "correct": q.correct,
+                            **res.to_jsonl(),
+                        }
+                    )
+                    + "\n"
+                )
+
+        per_task_acc: dict[str, dict[str, Any]] = {}
+        for task in tasks:
+            n_correct = 0
+            n_total = 0
+            for q, res in zip(questions, results):
+                if q.task != task:
+                    continue
+                n_total += 1
+                if (res.answer_letter or "").upper() == q.correct:
+                    n_correct += 1
+            acc = accuracy_with_wilson_ci(n_correct, n_total)
+            per_task_acc[task] = acc.to_dict()
+
+        macro = macro_accuracy(
+            {t: accuracy_with_wilson_ci(d["n_correct"], d["n_total"]) for t, d in per_task_acc.items()}
+        )
+        metrics = {"per_task": per_task_acc, "macro_accuracy": macro}
+
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "task_filter": task_filter,
+                "concurrency": concurrency,
+                "provider_model": ctx.provider_model,
+                "ingest_settings": ingest_settings,
+            },
+        )
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps(
+                {
+                    "suite": self.suite,
+                    "benchmark": self.name,
+                    "raw_path": "raw.jsonl",
+                    "metrics": metrics,
+                    "extra": artifact.extra,
+                },
+                indent=2,
+                sort_keys=True,
+            )
+            + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="MIRAGE — single-arm SurfSense per-task accuracy",
+                headline=False,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        per_task = latest.metrics.get("per_task", {})
+        macro = latest.metrics.get("macro_accuracy", 0.0)
+        lines: list[str] = []
+        lines.append(format_ingest_settings_md(latest.extra.get("ingest_settings")))
+        for task in _TASKS:
+            row = per_task.get(task)
+            if not row:
+                continue
+            acc = row.get("accuracy", 0.0)
+            low = row.get("ci_low", 0.0)
+            high = row.get("ci_high", 0.0)
+            lines.append(
+                f"- {task}: {acc * 100:.1f}% "
+                f"(Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%, "
+                f"n={row.get('n_total', '?')})"
+            )
+        if not lines:
+            lines.append("- (no per-task results)")
+        lines.append(f"- Macro accuracy: {macro * 100:.2f}%")
+        return ReportSection(
+            title="MIRAGE — single-arm SurfSense per-task accuracy",
+            headline=False,
+            body_md="\n".join(lines),
+            body_json=latest.metrics,
+        )
+
+
+__all__ = ["MirageBenchmark", "MirageQuestion"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py
new file mode 100644
index 000000000..22682ed3f
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/__init__.py
@@ -0,0 +1,14 @@
+"""Multimodal long-document benchmarks (PDFs with embedded images/charts/tables).
+
+Distinct from the medical suite because these documents are domain-mixed
+(research reports, financials, manuals, government, brochures, papers).
+The hypothesis being tested here is *general*: does SurfSense's
+chunking-based vision RAG preserve information that lives in pixels —
+across long PDFs, across pages — versus feeding the same PDF directly
+to a vision-capable model?
+
+Subpackages register themselves with ``core.registry`` on import. The
+``suites/__init__.py`` discovery walker imports them automatically.
+"""
+
+from __future__ import annotations
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py
new file mode 100644
index 000000000..1c2bfa84c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/__init__.py
@@ -0,0 +1,19 @@
+"""MMLongBench-Doc — head-to-head Native PDF (vision) vs SurfSense (vision RAG).
+
+Source: https://huggingface.co/datasets/yubo2333/MMLongBench-Doc
+Paper:  https://arxiv.org/abs/2407.01523 (NeurIPS 2024 D&B Track)
+
+* 135 long PDFs (avg 47 pages, multi-modal: text, images, charts, tables)
+* 1,091 expert-annotated questions
+* 33% require evidence from multiple pages
+* ~22% intentionally unanswerable (tests hallucination resistance)
+* 7 document types: research report, tutorial/workshop, academic paper,
+  financial report, brochure, government, manuals
+"""
+
+from __future__ import annotations
+
+from ....core import registry as _registry
+from .runner import MMLongBenchDocBenchmark
+
+_registry.register(MMLongBenchDocBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py
new file mode 100644
index 000000000..7edad73eb
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py
@@ -0,0 +1,236 @@
+"""Format-aware grader for MMLongBench-Doc answers.
+
+The dataset ships with five ``answer_format`` values per question:
+
+* ``Str``  — short factoid string
+* ``Int``  — integer count / year
+* ``Float`` — decimal number (often with units stripped)
+* ``List`` — comma- or semicolon-separated bag of items
+* ``None`` — gold answer is literally "Not answerable" (hallucination probe)
+
+The official MMLongBench-Doc paper grades with GPT-4 as judge. We
+implement a *deterministic* rule-based grader as the default (so two
+researchers running the same harness get the same number); an
+LLM-judge mode is exposed via ``--judge gpt5`` and routed through the
+same OpenRouter key the arms use, but is opt-in to keep cost down.
+
+Returned by every grading call:
+
+* ``correct: bool`` — final pass/fail used for accuracy + McNemar
+* ``f1: float``     — token-level F1 (continuous credit, useful when
+  comparing arms that get *most* of a list right)
+* ``method: str``   — which path graded the row (one of
+  ``str_norm`` / ``int_eq`` / ``float_tol`` / ``list_set`` /
+  ``none_match`` / ``llm_judge``).
+"""
+
+from __future__ import annotations
+
+import re
+import string
+from collections import Counter
+from dataclasses import dataclass
+
+# ---------------------------------------------------------------------------
+# Public types
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class GradeResult:
+    correct: bool
+    f1: float
+    method: str
+    normalised_pred: str = ""
+    normalised_gold: str = ""
+
+
+# ---------------------------------------------------------------------------
+# Normalisation helpers (shared)
+# ---------------------------------------------------------------------------
+
+_PUNCT_TABLE = str.maketrans({c: " " for c in string.punctuation})
+_ARTICLES = re.compile(r"\b(a|an|the)\b", re.IGNORECASE)
+_WS = re.compile(r"\s+")
+_NOT_ANSWERABLE_TOKENS = {
+    "not answerable",
+    "cannot be answered",
+    "cannot answer",
+    "no answer",
+    "unknown",
+    "none",
+    "not specified",
+    "not mentioned",
+    "not provided",
+    "the answer is not in the document",
+}
+
+# Abbreviations that should be matched literally on the lowercased
+# prediction (because normalisation strips their punctuation and
+# leaves them too short to be safe as substring tokens).
+_NOT_ANSWERABLE_LITERAL = {"n/a", "na/", "n.a.", "n a"}
+
+
+def _normalise_text(s: str) -> str:
+    """SQuAD-style normalisation: lowercase, drop punctuation/articles, squash whitespace."""
+
+    s = s.lower()
+    s = s.translate(_PUNCT_TABLE)
+    s = _ARTICLES.sub(" ", s)
+    s = _WS.sub(" ", s).strip()
+    return s
+
+
+# ---------------------------------------------------------------------------
+# Per-format graders
+# ---------------------------------------------------------------------------
+
+
+def _grade_str(pred: str, gold: str) -> GradeResult:
+    p = _normalise_text(pred)
+    g = _normalise_text(gold)
+    if not p:
+        return GradeResult(False, 0.0, "str_norm", p, g)
+    if p == g:
+        return GradeResult(True, 1.0, "str_norm", p, g)
+    # Substring match in either direction = correct (handles the common
+    # "model emits a fuller sentence containing the gold" case).
+    if g and (g in p or p in g):
+        return GradeResult(True, _f1_tokens(p, g), "str_norm", p, g)
+    return GradeResult(False, _f1_tokens(p, g), "str_norm", p, g)
+
+
+_INT_RE = re.compile(r"-?\d[\d,]*")
+
+
+def _grade_int(pred: str, gold: str) -> GradeResult:
+    g_match = _INT_RE.search(gold)
+    if g_match is None:
+        return _grade_str(pred, gold)
+    g_val = int(g_match.group(0).replace(",", ""))
+    p_match = _INT_RE.search(pred)
+    if p_match is None:
+        return GradeResult(False, 0.0, "int_eq", str(p_match), str(g_val))
+    p_val = int(p_match.group(0).replace(",", ""))
+    return GradeResult(p_val == g_val, 1.0 if p_val == g_val else 0.0,
+                       "int_eq", str(p_val), str(g_val))
+
+
+_FLOAT_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
+
+
+def _grade_float(pred: str, gold: str, *, rel_tol: float = 1e-2) -> GradeResult:
+    g_match = _FLOAT_RE.search(gold)
+    if g_match is None:
+        return _grade_str(pred, gold)
+    g_val = float(g_match.group(0).replace(",", "."))
+    p_match = _FLOAT_RE.search(pred)
+    if p_match is None:
+        return GradeResult(False, 0.0, "float_tol", "", str(g_val))
+    p_val = float(p_match.group(0).replace(",", "."))
+    # Tolerance: 1% relative or 0.01 absolute, whichever is looser.
+    abs_diff = abs(p_val - g_val)
+    tol = max(abs(g_val) * rel_tol, 0.01)
+    ok = abs_diff <= tol
+    return GradeResult(ok, 1.0 if ok else 0.0, "float_tol", str(p_val), str(g_val))
+
+
+_LIST_SPLIT = re.compile(r"[;,\n]")
+
+
+def _grade_list(pred: str, gold: str) -> GradeResult:
+    g_items = {_normalise_text(x) for x in _LIST_SPLIT.split(gold) if x.strip()}
+    p_items = {_normalise_text(x) for x in _LIST_SPLIT.split(pred) if x.strip()}
+    if not g_items:
+        return _grade_str(pred, gold)
+    inter = g_items & p_items
+    if not inter:
+        return GradeResult(False, 0.0, "list_set",
+                           ", ".join(sorted(p_items)),
+                           ", ".join(sorted(g_items)))
+    precision = len(inter) / len(p_items) if p_items else 0.0
+    recall = len(inter) / len(g_items)
+    f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
+    return GradeResult(f1 >= 0.999, f1, "list_set",
+                       ", ".join(sorted(p_items)),
+                       ", ".join(sorted(g_items)))
+
+
+def _grade_none(pred: str, gold: str) -> GradeResult:
+    """Gold == 'Not answerable'. The arm earns credit if its prediction
+    expresses inability to answer.
+
+    Two passes:
+
+    1. Literal-substring check on the lowercased+stripped pred for
+       ambiguous abbreviations like ``n/a`` (since normalisation
+       strips the punctuation and would over-match).
+    2. Word-boundary substring check on the normalised pred for the
+       multi-word phrases (``cannot answer``, ``not specified`` etc.).
+    """
+
+    raw_lower = (pred or "").strip().lower()
+    p = _normalise_text(pred)
+    expressed_unknown = False
+
+    # Pass 1: literal abbreviation hits on the raw lowercased text.
+    if any(lit in raw_lower for lit in _NOT_ANSWERABLE_LITERAL):
+        expressed_unknown = True
+
+    # Pass 2: word-boundary check on normalised tokens.
+    if not expressed_unknown:
+        p_padded = f" {p} "
+        for tok_raw in _NOT_ANSWERABLE_TOKENS:
+            tok = _normalise_text(tok_raw)
+            if not tok or len(tok) < 3:
+                continue
+            if f" {tok} " in p_padded:
+                expressed_unknown = True
+                break
+    return GradeResult(
+        expressed_unknown, 1.0 if expressed_unknown else 0.0,
+        "none_match", p, _normalise_text(gold),
+    )
+
+
+def _f1_tokens(pred: str, gold: str) -> float:
+    p_tok = pred.split()
+    g_tok = gold.split()
+    if not p_tok or not g_tok:
+        return 0.0
+    common = Counter(p_tok) & Counter(g_tok)
+    overlap = sum(common.values())
+    if overlap == 0:
+        return 0.0
+    precision = overlap / len(p_tok)
+    recall = overlap / len(g_tok)
+    return 2 * precision * recall / (precision + recall)
+
+
+# ---------------------------------------------------------------------------
+# Public dispatcher
+# ---------------------------------------------------------------------------
+
+
+_FORMAT_DISPATCH = {
+    "str": _grade_str,
+    "int": _grade_int,
+    "float": _grade_float,
+    "list": _grade_list,
+    "none": _grade_none,
+}
+
+
+def grade(*, pred: str, gold: str, answer_format: str) -> GradeResult:
+    """Grade a single (prediction, gold) pair.
+
+    ``answer_format`` is the dataset's ``answer_format`` column value.
+    Unknown / blank values fall through to string grading.
+    """
+
+    fmt = (answer_format or "").strip().lower()
+    fn = _FORMAT_DISPATCH.get(fmt, _grade_str)
+    return fn(pred or "", gold or "")
+
+
+__all__ = ["GradeResult", "grade"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py
new file mode 100644
index 000000000..cf0572df8
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/ingest.py
@@ -0,0 +1,365 @@
+"""MMLongBench-Doc ingestion.
+
+Steps:
+
+1. Pull the questions parquet from
+   ``hf://datasets/yubo2333/MMLongBench-Doc/data/`` and cache locally.
+2. Resolve the unique set of ``doc_id`` referenced by questions, and
+   download each PDF from
+   ``hf://datasets/yubo2333/MMLongBench-Doc/documents/<doc_id>``.
+   ``huggingface_hub.hf_hub_download`` is resumable + content-hash
+   verifying; we cache PDFs under ``<data_dir>/multimodal_doc/mmlongbench/pdfs/``.
+3. Upload every PDF to SurfSense via ``DocumentsClient.upload`` with
+   ``use_vision_llm=True`` so SurfSense's Pillow + LiteLLM vision
+   pipeline extracts captions / OCR for embedded images, charts, and
+   tables.
+4. Wait for ``processed`` status and persist
+   ``doc_id -> document_id`` in
+   ``<data_dir>/multimodal_doc/maps/mmlongbench_doc_map.jsonl``.
+
+By default we ingest **all** 135 PDFs (~660 MB, totally manageable).
+Operators can scope to a subset with ``--max-docs N`` if iterating on
+a slow vision pipeline.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+
+logger = logging.getLogger(__name__)
+
+
+HF_REPO_ID = "yubo2333/MMLongBench-Doc"
+HF_REPO_TYPE = "dataset"
+
+# Lazy import: huggingface_hub + pyarrow are heavyweight; keep the
+# benchmark module importable on machines that have only the core
+# install (e.g. CI lint jobs).
+def _hf_hub_download(*args, **kwargs):
+    from huggingface_hub import hf_hub_download
+
+    return hf_hub_download(*args, **kwargs)
+
+
+def _list_repo_files() -> list[str]:
+    from huggingface_hub import list_repo_files
+
+    return list_repo_files(repo_id=HF_REPO_ID, repo_type=HF_REPO_TYPE)
+
+
+# ---------------------------------------------------------------------------
+# Question parquet -> Python rows
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class MMLongBenchQuestion:
+    doc_id: str          # filename inside the documents/ folder
+    doc_type: str
+    question: str
+    answer: str
+    answer_format: str   # Str / Int / Float / List / None
+    evidence_pages: list[int]
+    evidence_sources: list[str]
+
+
+def _load_questions_from_parquet(parquet_path: Path) -> list[MMLongBenchQuestion]:
+    import pyarrow.parquet as pq
+
+    table = pq.read_table(parquet_path)
+    rows = table.to_pylist()
+    out: list[MMLongBenchQuestion] = []
+    for row in rows:
+        doc_id = str(row.get("doc_id") or "").strip()
+        if not doc_id:
+            continue
+        question = str(row.get("question") or "").strip()
+        if not question:
+            continue
+        out.append(
+            MMLongBenchQuestion(
+                doc_id=doc_id,
+                doc_type=str(row.get("doc_type") or "").strip(),
+                question=question,
+                answer=str(row.get("answer") or "").strip(),
+                answer_format=str(row.get("answer_format") or "").strip(),
+                evidence_pages=_parse_int_list(row.get("evidence_pages")),
+                evidence_sources=_parse_str_list(row.get("evidence_sources")),
+            )
+        )
+    return out
+
+
+def _parse_int_list(raw) -> list[int]:
+    if raw is None:
+        return []
+    if isinstance(raw, list):
+        out = []
+        for x in raw:
+            try:
+                out.append(int(x))
+            except (TypeError, ValueError):
+                continue
+        return out
+    text = str(raw).strip().strip("[]")
+    if not text:
+        return []
+    out: list[int] = []
+    for tok in text.split(","):
+        tok = tok.strip().strip("'\"")
+        if tok.isdigit():
+            out.append(int(tok))
+    return out
+
+
+def _parse_str_list(raw) -> list[str]:
+    if raw is None:
+        return []
+    if isinstance(raw, list):
+        return [str(x).strip().strip("'\"") for x in raw if str(x).strip()]
+    text = str(raw).strip().strip("[]")
+    if not text:
+        return []
+    return [tok.strip().strip("'\"") for tok in text.split(",") if tok.strip()]
+
+
+# ---------------------------------------------------------------------------
+# Download helpers
+# ---------------------------------------------------------------------------
+
+
+def _download_questions_parquet(cache_dir: Path) -> Path:
+    """Download every parquet under ``data/`` and concatenate.
+
+    The HF dataset usually publishes a single ``train`` split, but we
+    enumerate to be robust to repo restructuring.
+    """
+
+    parquet_paths: list[Path] = []
+    files = _list_repo_files()
+    data_files = [f for f in files if f.startswith("data/") and f.endswith(".parquet")]
+    if not data_files:
+        raise RuntimeError(
+            f"No parquet files found under data/ in {HF_REPO_ID}; "
+            f"upstream repo may have been restructured."
+        )
+    for rel in sorted(data_files):
+        local = _hf_hub_download(
+            repo_id=HF_REPO_ID,
+            filename=rel,
+            repo_type=HF_REPO_TYPE,
+            cache_dir=str(cache_dir),
+        )
+        parquet_paths.append(Path(local))
+        logger.info("Cached MMLongBench parquet shard %s -> %s", rel, local)
+    return parquet_paths[0] if len(parquet_paths) == 1 else _merge_parquets(parquet_paths, cache_dir)
+
+
+def _merge_parquets(paths: list[Path], cache_dir: Path) -> Path:
+    """Combine multiple parquet shards into one (rare branch, but correct)."""
+
+    import pyarrow as pa
+    import pyarrow.parquet as pq
+
+    tables = [pq.read_table(p) for p in paths]
+    merged = pa.concat_tables(tables, promote_options="default")
+    out = cache_dir / "merged_questions.parquet"
+    pq.write_table(merged, out)
+    return out
+
+
+def _download_pdf(doc_id: str, cache_dir: Path, pdfs_dir: Path) -> Path:
+    """Download a single PDF (resumable via huggingface_hub cache)."""
+
+    rel = f"documents/{doc_id}"
+    local = _hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=rel,
+        repo_type=HF_REPO_TYPE,
+        cache_dir=str(cache_dir),
+    )
+    # Materialise to a stable path inside our data/ tree so the runner
+    # has a deterministic location regardless of HF cache internals.
+    dest = pdfs_dir / doc_id
+    if not dest.exists() or dest.stat().st_size != Path(local).stat().st_size:
+        # Use a hardlink when possible (cheap), fall back to copy.
+        try:
+            if dest.exists():
+                dest.unlink()
+            os.link(local, dest)
+        except OSError:
+            from shutil import copy2
+
+            copy2(local, dest)
+    return dest
+
+
+# ---------------------------------------------------------------------------
+# Upload helpers
+# ---------------------------------------------------------------------------
+
+
+async def _upload_pdfs(
+    ctx: RunContext,
+    pdf_paths: Iterable[Path],
+    *,
+    batch_size: int,
+    settings: IngestSettings,
+) -> dict[str, int]:
+    """Upload PDFs in batches, return ``filename -> document_id`` map."""
+
+    docs_client = ctx.documents_client()
+    name_to_id: dict[str, int] = {}
+    pdf_list = list(pdf_paths)
+    for batch_start in range(0, len(pdf_list), batch_size):
+        batch = pdf_list[batch_start:batch_start + batch_size]
+        result = await docs_client.upload(
+            files=batch,
+            search_space_id=ctx.search_space_id,
+            should_summarize=settings.should_summarize,
+            use_vision_llm=settings.use_vision_llm,
+            processing_mode=settings.processing_mode,
+        )
+        all_ids = list(result.document_ids) + list(result.duplicate_document_ids)
+        if all_ids:
+            await docs_client.wait_until_ready(
+                search_space_id=ctx.search_space_id,
+                document_ids=result.document_ids,  # only newly added need polling
+                timeout_s=1800.0,  # vision pipeline is slow on long PDFs
+            )
+            statuses = await docs_client.get_status(
+                search_space_id=ctx.search_space_id,
+                document_ids=all_ids,
+            )
+            for s in statuses:
+                name_to_id[s.title] = s.document_id
+        logger.info(
+            "Uploaded MMLongBench batch %d-%d: %d new, %d duplicate",
+            batch_start, batch_start + len(batch),
+            len(result.document_ids), len(result.duplicate_document_ids),
+        )
+    return name_to_id
+
+
+# ---------------------------------------------------------------------------
+# Public entry point
+# ---------------------------------------------------------------------------
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    max_docs: int | None = None,
+    upload_batch_size: int = 8,
+    skip_upload: bool = False,
+    settings: IngestSettings | None = None,
+) -> None:
+    """Ingest MMLongBench-Doc into the multimodal_doc suite.
+
+    Parameters
+    ----------
+    max_docs : int | None
+        Cap the number of PDFs to download + upload. ``None`` = all 135.
+        Useful when iterating on the runner without paying for the full
+        vision pipeline pass each time.
+    upload_batch_size : int
+        How many PDFs to send per ``fileupload`` call. Smaller batches
+        recover faster from individual failures; larger batches reduce
+        round-trip overhead.
+    skip_upload : bool
+        Download + cache PDFs locally but skip SurfSense ingestion.
+        Useful for testing the native arm in isolation.
+    """
+
+    settings = settings or IngestSettings(use_vision_llm=True, processing_mode="basic")
+    bench_dir = ctx.benchmark_data_dir()
+    pdfs_dir = bench_dir / "pdfs"
+    pdfs_dir.mkdir(parents=True, exist_ok=True)
+    hf_cache = bench_dir / ".hf_cache"
+    hf_cache.mkdir(parents=True, exist_ok=True)
+
+    # Step 1: questions
+    parquet_path = _download_questions_parquet(hf_cache)
+    questions = _load_questions_from_parquet(parquet_path)
+    if not questions:
+        raise RuntimeError(
+            "MMLongBench-Doc parquet contains no parseable questions. "
+            "Upstream may have changed schema."
+        )
+
+    # Persist a copy alongside the PDFs so the runner has one place to read.
+    questions_jsonl = bench_dir / "questions.jsonl"
+    with questions_jsonl.open("w", encoding="utf-8") as fh:
+        for q in questions:
+            fh.write(json.dumps({
+                "doc_id": q.doc_id,
+                "doc_type": q.doc_type,
+                "question": q.question,
+                "answer": q.answer,
+                "answer_format": q.answer_format,
+                "evidence_pages": q.evidence_pages,
+                "evidence_sources": q.evidence_sources,
+            }) + "\n")
+    logger.info("Wrote %d MMLongBench questions to %s", len(questions), questions_jsonl)
+
+    # Step 2: download unique PDFs
+    unique_doc_ids = sorted({q.doc_id for q in questions})
+    if max_docs is not None and max_docs > 0:
+        unique_doc_ids = unique_doc_ids[:max_docs]
+    logger.info("MMLongBench: downloading %d unique PDFs", len(unique_doc_ids))
+
+    pdf_paths: dict[str, Path] = {}
+    for i, doc_id in enumerate(unique_doc_ids, start=1):
+        try:
+            pdf_paths[doc_id] = _download_pdf(doc_id, hf_cache, pdfs_dir)
+            if i % 10 == 0:
+                logger.info("  ... %d / %d PDFs cached", i, len(unique_doc_ids))
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("Failed to download MMLongBench PDF %s: %s", doc_id, exc)
+
+    # Step 3: upload to SurfSense
+    name_to_id: dict[str, int] = {}
+    if skip_upload:
+        logger.info("MMLongBench: --skip-upload set; skipping SurfSense ingestion")
+    else:
+        logger.info("MMLongBench upload settings: %s", settings.render_label())
+        name_to_id = await _upload_pdfs(
+            ctx,
+            pdf_paths.values(),
+            batch_size=upload_batch_size,
+            settings=settings,
+        )
+
+    # Step 4: persist doc_id -> document_id manifest
+    map_path = ctx.maps_dir() / "mmlongbench_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        # Header line records the resolved ingest settings
+        # (see core/ingest_settings.py).
+        fh.write(settings_header_line(settings) + "\n")
+        for doc_id in unique_doc_ids:
+            local = pdf_paths.get(doc_id)
+            if local is None:
+                continue
+            fh.write(json.dumps({
+                "doc_id": doc_id,
+                "document_id": name_to_id.get(local.name),
+                "pdf_path": str(local),
+                "n_questions": sum(1 for q in questions if q.doc_id == doc_id),
+            }) + "\n")
+    logger.info("Wrote MMLongBench doc map to %s", map_path)
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["mmlongbench"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+
+__all__ = ["MMLongBenchQuestion", "run_ingest"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py
new file mode 100644
index 000000000..27d6a0d00
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/prompt.py
@@ -0,0 +1,60 @@
+"""MMLongBench-Doc prompt template.
+
+Both arms get the same prompt — only the document delivery channel
+differs (native PDF embedded in the OpenRouter request vs SurfSense
+RAG retrieval). The format hint in the prompt mirrors what the
+upstream paper uses so the grader's regex can reliably extract the
+answer.
+"""
+
+from __future__ import annotations
+
+# ---------------------------------------------------------------------------
+# Per-format hint blocks
+# ---------------------------------------------------------------------------
+
+_FORMAT_HINTS: dict[str, str] = {
+    "str": (
+        "Respond with the answer as a short phrase, no full sentence. "
+        "Format your final line as `Answer: <text>`."
+    ),
+    "int": (
+        "Respond with a single integer only. "
+        "Format your final line as `Answer: <integer>`."
+    ),
+    "float": (
+        "Respond with a single decimal number only (no units). "
+        "Format your final line as `Answer: <number>`."
+    ),
+    "list": (
+        "Respond with a comma-separated list of items, no extra text. "
+        "Format your final line as `Answer: item1, item2, item3`."
+    ),
+    "none": (
+        "If the answer cannot be determined from the document, say so explicitly. "
+        "Format your final line as `Answer: Not answerable`."
+    ),
+}
+
+
+_PROMPT = """\
+You are a document-understanding assistant. Use ONLY the provided
+document to answer the question. The document may contain text,
+tables, charts, figures, and images. If the answer is in a chart or
+image, read it carefully. Do not use external knowledge.
+
+Question: {question}
+
+{format_hint}
+"""
+
+
+def build_prompt(question: str, *, answer_format: str) -> str:
+    """Assemble the full prompt for one MMLongBench question."""
+
+    fmt = (answer_format or "str").strip().lower()
+    hint = _FORMAT_HINTS.get(fmt, _FORMAT_HINTS["str"])
+    return _PROMPT.format(question=question.strip(), format_hint=hint)
+
+
+__all__ = ["build_prompt"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py
new file mode 100644
index 000000000..0e352d7ae
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/runner.py
@@ -0,0 +1,704 @@
+"""MMLongBench-Doc runner — head-to-head Native PDF (vision) vs SurfSense (vision RAG).
+
+Differences from a typical MCQ head-to-head:
+
+* Open-ended answers (Str / Int / Float / List / Not-answerable) — uses
+  ``extract_freeform_answer`` instead of ``extract_answer_letter``.
+* Format-aware grader (see ``.grader``) returns both binary correctness
+  (for accuracy / McNemar) and continuous F1 (for nuanced reporting).
+* Native arm requires a vision-capable model — we don't enforce this
+  in code (operator's choice via ``setup --provider-model``) but we
+  emit a warning if the pinned slug looks text-only.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, NativePdfArm, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+)
+from ....core.metrics.comparison import (
+    bootstrap_delta_ci,
+    mcnemar_test,
+    paired_aggregate,
+)
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci
+from ....core.parse.freeform_answer import extract_freeform_answer
+from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine
+from ....core.registry import (
+    ReportSection,
+    RunArtifact,
+    RunContext,
+)
+from ....core.scenarios import format_scenario_md
+from .grader import GradeResult, grade
+from .prompt import build_prompt
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Question + map row shapes
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class MMLBQuestion:
+    qid: str               # synthesised from doc_id + index
+    doc_id: str            # filename inside the documents/ folder
+    doc_type: str
+    question: str
+    gold_answer: str
+    answer_format: str
+    evidence_pages: list[int]
+    evidence_sources: list[str]
+    pdf_path: Path
+    document_id: int | None  # SurfSense doc id (None if upload skipped)
+
+
+def _load_doc_map(map_path: Path) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]:
+    """Read the doc map JSONL.
+
+    Returns ``(rows, settings)`` where ``settings`` is the
+    ``__settings__`` header blob (or ``{}`` for legacy maps).
+    """
+
+    rows: dict[str, dict[str, Any]] = {}
+    settings: dict[str, Any] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if is_settings_header(row):
+                settings = dict(row["__settings__"])
+                continue
+            rows[str(row["doc_id"])] = row
+    return rows, settings
+
+
+def _load_questions(
+    questions_jsonl: Path,
+    doc_map: dict[str, dict[str, Any]],
+    *,
+    doc_filter: list[str] | None,
+    format_filter: str | None,
+    sample_n: int | None,
+    skip_unanswerable: bool,
+) -> list[MMLBQuestion]:
+    out: list[MMLBQuestion] = []
+    per_doc_counter: dict[str, int] = {}
+    with questions_jsonl.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            doc_id = str(row.get("doc_id") or "").strip()
+            if not doc_id:
+                continue
+            if doc_filter and doc_id not in doc_filter:
+                continue
+            map_row = doc_map.get(doc_id)
+            if map_row is None:
+                logger.debug("No doc-map entry for %s; skipping", doc_id)
+                continue
+            answer_format = str(row.get("answer_format") or "").strip().lower()
+            if format_filter and format_filter != "all" and format_filter != answer_format:
+                continue
+            gold = str(row.get("answer") or "").strip()
+            if skip_unanswerable and answer_format == "none":
+                continue
+            idx = per_doc_counter.get(doc_id, 0)
+            per_doc_counter[doc_id] = idx + 1
+            out.append(MMLBQuestion(
+                qid=f"{doc_id}::Q{idx:03d}",
+                doc_id=doc_id,
+                doc_type=str(row.get("doc_type") or "").strip(),
+                question=str(row.get("question") or "").strip(),
+                gold_answer=gold,
+                answer_format=answer_format,
+                evidence_pages=list(row.get("evidence_pages") or []),
+                evidence_sources=list(row.get("evidence_sources") or []),
+                pdf_path=Path(map_row["pdf_path"]),
+                document_id=map_row.get("document_id"),
+            ))
+    out.sort(key=lambda q: (q.doc_id, q.qid))
+    if sample_n is not None and sample_n > 0:
+        out = out[:sample_n]
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Bounded concurrency helper
+# ---------------------------------------------------------------------------
+
+
+async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+# ---------------------------------------------------------------------------
+# Benchmark
+# ---------------------------------------------------------------------------
+
+
+_DESCRIPTION = (
+    "MMLongBench-Doc (135 long PDFs, 1,091 multimodal questions) — "
+    "Native PDF (vision) vs SurfSense (vision RAG) head-to-head."
+)
+
+
+_TEXT_ONLY_HINTS = ("gpt-5.4-mini", "gpt-3.5", "text-only", "instruct-")
+
+# MMLongBench-Doc PDFs are long documents with figures, charts, and
+# tables. Vision LLM at ingest is the whole point; flip --no-vision-llm
+# to measure how much SurfSense degrades on real document images.
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=True,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+class MMLongBenchDocBenchmark:
+    """Long-document multimodal RAG vs native vision."""
+
+    suite: str = "multimodal_doc"
+    name: str = "mmlongbench"
+    headline: bool = True
+    description: str = _DESCRIPTION
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--docs",
+            default=None,
+            help="Comma-separated doc_ids (filenames) to run (default: all).",
+        )
+        parser.add_argument(
+            "--format",
+            default="all",
+            choices=["all", "str", "int", "float", "list", "none"],
+            help="Filter to one answer format. 'none' = unanswerable probes only.",
+        )
+        parser.add_argument(
+            "--n", dest="sample_n", type=int, default=None,
+            help="Run only the first N questions after filters apply.",
+        )
+        parser.add_argument(
+            "--skip-unanswerable", dest="skip_unanswerable", action="store_true",
+            help="Drop ~22%% unanswerable questions (use to compare against baselines that don't include them).",
+        )
+        parser.add_argument(
+            "--concurrency", type=int, default=4,
+            help="Parallel question workers per arm.",
+        )
+        parser.add_argument(
+            "--no-mentions", dest="no_mentions", action="store_true",
+            help="SurfSense arm: skip mentioned_document_ids (unscoped retrieval).",
+        )
+        parser.add_argument(
+            "--pdf-engine", default="native",
+            choices=[e.value for e in PdfEngine],
+            help="OpenRouter file-parser engine for the native arm.",
+        )
+        parser.add_argument(
+            "--max-output-tokens", type=int, default=512,
+            help="Cap on completion length for both arms.",
+        )
+        # Ingest-only knobs (forwarded by the CLI to ingest.run_ingest).
+        parser.add_argument(
+            "--max-docs", dest="max_docs", type=int, default=None,
+            help="(ingest only) cap on number of unique PDFs to download + upload.",
+        )
+        parser.add_argument(
+            "--upload-batch-size", dest="upload_batch_size", type=int, default=8,
+            help="(ingest only) PDFs per fileupload call.",
+        )
+        parser.add_argument(
+            "--skip-upload", dest="skip_upload", action="store_true",
+            help="(ingest only) cache PDFs locally but don't push to SurfSense.",
+        )
+        # Per-upload knobs forwarded to /documents/fileupload at ingest;
+        # ignored at run-time (runner reads the resolved settings out of
+        # the doc-map manifest header).
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            max_docs=opts.get("max_docs"),
+            upload_batch_size=int(opts.get("upload_batch_size") or 8),
+            skip_upload=bool(opts.get("skip_upload", False)),
+            settings=settings,
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        docs_raw: str | None = opts.get("docs")
+        doc_filter = [d.strip() for d in docs_raw.split(",")] if docs_raw else None
+        format_filter = opts.get("format") or "all"
+        sample_n = opts.get("sample_n")
+        skip_unanswerable = bool(opts.get("skip_unanswerable"))
+        concurrency = int(opts.get("concurrency") or 4)
+        no_mentions = bool(opts.get("no_mentions"))
+        pdf_engine_name = opts.get("pdf_engine") or "native"
+        max_output_tokens = int(opts.get("max_output_tokens") or 512)
+
+        bench_dir = ctx.benchmark_data_dir()
+        questions_jsonl = bench_dir / "questions.jsonl"
+        map_path = ctx.maps_dir() / "mmlongbench_doc_map.jsonl"
+        if not questions_jsonl.exists() or not map_path.exists():
+            raise RuntimeError(
+                "MMLongBench-Doc not ingested for this suite. Run "
+                "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first."
+            )
+
+        doc_map, ingest_settings = _load_doc_map(map_path)
+        questions = _load_questions(
+            questions_jsonl, doc_map,
+            doc_filter=doc_filter,
+            format_filter=None if format_filter == "all" else format_filter,
+            sample_n=sample_n,
+            skip_unanswerable=skip_unanswerable,
+        )
+        if not questions:
+            raise RuntimeError(
+                "No MMLongBench questions matched the filters; broaden --docs/--format/--n."
+            )
+        logger.info("MMLongBench-Doc: scheduled %d questions", len(questions))
+
+        api_key = os.environ.get("OPENROUTER_API_KEY")
+        if not api_key:
+            raise RuntimeError(
+                "OPENROUTER_API_KEY env var is required for the native arm."
+            )
+
+        # Native arm slug differs from SurfSense slug only in cost-arbitrage
+        # scenario; otherwise both arms answer with provider_model.
+        native_arm_model = ctx.native_arm_model
+        if any(hint in native_arm_model.lower() for hint in _TEXT_ONLY_HINTS):
+            if ctx.scenario == "symmetric-cheap":
+                logger.info(
+                    "symmetric-cheap: native arm pinned to text-only %r as "
+                    "intended; expect it to lose on image-bearing pages "
+                    "(SurfSense answers from vision-extracted chunks).",
+                    native_arm_model,
+                )
+            else:
+                logger.warning(
+                    "Native arm slug %r looks text-only; image content in "
+                    "PDFs will be ignored. Re-pin via "
+                    "`setup --provider-model anthropic/claude-sonnet-4.5` "
+                    "(or pass --native-arm-model and --scenario cost-arbitrage "
+                    "to make this asymmetry explicit).",
+                    native_arm_model,
+                )
+
+        provider = OpenRouterPdfProvider(
+            api_key=api_key,
+            base_url=ctx.config.openrouter_base_url,
+            model=native_arm_model,
+            engine=PdfEngine(pdf_engine_name),
+        )
+        native_arm = NativePdfArm(provider=provider, max_output_tokens=max_output_tokens)
+        surf_arm = SurfSenseArm(
+            client=ctx.new_chat_client(),
+            search_space_id=ctx.search_space_id,
+            ephemeral_threads=True,
+        )
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+
+        async def _native_one(q: MMLBQuestion) -> ArmResult:
+            return await native_arm.answer(_make_native_request(q, max_output_tokens))
+
+        async def _surf_one(q: MMLBQuestion) -> ArmResult:
+            return await surf_arm.answer(_make_surfsense_request(q, no_mentions=no_mentions))
+
+        native_results, surf_results = await asyncio.gather(
+            _gather_with_limit((_native_one(q) for q in questions), concurrency=concurrency),
+            _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency),
+        )
+
+        native_grades = [_grade_one(q, r) for q, r in zip(questions, native_results, strict=False)]
+        surf_grades = [_grade_one(q, r) for q, r in zip(questions, surf_results, strict=False)]
+
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, n_res, s_res, n_g, s_g in zip(
+                questions, native_results, surf_results, native_grades, surf_grades, strict=False
+            ):
+                meta = {
+                    "qid": q.qid,
+                    "doc_id": q.doc_id,
+                    "doc_type": q.doc_type,
+                    "answer_format": q.answer_format,
+                    "gold": q.gold_answer,
+                    "evidence_pages": q.evidence_pages,
+                    "evidence_sources": q.evidence_sources,
+                    "document_id": q.document_id,
+                }
+                fh.write(json.dumps({
+                    **meta,
+                    **n_res.to_jsonl(),
+                    "graded": _grade_to_jsonl(n_g),
+                }) + "\n")
+                fh.write(json.dumps({
+                    **meta,
+                    **s_res.to_jsonl(),
+                    "graded": _grade_to_jsonl(s_g),
+                }) + "\n")
+
+        metrics = _compute_metrics(questions, native_results, surf_results, native_grades, surf_grades)
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "concurrency": concurrency,
+                "format_filter": format_filter,
+                "skip_unanswerable": skip_unanswerable,
+                "no_mentions": no_mentions,
+                "pdf_engine": pdf_engine_name,
+                "scenario": ctx.scenario,
+                "provider_model": ctx.provider_model,
+                "native_arm_model": native_arm_model,
+                "vision_provider_model": ctx.vision_provider_model,
+                "agent_llm_id": ctx.agent_llm_id,
+                "ingest_settings": ingest_settings,
+            },
+        )
+
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps({
+                "suite": self.suite,
+                "benchmark": self.name,
+                "raw_path": "raw.jsonl",
+                "metrics": metrics,
+                "extra": artifact.extra,
+            }, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="MMLongBench-Doc — Native PDF (vision) vs SurfSense (vision RAG)",
+                headline=True,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        native = m.get("native", {})
+        surf = m.get("surfsense", {})
+        delta = m.get("delta", {})
+        per_format = m.get("per_format", {})
+        extra = latest.extra
+
+        body_lines: list[str] = []
+        body_lines.append(
+            f"- Sample size: {extra.get('n_questions', '?')} questions "
+            f"(format filter: `{extra.get('format_filter', 'all')}`, "
+            f"skip-unanswerable: `{extra.get('skip_unanswerable', False)}`, "
+            f"engine: `{extra.get('pdf_engine', 'native')}`)."
+        )
+        body_lines.append(format_scenario_md(extra))
+        body_lines.append(format_ingest_settings_md(extra.get("ingest_settings")))
+        body_lines.append(
+            "- Native arm (OpenRouter `chat/completions` + file plugin, "
+            f"`{extra.get('native_arm_model') or extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(native, indent="  "))
+        body_lines.append(
+            "- SurfSense arm (`POST /api/v1/new_chat`, vision RAG over chunks, "
+            f"`{extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(surf, indent="  "))
+        body_lines.append("- Delta (paired):")
+        body_lines.append(
+            f"  - Accuracy: SurfSense {_pp(delta.get('accuracy_pp'))} pp "
+            f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, "
+            f"method={delta.get('mcnemar_method')})"
+        )
+        body_lines.append(
+            f"  - F1 (mean): SurfSense {_pp(delta.get('f1_pp'))} pp"
+        )
+        body_lines.append(
+            f"  - Bootstrap 95% CI on accuracy delta: "
+            f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]"
+        )
+        body_lines.append(
+            f"  - Cost / question: native ${_dollars(native.get('cost_micros_mean'))}, "
+            f"surfsense ${_dollars(surf.get('cost_micros_mean'))} "
+            f"(SurfSense delta {_pct_change(delta.get('cost_micros_pct'))})"
+        )
+        body_lines.append(
+            f"  - Latency p50: native {_ms_to_s(native.get('latency_ms_median'))}, "
+            f"surfsense {_ms_to_s(surf.get('latency_ms_median'))} "
+            f"(SurfSense delta {_pct_change(delta.get('latency_ms_pct'))})"
+        )
+        if per_format:
+            body_lines.append("- Per-format split (accuracy delta in pp):")
+            for fmt, vals in sorted(per_format.items()):
+                body_lines.append(
+                    f"  - {fmt}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
+                    f"(n={vals.get('n')}, native acc={vals.get('native_accuracy', 0)*100:.1f}%, "
+                    f"surf acc={vals.get('surfsense_accuracy', 0)*100:.1f}%)"
+                )
+
+        return ReportSection(
+            title="MMLongBench-Doc — Native PDF (vision) vs SurfSense (vision RAG)",
+            headline=True,
+            body_md="\n".join(body_lines),
+            body_json=m,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Per-question helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_native_request(q: MMLBQuestion, max_tokens: int) -> ArmRequest:
+    prompt = build_prompt(q.question, answer_format=q.answer_format)
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=prompt,
+        pdf_paths=[q.pdf_path],
+        options={"max_tokens": max_tokens},
+    )
+
+
+def _make_surfsense_request(q: MMLBQuestion, *, no_mentions: bool) -> ArmRequest:
+    prompt = build_prompt(q.question, answer_format=q.answer_format)
+    mentions: list[int] | None = None
+    if not no_mentions and q.document_id is not None:
+        mentions = [int(q.document_id)]
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=prompt,
+        mentioned_document_ids=mentions,
+    )
+
+
+def _grade_one(q: MMLBQuestion, result: ArmResult) -> GradeResult:
+    pred_text = extract_freeform_answer(result.raw_text or "")
+    return grade(pred=pred_text, gold=q.gold_answer, answer_format=q.answer_format)
+
+
+def _grade_to_jsonl(g: GradeResult) -> dict[str, Any]:
+    return {
+        "correct": g.correct,
+        "f1": g.f1,
+        "method": g.method,
+        "normalised_pred": g.normalised_pred,
+        "normalised_gold": g.normalised_gold,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Metrics aggregation
+# ---------------------------------------------------------------------------
+
+
+def _compute_metrics(
+    questions: list[MMLBQuestion],
+    native_results: list[ArmResult],
+    surf_results: list[ArmResult],
+    native_grades: list[GradeResult],
+    surf_grades: list[GradeResult],
+) -> dict[str, Any]:
+    native_correct = [g.correct for g in native_grades]
+    surf_correct = [g.correct for g in surf_grades]
+    native_f1 = [g.f1 for g in native_grades]
+    surf_f1 = [g.f1 for g in surf_grades]
+
+    native_costs = [float(r.cost_micros) for r in native_results]
+    surf_costs = [float(r.cost_micros) for r in surf_results]
+    native_latencies = [float(r.latency_ms) for r in native_results]
+    surf_latencies = [float(r.latency_ms) for r in surf_results]
+    native_in_tokens = [float(r.input_tokens) for r in native_results]
+    native_out_tokens = [float(r.output_tokens) for r in native_results]
+
+    native_acc = accuracy_with_wilson_ci(sum(native_correct), len(native_correct))
+    surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct))
+    mc = mcnemar_test(native_correct, surf_correct)
+    boot = bootstrap_delta_ci(native_correct, surf_correct, n_resamples=2000)
+
+    native_cost_agg = paired_aggregate(native_costs)
+    surf_cost_agg = paired_aggregate(surf_costs)
+    native_latency_agg = paired_aggregate(native_latencies)
+    surf_latency_agg = paired_aggregate(surf_latencies)
+
+    cost_pct = _safe_pct(surf_cost_agg.mean, native_cost_agg.mean)
+    latency_pct = _safe_pct(surf_latency_agg.median, native_latency_agg.median)
+
+    per_format_pairs: dict[str, list[tuple[bool, bool]]] = {}
+    for q, n_ok, s_ok in zip(questions, native_correct, surf_correct, strict=False):
+        per_format_pairs.setdefault(q.answer_format or "unknown", []).append((n_ok, s_ok))
+
+    per_format: dict[str, dict[str, Any]] = {}
+    for fmt, pairs in per_format_pairs.items():
+        n_correct = [a for a, _ in pairs]
+        s_correct = [b for _, b in pairs]
+        per_format[fmt] = {
+            "n": len(pairs),
+            "native_accuracy": (sum(n_correct) / len(pairs)) if pairs else 0.0,
+            "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
+            "delta_accuracy_pp": (
+                100.0 * (sum(s_correct) - sum(n_correct)) / len(pairs)
+                if pairs else 0.0
+            ),
+        }
+
+    native_f1_mean = sum(native_f1) / len(native_f1) if native_f1 else 0.0
+    surf_f1_mean = sum(surf_f1) / len(surf_f1) if surf_f1 else 0.0
+
+    return {
+        "native": {
+            **native_acc.to_dict(),
+            "f1_mean": native_f1_mean,
+            "cost_micros_mean": native_cost_agg.mean,
+            "cost_micros_median": native_cost_agg.median,
+            "latency_ms_mean": native_latency_agg.mean,
+            "latency_ms_median": native_latency_agg.median,
+            "latency_ms_p95": native_latency_agg.p95,
+            "input_tokens_mean": (sum(native_in_tokens) / len(native_in_tokens)) if native_in_tokens else 0.0,
+            "output_tokens_mean": (sum(native_out_tokens) / len(native_out_tokens)) if native_out_tokens else 0.0,
+        },
+        "surfsense": {
+            **surf_acc.to_dict(),
+            "f1_mean": surf_f1_mean,
+            "cost_micros_mean": surf_cost_agg.mean,
+            "cost_micros_median": surf_cost_agg.median,
+            "latency_ms_mean": surf_latency_agg.mean,
+            "latency_ms_median": surf_latency_agg.median,
+            "latency_ms_p95": surf_latency_agg.p95,
+        },
+        "delta": {
+            "accuracy_pp": 100.0 * (surf_acc.accuracy - native_acc.accuracy),
+            "f1_pp": 100.0 * (surf_f1_mean - native_f1_mean),
+            "mcnemar_p_value": mc.p_value,
+            "mcnemar_method": mc.method,
+            "mcnemar_b_native_only": mc.b,
+            "mcnemar_c_surfsense_only": mc.c,
+            "bootstrap_ci_low": 100.0 * boot.ci_low,
+            "bootstrap_ci_high": 100.0 * boot.ci_high,
+            "cost_micros_pct": cost_pct,
+            "latency_ms_pct": latency_pct,
+        },
+        "per_format": per_format,
+    }
+
+
+def _safe_pct(numerator: float, denominator: float) -> float | None:
+    if denominator == 0:
+        return None
+    return 100.0 * (numerator - denominator) / denominator
+
+
+# ---------------------------------------------------------------------------
+# Tiny formatting helpers used by report_section
+# ---------------------------------------------------------------------------
+
+
+def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str:
+    if not d:
+        return f"{indent}(no data)"
+    acc = d.get("accuracy", 0.0)
+    low = d.get("ci_low", 0.0)
+    high = d.get("ci_high", 0.0)
+    f1 = d.get("f1_mean", 0.0)
+    lines = [
+        f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)",
+        f"{indent}- F1 (token-level mean): {f1 * 100:.1f}%",
+        f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), "
+        f"${_dollars(d.get('cost_micros_median'))} (median)",
+        f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, "
+        f"p95 {_ms_to_s(d.get('latency_ms_p95'))}",
+    ]
+    if "input_tokens_mean" in d:
+        lines.append(
+            f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, "
+            f"out {d.get('output_tokens_mean', 0):.0f}"
+        )
+    return "\n".join(lines)
+
+
+def _dollars(micros: Any) -> str:
+    if micros is None:
+        return "?"
+    try:
+        return f"{(float(micros) / 1_000_000):.4f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _ms_to_s(ms: Any) -> str:
+    if ms is None:
+        return "?"
+    try:
+        return f"{float(ms) / 1000:.1f}s"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pp(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.1f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pct_change(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.0f}%"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _fmt(value: Any, ndigits: int) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):.{ndigits}f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+__all__ = ["MMLBQuestion", "MMLongBenchDocBenchmark"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/__init__.py
new file mode 100644
index 000000000..03fe24c02
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/__init__.py
@@ -0,0 +1,18 @@
+"""Research / multi-document RAG benchmarks.
+
+Distinct from ``multimodal_doc`` (PDF-bound) and ``medical`` (one
+question = one source PDF). Benchmarks here put *retrieval and
+reasoning across many documents* in the critical path — the regime
+where SurfSense's chunk-level RAG should shine versus "pour the
+entire document into the LLM" or "ask the LLM cold".
+
+* ``frames`` (google/frames-benchmark) — 824 multi-hop Wikipedia
+  questions; tests bare-LLM vs SurfSense over a shared ~330-doc
+  corpus.
+* ``crag``   (facebookresearch/CRAG, KDD Cup 2024) — 2,706 web QA
+  pairs with 5 pre-retrieved HTML pages each; tests bare-LLM vs
+  long-context-stuffed LLM vs SurfSense over the question's 5
+  scoped pages — the closest comparison to a competing RAG product.
+"""
+
+from __future__ import annotations
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
new file mode 100644
index 000000000..80358c474
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
@@ -0,0 +1,57 @@
+"""CRAG — Comprehensive RAG Benchmark (Yang et al., Meta, KDD Cup 2024).
+
+Source: https://github.com/facebookresearch/CRAG  (Tasks 1, 2, and 3)
+Paper:  https://arxiv.org/abs/2406.04744
+
+This package registers two siblings:
+
+* ``crag``    — Tasks 1 & 2: 5 candidate pages per question.
+* ``crag_t3`` — Task 3:       50 candidate pages per question. The
+  long-context arm is capped to the top-5 (the realistic "naive
+  RAG = pick top-K results" baseline); SurfSense retrieves over
+  all 50, where its rerank becomes the entire contribution.
+
+Both share the grader, prompt, runner, and report code; only the
+ingest path differs (single bz2 vs 4-part tar.bz2 streamed).
+
+CRAG ships ~2,706 factual QA pairs, each paired with **5 full HTML
+pages** retrieved as the top-5 of a real web search at ``query_time``
+(50 in Task 3).
+The benchmark spans 5 domains (finance, music, movie, sports, open)
+and 8 question types (simple, comparison, aggregation, set, multi-hop,
+post-processing, false_premise, simple_w_condition) — heads/torsos/
+tails of entity popularity — and an explicit static→real-time
+freshness axis.
+
+Why CRAG demonstrates SurfSense more clearly than FRAMES
+--------------------------------------------------------
+FRAMES tested SurfSense vs. *no retrieval at all* — a fair "naive
+prompting" baseline (the published 40.8% number) but not a competing
+RAG product. CRAG enables a three-way comparison:
+
+* ``bare_llm``      — chat completion with the question only. CRAG
+  paper: ≤34% accuracy ("LLM cold").
+* ``long_context``  — stuff all 5 extracted page texts straight into
+  the prompt (the "naive RAG" / "straightforward RAG" arm in the
+  paper). Published baseline: ~44%.
+* ``surfsense``     — POST ``/api/v1/new_chat`` with retrieval scoped
+  to the question's 5 ingested pages (``mentioned_document_ids``).
+
+So the headline becomes "SurfSense vs. context-stuffed long-context
+LLM, both fed the same 5 pages" — a head-to-head against the simplest
+realistic RAG strategy, not against an unarmed model.
+
+Scoring follows the CRAG paper: each prediction is graded as
+**correct** (+1), **missing/I-don't-know** (0), or **incorrect** (-1),
+and the headline metric is the *Truthfulness Score*:
+``(#correct - #incorrect) / total`` — penalising hallucinations
+relative to refusals.
+"""
+
+from __future__ import annotations
+
+from ....core import registry as _registry
+from .runner import CragBenchmark, CragTask3Benchmark
+
+_registry.register(CragBenchmark())
+_registry.register(CragTask3Benchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py
new file mode 100644
index 000000000..224dcae5c
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset.py
@@ -0,0 +1,335 @@
+"""CRAG dataset loader — download ``crag_task_1_and_2_dev_v4.jsonl.bz2`` and parse.
+
+The CRAG repo (``facebookresearch/CRAG``) ships Tasks 1 & 2 as a
+single bzip2-compressed JSONL on GitHub raw. Each row carries:
+
+* ``interaction_id``    — opaque per-question id (we keep verbatim)
+* ``query_time``        — wall clock of the original web search
+* ``domain``            — finance | music | movie | sports | open
+* ``question_type``     — simple | comparison | aggregation | set |
+                          multi-hop | post-processing | false_premise |
+                          simple_w_condition
+* ``static_or_dynamic`` — static | slow-changing | fast-changing | real-time
+* ``query``             — the question
+* ``answer``            — gold short answer
+* ``alt_ans``           — list[str] of alternative valid answers
+                          (paraphrases / synonyms / unit variants)
+* ``split``             — 0 = validation, 1 = public test
+* ``popularity``        — head | torso | tail (KG questions); empty for web
+* ``search_results``    — list of up to 5 ``{page_name, page_url,
+                          page_snippet, page_result, page_last_modified}``;
+                          ``page_result`` is full HTML.
+
+We materialise this into ``CragQuestion`` objects keeping ``pages`` as
+a list of ``CragPage`` so downstream ingest can save each as its own
+file and SurfSense can dedupe on filename.
+"""
+
+from __future__ import annotations
+
+import bz2
+import hashlib
+import io
+import json
+import logging
+import urllib.request
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+# Tasks 1 & 2 share the same JSONL on the public CRAG repo.
+CRAG_TASK_1_2_URL = (
+    "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/"
+    "crag_task_1_and_2_dev_v4.jsonl.bz2"
+)
+CRAG_TASK_1_2_FILENAME = "crag_task_1_and_2_dev_v4.jsonl.bz2"
+
+
+# ---------------------------------------------------------------------------
+# Question / page dataclasses
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class CragPage:
+    """One of the up-to-5 pre-retrieved web pages for a CRAG question."""
+
+    page_name: str
+    page_url: str
+    page_snippet: str
+    page_html: str
+    page_last_modified: str | None = None
+
+    @property
+    def url_hash(self) -> str:
+        """Stable 12-hex digest of the page URL for filename keys.
+
+        We can't use the raw URL as a filename (slashes, query strings,
+        unicode), and we *do* want collision-safety across the whole
+        ingest sample. ``sha1[:12]`` gives us 48 bits of namespace
+        which is overkill for a corpus capped at a few thousand pages.
+        """
+
+        return hashlib.sha1(self.page_url.encode("utf-8")).hexdigest()[:12]
+
+
+@dataclass
+class CragQuestion:
+    """One row of CRAG (Tasks 1 & 2)."""
+
+    qid: str                          # synthesised "C00000".."C02705"
+    interaction_id: str
+    query_time: str
+    query: str
+    gold_answer: str
+    alt_answers: list[str]
+    domain: str
+    question_type: str
+    static_or_dynamic: str
+    popularity: str                   # may be "" for web-sourced questions
+    split: int                        # 0=validation, 1=public_test
+    raw_index: int                    # row index in the source JSONL
+    pages: list[CragPage] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "qid": self.qid,
+            "interaction_id": self.interaction_id,
+            "query_time": self.query_time,
+            "query": self.query,
+            "gold_answer": self.gold_answer,
+            "alt_answers": list(self.alt_answers),
+            "domain": self.domain,
+            "question_type": self.question_type,
+            "static_or_dynamic": self.static_or_dynamic,
+            "popularity": self.popularity,
+            "split": self.split,
+            "raw_index": self.raw_index,
+            "n_pages": len(self.pages),
+            "page_urls": [p.page_url for p in self.pages],
+        }
+
+
+# ---------------------------------------------------------------------------
+# Download + decompress
+# ---------------------------------------------------------------------------
+
+
+def download_task_1_2(cache_dir: Path) -> Path:
+    """Download the bz2 archive into ``cache_dir`` (skip if cached).
+
+    Returns the path to the local ``.jsonl.bz2``. We use stdlib
+    ``urllib`` rather than ``httpx`` to keep the download synchronous
+    and trivially resumable (re-running the function is a no-op once
+    the file is on disk and non-empty).
+    """
+
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    dest = cache_dir / CRAG_TASK_1_2_FILENAME
+    if dest.exists() and dest.stat().st_size > 0:
+        logger.debug("CRAG bz2 already cached at %s", dest)
+        return dest
+
+    logger.info("Downloading CRAG (Tasks 1 & 2) from %s ...", CRAG_TASK_1_2_URL)
+    tmp = dest.with_suffix(dest.suffix + ".part")
+    req = urllib.request.Request(
+        CRAG_TASK_1_2_URL,
+        headers={"User-Agent": "SurfSense-Evals/0.1 (CRAG dataset fetch)"},
+    )
+    with urllib.request.urlopen(req, timeout=600) as response, tmp.open("wb") as fh:
+        chunk = response.read(1 << 20)
+        while chunk:
+            fh.write(chunk)
+            chunk = response.read(1 << 20)
+    tmp.replace(dest)
+    logger.info("CRAG bz2 downloaded: %s (%.1f MiB)", dest, dest.stat().st_size / 1024 / 1024)
+    return dest
+
+
+# ---------------------------------------------------------------------------
+# Parse
+# ---------------------------------------------------------------------------
+
+
+def _parse_pages(raw_search_results: Any) -> list[CragPage]:
+    if not isinstance(raw_search_results, list):
+        return []
+    pages: list[CragPage] = []
+    for entry in raw_search_results:
+        if not isinstance(entry, dict):
+            continue
+        url = str(entry.get("page_url") or "").strip()
+        html = str(entry.get("page_result") or "")
+        if not url or not html.strip():
+            # No URL or empty HTML => useless for retrieval.
+            continue
+        pages.append(CragPage(
+            page_name=str(entry.get("page_name") or "").strip(),
+            page_url=url,
+            page_snippet=str(entry.get("page_snippet") or "").strip(),
+            page_html=html,
+            page_last_modified=(
+                str(entry.get("page_last_modified")).strip()
+                if entry.get("page_last_modified") else None
+            ),
+        ))
+    return pages
+
+
+def _parse_alt_answers(raw: Any) -> list[str]:
+    if isinstance(raw, list):
+        return [str(x).strip() for x in raw if str(x).strip()]
+    if isinstance(raw, str) and raw.strip():
+        return [raw.strip()]
+    return []
+
+
+def iter_questions(jsonl_bz2_path: Path) -> list[CragQuestion]:
+    """Stream-decompress + parse the CRAG JSONL into ``CragQuestion`` objects.
+
+    The bz2 expansion ratio is ~10x and the decompressed file is
+    multi-GB; we therefore decompress *line by line* via
+    ``bz2.open(..., "rt")``. Each row is a single (potentially very
+    large, due to embedded HTML) JSON object. We keep the entire row
+    in memory because we materialise the pages to disk immediately
+    after parsing in the ingest pipeline — the runner never holds
+    more than the current sample's worth of HTML.
+    """
+
+    out: list[CragQuestion] = []
+    with bz2.open(jsonl_bz2_path, mode="rt", encoding="utf-8") as fh:
+        for raw_idx, line in enumerate(fh):
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                row = json.loads(line)
+            except json.JSONDecodeError as exc:
+                logger.warning("Skipping malformed CRAG row %d: %s", raw_idx, exc)
+                continue
+            query = str(row.get("query") or "").strip()
+            answer = str(row.get("answer") or "").strip()
+            if not query or not answer:
+                logger.debug("Skipping CRAG row %d with missing query/answer", raw_idx)
+                continue
+            interaction_id = str(row.get("interaction_id") or "").strip()
+            pages = _parse_pages(row.get("search_results"))
+            out.append(CragQuestion(
+                qid=f"C{raw_idx:05d}",
+                interaction_id=interaction_id,
+                query_time=str(row.get("query_time") or "").strip(),
+                query=query,
+                gold_answer=answer,
+                alt_answers=_parse_alt_answers(row.get("alt_ans")),
+                domain=str(row.get("domain") or "").strip().lower(),
+                question_type=str(row.get("question_type") or "").strip().lower(),
+                static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(),
+                popularity=str(row.get("popularity") or "").strip().lower(),
+                split=int(row.get("split") or 0),
+                raw_index=raw_idx,
+                pages=pages,
+            ))
+    return out
+
+
+def stratified_sample(
+    questions: list[CragQuestion],
+    *,
+    n: int,
+    seed: int = 17,
+) -> list[CragQuestion]:
+    """Take ``n`` questions that roughly preserve the domain × question-type mix.
+
+    CRAG is only ~2.7k rows so naive head-of-list sampling badly
+    over-weights ``finance`` (because the dataset isn't shuffled by
+    domain). We bucket on ``(domain, question_type)`` and round-robin
+    pick from each bucket until we hit ``n`` — this gives every
+    bucket a fair shot and keeps the sample composition stable across
+    re-runs (deterministic via the seeded shuffle inside each bucket).
+    """
+
+    if n <= 0 or n >= len(questions):
+        return list(questions)
+    import random
+
+    rng = random.Random(seed)
+    buckets: dict[tuple[str, str], list[CragQuestion]] = {}
+    for q in questions:
+        buckets.setdefault((q.domain, q.question_type), []).append(q)
+    for items in buckets.values():
+        rng.shuffle(items)
+
+    keys = sorted(buckets.keys())
+    chosen: list[CragQuestion] = []
+    cursor = 0
+    while len(chosen) < n and any(buckets[k] for k in keys):
+        key = keys[cursor % len(keys)]
+        cursor += 1
+        if buckets[key]:
+            chosen.append(buckets[key].pop())
+    chosen.sort(key=lambda q: q.raw_index)
+    return chosen
+
+
+def write_questions_jsonl(questions: list[CragQuestion], dest: Path) -> None:
+    """Persist a parsed copy (without page HTML) under the benchmark data dir."""
+
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    with dest.open("w", encoding="utf-8") as fh:
+        for q in questions:
+            fh.write(json.dumps(q.to_dict()) + "\n")
+
+
+# ---------------------------------------------------------------------------
+# Reading the lightweight questions.jsonl back
+# ---------------------------------------------------------------------------
+
+
+def load_questions_jsonl(path: Path) -> list[dict[str, Any]]:
+    """Re-load the lightweight (no-HTML) questions JSONL from disk."""
+
+    out: list[dict[str, Any]] = []
+    if not path.exists():
+        return out
+    with path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                out.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Convenience: decompress a snippet to memory for tests
+# ---------------------------------------------------------------------------
+
+
+def decompress_to_memory(jsonl_bz2_path: Path) -> io.StringIO:
+    """For tests / one-off scripts: read the whole bz2 into a StringIO.
+
+    Avoids leaking gigabytes; use ``iter_questions`` in production.
+    """
+
+    with bz2.open(jsonl_bz2_path, mode="rb") as fh:
+        return io.StringIO(fh.read().decode("utf-8"))
+
+
+__all__ = [
+    "CRAG_TASK_1_2_FILENAME",
+    "CRAG_TASK_1_2_URL",
+    "CragPage",
+    "CragQuestion",
+    "decompress_to_memory",
+    "download_task_1_2",
+    "iter_questions",
+    "load_questions_jsonl",
+    "stratified_sample",
+    "write_questions_jsonl",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
new file mode 100644
index 000000000..02bed5935
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
@@ -0,0 +1,263 @@
+"""CRAG Task 3 dataset loader — 4-part tar.bz2 → streaming JSONL.
+
+Task 3 ships ~7 GB of compressed data split into 4 parts on GitHub:
+
+    crag_task_3_dev_v4.tar.bz2.part1    (≈2 GB)
+    crag_task_3_dev_v4.tar.bz2.part2    (≈2 GB)
+    crag_task_3_dev_v4.tar.bz2.part3    (≈2 GB)
+    crag_task_3_dev_v4.tar.bz2.part4    (≈1.3 GB)
+
+Concatenated, they form a tar archive containing a single JSONL file.
+Decompressed, that JSONL is on the order of 30-50 GB because each row
+embeds 50 full HTML pages (vs 5 in Tasks 1 & 2).
+
+Materialising the JSONL would blow the disk budget (we have ~50 GB
+free at the time of writing), so we stream the whole thing instead:
+
+  1. Download parts (idempotent; ``scripts/download_crag_task3.py``).
+  2. Concat them into a virtual file via ``_MultiPartReader``.
+  3. Wrap in ``bz2.BZ2File`` for on-the-fly decompression.
+  4. Wrap in ``tarfile.open(fileobj=..., mode="r|")`` for streaming
+     tar member iteration.
+  5. For the JSONL member inside, ``tar.extractfile()`` returns a
+     binary file-like; we iterate lines and yield parsed dicts.
+
+The caller can ``break`` out as soon as they have enough samples —
+nothing past the consumed point is decompressed.
+
+Schema is identical to Tasks 1 & 2 (see ``dataset.py``); only
+``search_results`` is bigger (50 entries instead of 5).
+"""
+
+from __future__ import annotations
+
+import bz2
+import json
+import logging
+import tarfile
+from collections.abc import Iterator
+from pathlib import Path
+from typing import IO
+
+from .dataset import (
+    CragPage,
+    CragQuestion,
+    _parse_alt_answers,
+    _parse_pages,
+)
+
+logger = logging.getLogger(__name__)
+
+
+CRAG_TASK_3_PART_URLS: tuple[str, ...] = tuple(
+    "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/"
+    f"crag_task_3_dev_v4.tar.bz2.part{i}"
+    for i in (1, 2, 3, 4)
+)
+CRAG_TASK_3_PART_NAMES: tuple[str, ...] = tuple(
+    f"crag_task_3_dev_v4.tar.bz2.part{i}" for i in (1, 2, 3, 4)
+)
+
+
+# ---------------------------------------------------------------------------
+# Multi-part virtual file (concatenates N files transparently)
+# ---------------------------------------------------------------------------
+
+
+class _MultiPartReader:
+    """Read N files end-to-end as if they were one big file.
+
+    Implements just enough of the file protocol for ``bz2.BZ2File``
+    to consume it: ``read(n)``, ``readable()``, ``close()``.
+    Doesn't implement ``seek`` — the bz2 + tarfile streaming path
+    is forward-only, which is what we want here.
+    """
+
+    def __init__(self, paths: list[Path]) -> None:
+        if not paths:
+            raise ValueError("_MultiPartReader needs at least one path")
+        for p in paths:
+            if not p.exists():
+                raise FileNotFoundError(p)
+        self._paths = list(paths)
+        self._idx = 0
+        self._fh: IO[bytes] | None = self._paths[0].open("rb")
+        self._closed = False
+
+    def read(self, n: int = -1) -> bytes:
+        if self._closed:
+            raise ValueError("read of closed _MultiPartReader")
+        if n is None or n < 0:
+            chunks: list[bytes] = []
+            while self._fh is not None:
+                chunks.append(self._fh.read())
+                self._advance()
+            return b"".join(chunks)
+        out: list[bytes] = []
+        remaining = n
+        while remaining > 0 and self._fh is not None:
+            chunk = self._fh.read(remaining)
+            if not chunk:
+                self._advance()
+                continue
+            out.append(chunk)
+            remaining -= len(chunk)
+        return b"".join(out)
+
+    def _advance(self) -> None:
+        if self._fh is not None:
+            self._fh.close()
+            self._fh = None
+        self._idx += 1
+        if self._idx < len(self._paths):
+            self._fh = self._paths[self._idx].open("rb")
+
+    def readable(self) -> bool:
+        return not self._closed
+
+    def close(self) -> None:
+        if self._fh is not None:
+            self._fh.close()
+            self._fh = None
+        self._closed = True
+
+    def __enter__(self) -> _MultiPartReader:
+        return self
+
+    def __exit__(self, exc_type, exc, tb) -> None:  # type: ignore[no-untyped-def]
+        self.close()
+
+
+# ---------------------------------------------------------------------------
+# Stream the JSONL inside the tar.bz2
+# ---------------------------------------------------------------------------
+
+
+def _is_jsonl_member(name: str) -> bool:
+    return name.endswith(".jsonl") or name.endswith(".jsonl.txt")
+
+
+def iter_questions_task3(
+    parts_dir: Path,
+    *,
+    max_questions: int | None = None,
+) -> list[CragQuestion]:
+    """Stream-parse Task 3 rows into ``CragQuestion`` objects.
+
+    The Task 3 archive ships its 2,706 questions sharded across
+    multiple JSONL files inside the tar (e.g.
+    ``crag_task_3_dev_v4_0.jsonl``, ``..._1.jsonl``, …). We iterate
+    members in-stream, parse every JSONL one we encounter, and stop
+    as soon as ``max_questions`` is reached — at which point we
+    don't decompress any further members.
+
+    For a typical n=50 sample at ~3 MB per row we touch ~150 MB of
+    decompressed JSONL — almost always inside the first shard.
+    """
+
+    parts = [parts_dir / name for name in CRAG_TASK_3_PART_NAMES]
+    multi = _MultiPartReader(parts)
+    bz = bz2.BZ2File(multi, mode="rb")
+    tar = tarfile.open(fileobj=bz, mode="r|")
+    out: list[CragQuestion] = []
+    raw_idx = 0
+    found_jsonl = False
+    try:
+        for member in tar:
+            if not member.isfile() or not _is_jsonl_member(member.name):
+                continue
+            found_jsonl = True
+            logger.info(
+                "CRAG Task 3: streaming JSONL shard %s (size: %d bytes)",
+                member.name, member.size,
+            )
+            fh = tar.extractfile(member)
+            if fh is None:
+                logger.warning("tar.extractfile returned None for %s; skipping", member.name)
+                continue
+            try:
+                for raw_line in fh:
+                    line = raw_line.decode("utf-8", errors="replace").strip()
+                    if not line:
+                        continue
+                    try:
+                        row = json.loads(line)
+                    except json.JSONDecodeError as exc:
+                        logger.warning(
+                            "Skipping malformed CRAG Task 3 row %d in %s: %s",
+                            raw_idx, member.name, exc,
+                        )
+                        raw_idx += 1
+                        continue
+                    query = str(row.get("query") or "").strip()
+                    answer = str(row.get("answer") or "").strip()
+                    if not query or not answer:
+                        raw_idx += 1
+                        continue
+                    out.append(CragQuestion(
+                        qid=f"T3_{raw_idx:05d}",
+                        interaction_id=str(row.get("interaction_id") or "").strip(),
+                        query_time=str(row.get("query_time") or "").strip(),
+                        query=query,
+                        gold_answer=answer,
+                        alt_answers=_parse_alt_answers(row.get("alt_ans")),
+                        domain=str(row.get("domain") or "").strip().lower(),
+                        question_type=str(row.get("question_type") or "").strip().lower(),
+                        static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(),
+                        popularity=str(row.get("popularity") or "").strip().lower(),
+                        split=int(row.get("split") or 0),
+                        raw_index=raw_idx,
+                        pages=_parse_pages(row.get("search_results")),
+                    ))
+                    raw_idx += 1
+                    if max_questions is not None and len(out) >= max_questions:
+                        return out
+            finally:
+                try:
+                    fh.close()
+                except Exception:  # noqa: BLE001
+                    pass
+        if not found_jsonl:
+            raise RuntimeError(
+                "No JSONL member found inside Task 3 tar.bz2 archive; "
+                "schema may have changed upstream."
+            )
+    finally:
+        try:
+            tar.close()
+        except Exception:  # noqa: BLE001
+            pass
+        try:
+            bz.close()
+        except Exception:  # noqa: BLE001
+            pass
+        try:
+            multi.close()
+        except Exception:  # noqa: BLE001
+            pass
+    return out
+
+
+def parts_present(parts_dir: Path) -> bool:
+    """``True`` iff all 4 parts exist on disk and are non-empty."""
+
+    for name in CRAG_TASK_3_PART_NAMES:
+        p = parts_dir / name
+        if not p.exists() or p.stat().st_size == 0:
+            return False
+    return True
+
+
+# ---------------------------------------------------------------------------
+# Re-exports for convenience
+# ---------------------------------------------------------------------------
+
+
+__all__ = [
+    "CRAG_TASK_3_PART_NAMES",
+    "CRAG_TASK_3_PART_URLS",
+    "CragPage",
+    "CragQuestion",
+    "iter_questions_task3",
+    "parts_present",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py
new file mode 100644
index 000000000..63f66702b
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/grader.py
@@ -0,0 +1,540 @@
+"""CRAG 3-class grader: ``correct`` (+1) / ``missing`` (0) / ``incorrect`` (-1).
+
+The CRAG paper's headline metric is the **Truthfulness Score**:
+
+    score = (#correct - #incorrect) / total
+
+which rewards calibrated abstention — refusing to answer is neutral
+(0), guessing wrong is negative (-1). Grading is therefore a 3-class
+problem rather than the 2-class accuracy used for FRAMES.
+
+Pipeline per (pred, gold, alt_ans, question_type):
+
+1. Detect refusal first (``Answer: I don't know`` / "I don't know" /
+   "no information") → ``missing`` (deterministic, never billed).
+2. ``false_premise`` questions: gold is canonically "the question
+   contains a false premise" — reward any answer that flags the
+   false premise (substring "false premise" / "incorrect premise" /
+   "no such") as correct.
+3. Run the FRAMES-style deterministic shortcut (exact / numeric /
+   substring) on ``pred`` against ``gold ∪ alt_ans``. Hit → correct.
+4. Fall through to the LLM judge (if configured), which returns one
+   of ``{correct, missing, incorrect}`` — verbatim CRAG protocol.
+5. No judge configured → record ``incorrect`` (pessimistic but at
+   least monotone with the deterministic grader).
+
+The judge is throttled by an asyncio.Semaphore so it doesn't outrun
+the OpenRouter rate limit; the pre-judge deterministic pass keeps
+the bill bounded (most easy "Beyoncé"-vs-"Beyoncé Knowles" cases
+short-circuit before we burn judge tokens).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+import string
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Any, Literal
+
+from ....core.providers.openrouter_chat import OpenRouterChatProvider
+
+logger = logging.getLogger(__name__)
+
+
+GradeClass = Literal["correct", "missing", "incorrect"]
+
+
+# ---------------------------------------------------------------------------
+# Public type
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class CragGradeResult:
+    """One graded (pred, gold) pair under CRAG's 3-class rubric."""
+
+    grade: GradeClass
+    score: int                     # +1 / 0 / -1
+    method: str                    # exact, numeric, substring, refusal,
+                                   # false_premise_correct, false_premise_miss,
+                                   # llm_judge, lexical_miss, ...
+    normalised_pred: str = ""
+    normalised_gold: str = ""
+    judge_rationale: str = ""
+
+    @property
+    def correct(self) -> bool:
+        return self.grade == "correct"
+
+    @property
+    def missing(self) -> bool:
+        return self.grade == "missing"
+
+    @property
+    def incorrect(self) -> bool:
+        return self.grade == "incorrect"
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "grade": self.grade,
+            "score": self.score,
+            "method": self.method,
+            "normalised_pred": self.normalised_pred,
+            "normalised_gold": self.normalised_gold,
+            "judge_rationale": self.judge_rationale,
+        }
+
+
+def _grade_to_score(grade: GradeClass) -> int:
+    return {"correct": 1, "missing": 0, "incorrect": -1}[grade]
+
+
+# ---------------------------------------------------------------------------
+# Normalisation
+# ---------------------------------------------------------------------------
+
+
+_PUNCT_TABLE = str.maketrans({c: " " for c in string.punctuation})
+_ARTICLES = re.compile(r"\b(a|an|the)\b", re.IGNORECASE)
+_WS = re.compile(r"\s+")
+
+
+def _normalise(s: str) -> str:
+    s = (s or "").lower()
+    s = s.translate(_PUNCT_TABLE)
+    s = _ARTICLES.sub(" ", s)
+    s = _WS.sub(" ", s).strip()
+    return s
+
+
+_WORD_NUMBERS = {
+    "zero": 0, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5,
+    "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11,
+    "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15, "sixteen": 16,
+    "seventeen": 17, "eighteen": 18, "nineteen": 19, "twenty": 20,
+}
+
+_NUMERIC_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
+
+
+def _maybe_number(s: str) -> float | None:
+    """Extract a single numeric value from raw lowercased text."""
+
+    raw = (s or "").strip().lower()
+    if not raw:
+        return None
+    match = _NUMERIC_RE.search(raw)
+    if match:
+        try:
+            return float(match.group(0).replace(",", ""))
+        except ValueError:
+            pass
+    for tok in _normalise(s).split():
+        if tok in _WORD_NUMBERS:
+            return float(_WORD_NUMBERS[tok])
+    return None
+
+
+def _whole_word_substring(haystack: str, needle: str) -> bool:
+    if not needle:
+        return False
+    return f" {needle} " in f" {haystack} "
+
+
+# ---------------------------------------------------------------------------
+# Refusal detection
+# ---------------------------------------------------------------------------
+
+
+_REFUSAL_PATTERNS = [
+    re.compile(r"\bi\s+don'?t\s+know\b", re.IGNORECASE),
+    re.compile(r"\bi\s+do\s+not\s+know\b", re.IGNORECASE),
+    re.compile(r"\bnot\s+enough\s+information\b", re.IGNORECASE),
+    re.compile(r"\binsufficient\s+information\b", re.IGNORECASE),
+    re.compile(r"\bcannot\s+(?:be\s+)?(?:answered|determined)\b", re.IGNORECASE),
+    re.compile(r"\bunable\s+to\s+(?:answer|determine)\b", re.IGNORECASE),
+    re.compile(r"\bno\s+(?:information|data|evidence)\b", re.IGNORECASE),
+]
+
+
+def _is_refusal(pred: str) -> bool:
+    """Cheap deterministic check for "I don't know" -shaped responses."""
+
+    if not pred or not pred.strip():
+        return True  # empty answer is a de facto refusal
+    return any(p.search(pred) for p in _REFUSAL_PATTERNS)
+
+
+# ---------------------------------------------------------------------------
+# False-premise handling
+# ---------------------------------------------------------------------------
+
+
+_FALSE_PREMISE_PATTERNS = [
+    re.compile(r"false\s+premise", re.IGNORECASE),
+    re.compile(r"incorrect\s+premise", re.IGNORECASE),
+    re.compile(r"premise\s+(?:is|of)\s+the\s+question", re.IGNORECASE),
+    re.compile(r"\bno\s+such\b", re.IGNORECASE),
+    re.compile(r"never\s+(?:happened|occurred|existed)", re.IGNORECASE),
+    re.compile(r"\bdid\s+not\s+(?:happen|occur|exist)\b", re.IGNORECASE),
+    re.compile(r"\bdoes\s+not\s+exist\b", re.IGNORECASE),
+    re.compile(r"is\s+not\s+(?:true|correct|accurate)", re.IGNORECASE),
+    re.compile(r"\bisn'?t\s+(?:true|correct|accurate)\b", re.IGNORECASE),
+    re.compile(r"\binvalid\s+(?:premise|question|assumption)\b", re.IGNORECASE),
+]
+
+
+def _flags_false_premise(pred: str) -> bool:
+    return any(p.search(pred) for p in _FALSE_PREMISE_PATTERNS)
+
+
+# ---------------------------------------------------------------------------
+# Deterministic grader
+# ---------------------------------------------------------------------------
+
+
+def grade_deterministic(
+    *,
+    pred: str,
+    gold: str,
+    alt_answers: Sequence[str] = (),
+    question_type: str = "",
+) -> CragGradeResult:
+    """Try to grade without the LLM judge. Returns a final result.
+
+    Always returns *some* result — the caller checks ``method`` to
+    decide whether the LLM judge should overturn it. ``lexical_miss``
+    and ``false_premise_unclear`` are the two methods that trigger the
+    judge fallback.
+    """
+
+    qtype = (question_type or "").lower()
+    n_pred = _normalise(pred)
+    n_gold = _normalise(gold)
+
+    if _is_refusal(pred):
+        # CRAG protocol: refusal is *missing* (0), even on false-premise
+        # questions where one might argue refusal == correct. We
+        # follow the paper's grading literally.
+        return CragGradeResult(
+            grade="missing",
+            score=0,
+            method="refusal",
+            normalised_pred=n_pred,
+            normalised_gold=n_gold,
+        )
+
+    # Empty-gold guard (shouldn't happen, but defensively):
+    if not n_gold:
+        return CragGradeResult(
+            grade="incorrect",
+            score=-1,
+            method="empty_gold",
+            normalised_pred=n_pred,
+            normalised_gold=n_gold,
+        )
+
+    # False-premise questions: gold is typically "the question contains
+    # a false premise" / "no such X" / similar. Any answer that
+    # explicitly flags the false premise is correct.
+    if qtype == "false_premise":
+        if _flags_false_premise(pred):
+            return CragGradeResult(
+                grade="correct",
+                score=1,
+                method="false_premise_flagged",
+                normalised_pred=n_pred,
+                normalised_gold=n_gold,
+            )
+        # If the model commits to *any* concrete answer on a false-
+        # premise question without flagging the premise, it is wrong.
+        # But we don't classify ourselves — let the judge decide on
+        # the off chance the gold itself is e.g. "no" and the pred
+        # is "no" without explicit "false premise" wording.
+        return CragGradeResult(
+            grade="incorrect",
+            score=-1,
+            method="false_premise_unclear",
+            normalised_pred=n_pred,
+            normalised_gold=n_gold,
+        )
+
+    # All non-false-premise questions: try the standard chain against
+    # gold and each alt answer. First match wins.
+    candidates = [gold, *list(alt_answers)]
+    for candidate in candidates:
+        if not candidate or not str(candidate).strip():
+            continue
+        cand_norm = _normalise(candidate)
+        if not cand_norm:
+            continue
+        if n_pred == cand_norm:
+            return CragGradeResult(
+                grade="correct", score=1, method="exact",
+                normalised_pred=n_pred, normalised_gold=cand_norm,
+            )
+        p_num = _maybe_number(pred)
+        c_num = _maybe_number(candidate)
+        if p_num is not None and c_num is not None:
+            # Pure 1% relative tolerance for CRAG (currency, counts,
+            # ratios). Unlike FRAMES (which uses a 0.5 absolute floor
+            # for year-shaped answers), CRAG's numeric questions are
+            # often small-value (stock prices, percentages) where a
+            # 0.5 floor would let "$2.05" match "$2.17". The judge is
+            # the safety net for borderline rounding cases.
+            tol = abs(c_num) * 0.01
+            if abs(p_num - c_num) <= tol:
+                return CragGradeResult(
+                    grade="correct", score=1, method="numeric",
+                    normalised_pred=n_pred, normalised_gold=cand_norm,
+                )
+            # Numeric question with different numbers — keep looking
+            # at other candidates rather than declaring miss now;
+            # alt answers may include word forms that pass.
+        if _whole_word_substring(n_pred, cand_norm):
+            return CragGradeResult(
+                grade="correct", score=1, method="substring",
+                normalised_pred=n_pred, normalised_gold=cand_norm,
+            )
+        if _whole_word_substring(cand_norm, n_pred) and len(n_pred) >= 3:
+            return CragGradeResult(
+                grade="correct", score=1, method="substring_reverse",
+                normalised_pred=n_pred, normalised_gold=cand_norm,
+            )
+
+    return CragGradeResult(
+        grade="incorrect",
+        score=-1,
+        method="lexical_miss",
+        normalised_pred=n_pred,
+        normalised_gold=n_gold,
+    )
+
+
+# ---------------------------------------------------------------------------
+# LLM-as-judge (3-class)
+# ---------------------------------------------------------------------------
+
+
+_JUDGE_SYSTEM = (
+    "You are an impartial grader for short-answer factual questions, "
+    "following the CRAG benchmark rubric. Given a question, the gold "
+    "answer (and any alternative valid answers), and a model's "
+    "prediction, classify the prediction into exactly one of three "
+    "categories:\n\n"
+    "* \"correct\"   — the prediction expresses the same factual "
+    "content as the gold answer (paraphrasing OK; numbers as words "
+    "OK; partial-but-correct names OK; non-contradictory extra "
+    "detail OK).\n"
+    "* \"missing\"   — the prediction explicitly refuses, says \"I "
+    "don't know\", says there is insufficient information, or hedges "
+    "without committing.\n"
+    "* \"incorrect\" — the prediction commits to a fact that is "
+    "different from the gold answer, or fails to flag a false "
+    "premise when the question contains one.\n\n"
+    "Special case: if the question contains a false premise and the "
+    "gold answer says so, then a prediction that flags the false "
+    "premise is \"correct\".\n\n"
+    "Respond with ONLY a JSON object on a single line:\n"
+    '{\"grade\": \"correct\"|\"missing\"|\"incorrect\", \"rationale\": \"<one short sentence>\"}'
+)
+
+
+_JUDGE_TEMPLATE = """\
+Question: {question}
+Question type: {question_type}
+Gold answer: {gold}
+{alt_block}Model prediction: {pred}
+
+Decide whether the prediction is correct, missing, or incorrect.
+"""
+
+
+@dataclass
+class CragJudgeConfig:
+    api_key: str
+    model: str = "anthropic/claude-sonnet-4.5"
+    base_url: str = "https://openrouter.ai/api/v1"
+    max_tokens: int = 200
+    concurrency: int = 4
+
+
+class CragLlmJudge:
+    """Async LLM judge over OpenRouter chat completions, 3-class output."""
+
+    def __init__(self, *, config: CragJudgeConfig) -> None:
+        self._config = config
+        self._provider = OpenRouterChatProvider(
+            api_key=config.api_key,
+            base_url=config.base_url,
+            model=config.model,
+        )
+        self._sem = asyncio.Semaphore(max(1, config.concurrency))
+
+    @property
+    def model(self) -> str:
+        return self._config.model
+
+    async def judge(
+        self,
+        *,
+        question: str,
+        gold: str,
+        alt_answers: Sequence[str],
+        pred: str,
+        question_type: str = "",
+    ) -> tuple[GradeClass, str]:
+        """Return ``(grade, rationale)``. Errors return incorrect + reason."""
+
+        alt_block = ""
+        if alt_answers:
+            alt_lines = "\n".join(f"  - {a}" for a in alt_answers if a)
+            if alt_lines:
+                alt_block = f"Alternative valid answers:\n{alt_lines}\n"
+        prompt = _JUDGE_TEMPLATE.format(
+            question=question,
+            question_type=question_type or "unknown",
+            gold=gold,
+            alt_block=alt_block,
+            pred=pred,
+        )
+        try:
+            async with self._sem:
+                response = await self._provider.complete(
+                    prompt=prompt,
+                    system_prompt=_JUDGE_SYSTEM,
+                    max_tokens=self._config.max_tokens,
+                )
+        except Exception as exc:  # noqa: BLE001
+            return "incorrect", f"judge_error: {type(exc).__name__}: {exc}"
+        return _parse_judge_response(response.text)
+
+
+def _parse_judge_response(text: str) -> tuple[GradeClass, str]:
+    """Parse the judge reply into a 3-class label + rationale."""
+
+    if not text or not text.strip():
+        return "incorrect", "judge_returned_empty"
+    match = re.search(r"\{[^{}]*\}", text, flags=re.DOTALL)
+    candidate = match.group(0) if match else text
+    try:
+        data = json.loads(candidate)
+    except (json.JSONDecodeError, ValueError):
+        lowered = text.strip().lower()
+        if "correct" in lowered and "incorrect" not in lowered:
+            return "correct", "yes (parser_fallback)"
+        if "missing" in lowered or "i don" in lowered:
+            return "missing", "missing (parser_fallback)"
+        return "incorrect", f"unparseable_judge_response: {text[:200]}"
+    raw_grade = str(data.get("grade") or "").strip().lower()
+    rationale = str(data.get("rationale", "")).strip()[:280]
+    if raw_grade in {"correct", "missing", "incorrect"}:
+        return raw_grade, rationale  # type: ignore[return-value]
+    return "incorrect", f"unknown_grade={raw_grade!r}; {rationale}"
+
+
+# ---------------------------------------------------------------------------
+# Combined grader
+# ---------------------------------------------------------------------------
+
+
+# Methods that should *not* trigger the LLM judge — the deterministic
+# verdict is conclusive (refusal, exact match, numeric mismatch, etc.).
+_TERMINAL_METHODS = frozenset({
+    "refusal",
+    "exact",
+    "numeric",
+    "substring",
+    "substring_reverse",
+    "false_premise_flagged",
+    "empty_gold",
+})
+
+
+async def grade_with_judge(
+    *,
+    pred: str,
+    gold: str,
+    alt_answers: Sequence[str],
+    question: str,
+    question_type: str,
+    judge: CragLlmJudge | None,
+) -> CragGradeResult:
+    """One row → deterministic shortcut → optional LLM judge fallback."""
+
+    det = grade_deterministic(
+        pred=pred,
+        gold=gold,
+        alt_answers=alt_answers,
+        question_type=question_type,
+    )
+    if det.method in _TERMINAL_METHODS:
+        return det
+    if judge is None:
+        return det  # ``lexical_miss`` / ``false_premise_unclear`` → keep as-is
+    grade, rationale = await judge.judge(
+        question=question,
+        gold=gold,
+        alt_answers=alt_answers,
+        pred=pred,
+        question_type=question_type,
+    )
+    return CragGradeResult(
+        grade=grade,
+        score=_grade_to_score(grade),
+        method="llm_judge",
+        normalised_pred=det.normalised_pred,
+        normalised_gold=det.normalised_gold,
+        judge_rationale=rationale,
+    )
+
+
+@dataclass
+class CragGradeRow:
+    """One row to grade. Mirrors the FRAMES grader's tuple but typed."""
+
+    qid: str
+    question: str
+    gold: str
+    alt_answers: list[str]
+    pred: str
+    question_type: str = ""
+
+
+async def grade_many(
+    *,
+    rows: Sequence[CragGradeRow],
+    judge: CragLlmJudge | None,
+) -> list[CragGradeResult]:
+    """Grade every row concurrently. Judge enforces its own concurrency cap."""
+
+    if not rows:
+        return []
+    coros = [
+        grade_with_judge(
+            pred=r.pred,
+            gold=r.gold,
+            alt_answers=r.alt_answers,
+            question=r.question,
+            question_type=r.question_type,
+            judge=judge,
+        )
+        for r in rows
+    ]
+    return list(await asyncio.gather(*coros))
+
+
+__all__ = [
+    "CragGradeResult",
+    "CragGradeRow",
+    "CragJudgeConfig",
+    "CragLlmJudge",
+    "GradeClass",
+    "grade_deterministic",
+    "grade_many",
+    "grade_with_judge",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py
new file mode 100644
index 000000000..1b00aedc2
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/html_extract.py
@@ -0,0 +1,206 @@
+"""HTML → markdown for CRAG pages, with boilerplate removal.
+
+Each CRAG page is a *full* HTML document (nav, ads, recommended-for-
+you, footer, ...). Without removing that boilerplate, retrieval over
+the chunks would surface menu items and "subscribe to our newsletter"
+boxes instead of the actual page content. We use ``trafilatura``,
+which is purpose-built for main-content extraction (the same library
+Common Crawl downstream pipelines use). It outputs clean prose with
+section headers, lists, and tables preserved.
+
+Extraction policy:
+1. ``trafilatura.extract`` with ``output_format="markdown"`` — main
+   content only, headers preserved, tables kept.
+2. If extraction fails or returns < 200 chars (paywalled / JS-only
+   page / extraction confused), fall back to a plain stdlib
+   ``HTMLParser`` that strips tags and collapses whitespace. Some
+   text is better than no text — SurfSense's chunker handles noisy
+   prose.
+
+We *intentionally* keep the page name and URL as visible H1 / link
+metadata so the SurfSense chunker preserves doc identity at the top of
+the first chunk (mirrors what we do for FRAMES Wikipedia pages).
+"""
+
+from __future__ import annotations
+
+import html
+import logging
+import re
+from dataclasses import dataclass
+from html.parser import HTMLParser
+
+logger = logging.getLogger(__name__)
+
+
+_MIN_TRAFILATURA_LENGTH = 200
+_MAX_OUTPUT_CHARS = 200_000  # cap to keep upload payloads sane
+
+
+@dataclass
+class ExtractionResult:
+    """Outcome of converting one HTML blob to plain markdown."""
+
+    text: str
+    method: str          # "trafilatura" | "fallback_strip" | "empty"
+    n_chars: int
+
+    @property
+    def ok(self) -> bool:
+        return self.n_chars > 0
+
+
+# ---------------------------------------------------------------------------
+# Trafilatura wrapper (lazy import so tests / small scripts don't pay)
+# ---------------------------------------------------------------------------
+
+
+def _trafilatura_extract(html_text: str, *, url: str) -> str | None:
+    try:
+        import trafilatura
+    except ImportError:  # pragma: no cover - dependency is required
+        logger.warning("trafilatura not installed; falling back to strip-tags only")
+        return None
+    try:
+        text = trafilatura.extract(
+            html_text,
+            url=url or None,
+            output_format="markdown",
+            include_links=False,
+            include_images=False,
+            include_tables=True,
+            favor_recall=True,
+        )
+    except Exception as exc:  # noqa: BLE001 - trafilatura raises a zoo
+        logger.debug("trafilatura.extract crashed for %s: %s", url, exc)
+        return None
+    if not text:
+        return None
+    return text.strip()
+
+
+# ---------------------------------------------------------------------------
+# Stdlib fallback: strip HTML tags
+# ---------------------------------------------------------------------------
+
+
+class _StripHTMLParser(HTMLParser):
+    """Collect text content, treating block tags as paragraph breaks.
+
+    We deliberately drop ``<script>``, ``<style>``, ``<nav>``,
+    ``<header>``, ``<footer>``, and ``<aside>`` content — these are
+    almost always boilerplate and they are the dominant source of
+    noise SurfSense ends up retrieving against if not removed.
+    """
+
+    _SKIP_TAGS = frozenset({"script", "style", "nav", "header", "footer", "aside", "svg"})
+    _BLOCK_TAGS = frozenset({
+        "p", "div", "section", "article", "li", "ul", "ol",
+        "h1", "h2", "h3", "h4", "h5", "h6", "br", "tr",
+        "td", "th", "table", "blockquote", "pre",
+    })
+
+    def __init__(self) -> None:
+        super().__init__(convert_charrefs=True)
+        self._buffer: list[str] = []
+        self._skip_depth: int = 0
+
+    def handle_starttag(self, tag: str, attrs: list) -> None:  # noqa: ARG002
+        if tag in self._SKIP_TAGS:
+            self._skip_depth += 1
+        if tag in self._BLOCK_TAGS:
+            self._buffer.append("\n")
+
+    def handle_endtag(self, tag: str) -> None:
+        if tag in self._SKIP_TAGS and self._skip_depth > 0:
+            self._skip_depth -= 1
+        if tag in self._BLOCK_TAGS:
+            self._buffer.append("\n")
+
+    def handle_data(self, data: str) -> None:
+        if self._skip_depth:
+            return
+        self._buffer.append(data)
+
+    def get_text(self) -> str:
+        text = "".join(self._buffer)
+        # Decode any leftover entities and collapse whitespace.
+        text = html.unescape(text)
+        text = re.sub(r"[ \t]+", " ", text)
+        text = re.sub(r"\n[ \t]+", "\n", text)
+        text = re.sub(r"\n{3,}", "\n\n", text)
+        return text.strip()
+
+
+def _strip_tags(html_text: str) -> str:
+    parser = _StripHTMLParser()
+    try:
+        parser.feed(html_text)
+    except Exception as exc:  # noqa: BLE001 - HTMLParser is fragile on garbage input
+        logger.debug("HTMLParser failed; using regex strip: %s", exc)
+        no_tags = re.sub(r"<[^>]+>", " ", html_text)
+        return re.sub(r"\s+", " ", html.unescape(no_tags)).strip()
+    return parser.get_text()
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+
+def extract_main_content(
+    html_text: str,
+    *,
+    url: str = "",
+    page_name: str = "",
+    last_modified: str | None = None,
+) -> ExtractionResult:
+    """Convert one HTML blob into clean markdown for ingest.
+
+    The returned ``text`` is prefixed with a small metadata header
+    (``# {page_name}\\n\\nSource: {url}\\n``) so that:
+
+    * SurfSense's chunker has a stable doc-identity anchor at the top
+      of the first chunk (matches what we do for FRAMES Wikipedia).
+    * The retrieval-augmented arm sees the URL inline, which the LLM
+      can surface as a citation if the prompt asks for one.
+    """
+
+    body = ""
+    method = "empty"
+    if html_text and html_text.strip():
+        body = _trafilatura_extract(html_text, url=url) or ""
+        if body and len(body) >= _MIN_TRAFILATURA_LENGTH:
+            method = "trafilatura"
+        else:
+            stripped = _strip_tags(html_text)
+            # Prefer trafilatura output even if short, but only if it
+            # contained any prose at all — empty trafilatura fall-through
+            # to the stripped form.
+            if body and stripped and len(stripped) > len(body) * 1.5:
+                body = stripped
+                method = "fallback_strip"
+            elif not body and stripped:
+                body = stripped
+                method = "fallback_strip"
+            elif body:
+                method = "trafilatura"
+
+    body = body.strip()
+    if len(body) > _MAX_OUTPUT_CHARS:
+        body = body[:_MAX_OUTPUT_CHARS] + "\n\n[...truncated...]"
+
+    if not body:
+        return ExtractionResult(text="", method="empty", n_chars=0)
+
+    title_line = (page_name or url or "Untitled").strip()
+    header_lines = [f"# {title_line}"]
+    if url:
+        header_lines.append(f"Source: {url}")
+    if last_modified:
+        header_lines.append(f"Last modified: {last_modified}")
+    final = "\n".join(header_lines) + "\n\n" + body + "\n"
+    return ExtractionResult(text=final, method=method, n_chars=len(final))
+
+
+__all__ = ["ExtractionResult", "extract_main_content"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
new file mode 100644
index 000000000..1a6a1dfa7
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
@@ -0,0 +1,447 @@
+"""CRAG ingestion: download → extract → upload → per-question doc map.
+
+Steps:
+
+1. Download ``crag_task_1_and_2_dev_v4.jsonl.bz2`` from
+   ``facebookresearch/CRAG`` (skip if cached).
+2. Stream-parse into ``CragQuestion`` objects.
+3. Optionally cap to ``--n-questions N`` (and *stratified* sample
+   across ``(domain, question_type)`` so the smoke / partial run
+   isn't dominated by ``finance`` or ``simple``).
+4. For each question, extract the 5 web pages to clean markdown via
+   ``trafilatura`` and write them to
+   ``<bench_dir>/pages/<qid>__<page_idx>__<url_hash>.md``. The
+   filename is unique across the whole sample (so SurfSense's
+   ``(filename, search_space)`` dedup never collides between
+   questions) and round-trippable (the ``<qid>__`` prefix lets the
+   ingest infer doc-membership at the title level even before we
+   land on a stable status response).
+5. Upload all extracted pages to SurfSense in batches with text-only
+   ETL (``use_vision_llm=False, processing_mode="basic"``) — these
+   are extracted plaintext, no images involved.
+6. Persist a doc map at
+   ``<suite_data>/maps/crag_doc_map.jsonl`` with one row per question:
+
+       {"qid": "C00042",
+        "interaction_id": "<uuid>",
+        "question": "<text>",
+        "gold_answer": "<text>",
+        "alt_answers": [...],
+        "domain": "...", "question_type": "...",
+        "static_or_dynamic": "...", "popularity": "...",
+        "query_time": "...",
+        "page_filenames": ["C00042__0__abc123.md", ...],
+        "document_ids": [42101, 42102, ...],
+        "missing_pages": [...]   # filenames whose upload failed
+       }
+
+The runner uses ``document_ids`` to scope SurfSense retrieval to
+exactly the 5 pages of the question (matches CRAG protocol — the
+benchmark explicitly hands over its own retrieved pages).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.clients.documents import (
+    DocumentProcessingFailed,
+    DocumentProcessingTimeout,
+)
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+from .dataset import (
+    CragPage,
+    CragQuestion,
+    download_task_1_2,
+    iter_questions,
+    stratified_sample,
+    write_questions_jsonl,
+)
+from .html_extract import extract_main_content
+
+logger = logging.getLogger(__name__)
+
+
+_FILENAME_SAFE = re.compile(r"[^A-Za-z0-9._\-]+")
+
+
+def _page_filename(qid: str, page_idx: int, page: CragPage) -> str:
+    """Filesystem-safe, globally unique markdown filename for a CRAG page.
+
+    Format: ``<qid>__<idx>__<url_hash>.md``. Both the qid (``C00042``)
+    and the URL-hash (``[:12]``) are alphanumeric so we don't need to
+    sanitise them, but we strip anything else just in case.
+    """
+
+    qid_safe = _FILENAME_SAFE.sub("_", qid)
+    return f"{qid_safe}__{page_idx:02d}__{page.url_hash}.md"
+
+
+# ---------------------------------------------------------------------------
+# Stats
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class _IngestStats:
+    n_questions: int
+    n_pages_total: int
+    n_pages_extracted: int
+    n_pages_empty: int
+    n_uploaded: int
+    n_existing: int
+    bench_dir: Path
+    map_path: Path
+
+
+# ---------------------------------------------------------------------------
+# Page extraction
+# ---------------------------------------------------------------------------
+
+
+def _materialise_pages(
+    questions: list[CragQuestion],
+    *,
+    pages_dir: Path,
+    overwrite: bool = False,
+) -> tuple[dict[str, list[str]], dict[str, str]]:
+    """Extract every page in every question to ``pages_dir`` as markdown.
+
+    Returns:
+      * ``qid -> [filename, filename, ...]`` (in page order, only
+        successful extractions)
+      * ``filename -> source_url`` for diagnostics
+
+    Empty extractions (paywall / JS / parse-fail with no fallback
+    output) are skipped — better to retrieve from 4 pages than feed
+    SurfSense's chunker an empty file.
+    """
+
+    pages_dir.mkdir(parents=True, exist_ok=True)
+    qid_to_files: dict[str, list[str]] = {}
+    file_to_url: dict[str, str] = {}
+    method_counts: dict[str, int] = {}
+    n_empty = 0
+
+    for q in questions:
+        names: list[str] = []
+        for idx, page in enumerate(q.pages):
+            filename = _page_filename(q.qid, idx, page)
+            dest = pages_dir / filename
+            if dest.exists() and dest.stat().st_size > 0 and not overwrite:
+                method_counts["cache_hit"] = method_counts.get("cache_hit", 0) + 1
+                names.append(filename)
+                file_to_url[filename] = page.page_url
+                continue
+            result = extract_main_content(
+                page.page_html,
+                url=page.page_url,
+                page_name=page.page_name,
+                last_modified=page.page_last_modified,
+            )
+            method_counts[result.method] = method_counts.get(result.method, 0) + 1
+            if not result.ok:
+                n_empty += 1
+                continue
+            dest.write_text(result.text, encoding="utf-8")
+            names.append(filename)
+            file_to_url[filename] = page.page_url
+        qid_to_files[q.qid] = names
+
+    logger.info(
+        "CRAG page extraction: %s; empty=%d, total_files=%d across %d questions",
+        method_counts, n_empty, len(file_to_url), len(qid_to_files),
+    )
+    return qid_to_files, file_to_url
+
+
+# ---------------------------------------------------------------------------
+# Upload
+# ---------------------------------------------------------------------------
+
+
+async def _upload_pages(
+    ctx: RunContext,
+    *,
+    pages_dir: Path,
+    filenames: list[str],
+    batch_size: int,
+    settings: IngestSettings,
+) -> dict[str, int]:
+    """Upload ``filenames`` (already on disk under ``pages_dir``) and return name → doc_id."""
+
+    if not filenames:
+        return {}
+    docs_client = ctx.documents_client()
+    name_to_id: dict[str, int] = {}
+    paths = [pages_dir / fn for fn in filenames if (pages_dir / fn).exists()]
+
+    for batch_start in range(0, len(paths), batch_size):
+        batch = paths[batch_start : batch_start + batch_size]
+        result = await docs_client.upload(
+            files=batch,
+            search_space_id=ctx.search_space_id,
+            should_summarize=settings.should_summarize,
+            use_vision_llm=settings.use_vision_llm,
+            processing_mode=settings.processing_mode,
+        )
+        all_ids = list(result.document_ids) + list(result.duplicate_document_ids)
+        if result.document_ids:
+            try:
+                await docs_client.wait_until_ready(
+                    search_space_id=ctx.search_space_id,
+                    document_ids=result.document_ids,
+                    timeout_s=900.0,
+                )
+            except (DocumentProcessingFailed, DocumentProcessingTimeout) as exc:
+                logger.warning("CRAG batch processing issue: %s", exc)
+        if all_ids:
+            statuses = await docs_client.get_status(
+                search_space_id=ctx.search_space_id,
+                document_ids=all_ids,
+            )
+            for s in statuses:
+                stem = Path(s.title).stem if s.title.endswith(".md") else s.title
+                name_to_id[stem] = s.document_id
+                name_to_id[s.title] = s.document_id
+                if not s.title.endswith(".md"):
+                    name_to_id[f"{s.title}.md"] = s.document_id
+        logger.info(
+            "CRAG upload batch %d-%d: %d new, %d duplicate",
+            batch_start, batch_start + len(batch),
+            len(result.document_ids), len(result.duplicate_document_ids),
+        )
+    return name_to_id
+
+
+# ---------------------------------------------------------------------------
+# Doc map writer
+# ---------------------------------------------------------------------------
+
+
+def _resolve_question_doc_ids(
+    questions: list[CragQuestion],
+    qid_to_files: dict[str, list[str]],
+    name_to_id: dict[str, int],
+) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for q in questions:
+        filenames = qid_to_files.get(q.qid, [])
+        doc_ids: list[int] = []
+        missing: list[str] = []
+        for fn in filenames:
+            stem = Path(fn).stem
+            doc_id = name_to_id.get(stem) or name_to_id.get(fn)
+            if doc_id is not None and doc_id not in doc_ids:
+                doc_ids.append(doc_id)
+            else:
+                missing.append(fn)
+        rows.append({
+            "qid": q.qid,
+            "interaction_id": q.interaction_id,
+            "raw_index": q.raw_index,
+            "question": q.query,
+            "gold_answer": q.gold_answer,
+            "alt_answers": list(q.alt_answers),
+            "domain": q.domain,
+            "question_type": q.question_type,
+            "static_or_dynamic": q.static_or_dynamic,
+            "popularity": q.popularity,
+            "query_time": q.query_time,
+            "split": q.split,
+            "page_filenames": filenames,
+            "document_ids": doc_ids,
+            "missing_pages": missing,
+            "n_pages": len(filenames),
+        })
+    return rows
+
+
+# ---------------------------------------------------------------------------
+# Public entry
+# ---------------------------------------------------------------------------
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    n_questions: int | None = None,
+    upload_batch_size: int = 16,
+    skip_upload: bool = False,
+    overwrite_extract: bool = False,
+    settings: IngestSettings | None = None,
+    sample_seed: int = 17,
+) -> None:
+    """Ingest the CRAG benchmark (Tasks 1 & 2) into the research suite.
+
+    Parameters
+    ----------
+    n_questions
+        Cap on the number of CRAG questions to materialise.
+        ``None`` = all 2,706 (~13,500 pages — large; smoke runs
+        should pass 10-20 and full runs ~200).
+    upload_batch_size
+        Markdown files per ``/documents/fileupload`` call.
+    skip_upload
+        Extract + cache markdown locally but don't push to SurfSense
+        (useful for debugging the extraction step).
+    overwrite_extract
+        Re-run trafilatura even when a cached markdown file exists.
+        Default False so re-running ingest is idempotent.
+    settings
+        Override per-upload knobs. CRAG defaults to text-only basic
+        ETL — these are *extracted* plaintext, no images.
+    sample_seed
+        RNG seed for ``stratified_sample``. Pin this for reproducibility.
+    """
+
+    settings = settings or IngestSettings(
+        use_vision_llm=False,
+        processing_mode="basic",
+        should_summarize=False,
+    )
+    bench_dir = ctx.benchmark_data_dir()
+    pages_dir = bench_dir / "pages"
+    raw_cache = bench_dir / ".raw_cache"
+    raw_cache.mkdir(parents=True, exist_ok=True)
+
+    bz2_path = download_task_1_2(raw_cache)
+    logger.info("CRAG: parsing %s ...", bz2_path.name)
+    all_questions = iter_questions(bz2_path)
+    if not all_questions:
+        raise RuntimeError(
+            "CRAG JSONL contained no parseable rows; upstream may have changed schema."
+        )
+    logger.info("CRAG: parsed %d total questions", len(all_questions))
+
+    if n_questions is not None and n_questions > 0:
+        questions = stratified_sample(all_questions, n=n_questions, seed=sample_seed)
+        logger.info(
+            "CRAG: stratified sample of %d questions across %d (domain, qtype) buckets",
+            len(questions),
+            len({(q.domain, q.question_type) for q in questions}),
+        )
+    else:
+        questions = all_questions
+
+    questions_jsonl = bench_dir / "questions.jsonl"
+    write_questions_jsonl(questions, questions_jsonl)
+
+    n_pages_total = sum(len(q.pages) for q in questions)
+    logger.info(
+        "CRAG: extracting up to %d pages across %d questions ...",
+        n_pages_total, len(questions),
+    )
+    qid_to_files, file_to_url = _materialise_pages(
+        questions, pages_dir=pages_dir, overwrite=overwrite_extract,
+    )
+    n_pages_extracted = sum(len(v) for v in qid_to_files.values())
+
+    name_to_id: dict[str, int] = {}
+    if skip_upload:
+        logger.info("CRAG: --skip-upload; skipping SurfSense ingestion")
+    else:
+        all_filenames = sorted({fn for fns in qid_to_files.values() for fn in fns})
+        logger.info("CRAG: uploading %d unique pages ...", len(all_filenames))
+        name_to_id = await _upload_pages(
+            ctx,
+            pages_dir=pages_dir,
+            filenames=all_filenames,
+            batch_size=upload_batch_size,
+            settings=settings,
+        )
+
+    doc_rows = _resolve_question_doc_ids(questions, qid_to_files, name_to_id)
+    map_path = ctx.maps_dir() / "crag_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        fh.write(settings_header_line(settings) + "\n")
+        for row in doc_rows:
+            fh.write(json.dumps(row) + "\n")
+    logger.info("Wrote CRAG doc map to %s (%d rows)", map_path, len(doc_rows))
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["crag"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+    stats = _IngestStats(
+        n_questions=len(questions),
+        n_pages_total=n_pages_total,
+        n_pages_extracted=n_pages_extracted,
+        n_pages_empty=n_pages_total - n_pages_extracted,
+        n_uploaded=len(name_to_id),
+        n_existing=0,
+        bench_dir=bench_dir,
+        map_path=map_path,
+    )
+    logger.info("CRAG ingest done: %s", stats)
+
+
+# ---------------------------------------------------------------------------
+# For runner: read extracted page text back from disk
+# ---------------------------------------------------------------------------
+
+
+def read_page_markdown(bench_dir: Path, filename: str) -> str | None:
+    """Return the on-disk markdown body for a previously-extracted page.
+
+    Used by the long-context runner arm to assemble the prompt at
+    inference time — we don't keep all 5×N pages in memory between
+    ingest and run.
+    """
+
+    path = bench_dir / "pages" / filename
+    if not path.exists():
+        return None
+    try:
+        return path.read_text(encoding="utf-8")
+    except OSError:
+        return None
+
+
+async def _retry_upload_idempotent(  # noqa: D401 - hidden helper
+    ctx: RunContext,
+    *,
+    pages_dir: Path,
+    filenames: list[str],
+    batch_size: int,
+    settings: IngestSettings,
+    max_attempts: int = 2,
+) -> dict[str, int]:
+    """Future-proofing hook (unused today): retry the ingest upload pass."""
+
+    last_exc: Exception | None = None
+    for attempt in range(max_attempts):
+        try:
+            return await _upload_pages(
+                ctx,
+                pages_dir=pages_dir,
+                filenames=filenames,
+                batch_size=batch_size,
+                settings=settings,
+            )
+        except Exception as exc:  # noqa: BLE001
+            last_exc = exc
+            logger.warning("CRAG upload attempt %d failed: %s", attempt + 1, exc)
+            await asyncio.sleep(2.0 * (attempt + 1))
+    if last_exc is not None:
+        raise last_exc
+    return {}
+
+
+__all__ = [
+    "_IngestStats",
+    "_materialise_pages",
+    "_page_filename",
+    "_resolve_question_doc_ids",
+    "_upload_pages",
+    "read_page_markdown",
+    "run_ingest",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
new file mode 100644
index 000000000..e5440f382
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
@@ -0,0 +1,191 @@
+"""CRAG Task 3 ingestion: 4-part download → streaming JSONL → upload.
+
+Same flow as ``ingest.run_ingest`` for Tasks 1 & 2 (extract HTML →
+upload markdown → resolve doc_ids → write doc map), but:
+
+* Source: 4 .tar.bz2 parts streamed via ``dataset_task3``.
+* Page count: 50 per question instead of 5 — the whole point of
+  Task 3 (the long-context arm now structurally has to choose what
+  to keep, while SurfSense's retrieval becomes mandatory).
+* Stratified sampling re-uses the Task 1 helper since the question
+  schema is identical.
+
+Doc map lands at ``<suite_data>/maps/crag_t3_doc_map.jsonl`` with the
+same row shape as Task 1's map (so the runner only needs to know
+which file to load; everything else is shared).
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+from .dataset import stratified_sample, write_questions_jsonl
+from .dataset_task3 import (
+    CRAG_TASK_3_PART_NAMES,
+    iter_questions_task3,
+    parts_present,
+)
+from .ingest import (
+    _IngestStats,
+    _materialise_pages,
+    _resolve_question_doc_ids,
+    _upload_pages,
+)
+
+logger = logging.getLogger(__name__)
+
+
+_INSTRUCTIONS_TO_DOWNLOAD = (
+    "Run `python scripts/download_crag_task3.py` first to fetch the "
+    "4 tar.bz2 parts (~7 GB total) into "
+    "data/research/crag_t3/.raw_cache/. The downloader is idempotent "
+    "and parallel."
+)
+
+
+async def run_ingest_task3(
+    ctx: RunContext,
+    *,
+    n_questions: int | None = None,
+    upload_batch_size: int = 16,
+    skip_upload: bool = False,
+    overwrite_extract: bool = False,
+    settings: IngestSettings | None = None,
+    sample_seed: int = 17,
+    parse_cap: int | None = None,
+) -> None:
+    """Ingest CRAG Task 3 (50 pages per question) into the research suite.
+
+    Parameters
+    ----------
+    n_questions
+        Cap on the post-stratified-sample question count. ``None`` =
+        "use whatever ``parse_cap`` produced". For real runs aim for
+        50 (~2,500 pages) — n=200 (10k pages) is doable but slow.
+    parse_cap
+        Hard cap on how many rows we *parse* from the streaming
+        archive before stratified sampling. Defaults to
+        ``max(400, 6*n_questions)`` — enough to cover all (domain,
+        question_type) buckets ~5x but small enough to fit in the
+        first shard or two (each shard is ≈5 GB decompressed and
+        holds ~300 rows; bz2 throughput is ~50 MB/s). Lowering this
+        is the only knob that bounds streaming cost since we can
+        ``break`` out of the JSONL stream early without decompressing
+        the rest of the ~50 GB archive body.
+    upload_batch_size
+        Markdown files per ``/documents/fileupload`` call.
+    skip_upload
+        Extract markdown locally, don't push to SurfSense.
+    overwrite_extract
+        Re-run trafilatura even when a cached markdown is present.
+    settings
+        Per-upload knobs override (default: text-only basic ETL).
+    sample_seed
+        RNG seed for stratified sampling (deterministic).
+    """
+
+    settings = settings or IngestSettings(
+        use_vision_llm=False,
+        processing_mode="basic",
+        should_summarize=False,
+    )
+    bench_dir = ctx.benchmark_data_dir()
+    pages_dir = bench_dir / "pages"
+    raw_cache = bench_dir / ".raw_cache"
+    raw_cache.mkdir(parents=True, exist_ok=True)
+
+    if not parts_present(raw_cache):
+        missing = [
+            n for n in CRAG_TASK_3_PART_NAMES
+            if not (raw_cache / n).exists()
+        ]
+        raise RuntimeError(
+            f"CRAG Task 3 parts missing from {raw_cache}: {missing}. "
+            f"{_INSTRUCTIONS_TO_DOWNLOAD}"
+        )
+
+    # 1. Stream-parse (capped). For n=50 we don't need the full 2,706
+    #    rows — just enough that the stratified sampler can balance.
+    #    Each tar shard ~5 GB / ~300 rows / ~2 min decompress, so
+    #    400-500 rows = shard 0 + a slice of shard 1 ≈ 3-4 min.
+    parse_cap = parse_cap or (
+        max(400, 6 * (n_questions or 50)) if n_questions else None
+    )
+    logger.info(
+        "CRAG Task 3: streaming JSONL (parse_cap=%s) ...",
+        parse_cap if parse_cap else "no-cap",
+    )
+    all_questions = iter_questions_task3(raw_cache, max_questions=parse_cap)
+    logger.info("CRAG Task 3: parsed %d rows", len(all_questions))
+
+    if not all_questions:
+        raise RuntimeError("CRAG Task 3 streaming returned 0 rows; check archive integrity.")
+
+    if n_questions is not None and n_questions > 0:
+        questions = stratified_sample(all_questions, n=n_questions, seed=sample_seed)
+        logger.info(
+            "CRAG Task 3: stratified sample of %d questions across %d (domain, qtype) buckets",
+            len(questions),
+            len({(q.domain, q.question_type) for q in questions}),
+        )
+    else:
+        questions = all_questions
+
+    questions_jsonl = bench_dir / "questions.jsonl"
+    write_questions_jsonl(questions, questions_jsonl)
+
+    n_pages_total = sum(len(q.pages) for q in questions)
+    logger.info(
+        "CRAG Task 3: extracting up to %d pages across %d questions ...",
+        n_pages_total, len(questions),
+    )
+    qid_to_files, _file_to_url = _materialise_pages(
+        questions, pages_dir=pages_dir, overwrite=overwrite_extract,
+    )
+    n_pages_extracted = sum(len(v) for v in qid_to_files.values())
+
+    name_to_id: dict[str, int] = {}
+    if skip_upload:
+        logger.info("CRAG Task 3: --skip-upload; skipping SurfSense ingestion")
+    else:
+        all_filenames = sorted({fn for fns in qid_to_files.values() for fn in fns})
+        logger.info("CRAG Task 3: uploading %d unique pages ...", len(all_filenames))
+        name_to_id = await _upload_pages(
+            ctx,
+            pages_dir=pages_dir,
+            filenames=all_filenames,
+            batch_size=upload_batch_size,
+            settings=settings,
+        )
+
+    doc_rows = _resolve_question_doc_ids(questions, qid_to_files, name_to_id)
+    map_path = ctx.maps_dir() / "crag_t3_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        fh.write(settings_header_line(settings) + "\n")
+        for row in doc_rows:
+            fh.write(json.dumps(row) + "\n")
+    logger.info("Wrote CRAG Task 3 doc map to %s (%d rows)", map_path, len(doc_rows))
+
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["crag_t3"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+    stats = _IngestStats(
+        n_questions=len(questions),
+        n_pages_total=n_pages_total,
+        n_pages_extracted=n_pages_extracted,
+        n_pages_empty=n_pages_total - n_pages_extracted,
+        n_uploaded=len(name_to_id),
+        n_existing=0,
+        bench_dir=bench_dir,
+        map_path=map_path,
+    )
+    logger.info("CRAG Task 3 ingest done: %s", stats)
+
+
+__all__ = ["run_ingest_task3"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/prompt.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/prompt.py
new file mode 100644
index 000000000..626834505
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/prompt.py
@@ -0,0 +1,146 @@
+"""CRAG prompt templates for the three competing arms.
+
+The CRAG paper grades each prediction as one of:
+
+* **correct**   — answer matches gold (with paraphrasing tolerance)
+* **missing**   — model refuses or says "I don't know"
+* **incorrect** — model commits to a wrong answer (hallucination)
+
+The truthfulness score `(correct - incorrect) / total` rewards
+calibrated abstention, so the prompts below explicitly *invite* the
+model to refuse when it isn't confident — otherwise the bare-LLM arm
+gets penalised twice (no docs *and* a no-refusal prompt) and the
+comparison stops being fair to the LLM-only baseline.
+
+Three templates, byte-identical instructions:
+
+* ``build_bare_prompt(q)``         — question-only.
+* ``build_long_context_prompt(q, contexts)`` — question + concatenated
+  page extracts, all stuffed into the user message. Mirrors the
+  paper's "straightforward RAG" baseline.
+* ``build_surfsense_prompt(q)``    — question + a hint that retrieval
+  over the question's 5 ingested pages is available; the SurfSense
+  agent itself owns the retrieval step.
+
+The ``Answer:`` line at the end is parsed by ``extract_freeform_answer``
+in the runner, so the format is mandatory.
+"""
+
+from __future__ import annotations
+
+
+_BASE_INSTRUCTIONS = (
+    "You are a careful question-answering assistant. The question is a "
+    "real-world factual question that may be about finance, music, "
+    "movies, sports, or any other domain.\n\n"
+    "Important rules:\n"
+    "1. If the question contains a false premise (an assumption that "
+    "is factually wrong), say so explicitly in your final answer "
+    "rather than answering as if the premise were true.\n"
+    "2. If you are not confident in an answer, prefer saying \"I don't "
+    "know\" over guessing. A wrong commit is penalised more than a "
+    "refusal.\n"
+    "3. Keep the final answer short — a name, a number, a date, a "
+    "phrase. Do not repeat the question.\n\n"
+    "Format your final line EXACTLY as:\n"
+    "Answer: <short answer>\n"
+    "If you don't know, write `Answer: I don't know`."
+)
+
+
+_BARE_TEMPLATE = """\
+{instructions}
+
+Question: {question}
+Question time: {query_time}
+"""
+
+
+_SURFSENSE_TEMPLATE = """\
+{instructions}
+
+You have access to a search index of up to 5 web pages that were
+retrieved for this question. Use the retrieval tool to look up any
+facts you are not confident about. The pages may be partially or fully
+relevant; some may contradict each other (prefer the more authoritative
+or more recent source).
+
+Question: {question}
+Question time: {query_time}
+"""
+
+
+_LONG_CONTEXT_TEMPLATE = """\
+{instructions}
+
+You are given the full text of {n_contexts} web pages that were
+retrieved for this question. Read all of them, then answer. The
+pages may be partially or fully relevant; some may contradict each
+other (prefer the more authoritative or more recent source).
+
+{contexts}
+
+Question: {question}
+Question time: {query_time}
+"""
+
+
+def build_bare_prompt(question: str, *, query_time: str = "") -> str:
+    """Prompt for the no-retrieval baseline arm."""
+
+    return _BARE_TEMPLATE.format(
+        instructions=_BASE_INSTRUCTIONS,
+        question=question.strip(),
+        query_time=query_time.strip() or "unknown",
+    )
+
+
+def build_surfsense_prompt(question: str, *, query_time: str = "") -> str:
+    """Prompt for the SurfSense arm (agent does retrieval itself)."""
+
+    return _SURFSENSE_TEMPLATE.format(
+        instructions=_BASE_INSTRUCTIONS,
+        question=question.strip(),
+        query_time=query_time.strip() or "unknown",
+    )
+
+
+def build_long_context_prompt(
+    question: str,
+    *,
+    contexts: list[tuple[str, str]],
+    query_time: str = "",
+    per_page_char_cap: int = 12_000,
+) -> str:
+    """Prompt for the "stuff all pages into the prompt" baseline.
+
+    ``contexts`` is a list of ``(page_title_or_url, page_text)`` pairs.
+    Each page is truncated at ``per_page_char_cap`` (default 12k chars
+    ≈ 3k tokens) so a 5-page CRAG question fits well under any
+    modern long-context window with room for the question + reasoning.
+    """
+
+    blocks: list[str] = []
+    for idx, (title, text) in enumerate(contexts, start=1):
+        body = (text or "").strip()
+        if len(body) > per_page_char_cap:
+            body = body[:per_page_char_cap].rstrip() + "\n[...truncated...]"
+        title_clean = (title or f"page_{idx}").strip().replace("\n", " ")
+        blocks.append(
+            f"--- PAGE {idx}: {title_clean} ---\n{body}\n"
+        )
+    contexts_block = "\n".join(blocks) if blocks else "(no pages retrieved)"
+    return _LONG_CONTEXT_TEMPLATE.format(
+        instructions=_BASE_INSTRUCTIONS,
+        n_contexts=len(contexts),
+        contexts=contexts_block,
+        question=question.strip(),
+        query_time=query_time.strip() or "unknown",
+    )
+
+
+__all__ = [
+    "build_bare_prompt",
+    "build_long_context_prompt",
+    "build_surfsense_prompt",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
new file mode 100644
index 000000000..d6ba49294
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
@@ -0,0 +1,1053 @@
+"""CRAG runner — Bare LLM vs Long-Context LLM vs SurfSense.
+
+Three arms run paired on every question in the sample. All three
+answer with the same model (CRAG is a head-to-head benchmark, not a
+cost-arbitrage benchmark). The arms differ only in *what they see*:
+
+1. ``bare_llm``      — chat completion with the question only
+   (paper baseline ≤34%).
+2. ``long_context``  — same model, but the user message also includes
+   the extracted text of all 5 web pages (paper baseline ~44%).
+3. ``surfsense``     — POST ``/api/v1/new_chat`` with retrieval scoped
+   to the question's 5 ingested pages via ``mentioned_document_ids``.
+   The agent retrieves and reasons; we only grade the final answer.
+
+Grading: 3-class CRAG rubric — correct/missing/incorrect — with
+deterministic shortcuts and an LLM-as-judge fallback. Headline is
+the **truthfulness score** ``(#correct - #incorrect) / total``, the
+metric the CRAG paper and KDD Cup 2024 leaderboard use.
+
+We keep paired stats (McNemar + bootstrap CI) on the **correct**
+flag for each arm pair (long_context vs bare, surfsense vs
+long_context, surfsense vs bare) so the report can call out exactly
+where the lift comes from. Per-domain and per-question-type breakdowns
+surface where SurfSense beats long-context (e.g. multi-hop / set
+questions where retrieval-then-reason wins over stuff-it-all-in).
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, BareLlmArm, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+)
+from ....core.metrics.comparison import (
+    bootstrap_delta_ci,
+    mcnemar_test,
+    paired_aggregate,
+)
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci
+from ....core.parse.freeform_answer import extract_freeform_answer
+from ....core.providers.openrouter_chat import OpenRouterChatProvider
+from ....core.registry import ReportSection, RunArtifact, RunContext
+from ....core.scenarios import format_scenario_md
+from .grader import (
+    CragGradeResult,
+    CragGradeRow,
+    CragJudgeConfig,
+    CragLlmJudge,
+    grade_many,
+)
+from .ingest import read_page_markdown
+from .prompt import (
+    build_bare_prompt,
+    build_long_context_prompt,
+    build_surfsense_prompt,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Question shape (post-ingest)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class CragRunnerQuestion:
+    qid: str
+    raw_index: int
+    question: str
+    gold_answer: str
+    alt_answers: list[str]
+    domain: str
+    question_type: str
+    static_or_dynamic: str
+    popularity: str
+    query_time: str
+    page_filenames: list[str]
+    document_ids: list[int]
+    missing_pages: list[str] = field(default_factory=list)
+
+
+def _load_doc_map(map_path: Path) -> tuple[list[dict[str, Any]], dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    settings: dict[str, Any] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if is_settings_header(row):
+                settings = dict(row["__settings__"])
+                continue
+            rows.append(row)
+    return rows, settings
+
+
+def _filter_questions(
+    rows: list[dict[str, Any]],
+    *,
+    sample_n: int | None,
+    domain_filter: str | None,
+    qtype_filter: str | None,
+) -> list[CragRunnerQuestion]:
+    out: list[CragRunnerQuestion] = []
+    for row in rows:
+        domain = str(row.get("domain") or "").lower()
+        qtype = str(row.get("question_type") or "").lower()
+        if domain_filter and domain_filter != domain:
+            continue
+        if qtype_filter and qtype_filter not in qtype:
+            continue
+        out.append(CragRunnerQuestion(
+            qid=str(row.get("qid") or "").strip(),
+            raw_index=int(row.get("raw_index") or 0),
+            question=str(row.get("question") or "").strip(),
+            gold_answer=str(row.get("gold_answer") or "").strip(),
+            alt_answers=list(row.get("alt_answers") or []),
+            domain=domain,
+            question_type=qtype,
+            static_or_dynamic=str(row.get("static_or_dynamic") or "").lower(),
+            popularity=str(row.get("popularity") or "").lower(),
+            query_time=str(row.get("query_time") or "").strip(),
+            page_filenames=list(row.get("page_filenames") or []),
+            document_ids=list(row.get("document_ids") or []),
+            missing_pages=list(row.get("missing_pages") or []),
+        ))
+    out.sort(key=lambda q: q.raw_index)
+    if sample_n is not None and sample_n > 0:
+        out = out[:sample_n]
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Concurrency helper
+# ---------------------------------------------------------------------------
+
+
+async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+# ---------------------------------------------------------------------------
+# Benchmark
+# ---------------------------------------------------------------------------
+
+
+_DESCRIPTION = (
+    "CRAG (Comprehensive RAG Benchmark, Meta KDD Cup 2024) — three "
+    "arms (Bare LLM / Long-Context LLM / SurfSense) over the same "
+    "5-page-per-question CRAG corpus. Tests competitive RAG vs naive "
+    "context-stuffing; CRAG truthfulness score is the headline metric."
+)
+
+
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=False,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+class CragBenchmark:
+    """3-arm CRAG runner: bare vs long-context vs SurfSense."""
+
+    suite: str = "research"
+    name: str = "crag"
+    headline: bool = True
+    description: str = _DESCRIPTION
+
+    # Subclasses (e.g. Task 3) override these without re-implementing run().
+    doc_map_filename: str = "crag_doc_map.jsonl"
+    # 0 = use ALL pages in the long-context arm. Task 3 defaults to 5
+    # so the long-context arm models the realistic "stuff the top-5
+    # search results into the prompt" baseline rather than blowing
+    # past the 128k-token context window with all 50 pages.
+    default_long_context_top_n: int = 0
+    pages_per_question_label: str = "5 pages"
+    ingest_hint: str = (
+        "`python -m surfsense_evals ingest research crag --n-questions 200`"
+    )
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--n", dest="sample_n", type=int, default=None,
+            help="Run only the first N questions after filters.",
+        )
+        parser.add_argument(
+            "--domain", dest="domain_filter", default=None,
+            help="Filter to a single CRAG domain (finance|music|movie|sports|open).",
+        )
+        parser.add_argument(
+            "--qtype", dest="qtype_filter", default=None,
+            help=(
+                "Filter to questions whose question_type contains this "
+                "substring (case-insensitive). Examples: 'multi-hop', "
+                "'comparison', 'false_premise'."
+            ),
+        )
+        parser.add_argument(
+            "--concurrency", type=int, default=4,
+            help="Parallel question workers per arm.",
+        )
+        parser.add_argument(
+            "--max-output-tokens", type=int, default=512,
+            help="Cap on completion length for the chat-completion arms.",
+        )
+        parser.add_argument(
+            "--per-page-char-cap", dest="per_page_char_cap", type=int, default=12_000,
+            help="Long-context arm: max chars per page before truncation (default 12k).",
+        )
+        parser.add_argument(
+            "--long-context-top-n-pages", dest="long_context_top_n_pages",
+            type=int, default=self.default_long_context_top_n,
+            help=(
+                "Long-context arm: keep only the first N pages from the "
+                "question's candidate list (0 = use all). Task 3 defaults "
+                "to 5 (the realistic 'naive RAG' top-K baseline)."
+            ),
+        )
+        parser.add_argument(
+            "--skip-bare", dest="skip_bare", action="store_true",
+            help="Skip the bare-LLM arm (saves cost on re-runs).",
+        )
+        parser.add_argument(
+            "--skip-long-context", dest="skip_long_context", action="store_true",
+            help="Skip the long-context arm.",
+        )
+        parser.add_argument(
+            "--skip-surfsense", dest="skip_surfsense", action="store_true",
+            help="Skip the SurfSense arm (useful when iterating on the LLM arms only).",
+        )
+        parser.add_argument(
+            "--no-mention-scope", dest="no_mention_scope", action="store_true",
+            help=(
+                "SurfSense arm: don't pass mentioned_document_ids; let "
+                "the agent retrieve over the entire SearchSpace. Default "
+                "is to scope to the question's 5 ingested pages "
+                "(matches CRAG protocol)."
+            ),
+        )
+        parser.add_argument(
+            "--no-judge", dest="no_judge", action="store_true",
+            help="Disable the LLM-as-judge fallback grader.",
+        )
+        parser.add_argument(
+            "--judge-model", dest="judge_model",
+            default="anthropic/claude-sonnet-4.5",
+            help="OpenRouter slug for the LLM judge.",
+        )
+        parser.add_argument(
+            "--judge-concurrency", dest="judge_concurrency", type=int, default=4,
+            help="Parallel judge calls.",
+        )
+        # Ingest knobs
+        parser.add_argument(
+            "--n-questions", dest="n_questions", type=int, default=None,
+            help="(ingest only) cap on number of questions to materialise + ingest.",
+        )
+        parser.add_argument(
+            "--upload-batch-size", dest="upload_batch_size", type=int, default=16,
+            help="(ingest only) markdown files per fileupload call.",
+        )
+        parser.add_argument(
+            "--skip-upload", dest="skip_upload", action="store_true",
+            help="(ingest only) extract pages locally but don't push to SurfSense.",
+        )
+        parser.add_argument(
+            "--overwrite-extract", dest="overwrite_extract", action="store_true",
+            help="(ingest only) re-run trafilatura even when cached markdown exists.",
+        )
+        parser.add_argument(
+            "--sample-seed", dest="sample_seed", type=int, default=17,
+            help="(ingest only) RNG seed for the stratified sample.",
+        )
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            n_questions=opts.get("n_questions"),
+            upload_batch_size=int(opts.get("upload_batch_size") or 16),
+            skip_upload=bool(opts.get("skip_upload", False)),
+            overwrite_extract=bool(opts.get("overwrite_extract", False)),
+            settings=settings,
+            sample_seed=int(opts.get("sample_seed") or 17),
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        sample_n = opts.get("sample_n")
+        domain_filter = (opts.get("domain_filter") or "").strip().lower() or None
+        qtype_filter = (opts.get("qtype_filter") or "").strip().lower() or None
+        concurrency = int(opts.get("concurrency") or 4)
+        max_output_tokens = int(opts.get("max_output_tokens") or 512)
+        per_page_char_cap = int(opts.get("per_page_char_cap") or 12_000)
+        long_context_top_n_pages = int(
+            opts.get("long_context_top_n_pages")
+            if opts.get("long_context_top_n_pages") is not None
+            else self.default_long_context_top_n
+        )
+        skip_bare = bool(opts.get("skip_bare"))
+        skip_long_context = bool(opts.get("skip_long_context"))
+        skip_surfsense = bool(opts.get("skip_surfsense"))
+        no_mention_scope = bool(opts.get("no_mention_scope"))
+        no_judge = bool(opts.get("no_judge"))
+        judge_model = str(opts.get("judge_model") or "anthropic/claude-sonnet-4.5")
+        judge_concurrency = int(opts.get("judge_concurrency") or 4)
+
+        bench_dir = ctx.benchmark_data_dir()
+        map_path = ctx.maps_dir() / self.doc_map_filename
+        if not map_path.exists():
+            raise RuntimeError(
+                f"{self.name} not ingested for this suite. Run "
+                f"{self.ingest_hint} first."
+            )
+
+        rows, ingest_settings = _load_doc_map(map_path)
+        questions = _filter_questions(
+            rows,
+            sample_n=sample_n,
+            domain_filter=domain_filter,
+            qtype_filter=qtype_filter,
+        )
+        if not questions:
+            raise RuntimeError(
+                "No CRAG questions matched the filters; broaden --n / --domain / --qtype."
+            )
+        logger.info("CRAG: scheduled %d questions", len(questions))
+
+        api_key = os.environ.get("OPENROUTER_API_KEY")
+        if not api_key and not (skip_bare and skip_long_context):
+            raise RuntimeError(
+                "OPENROUTER_API_KEY env var is required for the bare / long-context arms."
+            )
+
+        bare_arm = long_context_arm = surf_arm = None
+        chat_provider: OpenRouterChatProvider | None = None
+        if not (skip_bare and skip_long_context):
+            chat_provider = OpenRouterChatProvider(
+                api_key=api_key or "",
+                base_url=ctx.config.openrouter_base_url,
+                model=ctx.native_arm_model,
+            )
+        if not skip_bare and chat_provider is not None:
+            bare_arm = BareLlmArm(
+                provider=chat_provider,
+                max_output_tokens=max_output_tokens,
+                name="bare_llm",
+            )
+        if not skip_long_context and chat_provider is not None:
+            long_context_arm = BareLlmArm(
+                provider=chat_provider,
+                max_output_tokens=max_output_tokens,
+                name="long_context",
+            )
+        if not skip_surfsense:
+            surf_arm = SurfSenseArm(
+                client=ctx.new_chat_client(),
+                search_space_id=ctx.search_space_id,
+                ephemeral_threads=True,
+            )
+
+        judge: CragLlmJudge | None = None
+        if not no_judge:
+            if not api_key:
+                logger.warning("CRAG: --no-judge implied (no OPENROUTER_API_KEY for judge)")
+            else:
+                judge = CragLlmJudge(config=CragJudgeConfig(
+                    api_key=api_key,
+                    model=judge_model,
+                    base_url=ctx.config.openrouter_base_url,
+                    concurrency=judge_concurrency,
+                ))
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+
+        async def _bare_one(q: CragRunnerQuestion) -> ArmResult:
+            assert bare_arm is not None
+            return await bare_arm.answer(_make_bare_request(q, max_output_tokens))
+
+        async def _long_context_one(q: CragRunnerQuestion) -> ArmResult:
+            assert long_context_arm is not None
+            return await long_context_arm.answer(
+                _make_long_context_request(
+                    q,
+                    bench_dir,
+                    max_output_tokens,
+                    per_page_char_cap,
+                    top_n_pages=long_context_top_n_pages,
+                )
+            )
+
+        async def _surf_one(q: CragRunnerQuestion) -> ArmResult:
+            assert surf_arm is not None
+            return await surf_arm.answer(
+                _make_surfsense_request(q, scope_to_pages=not no_mention_scope)
+            )
+
+        # Run all enabled arms concurrently. Each arm is itself
+        # internally concurrency-bounded.
+        tasks: list[Any] = []
+        if bare_arm is not None:
+            tasks.append(_gather_with_limit((_bare_one(q) for q in questions), concurrency=concurrency))
+        else:
+            tasks.append(_make_skipped_results(questions, "bare_llm"))
+        if long_context_arm is not None:
+            tasks.append(_gather_with_limit((_long_context_one(q) for q in questions), concurrency=concurrency))
+        else:
+            tasks.append(_make_skipped_results(questions, "long_context"))
+        if surf_arm is not None:
+            tasks.append(_gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency))
+        else:
+            tasks.append(_make_skipped_results(questions, "surfsense"))
+
+        bare_results, long_context_results, surf_results = await asyncio.gather(*tasks)
+
+        bare_grades = await _grade_results(questions, bare_results, judge=judge) if bare_arm else _empty_grades(questions)
+        lc_grades = await _grade_results(questions, long_context_results, judge=judge) if long_context_arm else _empty_grades(questions)
+        surf_grades = await _grade_results(questions, surf_results, judge=judge) if surf_arm else _empty_grades(questions)
+
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, b_res, l_res, s_res, b_g, l_g, s_g in zip(
+                questions,
+                bare_results, long_context_results, surf_results,
+                bare_grades, lc_grades, surf_grades,
+                strict=False,
+            ):
+                meta = {
+                    "qid": q.qid,
+                    "raw_index": q.raw_index,
+                    "domain": q.domain,
+                    "question_type": q.question_type,
+                    "static_or_dynamic": q.static_or_dynamic,
+                    "popularity": q.popularity,
+                    "n_pages": len(q.page_filenames),
+                    "n_doc_ids": len(q.document_ids),
+                    "gold": q.gold_answer,
+                    "alt_answers": q.alt_answers,
+                }
+                for res, grade in (
+                    (b_res, b_g), (l_res, l_g), (s_res, s_g),
+                ):
+                    fh.write(json.dumps({
+                        **meta,
+                        **res.to_jsonl(),
+                        "graded": grade.to_dict(),
+                    }) + "\n")
+
+        metrics = _compute_metrics(
+            questions=questions,
+            bare_results=bare_results, long_context_results=long_context_results, surf_results=surf_results,
+            bare_grades=bare_grades, lc_grades=lc_grades, surf_grades=surf_grades,
+            arms_active={
+                "bare_llm": bare_arm is not None,
+                "long_context": long_context_arm is not None,
+                "surfsense": surf_arm is not None,
+            },
+        )
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "concurrency": concurrency,
+                "domain_filter": domain_filter,
+                "qtype_filter": qtype_filter,
+                "no_mention_scope": no_mention_scope,
+                "no_judge": no_judge,
+                "judge_model": judge_model if not no_judge else None,
+                "scenario": ctx.scenario,
+                "provider_model": ctx.provider_model,
+                "native_arm_model": ctx.native_arm_model,
+                "vision_provider_model": ctx.vision_provider_model,
+                "agent_llm_id": ctx.agent_llm_id,
+                "ingest_settings": ingest_settings,
+                "per_page_char_cap": per_page_char_cap,
+                "long_context_top_n_pages": long_context_top_n_pages,
+                "pages_per_question_label": self.pages_per_question_label,
+                "max_output_tokens": max_output_tokens,
+                "arms_active": {
+                    "bare_llm": bare_arm is not None,
+                    "long_context": long_context_arm is not None,
+                    "surfsense": surf_arm is not None,
+                },
+            },
+        )
+
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps({
+                "suite": self.suite,
+                "benchmark": self.name,
+                "raw_path": "raw.jsonl",
+                "metrics": metrics,
+                "extra": artifact.extra,
+            }, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="CRAG — Bare LLM vs Long-Context LLM vs SurfSense",
+                headline=True,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        bare = m.get("bare_llm", {})
+        lc = m.get("long_context", {})
+        surf = m.get("surfsense", {})
+        deltas = m.get("deltas", {})
+        per_domain = m.get("per_domain", {})
+        per_qtype = m.get("per_question_type", {})
+        extra = latest.extra
+
+        body_lines: list[str] = []
+        body_lines.append(
+            f"- Sample size: {extra.get('n_questions', '?')} questions "
+            f"(domain filter: `{extra.get('domain_filter') or 'none'}`, "
+            f"qtype filter: `{extra.get('qtype_filter') or 'none'}`, "
+            f"judge: `{extra.get('judge_model') or 'deterministic-only'}`)."
+        )
+        body_lines.append(format_scenario_md(extra))
+        body_lines.append(format_ingest_settings_md(extra.get("ingest_settings")))
+        active = extra.get("arms_active") or {}
+        if not active.get("bare_llm", True):
+            body_lines.append("- Bare-LLM arm: SKIPPED.")
+        else:
+            body_lines.append(
+                f"- Bare-LLM arm (`{extra.get('native_arm_model') or '?'}`, no retrieval):"
+            )
+            body_lines.append(_arm_summary_lines(bare, indent="  "))
+        if not active.get("long_context", True):
+            body_lines.append("- Long-context arm: SKIPPED.")
+        else:
+            top_n = int(extra.get("long_context_top_n_pages") or 0)
+            page_phrase = (
+                f"top-{top_n} of {extra.get('pages_per_question_label') or 'pages'}"
+                if top_n > 0
+                else f"all of {extra.get('pages_per_question_label') or 'pages'}"
+            )
+            body_lines.append(
+                f"- Long-context arm (`{extra.get('native_arm_model') or '?'}`, "
+                f"{page_phrase} stuffed into prompt; per-page cap "
+                f"{extra.get('per_page_char_cap', 12_000):,} chars):"
+            )
+            body_lines.append(_arm_summary_lines(lc, indent="  "))
+        if not active.get("surfsense", True):
+            body_lines.append("- SurfSense arm: SKIPPED.")
+        else:
+            scope_phrase = (
+                "whole SearchSpace"
+                if extra.get("no_mention_scope")
+                else f"per-question {extra.get('pages_per_question_label') or 'pages'}"
+            )
+            body_lines.append(
+                f"- SurfSense arm (`{extra.get('provider_model', '?')}`, retrieval over "
+                f"{scope_phrase}):"
+            )
+            body_lines.append(_arm_summary_lines(surf, indent="  "))
+
+        body_lines.append("- Headline truthfulness scores (CRAG paper rubric):")
+        for label, key in (
+            ("Bare LLM", "bare_llm"), ("Long-Context", "long_context"), ("SurfSense", "surfsense"),
+        ):
+            d = m.get(key, {})
+            body_lines.append(
+                f"  - {label}: score={_signed_pct(d.get('truthfulness_score'))}, "
+                f"correct={_pct(d.get('correct_rate'))}, "
+                f"missing={_pct(d.get('missing_rate'))}, "
+                f"incorrect={_pct(d.get('incorrect_rate'))}"
+            )
+
+        if deltas:
+            body_lines.append("- Pairwise deltas (paired):")
+            for label, key in (
+                ("SurfSense vs Bare", "surfsense_vs_bare"),
+                ("SurfSense vs Long-Context", "surfsense_vs_long_context"),
+                ("Long-Context vs Bare", "long_context_vs_bare"),
+            ):
+                d = deltas.get(key)
+                if not d:
+                    continue
+                body_lines.append(
+                    f"  - {label}: accuracy {_pp(d.get('accuracy_pp'))} pp, "
+                    f"truthfulness {_pp(d.get('truthfulness_score_pp'))} pp "
+                    f"(McNemar p={_fmt(d.get('mcnemar_p_value'), 4)}, "
+                    f"method={d.get('mcnemar_method')}; bootstrap CI on accuracy "
+                    f"[{_pp(d.get('bootstrap_ci_low'))}pp, {_pp(d.get('bootstrap_ci_high'))}pp])"
+                )
+
+        if per_domain:
+            body_lines.append("- Per-domain truthfulness score (active arms):")
+            for domain in sorted(per_domain.keys()):
+                row = per_domain[domain]
+                pieces: list[str] = [f"  - {domain} (n={row.get('n')}):"]
+                for arm in ("bare_llm", "long_context", "surfsense"):
+                    if arm not in row:
+                        continue
+                    pieces.append(
+                        f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}"
+                    )
+                body_lines.append(" ".join(pieces))
+
+        if per_qtype:
+            body_lines.append("- Per-question-type truthfulness score (active arms):")
+            for qtype in sorted(per_qtype.keys()):
+                row = per_qtype[qtype]
+                pieces = [f"  - {qtype} (n={row.get('n')}):"]
+                for arm in ("bare_llm", "long_context", "surfsense"):
+                    if arm not in row:
+                        continue
+                    pieces.append(
+                        f"{arm}={_signed_pct(row[arm].get('truthfulness_score'))}"
+                    )
+                body_lines.append(" ".join(pieces))
+
+        return ReportSection(
+            title="CRAG — Bare LLM vs Long-Context LLM vs SurfSense",
+            headline=True,
+            body_md="\n".join(body_lines),
+            body_json=m,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Per-question helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_bare_request(q: CragRunnerQuestion, max_tokens: int) -> ArmRequest:
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=build_bare_prompt(q.question, query_time=q.query_time),
+        options={"max_tokens": max_tokens},
+    )
+
+
+def _make_long_context_request(
+    q: CragRunnerQuestion,
+    bench_dir: Path,
+    max_tokens: int,
+    per_page_char_cap: int,
+    *,
+    top_n_pages: int = 0,
+) -> ArmRequest:
+    # The CRAG search_results list is already ranked top-K from the
+    # original web search at query_time; slicing the prefix is the
+    # honest "naive RAG: take the top-K results" baseline.
+    page_iter = q.page_filenames
+    if top_n_pages and top_n_pages > 0:
+        page_iter = page_iter[:top_n_pages]
+    contexts: list[tuple[str, str]] = []
+    for fn in page_iter:
+        text = read_page_markdown(bench_dir, fn) or ""
+        if not text.strip():
+            continue
+        # Use the filename stem as a stable title fallback (URLs are
+        # already in the markdown body's "Source:" header line).
+        contexts.append((Path(fn).stem, text))
+    prompt = build_long_context_prompt(
+        q.question,
+        contexts=contexts,
+        query_time=q.query_time,
+        per_page_char_cap=per_page_char_cap,
+    )
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=prompt,
+        options={"max_tokens": max_tokens},
+    )
+
+
+def _make_surfsense_request(q: CragRunnerQuestion, *, scope_to_pages: bool) -> ArmRequest:
+    mentions: list[int] | None = None
+    if scope_to_pages and q.document_ids:
+        mentions = list(q.document_ids)
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=build_surfsense_prompt(q.question, query_time=q.query_time),
+        mentioned_document_ids=mentions,
+    )
+
+
+async def _grade_results(
+    questions: list[CragRunnerQuestion],
+    results: list[ArmResult],
+    *,
+    judge: CragLlmJudge | None,
+) -> list[CragGradeResult]:
+    rows: list[CragGradeRow] = []
+    for q, r in zip(questions, results, strict=False):
+        pred = extract_freeform_answer(r.raw_text or "")
+        rows.append(CragGradeRow(
+            qid=q.qid,
+            question=q.question,
+            gold=q.gold_answer,
+            alt_answers=q.alt_answers,
+            pred=pred,
+            question_type=q.question_type,
+        ))
+    return await grade_many(rows=rows, judge=judge)
+
+
+def _empty_grades(questions: list[CragRunnerQuestion]) -> list[CragGradeResult]:
+    return [
+        CragGradeResult(grade="missing", score=0, method="skipped_arm")
+        for _ in questions
+    ]
+
+
+async def _make_skipped_results(
+    questions: list[CragRunnerQuestion], arm_name: str,
+) -> list[ArmResult]:
+    """Stand-in results so downstream code can assume parallel lists."""
+
+    return [
+        ArmResult(arm=arm_name, question_id=q.qid, raw_text="", error="skipped")
+        for q in questions
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Metrics aggregation
+# ---------------------------------------------------------------------------
+
+
+def _arm_truthfulness(grades: list[CragGradeResult]) -> dict[str, Any]:
+    """Per-arm headline numbers — accuracy + 3-class rates + truthfulness."""
+
+    n = len(grades) or 1
+    n_correct = sum(g.correct for g in grades)
+    n_missing = sum(g.missing for g in grades)
+    n_incorrect = sum(g.incorrect for g in grades)
+    return {
+        "n_total": len(grades),
+        "n_correct": n_correct,
+        "n_missing": n_missing,
+        "n_incorrect": n_incorrect,
+        "correct_rate": n_correct / n,
+        "missing_rate": n_missing / n,
+        "incorrect_rate": n_incorrect / n,
+        "truthfulness_score": (n_correct - n_incorrect) / n,
+    }
+
+
+def _compute_metrics(
+    *,
+    questions: list[CragRunnerQuestion],
+    bare_results: list[ArmResult],
+    long_context_results: list[ArmResult],
+    surf_results: list[ArmResult],
+    bare_grades: list[CragGradeResult],
+    lc_grades: list[CragGradeResult],
+    surf_grades: list[CragGradeResult],
+    arms_active: dict[str, bool],
+) -> dict[str, Any]:
+    bare_correct = [g.correct for g in bare_grades]
+    lc_correct = [g.correct for g in lc_grades]
+    surf_correct = [g.correct for g in surf_grades]
+
+    bare_acc = accuracy_with_wilson_ci(sum(bare_correct), len(bare_correct))
+    lc_acc = accuracy_with_wilson_ci(sum(lc_correct), len(lc_correct))
+    surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct))
+
+    bare_t = _arm_truthfulness(bare_grades)
+    lc_t = _arm_truthfulness(lc_grades)
+    surf_t = _arm_truthfulness(surf_grades)
+
+    def _arm_block(
+        results: list[ArmResult],
+        acc: Any,
+        truthfulness: dict[str, Any],
+    ) -> dict[str, Any]:
+        costs = [float(r.cost_micros) for r in results]
+        latencies = [float(r.latency_ms) for r in results]
+        ins = [float(r.input_tokens) for r in results]
+        outs = [float(r.output_tokens) for r in results]
+        cost_agg = paired_aggregate(costs)
+        lat_agg = paired_aggregate(latencies)
+        return {
+            **acc.to_dict(),
+            **truthfulness,
+            "cost_micros_mean": cost_agg.mean,
+            "cost_micros_median": cost_agg.median,
+            "latency_ms_mean": lat_agg.mean,
+            "latency_ms_median": lat_agg.median,
+            "latency_ms_p95": lat_agg.p95,
+            "input_tokens_mean": (sum(ins) / len(ins)) if ins else 0.0,
+            "output_tokens_mean": (sum(outs) / len(outs)) if outs else 0.0,
+        }
+
+    out: dict[str, Any] = {
+        "bare_llm": _arm_block(bare_results, bare_acc, bare_t),
+        "long_context": _arm_block(long_context_results, lc_acc, lc_t),
+        "surfsense": _arm_block(surf_results, surf_acc, surf_t),
+    }
+
+    deltas: dict[str, Any] = {}
+    for label, ref_correct, ref_t, chal_correct, chal_t, both_active in (
+        ("surfsense_vs_bare", bare_correct, bare_t, surf_correct, surf_t,
+         arms_active.get("bare_llm") and arms_active.get("surfsense")),
+        ("surfsense_vs_long_context", lc_correct, lc_t, surf_correct, surf_t,
+         arms_active.get("long_context") and arms_active.get("surfsense")),
+        ("long_context_vs_bare", bare_correct, bare_t, lc_correct, lc_t,
+         arms_active.get("bare_llm") and arms_active.get("long_context")),
+    ):
+        if not both_active:
+            continue
+        mc = mcnemar_test(ref_correct, chal_correct)
+        boot = bootstrap_delta_ci(ref_correct, chal_correct, n_resamples=2000)
+        deltas[label] = {
+            "accuracy_pp": 100.0 * (sum(chal_correct) - sum(ref_correct)) / max(1, len(chal_correct)),
+            "truthfulness_score_pp": 100.0 * (chal_t["truthfulness_score"] - ref_t["truthfulness_score"]),
+            "mcnemar_p_value": mc.p_value,
+            "mcnemar_method": mc.method,
+            "mcnemar_b_ref_only": mc.b,
+            "mcnemar_c_challenger_only": mc.c,
+            "bootstrap_ci_low": 100.0 * boot.ci_low,
+            "bootstrap_ci_high": 100.0 * boot.ci_high,
+        }
+    out["deltas"] = deltas
+
+    out["per_domain"] = _per_facet_truthfulness(
+        questions, bare_grades, lc_grades, surf_grades,
+        arms_active=arms_active,
+        key_fn=lambda q: q.domain or "(unspecified)",
+    )
+    out["per_question_type"] = _per_facet_truthfulness(
+        questions, bare_grades, lc_grades, surf_grades,
+        arms_active=arms_active,
+        key_fn=lambda q: q.question_type or "(unspecified)",
+    )
+
+    out["grader_methods"] = {
+        "bare_llm": _count_methods(bare_grades) if arms_active.get("bare_llm") else {},
+        "long_context": _count_methods(lc_grades) if arms_active.get("long_context") else {},
+        "surfsense": _count_methods(surf_grades) if arms_active.get("surfsense") else {},
+    }
+    return out
+
+
+def _per_facet_truthfulness(
+    questions: list[CragRunnerQuestion],
+    bare_grades: list[CragGradeResult],
+    lc_grades: list[CragGradeResult],
+    surf_grades: list[CragGradeResult],
+    *,
+    arms_active: dict[str, bool],
+    key_fn: Any,
+) -> dict[str, Any]:
+    """Bucket truthfulness scores by ``key_fn(q)``."""
+
+    buckets: dict[str, dict[str, list[CragGradeResult]]] = {}
+    for q, b, l, s in zip(questions, bare_grades, lc_grades, surf_grades, strict=False):
+        key = key_fn(q)
+        bucket = buckets.setdefault(key, {"bare_llm": [], "long_context": [], "surfsense": []})
+        bucket["bare_llm"].append(b)
+        bucket["long_context"].append(l)
+        bucket["surfsense"].append(s)
+    out: dict[str, Any] = {}
+    for key, arms in buckets.items():
+        row: dict[str, Any] = {"n": len(arms["bare_llm"])}
+        for arm_name, grades in arms.items():
+            if not arms_active.get(arm_name):
+                continue
+            row[arm_name] = _arm_truthfulness(grades)
+        out[key] = row
+    return out
+
+
+def _count_methods(grades: list[CragGradeResult]) -> dict[str, int]:
+    out: dict[str, int] = {}
+    for g in grades:
+        out[g.method] = out.get(g.method, 0) + 1
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Tiny formatting helpers
+# ---------------------------------------------------------------------------
+
+
+def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str:
+    if not d:
+        return f"{indent}(no data)"
+    acc = d.get("accuracy", 0.0)
+    low = d.get("ci_low", 0.0)
+    high = d.get("ci_high", 0.0)
+    lines = [
+        f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)",
+        f"{indent}- 3-class: correct={d.get('correct_rate', 0)*100:.1f}%, "
+        f"missing={d.get('missing_rate', 0)*100:.1f}%, "
+        f"incorrect={d.get('incorrect_rate', 0)*100:.1f}%",
+        f"{indent}- Truthfulness score (correct - incorrect)/total: "
+        f"{d.get('truthfulness_score', 0)*100:+.1f}%",
+        f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), "
+        f"${_dollars(d.get('cost_micros_median'))} (median)",
+        f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, "
+        f"p95 {_ms_to_s(d.get('latency_ms_p95'))}",
+    ]
+    if d.get("input_tokens_mean") or d.get("output_tokens_mean"):
+        lines.append(
+            f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, "
+            f"out {d.get('output_tokens_mean', 0):.0f}"
+        )
+    return "\n".join(lines)
+
+
+def _dollars(micros: Any) -> str:
+    if micros is None:
+        return "?"
+    try:
+        return f"{(float(micros) / 1_000_000):.4f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _ms_to_s(ms: Any) -> str:
+    if ms is None:
+        return "?"
+    try:
+        return f"{float(ms) / 1000:.1f}s"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pp(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.1f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pct(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value)*100:.1f}%"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _signed_pct(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value)*100:+.1f}%"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _fmt(value: Any, ndigits: int) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):.{ndigits}f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+_TASK3_DESCRIPTION = (
+    "CRAG Task 3 (Meta KDD Cup 2024) — same 3 arms but the corpus per "
+    "question now has **50 candidate web pages** (vs 5 in Tasks 1 & 2). "
+    "The long-context arm uses only the top-5 (the realistic naive-RAG "
+    "baseline); SurfSense retrieves over all 50, where its rerank "
+    "becomes the actual contribution."
+)
+
+
+class CragTask3Benchmark(CragBenchmark):
+    """3-arm CRAG runner over Task 3 (50 pages per question).
+
+    Reuses the entire Task 1/2 runtime (grader, prompt, metrics,
+    reporting) — the only deltas are: the doc map filename, the
+    long-context arm's default page cap (5 instead of all 50), and
+    the ingest entrypoint (4-part archive instead of single bz2).
+    """
+
+    name: str = "crag_t3"
+    description: str = _TASK3_DESCRIPTION
+    doc_map_filename: str = "crag_t3_doc_map.jsonl"
+    default_long_context_top_n: int = 5
+    pages_per_question_label: str = "50 pages"
+    ingest_hint: str = (
+        "`python -m surfsense_evals ingest research crag_t3 --n-questions 50` "
+        "(after `python scripts/download_crag_task3.py`)"
+    )
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        # Local import: keep dataset_task3's lazy-streaming module out
+        # of the import graph until someone actually wants Task 3.
+        from .ingest_task3 import run_ingest_task3
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest_task3(
+            ctx,
+            n_questions=opts.get("n_questions"),
+            upload_batch_size=int(opts.get("upload_batch_size") or 16),
+            skip_upload=bool(opts.get("skip_upload", False)),
+            overwrite_extract=bool(opts.get("overwrite_extract", False)),
+            settings=settings,
+            sample_seed=int(opts.get("sample_seed") or 17),
+            parse_cap=opts.get("parse_cap"),
+        )
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        super().add_run_args(parser)
+        parser.add_argument(
+            "--parse-cap", dest="parse_cap", type=int, default=None,
+            help=(
+                "(ingest only) Hard cap on rows parsed from the streaming "
+                "Task 3 archive before stratified sampling. Default: "
+                "max(2000, 10 * n_questions). Lower = less decompression."
+            ),
+        )
+
+
+__all__ = ["CragBenchmark", "CragRunnerQuestion", "CragTask3Benchmark"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/__init__.py
new file mode 100644
index 000000000..4e556cd84
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/__init__.py
@@ -0,0 +1,29 @@
+"""FRAMES — multi-hop Wikipedia retrieval & reasoning (google/frames-benchmark).
+
+Source: https://huggingface.co/datasets/google/frames-benchmark
+Paper:  https://arxiv.org/abs/2409.12941 (Krishna et al., 2024)
+
+* 824 multi-hop questions, each requiring 2-15 Wikipedia articles
+* 5 reasoning types: numerical, tabular, multiple constraints,
+  temporal, post-processing
+* Published Gemini-Pro-1.5 baselines:
+  - Naive prompting (no retrieval):    40.8%
+  - BM25, top-4:                       47.4%
+  - Multi-step retrieval & reasoning:  66.0%
+  - Oracle retrieval (gold articles):  72.9%
+
+This is the benchmark that *finally* puts SurfSense's strongest claim
+on trial: cross-document iterative retrieval. The harness ingests
+every Wikipedia article referenced by any question in the run sample
+into a single SearchSpace; SurfSense answers without
+``mentioned_document_ids`` so its agent has to actually retrieve.
+The bare-LLM arm answers from the prompt only (the published 40.8%
+baseline number).
+"""
+
+from __future__ import annotations
+
+from ....core import registry as _registry
+from .runner import FramesBenchmark
+
+_registry.register(FramesBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/dataset.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/dataset.py
new file mode 100644
index 000000000..c3b6b878e
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/dataset.py
@@ -0,0 +1,174 @@
+"""FRAMES dataset loader — download ``test.tsv`` from HF and parse rows.
+
+The HF repo (``google/frames-benchmark``) ships a single tab-separated
+file at ``test.tsv`` (824 rows). Columns of interest for us:
+
+* unnamed first column → row index (``id`` we synthesise as ``Q000``..)
+* ``Prompt``  → the question (free-text, often multi-clause)
+* ``Answer``  → gold answer (short string: name, number, year, ...)
+* ``wikipedia_link_1`` ... ``wikipedia_link_11+`` → sparse per-question
+  link cells (we ignore in favour of the consolidated column below).
+* ``reasoning_types`` → pipe-separated tags (``"Numerical reasoning |
+  Tabular reasoning | Multiple constraints"``)
+* ``wiki_links`` → Python-list literal of every URL the question relies
+  on, e.g. ``"['https://en.wikipedia.org/wiki/...', '...']"``
+
+We use ``wiki_links`` (already deduplicated per row) and
+``ast.literal_eval`` to materialise it. The legacy
+``wikipedia_link_*`` columns are kept around only so a curious
+operator can compare cell-vs-list if upstream ever drift apart.
+"""
+
+from __future__ import annotations
+
+import ast
+import json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+HF_REPO_ID = "google/frames-benchmark"
+HF_REPO_TYPE = "dataset"
+HF_TEST_FILE = "test.tsv"
+
+
+def _hf_hub_download(*args: Any, **kwargs: Any) -> str:
+    from huggingface_hub import hf_hub_download
+
+    return hf_hub_download(*args, **kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Question dataclass
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FramesQuestion:
+    """One row of FRAMES (post-parse)."""
+
+    qid: str                   # synthesised "Q000" .. "Q823"
+    question: str
+    gold_answer: str
+    wiki_urls: list[str]       # deduped, in original order
+    reasoning_types: list[str] # split on "|"
+    raw_index: int             # row index from the TSV (for debugging)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "qid": self.qid,
+            "question": self.question,
+            "gold_answer": self.gold_answer,
+            "wiki_urls": list(self.wiki_urls),
+            "reasoning_types": list(self.reasoning_types),
+            "raw_index": self.raw_index,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Download + parse
+# ---------------------------------------------------------------------------
+
+
+def download_test_tsv(cache_dir: Path) -> Path:
+    """Resumable download of ``test.tsv`` via ``huggingface_hub``."""
+
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    local = _hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=HF_TEST_FILE,
+        repo_type=HF_REPO_TYPE,
+        cache_dir=str(cache_dir),
+    )
+    return Path(local)
+
+
+def _parse_wiki_links(raw: Any) -> list[str]:
+    """Convert the ``wiki_links`` cell (Python list literal) to ``list[str]``."""
+
+    if not raw:
+        return []
+    if isinstance(raw, list):
+        return [str(x).strip() for x in raw if str(x).strip()]
+    text = str(raw).strip()
+    if not text:
+        return []
+    try:
+        parsed = ast.literal_eval(text)
+    except (SyntaxError, ValueError):
+        # Fall back: maybe it's a comma-separated string with no quotes.
+        return [tok.strip() for tok in text.strip("[]").split(",") if tok.strip()]
+    if isinstance(parsed, (list, tuple)):
+        return [str(x).strip() for x in parsed if str(x).strip()]
+    return [str(parsed).strip()]
+
+
+def _parse_reasoning_types(raw: Any) -> list[str]:
+    if not raw:
+        return []
+    text = str(raw).strip()
+    if not text:
+        return []
+    return [tok.strip() for tok in text.split("|") if tok.strip()]
+
+
+def load_questions(tsv_path: Path) -> list[FramesQuestion]:
+    """Read FRAMES rows from disk into ``FramesQuestion`` objects.
+
+    Uses pandas for robust TSV parsing (tabs inside quoted strings are
+    rare in this dataset but pandas handles them; the stdlib ``csv``
+    module is fine too if pandas ever becomes a problem). We pin
+    ``index_col=0`` because the upstream TSV uses the first unnamed
+    column as the row index.
+    """
+
+    import pandas as pd
+
+    df = pd.read_csv(tsv_path, sep="\t", index_col=0, keep_default_na=False)
+    out: list[FramesQuestion] = []
+    for raw_idx, row in df.iterrows():
+        prompt = str(row.get("Prompt") or "").strip()
+        answer = str(row.get("Answer") or "").strip()
+        if not prompt or not answer:
+            logger.debug("Skipping FRAMES row %s with missing Prompt/Answer", raw_idx)
+            continue
+        urls = _parse_wiki_links(row.get("wiki_links"))
+        if not urls:
+            # Fall back to the per-cell ``wikipedia_link_*`` columns.
+            urls = []
+            for col in row.index:
+                if col.startswith("wikipedia_link"):
+                    val = str(row.get(col) or "").strip()
+                    if val and val not in urls:
+                        urls.append(val)
+        reasoning = _parse_reasoning_types(row.get("reasoning_types"))
+        out.append(FramesQuestion(
+            qid=f"Q{int(raw_idx):03d}",
+            question=prompt,
+            gold_answer=answer,
+            wiki_urls=urls,
+            reasoning_types=reasoning,
+            raw_index=int(raw_idx),
+        ))
+    return out
+
+
+def write_questions_jsonl(questions: list[FramesQuestion], dest: Path) -> None:
+    """Persist a parsed copy under the benchmark data dir."""
+
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    with dest.open("w", encoding="utf-8") as fh:
+        for q in questions:
+            fh.write(json.dumps(q.to_dict()) + "\n")
+
+
+__all__ = [
+    "FramesQuestion",
+    "download_test_tsv",
+    "load_questions",
+    "write_questions_jsonl",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/grader.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/grader.py
new file mode 100644
index 000000000..d280e3eaf
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/grader.py
@@ -0,0 +1,341 @@
+"""FRAMES grader: deterministic shortcut + LLM-as-judge fallback.
+
+FRAMES gold answers are short factoids (a name, a year, an ordinal,
+a count). The published paper uses an LLM judge for grading, citing
+the long tail of paraphrasing ("Jane Ballou" vs "Mrs. Ballou (Jane)";
+"5" vs "five"; "London, England" vs "London"). We replicate that
+faithfully *but* avoid burning judge tokens on the obvious cases.
+
+Pipeline per (pred, gold):
+
+1. Normalise both sides (SQuAD-style).
+2. If normalised pred == normalised gold → CORRECT (``method=exact``).
+3. Numeric path: if both extract to a single number and the values
+   match within 1% relative tolerance → CORRECT (``method=numeric``).
+4. Substring path: if normalised gold appears as a *whole-word phrase*
+   inside normalised pred (or vice versa) → CORRECT
+   (``method=substring``).
+5. Otherwise → call the LLM judge if a judge is wired; the judge
+   returns yes/no with a one-line rationale.
+6. If no judge is configured, fall through to ``False``
+   (``method=lexical_miss``).
+
+The judge is called *concurrently* across the run via a semaphore (so
+it doesn't outrun the upstream rate limit). Cached on
+``(arm, qid)`` so re-running ``report`` doesn't re-judge.
+
+Returned shape mirrors ``mmlongbench.grader.GradeResult`` to keep
+report writers uniform across benchmarks.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+import string
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Any
+
+from ....core.providers.openrouter_chat import OpenRouterChatProvider
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Public types
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class GradeResult:
+    """Shape mirrors mmlongbench.grader.GradeResult for report uniformity."""
+
+    correct: bool
+    f1: float
+    method: str
+    normalised_pred: str = ""
+    normalised_gold: str = ""
+    judge_rationale: str = ""
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "correct": self.correct,
+            "f1": self.f1,
+            "method": self.method,
+            "normalised_pred": self.normalised_pred,
+            "normalised_gold": self.normalised_gold,
+            "judge_rationale": self.judge_rationale,
+        }
+
+
+# ---------------------------------------------------------------------------
+# Normalisation
+# ---------------------------------------------------------------------------
+
+
+_PUNCT_TABLE = str.maketrans({c: " " for c in string.punctuation})
+_ARTICLES = re.compile(r"\b(a|an|the)\b", re.IGNORECASE)
+_WS = re.compile(r"\s+")
+
+
+def _normalise(s: str) -> str:
+    s = (s or "").lower()
+    s = s.translate(_PUNCT_TABLE)
+    s = _ARTICLES.sub(" ", s)
+    s = _WS.sub(" ", s).strip()
+    return s
+
+
+_WORD_NUMBERS = {
+    "zero": 0, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5,
+    "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11,
+    "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15, "sixteen": 16,
+    "seventeen": 17, "eighteen": 18, "nineteen": 19, "twenty": 20,
+}
+
+_NUMERIC_RE = re.compile(r"-?\d+(?:[.,]\d+)?")
+
+
+def _maybe_number(s: str) -> float | None:
+    """Extract a single numeric value, recognising digit and word forms.
+
+    Operates on the lowercased *raw* text (rather than the
+    punctuation-stripped normalisation) so that thousands separators
+    like ``1,234`` are preserved through the regex and parsed
+    correctly. We only fall back to ``_normalise`` for the word-number
+    pass, which doesn't care about punctuation.
+    """
+
+    raw = (s or "").strip().lower()
+    if not raw:
+        return None
+    match = _NUMERIC_RE.search(raw)
+    if match:
+        try:
+            return float(match.group(0).replace(",", ""))
+        except ValueError:
+            pass
+    for tok in _normalise(s).split():
+        if tok in _WORD_NUMBERS:
+            return float(_WORD_NUMBERS[tok])
+    return None
+
+
+def _whole_word_substring(haystack: str, needle: str) -> bool:
+    """Is ``needle`` present as a whole-word phrase in ``haystack``?"""
+
+    if not needle:
+        return False
+    pad_h = f" {haystack} "
+    pad_n = f" {needle} "
+    return pad_n in pad_h
+
+
+# ---------------------------------------------------------------------------
+# Deterministic shortcut
+# ---------------------------------------------------------------------------
+
+
+def grade_deterministic(*, pred: str, gold: str) -> GradeResult:
+    """Try to grade without the LLM judge. Returns a final-result object.
+
+    A ``False`` result with ``method == "lexical_miss"`` is the signal
+    to the caller that the LLM judge should be consulted (if available).
+    """
+
+    if not (pred or "").strip():
+        return GradeResult(False, 0.0, "empty_pred", "", _normalise(gold))
+
+    p = _normalise(pred)
+    g = _normalise(gold)
+    if not g:
+        # Defensively: gold should never be empty; if it is, we can't grade.
+        return GradeResult(False, 0.0, "empty_gold", p, g)
+
+    if p == g:
+        return GradeResult(True, 1.0, "exact", p, g)
+
+    p_num = _maybe_number(pred)
+    g_num = _maybe_number(gold)
+    if p_num is not None and g_num is not None:
+        # 1% relative tolerance, 0.5 absolute floor (handles year-ish answers).
+        tol = max(abs(g_num) * 0.01, 0.5)
+        if abs(p_num - g_num) <= tol:
+            return GradeResult(True, 1.0, "numeric", p, g)
+        return GradeResult(False, 0.0, "numeric_miss", p, g)
+
+    if _whole_word_substring(p, g):
+        return GradeResult(True, 1.0, "substring", p, g)
+    if _whole_word_substring(g, p) and len(p) >= 3:
+        # Be conservative the other direction — only credit if pred is
+        # at least 3 normalised chars (avoids "John" matching gold
+        # "John F. Kennedy" as correct).
+        return GradeResult(True, 1.0, "substring_reverse", p, g)
+
+    return GradeResult(False, 0.0, "lexical_miss", p, g)
+
+
+# ---------------------------------------------------------------------------
+# LLM-as-judge
+# ---------------------------------------------------------------------------
+
+
+_JUDGE_SYSTEM = (
+    "You are an impartial grader for short-answer factual questions. "
+    "Given a question, the gold answer, and a model's prediction, "
+    "decide whether the prediction is correct. The prediction is "
+    "correct if it expresses the same factual content as the gold "
+    "answer, allowing for paraphrasing, surface-level differences "
+    "(numbers as words, names with/without titles), and additional "
+    "non-contradictory detail. The prediction is incorrect if it "
+    "expresses a different fact, omits the central answer, or hedges "
+    "without committing.\n\n"
+    "Respond with ONLY a JSON object on a single line:\n"
+    '{\"correct\": true|false, \"rationale\": \"<one short sentence>\"}'
+)
+
+
+_JUDGE_TEMPLATE = """\
+Question: {question}
+Gold answer: {gold}
+Model prediction: {pred}
+
+Decide whether the prediction is correct.
+"""
+
+
+@dataclass
+class JudgeConfig:
+    """Configuration handed to ``LlmJudge`` at construction time."""
+
+    api_key: str
+    model: str = "anthropic/claude-sonnet-4.5"
+    base_url: str = "https://openrouter.ai/api/v1"
+    max_tokens: int = 200
+    concurrency: int = 4
+
+
+class LlmJudge:
+    """Async LLM judge over OpenRouter chat completions."""
+
+    def __init__(self, *, config: JudgeConfig) -> None:
+        self._config = config
+        self._provider = OpenRouterChatProvider(
+            api_key=config.api_key,
+            base_url=config.base_url,
+            model=config.model,
+        )
+        self._sem = asyncio.Semaphore(max(1, config.concurrency))
+
+    @property
+    def model(self) -> str:
+        return self._config.model
+
+    async def judge(
+        self,
+        *,
+        question: str,
+        gold: str,
+        pred: str,
+    ) -> tuple[bool, str]:
+        """Return ``(is_correct, rationale)``. Errors return False + reason."""
+
+        prompt = _JUDGE_TEMPLATE.format(question=question, gold=gold, pred=pred)
+        try:
+            async with self._sem:
+                response = await self._provider.complete(
+                    prompt=prompt,
+                    system_prompt=_JUDGE_SYSTEM,
+                    max_tokens=self._config.max_tokens,
+                )
+        except Exception as exc:  # noqa: BLE001
+            return False, f"judge_error: {type(exc).__name__}: {exc}"
+        return _parse_judge_response(response.text)
+
+
+def _parse_judge_response(text: str) -> tuple[bool, str]:
+    """Pull ``correct`` + ``rationale`` out of the judge's reply."""
+
+    if not text or not text.strip():
+        return False, "judge_returned_empty"
+    # Accept JSON anywhere in the message; some models prepend prose.
+    match = re.search(r"\{[^{}]*\}", text, flags=re.DOTALL)
+    candidate = match.group(0) if match else text
+    try:
+        data = json.loads(candidate)
+    except (json.JSONDecodeError, ValueError):
+        # Fallback: yes/no parsing.
+        lowered = text.strip().lower()
+        if lowered.startswith("yes") or "correct: yes" in lowered or '"correct": true' in lowered:
+            return True, "yes (parser_fallback)"
+        if lowered.startswith("no") or "correct: no" in lowered or '"correct": false' in lowered:
+            return False, "no (parser_fallback)"
+        return False, f"unparseable_judge_response: {text[:200]}"
+    correct = bool(data.get("correct"))
+    rationale = str(data.get("rationale", "")).strip()[:280]
+    return correct, rationale
+
+
+# ---------------------------------------------------------------------------
+# Combined grader
+# ---------------------------------------------------------------------------
+
+
+async def grade_with_judge(
+    *,
+    pred: str,
+    gold: str,
+    question: str,
+    judge: LlmJudge | None,
+) -> GradeResult:
+    """Grade one row: deterministic shortcut → optional LLM judge fallback."""
+
+    det = grade_deterministic(pred=pred, gold=gold)
+    if det.correct or det.method != "lexical_miss":
+        return det
+    if judge is None:
+        return det
+    is_correct, rationale = await judge.judge(question=question, gold=gold, pred=pred)
+    return GradeResult(
+        correct=is_correct,
+        f1=1.0 if is_correct else 0.0,
+        method="llm_judge",
+        normalised_pred=det.normalised_pred,
+        normalised_gold=det.normalised_gold,
+        judge_rationale=rationale,
+    )
+
+
+async def grade_many(
+    *,
+    rows: Sequence[tuple[str, str, str, str]],
+    judge: LlmJudge | None,
+) -> list[GradeResult]:
+    """Grade ``[(qid, question, gold, pred), ...]`` concurrently.
+
+    The judge already enforces its own concurrency cap; this just
+    schedules everything via ``asyncio.gather``. ``qid`` is unused
+    inside the grader but threaded through so callers can correlate
+    results back to their rows.
+    """
+
+    if not rows:
+        return []
+    coros = [
+        grade_with_judge(pred=p, gold=g, question=q, judge=judge)
+        for _qid, q, g, p in rows
+    ]
+    return list(await asyncio.gather(*coros))
+
+
+__all__ = [
+    "GradeResult",
+    "JudgeConfig",
+    "LlmJudge",
+    "grade_deterministic",
+    "grade_many",
+    "grade_with_judge",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/ingest.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/ingest.py
new file mode 100644
index 000000000..9780be4ed
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/ingest.py
@@ -0,0 +1,341 @@
+"""FRAMES ingestion: download → fetch Wikipedia → upload markdown.
+
+Steps:
+
+1. Download ``test.tsv`` from ``hf://datasets/google/frames-benchmark``.
+2. Parse rows into ``FramesQuestion`` objects.
+3. Optionally cap to the first ``--max-questions N`` so a smoke run
+   doesn't trigger a 1k-article fetch.
+4. Build the **deduplicated** set of Wikipedia URLs across the chosen
+   sample (questions share many articles — Q1 and Q42 might both
+   reference ``James_A._Garfield``).
+5. Fetch each unique article via ``WikiFetcher`` (polite 2 RPS) into
+   ``<bench_dir>/wiki/<title>.md``.
+6. Upload the resulting markdown files to SurfSense in batches with
+   ``use_vision_llm=False, processing_mode="basic"`` (text-only — no
+   reason to pay vision LLM costs on Wikipedia plaintext).
+7. Persist a doc map at
+   ``<suite_data>/maps/frames_doc_map.jsonl`` with one row per question
+   listing its ``document_ids`` (so the runner *could* scope retrieval
+   if requested, though by default we don't — see ``runner.py``).
+
+The doc map row shape:
+
+    {"qid": "Q000",
+     "wiki_titles": ["President of the United States", "James Buchanan", ...],
+     "document_ids": [123, 124, ...],
+     "missing_titles": []}
+
+We resolve titles → SurfSense document_ids via the post-upload
+``DocumentStatus.title`` field. SurfSense's title is the uploaded
+filename (without extension), so we round-trip via
+``cache_filename_for_title`` to match.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.clients.documents import (
+    DocumentProcessingFailed,
+    DocumentProcessingTimeout,
+)
+from ....core.config import set_suite_state
+from ....core.ingest_settings import IngestSettings, settings_header_line
+from ....core.registry import RunContext
+from .dataset import (
+    download_test_tsv,
+    load_questions,
+    write_questions_jsonl,
+)
+from .wiki_fetch import (
+    WikiArticle,
+    WikiFetcher,
+    cache_filename_for_title,
+    title_from_url,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class _IngestStats:
+    n_questions: int
+    n_unique_urls: int
+    n_fetched: int
+    n_cached_hits: int
+    n_missing: int
+    n_uploaded: int
+    n_existing: int
+    bench_dir: Path
+    map_path: Path
+
+
+async def _fetch_articles(
+    fetcher: WikiFetcher,
+    urls: list[str],
+) -> tuple[dict[str, WikiArticle], list[str]]:
+    """Fetch each URL serially (the WikiFetcher's rate-limiter serialises anyway).
+
+    Returns ``(url -> WikiArticle, missing_urls)``. Missing means
+    Wikipedia reported the title doesn't exist, the URL was non-wiki,
+    or the API returned an empty extract.
+    """
+
+    fetched: dict[str, WikiArticle] = {}
+    missing: list[str] = []
+    n_total = len(urls)
+    for i, url in enumerate(urls, start=1):
+        try:
+            article = await fetcher.fetch(url)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning("FRAMES wiki fetch %s failed: %s", url, exc)
+            missing.append(url)
+            continue
+        if article is None:
+            missing.append(url)
+            continue
+        fetched[url] = article
+        if i % 25 == 0 or i == n_total:
+            logger.info("  ... fetched %d / %d Wikipedia articles", i, n_total)
+    return fetched, missing
+
+
+async def _upload_markdowns(
+    ctx: RunContext,
+    articles: list[WikiArticle],
+    *,
+    batch_size: int,
+    settings: IngestSettings,
+) -> dict[str, int]:
+    """Upload deduplicated markdown files. Returns ``filename -> document_id``.
+
+    SurfSense dedupes uploads on ``(filename, search_space_id)``, so
+    re-running ingest after a crash is idempotent — duplicates land in
+    ``duplicate_document_ids`` and we still recover their ids via the
+    status endpoint.
+    """
+
+    if not articles:
+        return {}
+    docs_client = ctx.documents_client()
+    name_to_id: dict[str, int] = {}
+    paths = [a.markdown_path for a in articles]
+    for batch_start in range(0, len(paths), batch_size):
+        batch = paths[batch_start : batch_start + batch_size]
+        result = await docs_client.upload(
+            files=batch,
+            search_space_id=ctx.search_space_id,
+            should_summarize=settings.should_summarize,
+            use_vision_llm=settings.use_vision_llm,
+            processing_mode=settings.processing_mode,
+        )
+        all_ids = list(result.document_ids) + list(result.duplicate_document_ids)
+        if result.document_ids:
+            try:
+                await docs_client.wait_until_ready(
+                    search_space_id=ctx.search_space_id,
+                    document_ids=result.document_ids,
+                    timeout_s=900.0,
+                )
+            except (DocumentProcessingFailed, DocumentProcessingTimeout) as exc:
+                logger.warning("FRAMES batch processing issue: %s", exc)
+        if all_ids:
+            statuses = await docs_client.get_status(
+                search_space_id=ctx.search_space_id,
+                document_ids=all_ids,
+            )
+            for s in statuses:
+                # SurfSense stores the uploaded filename as ``title`` (no extension).
+                stem = Path(s.title).stem if s.title.endswith(".md") else s.title
+                name_to_id[stem] = s.document_id
+                name_to_id[s.title] = s.document_id
+        logger.info(
+            "FRAMES upload batch %d-%d: %d new, %d duplicate",
+            batch_start, batch_start + len(batch),
+            len(result.document_ids), len(result.duplicate_document_ids),
+        )
+    return name_to_id
+
+
+def _resolve_question_doc_ids(
+    questions: list[Any],
+    fetched: dict[str, WikiArticle],
+    name_to_id: dict[str, int],
+) -> list[dict[str, Any]]:
+    """For each question, list the document_ids of its (fetched) wiki articles."""
+
+    rows: list[dict[str, Any]] = []
+    for q in questions:
+        doc_ids: list[int] = []
+        titles: list[str] = []
+        missing: list[str] = []
+        for url in q.wiki_urls:
+            article = fetched.get(url)
+            if article is None:
+                missing.append(url)
+                continue
+            titles.append(article.title)
+            stem = Path(cache_filename_for_title(article.title)).stem
+            doc_id = name_to_id.get(stem) or name_to_id.get(article.markdown_path.name)
+            if doc_id is not None and doc_id not in doc_ids:
+                doc_ids.append(doc_id)
+        rows.append({
+            "qid": q.qid,
+            "raw_index": q.raw_index,
+            "n_wiki_urls": len(q.wiki_urls),
+            "wiki_titles": titles,
+            "document_ids": doc_ids,
+            "missing_urls": missing,
+        })
+    return rows
+
+
+# ---------------------------------------------------------------------------
+# Public entry point
+# ---------------------------------------------------------------------------
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    max_questions: int | None = None,
+    upload_batch_size: int = 16,
+    skip_upload: bool = False,
+    fetch_rate_limit_rps: float = 2.0,
+    settings: IngestSettings | None = None,
+) -> None:
+    """Ingest the FRAMES benchmark into the research suite.
+
+    Parameters
+    ----------
+    max_questions : int | None
+        Cap on the number of FRAMES questions to materialise. ``None`` =
+        all 824 (≈300+ unique articles). Smoke runs should pass 5-10.
+    upload_batch_size : int
+        Markdown files per ``/documents/fileupload`` call. Larger
+        batches reduce round-trip overhead; smaller batches recover
+        faster from individual processing failures.
+    skip_upload : bool
+        Fetch + cache Wikipedia articles locally but don't push to
+        SurfSense. Useful for debugging the fetcher in isolation.
+    fetch_rate_limit_rps : float
+        Maximum requests-per-second to the Wikipedia API. Default 2.0
+        is a polite ceiling; raise cautiously.
+    settings : IngestSettings | None
+        Override per-upload knobs. FRAMES defaults to text-only
+        (no vision LLM, basic mode) — the corpus is plain wikitext.
+    """
+
+    settings = settings or IngestSettings(
+        use_vision_llm=False,
+        processing_mode="basic",
+        should_summarize=False,
+    )
+    bench_dir = ctx.benchmark_data_dir()
+    wiki_cache = bench_dir / "wiki"
+    wiki_cache.mkdir(parents=True, exist_ok=True)
+    hf_cache = bench_dir / ".hf_cache"
+    hf_cache.mkdir(parents=True, exist_ok=True)
+
+    # 1. Download + parse questions.
+    tsv_path = download_test_tsv(hf_cache)
+    questions = load_questions(tsv_path)
+    if not questions:
+        raise RuntimeError(
+            "FRAMES test.tsv contained no parseable rows; upstream may "
+            "have changed schema."
+        )
+    logger.info("FRAMES: parsed %d questions from %s", len(questions), tsv_path.name)
+    if max_questions is not None and max_questions > 0:
+        questions = questions[:max_questions]
+        logger.info("FRAMES: capped to first %d questions", len(questions))
+
+    questions_jsonl = bench_dir / "questions.jsonl"
+    write_questions_jsonl(questions, questions_jsonl)
+
+    # 2. Build deduplicated URL set (preserving first-seen order).
+    seen_urls: dict[str, None] = {}
+    for q in questions:
+        for url in q.wiki_urls:
+            seen_urls.setdefault(url, None)
+    unique_urls = list(seen_urls.keys())
+    logger.info(
+        "FRAMES: %d unique Wikipedia URLs across %d questions",
+        len(unique_urls), len(questions),
+    )
+
+    # 3. Fetch (with cache).
+    fetcher = WikiFetcher(cache_dir=wiki_cache, rate_limit_rps=fetch_rate_limit_rps)
+    n_cached = sum(
+        1 for url in unique_urls
+        if (wiki_cache / cache_filename_for_title(_safe_title(url))).exists()
+    )
+    fetched, missing_urls = await _fetch_articles(fetcher, unique_urls)
+    logger.info(
+        "FRAMES: fetched=%d, cache_hits=%d, missing=%d",
+        len(fetched), n_cached, len(missing_urls),
+    )
+
+    # 4. Upload to SurfSense (deduped by filename).
+    name_to_id: dict[str, int] = {}
+    if skip_upload:
+        logger.info("FRAMES: --skip-upload; skipping SurfSense ingestion")
+    else:
+        unique_articles = list({a.markdown_path: a for a in fetched.values()}.values())
+        name_to_id = await _upload_markdowns(
+            ctx,
+            unique_articles,
+            batch_size=upload_batch_size,
+            settings=settings,
+        )
+
+    # 5. Persist per-question doc map.
+    doc_rows = _resolve_question_doc_ids(questions, fetched, name_to_id)
+
+    map_path = ctx.maps_dir() / "frames_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        fh.write(settings_header_line(settings) + "\n")
+        for row in doc_rows:
+            fh.write(json.dumps(row) + "\n")
+    logger.info("Wrote FRAMES doc map to %s (%d rows)", map_path, len(doc_rows))
+
+    # 6. Update suite state.
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["frames"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+    stats = _IngestStats(
+        n_questions=len(questions),
+        n_unique_urls=len(unique_urls),
+        n_fetched=len(fetched),
+        n_cached_hits=n_cached,
+        n_missing=len(missing_urls),
+        n_uploaded=len(name_to_id),
+        n_existing=0,
+        bench_dir=bench_dir,
+        map_path=map_path,
+    )
+    logger.info("FRAMES ingest done: %s", stats)
+
+
+def _safe_title(url: str) -> str:
+    """Pre-cache title resolution; returns ``""`` on bad URL."""
+
+    try:
+        return title_from_url(url)
+    except ValueError:
+        return ""
+
+
+__all__ = ["run_ingest"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/prompt.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/prompt.py
new file mode 100644
index 000000000..16bb06da4
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/prompt.py
@@ -0,0 +1,71 @@
+"""FRAMES prompt templates.
+
+Two templates: one for the bare-LLM arm (no retrieval), one for
+SurfSense (the agent retrieves; we mostly just instruct it on
+output format). Both arms must use byte-identical *content* for the
+question itself so the head-to-head is fair — the wrappers diverge
+only in framing.
+
+Format expectations (mirrors the FRAMES paper, section 4):
+
+* Short factual answer — names, dates, numbers, ordinals
+* No extra explanation in the final line; we anchor on
+  ``Answer: <text>`` for deterministic extraction
+* Free-text reasoning is *allowed* before the final ``Answer:`` line —
+  multi-hop questions often benefit from it. We just don't grade it.
+"""
+
+from __future__ import annotations
+
+
+_BASE_INSTRUCTIONS = (
+    "You are a careful question-answering assistant. The question may "
+    "require combining facts from multiple sources, doing arithmetic, "
+    "or reasoning about dates. Think step by step if needed, then give "
+    "the final answer.\n\n"
+    "Format your final line EXACTLY as:\n"
+    "Answer: <short answer>\n\n"
+    "The answer should be as short as possible — a name, a number, a "
+    "date, a single phrase. Do not repeat the question. Do not include "
+    "punctuation at the end unless it is part of the answer."
+)
+
+
+_BARE_TEMPLATE = """\
+{instructions}
+
+Question: {question}
+"""
+
+
+_SURFSENSE_TEMPLATE = """\
+{instructions}
+
+You have access to a Wikipedia knowledge base via retrieval. Use it
+to look up any facts you are not confident about. The corpus contains
+the Wikipedia articles needed to answer this question, but you must
+retrieve them yourself — they are not pre-selected.
+
+Question: {question}
+"""
+
+
+def build_bare_prompt(question: str) -> str:
+    """Prompt for the no-retrieval baseline arm."""
+
+    return _BARE_TEMPLATE.format(
+        instructions=_BASE_INSTRUCTIONS,
+        question=question.strip(),
+    )
+
+
+def build_surfsense_prompt(question: str) -> str:
+    """Prompt for the SurfSense arm (retrieval-augmented)."""
+
+    return _SURFSENSE_TEMPLATE.format(
+        instructions=_BASE_INSTRUCTIONS,
+        question=question.strip(),
+    )
+
+
+__all__ = ["build_bare_prompt", "build_surfsense_prompt"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/runner.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/runner.py
new file mode 100644
index 000000000..a8dde0dd2
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/runner.py
@@ -0,0 +1,686 @@
+"""FRAMES runner — Bare LLM (no retrieval) vs SurfSense (multi-hop RAG).
+
+Two arms run paired on every question in the sample:
+
+1. ``BareLlmArm``  — OpenRouter chat completion with the question only.
+   Reproduces the published "naive prompting" baseline (40.8% on
+   Gemini-Pro-1.5).
+2. ``SurfSenseArm`` — POST ``/api/v1/new_chat`` with **no**
+   ``mentioned_document_ids`` so the agent retrieves over the entire
+   ingested Wikipedia corpus. This is the "multi-step retrieval &
+   reasoning" cell in the FRAMES paper.
+
+Open-ended grading: deterministic shortcut + optional LLM-as-judge
+(``--no-judge`` to disable). Cost / latency / token aggregates are
+collected per arm. Paired stats (McNemar, bootstrap CI) for the
+accuracy delta. Per-reasoning-type breakdown to surface where one
+arm beats the other (numerical vs temporal vs multi-constraint, ...).
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import ArmRequest, ArmResult, BareLlmArm, SurfSenseArm
+from ....core.config import utc_iso_timestamp
+from ....core.ingest_settings import (
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+)
+from ....core.metrics.comparison import (
+    bootstrap_delta_ci,
+    mcnemar_test,
+    paired_aggregate,
+)
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci
+from ....core.parse.freeform_answer import extract_freeform_answer
+from ....core.providers.openrouter_chat import OpenRouterChatProvider
+from ....core.registry import ReportSection, RunArtifact, RunContext
+from ....core.scenarios import format_scenario_md
+from .grader import GradeResult, JudgeConfig, LlmJudge, grade_many
+from .prompt import build_bare_prompt, build_surfsense_prompt
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Question shape
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FramesRunnerQuestion:
+    qid: str
+    raw_index: int
+    question: str
+    gold_answer: str
+    reasoning_types: list[str]
+    document_ids: list[int]   # subset of corpus relevant to this Q (may be empty)
+    n_wiki_urls: int
+    missing_urls: list[str]
+
+
+def _load_doc_map(map_path: Path) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]:
+    rows: dict[str, dict[str, Any]] = {}
+    settings: dict[str, Any] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if is_settings_header(row):
+                settings = dict(row["__settings__"])
+                continue
+            rows[str(row["qid"])] = row
+    return rows, settings
+
+
+def _load_questions(
+    questions_jsonl: Path,
+    doc_map: dict[str, dict[str, Any]],
+    *,
+    sample_n: int | None,
+    reasoning_filter: str | None,
+) -> list[FramesRunnerQuestion]:
+    out: list[FramesRunnerQuestion] = []
+    with questions_jsonl.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            qid = str(row.get("qid") or "").strip()
+            if not qid:
+                continue
+            map_row = doc_map.get(qid, {})
+            reasoning = list(row.get("reasoning_types") or [])
+            if reasoning_filter and reasoning_filter not in [r.lower() for r in reasoning]:
+                continue
+            out.append(FramesRunnerQuestion(
+                qid=qid,
+                raw_index=int(row.get("raw_index") or 0),
+                question=str(row.get("question") or "").strip(),
+                gold_answer=str(row.get("gold_answer") or "").strip(),
+                reasoning_types=reasoning,
+                document_ids=list(map_row.get("document_ids") or []),
+                n_wiki_urls=int(map_row.get("n_wiki_urls") or 0),
+                missing_urls=list(map_row.get("missing_urls") or []),
+            ))
+    out.sort(key=lambda q: q.raw_index)
+    if sample_n is not None and sample_n > 0:
+        out = out[:sample_n]
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Bounded concurrency helper
+# ---------------------------------------------------------------------------
+
+
+async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+# ---------------------------------------------------------------------------
+# Benchmark
+# ---------------------------------------------------------------------------
+
+
+_DESCRIPTION = (
+    "FRAMES (824 multi-hop Wikipedia questions, 5 reasoning types) — "
+    "Bare LLM (no retrieval) vs SurfSense (multi-step RAG over the "
+    "Wikipedia corpus). Tests cross-document retrieval + reasoning."
+)
+
+
+_DEFAULT_INGEST_SETTINGS = IngestSettings(
+    use_vision_llm=False,
+    processing_mode="basic",
+    should_summarize=False,
+)
+
+
+class FramesBenchmark:
+    """Multi-hop Wikipedia RAG vs naive prompting."""
+
+    suite: str = "research"
+    name: str = "frames"
+    headline: bool = True
+    description: str = _DESCRIPTION
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--n", dest="sample_n", type=int, default=None,
+            help="Run only the first N questions after filters (default: all 824).",
+        )
+        parser.add_argument(
+            "--reasoning",
+            dest="reasoning_filter",
+            default=None,
+            help=(
+                "Filter to questions tagged with this reasoning type "
+                "(e.g. 'numerical reasoning', 'temporal reasoning'). "
+                "Case-insensitive substring against the upstream tags."
+            ),
+        )
+        parser.add_argument(
+            "--concurrency", type=int, default=4,
+            help="Parallel question workers per arm.",
+        )
+        parser.add_argument(
+            "--scope-mentions", dest="scope_mentions", action="store_true",
+            help=(
+                "SurfSense arm: scope retrieval to the per-question "
+                "document_ids (oracle-retrieval upper bound). Default "
+                "is full-corpus retrieval (the realistic FRAMES setting)."
+            ),
+        )
+        parser.add_argument(
+            "--max-output-tokens", type=int, default=512,
+            help="Cap on completion length for both arms.",
+        )
+        parser.add_argument(
+            "--no-judge", dest="no_judge", action="store_true",
+            help=(
+                "Disable LLM-as-judge fallback grading; use only the "
+                "deterministic grader (faster but more pessimistic)."
+            ),
+        )
+        parser.add_argument(
+            "--judge-model",
+            dest="judge_model",
+            default="anthropic/claude-sonnet-4.5",
+            help="OpenRouter slug for the LLM judge (default: claude-sonnet-4.5).",
+        )
+        parser.add_argument(
+            "--judge-concurrency",
+            dest="judge_concurrency",
+            type=int,
+            default=4,
+            help="Parallel judge calls (default: 4).",
+        )
+        # Ingest-only knobs.
+        parser.add_argument(
+            "--max-questions", dest="max_questions", type=int, default=None,
+            help="(ingest only) cap on number of questions to materialise + ingest.",
+        )
+        parser.add_argument(
+            "--upload-batch-size", dest="upload_batch_size", type=int, default=16,
+            help="(ingest only) markdown files per fileupload call.",
+        )
+        parser.add_argument(
+            "--skip-upload", dest="skip_upload", action="store_true",
+            help="(ingest only) cache wiki articles locally but don't push to SurfSense.",
+        )
+        parser.add_argument(
+            "--fetch-rps", dest="fetch_rate_limit_rps", type=float, default=2.0,
+            help="(ingest only) max requests/second to the Wikipedia API.",
+        )
+        add_ingest_settings_args(parser, defaults=_DEFAULT_INGEST_SETTINGS)
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
+        await run_ingest(
+            ctx,
+            max_questions=opts.get("max_questions"),
+            upload_batch_size=int(opts.get("upload_batch_size") or 16),
+            skip_upload=bool(opts.get("skip_upload", False)),
+            fetch_rate_limit_rps=float(opts.get("fetch_rate_limit_rps") or 2.0),
+            settings=settings,
+        )
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        sample_n = opts.get("sample_n")
+        reasoning_filter = opts.get("reasoning_filter")
+        if reasoning_filter:
+            reasoning_filter = reasoning_filter.strip().lower() or None
+        concurrency = int(opts.get("concurrency") or 4)
+        scope_mentions = bool(opts.get("scope_mentions"))
+        max_output_tokens = int(opts.get("max_output_tokens") or 512)
+        no_judge = bool(opts.get("no_judge"))
+        judge_model = str(opts.get("judge_model") or "anthropic/claude-sonnet-4.5")
+        judge_concurrency = int(opts.get("judge_concurrency") or 4)
+
+        bench_dir = ctx.benchmark_data_dir()
+        questions_jsonl = bench_dir / "questions.jsonl"
+        map_path = ctx.maps_dir() / "frames_doc_map.jsonl"
+        if not questions_jsonl.exists() or not map_path.exists():
+            raise RuntimeError(
+                "FRAMES not ingested for this suite. Run "
+                "`python -m surfsense_evals ingest research frames` first."
+            )
+
+        doc_map, ingest_settings = _load_doc_map(map_path)
+        questions = _load_questions(
+            questions_jsonl, doc_map,
+            sample_n=sample_n,
+            reasoning_filter=reasoning_filter,
+        )
+        if not questions:
+            raise RuntimeError(
+                "No FRAMES questions matched the filters; broaden --reasoning/--n."
+            )
+        logger.info("FRAMES: scheduled %d questions", len(questions))
+
+        api_key = os.environ.get("OPENROUTER_API_KEY")
+        if not api_key:
+            raise RuntimeError(
+                "OPENROUTER_API_KEY env var is required for the bare-LLM arm."
+            )
+
+        bare_provider = OpenRouterChatProvider(
+            api_key=api_key,
+            base_url=ctx.config.openrouter_base_url,
+            model=ctx.native_arm_model,
+        )
+        bare_arm = BareLlmArm(
+            provider=bare_provider,
+            max_output_tokens=max_output_tokens,
+        )
+        surf_arm = SurfSenseArm(
+            client=ctx.new_chat_client(),
+            search_space_id=ctx.search_space_id,
+            ephemeral_threads=True,
+        )
+
+        judge: LlmJudge | None = None
+        if not no_judge:
+            judge = LlmJudge(config=JudgeConfig(
+                api_key=api_key,
+                model=judge_model,
+                base_url=ctx.config.openrouter_base_url,
+                concurrency=judge_concurrency,
+            ))
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+
+        async def _bare_one(q: FramesRunnerQuestion) -> ArmResult:
+            return await bare_arm.answer(_make_bare_request(q, max_output_tokens))
+
+        async def _surf_one(q: FramesRunnerQuestion) -> ArmResult:
+            return await surf_arm.answer(
+                _make_surfsense_request(q, scope_mentions=scope_mentions)
+            )
+
+        bare_results, surf_results = await asyncio.gather(
+            _gather_with_limit((_bare_one(q) for q in questions), concurrency=concurrency),
+            _gather_with_limit((_surf_one(q) for q in questions), concurrency=concurrency),
+        )
+
+        bare_grades = await _grade_results(questions, bare_results, judge=judge)
+        surf_grades = await _grade_results(questions, surf_results, judge=judge)
+
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for q, b_res, s_res, b_g, s_g in zip(
+                questions, bare_results, surf_results, bare_grades, surf_grades, strict=False
+            ):
+                meta = {
+                    "qid": q.qid,
+                    "raw_index": q.raw_index,
+                    "reasoning_types": q.reasoning_types,
+                    "n_wiki_urls": q.n_wiki_urls,
+                    "n_resolved_doc_ids": len(q.document_ids),
+                    "n_missing_urls": len(q.missing_urls),
+                    "gold": q.gold_answer,
+                }
+                fh.write(json.dumps({
+                    **meta,
+                    **b_res.to_jsonl(),
+                    "graded": b_g.to_dict(),
+                }) + "\n")
+                fh.write(json.dumps({
+                    **meta,
+                    **s_res.to_jsonl(),
+                    "graded": s_g.to_dict(),
+                }) + "\n")
+
+        metrics = _compute_metrics(questions, bare_results, surf_results, bare_grades, surf_grades)
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "concurrency": concurrency,
+                "reasoning_filter": reasoning_filter,
+                "scope_mentions": scope_mentions,
+                "no_judge": no_judge,
+                "judge_model": judge_model if not no_judge else None,
+                "scenario": ctx.scenario,
+                "provider_model": ctx.provider_model,
+                "native_arm_model": ctx.native_arm_model,
+                "vision_provider_model": ctx.vision_provider_model,
+                "agent_llm_id": ctx.agent_llm_id,
+                "ingest_settings": ingest_settings,
+                "bare_arm_label": "bare_llm",
+            },
+        )
+
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps({
+                "suite": self.suite,
+                "benchmark": self.name,
+                "raw_path": "raw.jsonl",
+                "metrics": metrics,
+                "extra": artifact.extra,
+            }, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="FRAMES — Bare LLM vs SurfSense (multi-hop Wikipedia RAG)",
+                headline=True,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        bare = m.get("bare", {})
+        surf = m.get("surfsense", {})
+        delta = m.get("delta", {})
+        per_reasoning = m.get("per_reasoning", {})
+        extra = latest.extra
+
+        body_lines: list[str] = []
+        body_lines.append(
+            f"- Sample size: {extra.get('n_questions', '?')} questions "
+            f"(reasoning filter: `{extra.get('reasoning_filter') or 'none'}`, "
+            f"scope-mentions: `{extra.get('scope_mentions', False)}`, "
+            f"judge: `{extra.get('judge_model') or 'deterministic-only'}`)."
+        )
+        body_lines.append(format_scenario_md(extra))
+        body_lines.append(format_ingest_settings_md(extra.get("ingest_settings")))
+        body_lines.append(
+            "- Bare LLM arm (OpenRouter chat, no retrieval, "
+            f"`{extra.get('native_arm_model') or extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(bare, indent="  "))
+        body_lines.append(
+            "- SurfSense arm (`POST /api/v1/new_chat`, multi-step RAG, "
+            f"`{extra.get('provider_model', '?')}`):"
+        )
+        body_lines.append(_arm_summary_lines(surf, indent="  "))
+        body_lines.append("- Delta (paired):")
+        body_lines.append(
+            f"  - Accuracy: SurfSense {_pp(delta.get('accuracy_pp'))} pp "
+            f"(McNemar p={_fmt(delta.get('mcnemar_p_value'), 4)}, "
+            f"method={delta.get('mcnemar_method')})"
+        )
+        body_lines.append(
+            f"  - Bootstrap 95% CI on accuracy delta: "
+            f"[{_pp(delta.get('bootstrap_ci_low'))}pp, {_pp(delta.get('bootstrap_ci_high'))}pp]"
+        )
+        body_lines.append(
+            f"  - Cost / question: bare ${_dollars(bare.get('cost_micros_mean'))}, "
+            f"surfsense ${_dollars(surf.get('cost_micros_mean'))} "
+            f"(SurfSense delta {_pct_change(delta.get('cost_micros_pct'))})"
+        )
+        body_lines.append(
+            f"  - Latency p50: bare {_ms_to_s(bare.get('latency_ms_median'))}, "
+            f"surfsense {_ms_to_s(surf.get('latency_ms_median'))} "
+            f"(SurfSense delta {_pct_change(delta.get('latency_ms_pct'))})"
+        )
+        if per_reasoning:
+            body_lines.append("- Per-reasoning-type split (accuracy delta in pp):")
+            for tag, vals in sorted(per_reasoning.items()):
+                body_lines.append(
+                    f"  - {tag}: SurfSense {_pp(vals.get('delta_accuracy_pp'))} pp "
+                    f"(n={vals.get('n')}, bare acc={vals.get('bare_accuracy', 0)*100:.1f}%, "
+                    f"surf acc={vals.get('surfsense_accuracy', 0)*100:.1f}%)"
+                )
+
+        return ReportSection(
+            title="FRAMES — Bare LLM vs SurfSense (multi-hop Wikipedia RAG)",
+            headline=True,
+            body_md="\n".join(body_lines),
+            body_json=m,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Per-question helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_bare_request(q: FramesRunnerQuestion, max_tokens: int) -> ArmRequest:
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=build_bare_prompt(q.question),
+        options={"max_tokens": max_tokens},
+    )
+
+
+def _make_surfsense_request(q: FramesRunnerQuestion, *, scope_mentions: bool) -> ArmRequest:
+    mentions: list[int] | None = None
+    if scope_mentions and q.document_ids:
+        mentions = list(q.document_ids)
+    return ArmRequest(
+        question_id=q.qid,
+        prompt=build_surfsense_prompt(q.question),
+        mentioned_document_ids=mentions,
+    )
+
+
+async def _grade_results(
+    questions: list[FramesRunnerQuestion],
+    results: list[ArmResult],
+    *,
+    judge: LlmJudge | None,
+) -> list[GradeResult]:
+    rows: list[tuple[str, str, str, str]] = []
+    for q, r in zip(questions, results, strict=False):
+        pred = extract_freeform_answer(r.raw_text or "")
+        rows.append((q.qid, q.question, q.gold_answer, pred))
+    return await grade_many(rows=rows, judge=judge)
+
+
+# ---------------------------------------------------------------------------
+# Metrics aggregation
+# ---------------------------------------------------------------------------
+
+
+def _compute_metrics(
+    questions: list[FramesRunnerQuestion],
+    bare_results: list[ArmResult],
+    surf_results: list[ArmResult],
+    bare_grades: list[GradeResult],
+    surf_grades: list[GradeResult],
+) -> dict[str, Any]:
+    bare_correct = [g.correct for g in bare_grades]
+    surf_correct = [g.correct for g in surf_grades]
+
+    bare_costs = [float(r.cost_micros) for r in bare_results]
+    surf_costs = [float(r.cost_micros) for r in surf_results]
+    bare_latencies = [float(r.latency_ms) for r in bare_results]
+    surf_latencies = [float(r.latency_ms) for r in surf_results]
+    bare_in_tokens = [float(r.input_tokens) for r in bare_results]
+    bare_out_tokens = [float(r.output_tokens) for r in bare_results]
+
+    bare_acc = accuracy_with_wilson_ci(sum(bare_correct), len(bare_correct))
+    surf_acc = accuracy_with_wilson_ci(sum(surf_correct), len(surf_correct))
+    mc = mcnemar_test(bare_correct, surf_correct)
+    boot = bootstrap_delta_ci(bare_correct, surf_correct, n_resamples=2000)
+
+    bare_cost_agg = paired_aggregate(bare_costs)
+    surf_cost_agg = paired_aggregate(surf_costs)
+    bare_latency_agg = paired_aggregate(bare_latencies)
+    surf_latency_agg = paired_aggregate(surf_latencies)
+    cost_pct = _safe_pct(surf_cost_agg.mean, bare_cost_agg.mean)
+    latency_pct = _safe_pct(surf_latency_agg.median, bare_latency_agg.median)
+
+    # Per-reasoning-type breakdown. Each question may carry multiple
+    # reasoning tags; we count it under each tag (so totals don't
+    # equal len(questions) — the reader is expected to look at the
+    # per-tag ``n``).
+    per_reasoning_pairs: dict[str, list[tuple[bool, bool]]] = {}
+    for q, b_ok, s_ok in zip(questions, bare_correct, surf_correct, strict=False):
+        tags = q.reasoning_types or ["(untagged)"]
+        for tag in tags:
+            per_reasoning_pairs.setdefault(tag, []).append((b_ok, s_ok))
+
+    per_reasoning: dict[str, dict[str, Any]] = {}
+    for tag, pairs in per_reasoning_pairs.items():
+        b_correct = [a for a, _ in pairs]
+        s_correct = [b for _, b in pairs]
+        per_reasoning[tag] = {
+            "n": len(pairs),
+            "bare_accuracy": (sum(b_correct) / len(pairs)) if pairs else 0.0,
+            "surfsense_accuracy": (sum(s_correct) / len(pairs)) if pairs else 0.0,
+            "delta_accuracy_pp": (
+                100.0 * (sum(s_correct) - sum(b_correct)) / len(pairs)
+                if pairs else 0.0
+            ),
+        }
+
+    grader_methods = {
+        "bare": _count_methods(bare_grades),
+        "surfsense": _count_methods(surf_grades),
+    }
+
+    return {
+        "bare": {
+            **bare_acc.to_dict(),
+            "cost_micros_mean": bare_cost_agg.mean,
+            "cost_micros_median": bare_cost_agg.median,
+            "latency_ms_mean": bare_latency_agg.mean,
+            "latency_ms_median": bare_latency_agg.median,
+            "latency_ms_p95": bare_latency_agg.p95,
+            "input_tokens_mean": (sum(bare_in_tokens) / len(bare_in_tokens)) if bare_in_tokens else 0.0,
+            "output_tokens_mean": (sum(bare_out_tokens) / len(bare_out_tokens)) if bare_out_tokens else 0.0,
+        },
+        "surfsense": {
+            **surf_acc.to_dict(),
+            "cost_micros_mean": surf_cost_agg.mean,
+            "cost_micros_median": surf_cost_agg.median,
+            "latency_ms_mean": surf_latency_agg.mean,
+            "latency_ms_median": surf_latency_agg.median,
+            "latency_ms_p95": surf_latency_agg.p95,
+        },
+        "delta": {
+            "accuracy_pp": 100.0 * (surf_acc.accuracy - bare_acc.accuracy),
+            "mcnemar_p_value": mc.p_value,
+            "mcnemar_method": mc.method,
+            "mcnemar_b_bare_only": mc.b,
+            "mcnemar_c_surfsense_only": mc.c,
+            "bootstrap_ci_low": 100.0 * boot.ci_low,
+            "bootstrap_ci_high": 100.0 * boot.ci_high,
+            "cost_micros_pct": cost_pct,
+            "latency_ms_pct": latency_pct,
+        },
+        "per_reasoning": per_reasoning,
+        "grader_methods": grader_methods,
+    }
+
+
+def _count_methods(grades: list[GradeResult]) -> dict[str, int]:
+    out: dict[str, int] = {}
+    for g in grades:
+        out[g.method] = out.get(g.method, 0) + 1
+    return out
+
+
+def _safe_pct(numerator: float, denominator: float) -> float | None:
+    if denominator == 0:
+        return None
+    return 100.0 * (numerator - denominator) / denominator
+
+
+# ---------------------------------------------------------------------------
+# Tiny formatting helpers used by report_section
+# ---------------------------------------------------------------------------
+
+
+def _arm_summary_lines(d: dict[str, Any], *, indent: str) -> str:
+    if not d:
+        return f"{indent}(no data)"
+    acc = d.get("accuracy", 0.0)
+    low = d.get("ci_low", 0.0)
+    high = d.get("ci_high", 0.0)
+    lines = [
+        f"{indent}- Accuracy: {acc * 100:.1f}% (Wilson 95% CI: {low * 100:.1f}% – {high * 100:.1f}%)",
+        f"{indent}- Cost / question: ${_dollars(d.get('cost_micros_mean'))} (mean), "
+        f"${_dollars(d.get('cost_micros_median'))} (median)",
+        f"{indent}- Latency: p50 {_ms_to_s(d.get('latency_ms_median'))}, "
+        f"p95 {_ms_to_s(d.get('latency_ms_p95'))}",
+    ]
+    if "input_tokens_mean" in d:
+        lines.append(
+            f"{indent}- Mean tokens / question: in {d.get('input_tokens_mean', 0):.0f}, "
+            f"out {d.get('output_tokens_mean', 0):.0f}"
+        )
+    return "\n".join(lines)
+
+
+def _dollars(micros: Any) -> str:
+    if micros is None:
+        return "?"
+    try:
+        return f"{(float(micros) / 1_000_000):.4f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _ms_to_s(ms: Any) -> str:
+    if ms is None:
+        return "?"
+    try:
+        return f"{float(ms) / 1000:.1f}s"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pp(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.1f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _pct_change(value: Any) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):+.0f}%"
+    except (TypeError, ValueError):
+        return "?"
+
+
+def _fmt(value: Any, ndigits: int) -> str:
+    if value is None:
+        return "?"
+    try:
+        return f"{float(value):.{ndigits}f}"
+    except (TypeError, ValueError):
+        return "?"
+
+
+__all__ = ["FramesBenchmark", "FramesRunnerQuestion"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/frames/wiki_fetch.py b/surfsense_evals/src/surfsense_evals/suites/research/frames/wiki_fetch.py
new file mode 100644
index 000000000..7f6b63e50
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/research/frames/wiki_fetch.py
@@ -0,0 +1,241 @@
+"""Wikipedia article fetcher → plain-text markdown, with disk cache.
+
+We hit the MediaWiki action API for *plain text* extracts:
+
+    GET https://en.wikipedia.org/w/api.php
+        ?action=query&prop=extracts&explaintext=true
+        &redirects=1&titles=<Title>&format=json&formatversion=2
+
+This avoids HTML→markdown conversion (and its many edge cases). The
+``explaintext=true`` mode strips infoboxes / templates / wikilinks
+and returns clean section-headered prose, which is exactly what we
+want SurfSense to chunk + embed. We prepend ``# <Title>\n\n`` so the
+markdown has a visible H1 (helps SurfSense's chunker preserve doc
+identity at the top of the first chunk).
+
+Caching: every fetched article lands in
+``<bench_dir>/wiki/<sanitised-title>.md`` and is reused on subsequent
+runs. The cache key is the URL-decoded title (e.g.
+``Charlotte_Brontë`` regardless of source URL casing or
+percent-encoding).
+
+Politeness: 2 RPS rate limit + a descriptive User-Agent (Wikimedia
+asks for one). We don't parallelise above 2 RPS — this is a courtesy
+to Wikipedia and only ~300 articles for the n=100 sample.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+import urllib.parse
+from dataclasses import dataclass
+from pathlib import Path
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+
+WIKI_API = "https://en.wikipedia.org/w/api.php"
+USER_AGENT = (
+    "SurfSense-Evals/0.1 (https://github.com/MODSetter/SurfSense; "
+    "research-benchmark fetch; respects 2 RPS rate limit)"
+)
+
+
+@dataclass(frozen=True)
+class WikiArticle:
+    """One fetched article + metadata."""
+
+    title: str            # canonical title returned by MW (post-redirect)
+    source_url: str       # the URL we were asked to fetch
+    markdown_path: Path   # where the cached body lives on disk
+    n_chars: int          # length of the body (post-prepend H1)
+    redirected_from: str | None = None
+
+
+# ---------------------------------------------------------------------------
+# Title <-> URL helpers
+# ---------------------------------------------------------------------------
+
+
+_WIKI_PATH_RE = re.compile(r"^/wiki/(?P<title>[^?#]+)$")
+
+
+def title_from_url(url: str) -> str:
+    """Pull the page title out of a wiki URL.
+
+    ``https://en.wikipedia.org/wiki/Charlotte_Bront%C3%AB`` → ``Charlotte Brontë``.
+    Spaces are preserved (the API accepts spaces and underscores
+    interchangeably; we use spaces to keep cache filenames human-readable).
+    """
+
+    parsed = urllib.parse.urlparse(url)
+    if parsed.netloc and "wikipedia.org" not in parsed.netloc:
+        raise ValueError(f"Not a Wikipedia URL: {url!r}")
+    match = _WIKI_PATH_RE.match(parsed.path)
+    if not match:
+        raise ValueError(f"Unrecognised wiki path: {parsed.path!r}")
+    raw_title = urllib.parse.unquote(match.group("title"))
+    # MW treats underscores and spaces as equivalent; spaces are friendlier.
+    return raw_title.replace("_", " ").strip()
+
+
+_FILENAME_SAFE = re.compile(r"[^A-Za-z0-9._\- ]")
+
+
+def cache_filename_for_title(title: str) -> str:
+    """Map a title to a filesystem-safe filename.
+
+    Replaces every non-(alnum / ``._- `` / space) character with ``_``.
+    Title collisions are rare (FRAMES only has English Wikipedia titles)
+    and a final ``hash(title)[:8]`` would obscure the otherwise-readable
+    filenames; we accept the (vanishingly small) collision risk.
+    """
+
+    safe = _FILENAME_SAFE.sub("_", title)
+    safe = safe.strip().replace(" ", "_")
+    return f"{safe}.md"
+
+
+# ---------------------------------------------------------------------------
+# Async fetcher with rate limiting + retry
+# ---------------------------------------------------------------------------
+
+
+class WikiFetcher:
+    """Polite fetch + disk cache + redirect handling."""
+
+    def __init__(
+        self,
+        *,
+        cache_dir: Path,
+        rate_limit_rps: float = 2.0,
+        timeout_s: float = 30.0,
+        max_retries: int = 3,
+    ) -> None:
+        self._cache_dir = Path(cache_dir)
+        self._cache_dir.mkdir(parents=True, exist_ok=True)
+        self._min_interval = 1.0 / max(rate_limit_rps, 0.1)
+        self._last_request_at = 0.0
+        self._rate_lock = asyncio.Lock()
+        self._timeout = httpx.Timeout(timeout_s, connect=10.0)
+        self._max_retries = max_retries
+
+    async def _throttle(self) -> None:
+        async with self._rate_lock:
+            now = asyncio.get_event_loop().time()
+            wait = self._last_request_at + self._min_interval - now
+            if wait > 0:
+                await asyncio.sleep(wait)
+            self._last_request_at = asyncio.get_event_loop().time()
+
+    async def fetch(
+        self,
+        url: str,
+        *,
+        http: httpx.AsyncClient | None = None,
+    ) -> WikiArticle | None:
+        """Fetch one article. Returns ``None`` only if MW reports the title is missing.
+
+        Raises on transport errors after retries. Caller decides
+        whether to abort the whole ingest or continue with the
+        successfully-fetched subset.
+        """
+
+        try:
+            title = title_from_url(url)
+        except ValueError as exc:
+            logger.warning("Skipping non-wiki URL %s: %s", url, exc)
+            return None
+
+        cache_path = self._cache_dir / cache_filename_for_title(title)
+        if cache_path.exists() and cache_path.stat().st_size > 0:
+            return WikiArticle(
+                title=title,
+                source_url=url,
+                markdown_path=cache_path,
+                n_chars=cache_path.stat().st_size,
+            )
+
+        last_exc: Exception | None = None
+        for attempt in range(self._max_retries):
+            try:
+                await self._throttle()
+                payload = await self._fetch_extract(title, http=http)
+                break
+            except (httpx.HTTPError, RuntimeError) as exc:
+                last_exc = exc
+                wait = 1.0 * (2 ** attempt)
+                logger.warning(
+                    "wiki fetch %r attempt %d failed: %s; retry in %.1fs",
+                    title, attempt + 1, exc, wait,
+                )
+                await asyncio.sleep(wait)
+        else:
+            assert last_exc is not None
+            raise last_exc
+
+        page = payload.get("page") or {}
+        if not page or page.get("missing"):
+            logger.warning("Wikipedia reports missing page for %r (url=%s)", title, url)
+            return None
+
+        canonical_title = str(page.get("title") or title).strip()
+        body = str(page.get("extract") or "").strip()
+        if not body:
+            logger.warning("Wikipedia returned empty extract for %r", title)
+            return None
+        markdown = f"# {canonical_title}\n\n{body}\n"
+        cache_path.write_text(markdown, encoding="utf-8")
+        return WikiArticle(
+            title=canonical_title,
+            source_url=url,
+            markdown_path=cache_path,
+            n_chars=len(markdown),
+            redirected_from=title if canonical_title != title else None,
+        )
+
+    async def _fetch_extract(
+        self,
+        title: str,
+        *,
+        http: httpx.AsyncClient | None,
+    ) -> dict:
+        """One MW API call. Returns ``{'page': {...}}`` (formatversion=2)."""
+
+        params = {
+            "action": "query",
+            "prop": "extracts",
+            "explaintext": "true",
+            "redirects": "1",
+            "format": "json",
+            "formatversion": "2",
+            "titles": title,
+        }
+        headers = {"User-Agent": USER_AGENT, "Accept": "application/json"}
+        if http is not None:
+            response = await http.get(WIKI_API, params=params, headers=headers, timeout=self._timeout)
+        else:
+            async with httpx.AsyncClient(timeout=self._timeout) as client:
+                response = await client.get(WIKI_API, params=params, headers=headers, timeout=self._timeout)
+        response.raise_for_status()
+        data = response.json()
+        if "error" in data:
+            raise RuntimeError(f"MediaWiki API error: {data['error']!r}")
+        pages = (data.get("query") or {}).get("pages") or []
+        if not pages:
+            return {"page": {}}
+        return {"page": pages[0]}
+
+
+__all__ = [
+    "WIKI_API",
+    "USER_AGENT",
+    "WikiArticle",
+    "WikiFetcher",
+    "cache_filename_for_title",
+    "title_from_url",
+]
diff --git a/surfsense_evals/tests/__init__.py b/surfsense_evals/tests/__init__.py
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/surfsense_evals/tests/__init__.py
@@ -0,0 +1 @@
+
diff --git a/surfsense_evals/tests/conftest.py b/surfsense_evals/tests/conftest.py
new file mode 100644
index 000000000..1cb1d0faf
--- /dev/null
+++ b/surfsense_evals/tests/conftest.py
@@ -0,0 +1,34 @@
+"""Shared pytest fixtures for surfsense-evals."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.core.config import Config
+
+
+@pytest.fixture
+def tmp_env(monkeypatch, tmp_path: Path) -> Path:
+    """Isolate env vars + filesystem state per test.
+
+    Wipes every ``SURFSENSE_*`` / ``OPENROUTER_*`` / ``EVAL_*`` var so a
+    test that wants a specific credential mode can ``monkeypatch.setenv``
+    just what it needs without leakage from the caller's shell.
+    """
+
+    for key in list(os.environ):
+        if key.startswith(("SURFSENSE_", "OPENROUTER_", "EVAL_")):
+            monkeypatch.delenv(key, raising=False)
+    monkeypatch.setenv("EVAL_DATA_DIR", str(tmp_path / "data"))
+    monkeypatch.setenv("EVAL_REPORTS_DIR", str(tmp_path / "reports"))
+    return tmp_path
+
+
+@pytest.fixture
+def isolated_config(tmp_env: Path) -> Config:  # noqa: ARG001
+    from surfsense_evals.core.config import load_config
+
+    return load_config()
diff --git a/surfsense_evals/tests/core/__init__.py b/surfsense_evals/tests/core/__init__.py
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/surfsense_evals/tests/core/__init__.py
@@ -0,0 +1 @@
+
diff --git a/surfsense_evals/tests/core/test_auth.py b/surfsense_evals/tests/core/test_auth.py
new file mode 100644
index 000000000..43ec94b93
--- /dev/null
+++ b/surfsense_evals/tests/core/test_auth.py
@@ -0,0 +1,95 @@
+"""Auth credential resolution + 401 refresh hook."""
+
+from __future__ import annotations
+
+import httpx
+import pytest
+import respx
+
+from surfsense_evals.core.auth import (
+    CredentialError,
+    acquire_token,
+    client_with_auth,
+)
+from surfsense_evals.core.config import Config
+
+
+def _make_config(**overrides) -> Config:
+    base = {
+        "surfsense_api_base": "http://test",
+        "openrouter_api_key": None,
+        "openrouter_base_url": "https://openrouter.ai/api/v1",
+        "surfsense_jwt": None,
+        "surfsense_refresh_token": None,
+        "surfsense_user_email": None,
+        "surfsense_user_password": None,
+        "data_dir": None,
+        "reports_dir": None,
+    }
+    base.update(overrides)
+    # Path objects required by Config; tests don't touch the FS.
+    from pathlib import Path
+
+    base["data_dir"] = base["data_dir"] or Path("/tmp/eval_test_data")
+    base["reports_dir"] = base["reports_dir"] or Path("/tmp/eval_test_reports")
+    return Config(**base)
+
+
+@pytest.mark.asyncio
+async def test_acquire_token_jwt_mode_short_circuits():
+    config = _make_config(surfsense_jwt="abc", surfsense_refresh_token="ref")
+    bundle = await acquire_token(config)
+    assert bundle.access_token == "abc"
+    assert bundle.refresh_token == "ref"
+    assert bundle.mode == "jwt"
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_acquire_token_local_mode_posts_form():
+    respx.post("http://test/auth/jwt/login").mock(
+        return_value=httpx.Response(
+            200, json={"access_token": "T", "refresh_token": "R", "token_type": "bearer"}
+        )
+    )
+    config = _make_config(
+        surfsense_user_email="u@example.com", surfsense_user_password="pw"
+    )
+    bundle = await acquire_token(config)
+    assert bundle.access_token == "T"
+    assert bundle.refresh_token == "R"
+    assert bundle.mode == "local"
+
+
+@pytest.mark.asyncio
+async def test_acquire_token_no_credentials():
+    config = _make_config()
+    with pytest.raises(CredentialError) as exc:
+        await acquire_token(config)
+    assert "SURFSENSE_USER_EMAIL" in str(exc.value)
+    assert "SURFSENSE_JWT" in str(exc.value)
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_client_with_auth_refreshes_on_401():
+    config = _make_config(surfsense_jwt="old", surfsense_refresh_token="ref")
+    bundle = await acquire_token(config)
+
+    respx.post("http://test/auth/jwt/refresh").mock(
+        return_value=httpx.Response(200, json={"access_token": "new", "refresh_token": "ref2"})
+    )
+    # First call returns 401; the retry (post-refresh) returns 200.
+    respx.get("http://test/api/v1/searchspaces").mock(
+        side_effect=[
+            httpx.Response(401, json={"detail": "expired"}),
+            httpx.Response(200, json=[]),
+        ]
+    )
+
+    async with client_with_auth(config, bundle) as client:
+        response = await client.get("http://test/api/v1/searchspaces")
+
+    assert response.status_code == 200
+    assert bundle.access_token == "new"
+    assert bundle.refresh_token == "ref2"
diff --git a/surfsense_evals/tests/core/test_clients.py b/surfsense_evals/tests/core/test_clients.py
new file mode 100644
index 000000000..9e2c4ad75
--- /dev/null
+++ b/surfsense_evals/tests/core/test_clients.py
@@ -0,0 +1,262 @@
+"""respx-mocked tests for the SurfSense HTTP clients."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import httpx
+import pytest
+import respx
+
+from surfsense_evals.core.clients import (
+    DocumentsClient,
+    NewChatClient,
+    SearchSpaceClient,
+)
+from surfsense_evals.core.clients.new_chat import ThreadBusyError
+
+_BASE = "http://test"
+
+
+@pytest.fixture
+def http() -> httpx.AsyncClient:
+    return httpx.AsyncClient(base_url=_BASE)
+
+
+# ---------------------------------------------------------------------------
+# SearchSpaceClient
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_create_search_space_returns_row(respx_mock, http):
+    respx_mock.post("/api/v1/searchspaces").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "id": 99,
+                "name": "eval-medical-2026",
+                "description": None,
+                "user_id": "user-x",
+                "citations_enabled": True,
+                "qna_custom_instructions": None,
+            },
+        )
+    )
+    client = SearchSpaceClient(http, _BASE)
+    row = await client.create("eval-medical-2026")
+    assert row.id == 99
+    assert row.name == "eval-medical-2026"
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_delete_search_space_idempotent_on_404(respx_mock, http):
+    respx_mock.delete("/api/v1/searchspaces/42").mock(
+        return_value=httpx.Response(404, json={"detail": "gone"})
+    )
+    client = SearchSpaceClient(http, _BASE)
+    await client.delete(42)  # must not raise
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_set_llm_preferences_partial_update(respx_mock, http):
+    route = respx_mock.put("/api/v1/search-spaces/42/llm-preferences").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "agent_llm_id": -10042,
+                "document_summary_llm_id": None,
+                "image_generation_config_id": None,
+                "vision_llm_config_id": None,
+                "agent_llm": {
+                    "id": -10042,
+                    "provider": "OPENROUTER",
+                    "model_name": "anthropic/claude-sonnet-4.5",
+                },
+            },
+        )
+    )
+    client = SearchSpaceClient(http, _BASE)
+    prefs = await client.set_llm_preferences(42, agent_llm_id=-10042)
+    assert prefs.agent_llm_id == -10042
+    assert prefs.agent_llm["provider"] == "OPENROUTER"
+    sent_body = json.loads(route.calls[-1].request.content)
+    assert sent_body == {"agent_llm_id": -10042}
+
+
+# ---------------------------------------------------------------------------
+# DocumentsClient
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_documents_status_parses_state(respx_mock, http):
+    respx_mock.get("/api/v1/documents/status").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "items": [
+                    {"id": 1, "title": "a.pdf", "document_type": "FILE",
+                     "status": {"state": "ready", "reason": None}},
+                    {"id": 2, "title": "b.pdf", "document_type": "FILE",
+                     "status": {"state": "failed", "reason": "ETL boom"}},
+                ]
+            },
+        )
+    )
+    client = DocumentsClient(http, _BASE)
+    statuses = await client.get_status(search_space_id=1, document_ids=[1, 2])
+    assert {s.document_id for s in statuses} == {1, 2}
+    assert {s.is_ready for s in statuses} == {True, False}
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_documents_upload_returns_payload(respx_mock, http, tmp_path: Path):
+    f1 = tmp_path / "a.pdf"
+    f1.write_bytes(b"%PDF-1.4 small")
+    respx_mock.post("/api/v1/documents/fileupload").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "message": "Files uploaded",
+                "document_ids": [101],
+                "duplicate_document_ids": [],
+                "total_files": 1,
+                "pending_files": 1,
+                "skipped_duplicates": 0,
+            },
+        )
+    )
+    client = DocumentsClient(http, _BASE)
+    result = await client.upload(files=[f1], search_space_id=7)
+    assert result.document_ids == [101]
+    assert result.pending_files == 1
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_documents_list_chunks_paginated(respx_mock, http):
+    respx_mock.get("/api/v1/documents/5/chunks").mock(
+        side_effect=[
+            httpx.Response(200, json={
+                "items": [{"id": 1, "content": "a"}, {"id": 2, "content": "b"}],
+                "total": 3, "page": 0, "page_size": 2, "has_more": True,
+            }),
+            httpx.Response(200, json={
+                "items": [{"id": 3, "content": "c"}],
+                "total": 3, "page": 1, "page_size": 2, "has_more": False,
+            }),
+        ]
+    )
+    client = DocumentsClient(http, _BASE)
+    rows = await client.list_chunks(5, page_size=2)
+    assert [r.id for r in rows] == [1, 2, 3]
+
+
+# ---------------------------------------------------------------------------
+# NewChatClient
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_create_thread_returns_id(respx_mock, http):
+    respx_mock.post("/api/v1/threads").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "id": 555,
+                "title": "eval",
+                "archived": False,
+                "visibility": "PRIVATE",
+                "search_space_id": 1,
+                "messages": [],
+                "created_at": "2026-05-11T00:00:00Z",
+                "updated_at": "2026-05-11T00:00:00Z",
+            },
+        )
+    )
+    client = NewChatClient(http, _BASE)
+    tid = await client.create_thread(search_space_id=1)
+    assert tid == 555
+
+
+def _sse_body(events: list[dict]) -> bytes:
+    parts = []
+    for ev in events:
+        parts.append(f"data: {json.dumps(ev)}\n\n")
+    parts.append("data: [DONE]\n\n")
+    return "".join(parts).encode("utf-8")
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_ask_accumulates_text_deltas(respx_mock, http):
+    body = _sse_body([
+        {"type": "start", "messageId": "m1"},
+        {"type": "text-start", "id": "t1"},
+        {"type": "text-delta", "id": "t1", "delta": "Answer "},
+        {"type": "text-delta", "id": "t1", "delta": "is "},
+        {"type": "text-delta", "id": "t1", "delta": "B [citation:42]."},
+        {"type": "text-end", "id": "t1"},
+        {"type": "finish"},
+    ])
+    respx_mock.post("/api/v1/new_chat").mock(
+        return_value=httpx.Response(
+            200,
+            content=body,
+            headers={"Content-Type": "text/event-stream"},
+        )
+    )
+    client = NewChatClient(http, _BASE)
+    answer = await client.ask(
+        thread_id=1, search_space_id=2, user_query="What is the answer?"
+    )
+    assert answer.text == "Answer is B [citation:42]."
+    assert answer.finished_normally is True
+    assert any(c["chunk_id"] == 42 for c in answer.citations)
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_ask_409_thread_busy_retries(respx_mock, http):
+    body = _sse_body([
+        {"type": "text-delta", "id": "t1", "delta": "ok"},
+        {"type": "finish"},
+    ])
+    busy = httpx.Response(
+        409,
+        json={"detail": {"errorCode": "THREAD_BUSY", "message": "busy"}},
+        headers={"Retry-After": "1"},
+    )
+    success = httpx.Response(
+        200, content=body, headers={"Content-Type": "text/event-stream"}
+    )
+    respx_mock.post("/api/v1/new_chat").mock(side_effect=[busy, success])
+    client = NewChatClient(http, _BASE)
+    answer = await client.ask(
+        thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=2
+    )
+    assert answer.text == "ok"
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_ask_409_exhausts_retries(respx_mock, http):
+    busy = httpx.Response(
+        409,
+        json={"detail": {"errorCode": "TURN_CANCELLING", "message": "wait"}},
+        headers={"Retry-After": "1"},
+    )
+    respx_mock.post("/api/v1/new_chat").mock(return_value=busy)
+    client = NewChatClient(http, _BASE)
+    with pytest.raises(ThreadBusyError):
+        await client.ask(
+            thread_id=1, search_space_id=2, user_query="hi", max_busy_retries=1
+        )
diff --git a/surfsense_evals/tests/core/test_config.py b/surfsense_evals/tests/core/test_config.py
new file mode 100644
index 000000000..f7b8f7249
--- /dev/null
+++ b/surfsense_evals/tests/core/test_config.py
@@ -0,0 +1,160 @@
+"""Tests for env loading + state.json read/write."""
+
+from __future__ import annotations
+
+import json
+
+from surfsense_evals.core.config import (
+    DEFAULT_SCENARIO,
+    SCENARIOS,
+    SuiteState,
+    clear_suite_state,
+    get_suite_state,
+    load_config,
+    set_suite_state,
+)
+
+
+def test_load_config_defaults_to_localhost(tmp_env):  # noqa: ARG001
+    config = load_config()
+    assert config.surfsense_api_base == "http://localhost:8000"
+    assert config.has_jwt_mode() is False
+    assert config.has_local_mode() is False
+    assert config.credential_mode() == "none"
+
+
+def test_load_config_picks_up_jwt_env(tmp_env, monkeypatch):  # noqa: ARG001
+    monkeypatch.setenv("SURFSENSE_JWT", "tok")
+    config = load_config()
+    assert config.credential_mode() == "jwt"
+
+
+def test_load_config_picks_up_local_env(tmp_env, monkeypatch):  # noqa: ARG001
+    monkeypatch.setenv("SURFSENSE_USER_EMAIL", "u@x.com")
+    monkeypatch.setenv("SURFSENSE_USER_PASSWORD", "pw")
+    config = load_config()
+    assert config.credential_mode() == "local"
+
+
+def test_state_roundtrip_per_suite(tmp_env):  # noqa: ARG001
+    config = load_config()
+    assert get_suite_state(config, "medical") is None
+    state = SuiteState(
+        search_space_id=1,
+        agent_llm_id=-10042,
+        provider_model="anthropic/claude-sonnet-4.5",
+        created_at="2026-05-11T20-30-00Z",
+    )
+    set_suite_state(config, "medical", state)
+    legal = SuiteState(
+        search_space_id=2,
+        agent_llm_id=-1,
+        provider_model="openai/gpt-5",
+        created_at="2026-05-11T21-00-00Z",
+    )
+    set_suite_state(config, "legal", legal)
+
+    fetched = get_suite_state(config, "medical")
+    assert fetched.search_space_id == 1
+    assert fetched.provider_model == "anthropic/claude-sonnet-4.5"
+
+    # Other suite untouched after teardown.
+    cleared = clear_suite_state(config, "medical")
+    assert cleared is True
+    assert get_suite_state(config, "medical") is None
+    assert get_suite_state(config, "legal").search_space_id == 2
+
+    raw = json.loads(config.state_path.read_text(encoding="utf-8"))
+    assert "medical" not in raw["suites"]
+    assert "legal" in raw["suites"]
+
+
+def test_paths_are_per_suite(tmp_env):  # noqa: ARG001
+    config = load_config()
+    a = config.suite_data_dir("medical")
+    b = config.suite_data_dir("legal")
+    assert a != b
+    assert config.suite_reports_dir("medical").parent == config.reports_dir
+    assert config.suite_runs_dir("medical").name == "runs"
+    assert config.suite_maps_dir("medical").name == "maps"
+
+
+# ---------------------------------------------------------------------------
+# Scenario state — back-compat + new fields
+# ---------------------------------------------------------------------------
+
+
+def test_legacy_state_back_compat_defaults_to_head_to_head():
+    """state.json files written before scenarios shipped must still load.
+
+    Missing ``scenario`` / ``vision_*`` / ``native_arm_model`` keys all
+    default to ``head-to-head`` / ``None`` so old setups keep working
+    after upgrade — the runner's behaviour exactly mirrors the legacy
+    one (both arms answer with ``provider_model``).
+    """
+
+    legacy = {
+        "search_space_id": 7,
+        "agent_llm_id": -123,
+        "provider_model": "anthropic/claude-sonnet-4.5",
+        "created_at": "2026-05-11T20-30-00Z",
+        "ingestion_maps": {},
+    }
+    state = SuiteState.from_dict(legacy)
+    assert state.scenario == DEFAULT_SCENARIO == "head-to-head"
+    assert state.vision_llm_config_id is None
+    assert state.vision_provider_model is None
+    assert state.native_arm_model is None
+    # The native arm should still answer with the same slug as SurfSense.
+    assert state.effective_native_arm_model == state.provider_model
+
+
+def test_unknown_scenario_falls_back_to_default():
+    """Garbage scenario in state.json → default, not crash.
+
+    Defensive: we'd rather a stale state file render with the safe
+    head-to-head behaviour than break the whole run with a KeyError.
+    """
+
+    payload = {
+        "search_space_id": 1,
+        "agent_llm_id": -1,
+        "provider_model": "openai/gpt-5",
+        "scenario": "unknown-scenario-name",
+    }
+    state = SuiteState.from_dict(payload)
+    assert state.scenario == DEFAULT_SCENARIO
+
+
+def test_cost_arbitrage_state_persists_native_arm_model(tmp_env):  # noqa: ARG001
+    config = load_config()
+    state = SuiteState(
+        search_space_id=42,
+        agent_llm_id=-1,
+        provider_model="openai/gpt-5.4-mini",
+        created_at="2026-05-11T20-30-00Z",
+        scenario="cost-arbitrage",
+        vision_llm_config_id=-101,
+        vision_provider_model="anthropic/claude-sonnet-4.5",
+        native_arm_model="anthropic/claude-sonnet-4.5",
+    )
+    set_suite_state(config, "medical", state)
+
+    fetched = get_suite_state(config, "medical")
+    assert fetched.scenario == "cost-arbitrage"
+    assert fetched.vision_llm_config_id == -101
+    assert fetched.vision_provider_model == "anthropic/claude-sonnet-4.5"
+    assert fetched.native_arm_model == "anthropic/claude-sonnet-4.5"
+    # Cost arbitrage's whole point: native arm slug != surfsense slug.
+    assert fetched.effective_native_arm_model != fetched.provider_model
+    assert fetched.effective_native_arm_model == "anthropic/claude-sonnet-4.5"
+
+    raw = json.loads(config.state_path.read_text(encoding="utf-8"))
+    assert raw["suites"]["medical"]["scenario"] == "cost-arbitrage"
+
+
+def test_scenario_constants_are_stable():
+    """Pin the public scenario list; runners + tests key off these strings."""
+
+    assert SCENARIOS == ("head-to-head", "symmetric-cheap", "cost-arbitrage")
+    assert DEFAULT_SCENARIO == "head-to-head"
diff --git a/surfsense_evals/tests/core/test_ingest_settings.py b/surfsense_evals/tests/core/test_ingest_settings.py
new file mode 100644
index 000000000..acfac57a6
--- /dev/null
+++ b/surfsense_evals/tests/core/test_ingest_settings.py
@@ -0,0 +1,269 @@
+"""Unit tests for ``surfsense_evals.core.ingest_settings``.
+
+Covers:
+
+* ``IngestSettings.merge`` honours operator overrides and falls back
+  to per-benchmark defaults when the operator is silent.
+* ``add_ingest_settings_args`` exposes the three flag pairs and
+  argparse defaults of ``None`` correctly distinguish "not passed"
+  from "explicitly false".
+* ``settings_header_line`` / ``read_settings_header`` round-trip
+  through a JSONL file.
+* ``read_settings_header`` is fault-tolerant: missing files, missing
+  header, malformed JSON.
+* ``format_ingest_settings_md`` produces a stable Markdown bullet.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.core.ingest_settings import (
+    PROCESSING_MODES,
+    SETTINGS_HEADER_KEY,
+    IngestSettings,
+    add_ingest_settings_args,
+    format_ingest_settings_md,
+    is_settings_header,
+    read_settings_header,
+    settings_header_line,
+)
+
+# ---------------------------------------------------------------------------
+# IngestSettings.merge
+# ---------------------------------------------------------------------------
+
+
+class TestMerge:
+    def test_silent_operator_uses_defaults(self) -> None:
+        defaults = IngestSettings(use_vision_llm=True, processing_mode="basic", should_summarize=True)
+        merged = IngestSettings.merge(defaults, {})
+        assert merged == defaults
+
+    def test_explicit_false_overrides_default_true(self) -> None:
+        defaults = IngestSettings(use_vision_llm=True)
+        merged = IngestSettings.merge(
+            defaults, {"use_vision_llm": False}
+        )
+        assert merged.use_vision_llm is False
+
+    def test_explicit_true_overrides_default_false(self) -> None:
+        defaults = IngestSettings(use_vision_llm=False)
+        merged = IngestSettings.merge(
+            defaults, {"use_vision_llm": True}
+        )
+        assert merged.use_vision_llm is True
+
+    def test_none_means_silent(self) -> None:
+        # Argparse with BooleanOptionalAction yields None when the
+        # operator passed neither --use-vision-llm nor --no-vision-llm.
+        defaults = IngestSettings(use_vision_llm=True)
+        merged = IngestSettings.merge(
+            defaults, {"use_vision_llm": None}
+        )
+        assert merged.use_vision_llm is True
+
+    def test_processing_mode_override(self) -> None:
+        defaults = IngestSettings(processing_mode="basic")
+        merged = IngestSettings.merge(
+            defaults, {"processing_mode": "premium"}
+        )
+        assert merged.processing_mode == "premium"
+
+    def test_processing_mode_invalid_raises(self) -> None:
+        defaults = IngestSettings(processing_mode="basic")
+        with pytest.raises(ValueError, match="Invalid processing_mode"):
+            IngestSettings.merge(defaults, {"processing_mode": "exotic"})
+
+    def test_processing_mode_blank_falls_back(self) -> None:
+        defaults = IngestSettings(processing_mode="basic")
+        merged = IngestSettings.merge(defaults, {"processing_mode": ""})
+        assert merged.processing_mode == "basic"
+
+    def test_string_truthy_coerced(self) -> None:
+        defaults = IngestSettings(use_vision_llm=False)
+        merged = IngestSettings.merge(defaults, {"use_vision_llm": "yes"})
+        assert merged.use_vision_llm is True
+
+    def test_string_falsy_coerced(self) -> None:
+        defaults = IngestSettings(use_vision_llm=True)
+        merged = IngestSettings.merge(defaults, {"use_vision_llm": "false"})
+        assert merged.use_vision_llm is False
+
+    def test_other_keys_ignored(self) -> None:
+        # Benchmarks pass the whole opts dict; merge must tolerate
+        # unrelated keys without crashing.
+        defaults = IngestSettings(use_vision_llm=True, processing_mode="basic")
+        merged = IngestSettings.merge(
+            defaults,
+            {
+                "use_vision_llm": False,
+                "concurrency": 4,
+                "task_filter": "all",
+                "no_mentions": True,
+            },
+        )
+        assert merged.use_vision_llm is False
+        assert merged.processing_mode == "basic"
+
+    def test_to_dict_round_trips(self) -> None:
+        s = IngestSettings(use_vision_llm=True, processing_mode="premium", should_summarize=False)
+        d = s.to_dict()
+        assert d == {
+            "use_vision_llm": True,
+            "processing_mode": "premium",
+            "should_summarize": False,
+        }
+
+    def test_render_label_format(self) -> None:
+        s = IngestSettings(use_vision_llm=True, processing_mode="premium", should_summarize=True)
+        assert s.render_label() == "vision=on, mode=premium, summarize=on"
+
+
+# ---------------------------------------------------------------------------
+# add_ingest_settings_args
+# ---------------------------------------------------------------------------
+
+
+class TestAddArgs:
+    @pytest.fixture
+    def parser(self) -> argparse.ArgumentParser:
+        p = argparse.ArgumentParser()
+        add_ingest_settings_args(
+            p,
+            defaults=IngestSettings(
+                use_vision_llm=False, processing_mode="basic", should_summarize=False
+            ),
+        )
+        return p
+
+    def test_silent_invocation_yields_none(self, parser: argparse.ArgumentParser) -> None:
+        args = parser.parse_args([])
+        assert args.use_vision_llm is None
+        assert args.processing_mode is None
+        assert args.should_summarize is None
+
+    def test_use_vision_llm_flag(self, parser: argparse.ArgumentParser) -> None:
+        args = parser.parse_args(["--use-vision-llm"])
+        assert args.use_vision_llm is True
+
+    def test_no_vision_llm_flag(self, parser: argparse.ArgumentParser) -> None:
+        args = parser.parse_args(["--no-vision-llm"])
+        assert args.use_vision_llm is False
+
+    def test_processing_mode_choices(self, parser: argparse.ArgumentParser) -> None:
+        for mode in PROCESSING_MODES:
+            args = parser.parse_args(["--processing-mode", mode])
+            assert args.processing_mode == mode
+
+    def test_processing_mode_rejects_unknown(
+        self, parser: argparse.ArgumentParser
+    ) -> None:
+        with pytest.raises(SystemExit):
+            parser.parse_args(["--processing-mode", "exotic"])
+
+    def test_summarize_flag_pair(self, parser: argparse.ArgumentParser) -> None:
+        on = parser.parse_args(["--should-summarize"])
+        assert on.should_summarize is True
+        off = parser.parse_args(["--no-summarize"])
+        assert off.should_summarize is False
+
+    def test_vision_flags_mutually_exclusive(
+        self, parser: argparse.ArgumentParser
+    ) -> None:
+        with pytest.raises(SystemExit):
+            parser.parse_args(["--use-vision-llm", "--no-vision-llm"])
+
+    def test_full_pipeline(self, parser: argparse.ArgumentParser) -> None:
+        # Operator passes flags + defaults are reasonable. Merge
+        # should yield exactly what they asked for.
+        args = parser.parse_args(
+            ["--use-vision-llm", "--processing-mode", "premium"]
+        )
+        defaults = IngestSettings(
+            use_vision_llm=False, processing_mode="basic", should_summarize=False
+        )
+        merged = IngestSettings.merge(defaults, vars(args))
+        assert merged == IngestSettings(
+            use_vision_llm=True, processing_mode="premium", should_summarize=False
+        )
+
+
+# ---------------------------------------------------------------------------
+# Header round-trip + read_settings_header fault tolerance
+# ---------------------------------------------------------------------------
+
+
+class TestHeader:
+    def test_header_line_round_trip(self, tmp_path: Path) -> None:
+        s = IngestSettings(use_vision_llm=True, processing_mode="premium")
+        path = tmp_path / "map.jsonl"
+        with path.open("w", encoding="utf-8") as fh:
+            fh.write(settings_header_line(s) + "\n")
+            fh.write(json.dumps({"case_id": "x", "document_id": 1}) + "\n")
+        loaded = read_settings_header(path)
+        assert loaded == s.to_dict()
+
+    def test_is_settings_header_recognises(self) -> None:
+        assert is_settings_header({SETTINGS_HEADER_KEY: {}})
+        assert not is_settings_header({"case_id": "x"})
+
+    def test_missing_file_returns_empty(self, tmp_path: Path) -> None:
+        assert read_settings_header(tmp_path / "does_not_exist.jsonl") == {}
+
+    def test_empty_file_returns_empty(self, tmp_path: Path) -> None:
+        path = tmp_path / "empty.jsonl"
+        path.write_text("", encoding="utf-8")
+        assert read_settings_header(path) == {}
+
+    def test_no_header_returns_empty(self, tmp_path: Path) -> None:
+        path = tmp_path / "legacy.jsonl"
+        with path.open("w", encoding="utf-8") as fh:
+            fh.write(json.dumps({"case_id": "x", "document_id": 1}) + "\n")
+            fh.write(json.dumps({"case_id": "y", "document_id": 2}) + "\n")
+        assert read_settings_header(path) == {}
+
+    def test_malformed_json_returns_empty(self, tmp_path: Path) -> None:
+        path = tmp_path / "broken.jsonl"
+        path.write_text("not json\n", encoding="utf-8")
+        assert read_settings_header(path) == {}
+
+    def test_skips_blank_first_lines(self, tmp_path: Path) -> None:
+        s = IngestSettings(use_vision_llm=True)
+        path = tmp_path / "padded.jsonl"
+        with path.open("w", encoding="utf-8") as fh:
+            fh.write("\n\n")
+            fh.write(settings_header_line(s) + "\n")
+        assert read_settings_header(path) == s.to_dict()
+
+
+# ---------------------------------------------------------------------------
+# format_ingest_settings_md
+# ---------------------------------------------------------------------------
+
+
+class TestFormatMd:
+    def test_full_settings(self) -> None:
+        out = format_ingest_settings_md(
+            {"use_vision_llm": True, "processing_mode": "premium", "should_summarize": True}
+        )
+        assert "vision_llm=`on`" in out
+        assert "processing_mode=`premium`" in out
+        assert "summarize=`on`" in out
+
+    def test_default_off(self) -> None:
+        out = format_ingest_settings_md(
+            {"use_vision_llm": False, "processing_mode": "basic", "should_summarize": False}
+        )
+        assert "vision_llm=`off`" in out
+        assert "processing_mode=`basic`" in out
+        assert "summarize=`off`" in out
+
+    def test_missing_returns_re_ingest_hint(self) -> None:
+        # Empty dict + None + non-mapping should all degrade gracefully.
+        for raw in [None, {}, "not-a-mapping"]:
+            assert "(not recorded" in format_ingest_settings_md(raw)
diff --git a/surfsense_evals/tests/core/test_metrics.py b/surfsense_evals/tests/core/test_metrics.py
new file mode 100644
index 000000000..cde1bb957
--- /dev/null
+++ b/surfsense_evals/tests/core/test_metrics.py
@@ -0,0 +1,153 @@
+"""Metric correctness — Wilson, McNemar, retrieval scores."""
+
+from __future__ import annotations
+
+import math
+
+import pytest
+
+from surfsense_evals.core.metrics import (
+    accuracy_with_wilson_ci,
+    bootstrap_delta_ci,
+    mcnemar_test,
+    mrr,
+    ndcg_at_k,
+    recall_at_k,
+    score_run,
+    wilson_ci,
+)
+
+# ---------------------------------------------------------------------------
+# Wilson
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "k,n,low,high",
+    [
+        (80, 100, 0.7111, 0.8666),  # cross-checked vs statsmodels.proportion_confint(method='wilson')
+        (50, 100, 0.4038, 0.5962),
+        (0, 0, 0.0, 1.0),
+        (0, 10, 0.0, 0.2775),
+        (10, 10, 0.7225, 1.0),
+    ],
+)
+def test_wilson_ci_known_values(k, n, low, high):
+    result_low, result_high = wilson_ci(k, n)
+    assert math.isclose(result_low, low, abs_tol=5e-4), (k, n, result_low, low)
+    assert math.isclose(result_high, high, abs_tol=5e-4), (k, n, result_high, high)
+
+
+def test_accuracy_with_wilson_ci_object():
+    res = accuracy_with_wilson_ci(70, 100)
+    assert res.accuracy == 0.7
+    assert 0.0 < res.ci_low < res.ci_high < 1.0
+
+
+def test_invalid_inputs_raise():
+    with pytest.raises(ValueError):
+        accuracy_with_wilson_ci(-1, 10)
+    with pytest.raises(ValueError):
+        accuracy_with_wilson_ci(11, 10)
+
+
+# ---------------------------------------------------------------------------
+# McNemar
+# ---------------------------------------------------------------------------
+
+
+def test_mcnemar_degenerate_returns_p_value_one():
+    a = [True, True, False, False]
+    b = [True, True, False, False]
+    res = mcnemar_test(a, b)
+    assert res.b == 0 and res.c == 0
+    assert res.p_value == 1.0
+    assert res.method == "degenerate"
+
+
+def test_mcnemar_exact_branch_strong_signal():
+    """B = 0, C = 10 → exact two-sided binomial p == 2 * (1/2)**10."""
+
+    a = [True] * 10 + [False] * 10
+    b = [True] * 10 + [True] * 10  # surfsense beats native on the 10 native-wrong
+    res = mcnemar_test(a, b)
+    assert res.b == 0
+    assert res.c == 10
+    assert res.method == "exact"
+    expected = 2 * (0.5 ** 10)
+    assert math.isclose(res.p_value, expected, rel_tol=1e-9)
+
+
+def test_mcnemar_chi_square_approx_for_large_discordant():
+    # Construct b=15, c=5 with continuity-corrected chi^2 = (|10|-1)^2/20 = 4.05.
+    a = [True] * 15 + [False] * 5 + [True] * 30 + [False] * 30
+    b = [False] * 15 + [True] * 5 + [True] * 30 + [False] * 30
+    res = mcnemar_test(a, b)
+    assert res.method == "chi2_cc"
+    assert res.b == 15 and res.c == 5
+    assert math.isclose(res.statistic, ((abs(15 - 5) - 1) ** 2) / 20.0, rel_tol=1e-9)
+    # p ≈ chi2.sf(4.05, df=1) ≈ 0.04417
+    assert 0.04 < res.p_value < 0.05
+
+
+def test_mcnemar_length_mismatch():
+    with pytest.raises(ValueError):
+        mcnemar_test([True], [True, False])
+
+
+# ---------------------------------------------------------------------------
+# Bootstrap
+# ---------------------------------------------------------------------------
+
+
+def test_bootstrap_delta_ci_shape_and_determinism():
+    a = [True, True, False, True, False, False, True, True]
+    b = [True, True, True, True, True, False, True, False]
+    res1 = bootstrap_delta_ci(a, b, n_resamples=500, random_state=42)
+    res2 = bootstrap_delta_ci(a, b, n_resamples=500, random_state=42)
+    assert res1.delta == res2.delta
+    assert res1.ci_low == res2.ci_low
+    assert res1.ci_high == res2.ci_high
+    assert res1.ci_low <= res1.delta <= res1.ci_high
+    assert res1.n_resamples == 500
+
+
+# ---------------------------------------------------------------------------
+# Retrieval
+# ---------------------------------------------------------------------------
+
+
+def test_recall_at_k():
+    retrieved = ["a", "b", "c", "d"]
+    relevant = ["b", "d", "z"]
+    assert recall_at_k(retrieved, relevant, k=2) == pytest.approx(1 / 3)
+    assert recall_at_k(retrieved, relevant, k=4) == pytest.approx(2 / 3)
+
+
+def test_mrr():
+    assert mrr(["a", "b", "c"], ["c"]) == pytest.approx(1 / 3)
+    assert mrr(["x", "y"], ["z"]) == 0.0
+
+
+def test_ndcg_at_k_perfect_order():
+    qrels = {"a": 2, "b": 1}
+    assert ndcg_at_k(["a", "b"], qrels, k=2) == pytest.approx(1.0)
+
+
+def test_ndcg_at_k_irrelevant_first():
+    qrels = {"a": 2, "b": 1}
+    # Wrong order should still be > 0 but < 1
+    val = ndcg_at_k(["c", "a", "b"], qrels, k=3)
+    assert 0 < val < 1
+
+
+def test_score_run_aggregates_across_queries():
+    scores = score_run(
+        per_query_retrieved={"q1": ["a", "b"], "q2": ["x", "y", "z"]},
+        per_query_qrels={"q1": {"a": 1}, "q2": {"z": 2}},
+        ks=(1, 5),
+        ndcg_k=5,
+    )
+    assert scores.n_queries == 2
+    assert scores.recall_at_k[1] == pytest.approx((1 + 0) / 2)  # q1 hits @1, q2 doesn't
+    assert scores.mrr == pytest.approx((1.0 + 1 / 3) / 2)
diff --git a/surfsense_evals/tests/core/test_parse_answer_letter.py b/surfsense_evals/tests/core/test_parse_answer_letter.py
new file mode 100644
index 000000000..5adbf4bc3
--- /dev/null
+++ b/surfsense_evals/tests/core/test_parse_answer_letter.py
@@ -0,0 +1,27 @@
+"""Tests for the MCQ answer-letter extractor."""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.core.parse import extract_answer_letter
+from surfsense_evals.core.parse.answer_letter import AnswerLetterResult
+
+
+@pytest.mark.parametrize(
+    "text,expected_letter,expected_strategy",
+    [
+        ('```json\n{"step_by_step_thinking": "...", "answer_choice": "B"}\n```', "B", "json_envelope"),
+        ('Reasoning... {"step_by_step_thinking": "x", "answer_choice": "C"}', "C", "json_envelope"),
+        ("Long reasoning.\nAnswer: D", "D", "answer_line"),
+        ("The correct answer is (A).", "A", "answer_line"),
+        ("Final answer: e", "E", "answer_line"),
+        ("Long reasoning.\n\nB", "B", "bare_letter"),
+        ("Long reasoning.\n\n(C).", "C", "bare_letter"),
+        ("", None, "none"),
+        ("Just narrative without an answer.", None, "none"),
+    ],
+)
+def test_extract_answer_letter(text, expected_letter, expected_strategy):
+    result = extract_answer_letter(text)
+    assert result == AnswerLetterResult(expected_letter, expected_strategy)
diff --git a/surfsense_evals/tests/core/test_parse_citations.py b/surfsense_evals/tests/core/test_parse_citations.py
new file mode 100644
index 000000000..eb444dab2
--- /dev/null
+++ b/surfsense_evals/tests/core/test_parse_citations.py
@@ -0,0 +1,108 @@
+"""Parity tests for the citation regex.
+
+Each row mirrors a case from the canonical TS reference at
+``surfsense_web/lib/citations/citation-parser.ts``. If a future PR
+loosens or tightens the TS regex, these tests will start failing;
+that's the explicit signal to re-port the change.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.core.parse import (
+    CITATION_REGEX,
+    ChunkCitation,
+    UrlCitation,
+    parse_citations,
+)
+
+PARITY_TABLE = [
+    # (input, expected number of matches, expected first-token kind/value)
+    ("Plain text with no citation.", 0, None),
+    (
+        "The patient has fever [citation:42] and cough.",
+        1,
+        ChunkCitation(chunk_id=42, is_docs_chunk=False),
+    ),
+    (
+        "Negative chunk ids work [citation:-7].",
+        1,
+        ChunkCitation(chunk_id=-7, is_docs_chunk=False),
+    ),
+    (
+        "doc-prefix [citation:doc-12].",
+        1,
+        ChunkCitation(chunk_id=12, is_docs_chunk=True),
+    ),
+    (
+        "Multi id [citation:1, doc-2, -3].",
+        3,
+        ChunkCitation(chunk_id=1, is_docs_chunk=False),
+    ),
+    (
+        "URL form [citation:https://x.com/a].",
+        1,
+        UrlCitation(url="https://x.com/a"),
+    ),
+    (
+        "Chinese brackets【citation:5】.",
+        1,
+        ChunkCitation(chunk_id=5, is_docs_chunk=False),
+    ),
+    (
+        "ZWSP-decorated [\u200bcitation:9\u200b].",
+        1,
+        ChunkCitation(chunk_id=9, is_docs_chunk=False),
+    ),
+    (
+        "Whitespace [citation:  doc-100 ] tolerated.",
+        1,
+        ChunkCitation(chunk_id=100, is_docs_chunk=True),
+    ),
+    (
+        # The TS regex's URL char class excludes ']', so a trailing
+        # bracket isn't swallowed.
+        "Two URLs [citation:https://a.io] and [citation:https://b.io].",
+        2,
+        UrlCitation(url="https://a.io"),
+    ),
+    (
+        # Garbled form should match nothing.
+        "Citation-like but wrong [citation:].",
+        0,
+        None,
+    ),
+]
+
+
+@pytest.mark.parametrize("text,n_expected,first", PARITY_TABLE)
+def test_citation_regex_parity(text: str, n_expected: int, first):
+    tokens = parse_citations(text)
+    assert len(tokens) == n_expected, (text, tokens)
+    if first is not None:
+        assert tokens[0] == first, (text, tokens)
+
+
+def test_regex_pattern_matches_ts_source():
+    """Sanity: the compiled pattern carries the exact alternatives the TS source does."""
+
+    pattern = CITATION_REGEX.pattern
+    assert "https?://" in pattern
+    assert "urlcite" in pattern
+    assert "doc-" in pattern
+    assert "\u200B" in pattern
+    assert "【" in pattern and "】" in pattern
+
+
+def test_url_map_resolution():
+    text = "Inline placeholder [citation:urlcite0]."
+    tokens = parse_citations(text, url_map={"urlcite0": "https://resolved.example/x"})
+    assert tokens == [UrlCitation(url="https://resolved.example/x")]
+
+
+def test_url_map_missing_key_drops_token():
+    """Missing urlcite resolution returns no token (TS behaviour)."""
+
+    text = "[citation:urlcite99]"
+    assert parse_citations(text, url_map={}) == []
diff --git a/surfsense_evals/tests/core/test_parse_freeform_answer.py b/surfsense_evals/tests/core/test_parse_freeform_answer.py
new file mode 100644
index 000000000..bdc7d74fc
--- /dev/null
+++ b/surfsense_evals/tests/core/test_parse_freeform_answer.py
@@ -0,0 +1,73 @@
+"""Tests for ``surfsense_evals.core.parse.freeform_answer``."""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.core.parse.freeform_answer import extract_freeform_answer
+
+
+class TestExtractFreeformAnswer:
+    def test_empty_string_returns_empty(self) -> None:
+        assert extract_freeform_answer("") == ""
+        assert extract_freeform_answer("   \n\n  ") == ""
+
+    def test_simple_answer_marker(self) -> None:
+        assert extract_freeform_answer("Answer: 42") == "42"
+
+    def test_final_answer_marker(self) -> None:
+        assert extract_freeform_answer("Final answer: Paris") == "Paris"
+
+    def test_the_answer_is_marker(self) -> None:
+        assert extract_freeform_answer("The answer is: not answerable") == "not answerable"
+
+    def test_multiline_picks_last_answer_marker(self) -> None:
+        text = "Let me think...\nAnswer: 5\nAnswer: 7\n"
+        assert extract_freeform_answer(text) == "7"
+
+    def test_falls_back_to_last_nonempty_line(self) -> None:
+        text = "Some thinking here.\n\n42"
+        assert extract_freeform_answer(text) == "42"
+
+    def test_strips_quotes(self) -> None:
+        assert extract_freeform_answer('Answer: "Paris"') == "Paris"
+        assert extract_freeform_answer("Answer: 'Paris'") == "Paris"
+
+    def test_strips_backticks(self) -> None:
+        assert extract_freeform_answer("Answer: `42`") == "42"
+
+    def test_uses_fenced_block_when_no_marker(self) -> None:
+        text = "Here's my response:\n```\nfinal value\n```\n"
+        assert extract_freeform_answer(text) == "final value"
+
+    def test_case_insensitive_markers(self) -> None:
+        assert extract_freeform_answer("ANSWER: yes") == "yes"
+        assert extract_freeform_answer("answer: no") == "no"
+
+    @pytest.mark.parametrize("text,expected", [
+        ("Answer: 1, 2, 3", "1, 2, 3"),
+        ("Answer: 3.14", "3.14"),
+        ("Answer:    spaced   ", "spaced"),
+    ])
+    def test_various_payloads(self, text: str, expected: str) -> None:
+        assert extract_freeform_answer(text) == expected
+
+    def test_inline_answer_after_thinking_trace(self) -> None:
+        # Agent replies sometimes glue their thinking onto the same
+        # line as the final "Answer: ..." marker (no newline before it).
+        # The line-anchored regex misses this; the inline fallback
+        # should still extract the right value.
+        text = (
+            "Need the Charlotte Bronte book title/year and the rank "
+            "for a 128-foot NYC building.Answer: 128th"
+        )
+        assert extract_freeform_answer(text) == "128th"
+
+    def test_inline_picks_last_inline_answer(self) -> None:
+        text = "Thought: maybe Answer: 5 is right? Actually Answer: 7."
+        assert extract_freeform_answer(text) == "7."
+
+    def test_inline_does_not_override_proper_marker(self) -> None:
+        # When a clean line-anchored "Answer: ..." exists, that wins.
+        text = "Some preamble.Answer: 99\nAnswer: 42"
+        assert extract_freeform_answer(text) == "42"
diff --git a/surfsense_evals/tests/core/test_parse_sse.py b/surfsense_evals/tests/core/test_parse_sse.py
new file mode 100644
index 000000000..362717288
--- /dev/null
+++ b/surfsense_evals/tests/core/test_parse_sse.py
@@ -0,0 +1,84 @@
+"""Tests for the SSE consumer."""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.core.parse import iter_sse_events
+
+
+async def _alist(it):
+    out = []
+    async for x in it:
+        out.append(x)
+    return out
+
+
+async def _astream(lines):
+    for line in lines:
+        yield line
+
+
+@pytest.mark.asyncio
+async def test_basic_data_frame():
+    events = await _alist(
+        iter_sse_events(_astream([
+            'data: {"type": "text-delta", "delta": "hi"}',
+            "",
+            'data: {"type": "finish"}',
+            "",
+        ]))
+    )
+    assert [e.data for e in events] == [
+        '{"type": "text-delta", "delta": "hi"}',
+        '{"type": "finish"}',
+    ]
+
+
+@pytest.mark.asyncio
+async def test_done_sentinel_passes_through():
+    events = await _alist(
+        iter_sse_events(_astream([
+            "data: [DONE]",
+            "",
+        ]))
+    )
+    assert [e.data for e in events] == ["[DONE]"]
+
+
+@pytest.mark.asyncio
+async def test_multiline_data_joins_with_newline():
+    events = await _alist(
+        iter_sse_events(_astream([
+            "data: line1",
+            "data: line2",
+            "",
+        ]))
+    )
+    assert events[0].data == "line1\nline2"
+
+
+@pytest.mark.asyncio
+async def test_comments_and_other_fields_ignored():
+    events = await _alist(
+        iter_sse_events(_astream([
+            ": heartbeat",
+            "event: foo",
+            "id: 123",
+            "data: payload",
+            "",
+        ]))
+    )
+    assert [e.data for e in events] == ["payload"]
+
+
+@pytest.mark.asyncio
+async def test_handles_missing_trailing_blank():
+    """Some servers omit the final blank line; the consumer should still emit."""
+
+    events = await _alist(
+        iter_sse_events(_astream([
+            "data: only-one",
+        ]))
+    )
+    assert [e.data for e in events] == ["only-one"]
diff --git a/surfsense_evals/tests/core/test_pdf_render.py b/surfsense_evals/tests/core/test_pdf_render.py
new file mode 100644
index 000000000..facdabbe8
--- /dev/null
+++ b/surfsense_evals/tests/core/test_pdf_render.py
@@ -0,0 +1,51 @@
+"""Smoke tests for PDF rendering.
+
+We don't pull a full PDF parser into the test deps; the assertions
+are bytes-level (``%PDF`` magic, deterministic CreationDate scrub).
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from surfsense_evals.core.pdf import render_pdf, render_text_files_to_pdf
+
+
+def test_render_pdf_writes_pdf_with_magic(tmp_path: Path):
+    out = tmp_path / "out.pdf"
+    rendered = render_pdf(
+        title="Test",
+        sections=[("intro", "Hello world."), ("body", "Line one.\nLine two.")],
+        output_path=out,
+    )
+    assert rendered.path == out
+    assert out.exists()
+    assert out.read_bytes().startswith(b"%PDF-")
+
+
+def test_render_pdf_deterministic_dates(tmp_path: Path):
+    out_a = tmp_path / "a.pdf"
+    out_b = tmp_path / "b.pdf"
+    sections = [("only", "deterministic body content")]
+    render_pdf(title="Det", sections=sections, output_path=out_a)
+    render_pdf(title="Det", sections=sections, output_path=out_b)
+    # CreationDate / ModDate are scrubbed to a fixed value, so the two
+    # files should compare equal (modulo any other internal randomness
+    # — reportlab's basic outputs are deterministic given fixed inputs).
+    assert out_a.read_bytes() == out_b.read_bytes()
+
+
+def test_render_text_files_uses_filename_as_section(tmp_path: Path):
+    files_dir = tmp_path / "src"
+    files_dir.mkdir()
+    (files_dir / "admission_note.txt").write_text("history of present illness", encoding="utf-8")
+    (files_dir / "labs.txt").write_text("Na 138, K 4.0", encoding="utf-8")
+    out = tmp_path / "case.pdf"
+    rendered = render_text_files_to_pdf(
+        title="Case 1",
+        files=[files_dir / "admission_note.txt", files_dir / "labs.txt"],
+        output_path=out,
+    )
+    assert out.exists()
+    # We don't decode the PDF; the n_chars estimate should reflect both inputs.
+    assert rendered.n_chars >= len("history of present illness") + len("Na 138, K 4.0")
diff --git a/surfsense_evals/tests/core/test_pdf_render_with_images.py b/surfsense_evals/tests/core/test_pdf_render_with_images.py
new file mode 100644
index 000000000..c29503bc9
--- /dev/null
+++ b/surfsense_evals/tests/core/test_pdf_render_with_images.py
@@ -0,0 +1,73 @@
+"""Tests for ``render_pdf_with_images`` — covers image embedding +
+deterministic byte output, mirroring ``test_pdf_render.py`` for the
+text-only path.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.core.pdf import PdfImage, render_pdf_with_images
+
+
+@pytest.fixture
+def tiny_png(tmp_path: Path) -> Path:
+    """Generate a real 4x4 PNG via Pillow — embeds cleanly in reportlab.
+
+    Hand-crafted PNG headers tend to fail PIL's strict decoder, so we
+    delegate to Pillow which is already a transitive dep of reportlab.
+    """
+
+    from PIL import Image as PILImage
+
+    p = tmp_path / "pixel.png"
+    PILImage.new("RGB", (4, 4), color=(128, 128, 128)).save(p, format="PNG")
+    return p
+
+
+class TestRenderPdfWithImages:
+    def test_renders_pdf_with_no_images(self, tmp_path: Path) -> None:
+        out = tmp_path / "out.pdf"
+        rendered = render_pdf_with_images(
+            title="Test",
+            sections=[("Heading", "Body text here.", None)],
+            output_path=out,
+        )
+        assert rendered.path == out
+        assert out.exists()
+        assert out.read_bytes().startswith(b"%PDF-")
+
+    def test_renders_pdf_with_one_image(self, tmp_path: Path, tiny_png: Path) -> None:
+        out = tmp_path / "out.pdf"
+        render_pdf_with_images(
+            title="Test",
+            sections=[("Case", "Body text.", [PdfImage(path=tiny_png, caption="A pixel")])],
+            output_path=out,
+        )
+        assert out.exists()
+        assert out.stat().st_size > 200  # not empty
+
+    def test_deterministic_bytes(self, tmp_path: Path, tiny_png: Path) -> None:
+        out_a = tmp_path / "a.pdf"
+        out_b = tmp_path / "b.pdf"
+        sections = [
+            ("Case", "Some text.", [PdfImage(path=tiny_png, caption="cap")]),
+            ("Options", "A) one\nB) two", None),
+        ]
+        render_pdf_with_images(title="Test", sections=sections, output_path=out_a)
+        render_pdf_with_images(title="Test", sections=sections, output_path=out_b)
+        assert out_a.read_bytes() == out_b.read_bytes()
+
+    def test_skips_invalid_image_silently(self, tmp_path: Path) -> None:
+        """A bad image path should not abort the whole PDF render."""
+
+        out = tmp_path / "out.pdf"
+        render_pdf_with_images(
+            title="Test",
+            sections=[("Case", "Text", [PdfImage(path=tmp_path / "nope.jpg", caption="x")])],
+            output_path=out,
+        )
+        assert out.exists()
+        assert out.read_bytes().startswith(b"%PDF-")
diff --git a/surfsense_evals/tests/core/test_provider_openrouter.py b/surfsense_evals/tests/core/test_provider_openrouter.py
new file mode 100644
index 000000000..eb78aa053
--- /dev/null
+++ b/surfsense_evals/tests/core/test_provider_openrouter.py
@@ -0,0 +1,121 @@
+"""respx-mocked tests for the OpenRouter PDF provider."""
+
+from __future__ import annotations
+
+import base64
+import json
+from pathlib import Path
+
+import httpx
+import pytest
+import respx
+
+from surfsense_evals.core.providers.openrouter_pdf import (
+    OpenRouterPdfProvider,
+    PdfEngine,
+)
+
+_BASE = "https://openrouter.test"
+
+
+@pytest.fixture
+def tiny_pdf(tmp_path: Path) -> Path:
+    p = tmp_path / "case.pdf"
+    p.write_bytes(b"%PDF-1.4 minimal content")
+    return p
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_payload_shape_matches_openrouter_docs(respx_mock, tiny_pdf: Path):
+    captured = {}
+
+    def _capture(request):
+        captured["body"] = json.loads(request.content)
+        captured["headers"] = dict(request.headers)
+        return httpx.Response(
+            200,
+            json={
+                "choices": [{
+                    "message": {"content": "Answer: B"},
+                    "finish_reason": "stop",
+                }],
+                "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15, "cost": 0.0001},
+            },
+        )
+
+    respx_mock.post("/chat/completions").mock(side_effect=_capture)
+
+    provider = OpenRouterPdfProvider(
+        api_key="sk-or-test",
+        base_url=_BASE,
+        model="anthropic/claude-sonnet-4.5",
+        engine=PdfEngine.NATIVE,
+    )
+    response = await provider.complete(prompt="What is the diagnosis?", pdf_path=tiny_pdf)
+    body = captured["body"]
+    assert body["model"] == "anthropic/claude-sonnet-4.5"
+    assert body["plugins"] == [{"id": "file-parser", "pdf": {"engine": "native"}}]
+    user = body["messages"][-1]
+    assert user["role"] == "user"
+    file_part = user["content"][0]
+    assert file_part["type"] == "file"
+    assert file_part["file"]["filename"] == tiny_pdf.name
+    assert file_part["file"]["file_data"].startswith("data:application/pdf;base64,")
+    assert (
+        base64.b64decode(file_part["file"]["file_data"].split(",", 1)[1])
+        == tiny_pdf.read_bytes()  # noqa: ASYNC240 — test fixture, sync read is fine
+    )
+    assert user["content"][1] == {"type": "text", "text": "What is the diagnosis?"}
+    assert captured["headers"]["authorization"] == "Bearer sk-or-test"
+    assert captured["headers"].get("x-title") == "SurfSense-evals"
+
+    assert response.text == "Answer: B"
+    assert response.input_tokens == 10
+    assert response.output_tokens == 5
+    assert response.total_tokens == 15
+    # cost 0.0001 USD == 100 micros
+    assert response.cost_micros == 100
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_chat_array_content_concatenates(respx_mock, tiny_pdf: Path):
+    respx_mock.post("/chat/completions").mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "choices": [{
+                    "message": {
+                        "content": [
+                            {"type": "text", "text": "Hello "},
+                            {"type": "text", "text": "world"},
+                            {"type": "image_url", "image_url": "ignored"},
+                        ]
+                    }
+                }],
+                "usage": {"prompt_tokens": 1, "completion_tokens": 1},
+            },
+        )
+    )
+    provider = OpenRouterPdfProvider(
+        api_key="sk-or-test", base_url=_BASE, model="x/y"
+    )
+    response = await provider.complete(prompt="hi", pdf_path=tiny_pdf)
+    assert response.text == "Hello world"
+
+
+@pytest.mark.asyncio
+@respx.mock(base_url=_BASE)
+async def test_provider_raises_on_4xx(respx_mock, tiny_pdf: Path):
+    respx_mock.post("/chat/completions").mock(
+        return_value=httpx.Response(429, json={"error": {"message": "rate limited"}})
+    )
+    provider = OpenRouterPdfProvider(api_key="sk-or-test", base_url=_BASE, model="x/y")
+    with pytest.raises(httpx.HTTPStatusError):
+        await provider.complete(prompt="hi", pdf_path=tiny_pdf)
+
+
+def test_missing_api_key_raises():
+    with pytest.raises(ValueError):
+        OpenRouterPdfProvider(api_key="", base_url=_BASE, model="x/y")
diff --git a/surfsense_evals/tests/core/test_registry.py b/surfsense_evals/tests/core/test_registry.py
new file mode 100644
index 000000000..ffdbf2261
--- /dev/null
+++ b/surfsense_evals/tests/core/test_registry.py
@@ -0,0 +1,58 @@
+"""Registry + auto-discovery tests.
+
+* Auto-discovery skips packages starting with ``_`` (so test fixtures
+  don't leak into the production catalogue).
+* Manually importing a ``_demo`` benchmark fires its ``register(...)``
+  call and the CLI sees it.
+"""
+
+from __future__ import annotations
+
+import importlib
+
+from surfsense_evals.core import registry
+
+
+def _force_register_demo() -> None:
+    """Import (or reload) the demo module so its ``register(...)`` runs.
+
+    On a fresh interpreter, ``import_module`` triggers package
+    initialization. After the first call though, the module is cached
+    in ``sys.modules`` and a second ``import_module`` is a no-op — so
+    if a previous test already unregistered the entry, we have to
+    ``reload`` to re-execute the module body.
+    """
+
+    module = importlib.import_module("surfsense_evals.suites._demo.hello")
+    if ("_demo", "hello") not in registry.snapshot():
+        importlib.reload(module)
+
+
+def test_auto_discovery_skips_underscore_prefixed_subpackages():
+    from surfsense_evals.suites import discover_suites
+
+    discovered = discover_suites()
+    assert all(not part.startswith("_") for full in discovered for part in full.split("."))
+    # The medical suite's headline benchmark must always discover.
+    assert any(name.endswith(".medical.medxpertqa") for name in discovered)
+
+
+def test_demo_benchmark_registers_on_explicit_import():
+    _force_register_demo()
+    bench = registry.get("_demo", "hello")
+    assert bench is not None
+    assert bench.name == "hello"
+    assert bench.headline is False
+    # Cleanup so the test is idempotent under repeated runs.
+    registry.unregister("_demo", "hello")
+
+
+def test_register_unregister_roundtrip():
+    # Make sure no stale entry from a prior test in the session.
+    if ("_demo", "hello") in registry.snapshot():
+        registry.unregister("_demo", "hello")
+    snapshot_before = dict(registry.snapshot())
+    _force_register_demo()
+    assert ("_demo", "hello") in registry.snapshot()
+    registry.unregister("_demo", "hello")
+    assert dict(registry.snapshot()) == snapshot_before
diff --git a/surfsense_evals/tests/core/test_scenarios.py b/surfsense_evals/tests/core/test_scenarios.py
new file mode 100644
index 000000000..5e93c266b
--- /dev/null
+++ b/surfsense_evals/tests/core/test_scenarios.py
@@ -0,0 +1,68 @@
+"""Tests for the shared scenario formatter used in head-to-head reports."""
+
+from __future__ import annotations
+
+from surfsense_evals.core.scenarios import format_scenario_md
+
+
+def test_head_to_head_renders_both_arms_same_slug():
+    extra = {
+        "scenario": "head-to-head",
+        "provider_model": "anthropic/claude-sonnet-4.5",
+    }
+    line = format_scenario_md(extra)
+    assert "head-to-head" in line
+    assert "anthropic/claude-sonnet-4.5" in line
+
+
+def test_head_to_head_includes_vision_slug_when_recorded():
+    extra = {
+        "scenario": "head-to-head",
+        "provider_model": "anthropic/claude-sonnet-4.5",
+        "vision_provider_model": "anthropic/claude-sonnet-4.5",
+    }
+    line = format_scenario_md(extra)
+    assert "ingest VLM" in line
+    assert "claude-sonnet-4.5" in line
+
+
+def test_symmetric_cheap_calls_out_native_arm_disadvantage():
+    extra = {
+        "scenario": "symmetric-cheap",
+        "provider_model": "openai/gpt-5.4-mini",
+        "vision_provider_model": "anthropic/claude-sonnet-4.5",
+    }
+    line = format_scenario_md(extra)
+    assert "**symmetric-cheap**" in line
+    assert "gpt-5.4-mini" in line
+    # The "structurally loses" disclaimer must be there so reviewers
+    # don't read this as a fair comparison.
+    assert "structurally loses" in line.lower() or "structurally_loses" in line.lower()
+
+
+def test_cost_arbitrage_distinguishes_native_and_surfsense_slugs():
+    extra = {
+        "scenario": "cost-arbitrage",
+        "provider_model": "openai/gpt-5.4-mini",
+        "native_arm_model": "anthropic/claude-sonnet-4.5",
+        "vision_provider_model": "anthropic/claude-sonnet-4.5",
+    }
+    line = format_scenario_md(extra)
+    assert "**cost-arbitrage**" in line
+    # Both slugs surface; reader can see the asymmetry at a glance.
+    assert "anthropic/claude-sonnet-4.5" in line
+    assert "openai/gpt-5.4-mini" in line
+    assert "fraction of the per-query cost" in line
+
+
+def test_legacy_artifact_without_scenario_renders_as_head_to_head():
+    """Old run_artifact.json files don't have ``scenario`` — must still render."""
+
+    extra = {"provider_model": "anthropic/claude-sonnet-4.5"}
+    line = format_scenario_md(extra)
+    assert "head-to-head" in line
+
+
+def test_none_extra_does_not_crash():
+    line = format_scenario_md(None)
+    assert "head-to-head" in line
diff --git a/surfsense_evals/tests/core/test_vision_llm.py b/surfsense_evals/tests/core/test_vision_llm.py
new file mode 100644
index 000000000..5c3dfd719
--- /dev/null
+++ b/surfsense_evals/tests/core/test_vision_llm.py
@@ -0,0 +1,121 @@
+"""Tests for vision LLM auto-pick + explicit-slug resolution."""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.core.clients.search_space import VisionLlmConfigEntry
+from surfsense_evals.core.vision_llm import (
+    RECOMMENDED_VISION_PRIORITY,
+    VisionConfigError,
+    resolve_vision_llm,
+)
+
+
+def _entry(*, id: int, model_name: str, provider: str = "OPENROUTER") -> VisionLlmConfigEntry:
+    return VisionLlmConfigEntry(
+        id=id,
+        name=f"OpenRouter • {model_name}",
+        provider=provider,
+        model_name=model_name,
+        is_auto_mode=False,
+        raw={},
+    )
+
+
+# ---------------------------------------------------------------------------
+# Explicit slug resolution
+# ---------------------------------------------------------------------------
+
+
+def test_explicit_slug_resolves_to_matching_config_id():
+    candidates = [
+        _entry(id=-101, model_name="anthropic/claude-sonnet-4.5"),
+        _entry(id=-102, model_name="openai/gpt-5"),
+    ]
+    resolved = resolve_vision_llm(candidates, explicit_slug="openai/gpt-5")
+    assert resolved.config_id == -102
+    assert resolved.provider_model == "openai/gpt-5"
+    assert resolved.selected_via == "explicit"
+
+
+def test_explicit_slug_with_no_match_raises_with_helpful_listing():
+    candidates = [_entry(id=-101, model_name="anthropic/claude-sonnet-4.5")]
+    with pytest.raises(VisionConfigError) as exc_info:
+        resolve_vision_llm(candidates, explicit_slug="some/missing-slug")
+    msg = str(exc_info.value)
+    assert "some/missing-slug" in msg
+    assert "anthropic/claude-sonnet-4.5" in msg  # surfaced as a sample
+
+
+def test_explicit_slug_skips_non_openrouter_entries():
+    """A YAML BYOK entry with a colliding model_name shouldn't accidentally match."""
+
+    candidates = [
+        _entry(id=42, model_name="openai/gpt-5", provider="OPENAI"),
+        _entry(id=-101, model_name="openai/gpt-5"),
+    ]
+    resolved = resolve_vision_llm(candidates, explicit_slug="openai/gpt-5")
+    assert resolved.config_id == -101  # the OpenRouter one, not the BYOK one
+
+
+# ---------------------------------------------------------------------------
+# Auto-pick by recommended priority
+# ---------------------------------------------------------------------------
+
+
+def test_auto_pick_walks_priority_list_in_order():
+    candidates = [
+        _entry(id=-300, model_name="google/gemini-2.5-pro"),
+        _entry(id=-200, model_name="anthropic/claude-opus-4.7"),
+        _entry(id=-100, model_name="anthropic/claude-sonnet-4.5"),
+    ]
+    resolved = resolve_vision_llm(candidates, explicit_slug=None)
+    # claude-sonnet-4.5 is first in the priority tuple, so it wins.
+    assert resolved.config_id == -100
+    assert resolved.provider_model == "anthropic/claude-sonnet-4.5"
+    assert resolved.selected_via == "auto-priority"
+
+
+def test_auto_pick_skips_to_next_priority_when_first_unavailable():
+    candidates = [
+        _entry(id=-200, model_name="anthropic/claude-opus-4.7"),
+        _entry(id=-300, model_name="google/gemini-2.5-pro"),
+    ]
+    resolved = resolve_vision_llm(candidates, explicit_slug=None)
+    # claude-sonnet-4.5 not registered → claude-opus-4.7 is next in priority.
+    assert resolved.provider_model == "anthropic/claude-opus-4.7"
+    assert resolved.selected_via == "auto-priority"
+
+
+def test_auto_pick_falls_back_to_first_openrouter_when_no_recommended_match():
+    candidates = [
+        _entry(id=-700, model_name="some/exotic-vision-model"),
+        _entry(id=-800, model_name="another/exotic-vision-model"),
+    ]
+    resolved = resolve_vision_llm(candidates, explicit_slug=None)
+    # Neither matches the priority list → first OpenRouter entry wins.
+    assert resolved.config_id == -700
+    assert resolved.selected_via == "auto-fallback"
+
+
+def test_auto_pick_with_zero_openrouter_candidates_raises():
+    candidates: list[VisionLlmConfigEntry] = []
+    with pytest.raises(VisionConfigError) as exc_info:
+        resolve_vision_llm(candidates, explicit_slug=None)
+    assert "vision_enabled: true" in str(exc_info.value)
+
+
+def test_auto_pick_ignores_non_openrouter_entries():
+    candidates = [
+        _entry(id=99, model_name="anthropic/claude-sonnet-4.5", provider="ANTHROPIC"),
+    ]
+    with pytest.raises(VisionConfigError):
+        resolve_vision_llm(candidates, explicit_slug=None)
+
+
+def test_recommended_priority_is_a_stable_public_list():
+    """If you reorder this, update the README's auto-pick claim too."""
+
+    assert RECOMMENDED_VISION_PRIORITY[0] == "anthropic/claude-sonnet-4.5"
+    assert "google/gemini-2.5-pro" in RECOMMENDED_VISION_PRIORITY
diff --git a/surfsense_evals/tests/suites/__init__.py b/surfsense_evals/tests/suites/__init__.py
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/surfsense_evals/tests/suites/__init__.py
@@ -0,0 +1 @@
+
diff --git a/surfsense_evals/tests/suites/test_crag_dataset.py b/surfsense_evals/tests/suites/test_crag_dataset.py
new file mode 100644
index 000000000..36114b52e
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_crag_dataset.py
@@ -0,0 +1,224 @@
+"""Tests for the CRAG dataset loader (parser + sampling).
+
+The full bz2 download is excluded — these tests synthesise a tiny
+JSONL-bz2 in a tmp dir and verify the parser / stratified-sampler
+produce well-shaped objects.
+"""
+
+from __future__ import annotations
+
+import bz2
+import json
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.suites.research.crag.dataset import (
+    CragPage,
+    CragQuestion,
+    iter_questions,
+    stratified_sample,
+)
+
+
+def _make_jsonl_bz2(rows: list[dict], tmp_path: Path) -> Path:
+    """Write ``rows`` as one JSON object per line, bz2-compressed."""
+
+    dest = tmp_path / "fake.jsonl.bz2"
+    payload = "\n".join(json.dumps(r) for r in rows).encode("utf-8")
+    with bz2.open(dest, "wb") as fh:
+        fh.write(payload)
+    return dest
+
+
+def _row(
+    *,
+    interaction_id: str,
+    query: str,
+    answer: str,
+    domain: str = "movie",
+    question_type: str = "simple",
+    pages: list[dict] | None = None,
+    alt_ans: list[str] | None = None,
+    popularity: str = "head",
+    static_or_dynamic: str = "static",
+    split: int = 0,
+    query_time: str = "2024-04-01",
+) -> dict:
+    return {
+        "interaction_id": interaction_id,
+        "query_time": query_time,
+        "domain": domain,
+        "question_type": question_type,
+        "static_or_dynamic": static_or_dynamic,
+        "query": query,
+        "answer": answer,
+        "alt_ans": alt_ans or [],
+        "split": split,
+        "popularity": popularity,
+        "search_results": pages or [],
+    }
+
+
+class TestParser:
+    def test_basic_parse(self, tmp_path: Path) -> None:
+        rows = [
+            _row(
+                interaction_id="abc",
+                query="Who directed Inception?",
+                answer="Christopher Nolan",
+                pages=[{
+                    "page_name": "Inception (film)",
+                    "page_url": "https://en.wikipedia.org/wiki/Inception",
+                    "page_snippet": "snippet",
+                    "page_result": "<html>full html</html>",
+                    "page_last_modified": "2024-01-01",
+                }],
+            ),
+        ]
+        path = _make_jsonl_bz2(rows, tmp_path)
+        parsed = iter_questions(path)
+        assert len(parsed) == 1
+        q = parsed[0]
+        assert q.query == "Who directed Inception?"
+        assert q.gold_answer == "Christopher Nolan"
+        assert q.qid == "C00000"
+        assert q.domain == "movie"
+        assert q.question_type == "simple"
+        assert len(q.pages) == 1
+        page = q.pages[0]
+        assert page.page_name == "Inception (film)"
+        assert page.page_url == "https://en.wikipedia.org/wiki/Inception"
+
+    def test_skips_missing_query_or_answer(self, tmp_path: Path) -> None:
+        rows = [
+            _row(interaction_id="1", query="", answer="x"),
+            _row(interaction_id="2", query="ok?", answer=""),
+            _row(interaction_id="3", query="ok?", answer="x"),
+        ]
+        path = _make_jsonl_bz2(rows, tmp_path)
+        parsed = iter_questions(path)
+        assert len(parsed) == 1
+        assert parsed[0].interaction_id == "3"
+
+    def test_skips_empty_pages(self, tmp_path: Path) -> None:
+        rows = [
+            _row(
+                interaction_id="x",
+                query="q?",
+                answer="a",
+                pages=[
+                    {"page_url": "", "page_result": "<html/>"},  # no URL
+                    {"page_url": "https://x.test/", "page_result": ""},  # empty html
+                    {"page_url": "https://y.test/", "page_result": "<html>good</html>"},
+                ],
+            ),
+        ]
+        path = _make_jsonl_bz2(rows, tmp_path)
+        parsed = iter_questions(path)
+        assert len(parsed) == 1
+        assert len(parsed[0].pages) == 1
+        assert parsed[0].pages[0].page_url == "https://y.test/"
+
+    def test_alt_answers_parsed(self, tmp_path: Path) -> None:
+        rows = [
+            _row(interaction_id="z", query="q?", answer="42",
+                 alt_ans=["forty-two", "42.0"]),
+        ]
+        path = _make_jsonl_bz2(rows, tmp_path)
+        parsed = iter_questions(path)
+        assert parsed[0].alt_answers == ["forty-two", "42.0"]
+
+    def test_handles_malformed_line(self, tmp_path: Path) -> None:
+        # Manually construct a bz2 with one valid line and one garbage line.
+        good = json.dumps(_row(interaction_id="ok", query="q?", answer="a"))
+        path = tmp_path / "mixed.jsonl.bz2"
+        with bz2.open(path, "wb") as fh:
+            fh.write(b"not-json{\n")
+            fh.write((good + "\n").encode("utf-8"))
+        parsed = iter_questions(path)
+        # Malformed line is skipped; one good row survives at index 1.
+        assert len(parsed) == 1
+        assert parsed[0].interaction_id == "ok"
+
+
+class TestPageHash:
+    def test_url_hash_stable(self) -> None:
+        a = CragPage(
+            page_name="A", page_url="https://x.test/p?q=1",
+            page_snippet="", page_html="<html/>",
+        )
+        b = CragPage(
+            page_name="B", page_url="https://x.test/p?q=1",
+            page_snippet="", page_html="<html/>",
+        )
+        assert a.url_hash == b.url_hash
+        assert len(a.url_hash) == 12
+
+    def test_url_hash_unique(self) -> None:
+        a = CragPage(
+            page_name="A", page_url="https://x.test/a", page_snippet="", page_html="<html/>",
+        )
+        b = CragPage(
+            page_name="B", page_url="https://x.test/b", page_snippet="", page_html="<html/>",
+        )
+        assert a.url_hash != b.url_hash
+
+
+class TestStratifiedSample:
+    def _make_pool(self) -> list[CragQuestion]:
+        out: list[CragQuestion] = []
+        idx = 0
+        # 30 finance/simple, 20 movie/comparison, 5 sports/multi-hop.
+        for n, domain, qtype in (
+            (30, "finance", "simple"),
+            (20, "movie", "comparison"),
+            (5, "sports", "multi-hop"),
+        ):
+            for _ in range(n):
+                out.append(CragQuestion(
+                    qid=f"C{idx:05d}",
+                    interaction_id=f"i{idx}",
+                    query_time="2024-01-01",
+                    query=f"q{idx}?",
+                    gold_answer="a",
+                    alt_answers=[],
+                    domain=domain,
+                    question_type=qtype,
+                    static_or_dynamic="static",
+                    popularity="head",
+                    split=0,
+                    raw_index=idx,
+                    pages=[],
+                ))
+                idx += 1
+        return out
+
+    def test_sample_smaller_than_pool(self) -> None:
+        pool = self._make_pool()
+        sample = stratified_sample(pool, n=15, seed=7)
+        assert len(sample) == 15
+        # Should pull from all three buckets at least once.
+        domains = {q.domain for q in sample}
+        assert domains == {"finance", "movie", "sports"}
+
+    def test_sample_returns_pool_when_n_geq(self) -> None:
+        pool = self._make_pool()
+        sample = stratified_sample(pool, n=999, seed=1)
+        assert len(sample) == len(pool)
+
+    def test_sample_sorted_by_raw_index(self) -> None:
+        pool = self._make_pool()
+        sample = stratified_sample(pool, n=10, seed=42)
+        assert [q.raw_index for q in sample] == sorted(q.raw_index for q in sample)
+
+    def test_sample_deterministic(self) -> None:
+        pool = self._make_pool()
+        s1 = stratified_sample(pool, n=20, seed=11)
+        s2 = stratified_sample(pool, n=20, seed=11)
+        assert [q.qid for q in s1] == [q.qid for q in s2]
+
+    def test_n_zero_or_negative_returns_pool(self) -> None:
+        pool = self._make_pool()
+        assert len(stratified_sample(pool, n=0)) == len(pool)
+        assert len(stratified_sample(pool, n=-1)) == len(pool)
diff --git a/surfsense_evals/tests/suites/test_crag_dataset_task3.py b/surfsense_evals/tests/suites/test_crag_dataset_task3.py
new file mode 100644
index 000000000..123628350
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_crag_dataset_task3.py
@@ -0,0 +1,259 @@
+"""Unit tests for CRAG Task 3 streaming dataset loader.
+
+We don't (and shouldn't) hit the real 7 GB upstream archive in
+unit tests. Instead we construct tiny tar.bz2 archives split across
+N parts and verify:
+
+* ``_MultiPartReader`` correctly stitches N files together.
+* The streaming path (multi → bz2 → tar → JSONL) yields parsed
+  ``CragQuestion`` rows with the right shape.
+* ``max_questions`` cap is honoured (early break, no greedy read).
+* ``parts_present`` correctly detects missing/empty parts.
+"""
+
+from __future__ import annotations
+
+import bz2
+import io
+import json
+import tarfile
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.suites.research.crag.dataset_task3 import (
+    _MultiPartReader,
+    iter_questions_task3,
+    parts_present,
+)
+
+
+# ---------------------------------------------------------------------------
+# Fixtures: build a tiny synthetic Task 3 archive
+# ---------------------------------------------------------------------------
+
+
+def _make_jsonl_payload(n_rows: int) -> bytes:
+    rows = []
+    for i in range(n_rows):
+        rows.append({
+            "interaction_id": f"int_{i:04d}",
+            "query_time": "2024-01-01 00:00:00",
+            "domain": ["finance", "music", "movie", "sports", "open"][i % 5],
+            "question_type": ["simple", "comparison", "aggregation", "multi-hop"][i % 4],
+            "static_or_dynamic": "static",
+            "popularity": "head",
+            "split": 0,
+            "query": f"Synthetic CRAG question {i}?",
+            "answer": f"answer-{i}",
+            "alt_ans": [f"alt-{i}-a", f"alt-{i}-b"],
+            "search_results": [
+                {
+                    "page_name": f"Page {j} for q{i}",
+                    "page_url": f"https://example.com/q{i}/p{j}",
+                    "page_snippet": "snippet",
+                    "page_result": f"<html><body><p>q{i} p{j} body</p></body></html>",
+                    "page_last_modified": "",
+                }
+                for j in range(50)
+            ],
+        })
+    return b"\n".join(json.dumps(r).encode("utf-8") for r in rows) + b"\n"
+
+
+def _make_tar_bz2(jsonl_bytes: bytes, *, member_name: str = "data.jsonl") -> bytes:
+    bio = io.BytesIO()
+    with bz2.BZ2File(bio, mode="wb") as bz:
+        with tarfile.open(fileobj=bz, mode="w") as tar:
+            info = tarfile.TarInfo(name=member_name)
+            info.size = len(jsonl_bytes)
+            tar.addfile(info, io.BytesIO(jsonl_bytes))
+    return bio.getvalue()
+
+
+def _make_tar_bz2_multi(shards: list[tuple[str, bytes]]) -> bytes:
+    """Build a tar.bz2 archive containing multiple JSONL shards.
+
+    Mirrors the real CRAG Task 3 layout: one tar with N JSONL members
+    named ``crag_task_3_dev_v4_{i}.jsonl`` (or whatever the caller
+    passes in).
+    """
+
+    bio = io.BytesIO()
+    with bz2.BZ2File(bio, mode="wb") as bz:
+        with tarfile.open(fileobj=bz, mode="w") as tar:
+            for name, payload in shards:
+                info = tarfile.TarInfo(name=name)
+                info.size = len(payload)
+                tar.addfile(info, io.BytesIO(payload))
+    return bio.getvalue()
+
+
+def _split_into_parts(blob: bytes, n_parts: int) -> list[bytes]:
+    """Split byte string into N roughly-equal chunks (last gets remainder)."""
+    chunk = max(1, len(blob) // n_parts)
+    parts = [blob[i * chunk : (i + 1) * chunk] for i in range(n_parts - 1)]
+    parts.append(blob[(n_parts - 1) * chunk :])
+    return parts
+
+
+@pytest.fixture
+def task3_parts_dir(tmp_path: Path) -> Path:
+    """A directory containing a 4-part synthetic CRAG Task 3 archive (12 rows)."""
+    blob = _make_tar_bz2(_make_jsonl_payload(12))
+    parts = _split_into_parts(blob, 4)
+    parts_dir = tmp_path / ".raw_cache"
+    parts_dir.mkdir()
+    for i, b in enumerate(parts, start=1):
+        (parts_dir / f"crag_task_3_dev_v4.tar.bz2.part{i}").write_bytes(b)
+    return parts_dir
+
+
+# ---------------------------------------------------------------------------
+# _MultiPartReader
+# ---------------------------------------------------------------------------
+
+
+class TestMultiPartReader:
+    def test_concatenates_parts_in_order(self, tmp_path: Path) -> None:
+        a = tmp_path / "a"
+        b = tmp_path / "b"
+        c = tmp_path / "c"
+        a.write_bytes(b"hello, ")
+        b.write_bytes(b"streaming ")
+        c.write_bytes(b"world!")
+        with _MultiPartReader([a, b, c]) as r:
+            assert r.read() == b"hello, streaming world!"
+
+    def test_read_n_crosses_part_boundary(self, tmp_path: Path) -> None:
+        a = tmp_path / "a"
+        b = tmp_path / "b"
+        a.write_bytes(b"AAA")
+        b.write_bytes(b"BBBB")
+        with _MultiPartReader([a, b]) as r:
+            # Read 5 bytes — straddles boundary between parts.
+            assert r.read(5) == b"AAABB"
+            assert r.read(5) == b"BB"
+            assert r.read(5) == b""
+
+    def test_close_is_idempotent(self, tmp_path: Path) -> None:
+        a = tmp_path / "a"
+        a.write_bytes(b"x")
+        r = _MultiPartReader([a])
+        r.close()
+        r.close()
+        with pytest.raises(ValueError):
+            r.read(1)
+
+    def test_missing_part_raises(self, tmp_path: Path) -> None:
+        with pytest.raises(FileNotFoundError):
+            _MultiPartReader([tmp_path / "does-not-exist"])
+
+    def test_empty_paths_raises(self) -> None:
+        with pytest.raises(ValueError):
+            _MultiPartReader([])
+
+
+# ---------------------------------------------------------------------------
+# iter_questions_task3
+# ---------------------------------------------------------------------------
+
+
+@pytest.fixture
+def task3_multi_shard_dir(tmp_path: Path) -> Path:
+    """A 4-part archive whose tar contains 3 JSONL shards (4 + 4 + 4 rows)."""
+    payload_a = _make_jsonl_payload(4)
+    payload_b = _make_jsonl_payload(4)
+    payload_c = _make_jsonl_payload(4)
+    blob = _make_tar_bz2_multi([
+        ("crag_task_3_dev_v4_0.jsonl", payload_a),
+        ("crag_task_3_dev_v4_1.jsonl", payload_b),
+        ("crag_task_3_dev_v4_2.jsonl", payload_c),
+    ])
+    parts = _split_into_parts(blob, 4)
+    parts_dir = tmp_path / ".raw_cache"
+    parts_dir.mkdir()
+    for i, b in enumerate(parts, start=1):
+        (parts_dir / f"crag_task_3_dev_v4.tar.bz2.part{i}").write_bytes(b)
+    return parts_dir
+
+
+class TestIterQuestionsTask3:
+    def test_streams_full_archive(self, task3_parts_dir: Path) -> None:
+        questions = iter_questions_task3(task3_parts_dir)
+        assert len(questions) == 12
+        # All questions get the T3_ prefix and 50 pages each.
+        assert all(q.qid.startswith("T3_") for q in questions)
+        assert all(len(q.pages) == 50 for q in questions)
+        # Schema fields preserved.
+        first = questions[0]
+        assert first.query == "Synthetic CRAG question 0?"
+        assert first.gold_answer == "answer-0"
+        assert first.domain == "finance"
+        assert "alt-0-a" in first.alt_answers
+
+    def test_max_questions_caps_early(self, task3_parts_dir: Path) -> None:
+        questions = iter_questions_task3(task3_parts_dir, max_questions=3)
+        assert len(questions) == 3
+        # Sequential indices 0..2 — we don't skip rows.
+        assert [q.raw_index for q in questions] == [0, 1, 2]
+
+    def test_streams_multi_shard_archive(self, task3_multi_shard_dir: Path) -> None:
+        # Three shards × four rows each = twelve rows total.
+        questions = iter_questions_task3(task3_multi_shard_dir)
+        assert len(questions) == 12
+        # raw_index increments monotonically across shards.
+        assert [q.raw_index for q in questions] == list(range(12))
+        # qids are unique and sequential across shards.
+        assert len({q.qid for q in questions}) == 12
+
+    def test_max_questions_short_circuits_first_shard(self, task3_multi_shard_dir: Path) -> None:
+        # Cap < shard size — shouldn't touch shards 1 or 2 at all.
+        questions = iter_questions_task3(task3_multi_shard_dir, max_questions=2)
+        assert len(questions) == 2
+        # Both come from shard 0 (raw_index 0, 1).
+        assert [q.raw_index for q in questions] == [0, 1]
+
+    def test_max_questions_spans_shards(self, task3_multi_shard_dir: Path) -> None:
+        # Cap = 6 → all 4 from shard 0 + first 2 from shard 1.
+        questions = iter_questions_task3(task3_multi_shard_dir, max_questions=6)
+        assert len(questions) == 6
+        assert [q.raw_index for q in questions] == [0, 1, 2, 3, 4, 5]
+
+    def test_raises_when_no_jsonl_member(self, tmp_path: Path) -> None:
+        # Archive containing a non-jsonl member.
+        bio = io.BytesIO()
+        with bz2.BZ2File(bio, mode="wb") as bz:
+            with tarfile.open(fileobj=bz, mode="w") as tar:
+                info = tarfile.TarInfo(name="README.md")
+                payload = b"not jsonl"
+                info.size = len(payload)
+                tar.addfile(info, io.BytesIO(payload))
+        parts_dir = tmp_path / ".raw_cache"
+        parts_dir.mkdir()
+        for i, name in enumerate(
+            ("part1", "part2", "part3", "part4"), start=1,
+        ):
+            half = len(bio.getvalue()) // 4
+            chunk = bio.getvalue()[(i - 1) * half : i * half if i < 4 else len(bio.getvalue())]
+            (parts_dir / f"crag_task_3_dev_v4.tar.bz2.{name}").write_bytes(chunk)
+        with pytest.raises(RuntimeError, match="No JSONL member"):
+            iter_questions_task3(parts_dir)
+
+
+# ---------------------------------------------------------------------------
+# parts_present
+# ---------------------------------------------------------------------------
+
+
+class TestPartsPresent:
+    def test_all_present(self, task3_parts_dir: Path) -> None:
+        assert parts_present(task3_parts_dir) is True
+
+    def test_one_missing(self, task3_parts_dir: Path) -> None:
+        (task3_parts_dir / "crag_task_3_dev_v4.tar.bz2.part2").unlink()
+        assert parts_present(task3_parts_dir) is False
+
+    def test_one_empty(self, task3_parts_dir: Path) -> None:
+        (task3_parts_dir / "crag_task_3_dev_v4.tar.bz2.part3").write_bytes(b"")
+        assert parts_present(task3_parts_dir) is False
diff --git a/surfsense_evals/tests/suites/test_crag_grader.py b/surfsense_evals/tests/suites/test_crag_grader.py
new file mode 100644
index 000000000..93bf6f478
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_crag_grader.py
@@ -0,0 +1,248 @@
+"""Tests for the CRAG 3-class deterministic grader.
+
+The LLM-judge fallback is excluded here (network call); these tests
+exercise the deterministic shortcut + the special-case routing for
+``false_premise`` questions and refusal detection (``I don't know``).
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.suites.research.crag.grader import (
+    CragGradeResult,
+    _flags_false_premise,
+    _is_refusal,
+    _maybe_number,
+    _normalise,
+    _whole_word_substring,
+    grade_deterministic,
+)
+
+
+class TestNormalisation:
+    def test_lowercase_and_punct_stripped(self) -> None:
+        assert _normalise("Apple Inc.") == "apple inc"
+
+    def test_articles_removed(self) -> None:
+        assert _normalise("The Apple Watch") == "apple watch"
+
+    def test_empty_returns_empty(self) -> None:
+        assert _normalise("") == ""
+
+
+class TestNumericExtraction:
+    def test_simple_int(self) -> None:
+        assert _maybe_number("42") == 42.0
+
+    def test_int_with_commas(self) -> None:
+        assert _maybe_number("$1,234") == 1234.0
+
+    def test_year_in_sentence(self) -> None:
+        assert _maybe_number("released in 2008") == 2008.0
+
+    def test_word_number(self) -> None:
+        assert _maybe_number("seven") == 7.0
+
+
+class TestWholeWordSubstring:
+    def test_phrase_match(self) -> None:
+        assert _whole_word_substring("the new york yankees", "new york")
+
+    def test_word_boundary_required(self) -> None:
+        assert not _whole_word_substring("yorkshire", "york")
+
+
+class TestRefusalDetection:
+    def test_explicit_idk(self) -> None:
+        assert _is_refusal("Answer: I don't know")
+
+    def test_idk_no_apostrophe(self) -> None:
+        assert _is_refusal("I dont know")
+
+    def test_no_information(self) -> None:
+        assert _is_refusal("There is no information available about this.")
+
+    def test_unable_to_answer(self) -> None:
+        assert _is_refusal("I am unable to answer this question.")
+
+    def test_empty_is_refusal(self) -> None:
+        assert _is_refusal("")
+        assert _is_refusal("   ")
+
+    def test_real_answer_is_not_refusal(self) -> None:
+        assert not _is_refusal("Answer: Apple Inc")
+        assert not _is_refusal("The CEO is Tim Cook.")
+
+
+class TestFalsePremiseDetection:
+    def test_explicit_false_premise(self) -> None:
+        assert _flags_false_premise(
+            "The question contains a false premise; the company never had that product."
+        )
+
+    def test_no_such(self) -> None:
+        assert _flags_false_premise("There is no such album.")
+
+    def test_did_not_happen(self) -> None:
+        assert _flags_false_premise("That event did not happen.")
+
+    def test_does_not_exist(self) -> None:
+        assert _flags_false_premise("That movie does not exist.")
+
+    def test_normal_answer_is_not_premise_flag(self) -> None:
+        assert not _flags_false_premise("Apple, headquartered in Cupertino.")
+
+
+class TestGradeDeterministicHappyPath:
+    def test_exact_match_correct(self) -> None:
+        result = grade_deterministic(pred="Tim Cook", gold="Tim Cook", question_type="simple")
+        assert result.grade == "correct"
+        assert result.score == 1
+        assert result.method == "exact"
+
+    def test_substring_match(self) -> None:
+        result = grade_deterministic(
+            pred="The answer is Tim Cook, CEO of Apple.",
+            gold="Tim Cook",
+            question_type="simple",
+        )
+        assert result.grade == "correct"
+        assert result.method == "substring"
+
+    def test_alt_answer_match(self) -> None:
+        result = grade_deterministic(
+            pred="2,008",
+            gold="two thousand eight",
+            alt_answers=["2008"],
+            question_type="simple",
+        )
+        assert result.grade == "correct"
+        assert result.score == 1
+
+    def test_numeric_within_tolerance(self) -> None:
+        result = grade_deterministic(
+            pred="The revenue was $1,234,000 USD",
+            gold="$1,234,123",
+            question_type="aggregation",
+        )
+        assert result.grade == "correct"
+        assert result.method == "numeric"
+
+    def test_numeric_outside_tolerance(self) -> None:
+        result = grade_deterministic(
+            pred="100",
+            gold="500",
+            question_type="aggregation",
+        )
+        assert result.grade == "incorrect"
+        assert result.score == -1
+
+    def test_numeric_strict_small_currency(self) -> None:
+        # CRAG (unlike FRAMES) does not apply a 0.5 absolute floor —
+        # ``$2.05`` should NOT match ``$2.17`` (≈5.5% off, well over 1%).
+        result = grade_deterministic(
+            pred="$2.05",
+            gold="$2.17",
+            question_type="simple",
+        )
+        # Falls through to lexical_miss (no substring overlap either).
+        assert result.grade == "incorrect"
+        assert result.method == "lexical_miss"
+
+
+class TestGradeDeterministicRefusal:
+    def test_idk_maps_to_missing(self) -> None:
+        result = grade_deterministic(
+            pred="I don't know.", gold="Tim Cook", question_type="simple",
+        )
+        assert result.grade == "missing"
+        assert result.score == 0
+        assert result.method == "refusal"
+
+    def test_empty_pred_maps_to_missing(self) -> None:
+        result = grade_deterministic(pred="", gold="Tim Cook", question_type="simple")
+        assert result.grade == "missing"
+
+    def test_no_information_maps_to_missing(self) -> None:
+        result = grade_deterministic(
+            pred="There is not enough information to answer.",
+            gold="42",
+            question_type="simple",
+        )
+        assert result.grade == "missing"
+
+
+class TestGradeDeterministicFalsePremise:
+    def test_flagging_premise_is_correct(self) -> None:
+        result = grade_deterministic(
+            pred="The question contains a false premise; that movie does not exist.",
+            gold="invalid question",
+            question_type="false_premise",
+        )
+        assert result.grade == "correct"
+        assert result.method == "false_premise_flagged"
+
+    def test_committing_to_false_answer_is_unclear(self) -> None:
+        # Should land in false_premise_unclear → judge fallback territory.
+        result = grade_deterministic(
+            pred="The album was released in 2010.",
+            gold="invalid question",
+            question_type="false_premise",
+        )
+        assert result.grade == "incorrect"
+        assert result.method == "false_premise_unclear"
+
+    def test_idk_on_false_premise_is_missing(self) -> None:
+        # Refusal precedes false-premise routing.
+        result = grade_deterministic(
+            pred="I don't know.",
+            gold="invalid question",
+            question_type="false_premise",
+        )
+        assert result.grade == "missing"
+
+
+class TestGradeDeterministicLexicalMiss:
+    def test_unknown_paraphrase_routes_to_judge(self) -> None:
+        result = grade_deterministic(
+            pred="It is the technology giant in Cupertino.",
+            gold="Apple Inc",
+            question_type="simple",
+        )
+        # Without a judge, we fall through to lexical_miss → incorrect.
+        assert result.grade == "incorrect"
+        assert result.method == "lexical_miss"
+
+    def test_short_pred_no_substring_credit(self) -> None:
+        # Reverse-substring path requires len >= 3 to credit.
+        result = grade_deterministic(
+            pred="JK",
+            gold="JK Rowling",
+            question_type="simple",
+        )
+        assert result.grade == "incorrect"
+
+
+class TestGradeResultShape:
+    def test_to_dict_round_trip(self) -> None:
+        result = CragGradeResult(
+            grade="correct", score=1, method="exact",
+            normalised_pred="x", normalised_gold="x",
+        )
+        d = result.to_dict()
+        assert d["grade"] == "correct"
+        assert d["score"] == 1
+        assert d["method"] == "exact"
+
+    def test_score_matches_grade(self) -> None:
+        # Construct via grader so the score field is populated correctly.
+        for gold, pred, want_grade in (
+            ("hi", "hi", "correct"),
+            ("hi", "I don't know", "missing"),
+            ("hi", "bye", "incorrect"),
+        ):
+            result = grade_deterministic(pred=pred, gold=gold, question_type="simple")
+            assert result.grade == want_grade
+            expected_score = {"correct": 1, "missing": 0, "incorrect": -1}[want_grade]
+            assert result.score == expected_score
diff --git a/surfsense_evals/tests/suites/test_crag_html_extract.py b/surfsense_evals/tests/suites/test_crag_html_extract.py
new file mode 100644
index 000000000..a2b47aa45
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_crag_html_extract.py
@@ -0,0 +1,149 @@
+"""Tests for the CRAG HTML extractor.
+
+We don't network-fetch trafilatura; we just verify the wrapper:
+
+* Strips obvious boilerplate (nav/footer/scripts) out of the result.
+* Falls back to the stdlib stripper on degenerate input.
+* Caps output at the configured ceiling.
+* Always prepends a metadata header (``# title``) when content is
+  produced.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.suites.research.crag.html_extract import (
+    extract_main_content,
+)
+
+
+_RICH_HTML = """\
+<!DOCTYPE html>
+<html>
+<head><title>Apple Q3 Earnings</title>
+<script>const a=1;</script>
+<style>body{font-family:sans;}</style>
+</head>
+<body>
+<nav><a href="/home">Home</a><a href="/about">About</a></nav>
+<header><h1>Tech News Site</h1><p>Subscribe to our newsletter</p></header>
+<main>
+<article>
+  <h1>Apple posts $90B revenue in Q3 2024</h1>
+  <p>Apple Inc. announced its Q3 2024 financial results today, reporting
+  $90 billion in revenue, beating analyst expectations of $87 billion.</p>
+  <p>The company saw growth across iPhone, services, and wearables.
+  CEO Tim Cook attributed the performance to strong demand in emerging
+  markets, particularly India.</p>
+  <h2>Segment breakdown</h2>
+  <ul>
+    <li>iPhone: $45B</li>
+    <li>Services: $24B</li>
+    <li>Mac: $7B</li>
+  </ul>
+</article>
+</main>
+<footer><p>Copyright 2024 Tech News Site. All rights reserved.</p></footer>
+</body></html>
+"""
+
+
+class TestExtractMainContent:
+    def test_extracts_main_article(self) -> None:
+        result = extract_main_content(
+            _RICH_HTML,
+            url="https://example.com/apple",
+            page_name="Apple Q3 Earnings",
+        )
+        assert result.ok
+        assert "Apple" in result.text
+        assert "Q3 2024" in result.text
+        # Header line is prepended.
+        assert result.text.startswith("# Apple Q3 Earnings")
+        assert "Source: https://example.com/apple" in result.text
+
+    def test_strips_boilerplate(self) -> None:
+        result = extract_main_content(
+            _RICH_HTML,
+            url="https://example.com/apple",
+            page_name="Apple Q3 Earnings",
+        )
+        assert result.ok
+        # Boilerplate strings should NOT make it through.
+        assert "Subscribe to our newsletter" not in result.text
+        assert "Copyright 2024 Tech News Site" not in result.text
+        assert "const a=1" not in result.text  # script content
+
+    def test_includes_last_modified_when_provided(self) -> None:
+        result = extract_main_content(
+            _RICH_HTML,
+            url="https://example.com/apple",
+            page_name="Apple Q3 Earnings",
+            last_modified="2024-08-01",
+        )
+        assert "Last modified: 2024-08-01" in result.text
+
+    def test_empty_html_returns_empty_result(self) -> None:
+        result = extract_main_content("", url="https://x.test/")
+        assert not result.ok
+        assert result.method == "empty"
+        assert result.n_chars == 0
+
+    def test_whitespace_only_html_is_empty(self) -> None:
+        result = extract_main_content("   \n   ", url="https://x.test/")
+        assert not result.ok
+
+    def test_garbage_html_falls_back(self) -> None:
+        # Trafilatura should reject this, fallback strip should still yield text.
+        result = extract_main_content(
+            "<<weird>>not a tag>>>The brown fox<<jumped<<",
+            url="https://x.test/garbage",
+            page_name="Garbage",
+        )
+        # Either trafilatura recovers something or fallback_strip does.
+        if result.ok:
+            assert "brown fox" in result.text or "jumped" in result.text
+
+
+class TestFallbackStripper:
+    def test_extract_when_no_clear_main(self) -> None:
+        html = """
+        <html><body>
+        <p>This is content one.</p>
+        <p>This is content two.</p>
+        </body></html>
+        """
+        result = extract_main_content(
+            html, url="https://x.test/", page_name="Title",
+        )
+        assert result.ok
+        assert "content one" in result.text
+        assert "content two" in result.text
+
+    def test_html_entities_decoded(self) -> None:
+        html = """<html><body>
+        <article>
+        <p>Tom &amp; Jerry &mdash; classic cartoon &copy; 1940.</p>
+        <p>It's a story about a cat &lt;Tom&gt; and a mouse &lt;Jerry&gt;.</p>
+        </article>
+        </body></html>"""
+        result = extract_main_content(html, url="https://x.test/")
+        assert result.ok
+        # & should be decoded
+        assert "&amp;" not in result.text
+        assert "Tom" in result.text and "Jerry" in result.text
+
+
+class TestOutputCapping:
+    def test_long_output_is_truncated(self) -> None:
+        # Generate enough content to exceed 200k cap.
+        body = "<p>" + ("hello world " * 50_000) + "</p>"
+        html = f"<html><body><article>{body}</article></body></html>"
+        result = extract_main_content(html, url="https://x.test/", page_name="long")
+        assert result.ok
+        # The body text itself + the metadata header. Truncation marker
+        # appears either at the body limit or before EOF.
+        if "[...truncated...]" in result.text:
+            # The truncation kicked in.
+            assert len(result.text) <= 250_000  # header + 200k cap + slack
diff --git a/surfsense_evals/tests/suites/test_frames_dataset.py b/surfsense_evals/tests/suites/test_frames_dataset.py
new file mode 100644
index 000000000..e79e7db89
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_frames_dataset.py
@@ -0,0 +1,154 @@
+"""Tests for the FRAMES dataset parser.
+
+Network-free: we round-trip a tiny fixture TSV through pandas and
+``load_questions`` to confirm:
+
+* row indices become zero-padded ``Q###`` ids,
+* ``wiki_links`` (Python list literal) is materialised correctly,
+* ``reasoning_types`` is split on the pipe separator,
+* missing Prompt/Answer rows are dropped, and
+* the legacy ``wikipedia_link_*`` per-cell fallback works when
+  ``wiki_links`` is missing/empty.
+"""
+
+from __future__ import annotations
+
+import textwrap
+from pathlib import Path
+
+import pytest
+
+from surfsense_evals.suites.research.frames.dataset import (
+    FramesQuestion,
+    _parse_reasoning_types,
+    _parse_wiki_links,
+    load_questions,
+)
+
+
+# ---------------------------------------------------------------------------
+# Pure-function tests
+# ---------------------------------------------------------------------------
+
+
+class TestParseWikiLinks:
+    def test_python_list_literal(self) -> None:
+        s = "['https://en.wikipedia.org/wiki/A', 'https://en.wikipedia.org/wiki/B']"
+        assert _parse_wiki_links(s) == [
+            "https://en.wikipedia.org/wiki/A",
+            "https://en.wikipedia.org/wiki/B",
+        ]
+
+    def test_none_or_empty(self) -> None:
+        assert _parse_wiki_links(None) == []
+        assert _parse_wiki_links("") == []
+        assert _parse_wiki_links("[]") == []
+
+    def test_unquoted_csv_fallback(self) -> None:
+        # Defensive: non-Python-list strings still split on commas.
+        s = "https://a, https://b"
+        assert _parse_wiki_links(s) == ["https://a", "https://b"]
+
+    def test_already_a_list(self) -> None:
+        assert _parse_wiki_links(["x", "y"]) == ["x", "y"]
+
+
+class TestParseReasoningTypes:
+    def test_pipe_separated(self) -> None:
+        assert _parse_reasoning_types("Numerical reasoning | Multiple constraints") == [
+            "Numerical reasoning",
+            "Multiple constraints",
+        ]
+
+    def test_single_tag(self) -> None:
+        assert _parse_reasoning_types("Tabular reasoning") == ["Tabular reasoning"]
+
+    def test_empty(self) -> None:
+        assert _parse_reasoning_types(None) == []
+        assert _parse_reasoning_types("") == []
+
+
+# ---------------------------------------------------------------------------
+# Round-trip via pandas
+# ---------------------------------------------------------------------------
+
+
+def _write_tsv(path: Path, body: str) -> None:
+    """Helper that writes a tab-separated fixture exactly as the user typed it."""
+
+    path.write_text(textwrap.dedent(body), encoding="utf-8")
+
+
+def test_load_questions_basic(tmp_path: Path) -> None:
+    tsv = tmp_path / "test.tsv"
+    rows = [
+        # Header (first column is unnamed → pandas treats as index)
+        "\tPrompt\tAnswer\twikipedia_link_1\twikipedia_link_2\treasoning_types\twiki_links",
+        # Row 0
+        "0\tWho was the 15th president?\tJames Buchanan\t"
+        "https://en.wikipedia.org/wiki/James_Buchanan\t\t"
+        "Multiple constraints\t"
+        "['https://en.wikipedia.org/wiki/James_Buchanan']",
+        # Row 1
+        "1\tHow many years between A and B?\t87\t"
+        "https://en.wikipedia.org/wiki/A\thttps://en.wikipedia.org/wiki/B\t"
+        "Numerical reasoning | Temporal reasoning\t"
+        "['https://en.wikipedia.org/wiki/A', 'https://en.wikipedia.org/wiki/B']",
+        # Row 2 (intentionally missing Prompt — should be dropped)
+        "2\t\tunused\t\t\t\t",
+    ]
+    tsv.write_text("\n".join(rows) + "\n", encoding="utf-8")
+
+    questions = load_questions(tsv)
+    assert len(questions) == 2
+
+    q0, q1 = questions
+    assert isinstance(q0, FramesQuestion)
+    assert q0.qid == "Q000"
+    assert q0.raw_index == 0
+    assert q0.gold_answer == "James Buchanan"
+    assert q0.wiki_urls == ["https://en.wikipedia.org/wiki/James_Buchanan"]
+    assert q0.reasoning_types == ["Multiple constraints"]
+
+    assert q1.qid == "Q001"
+    assert q1.gold_answer == "87"
+    assert q1.wiki_urls == [
+        "https://en.wikipedia.org/wiki/A",
+        "https://en.wikipedia.org/wiki/B",
+    ]
+    assert q1.reasoning_types == ["Numerical reasoning", "Temporal reasoning"]
+
+
+def test_load_questions_falls_back_to_per_cell_links(tmp_path: Path) -> None:
+    """When ``wiki_links`` is empty, the loader should glue the
+    ``wikipedia_link_*`` cells back together."""
+
+    tsv = tmp_path / "test.tsv"
+    rows = [
+        "\tPrompt\tAnswer\twikipedia_link_1\twikipedia_link_2\treasoning_types\twiki_links",
+        "0\tQ?\tA\t"
+        "https://en.wikipedia.org/wiki/Cell1\thttps://en.wikipedia.org/wiki/Cell2\t"
+        "Numerical reasoning\t",
+    ]
+    tsv.write_text("\n".join(rows) + "\n", encoding="utf-8")
+    questions = load_questions(tsv)
+    assert len(questions) == 1
+    assert questions[0].wiki_urls == [
+        "https://en.wikipedia.org/wiki/Cell1",
+        "https://en.wikipedia.org/wiki/Cell2",
+    ]
+
+
+def test_load_questions_to_dict_round_trip(tmp_path: Path) -> None:
+    tsv = tmp_path / "test.tsv"
+    rows = [
+        "\tPrompt\tAnswer\treasoning_types\twiki_links",
+        "0\tQ?\tParis\tTemporal reasoning\t['https://en.wikipedia.org/wiki/Paris']",
+    ]
+    tsv.write_text("\n".join(rows) + "\n", encoding="utf-8")
+
+    [q] = load_questions(tsv)
+    d = q.to_dict()
+    assert d["qid"] == "Q000"
+    assert d["wiki_urls"] == ["https://en.wikipedia.org/wiki/Paris"]
+    assert d["reasoning_types"] == ["Temporal reasoning"]
diff --git a/surfsense_evals/tests/suites/test_frames_grader.py b/surfsense_evals/tests/suites/test_frames_grader.py
new file mode 100644
index 000000000..e6e38ff8a
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_frames_grader.py
@@ -0,0 +1,160 @@
+"""Tests for the FRAMES grader's deterministic shortcut.
+
+The LLM-judge fallback is excluded here (network call); we just
+confirm the rule-based path picks up obvious correct/incorrect
+cases and routes the ambiguous ones to ``lexical_miss`` so the
+runner knows to consult the judge.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.suites.research.frames.grader import (
+    GradeResult,
+    _maybe_number,
+    _normalise,
+    _whole_word_substring,
+    grade_deterministic,
+)
+
+
+class TestNormalisation:
+    def test_lowercase_and_punct_stripped(self) -> None:
+        assert _normalise("Jane Ballou.") == "jane ballou"
+
+    def test_articles_removed(self) -> None:
+        assert _normalise("The Eiffel Tower") == "eiffel tower"
+
+    def test_whitespace_squashed(self) -> None:
+        assert _normalise("  multi   space\tinput  ") == "multi space input"
+
+    def test_empty_returns_empty(self) -> None:
+        assert _normalise("") == ""
+        assert _normalise(None) == ""  # type: ignore[arg-type]
+
+
+class TestNumericExtraction:
+    def test_simple_int(self) -> None:
+        assert _maybe_number("42") == 42.0
+
+    def test_int_with_commas(self) -> None:
+        assert _maybe_number("1,234") == 1234.0
+
+    def test_year_in_sentence(self) -> None:
+        assert _maybe_number("It was published in 1847.") == 1847.0
+
+    def test_word_number(self) -> None:
+        assert _maybe_number("five") == 5.0
+        assert _maybe_number("Twenty") == 20.0
+
+    def test_no_number_returns_none(self) -> None:
+        assert _maybe_number("Jane Ballou") is None
+        assert _maybe_number("") is None
+
+
+class TestWholeWordSubstring:
+    def test_phrase_match(self) -> None:
+        assert _whole_word_substring("president of the united states", "united states")
+
+    def test_word_boundary_required(self) -> None:
+        # "states" should NOT match inside "statesman"
+        assert not _whole_word_substring("the renowned statesman", "states")
+
+    def test_empty_needle(self) -> None:
+        assert not _whole_word_substring("anything", "")
+
+
+class TestExactMatch:
+    def test_identical(self) -> None:
+        r = grade_deterministic(pred="Jane Ballou", gold="Jane Ballou")
+        assert r.correct is True
+        assert r.method == "exact"
+
+    def test_case_insensitive(self) -> None:
+        r = grade_deterministic(pred="paris", gold="Paris")
+        assert r.correct is True
+        assert r.method == "exact"
+
+    def test_punctuation_ignored(self) -> None:
+        r = grade_deterministic(pred="Jane Ballou.", gold="Jane Ballou")
+        assert r.correct is True
+
+
+class TestNumericPath:
+    def test_int_match(self) -> None:
+        r = grade_deterministic(pred="The answer is 87", gold="87")
+        assert r.correct is True
+        assert r.method == "numeric"
+
+    def test_word_number_matches_digit(self) -> None:
+        r = grade_deterministic(pred="five", gold="5")
+        assert r.correct is True
+        assert r.method == "numeric"
+
+    def test_off_by_more_than_tolerance_fails(self) -> None:
+        r = grade_deterministic(pred="86", gold="87")
+        # 86 vs 87, abs diff = 1, tol = max(0.01*87, 0.5) = 0.87 → fails
+        assert r.correct is False
+        assert r.method == "numeric_miss"
+
+    def test_within_one_percent_passes(self) -> None:
+        r = grade_deterministic(pred="100", gold="101")
+        # 1.0 abs diff, tol = max(0.01*101, 0.5) = 1.01 → passes
+        assert r.correct is True
+
+
+class TestSubstringPath:
+    def test_pred_contains_gold(self) -> None:
+        r = grade_deterministic(
+            pred="The answer is Jane Ballou according to records",
+            gold="Jane Ballou",
+        )
+        assert r.correct is True
+        assert r.method == "substring"
+
+    def test_gold_contains_pred_with_minimum_length(self) -> None:
+        # Gold = "John F Kennedy", pred = "Kennedy" → reverse substring,
+        # ≥3 chars, but the FRAMES style usually accepts this.
+        r = grade_deterministic(pred="Kennedy", gold="John F. Kennedy")
+        assert r.correct is True
+        assert r.method == "substring_reverse"
+
+    def test_too_short_pred_no_reverse_credit(self) -> None:
+        r = grade_deterministic(pred="of", gold="World of Warcraft")
+        # "of" passes length but is a stopword; the article-stripping
+        # normaliser removes it from gold, so substring fails. Either
+        # way, the grader should NOT credit this.
+        assert r.correct is False
+
+
+class TestLexicalMiss:
+    def test_completely_different_pred_falls_through(self) -> None:
+        r = grade_deterministic(pred="London", gold="Paris")
+        assert r.correct is False
+        assert r.method == "lexical_miss"
+
+    def test_empty_pred(self) -> None:
+        r = grade_deterministic(pred="", gold="Paris")
+        assert r.correct is False
+        assert r.method == "empty_pred"
+
+    def test_empty_gold_defensive(self) -> None:
+        r = grade_deterministic(pred="something", gold="")
+        # Defensive guard — gold should never be empty in practice.
+        assert r.correct is False
+        assert r.method == "empty_gold"
+
+
+class TestGradeResultShape:
+    def test_dict_has_all_expected_keys(self) -> None:
+        r = grade_deterministic(pred="Paris", gold="Paris")
+        d = r.to_dict()
+        assert set(d) >= {
+            "correct",
+            "f1",
+            "method",
+            "normalised_pred",
+            "normalised_gold",
+            "judge_rationale",
+        }
diff --git a/surfsense_evals/tests/suites/test_frames_wiki_fetch.py b/surfsense_evals/tests/suites/test_frames_wiki_fetch.py
new file mode 100644
index 000000000..4941756f4
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_frames_wiki_fetch.py
@@ -0,0 +1,112 @@
+"""Tests for the FRAMES Wikipedia fetcher.
+
+We mock the MW API with respx so tests are network-free. Coverage:
+
+* URL → title parsing (percent-encoded, underscores, redirects)
+* Filename safety (slashes, special chars)
+* Cache hit short-circuits the API call
+* Missing pages return ``None`` (not an exception)
+* Successful fetches write ``# Title`` markdown to disk
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import httpx
+import pytest
+import respx
+
+from surfsense_evals.suites.research.frames.wiki_fetch import (
+    WIKI_API,
+    WikiFetcher,
+    cache_filename_for_title,
+    title_from_url,
+)
+
+
+class TestTitleFromUrl:
+    def test_basic(self) -> None:
+        assert title_from_url("https://en.wikipedia.org/wiki/James_Buchanan") == "James Buchanan"
+
+    def test_percent_encoded(self) -> None:
+        assert (
+            title_from_url("https://en.wikipedia.org/wiki/Charlotte_Bront%C3%AB")
+            == "Charlotte Brontë"
+        )
+
+    def test_query_string_dropped(self) -> None:
+        assert title_from_url("https://en.wikipedia.org/wiki/Foo?action=edit") == "Foo"
+
+    def test_non_wiki_raises(self) -> None:
+        with pytest.raises(ValueError):
+            title_from_url("https://example.com/wiki/Foo")
+
+
+class TestCacheFilename:
+    def test_simple(self) -> None:
+        assert cache_filename_for_title("James Buchanan") == "James_Buchanan.md"
+
+    def test_unicode_replaced_with_underscore(self) -> None:
+        # Brontë's diaeresis is non-ASCII so the regex replaces it with `_`.
+        # The space → `_` happens after the unicode swap, so the final
+        # name has exactly one underscore for the diaeresis. Acceptable:
+        # filenames stay round-trippable as long as the rule is deterministic.
+        assert cache_filename_for_title("Charlotte Brontë") == "Charlotte_Bront_.md"
+
+    def test_slashes_replaced(self) -> None:
+        # Wikipedia titles can contain ``/`` (e.g. "I/O"), which would
+        # break the filesystem layout if not sanitised.
+        assert cache_filename_for_title("I/O") == "I_O.md"
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_fetch_success_writes_markdown(tmp_path: Path) -> None:
+    respx.get(WIKI_API).mock(return_value=httpx.Response(
+        200,
+        json={"query": {"pages": [{
+            "pageid": 1,
+            "title": "James Buchanan",
+            "extract": "James Buchanan was the 15th president of the United States.",
+        }]}},
+    ))
+    fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100)  # disable throttle
+    article = await fetcher.fetch("https://en.wikipedia.org/wiki/James_Buchanan")
+    assert article is not None
+    assert article.title == "James Buchanan"
+    body = article.markdown_path.read_text(encoding="utf-8")
+    assert body.startswith("# James Buchanan")
+    assert "15th president" in body
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_fetch_missing_page_returns_none(tmp_path: Path) -> None:
+    respx.get(WIKI_API).mock(return_value=httpx.Response(
+        200,
+        json={"query": {"pages": [{
+            "title": "DoesNotExist",
+            "missing": True,
+        }]}},
+    ))
+    fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100)
+    article = await fetcher.fetch("https://en.wikipedia.org/wiki/DoesNotExist")
+    assert article is None
+    assert not (tmp_path / "DoesNotExist.md").exists()
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_fetch_cache_hit_skips_api(tmp_path: Path) -> None:
+    # Pre-populate the cache.
+    cached = tmp_path / cache_filename_for_title("Cached Page")
+    cached.write_text("# Cached Page\n\nfrom disk\n", encoding="utf-8")
+    fetcher = WikiFetcher(cache_dir=tmp_path, rate_limit_rps=100)
+
+    # No respx mock registered; if the fetcher hits the network, respx
+    # would error out (it intercepts everything inside the decorator).
+    article = await fetcher.fetch("https://en.wikipedia.org/wiki/Cached_Page")
+    assert article is not None
+    assert article.markdown_path == cached
+    assert article.markdown_path.read_text(encoding="utf-8").endswith("from disk\n")
diff --git a/surfsense_evals/tests/suites/test_mmlongbench_grader.py b/surfsense_evals/tests/suites/test_mmlongbench_grader.py
new file mode 100644
index 000000000..92cd5f0cb
--- /dev/null
+++ b/surfsense_evals/tests/suites/test_mmlongbench_grader.py
@@ -0,0 +1,129 @@
+"""Tests for the MMLongBench-Doc format-aware grader.
+
+The grader is the critical correctness piece for the open-ended
+benchmark (no MCQ shortcut), so we cover all five formats with
+representative happy-path + edge-case rows.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from surfsense_evals.suites.multimodal_doc.mmlongbench.grader import grade
+
+
+class TestStrFormat:
+    def test_exact_match(self) -> None:
+        r = grade(pred="Apollo 11", gold="Apollo 11", answer_format="Str")
+        assert r.correct is True
+        assert r.f1 == 1.0
+        assert r.method == "str_norm"
+
+    def test_lowercase_normalised(self) -> None:
+        r = grade(pred="paris", gold="Paris", answer_format="Str")
+        assert r.correct is True
+
+    def test_punctuation_difference_drops_to_substring(self) -> None:
+        # "N.A.S.A." normalises to "n a s a" (whitespace tokens) which
+        # doesn't equal "nasa" — but the F1 token overlap is still 0
+        # because none of the single letters appear standalone in "nasa".
+        # We assert the grader fails closed rather than over-claiming.
+        r = grade(pred="N.A.S.A.", gold="NASA", answer_format="Str")
+        assert r.correct is False  # explicit: this is a failure mode we accept
+
+    def test_substring_credit(self) -> None:
+        r = grade(pred="The answer is Paris.", gold="Paris", answer_format="Str")
+        assert r.correct is True
+
+    def test_completely_wrong(self) -> None:
+        r = grade(pred="London", gold="Paris", answer_format="Str")
+        assert r.correct is False
+        assert r.f1 < 0.5
+
+    def test_empty_pred(self) -> None:
+        r = grade(pred="", gold="Paris", answer_format="Str")
+        assert r.correct is False
+        assert r.f1 == 0.0
+
+
+class TestIntFormat:
+    def test_exact_int(self) -> None:
+        assert grade(pred="42", gold="42", answer_format="Int").correct is True
+
+    def test_int_in_sentence(self) -> None:
+        assert grade(pred="The answer is 42 years.", gold="42", answer_format="Int").correct is True
+
+    def test_int_with_commas(self) -> None:
+        assert grade(pred="1,500", gold="1500", answer_format="Int").correct is True
+
+    def test_wrong_int(self) -> None:
+        assert grade(pred="41", gold="42", answer_format="Int").correct is False
+
+    def test_no_int_in_pred(self) -> None:
+        assert grade(pred="not answerable", gold="42", answer_format="Int").correct is False
+
+
+class TestFloatFormat:
+    def test_exact_float(self) -> None:
+        assert grade(pred="3.14", gold="3.14", answer_format="Float").correct is True
+
+    def test_within_tolerance(self) -> None:
+        # 1% tolerance — 3.14 vs 3.13 is well within.
+        assert grade(pred="3.13", gold="3.14", answer_format="Float").correct is True
+
+    def test_outside_tolerance(self) -> None:
+        assert grade(pred="3.5", gold="3.14", answer_format="Float").correct is False
+
+    def test_european_decimal_comma(self) -> None:
+        # ``3,14`` should parse as 3.14
+        assert grade(pred="3,14", gold="3.14", answer_format="Float").correct is True
+
+    def test_zero_gold_with_small_abs_diff(self) -> None:
+        # Absolute tolerance of 0.01 should kick in for near-zero golds.
+        assert grade(pred="0.005", gold="0", answer_format="Float").correct is True
+
+
+class TestListFormat:
+    def test_exact_set_match(self) -> None:
+        r = grade(pred="apple, banana, cherry", gold="apple, banana, cherry", answer_format="List")
+        assert r.correct is True
+        assert r.f1 == pytest.approx(1.0)
+
+    def test_set_match_different_order(self) -> None:
+        r = grade(pred="cherry, apple, banana", gold="apple, banana, cherry", answer_format="List")
+        assert r.correct is True
+
+    def test_partial_overlap_gives_f1(self) -> None:
+        r = grade(pred="apple, banana", gold="apple, banana, cherry", answer_format="List")
+        assert r.correct is False
+        assert 0.0 < r.f1 < 1.0
+
+    def test_extra_items_lower_precision(self) -> None:
+        r = grade(pred="apple, banana, cherry, date", gold="apple, banana, cherry", answer_format="List")
+        assert 0.0 < r.f1 < 1.0
+        # Recall=1, precision=3/4 → F1 ~= 0.857
+        assert r.f1 == pytest.approx(2 * (3 / 4) * 1 / (3 / 4 + 1), rel=1e-3)
+
+
+class TestNoneFormat:
+    def test_unknown_phrase_credited(self) -> None:
+        for phrase in ("Not answerable", "I cannot answer this.", "No answer", "N/A"):
+            r = grade(pred=phrase, gold="Not answerable", answer_format="None")
+            assert r.correct is True, phrase
+
+    def test_actual_answer_marked_wrong(self) -> None:
+        # The arm hallucinated an answer when it should have said "I don't know".
+        r = grade(pred="The answer is 42.", gold="Not answerable", answer_format="None")
+        assert r.correct is False
+
+
+class TestUnknownFormatFallsBackToStr:
+    def test_blank_format_uses_str_grader(self) -> None:
+        r = grade(pred="Paris", gold="Paris", answer_format="")
+        assert r.correct is True
+        assert r.method == "str_norm"
+
+    def test_garbage_format_uses_str_grader(self) -> None:
+        r = grade(pred="Paris", gold="Paris", answer_format="quux")
+        assert r.correct is True
+        assert r.method == "str_norm"
diff --git a/surfsense_evals/tests/test_integration_smoke.py b/surfsense_evals/tests/test_integration_smoke.py
new file mode 100644
index 000000000..493c04c25
--- /dev/null
+++ b/surfsense_evals/tests/test_integration_smoke.py
@@ -0,0 +1,35 @@
+"""Opt-in integration smoke against ``http://localhost:8000``.
+
+Run with ``pytest -m integration``. Skipped by default. Touches the
+real backend — requires it to be reachable, OPENROUTER_API_KEY
+unrelated, and one credential mode set.
+"""
+
+from __future__ import annotations
+
+import os
+
+import httpx
+import pytest
+
+from surfsense_evals.core.auth import acquire_token, client_with_auth
+from surfsense_evals.core.config import load_config
+
+pytestmark = pytest.mark.integration
+
+
+@pytest.mark.asyncio
+async def test_smoke_against_localhost():
+    if "SURFSENSE_API_BASE" not in os.environ:
+        pytest.skip("SURFSENSE_API_BASE not set; skipping integration smoke")
+    config = load_config()
+    if config.credential_mode() == "none":
+        pytest.skip("No credentials in environment; skipping integration smoke")
+    bundle = await acquire_token(config)
+    async with client_with_auth(config, bundle) as client:
+        response = await client.get(f"{config.surfsense_api_base}/api/v1/global-new-llm-configs")
+        try:
+            response.raise_for_status()
+        except httpx.HTTPStatusError as exc:
+            pytest.fail(f"Backend rejected smoke call: {exc!s}")
+        assert isinstance(response.json(), list)
diff --git a/surfsense_evals/uv.lock b/surfsense_evals/uv.lock
new file mode 100644
index 000000000..6c4fd7283
--- /dev/null
+++ b/surfsense_evals/uv.lock
@@ -0,0 +1,1742 @@
+version = 1
+revision = 1
+requires-python = ">=3.12"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/6f/353954c29e7dcce7cf00280a02c75f30e133c00793c7a2ed3776d7b2f426/aiohttp-3.13.5-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:023ecba036ddd840b0b19bf195bfae970083fd7024ce1ac22e9bba90464620e9", size = 748876 },
+    { url = "https://files.pythonhosted.org/packages/f5/1b/428a7c64687b3b2e9cd293186695affc0e1e54a445d0361743b231f11066/aiohttp-3.13.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15c933ad7920b7d9a20de151efcd05a6e38302cbf0e10c9b2acb9a42210a2416", size = 499557 },
+    { url = "https://files.pythonhosted.org/packages/29/47/7be41556bfbb6917069d6a6634bb7dd5e163ba445b783a90d40f5ac7e3a7/aiohttp-3.13.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ab2899f9fa2f9f741896ebb6fa07c4c883bfa5c7f2ddd8cf2aafa86fa981b2d2", size = 500258 },
+    { url = "https://files.pythonhosted.org/packages/67/84/c9ecc5828cb0b3695856c07c0a6817a99d51e2473400f705275a2b3d9239/aiohttp-3.13.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a60eaa2d440cd4707696b52e40ed3e2b0f73f65be07fd0ef23b6b539c9c0b0b4", size = 1749199 },
+    { url = "https://files.pythonhosted.org/packages/f0/d3/3c6d610e66b495657622edb6ae7c7fd31b2e9086b4ec50b47897ad6042a9/aiohttp-3.13.5-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:55b3bdd3292283295774ab585160c4004f4f2f203946997f49aac032c84649e9", size = 1721013 },
+    { url = "https://files.pythonhosted.org/packages/49/a0/24409c12217456df0bae7babe3b014e460b0b38a8e60753d6cb339f6556d/aiohttp-3.13.5-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c2b2355dc094e5f7d45a7bb262fe7207aa0460b37a0d87027dcf21b5d890e7d5", size = 1781501 },
+    { url = "https://files.pythonhosted.org/packages/98/9d/b65ec649adc5bccc008b0957a9a9c691070aeac4e41cea18559fef49958b/aiohttp-3.13.5-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b38765950832f7d728297689ad78f5f2cf79ff82487131c4d26fe6ceecdc5f8e", size = 1878981 },
+    { url = "https://files.pythonhosted.org/packages/57/d8/8d44036d7eb7b6a8ec4c5494ea0c8c8b94fbc0ed3991c1a7adf230df03bf/aiohttp-3.13.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b18f31b80d5a33661e08c89e202edabf1986e9b49c42b4504371daeaa11b47c1", size = 1767934 },
+    { url = "https://files.pythonhosted.org/packages/31/04/d3f8211f273356f158e3464e9e45484d3fb8c4ce5eb2f6fe9405c3273983/aiohttp-3.13.5-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:33add2463dde55c4f2d9635c6ab33ce154e5ecf322bd26d09af95c5f81cfa286", size = 1566671 },
+    { url = "https://files.pythonhosted.org/packages/41/db/073e4ebe00b78e2dfcacff734291651729a62953b48933d765dc513bf798/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:327cc432fdf1356fb4fbc6fe833ad4e9f6aacb71a8acaa5f1855e4b25910e4a9", size = 1705219 },
+    { url = "https://files.pythonhosted.org/packages/48/45/7dfba71a2f9fd97b15c95c06819de7eb38113d2cdb6319669195a7d64270/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:7c35b0bf0b48a70b4cb4fc5d7bed9b932532728e124874355de1a0af8ec4bc88", size = 1743049 },
+    { url = "https://files.pythonhosted.org/packages/18/71/901db0061e0f717d226386a7f471bb59b19566f2cae5f0d93874b017271f/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:df23d57718f24badef8656c49743e11a89fd6f5358fa8a7b96e728fda2abf7d3", size = 1749557 },
+    { url = "https://files.pythonhosted.org/packages/08/d5/41eebd16066e59cd43728fe74bce953d7402f2b4ddfdfef2c0e9f17ca274/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:02e048037a6501a5ec1f6fc9736135aec6eb8a004ce48838cb951c515f32c80b", size = 1558931 },
+    { url = "https://files.pythonhosted.org/packages/30/e6/4a799798bf05740e66c3a1161079bda7a3dd8e22ca392481d7a7f9af82a6/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:31cebae8b26f8a615d2b546fee45d5ffb76852ae6450e2a03f42c9102260d6fe", size = 1774125 },
+    { url = "https://files.pythonhosted.org/packages/84/63/7749337c90f92bc2cb18f9560d67aa6258c7060d1397d21529b8004fcf6f/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:888e78eb5ca55a615d285c3c09a7a91b42e9dd6fc699b166ebd5dee87c9ccf14", size = 1732427 },
+    { url = "https://files.pythonhosted.org/packages/98/de/cf2f44ff98d307e72fb97d5f5bbae3bfcb442f0ea9790c0bf5c5c2331404/aiohttp-3.13.5-cp312-cp312-win32.whl", hash = "sha256:8bd3ec6376e68a41f9f95f5ed170e2fcf22d4eb27a1f8cb361d0508f6e0557f3", size = 433534 },
+    { url = "https://files.pythonhosted.org/packages/aa/ca/eadf6f9c8fa5e31d40993e3db153fb5ed0b11008ad5d9de98a95045bed84/aiohttp-3.13.5-cp312-cp312-win_amd64.whl", hash = "sha256:110e448e02c729bcebb18c60b9214a87ba33bac4a9fa5e9a5f139938b56c6cb1", size = 460446 },
+    { url = "https://files.pythonhosted.org/packages/78/e9/d76bf503005709e390122d34e15256b88f7008e246c4bdbe915cd4f1adce/aiohttp-3.13.5-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5029cc80718bbd545123cd8fe5d15025eccaaaace5d0eeec6bd556ad6163d61", size = 742930 },
+    { url = "https://files.pythonhosted.org/packages/57/00/4b7b70223deaebd9bb85984d01a764b0d7bd6526fcdc73cca83bcbe7243e/aiohttp-3.13.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4bb6bf5811620003614076bdc807ef3b5e38244f9d25ca5fe888eaccea2a9832", size = 496927 },
+    { url = "https://files.pythonhosted.org/packages/9c/f5/0fb20fb49f8efdcdce6cd8127604ad2c503e754a8f139f5e02b01626523f/aiohttp-3.13.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a84792f8631bf5a94e52d9cc881c0b824ab42717165a5579c760b830d9392ac9", size = 497141 },
+    { url = "https://files.pythonhosted.org/packages/3b/86/b7c870053e36a94e8951b803cb5b909bfbc9b90ca941527f5fcafbf6b0fa/aiohttp-3.13.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57653eac22c6a4c13eb22ecf4d673d64a12f266e72785ab1c8b8e5940d0e8090", size = 1732476 },
+    { url = "https://files.pythonhosted.org/packages/b5/e5/4e161f84f98d80c03a238671b4136e6530453d65262867d989bbe78244d0/aiohttp-3.13.5-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e5e5f7debc7a57af53fdf5c5009f9391d9f4c12867049d509bf7bb164a6e295b", size = 1706507 },
+    { url = "https://files.pythonhosted.org/packages/d4/56/ea11a9f01518bd5a2a2fcee869d248c4b8a0cfa0bb13401574fa31adf4d4/aiohttp-3.13.5-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c719f65bebcdf6716f10e9eff80d27567f7892d8988c06de12bbbd39307c6e3a", size = 1773465 },
+    { url = "https://files.pythonhosted.org/packages/eb/40/333ca27fb74b0383f17c90570c748f7582501507307350a79d9f9f3c6eb1/aiohttp-3.13.5-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d97f93fdae594d886c5a866636397e2bcab146fd7a132fd6bb9ce182224452f8", size = 1873523 },
+    { url = "https://files.pythonhosted.org/packages/f0/d2/e2f77eef1acb7111405433c707dc735e63f67a56e176e72e9e7a2cd3f493/aiohttp-3.13.5-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3df334e39d4c2f899a914f1dba283c1aadc311790733f705182998c6f7cae665", size = 1754113 },
+    { url = "https://files.pythonhosted.org/packages/fb/56/3f653d7f53c89669301ec9e42c95233e2a0c0a6dd051269e6e678db4fdb0/aiohttp-3.13.5-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fe6970addfea9e5e081401bcbadf865d2b6da045472f58af08427e108d618540", size = 1562351 },
+    { url = "https://files.pythonhosted.org/packages/ec/a6/9b3e91eb8ae791cce4ee736da02211c85c6f835f1bdfac0594a8a3b7018c/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7becdf835feff2f4f335d7477f121af787e3504b48b449ff737afb35869ba7bb", size = 1693205 },
+    { url = "https://files.pythonhosted.org/packages/98/fc/bfb437a99a2fcebd6b6eaec609571954de2ed424f01c352f4b5504371dd3/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:676e5651705ad5d8a70aeb8eb6936c436d8ebbd56e63436cb7dd9bb36d2a9a46", size = 1730618 },
+    { url = "https://files.pythonhosted.org/packages/e4/b6/c8534862126191a034f68153194c389addc285a0f1347d85096d349bbc15/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:9b16c653d38eb1a611cc898c41e76859ca27f119d25b53c12875fd0474ae31a8", size = 1745185 },
+    { url = "https://files.pythonhosted.org/packages/0b/93/4ca8ee2ef5236e2707e0fd5fecb10ce214aee1ff4ab307af9c558bda3b37/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:999802d5fa0389f58decd24b537c54aa63c01c3219ce17d1214cbda3c2b22d2d", size = 1557311 },
+    { url = "https://files.pythonhosted.org/packages/57/ae/76177b15f18c5f5d094f19901d284025db28eccc5ae374d1d254181d33f4/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ec707059ee75732b1ba130ed5f9580fe10ff75180c812bc267ded039db5128c6", size = 1773147 },
+    { url = "https://files.pythonhosted.org/packages/01/a4/62f05a0a98d88af59d93b7fcac564e5f18f513cb7471696ac286db970d6a/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2d6d44a5b48132053c2f6cd5c8cb14bc67e99a63594e336b0f2af81e94d5530c", size = 1730356 },
+    { url = "https://files.pythonhosted.org/packages/e4/85/fc8601f59dfa8c9523808281f2da571f8b4699685f9809a228adcc90838d/aiohttp-3.13.5-cp313-cp313-win32.whl", hash = "sha256:329f292ed14d38a6c4c435e465f48bebb47479fd676a0411936cc371643225cc", size = 432637 },
+    { url = "https://files.pythonhosted.org/packages/c0/1b/ac685a8882896acf0f6b31d689e3792199cfe7aba37969fa91da63a7fa27/aiohttp-3.13.5-cp313-cp313-win_amd64.whl", hash = "sha256:69f571de7500e0557801c0b51f4780482c0ec5fe2ac851af5a92cfce1af1cb83", size = 458896 },
+    { url = "https://files.pythonhosted.org/packages/5d/ce/46572759afc859e867a5bc8ec3487315869013f59281ce61764f76d879de/aiohttp-3.13.5-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:eb4639f32fd4a9904ab8fb45bf3383ba71137f3d9d4ba25b3b3f3109977c5b8c", size = 745721 },
+    { url = "https://files.pythonhosted.org/packages/13/fe/8a2efd7626dbe6049b2ef8ace18ffda8a4dfcbe1bcff3ac30c0c7575c20b/aiohttp-3.13.5-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:7e5dc4311bd5ac493886c63cbf76ab579dbe4641268e7c74e48e774c74b6f2be", size = 497663 },
+    { url = "https://files.pythonhosted.org/packages/9b/91/cc8cc78a111826c54743d88651e1687008133c37e5ee615fee9b57990fac/aiohttp-3.13.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:756c3c304d394977519824449600adaf2be0ccee76d206ee339c5e76b70ded25", size = 499094 },
+    { url = "https://files.pythonhosted.org/packages/0a/33/a8362cb15cf16a3af7e86ed11962d5cd7d59b449202dc576cdc731310bde/aiohttp-3.13.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecc26751323224cf8186efcf7fbcbc30f4e1d8c7970659daf25ad995e4032a56", size = 1726701 },
+    { url = "https://files.pythonhosted.org/packages/45/0c/c091ac5c3a17114bd76cbf85d674650969ddf93387876cf67f754204bd77/aiohttp-3.13.5-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:10a75acfcf794edf9d8db50e5a7ec5fc818b2a8d3f591ce93bc7b1210df016d2", size = 1683360 },
+    { url = "https://files.pythonhosted.org/packages/23/73/bcee1c2b79bc275e964d1446c55c54441a461938e70267c86afaae6fba27/aiohttp-3.13.5-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:0f7a18f258d124cd678c5fe072fe4432a4d5232b0657fca7c1847f599233c83a", size = 1773023 },
+    { url = "https://files.pythonhosted.org/packages/c7/ef/720e639df03004fee2d869f771799d8c23046dec47d5b81e396c7cda583a/aiohttp-3.13.5-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:df6104c009713d3a89621096f3e3e88cc323fd269dbd7c20afe18535094320be", size = 1853795 },
+    { url = "https://files.pythonhosted.org/packages/bd/c9/989f4034fb46841208de7aeeac2c6d8300745ab4f28c42f629ba77c2d916/aiohttp-3.13.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:241a94f7de7c0c3b616627aaad530fe2cb620084a8b144d3be7b6ecfe95bae3b", size = 1730405 },
+    { url = "https://files.pythonhosted.org/packages/ce/75/ee1fd286ca7dc599d824b5651dad7b3be7ff8d9a7e7b3fe9820d9180f7db/aiohttp-3.13.5-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c974fb66180e58709b6fc402846f13791240d180b74de81d23913abe48e96d94", size = 1558082 },
+    { url = "https://files.pythonhosted.org/packages/c3/20/1e9e6650dfc436340116b7aa89ff8cb2bbdf0abc11dfaceaad8f74273a10/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:6e27ea05d184afac78aabbac667450c75e54e35f62238d44463131bd3f96753d", size = 1692346 },
+    { url = "https://files.pythonhosted.org/packages/d8/40/8ebc6658d48ea630ac7903912fe0dd4e262f0e16825aa4c833c56c9f1f56/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a79a6d399cef33a11b6f004c67bb07741d91f2be01b8d712d52c75711b1e07c7", size = 1698891 },
+    { url = "https://files.pythonhosted.org/packages/d8/78/ea0ae5ec8ba7a5c10bdd6e318f1ba5e76fcde17db8275188772afc7917a4/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:c632ce9c0b534fbe25b52c974515ed674937c5b99f549a92127c85f771a78772", size = 1742113 },
+    { url = "https://files.pythonhosted.org/packages/8a/66/9d308ed71e3f2491be1acb8769d96c6f0c47d92099f3bc9119cada27b357/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:fceedde51fbd67ee2bcc8c0b33d0126cc8b51ef3bbde2f86662bd6d5a6f10ec5", size = 1553088 },
+    { url = "https://files.pythonhosted.org/packages/da/a6/6cc25ed8dfc6e00c90f5c6d126a98e2cf28957ad06fa1036bd34b6f24a2c/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f92995dfec9420bb69ae629abf422e516923ba79ba4403bc750d94fb4a6c68c1", size = 1757976 },
+    { url = "https://files.pythonhosted.org/packages/c1/2b/cce5b0ffe0de99c83e5e36d8f828e4161e415660a9f3e58339d07cce3006/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:20ae0ff08b1f2c8788d6fb85afcb798654ae6ba0b747575f8562de738078457b", size = 1712444 },
+    { url = "https://files.pythonhosted.org/packages/6c/cf/9e1795b4160c58d29421eafd1a69c6ce351e2f7c8d3c6b7e4ca44aea1a5b/aiohttp-3.13.5-cp314-cp314-win32.whl", hash = "sha256:b20df693de16f42b2472a9c485e1c948ee55524786a0a34345511afdd22246f3", size = 438128 },
+    { url = "https://files.pythonhosted.org/packages/22/4d/eaedff67fc805aeba4ba746aec891b4b24cebb1a7d078084b6300f79d063/aiohttp-3.13.5-cp314-cp314-win_amd64.whl", hash = "sha256:f85c6f327bf0b8c29da7d93b1cabb6363fb5e4e160a32fa241ed2dce21b73162", size = 464029 },
+    { url = "https://files.pythonhosted.org/packages/79/11/c27d9332ee20d68dd164dc12a6ecdef2e2e35ecc97ed6cf0d2442844624b/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:1efb06900858bb618ff5cee184ae2de5828896c448403d51fb633f09e109be0a", size = 778758 },
+    { url = "https://files.pythonhosted.org/packages/04/fb/377aead2e0a3ba5f09b7624f702a964bdf4f08b5b6728a9799830c80041e/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:fee86b7c4bd29bdaf0d53d14739b08a106fdda809ca5fe032a15f52fae5fe254", size = 512883 },
+    { url = "https://files.pythonhosted.org/packages/bb/a6/aa109a33671f7a5d3bd78b46da9d852797c5e665bfda7d6b373f56bff2ec/aiohttp-3.13.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:20058e23909b9e65f9da62b396b77dfa95965cbe840f8def6e572538b1d32e36", size = 516668 },
+    { url = "https://files.pythonhosted.org/packages/79/b3/ca078f9f2fa9563c36fb8ef89053ea2bb146d6f792c5104574d49d8acb63/aiohttp-3.13.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cf20a8d6868cb15a73cab329ffc07291ba8c22b1b88176026106ae39aa6df0f", size = 1883461 },
+    { url = "https://files.pythonhosted.org/packages/b7/e3/a7ad633ca1ca497b852233a3cce6906a56c3225fb6d9217b5e5e60b7419d/aiohttp-3.13.5-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:330f5da04c987f1d5bdb8ae189137c77139f36bd1cb23779ca1a354a4b027800", size = 1747661 },
+    { url = "https://files.pythonhosted.org/packages/33/b9/cd6fe579bed34a906d3d783fe60f2fa297ef55b27bb4538438ee49d4dc41/aiohttp-3.13.5-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6f1cbf0c7926d315c3c26c2da41fd2b5d2fe01ac0e157b78caefc51a782196cf", size = 1863800 },
+    { url = "https://files.pythonhosted.org/packages/c0/3f/2c1e2f5144cefa889c8afd5cf431994c32f3b29da9961698ff4e3811b79a/aiohttp-3.13.5-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:53fc049ed6390d05423ba33103ded7281fe897cf97878f369a527070bd95795b", size = 1958382 },
+    { url = "https://files.pythonhosted.org/packages/66/1d/f31ec3f1013723b3babe3609e7f119c2c2fb6ef33da90061a705ef3e1bc8/aiohttp-3.13.5-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:898703aa2667e3c5ca4c54ca36cd73f58b7a38ef87a5606414799ebce4d3fd3a", size = 1803724 },
+    { url = "https://files.pythonhosted.org/packages/0e/b4/57712dfc6f1542f067daa81eb61da282fab3e6f1966fca25db06c4fc62d5/aiohttp-3.13.5-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0494a01ca9584eea1e5fbd6d748e61ecff218c51b576ee1999c23db7066417d8", size = 1640027 },
+    { url = "https://files.pythonhosted.org/packages/25/3c/734c878fb43ec083d8e31bf029daae1beafeae582d1b35da234739e82ee7/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:6cf81fe010b8c17b09495cbd15c1d35afbc8fb405c0c9cf4738e5ae3af1d65be", size = 1806644 },
+    { url = "https://files.pythonhosted.org/packages/20/a5/f671e5cbec1c21d044ff3078223f949748f3a7f86b14e34a365d74a5d21f/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:c564dd5f09ddc9d8f2c2d0a301cd30a79a2cc1b46dd1a73bef8f0038863d016b", size = 1791630 },
+    { url = "https://files.pythonhosted.org/packages/0b/63/fb8d0ad63a0b8a99be97deac8c04dacf0785721c158bdf23d679a87aa99e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:2994be9f6e51046c4f864598fd9abeb4fba6e88f0b2152422c9666dcd4aea9c6", size = 1809403 },
+    { url = "https://files.pythonhosted.org/packages/59/0c/bfed7f30662fcf12206481c2aac57dedee43fe1c49275e85b3a1e1742294/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:157826e2fa245d2ef46c83ea8a5faf77ca19355d278d425c29fda0beb3318037", size = 1634924 },
+    { url = "https://files.pythonhosted.org/packages/17/d6/fd518d668a09fd5a3319ae5e984d4d80b9a4b3df4e21c52f02251ef5a32e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:a8aca50daa9493e9e13c0f566201a9006f080e7c50e5e90d0b06f53146a54500", size = 1836119 },
+    { url = "https://files.pythonhosted.org/packages/78/b7/15fb7a9d52e112a25b621c67b69c167805cb1f2ab8f1708a5c490d1b52fe/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3b13560160d07e047a93f23aaa30718606493036253d5430887514715b67c9d9", size = 1772072 },
+    { url = "https://files.pythonhosted.org/packages/7e/df/57ba7f0c4a553fc2bd8b6321df236870ec6fd64a2a473a8a13d4f733214e/aiohttp-3.13.5-cp314-cp314t-win32.whl", hash = "sha256:9a0f4474b6ea6818b41f82172d799e4b3d29e22c2c520ce4357856fced9af2f8", size = 471819 },
+    { url = "https://files.pythonhosted.org/packages/62/29/2f8418269e46454a26171bfdd6a055d74febf32234e474930f2f60a17145/aiohttp-3.13.5-cp314-cp314t-win_amd64.whl", hash = "sha256:18a2f6c1182c51baa1d28d68fea51513cb2a76612f038853c0ad3c145423d3d9", size = 505441 },
+]
+
+[[package]]
+name = "aiosignal"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "frozenlist" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490 },
+]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303 },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+]
+
+[[package]]
+name = "anyio"
+version = "4.13.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353 },
+]
+
+[[package]]
+name = "attrs"
+version = "26.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548 },
+]
+
+[[package]]
+name = "certifi"
+version = "2026.4.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/25/ee/6caf7a40c36a1220410afe15a1cc64993a1f864871f698c0f93acb72842a/certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580", size = 137077 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707 },
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0c/eb/4fc8d0a7110eb5fc9cc161723a34a8a6c200ce3b4fbf681bc86feee22308/charset_normalizer-3.4.7-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46", size = 311328 },
+    { url = "https://files.pythonhosted.org/packages/f8/e3/0fadc706008ac9d7b9b5be6dc767c05f9d3e5df51744ce4cc9605de7b9f4/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2", size = 208061 },
+    { url = "https://files.pythonhosted.org/packages/42/f0/3dd1045c47f4a4604df85ec18ad093912ae1344ac706993aff91d38773a2/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e1421b502d83040e6d7fb2fb18dff63957f720da3d77b2fbd3187ceb63755d7b", size = 229031 },
+    { url = "https://files.pythonhosted.org/packages/dc/67/675a46eb016118a2fbde5a277a5d15f4f69d5f3f5f338e5ee2f8948fcf43/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:edac0f1ab77644605be2cbba52e6b7f630731fc42b34cb0f634be1a6eface56a", size = 225239 },
+    { url = "https://files.pythonhosted.org/packages/4b/f8/d0118a2f5f23b02cd166fa385c60f9b0d4f9194f574e2b31cef350ad7223/charset_normalizer-3.4.7-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5649fd1c7bade02f320a462fdefd0b4bd3ce036065836d4f42e0de958038e116", size = 216589 },
+    { url = "https://files.pythonhosted.org/packages/b1/f1/6d2b0b261b6c4ceef0fcb0d17a01cc5bc53586c2d4796fa04b5c540bc13d/charset_normalizer-3.4.7-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:203104ed3e428044fd943bc4bf45fa73c0730391f9621e37fe39ecf477b128cb", size = 202733 },
+    { url = "https://files.pythonhosted.org/packages/6f/c0/7b1f943f7e87cc3db9626ba17807d042c38645f0a1d4415c7a14afb5591f/charset_normalizer-3.4.7-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:298930cec56029e05497a76988377cbd7457ba864beeea92ad7e844fe74cd1f1", size = 212652 },
+    { url = "https://files.pythonhosted.org/packages/38/dd/5a9ab159fe45c6e72079398f277b7d2b523e7f716acc489726115a910097/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:708838739abf24b2ceb208d0e22403dd018faeef86ddac04319a62ae884c4f15", size = 211229 },
+    { url = "https://files.pythonhosted.org/packages/d5/ff/531a1cad5ca855d1c1a8b69cb71abfd6d85c0291580146fda7c82857caa1/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:0f7eb884681e3938906ed0434f20c63046eacd0111c4ba96f27b76084cd679f5", size = 203552 },
+    { url = "https://files.pythonhosted.org/packages/c1/4c/a5fb52d528a8ca41f7598cb619409ece30a169fbdf9cdce592e53b46c3a6/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:4dc1e73c36828f982bfe79fadf5919923f8a6f4df2860804db9a98c48824ce8d", size = 230806 },
+    { url = "https://files.pythonhosted.org/packages/59/7a/071feed8124111a32b316b33ae4de83d36923039ef8cf48120266844285b/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:aed52fea0513bac0ccde438c188c8a471c4e0f457c2dd20cdbf6ea7a450046c7", size = 212316 },
+    { url = "https://files.pythonhosted.org/packages/fd/35/f7dba3994312d7ba508e041eaac39a36b120f32d4c8662b8814dab876431/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:fea24543955a6a729c45a73fe90e08c743f0b3334bbf3201e6c4bc1b0c7fa464", size = 227274 },
+    { url = "https://files.pythonhosted.org/packages/8a/2d/a572df5c9204ab7688ec1edc895a73ebded3b023bb07364710b05dd1c9be/charset_normalizer-3.4.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:bb6d88045545b26da47aa879dd4a89a71d1dce0f0e549b1abcb31dfe4a8eac49", size = 218468 },
+    { url = "https://files.pythonhosted.org/packages/86/eb/890922a8b03a568ca2f336c36585a4713c55d4d67bf0f0c78924be6315ca/charset_normalizer-3.4.7-cp312-cp312-win32.whl", hash = "sha256:2257141f39fe65a3fdf38aeccae4b953e5f3b3324f4ff0daf9f15b8518666a2c", size = 148460 },
+    { url = "https://files.pythonhosted.org/packages/35/d9/0e7dffa06c5ab081f75b1b786f0aefc88365825dfcd0ac544bdb7b2b6853/charset_normalizer-3.4.7-cp312-cp312-win_amd64.whl", hash = "sha256:5ed6ab538499c8644b8a3e18debabcd7ce684f3fa91cf867521a7a0279cab2d6", size = 159330 },
+    { url = "https://files.pythonhosted.org/packages/9e/5d/481bcc2a7c88ea6b0878c299547843b2521ccbc40980cb406267088bc701/charset_normalizer-3.4.7-cp312-cp312-win_arm64.whl", hash = "sha256:56be790f86bfb2c98fb742ce566dfb4816e5a83384616ab59c49e0604d49c51d", size = 147828 },
+    { url = "https://files.pythonhosted.org/packages/c1/3b/66777e39d3ae1ddc77ee606be4ec6d8cbd4c801f65e5a1b6f2b11b8346dd/charset_normalizer-3.4.7-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f496c9c3cc02230093d8330875c4c3cdfc3b73612a5fd921c65d39cbcef08063", size = 309627 },
+    { url = "https://files.pythonhosted.org/packages/2e/4e/b7f84e617b4854ade48a1b7915c8ccfadeba444d2a18c291f696e37f0d3b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c", size = 207008 },
+    { url = "https://files.pythonhosted.org/packages/c4/bb/ec73c0257c9e11b268f018f068f5d00aa0ef8c8b09f7753ebd5f2880e248/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a277ab8928b9f299723bc1a2dabb1265911b1a76341f90a510368ca44ad9ab66", size = 228303 },
+    { url = "https://files.pythonhosted.org/packages/85/fb/32d1f5033484494619f701e719429c69b766bfc4dbc61aa9e9c8c166528b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3bec022aec2c514d9cf199522a802bd007cd588ab17ab2525f20f9c34d067c18", size = 224282 },
+    { url = "https://files.pythonhosted.org/packages/fa/07/330e3a0dda4c404d6da83b327270906e9654a24f6c546dc886a0eb0ffb23/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd", size = 215595 },
+    { url = "https://files.pythonhosted.org/packages/e3/7c/fc890655786e423f02556e0216d4b8c6bcb6bdfa890160dc66bf52dee468/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:f495a1652cf3fbab2eb0639776dad966c2fb874d79d87ca07f9d5f059b8bd215", size = 201986 },
+    { url = "https://files.pythonhosted.org/packages/d8/97/bfb18b3db2aed3b90cf54dc292ad79fdd5ad65c4eae454099475cbeadd0d/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e712b419df8ba5e42b226c510472b37bd57b38e897d3eca5e8cfd410a29fa859", size = 211711 },
+    { url = "https://files.pythonhosted.org/packages/6f/a5/a581c13798546a7fd557c82614a5c65a13df2157e9ad6373166d2a3e645d/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7804338df6fcc08105c7745f1502ba68d900f45fd770d5bdd5288ddccb8a42d8", size = 210036 },
+    { url = "https://files.pythonhosted.org/packages/8c/bf/b3ab5bcb478e4193d517644b0fb2bf5497fbceeaa7a1bc0f4d5b50953861/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:481551899c856c704d58119b5025793fa6730adda3571971af568f66d2424bb5", size = 202998 },
+    { url = "https://files.pythonhosted.org/packages/e7/4e/23efd79b65d314fa320ec6017b4b5834d5c12a58ba4610aa353af2e2f577/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f59099f9b66f0d7145115e6f80dd8b1d847176df89b234a5a6b3f00437aa0832", size = 230056 },
+    { url = "https://files.pythonhosted.org/packages/b9/9f/1e1941bc3f0e01df116e68dc37a55c4d249df5e6fa77f008841aef68264f/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:f59ad4c0e8f6bba240a9bb85504faa1ab438237199d4cce5f622761507b8f6a6", size = 211537 },
+    { url = "https://files.pythonhosted.org/packages/80/0f/088cbb3020d44428964a6c97fe1edfb1b9550396bf6d278330281e8b709c/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:3dedcc22d73ec993f42055eff4fcfed9318d1eeb9a6606c55892a26964964e48", size = 226176 },
+    { url = "https://files.pythonhosted.org/packages/6a/9f/130394f9bbe06f4f63e22641d32fc9b202b7e251c9aef4db044324dac493/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:64f02c6841d7d83f832cd97ccf8eb8a906d06eb95d5276069175c696b024b60a", size = 217723 },
+    { url = "https://files.pythonhosted.org/packages/73/55/c469897448a06e49f8fa03f6caae97074fde823f432a98f979cc42b90e69/charset_normalizer-3.4.7-cp313-cp313-win32.whl", hash = "sha256:4042d5c8f957e15221d423ba781e85d553722fc4113f523f2feb7b188cc34c5e", size = 148085 },
+    { url = "https://files.pythonhosted.org/packages/5d/78/1b74c5bbb3f99b77a1715c91b3e0b5bdb6fe302d95ace4f5b1bec37b0167/charset_normalizer-3.4.7-cp313-cp313-win_amd64.whl", hash = "sha256:3946fa46a0cf3e4c8cb1cc52f56bb536310d34f25f01ca9b6c16afa767dab110", size = 158819 },
+    { url = "https://files.pythonhosted.org/packages/68/86/46bd42279d323deb8687c4a5a811fd548cb7d1de10cf6535d099877a9a9f/charset_normalizer-3.4.7-cp313-cp313-win_arm64.whl", hash = "sha256:80d04837f55fc81da168b98de4f4b797ef007fc8a79ab71c6ec9bc4dd662b15b", size = 147915 },
+    { url = "https://files.pythonhosted.org/packages/97/c8/c67cb8c70e19ef1960b97b22ed2a1567711de46c4ddf19799923adc836c2/charset_normalizer-3.4.7-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:c36c333c39be2dbca264d7803333c896ab8fa7d4d6f0ab7edb7dfd7aea6e98c0", size = 309234 },
+    { url = "https://files.pythonhosted.org/packages/99/85/c091fdee33f20de70d6c8b522743b6f831a2f1cd3ff86de4c6a827c48a76/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c2aed2e5e41f24ea8ef1590b8e848a79b56f3a5564a65ceec43c9d692dc7d8a", size = 208042 },
+    { url = "https://files.pythonhosted.org/packages/87/1c/ab2ce611b984d2fd5d86a5a8a19c1ae26acac6bad967da4967562c75114d/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54523e136b8948060c0fa0bc7b1b50c32c186f2fceee897a495406bb6e311d2b", size = 228706 },
+    { url = "https://files.pythonhosted.org/packages/a8/29/2b1d2cb00bf085f59d29eb773ce58ec2d325430f8c216804a0a5cd83cbca/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:715479b9a2802ecac752a3b0efa2b0b60285cf962ee38414211abdfccc233b41", size = 224727 },
+    { url = "https://files.pythonhosted.org/packages/47/5c/032c2d5a07fe4d4855fea851209cca2b6f03ebeb6d4e3afdb3358386a684/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bd6c2a1c7573c64738d716488d2cdd3c00e340e4835707d8fdb8dc1a66ef164e", size = 215882 },
+    { url = "https://files.pythonhosted.org/packages/2c/c2/356065d5a8b78ed04499cae5f339f091946a6a74f91e03476c33f0ab7100/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:c45e9440fb78f8ddabcf714b68f936737a121355bf59f3907f4e17721b9d1aae", size = 200860 },
+    { url = "https://files.pythonhosted.org/packages/0c/cd/a32a84217ced5039f53b29f460962abb2d4420def55afabe45b1c3c7483d/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3534e7dcbdcf757da6b85a0bbf5b6868786d5982dd959b065e65481644817a18", size = 211564 },
+    { url = "https://files.pythonhosted.org/packages/44/86/58e6f13ce26cc3b8f4a36b94a0f22ae2f00a72534520f4ae6857c4b81f89/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e8ac484bf18ce6975760921bb6148041faa8fef0547200386ea0b52b5d27bf7b", size = 211276 },
+    { url = "https://files.pythonhosted.org/packages/8f/fe/d17c32dc72e17e155e06883efa84514ca375f8a528ba2546bee73fc4df81/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a5fe03b42827c13cdccd08e6c0247b6a6d4b5e3cdc53fd1749f5896adcdc2356", size = 201238 },
+    { url = "https://files.pythonhosted.org/packages/6a/29/f33daa50b06525a237451cdb6c69da366c381a3dadcd833fa5676bc468b3/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2d6eb928e13016cea4f1f21d1e10c1cebd5a421bc57ddf5b1142ae3f86824fab", size = 230189 },
+    { url = "https://files.pythonhosted.org/packages/b6/6e/52c84015394a6a0bdcd435210a7e944c5f94ea1055f5cc5d56c5fe368e7b/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e74327fb75de8986940def6e8dee4f127cc9752bee7355bb323cc5b2659b6d46", size = 211352 },
+    { url = "https://files.pythonhosted.org/packages/8c/d7/4353be581b373033fb9198bf1da3cf8f09c1082561e8e922aa7b39bf9fe8/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44", size = 227024 },
+    { url = "https://files.pythonhosted.org/packages/30/45/99d18aa925bd1740098ccd3060e238e21115fffbfdcb8f3ece837d0ace6c/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7579e913a5339fb8fa133f6bbcfd8e6749696206cf05acdbdca71a1b436d8e72", size = 217869 },
+    { url = "https://files.pythonhosted.org/packages/5c/05/5ee478aa53f4bb7996482153d4bfe1b89e0f087f0ab6b294fcf92d595873/charset_normalizer-3.4.7-cp314-cp314-win32.whl", hash = "sha256:5b77459df20e08151cd6f8b9ef8ef1f961ef73d85c21a555c7eed5b79410ec10", size = 148541 },
+    { url = "https://files.pythonhosted.org/packages/48/77/72dcb0921b2ce86420b2d79d454c7022bf5be40202a2a07906b9f2a35c97/charset_normalizer-3.4.7-cp314-cp314-win_amd64.whl", hash = "sha256:92a0a01ead5e668468e952e4238cccd7c537364eb7d851ab144ab6627dbbe12f", size = 159634 },
+    { url = "https://files.pythonhosted.org/packages/c6/a3/c2369911cd72f02386e4e340770f6e158c7980267da16af8f668217abaa0/charset_normalizer-3.4.7-cp314-cp314-win_arm64.whl", hash = "sha256:67f6279d125ca0046a7fd386d01b311c6363844deac3e5b069b514ba3e63c246", size = 148384 },
+    { url = "https://files.pythonhosted.org/packages/94/09/7e8a7f73d24dba1f0035fbbf014d2c36828fc1bf9c88f84093e57d315935/charset_normalizer-3.4.7-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:effc3f449787117233702311a1b7d8f59cba9ced946ba727bdc329ec69028e24", size = 330133 },
+    { url = "https://files.pythonhosted.org/packages/8d/da/96975ddb11f8e977f706f45cddd8540fd8242f71ecdb5d18a80723dcf62c/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fbccdc05410c9ee21bbf16a35f4c1d16123dcdeb8a1d38f33654fa21d0234f79", size = 216257 },
+    { url = "https://files.pythonhosted.org/packages/e5/e8/1d63bf8ef2d388e95c64b2098f45f84758f6d102a087552da1485912637b/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:733784b6d6def852c814bce5f318d25da2ee65dd4839a0718641c696e09a2960", size = 234851 },
+    { url = "https://files.pythonhosted.org/packages/9b/40/e5ff04233e70da2681fa43969ad6f66ca5611d7e669be0246c4c7aaf6dc8/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a89c23ef8d2c6b27fd200a42aa4ac72786e7c60d40efdc76e6011260b6e949c4", size = 233393 },
+    { url = "https://files.pythonhosted.org/packages/be/c1/06c6c49d5a5450f76899992f1ee40b41d076aee9279b49cf9974d2f313d5/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c114670c45346afedc0d947faf3c7f701051d2518b943679c8ff88befe14f8e", size = 223251 },
+    { url = "https://files.pythonhosted.org/packages/2b/9f/f2ff16fb050946169e3e1f82134d107e5d4ae72647ec8a1b1446c148480f/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:a180c5e59792af262bf263b21a3c49353f25945d8d9f70628e73de370d55e1e1", size = 206609 },
+    { url = "https://files.pythonhosted.org/packages/69/d5/a527c0cd8d64d2eab7459784fb4169a0ac76e5a6fc5237337982fd61347e/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3c9a494bc5ec77d43cea229c4f6db1e4d8fe7e1bbffa8b6f0f0032430ff8ab44", size = 220014 },
+    { url = "https://files.pythonhosted.org/packages/7e/80/8a7b8104a3e203074dc9aa2c613d4b726c0e136bad1cc734594b02867972/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d828b6667a32a728a1ad1d93957cdf37489c57b97ae6c4de2860fa749b8fc1e", size = 218979 },
+    { url = "https://files.pythonhosted.org/packages/02/9a/b759b503d507f375b2b5c153e4d2ee0a75aa215b7f2489cf314f4541f2c0/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:cf1493cd8607bec4d8a7b9b004e699fcf8f9103a9284cc94962cb73d20f9d4a3", size = 209238 },
+    { url = "https://files.pythonhosted.org/packages/c2/4e/0f3f5d47b86bdb79256e7290b26ac847a2832d9a4033f7eb2cd4bcf4bb5b/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0", size = 236110 },
+    { url = "https://files.pythonhosted.org/packages/96/23/bce28734eb3ed2c91dcf93abeb8a5cf393a7b2749725030bb630e554fdd8/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:752a45dc4a6934060b3b0dab47e04edc3326575f82be64bc4fc293914566503e", size = 219824 },
+    { url = "https://files.pythonhosted.org/packages/2c/6f/6e897c6984cc4d41af319b077f2f600fc8214eb2fe2d6bcb79141b882400/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:8778f0c7a52e56f75d12dae53ae320fae900a8b9b4164b981b9c5ce059cd1fcb", size = 233103 },
+    { url = "https://files.pythonhosted.org/packages/76/22/ef7bd0fe480a0ae9b656189ec00744b60933f68b4f42a7bb06589f6f576a/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ce3412fbe1e31eb81ea42f4169ed94861c56e643189e1e75f0041f3fe7020abe", size = 225194 },
+    { url = "https://files.pythonhosted.org/packages/c5/a7/0e0ab3e0b5bc1219bd80a6a0d4d72ca74d9250cb2382b7c699c147e06017/charset_normalizer-3.4.7-cp314-cp314t-win32.whl", hash = "sha256:c03a41a8784091e67a39648f70c5f97b5b6a37f216896d44d2cdcb82615339a0", size = 159827 },
+    { url = "https://files.pythonhosted.org/packages/7a/1d/29d32e0fb40864b1f878c7f5a0b343ae676c6e2b271a2d55cc3a152391da/charset_normalizer-3.4.7-cp314-cp314t-win_amd64.whl", hash = "sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c", size = 174168 },
+    { url = "https://files.pythonhosted.org/packages/de/32/d92444ad05c7a6e41fb2036749777c163baf7a0301a040cb672d6b2b1ae9/charset_normalizer-3.4.7-cp314-cp314t-win_arm64.whl", hash = "sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d", size = 153018 },
+    { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958 },
+]
+
+[[package]]
+name = "click"
+version = "8.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bb/63/f9e1ea081ce35720d8b92acde70daaedace594dc93b693c869e0d5910718/click-8.3.3.tar.gz", hash = "sha256:398329ad4837b2ff7cbe1dd166a4c0f8900c3ca3a218de04466f38f6497f18a2", size = 328061 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ae/44/c1221527f6a71a01ec6fbad7fa78f1d50dfa02217385cf0fa3eec7087d59/click-8.3.3-py3-none-any.whl", hash = "sha256:a2bf429bb3033c89fa4936ffb35d5cb471e3719e1f3c8a7c3fff0b8314305613", size = 110502 },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
+]
+
+[[package]]
+name = "datasets"
+version = "4.8.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "dill" },
+    { name = "filelock" },
+    { name = "fsspec", extra = ["http"] },
+    { name = "httpx" },
+    { name = "huggingface-hub" },
+    { name = "multiprocess" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+    { name = "pyyaml" },
+    { name = "requests" },
+    { name = "tqdm" },
+    { name = "xxhash" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/34/14cd8e76f907f7d4dca2334cfeec9f81d30fd15c25a015f99aaea694eaed/datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772", size = 605649 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/65/99/00f3196036501b53032c4b1ab8337a0b978dee832ed276dae3815df4e8b5/datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff", size = 528973 },
+]
+
+[[package]]
+name = "dill"
+version = "0.4.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019 },
+]
+
+[[package]]
+name = "filelock"
+version = "3.29.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812 },
+]
+
+[[package]]
+name = "frozenlist"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2d/f5/c831fac6cc817d26fd54c7eaccd04ef7e0288806943f7cc5bbf69f3ac1f0/frozenlist-1.8.0.tar.gz", hash = "sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad", size = 45875 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/29/948b9aa87e75820a38650af445d2ef2b6b8a6fab1a23b6bb9e4ef0be2d59/frozenlist-1.8.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:78f7b9e5d6f2fdb88cdde9440dc147259b62b9d3b019924def9f6478be254ac1", size = 87782 },
+    { url = "https://files.pythonhosted.org/packages/64/80/4f6e318ee2a7c0750ed724fa33a4bdf1eacdc5a39a7a24e818a773cd91af/frozenlist-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:229bf37d2e4acdaf808fd3f06e854a4a7a3661e871b10dc1f8f1896a3b05f18b", size = 50594 },
+    { url = "https://files.pythonhosted.org/packages/2b/94/5c8a2b50a496b11dd519f4a24cb5496cf125681dd99e94c604ccdea9419a/frozenlist-1.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f833670942247a14eafbb675458b4e61c82e002a148f49e68257b79296e865c4", size = 50448 },
+    { url = "https://files.pythonhosted.org/packages/6a/bd/d91c5e39f490a49df14320f4e8c80161cfcce09f1e2cde1edd16a551abb3/frozenlist-1.8.0-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:494a5952b1c597ba44e0e78113a7266e656b9794eec897b19ead706bd7074383", size = 242411 },
+    { url = "https://files.pythonhosted.org/packages/8f/83/f61505a05109ef3293dfb1ff594d13d64a2324ac3482be2cedc2be818256/frozenlist-1.8.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f423a119f4777a4a056b66ce11527366a8bb92f54e541ade21f2374433f6d4", size = 243014 },
+    { url = "https://files.pythonhosted.org/packages/d8/cb/cb6c7b0f7d4023ddda30cf56b8b17494eb3a79e3fda666bf735f63118b35/frozenlist-1.8.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3462dd9475af2025c31cc61be6652dfa25cbfb56cbbf52f4ccfe029f38decaf8", size = 234909 },
+    { url = "https://files.pythonhosted.org/packages/31/c5/cd7a1f3b8b34af009fb17d4123c5a778b44ae2804e3ad6b86204255f9ec5/frozenlist-1.8.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4c800524c9cd9bac5166cd6f55285957fcfc907db323e193f2afcd4d9abd69b", size = 250049 },
+    { url = "https://files.pythonhosted.org/packages/c0/01/2f95d3b416c584a1e7f0e1d6d31998c4a795f7544069ee2e0962a4b60740/frozenlist-1.8.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d6a5df73acd3399d893dafc71663ad22534b5aa4f94e8a2fabfe856c3c1b6a52", size = 256485 },
+    { url = "https://files.pythonhosted.org/packages/ce/03/024bf7720b3abaebcff6d0793d73c154237b85bdf67b7ed55e5e9596dc9a/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:405e8fe955c2280ce66428b3ca55e12b3c4e9c336fb2103a4937e891c69a4a29", size = 237619 },
+    { url = "https://files.pythonhosted.org/packages/69/fa/f8abdfe7d76b731f5d8bd217827cf6764d4f1d9763407e42717b4bed50a0/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:908bd3f6439f2fef9e85031b59fd4f1297af54415fb60e4254a95f75b3cab3f3", size = 250320 },
+    { url = "https://files.pythonhosted.org/packages/f5/3c/b051329f718b463b22613e269ad72138cc256c540f78a6de89452803a47d/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:294e487f9ec720bd8ffcebc99d575f7eff3568a08a253d1ee1a0378754b74143", size = 246820 },
+    { url = "https://files.pythonhosted.org/packages/0f/ae/58282e8f98e444b3f4dd42448ff36fa38bef29e40d40f330b22e7108f565/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:74c51543498289c0c43656701be6b077f4b265868fa7f8a8859c197006efb608", size = 250518 },
+    { url = "https://files.pythonhosted.org/packages/8f/96/007e5944694d66123183845a106547a15944fbbb7154788cbf7272789536/frozenlist-1.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:776f352e8329135506a1d6bf16ac3f87bc25b28e765949282dcc627af36123aa", size = 239096 },
+    { url = "https://files.pythonhosted.org/packages/66/bb/852b9d6db2fa40be96f29c0d1205c306288f0684df8fd26ca1951d461a56/frozenlist-1.8.0-cp312-cp312-win32.whl", hash = "sha256:433403ae80709741ce34038da08511d4a77062aa924baf411ef73d1146e74faf", size = 39985 },
+    { url = "https://files.pythonhosted.org/packages/b8/af/38e51a553dd66eb064cdf193841f16f077585d4d28394c2fa6235cb41765/frozenlist-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:34187385b08f866104f0c0617404c8eb08165ab1272e884abc89c112e9c00746", size = 44591 },
+    { url = "https://files.pythonhosted.org/packages/a7/06/1dc65480ab147339fecc70797e9c2f69d9cea9cf38934ce08df070fdb9cb/frozenlist-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:fe3c58d2f5db5fbd18c2987cba06d51b0529f52bc3a6cdc33d3f4eab725104bd", size = 40102 },
+    { url = "https://files.pythonhosted.org/packages/2d/40/0832c31a37d60f60ed79e9dfb5a92e1e2af4f40a16a29abcc7992af9edff/frozenlist-1.8.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8d92f1a84bb12d9e56f818b3a746f3efba93c1b63c8387a73dde655e1e42282a", size = 85717 },
+    { url = "https://files.pythonhosted.org/packages/30/ba/b0b3de23f40bc55a7057bd38434e25c34fa48e17f20ee273bbde5e0650f3/frozenlist-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:96153e77a591c8adc2ee805756c61f59fef4cf4073a9275ee86fe8cba41241f7", size = 49651 },
+    { url = "https://files.pythonhosted.org/packages/0c/ab/6e5080ee374f875296c4243c381bbdef97a9ac39c6e3ce1d5f7d42cb78d6/frozenlist-1.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f21f00a91358803399890ab167098c131ec2ddd5f8f5fd5fe9c9f2c6fcd91e40", size = 49417 },
+    { url = "https://files.pythonhosted.org/packages/d5/4e/e4691508f9477ce67da2015d8c00acd751e6287739123113a9fca6f1604e/frozenlist-1.8.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fb30f9626572a76dfe4293c7194a09fb1fe93ba94c7d4f720dfae3b646b45027", size = 234391 },
+    { url = "https://files.pythonhosted.org/packages/40/76/c202df58e3acdf12969a7895fd6f3bc016c642e6726aa63bd3025e0fc71c/frozenlist-1.8.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eaa352d7047a31d87dafcacbabe89df0aa506abb5b1b85a2fb91bc3faa02d822", size = 233048 },
+    { url = "https://files.pythonhosted.org/packages/f9/c0/8746afb90f17b73ca5979c7a3958116e105ff796e718575175319b5bb4ce/frozenlist-1.8.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:03ae967b4e297f58f8c774c7eabcce57fe3c2434817d4385c50661845a058121", size = 226549 },
+    { url = "https://files.pythonhosted.org/packages/7e/eb/4c7eefc718ff72f9b6c4893291abaae5fbc0c82226a32dcd8ef4f7a5dbef/frozenlist-1.8.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f6292f1de555ffcc675941d65fffffb0a5bcd992905015f85d0592201793e0e5", size = 239833 },
+    { url = "https://files.pythonhosted.org/packages/c2/4e/e5c02187cf704224f8b21bee886f3d713ca379535f16893233b9d672ea71/frozenlist-1.8.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:29548f9b5b5e3460ce7378144c3010363d8035cea44bc0bf02d57f5a685e084e", size = 245363 },
+    { url = "https://files.pythonhosted.org/packages/1f/96/cb85ec608464472e82ad37a17f844889c36100eed57bea094518bf270692/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ec3cc8c5d4084591b4237c0a272cc4f50a5b03396a47d9caaf76f5d7b38a4f11", size = 229314 },
+    { url = "https://files.pythonhosted.org/packages/5d/6f/4ae69c550e4cee66b57887daeebe006fe985917c01d0fff9caab9883f6d0/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:517279f58009d0b1f2e7c1b130b377a349405da3f7621ed6bfae50b10adf20c1", size = 243365 },
+    { url = "https://files.pythonhosted.org/packages/7a/58/afd56de246cf11780a40a2c28dc7cbabbf06337cc8ddb1c780a2d97e88d8/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:db1e72ede2d0d7ccb213f218df6a078a9c09a7de257c2fe8fcef16d5925230b1", size = 237763 },
+    { url = "https://files.pythonhosted.org/packages/cb/36/cdfaf6ed42e2644740d4a10452d8e97fa1c062e2a8006e4b09f1b5fd7d63/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b4dec9482a65c54a5044486847b8a66bf10c9cb4926d42927ec4e8fd5db7fed8", size = 240110 },
+    { url = "https://files.pythonhosted.org/packages/03/a8/9ea226fbefad669f11b52e864c55f0bd57d3c8d7eb07e9f2e9a0b39502e1/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:21900c48ae04d13d416f0e1e0c4d81f7931f73a9dfa0b7a8746fb2fe7dd970ed", size = 233717 },
+    { url = "https://files.pythonhosted.org/packages/1e/0b/1b5531611e83ba7d13ccc9988967ea1b51186af64c42b7a7af465dcc9568/frozenlist-1.8.0-cp313-cp313-win32.whl", hash = "sha256:8b7b94a067d1c504ee0b16def57ad5738701e4ba10cec90529f13fa03c833496", size = 39628 },
+    { url = "https://files.pythonhosted.org/packages/d8/cf/174c91dbc9cc49bc7b7aab74d8b734e974d1faa8f191c74af9b7e80848e6/frozenlist-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:878be833caa6a3821caf85eb39c5ba92d28e85df26d57afb06b35b2efd937231", size = 43882 },
+    { url = "https://files.pythonhosted.org/packages/c1/17/502cd212cbfa96eb1388614fe39a3fc9ab87dbbe042b66f97acb57474834/frozenlist-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:44389d135b3ff43ba8cc89ff7f51f5a0bb6b63d829c8300f79a2fe4fe61bcc62", size = 39676 },
+    { url = "https://files.pythonhosted.org/packages/d2/5c/3bbfaa920dfab09e76946a5d2833a7cbdf7b9b4a91c714666ac4855b88b4/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:e25ac20a2ef37e91c1b39938b591457666a0fa835c7783c3a8f33ea42870db94", size = 89235 },
+    { url = "https://files.pythonhosted.org/packages/d2/d6/f03961ef72166cec1687e84e8925838442b615bd0b8854b54923ce5b7b8a/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:07cdca25a91a4386d2e76ad992916a85038a9b97561bf7a3fd12d5d9ce31870c", size = 50742 },
+    { url = "https://files.pythonhosted.org/packages/1e/bb/a6d12b7ba4c3337667d0e421f7181c82dda448ce4e7ad7ecd249a16fa806/frozenlist-1.8.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4e0c11f2cc6717e0a741f84a527c52616140741cd812a50422f83dc31749fb52", size = 51725 },
+    { url = "https://files.pythonhosted.org/packages/bc/71/d1fed0ffe2c2ccd70b43714c6cab0f4188f09f8a67a7914a6b46ee30f274/frozenlist-1.8.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b3210649ee28062ea6099cfda39e147fa1bc039583c8ee4481cb7811e2448c51", size = 284533 },
+    { url = "https://files.pythonhosted.org/packages/c9/1f/fb1685a7b009d89f9bf78a42d94461bc06581f6e718c39344754a5d9bada/frozenlist-1.8.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581ef5194c48035a7de2aefc72ac6539823bb71508189e5de01d60c9dcd5fa65", size = 292506 },
+    { url = "https://files.pythonhosted.org/packages/e6/3b/b991fe1612703f7e0d05c0cf734c1b77aaf7c7d321df4572e8d36e7048c8/frozenlist-1.8.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3ef2d026f16a2b1866e1d86fc4e1291e1ed8a387b2c333809419a2f8b3a77b82", size = 274161 },
+    { url = "https://files.pythonhosted.org/packages/ca/ec/c5c618767bcdf66e88945ec0157d7f6c4a1322f1473392319b7a2501ded7/frozenlist-1.8.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5500ef82073f599ac84d888e3a8c1f77ac831183244bfd7f11eaa0289fb30714", size = 294676 },
+    { url = "https://files.pythonhosted.org/packages/7c/ce/3934758637d8f8a88d11f0585d6495ef54b2044ed6ec84492a91fa3b27aa/frozenlist-1.8.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50066c3997d0091c411a66e710f4e11752251e6d2d73d70d8d5d4c76442a199d", size = 300638 },
+    { url = "https://files.pythonhosted.org/packages/fc/4f/a7e4d0d467298f42de4b41cbc7ddaf19d3cfeabaf9ff97c20c6c7ee409f9/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5c1c8e78426e59b3f8005e9b19f6ff46e5845895adbde20ece9218319eca6506", size = 283067 },
+    { url = "https://files.pythonhosted.org/packages/dc/48/c7b163063d55a83772b268e6d1affb960771b0e203b632cfe09522d67ea5/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:eefdba20de0d938cec6a89bd4d70f346a03108a19b9df4248d3cf0d88f1b0f51", size = 292101 },
+    { url = "https://files.pythonhosted.org/packages/9f/d0/2366d3c4ecdc2fd391e0afa6e11500bfba0ea772764d631bbf82f0136c9d/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cf253e0e1c3ceb4aaff6df637ce033ff6535fb8c70a764a8f46aafd3d6ab798e", size = 289901 },
+    { url = "https://files.pythonhosted.org/packages/b8/94/daff920e82c1b70e3618a2ac39fbc01ae3e2ff6124e80739ce5d71c9b920/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:032efa2674356903cd0261c4317a561a6850f3ac864a63fc1583147fb05a79b0", size = 289395 },
+    { url = "https://files.pythonhosted.org/packages/e3/20/bba307ab4235a09fdcd3cc5508dbabd17c4634a1af4b96e0f69bfe551ebd/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6da155091429aeba16851ecb10a9104a108bcd32f6c1642867eadaee401c1c41", size = 283659 },
+    { url = "https://files.pythonhosted.org/packages/fd/00/04ca1c3a7a124b6de4f8a9a17cc2fcad138b4608e7a3fc5877804b8715d7/frozenlist-1.8.0-cp313-cp313t-win32.whl", hash = "sha256:0f96534f8bfebc1a394209427d0f8a63d343c9779cda6fc25e8e121b5fd8555b", size = 43492 },
+    { url = "https://files.pythonhosted.org/packages/59/5e/c69f733a86a94ab10f68e496dc6b7e8bc078ebb415281d5698313e3af3a1/frozenlist-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5d63a068f978fc69421fb0e6eb91a9603187527c86b7cd3f534a5b77a592b888", size = 48034 },
+    { url = "https://files.pythonhosted.org/packages/16/6c/be9d79775d8abe79b05fa6d23da99ad6e7763a1d080fbae7290b286093fd/frozenlist-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf0a7e10b077bf5fb9380ad3ae8ce20ef919a6ad93b4552896419ac7e1d8e042", size = 41749 },
+    { url = "https://files.pythonhosted.org/packages/f1/c8/85da824b7e7b9b6e7f7705b2ecaf9591ba6f79c1177f324c2735e41d36a2/frozenlist-1.8.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cee686f1f4cadeb2136007ddedd0aaf928ab95216e7691c63e50a8ec066336d0", size = 86127 },
+    { url = "https://files.pythonhosted.org/packages/8e/e8/a1185e236ec66c20afd72399522f142c3724c785789255202d27ae992818/frozenlist-1.8.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:119fb2a1bd47307e899c2fac7f28e85b9a543864df47aa7ec9d3c1b4545f096f", size = 49698 },
+    { url = "https://files.pythonhosted.org/packages/a1/93/72b1736d68f03fda5fdf0f2180fb6caaae3894f1b854d006ac61ecc727ee/frozenlist-1.8.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4970ece02dbc8c3a92fcc5228e36a3e933a01a999f7094ff7c23fbd2beeaa67c", size = 49749 },
+    { url = "https://files.pythonhosted.org/packages/a7/b2/fabede9fafd976b991e9f1b9c8c873ed86f202889b864756f240ce6dd855/frozenlist-1.8.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cba69cb73723c3f329622e34bdbf5ce1f80c21c290ff04256cff1cd3c2036ed2", size = 231298 },
+    { url = "https://files.pythonhosted.org/packages/3a/3b/d9b1e0b0eed36e70477ffb8360c49c85c8ca8ef9700a4e6711f39a6e8b45/frozenlist-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:778a11b15673f6f1df23d9586f83c4846c471a8af693a22e066508b77d201ec8", size = 232015 },
+    { url = "https://files.pythonhosted.org/packages/dc/94/be719d2766c1138148564a3960fc2c06eb688da592bdc25adcf856101be7/frozenlist-1.8.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0325024fe97f94c41c08872db482cf8ac4800d80e79222c6b0b7b162d5b13686", size = 225038 },
+    { url = "https://files.pythonhosted.org/packages/e4/09/6712b6c5465f083f52f50cf74167b92d4ea2f50e46a9eea0523d658454ae/frozenlist-1.8.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:97260ff46b207a82a7567b581ab4190bd4dfa09f4db8a8b49d1a958f6aa4940e", size = 240130 },
+    { url = "https://files.pythonhosted.org/packages/f8/d4/cd065cdcf21550b54f3ce6a22e143ac9e4836ca42a0de1022da8498eac89/frozenlist-1.8.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54b2077180eb7f83dd52c40b2750d0a9f175e06a42e3213ce047219de902717a", size = 242845 },
+    { url = "https://files.pythonhosted.org/packages/62/c3/f57a5c8c70cd1ead3d5d5f776f89d33110b1addae0ab010ad774d9a44fb9/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2f05983daecab868a31e1da44462873306d3cbfd76d1f0b5b69c473d21dbb128", size = 229131 },
+    { url = "https://files.pythonhosted.org/packages/6c/52/232476fe9cb64f0742f3fde2b7d26c1dac18b6d62071c74d4ded55e0ef94/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:33f48f51a446114bc5d251fb2954ab0164d5be02ad3382abcbfe07e2531d650f", size = 240542 },
+    { url = "https://files.pythonhosted.org/packages/5f/85/07bf3f5d0fb5414aee5f47d33c6f5c77bfe49aac680bfece33d4fdf6a246/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:154e55ec0655291b5dd1b8731c637ecdb50975a2ae70c606d100750a540082f7", size = 237308 },
+    { url = "https://files.pythonhosted.org/packages/11/99/ae3a33d5befd41ac0ca2cc7fd3aa707c9c324de2e89db0e0f45db9a64c26/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:4314debad13beb564b708b4a496020e5306c7333fa9a3ab90374169a20ffab30", size = 238210 },
+    { url = "https://files.pythonhosted.org/packages/b2/60/b1d2da22f4970e7a155f0adde9b1435712ece01b3cd45ba63702aea33938/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:073f8bf8becba60aa931eb3bc420b217bb7d5b8f4750e6f8b3be7f3da85d38b7", size = 231972 },
+    { url = "https://files.pythonhosted.org/packages/3f/ab/945b2f32de889993b9c9133216c068b7fcf257d8595a0ac420ac8677cab0/frozenlist-1.8.0-cp314-cp314-win32.whl", hash = "sha256:bac9c42ba2ac65ddc115d930c78d24ab8d4f465fd3fc473cdedfccadb9429806", size = 40536 },
+    { url = "https://files.pythonhosted.org/packages/59/ad/9caa9b9c836d9ad6f067157a531ac48b7d36499f5036d4141ce78c230b1b/frozenlist-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:3e0761f4d1a44f1d1a47996511752cf3dcec5bbdd9cc2b4fe595caf97754b7a0", size = 44330 },
+    { url = "https://files.pythonhosted.org/packages/82/13/e6950121764f2676f43534c555249f57030150260aee9dcf7d64efda11dd/frozenlist-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:d1eaff1d00c7751b7c6662e9c5ba6eb2c17a2306ba5e2a37f24ddf3cc953402b", size = 40627 },
+    { url = "https://files.pythonhosted.org/packages/c0/c7/43200656ecc4e02d3f8bc248df68256cd9572b3f0017f0a0c4e93440ae23/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d3bb933317c52d7ea5004a1c442eef86f426886fba134ef8cf4226ea6ee1821d", size = 89238 },
+    { url = "https://files.pythonhosted.org/packages/d1/29/55c5f0689b9c0fb765055629f472c0de484dcaf0acee2f7707266ae3583c/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8009897cdef112072f93a0efdce29cd819e717fd2f649ee3016efd3cd885a7ed", size = 50738 },
+    { url = "https://files.pythonhosted.org/packages/ba/7d/b7282a445956506fa11da8c2db7d276adcbf2b17d8bb8407a47685263f90/frozenlist-1.8.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2c5dcbbc55383e5883246d11fd179782a9d07a986c40f49abe89ddf865913930", size = 51739 },
+    { url = "https://files.pythonhosted.org/packages/62/1c/3d8622e60d0b767a5510d1d3cf21065b9db874696a51ea6d7a43180a259c/frozenlist-1.8.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:39ecbc32f1390387d2aa4f5a995e465e9e2f79ba3adcac92d68e3e0afae6657c", size = 284186 },
+    { url = "https://files.pythonhosted.org/packages/2d/14/aa36d5f85a89679a85a1d44cd7a6657e0b1c75f61e7cad987b203d2daca8/frozenlist-1.8.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92db2bf818d5cc8d9c1f1fc56b897662e24ea5adb36ad1f1d82875bd64e03c24", size = 292196 },
+    { url = "https://files.pythonhosted.org/packages/05/23/6bde59eb55abd407d34f77d39a5126fb7b4f109a3f611d3929f14b700c66/frozenlist-1.8.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2dc43a022e555de94c3b68a4ef0b11c4f747d12c024a520c7101709a2144fb37", size = 273830 },
+    { url = "https://files.pythonhosted.org/packages/d2/3f/22cff331bfad7a8afa616289000ba793347fcd7bc275f3b28ecea2a27909/frozenlist-1.8.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb89a7f2de3602cfed448095bab3f178399646ab7c61454315089787df07733a", size = 294289 },
+    { url = "https://files.pythonhosted.org/packages/a4/89/5b057c799de4838b6c69aa82b79705f2027615e01be996d2486a69ca99c4/frozenlist-1.8.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:33139dc858c580ea50e7e60a1b0ea003efa1fd42e6ec7fdbad78fff65fad2fd2", size = 300318 },
+    { url = "https://files.pythonhosted.org/packages/30/de/2c22ab3eb2a8af6d69dc799e48455813bab3690c760de58e1bf43b36da3e/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:168c0969a329b416119507ba30b9ea13688fafffac1b7822802537569a1cb0ef", size = 282814 },
+    { url = "https://files.pythonhosted.org/packages/59/f7/970141a6a8dbd7f556d94977858cfb36fa9b66e0892c6dd780d2219d8cd8/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:28bd570e8e189d7f7b001966435f9dac6718324b5be2990ac496cf1ea9ddb7fe", size = 291762 },
+    { url = "https://files.pythonhosted.org/packages/c1/15/ca1adae83a719f82df9116d66f5bb28bb95557b3951903d39135620ef157/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b2a095d45c5d46e5e79ba1e5b9cb787f541a8dee0433836cea4b96a2c439dcd8", size = 289470 },
+    { url = "https://files.pythonhosted.org/packages/ac/83/dca6dc53bf657d371fbc88ddeb21b79891e747189c5de990b9dfff2ccba1/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:eab8145831a0d56ec9c4139b6c3e594c7a83c2c8be25d5bcf2d86136a532287a", size = 289042 },
+    { url = "https://files.pythonhosted.org/packages/96/52/abddd34ca99be142f354398700536c5bd315880ed0a213812bc491cff5e4/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:974b28cf63cc99dfb2188d8d222bc6843656188164848c4f679e63dae4b0708e", size = 283148 },
+    { url = "https://files.pythonhosted.org/packages/af/d3/76bd4ed4317e7119c2b7f57c3f6934aba26d277acc6309f873341640e21f/frozenlist-1.8.0-cp314-cp314t-win32.whl", hash = "sha256:342c97bf697ac5480c0a7ec73cd700ecfa5a8a40ac923bd035484616efecc2df", size = 44676 },
+    { url = "https://files.pythonhosted.org/packages/89/76/c615883b7b521ead2944bb3480398cbb07e12b7b4e4d073d3752eb721558/frozenlist-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:06be8f67f39c8b1dc671f5d83aaefd3358ae5cdcf8314552c57e7ed3e6475bdd", size = 49451 },
+    { url = "https://files.pythonhosted.org/packages/e0/a3/5982da14e113d07b325230f95060e2169f5311b1017ea8af2a29b374c289/frozenlist-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:102e6314ca4da683dca92e3b1355490fed5f313b768500084fbe6371fddfdb79", size = 42507 },
+    { url = "https://files.pythonhosted.org/packages/9a/9a/e35b4a917281c0b8419d4207f4334c8e8c5dbf4f3f5f9ada73958d937dcc/frozenlist-1.8.0-py3-none-any.whl", hash = "sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d", size = 13409 },
+]
+
+[[package]]
+name = "fsspec"
+version = "2026.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/7c/f60c259dcbf4f0c47cc4ddb8f7720d2dcdc8888c8e5ad84c73ea4531cc5b/fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff", size = 313441 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/ab/fb21f4c939bb440104cc2b396d3be1d9b7a9fd3c6c2a53d98c45b3d7c954/fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437", size = 202505 },
+]
+
+[package.optional-dependencies]
+http = [
+    { name = "aiohttp" },
+]
+
+[[package]]
+name = "h11"
+version = "0.16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515 },
+]
+
+[[package]]
+name = "hf-xet"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/74/d8/5c06fc76461418326a7decf8367480c35be11a41fd938633929c60a9ec6b/hf_xet-1.5.0.tar.gz", hash = "sha256:e0fb0a34d9f406eed88233e829a67ec016bec5af19e480eac65a233ea289a948", size = 837196 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/68/9b/6912c99070915a4f28119e3c5b52a9abd1eec0ad5cb293b8c967a0c6f5a2/hf_xet-1.5.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:7d70fe2ce97b9db73b9c9b9c81fe3693640aec83416a966c446afea54acfae3c", size = 4023383 },
+    { url = "https://files.pythonhosted.org/packages/0f/6d/9563cfde59b5d8128a9c7ec972a087f4c782e4f7bac5a85234edfd5d5e49/hf_xet-1.5.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:73a0dae8c71de3b0633a45c73f4a4a5ed09e94b43441d82981a781d4f12baa42", size = 3792751 },
+    { url = "https://files.pythonhosted.org/packages/07/a5/ed5a0cf35b49a0571af5a8f53416dad1877a718c021c9937c3a53cb45781/hf_xet-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a60290ec57e9b71767fba7c3645ddafdd0759974b540441510c629c6db6db24a", size = 4456058 },
+    { url = "https://files.pythonhosted.org/packages/60/fb/3ae8bf2a7a37a4197d0195d7247fd25b3952e15cb8a599e285dfaa6f52b3/hf_xet-1.5.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:e5de0f6deada0dada870bb376a11bcd1f08abf3a968a6d118f33e72d1b1eb480", size = 4250783 },
+    { url = "https://files.pythonhosted.org/packages/a2/9b/8bae40d4d91525085137196e84eb0ed49cf65b5e96e5c3ecdadd8bd0fac2/hf_xet-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c799d49f1a5544a0ef7591c0ee75e0d6b93d6f56dc7a4979f59f7518d2872216", size = 4445594 },
+    { url = "https://files.pythonhosted.org/packages/13/59/c74efbbd4e8728172b2cc72a2bc014d2947a4b7bdced932fbd3f5da1a4e5/hf_xet-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2baea1b0b989e5c152fe81425f7745ddc8901280ba3d97c98d8cdece7b706c60", size = 4663995 },
+    { url = "https://files.pythonhosted.org/packages/73/32/8e1e0410af64cda9b139d1dcebdc993a8ff9c8c7c0e2696ae356d75ccc0d/hf_xet-1.5.0-cp313-cp313t-win_amd64.whl", hash = "sha256:526345b3ed45f374f6317349df489167606736c876241ba984105afe7fd4839d", size = 3966608 },
+    { url = "https://files.pythonhosted.org/packages/fc/34/a8febc8f4edbea8b3e21b02ebc8b628679b84ba7e45cde624a7736b51500/hf_xet-1.5.0-cp313-cp313t-win_arm64.whl", hash = "sha256:786d28e2eb8315d5035544b9d137b4a842d600c434bb91bf7d0d953cce906ad4", size = 3796946 },
+    { url = "https://files.pythonhosted.org/packages/2a/20/8fc8996afe5815fa1a6be8e9e5c02f24500f409d599e905800d498a4e14d/hf_xet-1.5.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:872d5601e6deea30d15865ede55d29eac6daf5a534ab417b99b6ef6b076dd96c", size = 4023495 },
+    { url = "https://files.pythonhosted.org/packages/32/6a/93d84463c00cecb561a7508aa6303e35ee2894294eac14245526924415fe/hf_xet-1.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9929561f5abf4581c8ea79587881dfef6b8abb2a0d8a51915936fc2a614f4e73", size = 3792731 },
+    { url = "https://files.pythonhosted.org/packages/9d/5a/8ec8e0c863b382d00b3c2e2af6ded6b06371be617144a625903a6d562f4b/hf_xet-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f7b7bbae318e583a86fb21e5a4a175d6721d628a2874f4bd022d0e660c32a682", size = 4456738 },
+    { url = "https://files.pythonhosted.org/packages/c5/ca/f7effa1a67717da2bcc6b6c28f71c6ca648c77acaec4e2c32f40cbe16d85/hf_xet-1.5.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:cf7b2dc6f31a4ea754bb50f74cde482dcf5d366d184076d8530b9872787f3761", size = 4251622 },
+    { url = "https://files.pythonhosted.org/packages/65/f2/19247dba3e231cf77dec59ddfb878f00057635ff773d099c9b59d37812c3/hf_xet-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8dbcbab554c9ef158ef2c991545c3e970ddd8cc7acdcd0a78c5a41095dab4ded", size = 4445667 },
+    { url = "https://files.pythonhosted.org/packages/7f/64/6f116801a3bcfb6f59f5c251f48cadc47ea54026441c4a385079286a94fa/hf_xet-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5906bf7718d3636dc13402914736abe723492cb730f744834f5f5b67d3a12702", size = 4664619 },
+    { url = "https://files.pythonhosted.org/packages/5c/e8/069542d37946ed08669b127e1496fa99e78196d71de8d41eda5e9f1b7a58/hf_xet-1.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:5f3dc2248fc01cc0a00cd392ab497f1ca373fcbc7e3f2da1f452480b384e839e", size = 3966802 },
+    { url = "https://files.pythonhosted.org/packages/f9/91/fc6fdec27b14d04e88c386ac0a0129732b53fa23f7c4a78f4b83a039c567/hf_xet-1.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:b285cea1b5bab46b758772716ba8d6854a1a0310fed1c249d678a8b38601e5a0", size = 3797168 },
+    { url = "https://files.pythonhosted.org/packages/3d/fb/69ff198a82cae7eb1a69fb84d93b3a3e4816564d76817fe541ddc96874eb/hf_xet-1.5.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:dad0dc84e941b8ba3c860659fe1fdc35c049d47cce293f003287757e971a8f56", size = 4030814 },
+    { url = "https://files.pythonhosted.org/packages/9b/ff/edcc2b40162bef3ff78e14ab637e5f3b89243d6aee72f5949d3bb6a5af83/hf_xet-1.5.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fd6e5a9b0fdac4ed03ed45ef79254a655b1aaab514a02202617fbf643f5fdf7a", size = 3798444 },
+    { url = "https://files.pythonhosted.org/packages/49/4d/103f76b04310e5e57656696cc184690d20c466af0bca3ca88f8c8ea5d4f3/hf_xet-1.5.0-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3531b1823a0e6d77d80f9ed15ca0e00f0d115094f8ac033d5cae88f4564cc949", size = 4465986 },
+    { url = "https://files.pythonhosted.org/packages/c4/a2/546f47f464737b3edbab6f8ddb57f2599b93d2cbb66f06abb475ccb48651/hf_xet-1.5.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:9a0ee58cd18d5ea799f7ed11290bbccbe56bdd8b1d97ca74b9cc49a3945d7a3b", size = 4259865 },
+    { url = "https://files.pythonhosted.org/packages/95/7f/1be593c1f28613be2e196473481cd81bfc5910795e30a34e8f744f6cac4f/hf_xet-1.5.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1e60df5a42e9bed8628b6416af2cba4cba57ae9f02de226a06b020d98e1aab18", size = 4459835 },
+    { url = "https://files.pythonhosted.org/packages/aa/b2/703569fc881f3284487e68cda7b42179978480da3c438042a6bbbb4a671c/hf_xet-1.5.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4b35549ce62601b84da4ff9b24d970032ace3d4430f52d91bcbb26c901d6c690", size = 4672414 },
+    { url = "https://files.pythonhosted.org/packages/af/37/1b6def445c567286b50aa3b33828158e135b1be44938dde59f11382a500c/hf_xet-1.5.0-cp37-abi3-win_amd64.whl", hash = "sha256:2806c7c17b4d23f8d88f7c4814f838c3b6150773fe339c20af23e1cfaf2797e4", size = 3977238 },
+    { url = "https://files.pythonhosted.org/packages/62/94/3b66b148778ee100dcfd69c2ca22b57b41b44d3063ceec934f209e9184ce/hf_xet-1.5.0-cp37-abi3-win_arm64.whl", hash = "sha256:b6c9df403040248c76d808d3e047d64db2d923bae593eb244c41e425cf6cd7be", size = 3806916 },
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "h11" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784 },
+]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "certifi" },
+    { name = "httpcore" },
+    { name = "idna" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517 },
+]
+
+[[package]]
+name = "httpx-sse"
+version = "0.4.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/4c/751061ffa58615a32c31b2d82e8482be8dd4a89154f003147acee90f2be9/httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d", size = 15943 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/fd/6668e5aec43ab844de6fc74927e155a3b37bf40d7c3790e49fc0406b6578/httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc", size = 8960 },
+]
+
+[[package]]
+name = "huggingface-hub"
+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "filelock" },
+    { name = "fsspec" },
+    { name = "hf-xet", marker = "platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" },
+    { name = "httpx" },
+    { name = "packaging" },
+    { name = "pyyaml" },
+    { name = "tqdm" },
+    { name = "typer" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/39/40/43109e943fd718b0ccd0cd61eb4f1c347df22bf81f5874c6f22adf44bcff/huggingface_hub-1.14.0.tar.gz", hash = "sha256:d6d2c9cd6be1d02ae9ec6672d5587d10a427f377db688e82528f426a041622c2", size = 782365 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/89/a5/33b49ba7bea7c41bb37f74ec0f8beea0831e052330196633fe2c77516ea6/huggingface_hub-1.14.0-py3-none-any.whl", hash = "sha256:efe075535c62e130b30e836b138e13785f6f043d1f0539e0a39aa411a99e90b8", size = 661479 },
+]
+
+[[package]]
+name = "idna"
+version = "3.15"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/77/7b3966d0b9d1d31a36ddf1746926a11dface89a83409bf1483f0237aa758/idna-3.15.tar.gz", hash = "sha256:ca962446ea538f7092a95e057da437618e886f4d349216d2b1e294abfdb65fdc", size = 199245 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/23/408243171aa9aaba178d3e2559159c24c1171a641aa83b67bdd3394ead8e/idna-3.15-py3-none-any.whl", hash = "sha256:048adeaf8c2d788c40fee287673ccaa74c24ffd8dcf09ffa555a2fbb59f10ac8", size = 72340 },
+]
+
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 },
+]
+
+[[package]]
+name = "joblib"
+version = "1.5.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071 },
+]
+
+[[package]]
+name = "markdown-it-py"
+version = "4.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/ff/7841249c247aa650a76b9ee4bbaeae59370dc8bfd2f6c01f3630c35eb134/markdown_it_py-4.2.0.tar.gz", hash = "sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49", size = 82454 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/81/4da04ced5a082363ecfa159c010d200ecbd959ae410c10c0264a38cac0f5/markdown_it_py-4.2.0-py3-none-any.whl", hash = "sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a", size = 91687 },
+]
+
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979 },
+]
+
+[[package]]
+name = "multidict"
+version = "6.7.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1a/c2/c2d94cbe6ac1753f3fc980da97b3d930efe1da3af3c9f5125354436c073d/multidict-6.7.1.tar.gz", hash = "sha256:ec6652a1bee61c53a3e5776b6049172c53b6aaba34f18c9ad04f82712bac623d", size = 102010 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8d/9c/f20e0e2cf80e4b2e4b1c365bf5fe104ee633c751a724246262db8f1a0b13/multidict-6.7.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:a90f75c956e32891a4eda3639ce6dd86e87105271f43d43442a3aedf3cddf172", size = 76893 },
+    { url = "https://files.pythonhosted.org/packages/fe/cf/18ef143a81610136d3da8193da9d80bfe1cb548a1e2d1c775f26b23d024a/multidict-6.7.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:3fccb473e87eaa1382689053e4a4618e7ba7b9b9b8d6adf2027ee474597128cd", size = 45456 },
+    { url = "https://files.pythonhosted.org/packages/a9/65/1caac9d4cd32e8433908683446eebc953e82d22b03d10d41a5f0fefe991b/multidict-6.7.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b0fa96985700739c4c7853a43c0b3e169360d6855780021bfc6d0f1ce7c123e7", size = 43872 },
+    { url = "https://files.pythonhosted.org/packages/cf/3b/d6bd75dc4f3ff7c73766e04e705b00ed6dbbaccf670d9e05a12b006f5a21/multidict-6.7.1-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:cb2a55f408c3043e42b40cc8eecd575afa27b7e0b956dfb190de0f8499a57a53", size = 251018 },
+    { url = "https://files.pythonhosted.org/packages/fd/80/c959c5933adedb9ac15152e4067c702a808ea183a8b64cf8f31af8ad3155/multidict-6.7.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eb0ce7b2a32d09892b3dd6cc44877a0d02a33241fafca5f25c8b6b62374f8b75", size = 258883 },
+    { url = "https://files.pythonhosted.org/packages/86/85/7ed40adafea3d4f1c8b916e3b5cc3a8e07dfcdcb9cd72800f4ed3ca1b387/multidict-6.7.1-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c3a32d23520ee37bf327d1e1a656fec76a2edd5c038bf43eddfa0572ec49c60b", size = 242413 },
+    { url = "https://files.pythonhosted.org/packages/d2/57/b8565ff533e48595503c785f8361ff9a4fde4d67de25c207cd0ba3befd03/multidict-6.7.1-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9c90fed18bffc0189ba814749fdcc102b536e83a9f738a9003e569acd540a733", size = 268404 },
+    { url = "https://files.pythonhosted.org/packages/e0/50/9810c5c29350f7258180dfdcb2e52783a0632862eb334c4896ac717cebcb/multidict-6.7.1-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:da62917e6076f512daccfbbde27f46fed1c98fee202f0559adec8ee0de67f71a", size = 269456 },
+    { url = "https://files.pythonhosted.org/packages/f3/8d/5e5be3ced1d12966fefb5c4ea3b2a5b480afcea36406559442c6e31d4a48/multidict-6.7.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bfde23ef6ed9db7eaee6c37dcec08524cb43903c60b285b172b6c094711b3961", size = 256322 },
+    { url = "https://files.pythonhosted.org/packages/31/6e/d8a26d81ac166a5592782d208dd90dfdc0a7a218adaa52b45a672b46c122/multidict-6.7.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3758692429e4e32f1ba0df23219cd0b4fc0a52f476726fff9337d1a57676a582", size = 253955 },
+    { url = "https://files.pythonhosted.org/packages/59/4c/7c672c8aad41534ba619bcd4ade7a0dc87ed6b8b5c06149b85d3dd03f0cd/multidict-6.7.1-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:398c1478926eca669f2fd6a5856b6de9c0acf23a2cb59a14c0ba5844fa38077e", size = 251254 },
+    { url = "https://files.pythonhosted.org/packages/7b/bd/84c24de512cbafbdbc39439f74e967f19570ce7924e3007174a29c348916/multidict-6.7.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:c102791b1c4f3ab36ce4101154549105a53dc828f016356b3e3bcae2e3a039d3", size = 252059 },
+    { url = "https://files.pythonhosted.org/packages/fa/ba/f5449385510825b73d01c2d4087bf6d2fccc20a2d42ac34df93191d3dd03/multidict-6.7.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a088b62bd733e2ad12c50dad01b7d0166c30287c166e137433d3b410add807a6", size = 263588 },
+    { url = "https://files.pythonhosted.org/packages/d7/11/afc7c677f68f75c84a69fe37184f0f82fce13ce4b92f49f3db280b7e92b3/multidict-6.7.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:3d51ff4785d58d3f6c91bdbffcb5e1f7ddfda557727043aa20d20ec4f65e324a", size = 259642 },
+    { url = "https://files.pythonhosted.org/packages/2b/17/ebb9644da78c4ab36403739e0e6e0e30ebb135b9caf3440825001a0bddcb/multidict-6.7.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fc5907494fccf3e7d3f94f95c91d6336b092b5fc83811720fae5e2765890dfba", size = 251377 },
+    { url = "https://files.pythonhosted.org/packages/ca/a4/840f5b97339e27846c46307f2530a2805d9d537d8b8bd416af031cad7fa0/multidict-6.7.1-cp312-cp312-win32.whl", hash = "sha256:28ca5ce2fd9716631133d0e9a9b9a745ad7f60bac2bccafb56aa380fc0b6c511", size = 41887 },
+    { url = "https://files.pythonhosted.org/packages/80/31/0b2517913687895f5904325c2069d6a3b78f66cc641a86a2baf75a05dcbb/multidict-6.7.1-cp312-cp312-win_amd64.whl", hash = "sha256:fcee94dfbd638784645b066074b338bc9cc155d4b4bffa4adce1615c5a426c19", size = 46053 },
+    { url = "https://files.pythonhosted.org/packages/0c/5b/aba28e4ee4006ae4c7df8d327d31025d760ffa992ea23812a601d226e682/multidict-6.7.1-cp312-cp312-win_arm64.whl", hash = "sha256:ba0a9fb644d0c1a2194cf7ffb043bd852cea63a57f66fbd33959f7dae18517bf", size = 43307 },
+    { url = "https://files.pythonhosted.org/packages/f2/22/929c141d6c0dba87d3e1d38fbdf1ba8baba86b7776469f2bc2d3227a1e67/multidict-6.7.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2b41f5fed0ed563624f1c17630cb9941cf2309d4df00e494b551b5f3e3d67a23", size = 76174 },
+    { url = "https://files.pythonhosted.org/packages/c7/75/bc704ae15fee974f8fccd871305e254754167dce5f9e42d88a2def741a1d/multidict-6.7.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:84e61e3af5463c19b67ced91f6c634effb89ef8bfc5ca0267f954451ed4bb6a2", size = 45116 },
+    { url = "https://files.pythonhosted.org/packages/79/76/55cd7186f498ed080a18440c9013011eb548f77ae1b297206d030eb1180a/multidict-6.7.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:935434b9853c7c112eee7ac891bc4cb86455aa631269ae35442cb316790c1445", size = 43524 },
+    { url = "https://files.pythonhosted.org/packages/e9/3c/414842ef8d5a1628d68edee29ba0e5bcf235dbfb3ccd3ea303a7fe8c72ff/multidict-6.7.1-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:432feb25a1cb67fe82a9680b4d65fb542e4635cb3166cd9c01560651ad60f177", size = 249368 },
+    { url = "https://files.pythonhosted.org/packages/f6/32/befed7f74c458b4a525e60519fe8d87eef72bb1e99924fa2b0f9d97a221e/multidict-6.7.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e82d14e3c948952a1a85503817e038cba5905a3352de76b9a465075d072fba23", size = 256952 },
+    { url = "https://files.pythonhosted.org/packages/03/d6/c878a44ba877f366630c860fdf74bfb203c33778f12b6ac274936853c451/multidict-6.7.1-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4cfb48c6ea66c83bcaaf7e4dfa7ec1b6bbcf751b7db85a328902796dfde4c060", size = 240317 },
+    { url = "https://files.pythonhosted.org/packages/68/49/57421b4d7ad2e9e60e25922b08ceb37e077b90444bde6ead629095327a6f/multidict-6.7.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1d540e51b7e8e170174555edecddbd5538105443754539193e3e1061864d444d", size = 267132 },
+    { url = "https://files.pythonhosted.org/packages/b7/fe/ec0edd52ddbcea2a2e89e174f0206444a61440b40f39704e64dc807a70bd/multidict-6.7.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:273d23f4b40f3dce4d6c8a821c741a86dec62cded82e1175ba3d99be128147ed", size = 268140 },
+    { url = "https://files.pythonhosted.org/packages/b0/73/6e1b01cbeb458807aa0831742232dbdd1fa92bfa33f52a3f176b4ff3dc11/multidict-6.7.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d624335fd4fa1c08a53f8b4be7676ebde19cd092b3895c421045ca87895b429", size = 254277 },
+    { url = "https://files.pythonhosted.org/packages/6a/b2/5fb8c124d7561a4974c342bc8c778b471ebbeb3cc17df696f034a7e9afe7/multidict-6.7.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:12fad252f8b267cc75b66e8fc51b3079604e8d43a75428ffe193cd9e2195dfd6", size = 252291 },
+    { url = "https://files.pythonhosted.org/packages/5a/96/51d4e4e06bcce92577fcd488e22600bd38e4fd59c20cb49434d054903bd2/multidict-6.7.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:03ede2a6ffbe8ef936b92cb4529f27f42be7f56afcdab5ab739cd5f27fb1cbf9", size = 250156 },
+    { url = "https://files.pythonhosted.org/packages/db/6b/420e173eec5fba721a50e2a9f89eda89d9c98fded1124f8d5c675f7a0c0f/multidict-6.7.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:90efbcf47dbe33dcf643a1e400d67d59abeac5db07dc3f27d6bdeae497a2198c", size = 249742 },
+    { url = "https://files.pythonhosted.org/packages/44/a3/ec5b5bd98f306bc2aa297b8c6f11a46714a56b1e6ef5ebda50a4f5d7c5fb/multidict-6.7.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:5c4b9bfc148f5a91be9244d6264c53035c8a0dcd2f51f1c3c6e30e30ebaa1c84", size = 262221 },
+    { url = "https://files.pythonhosted.org/packages/cd/f7/e8c0d0da0cd1e28d10e624604e1a36bcc3353aaebdfdc3a43c72bc683a12/multidict-6.7.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:401c5a650f3add2472d1d288c26deebc540f99e2fb83e9525007a74cd2116f1d", size = 258664 },
+    { url = "https://files.pythonhosted.org/packages/52/da/151a44e8016dd33feed44f730bd856a66257c1ee7aed4f44b649fb7edeb3/multidict-6.7.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:97891f3b1b3ffbded884e2916cacf3c6fc87b66bb0dde46f7357404750559f33", size = 249490 },
+    { url = "https://files.pythonhosted.org/packages/87/af/a3b86bf9630b732897f6fc3f4c4714b90aa4361983ccbdcd6c0339b21b0c/multidict-6.7.1-cp313-cp313-win32.whl", hash = "sha256:e1c5988359516095535c4301af38d8a8838534158f649c05dd1050222321bcb3", size = 41695 },
+    { url = "https://files.pythonhosted.org/packages/b2/35/e994121b0e90e46134673422dd564623f93304614f5d11886b1b3e06f503/multidict-6.7.1-cp313-cp313-win_amd64.whl", hash = "sha256:960c83bf01a95b12b08fd54324a4eb1d5b52c88932b5cba5d6e712bb3ed12eb5", size = 45884 },
+    { url = "https://files.pythonhosted.org/packages/ca/61/42d3e5dbf661242a69c97ea363f2d7b46c567da8eadef8890022be6e2ab0/multidict-6.7.1-cp313-cp313-win_arm64.whl", hash = "sha256:563fe25c678aaba333d5399408f5ec3c383ca5b663e7f774dd179a520b8144df", size = 43122 },
+    { url = "https://files.pythonhosted.org/packages/6d/b3/e6b21c6c4f314bb956016b0b3ef2162590a529b84cb831c257519e7fde44/multidict-6.7.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:c76c4bec1538375dad9d452d246ca5368ad6e1c9039dadcf007ae59c70619ea1", size = 83175 },
+    { url = "https://files.pythonhosted.org/packages/fb/76/23ecd2abfe0957b234f6c960f4ade497f55f2c16aeb684d4ecdbf1c95791/multidict-6.7.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:57b46b24b5d5ebcc978da4ec23a819a9402b4228b8a90d9c656422b4bdd8a963", size = 48460 },
+    { url = "https://files.pythonhosted.org/packages/c4/57/a0ed92b23f3a042c36bc4227b72b97eca803f5f1801c1ab77c8a212d455e/multidict-6.7.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e954b24433c768ce78ab7929e84ccf3422e46deb45a4dc9f93438f8217fa2d34", size = 46930 },
+    { url = "https://files.pythonhosted.org/packages/b5/66/02ec7ace29162e447f6382c495dc95826bf931d3818799bbef11e8f7df1a/multidict-6.7.1-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3bd231490fa7217cc832528e1cd8752a96f0125ddd2b5749390f7c3ec8721b65", size = 242582 },
+    { url = "https://files.pythonhosted.org/packages/58/18/64f5a795e7677670e872673aca234162514696274597b3708b2c0d276cce/multidict-6.7.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:253282d70d67885a15c8a7716f3a73edf2d635793ceda8173b9ecc21f2fb8292", size = 250031 },
+    { url = "https://files.pythonhosted.org/packages/c8/ed/e192291dbbe51a8290c5686f482084d31bcd9d09af24f63358c3d42fd284/multidict-6.7.1-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0b4c48648d7649c9335cf1927a8b87fa692de3dcb15faa676c6a6f1f1aabda43", size = 228596 },
+    { url = "https://files.pythonhosted.org/packages/1e/7e/3562a15a60cf747397e7f2180b0a11dc0c38d9175a650e75fa1b4d325e15/multidict-6.7.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:98bc624954ec4d2c7cb074b8eefc2b5d0ce7d482e410df446414355d158fe4ca", size = 257492 },
+    { url = "https://files.pythonhosted.org/packages/24/02/7d0f9eae92b5249bb50ac1595b295f10e263dd0078ebb55115c31e0eaccd/multidict-6.7.1-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1b99af4d9eec0b49927b4402bcbb58dea89d3e0db8806a4086117019939ad3dd", size = 255899 },
+    { url = "https://files.pythonhosted.org/packages/00/e3/9b60ed9e23e64c73a5cde95269ef1330678e9c6e34dd4eb6b431b85b5a10/multidict-6.7.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6aac4f16b472d5b7dc6f66a0d49dd57b0e0902090be16594dc9ebfd3d17c47e7", size = 247970 },
+    { url = "https://files.pythonhosted.org/packages/3e/06/538e58a63ed5cfb0bd4517e346b91da32fde409d839720f664e9a4ae4f9d/multidict-6.7.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:21f830fe223215dffd51f538e78c172ed7c7f60c9b96a2bf05c4848ad49921c3", size = 245060 },
+    { url = "https://files.pythonhosted.org/packages/b2/2f/d743a3045a97c895d401e9bd29aaa09b94f5cbdf1bd561609e5a6c431c70/multidict-6.7.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:f5dd81c45b05518b9aa4da4aa74e1c93d715efa234fd3e8a179df611cc85e5f4", size = 235888 },
+    { url = "https://files.pythonhosted.org/packages/38/83/5a325cac191ab28b63c52f14f1131f3b0a55ba3b9aa65a6d0bf2a9b921a0/multidict-6.7.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:eb304767bca2bb92fb9c5bd33cedc95baee5bb5f6c88e63706533a1c06ad08c8", size = 243554 },
+    { url = "https://files.pythonhosted.org/packages/20/1f/9d2327086bd15da2725ef6aae624208e2ef828ed99892b17f60c344e57ed/multidict-6.7.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c9035dde0f916702850ef66460bc4239d89d08df4d02023a5926e7446724212c", size = 252341 },
+    { url = "https://files.pythonhosted.org/packages/e8/2c/2a1aa0280cf579d0f6eed8ee5211c4f1730bd7e06c636ba2ee6aafda302e/multidict-6.7.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:af959b9beeb66c822380f222f0e0a1889331597e81f1ded7f374f3ecb0fd6c52", size = 246391 },
+    { url = "https://files.pythonhosted.org/packages/e5/03/7ca022ffc36c5a3f6e03b179a5ceb829be9da5783e6fe395f347c0794680/multidict-6.7.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:41f2952231456154ee479651491e94118229844dd7226541788be783be2b5108", size = 243422 },
+    { url = "https://files.pythonhosted.org/packages/dc/1d/b31650eab6c5778aceed46ba735bd97f7c7d2f54b319fa916c0f96e7805b/multidict-6.7.1-cp313-cp313t-win32.whl", hash = "sha256:df9f19c28adcb40b6aae30bbaa1478c389efd50c28d541d76760199fc1037c32", size = 47770 },
+    { url = "https://files.pythonhosted.org/packages/ac/5b/2d2d1d522e51285bd61b1e20df8f47ae1a9d80839db0b24ea783b3832832/multidict-6.7.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d54ecf9f301853f2c5e802da559604b3e95bb7a3b01a9c295c6ee591b9882de8", size = 53109 },
+    { url = "https://files.pythonhosted.org/packages/3d/a3/cc409ba012c83ca024a308516703cf339bdc4b696195644a7215a5164a24/multidict-6.7.1-cp313-cp313t-win_arm64.whl", hash = "sha256:5a37ca18e360377cfda1d62f5f382ff41f2b8c4ccb329ed974cc2e1643440118", size = 45573 },
+    { url = "https://files.pythonhosted.org/packages/91/cc/db74228a8be41884a567e88a62fd589a913708fcf180d029898c17a9a371/multidict-6.7.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8f333ec9c5eb1b7105e3b84b53141e66ca05a19a605368c55450b6ba208cb9ee", size = 75190 },
+    { url = "https://files.pythonhosted.org/packages/d5/22/492f2246bb5b534abd44804292e81eeaf835388901f0c574bac4eeec73c5/multidict-6.7.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:a407f13c188f804c759fc6a9f88286a565c242a76b27626594c133b82883b5c2", size = 44486 },
+    { url = "https://files.pythonhosted.org/packages/f1/4f/733c48f270565d78b4544f2baddc2fb2a245e5a8640254b12c36ac7ac68e/multidict-6.7.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0e161ddf326db5577c3a4cc2d8648f81456e8a20d40415541587a71620d7a7d1", size = 43219 },
+    { url = "https://files.pythonhosted.org/packages/24/bb/2c0c2287963f4259c85e8bcbba9182ced8d7fca65c780c38e99e61629d11/multidict-6.7.1-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:1e3a8bb24342a8201d178c3b4984c26ba81a577c80d4d525727427460a50c22d", size = 245132 },
+    { url = "https://files.pythonhosted.org/packages/a7/f9/44d4b3064c65079d2467888794dea218d1601898ac50222ab8a9a8094460/multidict-6.7.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97231140a50f5d447d3164f994b86a0bed7cd016e2682f8650d6a9158e14fd31", size = 252420 },
+    { url = "https://files.pythonhosted.org/packages/8b/13/78f7275e73fa17b24c9a51b0bd9d73ba64bb32d0ed51b02a746eb876abe7/multidict-6.7.1-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:6b10359683bd8806a200fd2909e7c8ca3a7b24ec1d8132e483d58e791d881048", size = 233510 },
+    { url = "https://files.pythonhosted.org/packages/4b/25/8167187f62ae3cbd52da7893f58cb036b47ea3fb67138787c76800158982/multidict-6.7.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:283ddac99f7ac25a4acadbf004cb5ae34480bbeb063520f70ce397b281859362", size = 264094 },
+    { url = "https://files.pythonhosted.org/packages/a1/e7/69a3a83b7b030cf283fb06ce074a05a02322359783424d7edf0f15fe5022/multidict-6.7.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:538cec1e18c067d0e6103aa9a74f9e832904c957adc260e61cd9d8cf0c3b3d37", size = 260786 },
+    { url = "https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7eee46ccb30ff48a1e35bb818cc90846c6be2b68240e42a78599166722cea709", size = 248483 },
+    { url = "https://files.pythonhosted.org/packages/48/5a/d5a99e3acbca0e29c5d9cba8f92ceb15dce78bab963b308ae692981e3a5d/multidict-6.7.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa263a02f4f2dd2d11a7b1bb4362aa7cb1049f84a9235d31adf63f30143469a0", size = 248403 },
+    { url = "https://files.pythonhosted.org/packages/35/48/e58cd31f6c7d5102f2a4bf89f96b9cf7e00b6c6f3d04ecc44417c00a5a3c/multidict-6.7.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:2e1425e2f99ec5bd36c15a01b690a1a2456209c5deed58f95469ffb46039ccbb", size = 240315 },
+    { url = "https://files.pythonhosted.org/packages/94/33/1cd210229559cb90b6786c30676bb0c58249ff42f942765f88793b41fdce/multidict-6.7.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:497394b3239fc6f0e13a78a3e1b61296e72bf1c5f94b4c4eb80b265c37a131cd", size = 245528 },
+    { url = "https://files.pythonhosted.org/packages/64/f2/6e1107d226278c876c783056b7db43d800bb64c6131cec9c8dfb6903698e/multidict-6.7.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:233b398c29d3f1b9676b4b6f75c518a06fcb2ea0b925119fb2c1bc35c05e1601", size = 258784 },
+    { url = "https://files.pythonhosted.org/packages/4d/c1/11f664f14d525e4a1b5327a82d4de61a1db604ab34c6603bb3c2cc63ad34/multidict-6.7.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:93b1818e4a6e0930454f0f2af7dfce69307ca03cdcfb3739bf4d91241967b6c1", size = 251980 },
+    { url = "https://files.pythonhosted.org/packages/e1/9f/75a9ac888121d0c5bbd4ecf4eead45668b1766f6baabfb3b7f66a410e231/multidict-6.7.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f33dc2a3abe9249ea5d8360f969ec7f4142e7ac45ee7014d8f8d5acddf178b7b", size = 243602 },
+    { url = "https://files.pythonhosted.org/packages/9a/e7/50bf7b004cc8525d80dbbbedfdc7aed3e4c323810890be4413e589074032/multidict-6.7.1-cp314-cp314-win32.whl", hash = "sha256:3ab8b9d8b75aef9df299595d5388b14530839f6422333357af1339443cff777d", size = 40930 },
+    { url = "https://files.pythonhosted.org/packages/e0/bf/52f25716bbe93745595800f36fb17b73711f14da59ed0bb2eba141bc9f0f/multidict-6.7.1-cp314-cp314-win_amd64.whl", hash = "sha256:5e01429a929600e7dab7b166062d9bb54a5eed752384c7384c968c2afab8f50f", size = 45074 },
+    { url = "https://files.pythonhosted.org/packages/97/ab/22803b03285fa3a525f48217963da3a65ae40f6a1b6f6cf2768879e208f9/multidict-6.7.1-cp314-cp314-win_arm64.whl", hash = "sha256:4885cb0e817aef5d00a2e8451d4665c1808378dc27c2705f1bf4ef8505c0d2e5", size = 42471 },
+    { url = "https://files.pythonhosted.org/packages/e0/6d/f9293baa6146ba9507e360ea0292b6422b016907c393e2f63fc40ab7b7b5/multidict-6.7.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:0458c978acd8e6ea53c81eefaddbbee9c6c5e591f41b3f5e8e194780fe026581", size = 82401 },
+    { url = "https://files.pythonhosted.org/packages/7a/68/53b5494738d83558d87c3c71a486504d8373421c3e0dbb6d0db48ad42ee0/multidict-6.7.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:c0abd12629b0af3cf590982c0b413b1e7395cd4ec026f30986818ab95bfaa94a", size = 48143 },
+    { url = "https://files.pythonhosted.org/packages/37/e8/5284c53310dcdc99ce5d66563f6e5773531a9b9fe9ec7a615e9bc306b05f/multidict-6.7.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:14525a5f61d7d0c94b368a42cff4c9a4e7ba2d52e2672a7b23d84dc86fb02b0c", size = 46507 },
+    { url = "https://files.pythonhosted.org/packages/e4/fc/6800d0e5b3875568b4083ecf5f310dcf91d86d52573160834fb4bfcf5e4f/multidict-6.7.1-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:17307b22c217b4cf05033dabefe68255a534d637c6c9b0cc8382718f87be4262", size = 239358 },
+    { url = "https://files.pythonhosted.org/packages/41/75/4ad0973179361cdf3a113905e6e088173198349131be2b390f9fa4da5fc6/multidict-6.7.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a7e590ff876a3eaf1c02a4dfe0724b6e69a9e9de6d8f556816f29c496046e59", size = 246884 },
+    { url = "https://files.pythonhosted.org/packages/c3/9c/095bb28b5da139bd41fb9a5d5caff412584f377914bd8787c2aa98717130/multidict-6.7.1-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:5fa6a95dfee63893d80a34758cd0e0c118a30b8dcb46372bf75106c591b77889", size = 225878 },
+    { url = "https://files.pythonhosted.org/packages/07/d0/c0a72000243756e8f5a277b6b514fa005f2c73d481b7d9e47cd4568aa2e4/multidict-6.7.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a0543217a6a017692aa6ae5cc39adb75e587af0f3a82288b1492eb73dd6cc2a4", size = 253542 },
+    { url = "https://files.pythonhosted.org/packages/c0/6b/f69da15289e384ecf2a68837ec8b5ad8c33e973aa18b266f50fe55f24b8c/multidict-6.7.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f99fe611c312b3c1c0ace793f92464d8cd263cc3b26b5721950d977b006b6c4d", size = 252403 },
+    { url = "https://files.pythonhosted.org/packages/a2/76/b9669547afa5a1a25cd93eaca91c0da1c095b06b6d2d8ec25b713588d3a1/multidict-6.7.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9004d8386d133b7e6135679424c91b0b854d2d164af6ea3f289f8f2761064609", size = 244889 },
+    { url = "https://files.pythonhosted.org/packages/7e/a9/a50d2669e506dad33cfc45b5d574a205587b7b8a5f426f2fbb2e90882588/multidict-6.7.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e628ef0e6859ffd8273c69412a2465c4be4a9517d07261b33334b5ec6f3c7489", size = 241982 },
+    { url = "https://files.pythonhosted.org/packages/c5/bb/1609558ad8b456b4827d3c5a5b775c93b87878fd3117ed3db3423dfbce1b/multidict-6.7.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:841189848ba629c3552035a6a7f5bf3b02eb304e9fea7492ca220a8eda6b0e5c", size = 232415 },
+    { url = "https://files.pythonhosted.org/packages/d8/59/6f61039d2aa9261871e03ab9dc058a550d240f25859b05b67fd70f80d4b3/multidict-6.7.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:ce1bbd7d780bb5a0da032e095c951f7014d6b0a205f8318308140f1a6aba159e", size = 240337 },
+    { url = "https://files.pythonhosted.org/packages/a1/29/fdc6a43c203890dc2ae9249971ecd0c41deaedfe00d25cb6564b2edd99eb/multidict-6.7.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b26684587228afed0d50cf804cc71062cc9c1cdf55051c4c6345d372947b268c", size = 248788 },
+    { url = "https://files.pythonhosted.org/packages/a9/14/a153a06101323e4cf086ecee3faadba52ff71633d471f9685c42e3736163/multidict-6.7.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:9f9af11306994335398293f9958071019e3ab95e9a707dc1383a35613f6abcb9", size = 242842 },
+    { url = "https://files.pythonhosted.org/packages/41/5f/604ae839e64a4a6efc80db94465348d3b328ee955e37acb24badbcd24d83/multidict-6.7.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b4938326284c4f1224178a560987b6cf8b4d38458b113d9b8c1db1a836e640a2", size = 240237 },
+    { url = "https://files.pythonhosted.org/packages/5f/60/c3a5187bf66f6fb546ff4ab8fb5a077cbdd832d7b1908d4365c7f74a1917/multidict-6.7.1-cp314-cp314t-win32.whl", hash = "sha256:98655c737850c064a65e006a3df7c997cd3b220be4ec8fe26215760b9697d4d7", size = 48008 },
+    { url = "https://files.pythonhosted.org/packages/0c/f7/addf1087b860ac60e6f382240f64fb99f8bfb532bb06f7c542b83c29ca61/multidict-6.7.1-cp314-cp314t-win_amd64.whl", hash = "sha256:497bde6223c212ba11d462853cfa4f0ae6ef97465033e7dc9940cdb3ab5b48e5", size = 53542 },
+    { url = "https://files.pythonhosted.org/packages/4c/81/4629d0aa32302ef7b2ec65c75a728cc5ff4fa410c50096174c1632e70b3e/multidict-6.7.1-cp314-cp314t-win_arm64.whl", hash = "sha256:2bbd113e0d4af5db41d5ebfe9ccaff89de2120578164f86a5d17d5a576d1e5b2", size = 44719 },
+    { url = "https://files.pythonhosted.org/packages/81/08/7036c080d7117f28a4af526d794aab6a84463126db031b007717c1a6676e/multidict-6.7.1-py3-none-any.whl", hash = "sha256:55d97cc6dae627efa6a6e548885712d4864b81110ac76fa4e534c03819fa4a56", size = 12319 },
+]
+
+[[package]]
+name = "multiprocess"
+version = "0.70.19"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "dill" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a2/f2/e783ac7f2aeeed14e9e12801f22529cc7e6b7ab80928d6dcce4e9f00922d/multiprocess-0.70.19.tar.gz", hash = "sha256:952021e0e6c55a4a9fe4cd787895b86e239a40e76802a789d6305398d3975897", size = 2079989 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e3/45/8004d1e6b9185c1a444d6b55ac5682acf9d98035e54386d967366035a03a/multiprocess-0.70.19-py310-none-any.whl", hash = "sha256:97404393419dcb2a8385910864eedf47a3cadf82c66345b44f036420eb0b5d87", size = 134948 },
+    { url = "https://files.pythonhosted.org/packages/86/c2/dec9722dc3474c164a0b6bcd9a7ed7da542c98af8cabce05374abab35edd/multiprocess-0.70.19-py311-none-any.whl", hash = "sha256:928851ae7973aea4ce0eaf330bbdafb2e01398a91518d5c8818802845564f45c", size = 144457 },
+    { url = "https://files.pythonhosted.org/packages/71/70/38998b950a97ea279e6bd657575d22d1a2047256caf707d9a10fbce4f065/multiprocess-0.70.19-py312-none-any.whl", hash = "sha256:3a56c0e85dd5025161bac5ce138dcac1e49174c7d8e74596537e729fd5c53c28", size = 150281 },
+    { url = "https://files.pythonhosted.org/packages/7f/74/d2c27e03cb84251dfe7249b8e82923643c6d48fa4883b9476b025e7dc7eb/multiprocess-0.70.19-py313-none-any.whl", hash = "sha256:8d5eb4ec5017ba2fab4e34a747c6d2c2b6fecfe9e7236e77988db91580ada952", size = 156414 },
+    { url = "https://files.pythonhosted.org/packages/a0/61/af9115673a5870fd885247e2f1b68c4f1197737da315b520a91c757a861a/multiprocess-0.70.19-py314-none-any.whl", hash = "sha256:e8cc7fbdff15c0613f0a1f1f8744bef961b0a164c0ca29bdff53e9d2d93c5e5f", size = 160318 },
+    { url = "https://files.pythonhosted.org/packages/7e/82/69e539c4c2027f1e1697e09aaa2449243085a0edf81ae2c6341e84d769b6/multiprocess-0.70.19-py39-none-any.whl", hash = "sha256:0d4b4397ed669d371c81dcd1ef33fd384a44d6c3de1bd0ca7ac06d837720d3c5", size = 133477 },
+]
+
+[[package]]
+name = "numpy"
+version = "2.4.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/9f/b8cef5bffa569759033adda9481211426f12f53299629b410340795c2514/numpy-2.4.4.tar.gz", hash = "sha256:2d390634c5182175533585cc89f3608a4682ccb173cc9bb940b2881c8d6f8fa0", size = 20731587 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/28/05/32396bec30fb2263770ee910142f49c1476d08e8ad41abf8403806b520ce/numpy-2.4.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15716cfef24d3a9762e3acdf87e27f58dc823d1348f765bbea6bef8c639bfa1b", size = 16689272 },
+    { url = "https://files.pythonhosted.org/packages/c5/f3/a983d28637bfcd763a9c7aafdb6d5c0ebf3d487d1e1459ffdb57e2f01117/numpy-2.4.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:23cbfd4c17357c81021f21540da84ee282b9c8fba38a03b7b9d09ba6b951421e", size = 14699573 },
+    { url = "https://files.pythonhosted.org/packages/9b/fd/e5ecca1e78c05106d98028114f5c00d3eddb41207686b2b7de3e477b0e22/numpy-2.4.4-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:8b3b60bb7cba2c8c81837661c488637eee696f59a877788a396d33150c35d842", size = 5204782 },
+    { url = "https://files.pythonhosted.org/packages/de/2f/702a4594413c1a8632092beae8aba00f1d67947389369b3777aed783fdca/numpy-2.4.4-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:e4a010c27ff6f210ff4c6ef34394cd61470d01014439b192ec22552ee867f2a8", size = 6552038 },
+    { url = "https://files.pythonhosted.org/packages/7f/37/eed308a8f56cba4d1fdf467a4fc67ef4ff4bf1c888f5fc980481890104b1/numpy-2.4.4-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f9e75681b59ddaa5e659898085ae0eaea229d054f2ac0c7e563a62205a700121", size = 15670666 },
+    { url = "https://files.pythonhosted.org/packages/0a/0d/0e3ecece05b7a7e87ab9fb587855548da437a061326fff64a223b6dcb78a/numpy-2.4.4-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:81f4a14bee47aec54f883e0cad2d73986640c1590eb9bfaaba7ad17394481e6e", size = 16645480 },
+    { url = "https://files.pythonhosted.org/packages/34/49/f2312c154b82a286758ee2f1743336d50651f8b5195db18cdb63675ff649/numpy-2.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:62d6b0f03b694173f9fcb1fb317f7222fd0b0b103e784c6549f5e53a27718c44", size = 17020036 },
+    { url = "https://files.pythonhosted.org/packages/7b/e9/736d17bd77f1b0ec4f9901aaec129c00d59f5d84d5e79bba540ef12c2330/numpy-2.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fbc356aae7adf9e6336d336b9c8111d390a05df88f1805573ebb0807bd06fd1d", size = 18368643 },
+    { url = "https://files.pythonhosted.org/packages/63/f6/d417977c5f519b17c8a5c3bc9e8304b0908b0e21136fe43bf628a1343914/numpy-2.4.4-cp312-cp312-win32.whl", hash = "sha256:0d35aea54ad1d420c812bfa0385c71cd7cc5bcf7c65fed95fc2cd02fe8c79827", size = 5961117 },
+    { url = "https://files.pythonhosted.org/packages/2d/5b/e1deebf88ff431b01b7406ca3583ab2bbb90972bbe1c568732e49c844f7e/numpy-2.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:b5f0362dc928a6ecd9db58868fca5e48485205e3855957bdedea308f8672ea4a", size = 12320584 },
+    { url = "https://files.pythonhosted.org/packages/58/89/e4e856ac82a68c3ed64486a544977d0e7bdd18b8da75b78a577ca31c4395/numpy-2.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:846300f379b5b12cc769334464656bc882e0735d27d9726568bc932fdc49d5ec", size = 10221450 },
+    { url = "https://files.pythonhosted.org/packages/14/1d/d0a583ce4fefcc3308806a749a536c201ed6b5ad6e1322e227ee4848979d/numpy-2.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:08f2e31ed5e6f04b118e49821397f12767934cfdd12a1ce86a058f91e004ee50", size = 16684933 },
+    { url = "https://files.pythonhosted.org/packages/c1/62/2b7a48fbb745d344742c0277f01286dead15f3f68e4f359fbfcf7b48f70f/numpy-2.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e823b8b6edc81e747526f70f71a9c0a07ac4e7ad13020aa736bb7c9d67196115", size = 14694532 },
+    { url = "https://files.pythonhosted.org/packages/e5/87/499737bfba066b4a3bebff24a8f1c5b2dee410b209bc6668c9be692580f0/numpy-2.4.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4a19d9dba1a76618dd86b164d608566f393f8ec6ac7c44f0cc879011c45e65af", size = 5199661 },
+    { url = "https://files.pythonhosted.org/packages/cd/da/464d551604320d1491bc345efed99b4b7034143a85787aab78d5691d5a0e/numpy-2.4.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d2a8490669bfe99a233298348acc2d824d496dee0e66e31b66a6022c2ad74a5c", size = 6547539 },
+    { url = "https://files.pythonhosted.org/packages/7d/90/8d23e3b0dafd024bf31bdec225b3bb5c2dbfa6912f8a53b8659f21216cbf/numpy-2.4.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45dbed2ab436a9e826e302fcdcbe9133f9b0006e5af7168afb8963a6520da103", size = 15668806 },
+    { url = "https://files.pythonhosted.org/packages/d1/73/a9d864e42a01896bb5974475438f16086be9ba1f0d19d0bb7a07427c4a8b/numpy-2.4.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c901b15172510173f5cb310eae652908340f8dede90fff9e3bf6c0d8dfd92f83", size = 16632682 },
+    { url = "https://files.pythonhosted.org/packages/34/fb/14570d65c3bde4e202a031210475ae9cde9b7686a2e7dc97ee67d2833b35/numpy-2.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:99d838547ace2c4aace6c4f76e879ddfe02bb58a80c1549928477862b7a6d6ed", size = 17019810 },
+    { url = "https://files.pythonhosted.org/packages/8a/77/2ba9d87081fd41f6d640c83f26fb7351e536b7ce6dd9061b6af5904e8e46/numpy-2.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0aec54fd785890ecca25a6003fd9a5aed47ad607bbac5cd64f836ad8666f4959", size = 18357394 },
+    { url = "https://files.pythonhosted.org/packages/a2/23/52666c9a41708b0853fa3b1a12c90da38c507a3074883823126d4e9d5b30/numpy-2.4.4-cp313-cp313-win32.whl", hash = "sha256:07077278157d02f65c43b1b26a3886bce886f95d20aabd11f87932750dfb14ed", size = 5959556 },
+    { url = "https://files.pythonhosted.org/packages/57/fb/48649b4971cde70d817cf97a2a2fdc0b4d8308569f1dd2f2611959d2e0cf/numpy-2.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:5c70f1cc1c4efbe316a572e2d8b9b9cc44e89b95f79ca3331553fbb63716e2bf", size = 12317311 },
+    { url = "https://files.pythonhosted.org/packages/ba/d8/11490cddd564eb4de97b4579ef6bfe6a736cc07e94c1598590ae25415e01/numpy-2.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:ef4059d6e5152fa1a39f888e344c73fdc926e1b2dd58c771d67b0acfbf2aa67d", size = 10222060 },
+    { url = "https://files.pythonhosted.org/packages/99/5d/dab4339177a905aad3e2221c915b35202f1ec30d750dd2e5e9d9a72b804b/numpy-2.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4bbc7f303d125971f60ec0aaad5e12c62d0d2c925f0ab1273debd0e4ba37aba5", size = 14822302 },
+    { url = "https://files.pythonhosted.org/packages/eb/e4/0564a65e7d3d97562ed6f9b0fd0fb0a6f559ee444092f105938b50043876/numpy-2.4.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:4d6d57903571f86180eb98f8f0c839fa9ebbfb031356d87f1361be91e433f5b7", size = 5327407 },
+    { url = "https://files.pythonhosted.org/packages/29/8d/35a3a6ce5ad371afa58b4700f1c820f8f279948cca32524e0a695b0ded83/numpy-2.4.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:4636de7fd195197b7535f231b5de9e4b36d2c440b6e566d2e4e4746e6af0ca93", size = 6647631 },
+    { url = "https://files.pythonhosted.org/packages/f4/da/477731acbd5a58a946c736edfdabb2ac5b34c3d08d1ba1a7b437fa0884df/numpy-2.4.4-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ad2e2ef14e0b04e544ea2fa0a36463f847f113d314aa02e5b402fdf910ef309e", size = 15727691 },
+    { url = "https://files.pythonhosted.org/packages/e6/db/338535d9b152beabeb511579598418ba0212ce77cf9718edd70262cc4370/numpy-2.4.4-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a285b3b96f951841799528cd1f4f01cd70e7e0204b4abebac9463eecfcf2a40", size = 16681241 },
+    { url = "https://files.pythonhosted.org/packages/e2/a9/ad248e8f58beb7a0219b413c9c7d8151c5d285f7f946c3e26695bdbbe2df/numpy-2.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f8474c4241bc18b750be2abea9d7a9ec84f46ef861dbacf86a4f6e043401f79e", size = 17085767 },
+    { url = "https://files.pythonhosted.org/packages/b5/1a/3b88ccd3694681356f70da841630e4725a7264d6a885c8d442a697e1146b/numpy-2.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4e874c976154687c1f71715b034739b45c7711bec81db01914770373d125e392", size = 18403169 },
+    { url = "https://files.pythonhosted.org/packages/c2/c9/fcfd5d0639222c6eac7f304829b04892ef51c96a75d479214d77e3ce6e33/numpy-2.4.4-cp313-cp313t-win32.whl", hash = "sha256:9c585a1790d5436a5374bac930dad6ed244c046ed91b2b2a3634eb2971d21008", size = 6083477 },
+    { url = "https://files.pythonhosted.org/packages/d5/e3/3938a61d1c538aaec8ed6fd6323f57b0c2d2d2219512434c5c878db76553/numpy-2.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:93e15038125dc1e5345d9b5b68aa7f996ec33b98118d18c6ca0d0b7d6198b7e8", size = 12457487 },
+    { url = "https://files.pythonhosted.org/packages/97/6a/7e345032cc60501721ef94e0e30b60f6b0bd601f9174ebd36389a2b86d40/numpy-2.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:0dfd3f9d3adbe2920b68b5cd3d51444e13a10792ec7154cd0a2f6e74d4ab3233", size = 10292002 },
+    { url = "https://files.pythonhosted.org/packages/6e/06/c54062f85f673dd5c04cbe2f14c3acb8c8b95e3384869bb8cc9bff8cb9df/numpy-2.4.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f169b9a863d34f5d11b8698ead99febeaa17a13ca044961aa8e2662a6c7766a0", size = 16684353 },
+    { url = "https://files.pythonhosted.org/packages/4c/39/8a320264a84404c74cc7e79715de85d6130fa07a0898f67fb5cd5bd79908/numpy-2.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2483e4584a1cb3092da4470b38866634bafb223cbcd551ee047633fd2584599a", size = 14704914 },
+    { url = "https://files.pythonhosted.org/packages/91/fb/287076b2614e1d1044235f50f03748f31fa287e3dbe6abeb35cdfa351eca/numpy-2.4.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:2d19e6e2095506d1736b7d80595e0f252d76b89f5e715c35e06e937679ea7d7a", size = 5210005 },
+    { url = "https://files.pythonhosted.org/packages/63/eb/fcc338595309910de6ecabfcef2419a9ce24399680bfb149421fa2df1280/numpy-2.4.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:6a246d5914aa1c820c9443ddcee9c02bec3e203b0c080349533fae17727dfd1b", size = 6544974 },
+    { url = "https://files.pythonhosted.org/packages/44/5d/e7e9044032a716cdfaa3fba27a8e874bf1c5f1912a1ddd4ed071bf8a14a6/numpy-2.4.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989824e9faf85f96ec9c7761cd8d29c531ad857bfa1daa930cba85baaecf1a9a", size = 15684591 },
+    { url = "https://files.pythonhosted.org/packages/98/7c/21252050676612625449b4807d6b695b9ce8a7c9e1c197ee6216c8a65c7c/numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27a8d92cd10f1382a67d7cf4db7ce18341b66438bdd9f691d7b0e48d104c2a9d", size = 16637700 },
+    { url = "https://files.pythonhosted.org/packages/b1/29/56d2bbef9465db24ef25393383d761a1af4f446a1df9b8cded4fe3a5a5d7/numpy-2.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e44319a2953c738205bf3354537979eaa3998ed673395b964c1176083dd46252", size = 17035781 },
+    { url = "https://files.pythonhosted.org/packages/e3/2b/a35a6d7589d21f44cea7d0a98de5ddcbb3d421b2622a5c96b1edf18707c3/numpy-2.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e892aff75639bbef0d2a2cfd55535510df26ff92f63c92cd84ef8d4ba5a5557f", size = 18362959 },
+    { url = "https://files.pythonhosted.org/packages/64/c9/d52ec581f2390e0f5f85cbfd80fb83d965fc15e9f0e1aec2195faa142cde/numpy-2.4.4-cp314-cp314-win32.whl", hash = "sha256:1378871da56ca8943c2ba674530924bb8ca40cd228358a3b5f302ad60cf875fc", size = 6008768 },
+    { url = "https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74", size = 12449181 },
+    { url = "https://files.pythonhosted.org/packages/70/2e/14cda6f4d8e396c612d1bf97f22958e92148801d7e4f110cabebdc0eef4b/numpy-2.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:2c194dd721e54ecad9ad387c1d35e63dce5c4450c6dc7dd5611283dda239aabb", size = 10496035 },
+    { url = "https://files.pythonhosted.org/packages/b1/e8/8fed8c8d848d7ecea092dc3469643f9d10bc3a134a815a3b033da1d2039b/numpy-2.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2aa0613a5177c264ff5921051a5719d20095ea586ca88cc802c5c218d1c67d3e", size = 14824958 },
+    { url = "https://files.pythonhosted.org/packages/05/1a/d8007a5138c179c2bf33ef44503e83d70434d2642877ee8fbb230e7c0548/numpy-2.4.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:42c16925aa5a02362f986765f9ebabf20de75cdefdca827d14315c568dcab113", size = 5330020 },
+    { url = "https://files.pythonhosted.org/packages/99/64/ffb99ac6ae93faf117bcbd5c7ba48a7f45364a33e8e458545d3633615dda/numpy-2.4.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:874f200b2a981c647340f841730fc3a2b54c9d940566a3c4149099591e2c4c3d", size = 6650758 },
+    { url = "https://files.pythonhosted.org/packages/6e/6e/795cc078b78a384052e73b2f6281ff7a700e9bf53bcce2ee579d4f6dd879/numpy-2.4.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9b39d38a9bd2ae1becd7eac1303d031c5c110ad31f2b319c6e7d98b135c934d", size = 15729948 },
+    { url = "https://files.pythonhosted.org/packages/5f/86/2acbda8cc2af5f3d7bfc791192863b9e3e19674da7b5e533fded124d1299/numpy-2.4.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b268594bccac7d7cf5844c7732e3f20c50921d94e36d7ec9b79e9857694b1b2f", size = 16679325 },
+    { url = "https://files.pythonhosted.org/packages/bc/59/cafd83018f4aa55e0ac6fa92aa066c0a1877b77a615ceff1711c260ffae8/numpy-2.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ac6b31e35612a26483e20750126d30d0941f949426974cace8e6b5c58a3657b0", size = 17084883 },
+    { url = "https://files.pythonhosted.org/packages/f0/85/a42548db84e65ece46ab2caea3d3f78b416a47af387fcbb47ec28e660dc2/numpy-2.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8e3ed142f2728df44263aaf5fb1f5b0b99f4070c553a0d7f033be65338329150", size = 18403474 },
+    { url = "https://files.pythonhosted.org/packages/ed/ad/483d9e262f4b831000062e5d8a45e342166ec8aaa1195264982bca267e62/numpy-2.4.4-cp314-cp314t-win32.whl", hash = "sha256:dddbbd259598d7240b18c9d87c56a9d2fb3b02fe266f49a7c101532e78c1d871", size = 6155500 },
+    { url = "https://files.pythonhosted.org/packages/c7/03/2fc4e14c7bd4ff2964b74ba90ecb8552540b6315f201df70f137faa5c589/numpy-2.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:a7164afb23be6e37ad90b2f10426149fd75aee07ca55653d2aa41e66c4ef697e", size = 12637755 },
+    { url = "https://files.pythonhosted.org/packages/58/78/548fb8e07b1a341746bfbecb32f2c268470f45fa028aacdbd10d9bc73aab/numpy-2.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:ba203255017337d39f89bdd58417f03c4426f12beed0440cfd933cb15f8669c7", size = 10566643 },
+]
+
+[[package]]
+name = "packaging"
+version = "26.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195 },
+]
+
+[[package]]
+name = "pandas"
+version = "3.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "python-dateutil" },
+    { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f8/87/4341c6252d1c47b08768c3d25ac487362bf403f0313ddae4a2a26c9b1b4c/pandas-3.0.3.tar.gz", hash = "sha256:696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc", size = 4651414 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/24/f1/392f8c5bfc16f66a0d2d41561c01627c228fe7ed2a0d056ef11315042570/pandas-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fed2ff7fd9779120e388e285fc029bd5cf9490cdd2e4166a9ee22c0e49a9ab09", size = 10357846 },
+    { url = "https://files.pythonhosted.org/packages/cf/3d/b16412745651e855f357e5e66930248688378853a6e2698a214e331fba1f/pandas-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b168fc218fd80a6cbdbdbc1a97ddc7889ed057d7eb45f50d866ceab5f39904c4", size = 9899550 },
+    { url = "https://files.pythonhosted.org/packages/31/a8/fa2535168fffcedf67f4f6de28d2dd903a747ca7c8ea6989451aaeb3a92f/pandas-3.0.3-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c", size = 10412965 },
+    { url = "https://files.pythonhosted.org/packages/65/b6/09b01cdbc15224e2850365192d17b7bdebb8bdbd8780ed221fcdf0d9a515/pandas-3.0.3-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6dc0b3fd2169c9157deed50b4d519553a3655c8c6a96027136d654592be973a9", size = 10894600 },
+    { url = "https://files.pythonhosted.org/packages/c9/a4/2eb28f2fccb4ced4a2c79ab2a5dee9ade1ebf44922ebad6fea158c9f95d4/pandas-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7e65d5407dc0b394f509699650e4a2ec01c0514f21850f453fa60f3be79a5dbf", size = 11422824 },
+    { url = "https://files.pythonhosted.org/packages/f8/45/830bb57f533a4604b355e07edcb8ea18cf88b5f94e5fca92f27052d7c597/pandas-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f8894dc474d648fe7b6ff0ca9b0bd73950d19952bc1a6534540762c5d79d305c", size = 11950889 },
+    { url = "https://files.pythonhosted.org/packages/b9/c5/fc1b368f303087d20e8c9bf3d6ceb186263cfac0ade735cd938538bea839/pandas-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:c7be265b62cef88e253a941e4698604973736dcfe242fdb5198f0f7bc473cdcc", size = 9755463 },
+    { url = "https://files.pythonhosted.org/packages/86/bd/fda8f9705b1b09c6ebe14bfc0fa0e4ec8584d54ea673628f157ff55131af/pandas-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:557409bc4178e70ee8d9ddb494798e51ebf6ea59330f6be22c51bab2a7db6c49", size = 9066158 },
+    { url = "https://files.pythonhosted.org/packages/c5/90/62d8302883c44308c477e222c3daf7c813a34c8e96985882fbd53d964352/pandas-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:67b3b64c11910cfa29f4e94a14d3bff9ee693b6fc76055e7cad549cee0aec5fa", size = 10331071 },
+    { url = "https://files.pythonhosted.org/packages/7f/ae/6a6493c783a101f165e4356953ba3c74d6f77f0042fa7d753da9dfbb640c/pandas-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:39436b377d56d2a2e52d0395bdbee171f01068e99af5250509aceeb929f765c7", size = 9875690 },
+    { url = "https://files.pythonhosted.org/packages/62/7c/5df8e9f56c69a2769fbe9382a5ef8f2658c007e376434e1e2cbb57ad895f/pandas-3.0.3-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d4be06d68f9ddcfc645b87534911da79a8fbffc7573c80e0edcf42a5020624d8", size = 10381634 },
+    { url = "https://files.pythonhosted.org/packages/99/68/1237369725aa617bb358263d535803e3053fdbc593513ec5ed9c9896b5b6/pandas-3.0.3-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a4eeb6830daf35a71cc09649bd823e2b542dac246cdee9614c6e4bd65028cd6a", size = 10891243 },
+    { url = "https://files.pythonhosted.org/packages/25/93/77d108e8af7222b4a503ebde0e30215b1c2e4f8e53a526431890f22d5586/pandas-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1928e07221f82db493cd4af1e23c1bfca524a19a4699887975bff68f49a72bfb", size = 11388659 },
+    { url = "https://files.pythonhosted.org/packages/d0/bd/eff5b4399f332ac386c853f6cd2bd3fa2ca0061b9f36ecd9c4d7c4265649/pandas-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:51b1fe551acb77dac643c6fda86084d8d446c10fe64b06a9cc29c4cc8540e7f2", size = 11942880 },
+    { url = "https://files.pythonhosted.org/packages/2c/20/559ace4200982c3887d0b86bfd0d856a2143ef8ddab63cc07934951a964c/pandas-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:a82d532a3351d435432cd913edbccaf8b8e01d4dd0e5ced5a8d2e8ecd94c7e44", size = 9757091 },
+    { url = "https://files.pythonhosted.org/packages/3a/66/69055a09fe200f29f922a3eeec4804611900b95f52d932ece3393c3c0c19/pandas-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:275c14e0fce14a2ec20eee474aecd305478ea3c1e6f6a9d8fe219a165542717e", size = 9057282 },
+    { url = "https://files.pythonhosted.org/packages/57/0e/efe801b0e6811e8e650cd21b7f2608e30f08a7067e2bf6e8752b0d56ee3c/pandas-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:46997386d528eb40376ecd6b033cf4a8a1e5282580f68f43de875b78cba2199d", size = 10767016 },
+    { url = "https://files.pythonhosted.org/packages/ea/dc/eb55135a1d5f0f0519f28da1f609a206d2cad1f9c35c32d51e38dd7261ae/pandas-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:261e308dfb22448384b7580cf719d2f998fe2966c92893c3e77d14008af1f066", size = 10420210 },
+    { url = "https://files.pythonhosted.org/packages/c6/3e/b1d5d955ce33ffecb407465a60bc32769d74fcf68224b7ae67ae11d4dea4/pandas-3.0.3-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dd1a5d1def6a46002e964510bdc67c368aa0951df5d1d9f8365336f5a1f490cd", size = 10336126 },
+    { url = "https://files.pythonhosted.org/packages/f5/76/a01261711ab60a22d71b862f0de20e4c504bf80457270ad8cb42110f6abc/pandas-3.0.3-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d72828c20c6d6e83e1e22a6a3b47b326b71664112fa9705dcbccfd7a39b62085", size = 10728051 },
+    { url = "https://files.pythonhosted.org/packages/e9/21/ea191195e587b18cf682e97f433f81b2d0fbe341380e80a3e0d6e4403c8e/pandas-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d26cbe1fcfc12e8fd900e2454163e466b2d3af84f7c75481df7683ffc073d870", size = 11350796 },
+    { url = "https://files.pythonhosted.org/packages/64/69/f0eaaf54939f0e8c6768fd06be9af2cef9b36048b96dfb9e1b2c685a807e/pandas-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:3e91cec1879ada0624fc3dc9953c5cbd60208e59c0db28f540c5d6d47502422f", size = 11799741 },
+    { url = "https://files.pythonhosted.org/packages/45/a4/865e0e510cae5fc2194de4db28be638952de942571ba9125934fd9c01d47/pandas-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:08d789b41f87e0905880e293cedf6197ce71fe67cc081358b1e148a491b9bd13", size = 10499958 },
+    { url = "https://files.pythonhosted.org/packages/86/54/effdcc3c0ff7a08037889200e148ebe94c16c4f653be078c7b3675955df1/pandas-3.0.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:3650109c0f22879df8bd6179ab9ee3d7f1d1d4e7e0094a3f0032d9f51e2e64ac", size = 10336065 },
+    { url = "https://files.pythonhosted.org/packages/68/10/bf2d6738d72748b961a3751ab89522d58c54efc36a8e1a12161216cd45cf/pandas-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:bab900348131a7db1f69a7309ef141fd5680f1487094193bcbbb61791573bf8f", size = 9926101 },
+    { url = "https://files.pythonhosted.org/packages/ae/e9/e35cf11c8a136e757b956f5f0efdcaa50aecde85ea055f1898dfc68262f3/pandas-3.0.3-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba7e08b9ac1d54569cd1e256e3668975ed624d6826f7b68df0342b012007bddb", size = 10457553 },
+    { url = "https://files.pythonhosted.org/packages/58/3b/1cdec6772bdbaf7b25dab360c59f03cadf05492dd724c6540af905389b07/pandas-3.0.3-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d71c63ae4ebdbf70209742096f1fc46a83a0613c99d4b23766cced9ff8cd62a", size = 10914065 },
+    { url = "https://files.pythonhosted.org/packages/c4/c2/1ef644445fcd72e3627bceec77e3560636f87ddce4ed841afe76b83b5bf9/pandas-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e3a2ec42c98ffa2565a67e08e218d06d72576d758d90facb7c00805194d8f360", size = 11459188 },
+    { url = "https://files.pythonhosted.org/packages/7e/49/4d8d4f42cbc9c4adc7a1870f269c02cbd6cd40d059622c06fb298addcbad/pandas-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:335f62418ed562cfc3c49e9e196375c28b729dcef8543abf4f9438e381bf3c76", size = 11982966 },
+    { url = "https://files.pythonhosted.org/packages/38/55/792619469bab9882d8bbd5865d45a72f6478762d04a9af4bf0d08c503e95/pandas-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:3c20a521bbb85902f79f7270c80a59e1b5452d96d170c034f207181870f97ac5", size = 9876755 },
+    { url = "https://files.pythonhosted.org/packages/2a/af/33c469653b0ba03b50c3a98192d4c07f0c75c66b263ceb097fce0ee97d31/pandas-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:a2d2dff8a04f3917b55ab3910c32990f8ddf7eceba114947838cefa976a68977", size = 9198658 },
+    { url = "https://files.pythonhosted.org/packages/a2/fa/b8c257bd76b8bd060c3a9151c1fca05e9b9c5e3af5d0f549c0356f6d143d/pandas-3.0.3-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:0d589105b3c14645af1738ff279b2995102d8f7a03b0a66dc8d95550eb513e04", size = 10787242 },
+    { url = "https://files.pythonhosted.org/packages/54/eb/f19206ffb0bf1919002969aa448b4702c6594845156a6f8050674855aac3/pandas-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:13fc1e853d9e04743d11ba75a985ccbc2a317fe07d8af61e445a6fd24dacd6a6", size = 10436369 },
+    { url = "https://files.pythonhosted.org/packages/fd/24/c7c39fb4fe22b71a0c2d78bf0c585c600092d85f94f086d2b3b2f6ca27e2/pandas-3.0.3-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:819959dab7bbd0049c15623fbac4e29a191b9528160a61fb1032242d8ced2d9c", size = 10358306 },
+    { url = "https://files.pythonhosted.org/packages/16/ec/dd2a9eb7fa1204df88c0864164e35b228ac581062ac612ba0a67fd812e4c/pandas-3.0.3-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:60ae316d3fd75d1858d450d0db0103ea2be3e7d4a95ec2f064f7e2ae63f7b028", size = 10758394 },
+    { url = "https://files.pythonhosted.org/packages/95/6e/00c61ea8e85b4f6d8d35e11852a1a4998fc7fafc91c6a602d1cc9c972d64/pandas-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bd3a518890b400d32f9023722dc9a9a5c969f00b415419a3c06c043f09bb5d7d", size = 11375717 },
+    { url = "https://files.pythonhosted.org/packages/31/89/8fc1c268969fac43688d65fd92e67df24bd128d53cb4d2eee534cd307399/pandas-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9c39be2d709d01fa972a0cabc522389fceca4f3969332ba25a7d6c5802cf976a", size = 11828897 },
+    { url = "https://files.pythonhosted.org/packages/56/3b/e7d20dea247a3e6dc0bd8a6953854afbedc03951def4e7371e05e7263e25/pandas-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4db8c527972a821cf5286b40ccc57642a39bc62e62022b42f99f8a67fca8c3a1", size = 10900855 },
+    { url = "https://files.pythonhosted.org/packages/0f/54/68a0978d1ef8502b8492099beaa6e7a0c1b32e3b5d4f677f5810cb08711c/pandas-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b2c95f8bfc1ee412bf482605d7bfd30c12d1d26bd59fdd91efeef1d4718decb1", size = 9466464 },
+]
+
+[[package]]
+name = "pillow"
+version = "12.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8c/21/c2bcdd5906101a30244eaffc1b6e6ce71a31bd0742a01eb89e660ebfac2d/pillow-12.2.0.tar.gz", hash = "sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5", size = 46987819 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/58/be/7482c8a5ebebbc6470b3eb791812fff7d5e0216c2be3827b30b8bb6603ed/pillow-12.2.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2d192a155bbcec180f8564f693e6fd9bccff5a7af9b32e2e4bf8c9c69dbad6b5", size = 5308279 },
+    { url = "https://files.pythonhosted.org/packages/d8/95/0a351b9289c2b5cbde0bacd4a83ebc44023e835490a727b2a3bd60ddc0f4/pillow-12.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f3f40b3c5a968281fd507d519e444c35f0ff171237f4fdde090dd60699458421", size = 4695490 },
+    { url = "https://files.pythonhosted.org/packages/de/af/4e8e6869cbed569d43c416fad3dc4ecb944cb5d9492defaed89ddd6fe871/pillow-12.2.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:03e7e372d5240cc23e9f07deca4d775c0817bffc641b01e9c3af208dbd300987", size = 6284462 },
+    { url = "https://files.pythonhosted.org/packages/e9/9e/c05e19657fd57841e476be1ab46c4d501bffbadbafdc31a6d665f8b737b6/pillow-12.2.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b86024e52a1b269467a802258c25521e6d742349d760728092e1bc2d135b4d76", size = 8094744 },
+    { url = "https://files.pythonhosted.org/packages/2b/54/1789c455ed10176066b6e7e6da1b01e50e36f94ba584dc68d9eebfe9156d/pillow-12.2.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7371b48c4fa448d20d2714c9a1f775a81155050d383333e0a6c15b1123dda005", size = 6398371 },
+    { url = "https://files.pythonhosted.org/packages/43/e3/fdc657359e919462369869f1c9f0e973f353f9a9ee295a39b1fea8ee1a77/pillow-12.2.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:62f5409336adb0663b7caa0da5c7d9e7bdbaae9ce761d34669420c2a801b2780", size = 7087215 },
+    { url = "https://files.pythonhosted.org/packages/8b/f8/2f6825e441d5b1959d2ca5adec984210f1ec086435b0ed5f52c19b3b8a6e/pillow-12.2.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:01afa7cf67f74f09523699b4e88c73fb55c13346d212a59a2db1f86b0a63e8c5", size = 6509783 },
+    { url = "https://files.pythonhosted.org/packages/67/f9/029a27095ad20f854f9dba026b3ea6428548316e057e6fc3545409e86651/pillow-12.2.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fc3d34d4a8fbec3e88a79b92e5465e0f9b842b628675850d860b8bd300b159f5", size = 7212112 },
+    { url = "https://files.pythonhosted.org/packages/be/42/025cfe05d1be22dbfdb4f264fe9de1ccda83f66e4fc3aac94748e784af04/pillow-12.2.0-cp312-cp312-win32.whl", hash = "sha256:58f62cc0f00fd29e64b29f4fd923ffdb3859c9f9e6105bfc37ba1d08994e8940", size = 6378489 },
+    { url = "https://files.pythonhosted.org/packages/5d/7b/25a221d2c761c6a8ae21bfa3874988ff2583e19cf8a27bf2fee358df7942/pillow-12.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:7f84204dee22a783350679a0333981df803dac21a0190d706a50475e361c93f5", size = 7084129 },
+    { url = "https://files.pythonhosted.org/packages/10/e1/542a474affab20fd4a0f1836cb234e8493519da6b76899e30bcc5d990b8b/pillow-12.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:af73337013e0b3b46f175e79492d96845b16126ddf79c438d7ea7ff27783a414", size = 2463612 },
+    { url = "https://files.pythonhosted.org/packages/4a/01/53d10cf0dbad820a8db274d259a37ba50b88b24768ddccec07355382d5ad/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:8297651f5b5679c19968abefd6bb84d95fe30ef712eb1b2d9b2d31ca61267f4c", size = 4100837 },
+    { url = "https://files.pythonhosted.org/packages/0f/98/f3a6657ecb698c937f6c76ee564882945f29b79bad496abcba0e84659ec5/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:50d8520da2a6ce0af445fa6d648c4273c3eeefbc32d7ce049f22e8b5c3daecc2", size = 4176528 },
+    { url = "https://files.pythonhosted.org/packages/69/bc/8986948f05e3ea490b8442ea1c1d4d990b24a7e43d8a51b2c7d8b1dced36/pillow-12.2.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:766cef22385fa1091258ad7e6216792b156dc16d8d3fa607e7545b2b72061f1c", size = 3640401 },
+    { url = "https://files.pythonhosted.org/packages/34/46/6c717baadcd62bc8ed51d238d521ab651eaa74838291bda1f86fe1f864c9/pillow-12.2.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5d2fd0fa6b5d9d1de415060363433f28da8b1526c1c129020435e186794b3795", size = 5308094 },
+    { url = "https://files.pythonhosted.org/packages/71/43/905a14a8b17fdb1ccb58d282454490662d2cb89a6bfec26af6d3520da5ec/pillow-12.2.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:56b25336f502b6ed02e889f4ece894a72612fe885889a6e8c4c80239ff6e5f5f", size = 4695402 },
+    { url = "https://files.pythonhosted.org/packages/73/dd/42107efcb777b16fa0393317eac58f5b5cf30e8392e266e76e51cff28c3d/pillow-12.2.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f1c943e96e85df3d3478f7b691f229887e143f81fedab9b20205349ab04d73ed", size = 6280005 },
+    { url = "https://files.pythonhosted.org/packages/a8/68/b93e09e5e8549019e61acf49f65b1a8530765a7f812c77a7461bca7e4494/pillow-12.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:03f6fab9219220f041c74aeaa2939ff0062bd5c364ba9ce037197f4c6d498cd9", size = 8090669 },
+    { url = "https://files.pythonhosted.org/packages/4b/6e/3ccb54ce8ec4ddd1accd2d89004308b7b0b21c4ac3d20fa70af4760a4330/pillow-12.2.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5cdfebd752ec52bf5bb4e35d9c64b40826bc5b40a13df7c3cda20a2c03a0f5ed", size = 6395194 },
+    { url = "https://files.pythonhosted.org/packages/67/ee/21d4e8536afd1a328f01b359b4d3997b291ffd35a237c877b331c1c3b71c/pillow-12.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eedf4b74eda2b5a4b2b2fb4c006d6295df3bf29e459e198c90ea48e130dc75c3", size = 7082423 },
+    { url = "https://files.pythonhosted.org/packages/78/5f/e9f86ab0146464e8c133fe85df987ed9e77e08b29d8d35f9f9f4d6f917ba/pillow-12.2.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:00a2865911330191c0b818c59103b58a5e697cae67042366970a6b6f1b20b7f9", size = 6505667 },
+    { url = "https://files.pythonhosted.org/packages/ed/1e/409007f56a2fdce61584fd3acbc2bbc259857d555196cedcadc68c015c82/pillow-12.2.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e1757442ed87f4912397c6d35a0db6a7b52592156014706f17658ff58bbf795", size = 7208580 },
+    { url = "https://files.pythonhosted.org/packages/23/c4/7349421080b12fb35414607b8871e9534546c128a11965fd4a7002ccfbee/pillow-12.2.0-cp313-cp313-win32.whl", hash = "sha256:144748b3af2d1b358d41286056d0003f47cb339b8c43a9ea42f5fea4d8c66b6e", size = 6375896 },
+    { url = "https://files.pythonhosted.org/packages/3f/82/8a3739a5e470b3c6cbb1d21d315800d8e16bff503d1f16b03a4ec3212786/pillow-12.2.0-cp313-cp313-win_amd64.whl", hash = "sha256:390ede346628ccc626e5730107cde16c42d3836b89662a115a921f28440e6a3b", size = 7081266 },
+    { url = "https://files.pythonhosted.org/packages/c3/25/f968f618a062574294592f668218f8af564830ccebdd1fa6200f598e65c5/pillow-12.2.0-cp313-cp313-win_arm64.whl", hash = "sha256:8023abc91fba39036dbce14a7d6535632f99c0b857807cbbbf21ecc9f4717f06", size = 2463508 },
+    { url = "https://files.pythonhosted.org/packages/4d/a4/b342930964e3cb4dce5038ae34b0eab4653334995336cd486c5a8c25a00c/pillow-12.2.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:042db20a421b9bafecc4b84a8b6e444686bd9d836c7fd24542db3e7df7baad9b", size = 5309927 },
+    { url = "https://files.pythonhosted.org/packages/9f/de/23198e0a65a9cf06123f5435a5d95cea62a635697f8f03d134d3f3a96151/pillow-12.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:dd025009355c926a84a612fecf58bb315a3f6814b17ead51a8e48d3823d9087f", size = 4698624 },
+    { url = "https://files.pythonhosted.org/packages/01/a6/1265e977f17d93ea37aa28aa81bad4fa597933879fac2520d24e021c8da3/pillow-12.2.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:88ddbc66737e277852913bd1e07c150cc7bb124539f94c4e2df5344494e0a612", size = 6321252 },
+    { url = "https://files.pythonhosted.org/packages/3c/83/5982eb4a285967baa70340320be9f88e57665a387e3a53a7f0db8231a0cd/pillow-12.2.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d362d1878f00c142b7e1a16e6e5e780f02be8195123f164edf7eddd911eefe7c", size = 8126550 },
+    { url = "https://files.pythonhosted.org/packages/4e/48/6ffc514adce69f6050d0753b1a18fd920fce8cac87620d5a31231b04bfc5/pillow-12.2.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c727a6d53cb0018aadd8018c2b938376af27914a68a492f59dfcaca650d5eea", size = 6433114 },
+    { url = "https://files.pythonhosted.org/packages/36/a3/f9a77144231fb8d40ee27107b4463e205fa4677e2ca2548e14da5cf18dce/pillow-12.2.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd8c21c98c5cc60653bcb311bef2ce0401642b7ce9d09e03a7da87c878289d4", size = 7115667 },
+    { url = "https://files.pythonhosted.org/packages/c1/fc/ac4ee3041e7d5a565e1c4fd72a113f03b6394cc72ab7089d27608f8aaccb/pillow-12.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9f08483a632889536b8139663db60f6724bfcb443c96f1b18855860d7d5c0fd4", size = 6538966 },
+    { url = "https://files.pythonhosted.org/packages/c0/a8/27fb307055087f3668f6d0a8ccb636e7431d56ed0750e07a60547b1e083e/pillow-12.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dac8d77255a37e81a2efcbd1fc05f1c15ee82200e6c240d7e127e25e365c39ea", size = 7238241 },
+    { url = "https://files.pythonhosted.org/packages/ad/4b/926ab182c07fccae9fcb120043464e1ff1564775ec8864f21a0ebce6ac25/pillow-12.2.0-cp313-cp313t-win32.whl", hash = "sha256:ee3120ae9dff32f121610bb08e4313be87e03efeadfc6c0d18f89127e24d0c24", size = 6379592 },
+    { url = "https://files.pythonhosted.org/packages/c2/c4/f9e476451a098181b30050cc4c9a3556b64c02cf6497ea421ac047e89e4b/pillow-12.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:325ca0528c6788d2a6c3d40e3568639398137346c3d6e66bb61db96b96511c98", size = 7085542 },
+    { url = "https://files.pythonhosted.org/packages/00/a4/285f12aeacbe2d6dc36c407dfbbe9e96d4a80b0fb710a337f6d2ad978c75/pillow-12.2.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2e5a76d03a6c6dcef67edabda7a52494afa4035021a79c8558e14af25313d453", size = 2465765 },
+    { url = "https://files.pythonhosted.org/packages/bf/98/4595daa2365416a86cb0d495248a393dfc84e96d62ad080c8546256cb9c0/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:3adc9215e8be0448ed6e814966ecf3d9952f0ea40eb14e89a102b87f450660d8", size = 4100848 },
+    { url = "https://files.pythonhosted.org/packages/0b/79/40184d464cf89f6663e18dfcf7ca21aae2491fff1a16127681bf1fa9b8cf/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:6a9adfc6d24b10f89588096364cc726174118c62130c817c2837c60cf08a392b", size = 4176515 },
+    { url = "https://files.pythonhosted.org/packages/b0/63/703f86fd4c422a9cf722833670f4f71418fb116b2853ff7da722ea43f184/pillow-12.2.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:6a6e67ea2e6feda684ed370f9a1c52e7a243631c025ba42149a2cc5934dec295", size = 3640159 },
+    { url = "https://files.pythonhosted.org/packages/71/e0/fb22f797187d0be2270f83500aab851536101b254bfa1eae10795709d283/pillow-12.2.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2bb4a8d594eacdfc59d9e5ad972aa8afdd48d584ffd5f13a937a664c3e7db0ed", size = 5312185 },
+    { url = "https://files.pythonhosted.org/packages/ba/8c/1a9e46228571de18f8e28f16fabdfc20212a5d019f3e3303452b3f0a580d/pillow-12.2.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:80b2da48193b2f33ed0c32c38140f9d3186583ce7d516526d462645fd98660ae", size = 4695386 },
+    { url = "https://files.pythonhosted.org/packages/70/62/98f6b7f0c88b9addd0e87c217ded307b36be024d4ff8869a812b241d1345/pillow-12.2.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22db17c68434de69d8ecfc2fe821569195c0c373b25cccb9cbdacf2c6e53c601", size = 6280384 },
+    { url = "https://files.pythonhosted.org/packages/5e/03/688747d2e91cfbe0e64f316cd2e8005698f76ada3130d0194664174fa5de/pillow-12.2.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7b14cc0106cd9aecda615dd6903840a058b4700fcb817687d0ee4fc8b6e389be", size = 8091599 },
+    { url = "https://files.pythonhosted.org/packages/f6/35/577e22b936fcdd66537329b33af0b4ccfefaeabd8aec04b266528cddb33c/pillow-12.2.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cbeb542b2ebc6fcdacabf8aca8c1a97c9b3ad3927d46b8723f9d4f033288a0f", size = 6396021 },
+    { url = "https://files.pythonhosted.org/packages/11/8d/d2532ad2a603ca2b93ad9f5135732124e57811d0168155852f37fbce2458/pillow-12.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4bfd07bc812fbd20395212969e41931001fd59eb55a60658b0e5710872e95286", size = 7083360 },
+    { url = "https://files.pythonhosted.org/packages/5e/26/d325f9f56c7e039034897e7380e9cc202b1e368bfd04d4cbe6a441f02885/pillow-12.2.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9aba9a17b623ef750a4d11b742cbafffeb48a869821252b30ee21b5e91392c50", size = 6507628 },
+    { url = "https://files.pythonhosted.org/packages/5f/f7/769d5632ffb0988f1c5e7660b3e731e30f7f8ec4318e94d0a5d674eb65a4/pillow-12.2.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:deede7c263feb25dba4e82ea23058a235dcc2fe1f6021025dc71f2b618e26104", size = 7209321 },
+    { url = "https://files.pythonhosted.org/packages/6a/7a/c253e3c645cd47f1aceea6a8bacdba9991bf45bb7dfe927f7c893e89c93c/pillow-12.2.0-cp314-cp314-win32.whl", hash = "sha256:632ff19b2778e43162304d50da0181ce24ac5bb8180122cbe1bf4673428328c7", size = 6479723 },
+    { url = "https://files.pythonhosted.org/packages/cd/8b/601e6566b957ca50e28725cb6c355c59c2c8609751efbecd980db44e0349/pillow-12.2.0-cp314-cp314-win_amd64.whl", hash = "sha256:4e6c62e9d237e9b65fac06857d511e90d8461a32adcc1b9065ea0c0fa3a28150", size = 7217400 },
+    { url = "https://files.pythonhosted.org/packages/d6/94/220e46c73065c3e2951bb91c11a1fb636c8c9ad427ac3ce7d7f3359b9b2f/pillow-12.2.0-cp314-cp314-win_arm64.whl", hash = "sha256:b1c1fbd8a5a1af3412a0810d060a78b5136ec0836c8a4ef9aa11807f2a22f4e1", size = 2554835 },
+    { url = "https://files.pythonhosted.org/packages/b6/ab/1b426a3974cb0e7da5c29ccff4807871d48110933a57207b5a676cccc155/pillow-12.2.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:57850958fe9c751670e49b2cecf6294acc99e562531f4bd317fa5ddee2068463", size = 5314225 },
+    { url = "https://files.pythonhosted.org/packages/19/1e/dce46f371be2438eecfee2a1960ee2a243bbe5e961890146d2dee1ff0f12/pillow-12.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d5d38f1411c0ed9f97bcb49b7bd59b6b7c314e0e27420e34d99d844b9ce3b6f3", size = 4698541 },
+    { url = "https://files.pythonhosted.org/packages/55/c3/7fbecf70adb3a0c33b77a300dc52e424dc22ad8cdc06557a2e49523b703d/pillow-12.2.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5c0a9f29ca8e79f09de89293f82fc9b0270bb4af1d58bc98f540cc4aedf03166", size = 6322251 },
+    { url = "https://files.pythonhosted.org/packages/1c/3c/7fbc17cfb7e4fe0ef1642e0abc17fc6c94c9f7a16be41498e12e2ba60408/pillow-12.2.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1610dd6c61621ae1cf811bef44d77e149ce3f7b95afe66a4512f8c59f25d9ebe", size = 8127807 },
+    { url = "https://files.pythonhosted.org/packages/ff/c3/a8ae14d6defd2e448493ff512fae903b1e9bd40b72efb6ec55ce0048c8ce/pillow-12.2.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0a34329707af4f73cf1782a36cd2289c0368880654a2c11f027bcee9052d35dd", size = 6433935 },
+    { url = "https://files.pythonhosted.org/packages/6e/32/2880fb3a074847ac159d8f902cb43278a61e85f681661e7419e6596803ed/pillow-12.2.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8e9c4f5b3c546fa3458a29ab22646c1c6c787ea8f5ef51300e5a60300736905e", size = 7116720 },
+    { url = "https://files.pythonhosted.org/packages/46/87/495cc9c30e0129501643f24d320076f4cc54f718341df18cc70ec94c44e1/pillow-12.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:fb043ee2f06b41473269765c2feae53fc2e2fbf96e5e22ca94fb5ad677856f06", size = 6540498 },
+    { url = "https://files.pythonhosted.org/packages/18/53/773f5edca692009d883a72211b60fdaf8871cbef075eaa9d577f0a2f989e/pillow-12.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f278f034eb75b4e8a13a54a876cc4a5ab39173d2cdd93a638e1b467fc545ac43", size = 7239413 },
+    { url = "https://files.pythonhosted.org/packages/c9/e4/4b64a97d71b2a83158134abbb2f5bd3f8a2ea691361282f010998f339ec7/pillow-12.2.0-cp314-cp314t-win32.whl", hash = "sha256:6bb77b2dcb06b20f9f4b4a8454caa581cd4dd0643a08bacf821216a16d9c8354", size = 6482084 },
+    { url = "https://files.pythonhosted.org/packages/ba/13/306d275efd3a3453f72114b7431c877d10b1154014c1ebbedd067770d629/pillow-12.2.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6562ace0d3fb5f20ed7290f1f929cae41b25ae29528f2af1722966a0a02e2aa1", size = 7225152 },
+    { url = "https://files.pythonhosted.org/packages/ff/6e/cf826fae916b8658848d7b9f38d88da6396895c676e8086fc0988073aaf8/pillow-12.2.0-cp314-cp314t-win_arm64.whl", hash = "sha256:aa88ccfe4e32d362816319ed727a004423aab09c5cea43c01a4b435643fa34eb", size = 2556579 },
+]
+
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538 },
+]
+
+[[package]]
+name = "propcache"
+version = "0.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ec/44/c87281c333769159c50594f22610f77398a47ccbfbbf23074e744e86f87c/propcache-0.5.2.tar.gz", hash = "sha256:01c4fc7480cd0598bb4b57022df55b9ca296da7fc5a8760bd8451a7e63a7d427", size = 50208 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4a/cb/e27bc2b2737a0bb49962b275efa051e8f1c35a936df7d5139b6b658b7dc9/propcache-0.5.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:806719138ecd720339a12410fb9614ac9b2b2d3a5fdf8235d56981c36f4039ba", size = 95887 },
+    { url = "https://files.pythonhosted.org/packages/e6/13/b8ae04c59392f8d11c6cd9fb4011d1dc7c86b81225c770280300e259ffe1/propcache-0.5.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:db2b80ea58eab4f86b2beec3cc8b39e8ff9276ac20e96b7cce43c8ae84cd6b5a", size = 54654 },
+    { url = "https://files.pythonhosted.org/packages/2c/7d/49777a3e20b55863d4794384a38acd460c04157b0a00f8602b0d508b8431/propcache-0.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e5cbfac9f61484f7e9f3597775500cd3ebe8274e9b050c38f9525c77c97520bf", size = 55190 },
+    { url = "https://files.pythonhosted.org/packages/44/c7/085d0cd63062e84044e3f05797749c3f8e3938ff3aeb0eb2f69d43fafc91/propcache-0.5.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5dbc581d2814337da56222fab8dc5f161cd798a434e49bac27930aaef798e144", size = 59995 },
+    { url = "https://files.pythonhosted.org/packages/9c/42/32cf8e3009e92b2645cf1e944f701e8ea4e924dffde1ee26db860bcbf7e4/propcache-0.5.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:857187f381f88c8e2fa2fe56ab94879d011b883d5a2ee5a1b60a8cd2a06846d9", size = 63422 },
+    { url = "https://files.pythonhosted.org/packages/9e/1b/f112433f99fc979431b87a39ef169e3f8df070d99a72792c56d6937ac48b/propcache-0.5.2-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:178b4a2cdaac1818e2bf1c5a99b94383fa73ea5382e032a48dec07dc5668dc42", size = 64342 },
+    { url = "https://files.pythonhosted.org/packages/14/15/5574111ae50dd6e879456888c0eadd4c5a869959775854e18e18a6b345f3/propcache-0.5.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f328175a2cde1f0ff2c4ed8ce968b9dcfb55f3a7153f39e2957ed994da13476", size = 61639 },
+    { url = "https://files.pythonhosted.org/packages/cc/da/4d775080b1490c0ae604acda868bd71aabe3a89ed16f2aa4339eb8a283e7/propcache-0.5.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:5671d09a36b06d0fd4a3da0fccbcae360e9b1570924171a15e9e0997f0249fba", size = 61588 },
+    { url = "https://files.pythonhosted.org/packages/04/ac/f076982cbe2195ee9cf32de5a1e46951d9fb399fc207f390562dd0fd8fb2/propcache-0.5.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:80168e2ebe4d3ec6599d10ad8f520304ae1cad9b6c5a95372aef1b66b7bfb53a", size = 60029 },
+    { url = "https://files.pythonhosted.org/packages/70/60/189be62e0dd898dce3b331e1b8c7a543cd3a405ac0c81fe8ee8a9d5d77e1/propcache-0.5.2-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:45f11346f884bc47444f6e6647131055844134c3175b629f84952e2b5cd62b64", size = 56774 },
+    { url = "https://files.pythonhosted.org/packages/ea/9e/93377b9c7939c1ffae98f878dee955efadfd638078bc86dbc21f9d52f651/propcache-0.5.2-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:8e778ebd44ef4f66ed60a0416b06b489687db264a9c0b3620362f26489492913", size = 63532 },
+    { url = "https://files.pythonhosted.org/packages/14/f9/590ef6cfb9b8028d516d287812ece32bb0bc5f11fbb9c8bf6b2e6313fec8/propcache-0.5.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:c0cb9ed24c8964e172768d455a38254c2dd8a552905729ce006cad3d3dda59b1", size = 61592 },
+    { url = "https://files.pythonhosted.org/packages/b4/5e/70958b3034c297a630bba2f17ca7abc2d5f39a803ad7e370ab79d1ecd022/propcache-0.5.2-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:1d1ad32d9d4355e2be65574fd0bfd3677e7066b009cd5b9b2dee8aa6a6393b33", size = 64788 },
+    { url = "https://files.pythonhosted.org/packages/12/fd/77fe5936d8c3086ca9048f7f415f122ed82e53884a9ec193646b42deef06/propcache-0.5.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c80f4ba3e8f00189165999a742ee526ebeccedf6c3f7beb0c7df821e9772435a", size = 62514 },
+    { url = "https://files.pythonhosted.org/packages/cf/74/66bd798b5b3be70aa1b391f5cc9d6a0a5532d7fd3b19ec0b213e72e6ad9d/propcache-0.5.2-cp312-cp312-win32.whl", hash = "sha256:8c7972d8f193740d9175f0998ab38717e6cd322d5935c5b0fef8c0d323fd9031", size = 39018 },
+    { url = "https://files.pythonhosted.org/packages/61/7c/5c0d34aa3024694d6dcb9271cdbdd08c4e47c1c0ad95ec7e7bc74cdea145/propcache-0.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:d9ee8826a7d47863a08ac44e1a5f611a462eefc3a194b492da242128bec75b42", size = 42322 },
+    { url = "https://files.pythonhosted.org/packages/4d/91/875812f1a3feb20ceba818ef39fbe4d92f1081e04ac815c822496d0d038b/propcache-0.5.2-cp312-cp312-win_arm64.whl", hash = "sha256:2800a4a8ead6b28cccd1ec54b59346f0def7922ee1c7598e8499c733cfbb7c84", size = 38172 },
+    { url = "https://files.pythonhosted.org/packages/c5/09/f049e45385503fe67db75a6b6186a7b9f0c3930366dc960522c312a825b1/propcache-0.5.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:099aaf4b4d1a02265b92a977edf00b5c4f63b3b17ac6de39b0d637c9cac0188a", size = 94457 },
+    { url = "https://files.pythonhosted.org/packages/6b/65/83d1d05655baf63113731bd5a1008435e14f8d1e5a06cbe4ec5b23ad7a31/propcache-0.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:68ce1c44c7a813a7f71ea04315a8c7b330b63db99d059a797a4651bb6f69f117", size = 53835 },
+    { url = "https://files.pythonhosted.org/packages/a9/12/a6ba6482bb5ea3260c000c9b20881c95fa11c6b30173715668259f844ed7/propcache-0.5.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:fc299c129490f55f254cd90be0deca4764e36e9a7c08b4aa588479a3bbed3098", size = 54545 },
+    { url = "https://files.pythonhosted.org/packages/a9/19/7fa086f5764c59ec8a8e157cd93aa8497acc00aba9dcdec56bfffb32602d/propcache-0.5.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6ae2198be502c10f09b2516e7b5d019816924bc3183a43ce792a7bd6625e6f4", size = 59886 },
+    { url = "https://files.pythonhosted.org/packages/a1/e4/5d7663dc8235956c8f5281698a3af1d351d8820341ddd890f59d9a9127f2/propcache-0.5.2-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6041d31504dc1779d700e1edcfb08eea334b357620b06681a4eabb57a74e574e", size = 63261 },
+    { url = "https://files.pythonhosted.org/packages/4a/4a/15a03adee24d6350da4292caeac44c34c033d2afe5e87eb370f38854560f/propcache-0.5.2-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f7eabc04151c78a9f4d5bbb5f1faf571e4defeb4b585e0fe95b60ff2dbe4d3d7", size = 64184 },
+    { url = "https://files.pythonhosted.org/packages/8b/c6/979176efdaa3d239e36d503d5af63a0a773b36662ed8f52e5b6a6d9fd40e/propcache-0.5.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4db0ba63d693afd40d249bd93f842b5f144f8fcbb83de05660373bcf30517b1d", size = 61534 },
+    { url = "https://files.pythonhosted.org/packages/c8/22/63e8cd1bae4c2d2be6493b6b7d10566ddafad88137cfbc99964a1119853c/propcache-0.5.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1dbcf7675229b35d31abb6547d8ebc8c27a830ac3f9a794edff6254873ec7c0a", size = 61500 },
+    { url = "https://files.pythonhosted.org/packages/60/5a/28e5d9acbac1cc9ccb67045e8c1b943aa8d79fdf39c93bd73cacd68008ea/propcache-0.5.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d310c013aad2c72f1c3f2f8dd3279d460a858c551f97aeb8c63e4693cca7b4d2", size = 59994 },
+    { url = "https://files.pythonhosted.org/packages/f3/40/db650677f554a95b9c01a7c9d93d629e93a15562f5deb4573c9ee136fed2/propcache-0.5.2-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:06187263ddad280d05b4d8a8b3bb7d164cbebd469236544a42e6d9b28ac6a4fa", size = 56884 },
+    { url = "https://files.pythonhosted.org/packages/80/45/70b39b89516ff8b96bf732fa6fded8cef20f293cb1508690101c3c07ec51/propcache-0.5.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3115559b8effafd63b142ea5ed53d63a16ea6469cbc63dce4ee194b42db5d853", size = 63464 },
+    { url = "https://files.pythonhosted.org/packages/f9/e2/fa59d3a89eac5534293124af4f1d0d0ada091ce4a0ab4610ce03fd2bdd8d/propcache-0.5.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c60462af8e6dc30c35407c7237ea908d777b22862bbee27bc4699c0d8bcdc45a", size = 61588 },
+    { url = "https://files.pythonhosted.org/packages/0b/97/efb547a55c4bc7381cfb202d6a2239ac621045277bc1ea5dfd3a7f0516c0/propcache-0.5.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:40314bca9ac559716fe374094fc81c11dcc34b64fd6c585360f5775690505704", size = 64667 },
+    { url = "https://files.pythonhosted.org/packages/92/56/f5c7d9b4b7595d5127da38974d791b2153f3d1eae6c674af3583ace92ad3/propcache-0.5.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:cfa21e036ce1e1db2be04ba3b85d2df1bb1702fa01932d984c5464c665228ff4", size = 62463 },
+    { url = "https://files.pythonhosted.org/packages/bd/3b/484a3a65fc9f9f60c41dcd17b428bace5389544e2c680994534a20755066/propcache-0.5.2-cp313-cp313-win32.whl", hash = "sha256:f156a3529f38063b6dbaf356e15602a7f95f8055b1295a438433a6386f10463d", size = 38621 },
+    { url = "https://files.pythonhosted.org/packages/1c/fd/3f0f10dba4dabad3bf53102be007abf55481067952bde0fdddff439e7c61/propcache-0.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:dfed59d0a5aeb01e242e66ff0300bc4a265a7c05f612d30016f0b60b1017d757", size = 41649 },
+    { url = "https://files.pythonhosted.org/packages/90/ec/6ce619cc32bb500a482f811f9cd509368b4e58e638d13f2c68f370d6b475/propcache-0.5.2-cp313-cp313-win_arm64.whl", hash = "sha256:ba338430e87ceb9c8f0cf754de38a9860560261e56c00376debd628698a7364f", size = 37636 },
+    { url = "https://files.pythonhosted.org/packages/1b/82/c1d268bbbf2ef981c5bf0fbbe746db617c66e3bcefe431a1aa8943fbe23a/propcache-0.5.2-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:a592f5f3da71c8691c788c13cb6734b6d17663d2e1cb8caddf0673d01ef8847d", size = 98872 },
+    { url = "https://files.pythonhosted.org/packages/f4/d4/52c871e73e864e6b34c0e2d58ac1ec5ccd149497ddc7ad2137ae98323a35/propcache-0.5.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:6a997d0489e9668a384fcfd5061b857aa5361de73191cac204d04b889cfbbafa", size = 56257 },
+    { url = "https://files.pythonhosted.org/packages/67/f0/9b90ca2a210b3d09bcfcd96ecd0f55545c091535abce2a45de2775cfd357/propcache-0.5.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:10734b5484ea113152ee25a91dccedf81631791805d2c9ccb054958e51842c94", size = 56696 },
+    { url = "https://files.pythonhosted.org/packages/9d/0e/6e9d4ba07c8e56e21ddec1e75f12148142b21ca83a51871babce095334f4/propcache-0.5.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cafca7e56c12bb02ae16d283742bef25a61122e9dab2b5b3f2ccbe589ce32164", size = 62378 },
+    { url = "https://files.pythonhosted.org/packages/65/19/c10badaa463dde8a27ce884f8ee2ec37e6035b7c9f5ff0c8f74f06f08dac/propcache-0.5.2-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f064f8d2b59177878b7615df1735cd8fe3462ed6be8c7b217d17a276489c2b7f", size = 65283 },
+    { url = "https://files.pythonhosted.org/packages/b0/b6/93bea99ca80e19cef6512a8580e5b7857bbe09422d9daa7fd4ef5723306c/propcache-0.5.2-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f78abfa8dfc32376fd1aacf597b2f2fbbe0ea751419aee718af5d4f82537ef8c", size = 66616 },
+    { url = "https://files.pythonhosted.org/packages/83/e4/5c7462e50625f051f37fb38b8224f7639f667184bbd34424ec83819bb1b7/propcache-0.5.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f7467da8a9822bf1a55336f877340c5bcbd3c482afc43a99771169f74a26dedc", size = 63773 },
+    { url = "https://files.pythonhosted.org/packages/ca/b6/99238894047b13c823be25027e736626cd414a52a5e30d2c3347c2733529/propcache-0.5.2-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a6ddc6ac9e25de626c1f129c1b467d7ecd33ce2237d3fd0c4e429feef0a7ee1f", size = 63664 },
+    { url = "https://files.pythonhosted.org/packages/85/1e/a3a1a63116a2b8edb415a8bb9a6f0c34bd03830b1e18e8ce2904e1dc1cf4/propcache-0.5.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:2f22cbbac9e26a8e864c0985ff1268d5d939d53d9d9411a9824279097e03a2cb", size = 62643 },
+    { url = "https://files.pythonhosted.org/packages/e4/03/893cf147de2fc6543c5eaa07ad833170e7e2a2385725bbebe8c0503723bb/propcache-0.5.2-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:fc76378c62a0f04d0cd82fbb1a2cd2d7e28fcb40d5873f28a6c44e388aaa2751", size = 59595 },
+    { url = "https://files.pythonhosted.org/packages/86/3b/04c1a2e12c57766568ba75ba72b3bf2042818d4c1425fab6fc07155c7cff/propcache-0.5.2-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:acd2c8edba48e31e58a363b8cf4e5c7db3b04b3f9e371f601df30d9b0d244836", size = 65711 },
+    { url = "https://files.pythonhosted.org/packages/1c/34/80f8d0099f8d6bacc4de1624c85672681c8cd1149ca2da0e38fd120b817f/propcache-0.5.2-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:452b5065457eb9991ec5eb38ff41d6cd4c991c9ac7c531c4d5849ae473a9a13f", size = 64247 },
+    { url = "https://files.pythonhosted.org/packages/f3/1a/8b08f3a5f1037e9e370c55883ceeeee0f6dd0416fb2d2d67b8bfc91f2a79/propcache-0.5.2-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:3430bb2bfe1331885c427745a751e774ee679fd4344f80b97bf879815fe8fa55", size = 67102 },
+    { url = "https://files.pythonhosted.org/packages/34/68/8bdb7bb7756d76e005490649d10e4a8369e610c74d619f71e1aedf889e9c/propcache-0.5.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:cef6cea3922890dd6c9654971001fa797b526c16ab5e1e46c05fd6f877be7568", size = 64964 },
+    { url = "https://files.pythonhosted.org/packages/0a/aa/50fb0b5d3968b61a510926ff8b8465f1d6e976b3ab74496d7a4b9fc42515/propcache-0.5.2-cp313-cp313t-win32.whl", hash = "sha256:72d61e16dd78228b58c5d47be830ff3da7e5f139abdf0aef9d86cde1c5cf2191", size = 42546 },
+    { url = "https://files.pythonhosted.org/packages/ae/4c/0ddbae64321bd4a95bcbfc19307238016b5b1fee645c84626c8d539e5b74/propcache-0.5.2-cp313-cp313t-win_amd64.whl", hash = "sha256:0958834041a0166d343b8d2cedcd8bcbaeb4fdbe0cf08320c5379f143c3be6e7", size = 46330 },
+    { url = "https://files.pythonhosted.org/packages/00/d9/9cddc8efb78d8af264c5ec9f6d10b62f57c515feda8d321595f56010fb23/propcache-0.5.2-cp313-cp313t-win_arm64.whl", hash = "sha256:6de8bd93ddde9b992cf2b2e0d796d501a19026b5b9fd87356d7d0779531a8d96", size = 40521 },
+    { url = "https://files.pythonhosted.org/packages/e2/ea/23ee535d90ce8bcc465a3028eb3cc0ce3bd1005f4bb27710b30587de798d/propcache-0.5.2-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:46088abff4cba581dea21ae0467a480526cb25aa5f3c269e909f800328bc3999", size = 94662 },
+    { url = "https://files.pythonhosted.org/packages/b5/06/c5a52f419b5d8972f8d46a7577476090d8e3263ff589ce40b5ca4968d5be/propcache-0.5.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:fc88b26f08d634f7bc819a7852e5214f5802641ab8d9fd5326892292eee1993e", size = 53928 },
+    { url = "https://files.pythonhosted.org/packages/63/b1/4260d67d6bd85e58a66b72d54ce15d5de789b6f3870cc6bedf8ff9667401/propcache-0.5.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:97797ebb098e670a2f92dd66f32897e30d7615b14e7f59711de23e30a9072539", size = 54650 },
+    { url = "https://files.pythonhosted.org/packages/70/06/2f46c318e3307cd7a6a7481def374ce838c0fe20084b39dd54b0879d0e99/propcache-0.5.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba57fffe4ac99c5d30076161b5866336d97600769bad35cc68f7774b15298a4e", size = 59912 },
+    { url = "https://files.pythonhosted.org/packages/4c/29/fe1aebec2ce57ab985a9c382bded1124431f85078113aa222c5d278430d4/propcache-0.5.2-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:583c19759d9eec1e5b69e2fbef36a7d9c326041be9746cb822d335c8cedc2979", size = 63300 },
+    { url = "https://files.pythonhosted.org/packages/b4/18/2334b26768b6c82be8c69e83671b767d5ef426aa09b0cba6c2ea47816774/propcache-0.5.2-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d0326e2e5e1f3163fa306c834e48e8d490e5fae607a097a40c0648109b47ba80", size = 64208 },
+    { url = "https://files.pythonhosted.org/packages/2b/76/7f1bfd6afff4c5e38e36a3c6d68eb5f4b7311ea80baf693db78d95b603c4/propcache-0.5.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e00820e192c8dbebcafb383ebbf99030895f09905e7a0eb2e0340a0bcc2bc825", size = 61633 },
+    { url = "https://files.pythonhosted.org/packages/c4/46/b3ff8aba2b4953a3e50de2cf72f1b5748b8eca93b15f3dc2c84339084c09/propcache-0.5.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c66afea89b1e43725731d2004732a046fe6fe955d51f952c3e95a7314a284a39", size = 61724 },
+    { url = "https://files.pythonhosted.org/packages/c5/01/814cfcafbcff954f94c01cf30e097ddc88a076b5440fbcf4570753437d40/propcache-0.5.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d4dc37dec6c6cdad0b57881a5658fd14fbf53e333b1a86cf86559f190e1d9ec4", size = 60069 },
+    { url = "https://files.pythonhosted.org/packages/da/68/5c6f7622d510cc666a300687e06fd060c1a43361c0c9b20d284f06d8096a/propcache-0.5.2-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:5570dbcc97571c15f68068e529c92715a12f8d54030e272d264b377e22bd17a5", size = 57099 },
+    { url = "https://files.pythonhosted.org/packages/55/27/9cb0b4c679124085327957d42521c99dba04c88c90c3e55a6f0b633ebccc/propcache-0.5.2-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:f814362777a9f841adddb200ecdf8f5cb1e5a3c4b7a86378edbd6ccb26edd702", size = 63391 },
+    { url = "https://files.pythonhosted.org/packages/f0/9d/7258aaa5bdf60fc6f27591eef6fe52768cb0beda7140be477c8b12c9794a/propcache-0.5.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:196913dea116aeb5a2ba95af4ddcb7ea85559ae07d8eee8751688310d09168c3", size = 61626 },
+    { url = "https://files.pythonhosted.org/packages/8e/0d/41c602003e8a9b16fe1e7eadf62c7bfba9d5474370b24200bf48b315f45f/propcache-0.5.2-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:6e7b8719005dd1175be4ab1cd25e9b98659a5e0347331506ec6760d2773a7fb5", size = 64781 },
+    { url = "https://files.pythonhosted.org/packages/8b/f3/38e66b1856e9bd079deea015bc4a55f7767c0e4db2f7dcf69e7e680ba4ce/propcache-0.5.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:51f96d685ab16e88cab128cd37a52c5da540809c8b879fa047731bfcb4ad35a4", size = 62570 },
+    { url = "https://files.pythonhosted.org/packages/95/ca/bbfe9b910ce57dde8bb4876b4520fc02a4e89497c10de26be936758a3aaa/propcache-0.5.2-cp314-cp314-win32.whl", hash = "sha256:cc6fc3cc62e8501d3ed62894425040d2728ecddb1ed072737a5c70bd537aa9f0", size = 39436 },
+    { url = "https://files.pythonhosted.org/packages/61/d2/45c9defbaa1ea297035d9d4cce9e8f80daafbf19319c6007f157c6256ea9/propcache-0.5.2-cp314-cp314-win_amd64.whl", hash = "sha256:81e3a30b0bb60caa22033dd0f8a3618d1d67356212514f62c57db75cb0ef410c", size = 42373 },
+    { url = "https://files.pythonhosted.org/packages/44/68/9ea5103f41d5217d7d6ec24db90018e23aebec070c3f9a6e54d12b841fd8/propcache-0.5.2-cp314-cp314-win_arm64.whl", hash = "sha256:0d2c9bf8528f135dbb805ce027567e09164f7efa51a2be07458a2c0420f292d0", size = 38554 },
+    { url = "https://files.pythonhosted.org/packages/8a/81/fadf555f42d3b762eea8a53950b0489fdc0aa9da5f8ed9e10ce0a4e01b48/propcache-0.5.2-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:4bc8ff1feffc6a61c7002ffe84634c41b822e104990ae009f44a0834430070bb", size = 99395 },
+    { url = "https://files.pythonhosted.org/packages/f5/c9/c61e134a686949cf7971af3a390148b1156f7be81c73bc0cd12c873e2d48/propcache-0.5.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:79aa3ff0a9b566633b642fa9caf7e21ed1c13d6feca718187873f199e1514078", size = 56653 },
+    { url = "https://files.pythonhosted.org/packages/cb/73/daf935ea7048ddd7ec8eec5345b4a40b619d2d178b3c0a0900796bc3c794/propcache-0.5.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1b31822f4474c4036bae62de9402710051d431a606d6a0f907fec79935a071aa", size = 56914 },
+    { url = "https://files.pythonhosted.org/packages/79/9f/aba959b435ea18617edd7cf0a7ad0b9c574b8fc7e3d2cd55fb59cb255d33/propcache-0.5.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:13fef48778b5a2a756523fdb781326b028ca75e32858b04f2cdd19f394564917", size = 62567 },
+    { url = "https://files.pythonhosted.org/packages/6c/a1/859942de9a791ff42f6141736f5b37749b8f53e65edfa49638c67dd67e6a/propcache-0.5.2-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8b73ab70f1a3351fbc71f663b3e645af6dd0329100c353081cf69c37433fc6fe", size = 65542 },
+    { url = "https://files.pythonhosted.org/packages/b5/61/315bc0fd6c0fc7f80a528b8afd209e5fc4a875ea79571b91b8f50f442907/propcache-0.5.2-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5538d2c13d93e4698af7e092b57bc7298fd35d1d58e656ae18f23ee0d0378e03", size = 66845 },
+    { url = "https://files.pythonhosted.org/packages/47/f7/9f8122e3132e8e354ac41975ef8f1099be7d5a16bc7ae562734e993665c0/propcache-0.5.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cd645f03898405cabe694fb8bc35241e3a9c332ec85627584fe3de201452b335", size = 63985 },
+    { url = "https://files.pythonhosted.org/packages/c8/54/c317819ec157cbf6f35df9df9657a6f82daf34d5faf15948b2f639c2192e/propcache-0.5.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a473b3440261e0c60706e732b2ed2f517857344fc21bf48fdfe211e2d98eb285", size = 63999 },
+    { url = "https://files.pythonhosted.org/packages/5a/56/387e3f7dfce0a9233df41fb888aa1c30222cb4bbbf09537c02dd9bd85fe2/propcache-0.5.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7afa37062e6650640e932e4cc9297d81f9f42d9944029cc386b8247dea4da837", size = 62779 },
+    { url = "https://files.pythonhosted.org/packages/a1/9c/596784cb5824ed61ee960d3f8655a3f0993e107c6e98ab6c818b7fb92ccb/propcache-0.5.2-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:8a90efd5777e996e42d568db9ac740b944d691e565cbfd31b2f7832f9184b2b8", size = 59796 },
+    { url = "https://files.pythonhosted.org/packages/c2/3d/1a6cfa1726a48542c1e8784a0761421476a5b68e09b7f36bf95eb954aaba/propcache-0.5.2-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:f19bb891234d72535764d703bfed1153cc34f4214d5bd7150aee1eec9e8f4366", size = 66023 },
+    { url = "https://files.pythonhosted.org/packages/e4/0e/05fd6990369477076e4e280bcb970de760fddf0161a46e988bc95f7940ec/propcache-0.5.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:32775082acd2d807ee3db715c7770d38767b817870acfa08c29e057f3c4d5b56", size = 64448 },
+    { url = "https://files.pythonhosted.org/packages/cd/86/5f8da315a4309c62c10c0b2516b17492d5d3bbe1bb862b96604db67e2a37/propcache-0.5.2-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:9282fb1a3bccd038da9f768b927b24a0c753e466c086b7c4f3c6982851eefb2d", size = 67329 },
+    { url = "https://files.pythonhosted.org/packages/da/d3/3368efe79ab21f0cdf86ef49895811c9cc933131d4cde1f28a624e22e712/propcache-0.5.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cc49723e2f60d6b32a0f0b08a3fd6d13203c07f1cd9566cfce0f12a917c967a2", size = 65172 },
+    { url = "https://files.pythonhosted.org/packages/d5/07/127e8b0bacfb325396196f9d976a22453049b89b9b2b08477cc3145faa44/propcache-0.5.2-cp314-cp314t-win32.whl", hash = "sha256:2d7aa89ebca5acc98cba9d1472d976e394782f587bad6661003602a619fd1821", size = 43813 },
+    { url = "https://files.pythonhosted.org/packages/88/fb/46dad6c0ae49ed230ab1b16c890c2b6314e2403e6c412976f4a72d64a527/propcache-0.5.2-cp314-cp314t-win_amd64.whl", hash = "sha256:d447bb0b3054be5818458fbb171208b1d9ff11eba14e18ca18b90cbb45767370", size = 47764 },
+    { url = "https://files.pythonhosted.org/packages/e7/c4/a47d0a63aa309d10d59ede6e9d4cff03a344a79d1f0f4cd0cd74997b53e0/propcache-0.5.2-cp314-cp314t-win_arm64.whl", hash = "sha256:fe67a3d11cd9b4efabfa45c3d00ffba2b26811442a73a581a94b67c2b5faccf6", size = 41140 },
+    { url = "https://files.pythonhosted.org/packages/3a/ed/1cdcab6ba3d6ab7feca11fc14f0eeea80755bb53ef4e892079f31b10a25f/propcache-0.5.2-py3-none-any.whl", hash = "sha256:be1ddfcbb376e3de5d2e2db1d58d6d67463e6b4f9f040c000de8e300295465fe", size = 14036 },
+]
+
+[[package]]
+name = "pyarrow"
+version = "24.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/13/13e1069b351bdc3881266e11147ffccf687505dbb0ea74036237f5d454a5/pyarrow-24.0.0.tar.gz", hash = "sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83", size = 1180261 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b4/a9/9686d9f07837f91f775e8932659192e02c74f9d8920524b480b85212cc68/pyarrow-24.0.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:6233c9ed9ab9d1db47de57d9753256d9dcffbf42db341576099f0fd9f6bf4810", size = 34981559 },
+    { url = "https://files.pythonhosted.org/packages/80/b6/0ddf0e9b6ead3474ab087ae598c76b031fc45532bf6a63f3a553440fb258/pyarrow-24.0.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:f7616236ec1bc2b15bfdec22a71ab38851c86f8f05ff64f379e1278cf20c634a", size = 36663654 },
+    { url = "https://files.pythonhosted.org/packages/7c/3b/926382efe8ce27ba729071d3566ade6dfb86bdf112f366000196b2f5780a/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:1617043b99bd33e5318ae18eb2919af09c71322ef1ca46566cdafc6e6712fb66", size = 45679394 },
+    { url = "https://files.pythonhosted.org/packages/b3/7a/829f7d9dfd37c207206081d6dad474d81dde29952401f07f2ba507814818/pyarrow-24.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6165461f55ef6314f026de6638d661188e3455d3ec49834556a0ebbdbace18bb", size = 48863122 },
+    { url = "https://files.pythonhosted.org/packages/5f/e8/f88ce625fe8babaae64e8db2d417c7653adb3019b08aae85c5ed787dc816/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3b13dedfe76a0ad2d1d859b0811b53827a4e9d93a0bcb05cf59333ab4980cc7e", size = 49376032 },
+    { url = "https://files.pythonhosted.org/packages/36/7a/82c363caa145fff88fb475da50d3bf52bb024f61917be5424c3392eaf878/pyarrow-24.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:25ea65d868eb04015cd18e6df2fbe98f07e5bda2abefabcb88fce39a947716f6", size = 51929490 },
+    { url = "https://files.pythonhosted.org/packages/66/1c/e3e72c8014ad2743ca64a701652c733cc5cbcee15c0463a32a8c55518d9e/pyarrow-24.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:295f0a7f2e242dabd513737cf076007dc5b2d59237e3eca37b05c0c6446f3826", size = 27355660 },
+    { url = "https://files.pythonhosted.org/packages/6f/d3/a1abf004482026ddc17f4503db227787fa3cfe41ec5091ff20e4fea55e57/pyarrow-24.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba", size = 34976759 },
+    { url = "https://files.pythonhosted.org/packages/4f/4a/34f0a36d28a2dd32225301b79daad44e243dc1a2bb77d43b60749be255c4/pyarrow-24.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68", size = 36658471 },
+    { url = "https://files.pythonhosted.org/packages/1f/78/543b94712ae8bb1a6023bcc1acf1a740fbff8286747c289cd9468fced2a5/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2", size = 45675981 },
+    { url = "https://files.pythonhosted.org/packages/84/9f/8fb7c222b100d314137fa40ec050de56cd8c6d957d1cfff685ce72f15b17/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0", size = 48859172 },
+    { url = "https://files.pythonhosted.org/packages/a7/d3/1ea72538e6c8b3b475ed78d1049a2c518e655761ea50fe1171fc855fcab7/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495", size = 49385733 },
+    { url = "https://files.pythonhosted.org/packages/c3/be/c3d8b06a1ba35f2260f8e1f771abbee7d5e345c0937aab90675706b1690a/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f", size = 51934335 },
+    { url = "https://files.pythonhosted.org/packages/9c/62/89e07a1e7329d2cde3e3c6994ba0839a24977a2beda8be6005ea3d860b99/pyarrow-24.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91", size = 27271748 },
+    { url = "https://files.pythonhosted.org/packages/17/1a/cff3a59f80b5b1658549d46611b67163f65e0664431c076ad728bf9d5af4/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275", size = 35238554 },
+    { url = "https://files.pythonhosted.org/packages/a8/99/cce0f42a327bfef2c420fb6078a3eb834826e5d6697bf3009fe11d2ad051/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b", size = 36782301 },
+    { url = "https://files.pythonhosted.org/packages/2a/66/8e560d5ff6793ca29aca213c53eec0dd482dd46cb93b2819e5aab52e4252/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42", size = 45721929 },
+    { url = "https://files.pythonhosted.org/packages/27/0c/a26e25505d030716e078d9f16eb74973cbf0b33b672884e9f9da1c83b871/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b", size = 48825365 },
+    { url = "https://files.pythonhosted.org/packages/5f/eb/771f9ecb0c65e73fe9dccdd1717901b9594f08c4515d000c7c62df573811/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37", size = 49451819 },
+    { url = "https://files.pythonhosted.org/packages/48/da/61ae89a88732f5a785646f3ec6125dbb640fa98a540eb2b9889caa561403/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca", size = 51909252 },
+    { url = "https://files.pythonhosted.org/packages/cb/1a/8dd5cafab7b66573fa91c03d06d213356ad4edd71813aa75e08ce2b3a844/pyarrow-24.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d", size = 27388127 },
+    { url = "https://files.pythonhosted.org/packages/ad/80/d022a34ff05d2cbedd8ccf841fc1f532ecfa9eb5ed1711b56d0e0ea71fc9/pyarrow-24.0.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838", size = 35007997 },
+    { url = "https://files.pythonhosted.org/packages/1a/ff/f01485fda6f4e5d441afb8dd5e7681e4db18826c1e271852f5d3957d6a80/pyarrow-24.0.0-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b", size = 36678720 },
+    { url = "https://files.pythonhosted.org/packages/9e/c2/2d2d5fea814237923f71b36495211f20b43a1576f9a4d6da7e751a64ec6f/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795", size = 45741852 },
+    { url = "https://files.pythonhosted.org/packages/8e/3a/28ba9c1c1ebdbb5f1b94dfebb46f207e52e6a554b7fe4132540fde29a3a0/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26", size = 48889852 },
+    { url = "https://files.pythonhosted.org/packages/df/51/4a389acfd31dca009f8fb82d7f510bb4130f2b3a8e18cf00194d0687d8ac/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde", size = 49445207 },
+    { url = "https://files.pythonhosted.org/packages/19/4b/0bab2b23d2ae901b1b9a03c0efd4b2d070256f8ce3fc43f6e58c167b2081/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76", size = 51954117 },
+    { url = "https://files.pythonhosted.org/packages/29/88/f4e9145da0417b3d2c12035a8492b35ff4a3dbc653e614fcfb51d9dedb38/pyarrow-24.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e", size = 28001155 },
+    { url = "https://files.pythonhosted.org/packages/79/4f/46a49a63f43526da895b1a45bbb51d5baf8e4d77159f8528fc3e5490007f/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05", size = 35250387 },
+    { url = "https://files.pythonhosted.org/packages/a0/da/d5e0cd5ef00796922404806d5f00325cdadc3441ce2c13fe7115f2df9a64/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a", size = 36797102 },
+    { url = "https://files.pythonhosted.org/packages/34/c7/5904145b0a593a05236c882933d439b5720f0a145381179063722fbfc123/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072", size = 45745118 },
+    { url = "https://files.pythonhosted.org/packages/13/d3/cca42fe166d1c6e4d5b80e530b7949104d10e17508a90ae202dac205ce2a/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931", size = 48844765 },
+    { url = "https://files.pythonhosted.org/packages/b0/49/942c3b79878ba928324d1e17c274ed84581db8c0a749b24bcf4cbdf15bd3/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699", size = 49471890 },
+    { url = "https://files.pythonhosted.org/packages/76/97/ff71431000a75d84135a1ace5ca4ba11726a231a8007bbb320a4c54075d5/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136", size = 51932250 },
+    { url = "https://files.pythonhosted.org/packages/51/be/6f79d55816d5c22557cf27533543d5d70dfe692adfbee4b99f2760674f38/pyarrow-24.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19", size = 28131282 },
+]
+
+[[package]]
+name = "pydantic"
+version = "2.13.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-types" },
+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/18/a5/b60d21ac674192f8ab0ba4e9fd860690f9b4a6e51ca5df118733b487d8d6/pydantic-2.13.4.tar.gz", hash = "sha256:c40756b57adaa8b1efeeced5c196f3f3b7c435f90e84ea7f443901bec8099ef6", size = 844775 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl", hash = "sha256:45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba", size = 472262 },
+]
+
+[[package]]
+name = "pydantic-core"
+version = "2.46.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/56/921726b776ace8d8f5db44c4ef961006580d91dc52b803c489fafd1aa249/pydantic_core-2.46.4.tar.gz", hash = "sha256:62f875393d7f270851f20523dd2e29f082bcc82292d66db2b64ea71f64b6e1c1", size = 471464 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/8c/af022f0af448d7747c5154288d46b5f2bc5f17366eaa0e23e9aa04d59f3b/pydantic_core-2.46.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:3245406455a5d98187ec35530fd772b1d799b26667980872c8d4614991e2c4a2", size = 2106158 },
+    { url = "https://files.pythonhosted.org/packages/19/95/6195171e385007300f0f5574592e467c568becce2d937a0b6804f218bc49/pydantic_core-2.46.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:962ccbab7b642487b1d8b7df90ef677e03134cf1fd8880bf698649b22a69371f", size = 1951724 },
+    { url = "https://files.pythonhosted.org/packages/8e/bc/f47d1ff9cbb1620e1b5b697eef06010035735f07820180e74178226b27b3/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8233f2947cf85404441fd7e0085f53b10c93e0ee78611099b5c7237e36aacbf7", size = 1975742 },
+    { url = "https://files.pythonhosted.org/packages/5b/11/9b9a5b0306345664a2da6410877af6e8082481b5884b3ddd78d47c6013ce/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3a233125ac121aa3ffba9a2b59edfc4a985a76092dc8279586ab4b71390875e7", size = 2052418 },
+    { url = "https://files.pythonhosted.org/packages/f1/b7/a65fec226f5d78fc39f4a13c4cc0c768c22b113438f60c14adc9d2865038/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b712b53160b79a5850310b912a5ef8e57e56947c8ad690c227f5c9d7e561712", size = 2232274 },
+    { url = "https://files.pythonhosted.org/packages/68/f0/92039db98b907ef49269a8271f67db9cb78ae2fc68062ef7e4e77adb5f61/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9401557acd873c3a7f3eb9383edef8ac4968f9510e340f4808d427e75667e7b4", size = 2309940 },
+    { url = "https://files.pythonhosted.org/packages/5f/97/2aab507d3d00ca626e8e57c1eac6a79e4e5fbcc63eb99733ff55d1717f65/pydantic_core-2.46.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:926c9541b14b12b1681dca8a0b75feb510b06c6341b70a8e500c2fdcff837cce", size = 2094516 },
+    { url = "https://files.pythonhosted.org/packages/22/37/a8aca44d40d737dde2bc05b3c6c07dff0de07ce6f82e9f3167aeaf4d5dea/pydantic_core-2.46.4-cp312-cp312-manylinux_2_31_riscv64.whl", hash = "sha256:56cb4851bcaf3d117eddcef4fe66afd750a50274b0da8e22be256d10e5611987", size = 2136854 },
+    { url = "https://files.pythonhosted.org/packages/24/99/fcef1b79238c06a8cbec70819ac722ba76e02bc8ada9b0fd66eba40da01b/pydantic_core-2.46.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c68fcd102d71ea85c5b2dfac3f4f8476eff42a9e078fd5faefff6d145063536b", size = 2180306 },
+    { url = "https://files.pythonhosted.org/packages/ae/6c/fc44000918855b42779d007ae63b0532794739027b2f417321cddbc44f6a/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b2f69dec1725e79a012d920df1707de5caf7ed5e08f3be4435e25803efc47458", size = 2190044 },
+    { url = "https://files.pythonhosted.org/packages/6b/65/d9cadc9f1920d7a127ad2edba16c1db7916e59719285cd6c94600b0080ba/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_armv7l.whl", hash = "sha256:8d0820e8192167f80d88d64038e609c31452eeca865b4e1d9950a27a4609b00b", size = 2329133 },
+    { url = "https://files.pythonhosted.org/packages/d0/cf/c873d91679f3a30bcf5e7ac280ce5573483e72295307685120d0d5ad3416/pydantic_core-2.46.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:fbdb89b3e1c94a30cc5edfce477c6e6a5dc4d8f84665b455c27582f211a1c72c", size = 2374464 },
+    { url = "https://files.pythonhosted.org/packages/47/bd/6f2fc8188f31bf10590f1e98e7b306336161fac930a8c514cd7bd828c7dc/pydantic_core-2.46.4-cp312-cp312-win32.whl", hash = "sha256:9aa768456404a8bf48a4406685ac2bec8e72b62c69313734fa3b73cf33b3a894", size = 1974823 },
+    { url = "https://files.pythonhosted.org/packages/40/8c/985c1d41ea1107c2534abd9870e4ed5c8e7669b5c308297835c001e7a1c4/pydantic_core-2.46.4-cp312-cp312-win_amd64.whl", hash = "sha256:e9c26f834c65f5752f3f06cb08cb86a913ceb7274d0db6e267808a708b46bc89", size = 2072919 },
+    { url = "https://files.pythonhosted.org/packages/c4/ba/f463d006e0c47373ca7ec5e1a261c59dc01ef4d62b2657af925fb0deee3a/pydantic_core-2.46.4-cp312-cp312-win_arm64.whl", hash = "sha256:4fc73cb559bdb54b1134a706a2802a4cddd27a0633f5abb7e53056268751ac6a", size = 2027604 },
+    { url = "https://files.pythonhosted.org/packages/51/a2/5d30b469c5267a17b39dec53208222f76a8d351dfac4af661888c5aee77d/pydantic_core-2.46.4-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:5d5902252db0d3cedf8d4a1bc68f70eeb430f7e4c7104c8c476753519b423008", size = 2106306 },
+    { url = "https://files.pythonhosted.org/packages/c1/81/4fa520eaffa8bd7d1525e644cd6d39e7d60b1592bc5b516693c7340b50f1/pydantic_core-2.46.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c94f0688e7b8d0a67abf40e57a7eaaecd17cc9586706a31b76c031f63df052b4", size = 1951906 },
+    { url = "https://files.pythonhosted.org/packages/03/d5/fd02da45b659668b05923b17ba3a0100a0a3d5541e3bd8fcc4ecb711309e/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f027324c56cd5406ca49c124b0db10e56c69064fec039acc571c29020cc87c76", size = 1976802 },
+    { url = "https://files.pythonhosted.org/packages/21/f2/95727e1368be3d3ed485eaab7adbd7dda408f33f7a36e8b48e0144002b91/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e739fee756ba1010f8bcccb534252e85a35fe45ae92c295a06059ce58b74ccd3", size = 2052446 },
+    { url = "https://files.pythonhosted.org/packages/9c/86/5d99feea3f77c7234b8718075b23db11532773c1a0dbd9b9490215dc2eeb/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9d56801be94b86a9da183e5f3766e6310752b99ff647e38b09a9500d88e46e76", size = 2232757 },
+    { url = "https://files.pythonhosted.org/packages/d2/3a/508ac615935ef7588cf6d9e9b91309fdc2da751af865e02a9098de88258c/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2412e734dcb48da14d4e4006b82b46b74f2518b8a26ee7e58c6844a6cd6d03c4", size = 2309275 },
+    { url = "https://files.pythonhosted.org/packages/07/f8/41db9de19d7987d6b04715a02b3b40aea467000275d9d758ffaa31af7d50/pydantic_core-2.46.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9551187363ffc0de2a00b2e47c25aeaeb1020b69b668762966df15fc5659dd5a", size = 2094467 },
+    { url = "https://files.pythonhosted.org/packages/2c/e2/f35033184cb11d0052daf4416e8e10a502ea2ac006fc4f459aee872727d1/pydantic_core-2.46.4-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:0186750b482eefa11d7f435892b09c5c606193ef3375bcf94aa00ae6bfb66262", size = 2134417 },
+    { url = "https://files.pythonhosted.org/packages/7e/7b/6ceeb1cc90e193862f444ebe373d8fdf613f0a82572dde03fb10734c6c71/pydantic_core-2.46.4-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5855698a4856556d86e8e6cd8434bc3ac0314ee8e12089ae0e143f64c6256e4e", size = 2179782 },
+    { url = "https://files.pythonhosted.org/packages/5a/f2/c8d7773ede6af08036423a00ae0ceffce266c3c52a096c435d68c896083f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:cbaf13819775b7f769bf4a1f066cb6df7a28d4480081a589828ef190226881cd", size = 2188782 },
+    { url = "https://files.pythonhosted.org/packages/59/31/0c864784e31f09f05cdd87606f08923b9c9e7f6e51dd27f20f62f975ce9f/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:633147d34cf4550417f12e2b1a0383973bdf5cdfde212cb09e9a581cf10820be", size = 2328334 },
+    { url = "https://files.pythonhosted.org/packages/c2/eb/4f6c8a41efa30baa755590f4141abf3a8c370fab610915733e74134a7270/pydantic_core-2.46.4-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:82cf5301172168103724d49a1444d3378cb20cdee30b116a1bd6031236298a5d", size = 2372986 },
+    { url = "https://files.pythonhosted.org/packages/5b/24/b375a480d53113860c299764bfe9f349a3dc9108b3adc0d7f0d786492ebf/pydantic_core-2.46.4-cp313-cp313-win32.whl", hash = "sha256:9fa8ae11da9e2b3126c6426f147e0fba88d96d65921799bb30c6abd1cb2c97fb", size = 1973693 },
+    { url = "https://files.pythonhosted.org/packages/7e/e8/cff247591966f2d22ec8c003cd7587e27b7ba7b81ab2fb888e3ab75dc285/pydantic_core-2.46.4-cp313-cp313-win_amd64.whl", hash = "sha256:6b3ace8194b0e5204818c92802dcdca7fc6d88aabbb799d7c795540d9cd6d292", size = 2071819 },
+    { url = "https://files.pythonhosted.org/packages/c6/1a/f4aee670d5670e9e148e0c82c7db98d780be566c6e6a97ee8035528ca0b3/pydantic_core-2.46.4-cp313-cp313-win_arm64.whl", hash = "sha256:184c081504d17f1c1066e430e117142b2c77d9448a97f7b65c6ac9fd9aee238d", size = 2027411 },
+    { url = "https://files.pythonhosted.org/packages/8d/74/228a26ddad29c6672b805d9fd78e8d251cd04004fa7eed0e622096cd0250/pydantic_core-2.46.4-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:428e04521a40150c85216fc8b85e8d39fece235a9cf5e383761238c7fa9b96fb", size = 2102079 },
+    { url = "https://files.pythonhosted.org/packages/ad/1f/8970b150a4b4365623ae00fc88603491f763c627311ae8031e3111356d6e/pydantic_core-2.46.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:23ace664830ee0bfe014a0c7bc248b1f7f25ed7ad103852c317624a1083af462", size = 1952179 },
+    { url = "https://files.pythonhosted.org/packages/95/30/5211a831ae054928054b2f79731661087a2bc5c01e825c672b3a4a8f1b3e/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce5c1d2a8b27468f433ca974829c44060b8097eedc39933e3c206a90ee49c4a9", size = 1978926 },
+    { url = "https://files.pythonhosted.org/packages/57/e9/689668733b1eb67adeef047db3c2e8788fcf65a7fd9c9e2b46b7744fe245/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7283d57845ecf5a163403eb0702dfc220cc4fbdd18919cb5ccea4f95ee1cdab4", size = 2046785 },
+    { url = "https://files.pythonhosted.org/packages/60/d9/6715260422ff50a2109878fd24d948a6c3446bb2664f34ee78cd972b3acd/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8daafc69c93ee8a0204506a3b6b30f586ef54028f52aeeeb5c4cfc5184fd5914", size = 2228733 },
+    { url = "https://files.pythonhosted.org/packages/18/ae/fdb2f64316afca925640f8e70bb1a564b0ec2721c1389e25b8eb4bf9a299/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd2213145bcc2ba85884d0ac63d222fece9209678f77b9b4d76f054c561adb28", size = 2307534 },
+    { url = "https://files.pythonhosted.org/packages/89/1d/8eff589b45bb8190a9d12c49cfad0f176a5cbd1534908a6b5125e2886239/pydantic_core-2.46.4-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a5f930472650a82629163023e630d160863fce524c616f4e5186e5de9d9a49b", size = 2099732 },
+    { url = "https://files.pythonhosted.org/packages/06/d5/ee5a3366637fee41dee51a1fc91562dcf12ddbc68fda34e6b253da2324bb/pydantic_core-2.46.4-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:c1b3f518abeca3aa13c712fd202306e145abf59a18b094a6bafb2d2bbf59192c", size = 2129627 },
+    { url = "https://files.pythonhosted.org/packages/94/33/2414be571d2c6a6c4d08be21f9292b6d3fdb08949a97b6dfe985017821db/pydantic_core-2.46.4-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1a7dd0b3ee80d90150e3495a3a13ac34dbcbfd4f012996a6a1d8900e91b5c0fb", size = 2179141 },
+    { url = "https://files.pythonhosted.org/packages/7b/79/7daa95be995be0eecc4cf75064cb33f9bbbfe3fe0158caf2f0d4a996a5c7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:3fb702cd90b0446a3a1c5e470bfa0dd23c0233b676a9099ddcc964fa6ca13898", size = 2184325 },
+    { url = "https://files.pythonhosted.org/packages/9f/cb/d0a382f5c0de8a222dc61c65348e0ce831b1f68e0a018450d31c2cace3a5/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:b8458003118a712e66286df6a707db01c52c0f52f7db8e4a38f0da1d3b94fc4e", size = 2323990 },
+    { url = "https://files.pythonhosted.org/packages/05/db/d9ba624cc4a5aced1598e88c04fdbd8310c8a69b9d38b9a3d39ce3a61ed7/pydantic_core-2.46.4-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:372429a130e469c9cd698925ce5fc50940b7a1336b0d82038e63d5bbc4edc519", size = 2369978 },
+    { url = "https://files.pythonhosted.org/packages/f2/20/d15df15ba918c423461905802bfd2981c3af0bfa0e40d05e13edbfa48bc3/pydantic_core-2.46.4-cp314-cp314-win32.whl", hash = "sha256:85bb3611ff1802f3ee7fdd7dbff26b56f343fb432d57a4728fdd49b6ef35e2f4", size = 1966354 },
+    { url = "https://files.pythonhosted.org/packages/fc/b6/6b8de4c0a7d7ab3004c439c80c5c1e0a3e8d78bbae19379b01960383d9e5/pydantic_core-2.46.4-cp314-cp314-win_amd64.whl", hash = "sha256:811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac", size = 2072238 },
+    { url = "https://files.pythonhosted.org/packages/32/36/51eb763beec1f4cf59b1db243a7dcc39cbb41230f050a09b9d69faaf0a48/pydantic_core-2.46.4-cp314-cp314-win_arm64.whl", hash = "sha256:bfec22eab3c8cc2ceec0248aec886624116dc079afa027ecc8ad4a7e62010f8a", size = 2018251 },
+    { url = "https://files.pythonhosted.org/packages/e8/91/855af51d625b23aa987116a19e231d2aaef9c4a415273ddc189b79a45fee/pydantic_core-2.46.4-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:af8244b2bef6aaad6d92cda81372de7f8c8d36c9f0c3ea36e827c60e7d9467a0", size = 2099593 },
+    { url = "https://files.pythonhosted.org/packages/fb/1b/8784a54c65edb5f49f0a14d6977cf1b209bba85a4c77445b255c2de58ab3/pydantic_core-2.46.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5a4330cdbc57162e4b3aa303f588ba752257694c9c9be3e7ebb11b4aca659b5d", size = 1935226 },
+    { url = "https://files.pythonhosted.org/packages/e8/e7/1955d28d1afc56dd4b3ad7cc0cf39df1b9852964cf16e5d13912756d6d6b/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c61fc04a3d840155ff08e475a04809278972fe6aef51e2720554e96367e34b", size = 1974605 },
+    { url = "https://files.pythonhosted.org/packages/93/e2/3fedbf0ba7a22850e6e9fd78117f1c0f10f950182344d8a6c535d468fdd8/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c50f2528cf200c5eed56faf3f4e22fcd5f38c157a8b78576e6ba3168ec35f000", size = 2030777 },
+    { url = "https://files.pythonhosted.org/packages/f8/61/46be275fcaaba0b4f5b9669dd852267ce1ff616592dccf7a7845588df091/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:0cbe8b01f948de4286c74cdd6c667aceb38f5c1e26f0693b3983d9d74887c65e", size = 2236641 },
+    { url = "https://files.pythonhosted.org/packages/60/db/12e93e46a8bac9988be3c016860f83293daea8c716c029c9ace279036f2f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:617d7e2ca7dcb8c5cf6bcb8c59b8832c94b36196bbf1cbd1bfb56ed341905edd", size = 2286404 },
+    { url = "https://files.pythonhosted.org/packages/e2/4a/4d8b19008f38d31c53b8219cfedc2e3d5de5fe99d90076b7e767de29274f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7027560ee92211647d0d34e3f7cd6f50da56399d26a9c8ad0da286d3869a53f3", size = 2109219 },
+    { url = "https://files.pythonhosted.org/packages/88/70/3cbc40978fefb7bb09c6708d40d4ad1a5d70fd7213c3d17f971de868ec1f/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:f99626688942fb746e545232e7726926f3be91b5975f8b55327665fafda991c7", size = 2110594 },
+    { url = "https://files.pythonhosted.org/packages/9d/20/b8d36736216e29491125531685b2f9e61aa5b4b2599893f8268551da3338/pydantic_core-2.46.4-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fc3e9034a63de20e15e8ade85358bc6efc614008cab72898b4b4952bea0509ff", size = 2159542 },
+    { url = "https://files.pythonhosted.org/packages/1d/a2/367df868eb584dacf6bf82a389272406d7178e301c4ac82545ab98bc2dd9/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:97e7cf2be5c77b7d1a9713a05605d49460d02c6078d38d8bef3cbe323c548424", size = 2168146 },
+    { url = "https://files.pythonhosted.org/packages/c1/b8/4460f77f7e201893f649a29ab355dddd3beee8a97bcb1a320db414f9a06e/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:3bf92c5d0e00fefaab325a4d27828fe6b6e2a21848686b5b60d2d9eeb09d76c6", size = 2306309 },
+    { url = "https://files.pythonhosted.org/packages/64/c4/be2639293acd87dc8ddbcec41a73cee9b2ebf996fe6d892a1a74e88ad3f7/pydantic_core-2.46.4-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:3ecbc122d18468d06ca279dc26a8c2e2d5acb10943bb35e36ae92096dc3b5565", size = 2369736 },
+    { url = "https://files.pythonhosted.org/packages/30/a6/9f9f380dbb301f67023bf8f707aaa75daadf84f7152d95c410fd7e81d994/pydantic_core-2.46.4-cp314-cp314t-win32.whl", hash = "sha256:e846ae7835bf0703ae43f534ab79a867146dadd59dc9ca5c8b53d5c8f7c9ef02", size = 1955575 },
+    { url = "https://files.pythonhosted.org/packages/40/1f/f1eb9eb350e795d1af8586289746f5c5677d16043040d63710e22abc43c9/pydantic_core-2.46.4-cp314-cp314t-win_amd64.whl", hash = "sha256:2108ba5c1c1eca18030634489dc544844144ee36357f2f9f780b93e7ddbb44b5", size = 2051624 },
+    { url = "https://files.pythonhosted.org/packages/f6/d2/42dd53d0a85c27606f316d3aa5d2869c4e8470a5ed6dec30e4a1abe19192/pydantic_core-2.46.4-cp314-cp314t-win_arm64.whl", hash = "sha256:4fcbe087dbc2068af7eda3aa87634eba216dbda64d1ae73c8684b621d33f6596", size = 2017325 },
+]
+
+[[package]]
+name = "pygments"
+version = "2.20.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151 },
+]
+
+[[package]]
+name = "pytest"
+version = "9.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249 },
+]
+
+[[package]]
+name = "pytest-asyncio"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pytest" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/90/2c/8af215c0f776415f3590cac4f9086ccefd6fd463befeae41cd4d3f193e5a/pytest_asyncio-1.3.0.tar.gz", hash = "sha256:d7f52f36d231b80ee124cd216ffb19369aa168fc10095013c6b014a34d3ee9e5", size = 50087 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e5/35/f8b19922b6a25bc0880171a2f1a003eaeb93657475193ab516fd87cac9da/pytest_asyncio-1.3.0-py3-none-any.whl", hash = "sha256:611e26147c7f77640e6d0a92a38ed17c3e9848063698d5c93d5aa7aa11cebff5", size = 15075 },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
+]
+
+[[package]]
+name = "python-dotenv"
+version = "1.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101 },
+]
+
+[[package]]
+name = "pyyaml"
+version = "6.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063 },
+    { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973 },
+    { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116 },
+    { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011 },
+    { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870 },
+    { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089 },
+    { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181 },
+    { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658 },
+    { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003 },
+    { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344 },
+    { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669 },
+    { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252 },
+    { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081 },
+    { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159 },
+    { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626 },
+    { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613 },
+    { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115 },
+    { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427 },
+    { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090 },
+    { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246 },
+    { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814 },
+    { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809 },
+    { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454 },
+    { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355 },
+    { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175 },
+    { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228 },
+    { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194 },
+    { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429 },
+    { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912 },
+    { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108 },
+    { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641 },
+    { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901 },
+    { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132 },
+    { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261 },
+    { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272 },
+    { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923 },
+    { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062 },
+    { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341 },
+]
+
+[[package]]
+name = "reportlab"
+version = "4.5.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "charset-normalizer" },
+    { name = "pillow" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4d/3f/b3861b7e40c9d66f4a04e018958d681d16b948bfd1963c962d43a8c23f66/reportlab-4.5.1.tar.gz", hash = "sha256:9fdf68f4de9171ec66acb4a5feed8f8ca2af43479e707a6fbb0daa75d88e5494", size = 3939748 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/45/ea7fad10122440de6e845568d106bffdc456ca0e8a1d8ae10b46016087e4/reportlab-4.5.1-py3-none-any.whl", hash = "sha256:06fce8cb56c83307cfa4909cdf4e6a2ddbb44e5d6ef4d2edca896d7e9769f091", size = 1957812 },
+]
+
+[[package]]
+name = "requests"
+version = "2.34.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "idna" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/43/b8/7a707d60fea4c49094e40262cc0e2ca6c768cca21587e34d3f705afec47e/requests-2.34.0.tar.gz", hash = "sha256:7d62fe92f50eb82c529b0916bb445afa1531a566fc8f35ffdc64446e771b856a", size = 142436 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/e6/e300fce5fe83c30520607a015dabd985df3251e188d234bfe9492e17a389/requests-2.34.0-py3-none-any.whl", hash = "sha256:917520a21b767485ce7c588f4ebb917c436b24a31231b44228715eaeb5a52c60", size = 73021 },
+]
+
+[[package]]
+name = "respx"
+version = "0.23.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/43/98/4e55c9c486404ec12373708d015ebce157966965a5ebe7f28ff2c784d41b/respx-0.23.1.tar.gz", hash = "sha256:242dcc6ce6b5b9bf621f5870c82a63997e8e82bc7c947f9ffe272b8f3dd5a780", size = 29243 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/4a/221da6ca167db45693d8d26c7dc79ccfc978a440251bf6721c9aaf251ac0/respx-0.23.1-py2.py3-none-any.whl", hash = "sha256:b18004b029935384bccfa6d7d9d74b4ec9af73a081cc28600fffc0447f4b8c1a", size = 25557 },
+]
+
+[[package]]
+name = "rich"
+version = "15.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c0/8f/0722ca900cc807c13a6a0c696dacf35430f72e0ec571c4275d2371fca3e9/rich-15.0.0.tar.gz", hash = "sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36", size = 230680 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/82/3b/64d4899d73f91ba49a8c18a8ff3f0ea8f1c1d75481760df8c68ef5235bf5/rich-15.0.0-py3-none-any.whl", hash = "sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb", size = 310654 },
+]
+
+[[package]]
+name = "ruff"
+version = "0.15.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/99/43/3291f1cc9106f4c63bdce7a8d0df5047fe8422a75b091c16b5e9355e0b11/ruff-0.15.12.tar.gz", hash = "sha256:ecea26adb26b4232c0c2ca19ccbc0083a68344180bba2a600605538ce51a40a6", size = 4643852 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/6e/e78ffb61d4686f3d96ba3df2c801161843746dcbcbb17a1e927d4829312b/ruff-0.15.12-py3-none-linux_armv6l.whl", hash = "sha256:f86f176e188e94d6bdbc09f09bfd9dc729059ad93d0e7390b5a73efe19f8861c", size = 10640713 },
+    { url = "https://files.pythonhosted.org/packages/ae/08/a317bc231fb9e7b93e4ef3089501e51922ff88d6936ce5cf870c4fe55419/ruff-0.15.12-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:e3bcd123364c3770b8e1b7baaf343cc99a35f197c5c6e8af79015c666c423a6c", size = 11069267 },
+    { url = "https://files.pythonhosted.org/packages/aa/a4/f828e9718d3dce1f5f11c39c4f65afd32783c8b2aebb2e3d259e492c47bd/ruff-0.15.12-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fe87510d000220aa1ed530d4448a7c696a0cae1213e5ec30e5874287b66557b5", size = 10397182 },
+    { url = "https://files.pythonhosted.org/packages/71/e0/3310fc6d1b5e1fdea22bf3b1b807c7e187b581021b0d7d4514cccdb5fb71/ruff-0.15.12-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84a1630093121375a3e2a95b4a6dc7b59e2b4ee76216e32d81aae550a832d002", size = 10758012 },
+    { url = "https://files.pythonhosted.org/packages/11/c1/a606911aee04c324ddaa883ae418f3569792fd3c4a10c50e0dd0a2311e1e/ruff-0.15.12-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fb129f40f114f089ebe0ca56c0d251cf2061b17651d464bb6478dc01e69f11f5", size = 10447479 },
+    { url = "https://files.pythonhosted.org/packages/9d/68/4201e8444f0894f21ab4aeeaee68aa4f10b51613514a20d80bd628d57e88/ruff-0.15.12-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b0c862b172d695db7598426b8af465e7e9ac00a3ea2a3630ee67eb82e366aaa6", size = 11234040 },
+    { url = "https://files.pythonhosted.org/packages/34/ff/8a6d6cf4ccc23fd67060874e832c18919d1557a0611ebef03fdb01fff11e/ruff-0.15.12-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2849ea9f3484c3aca43a82f484210370319e7170df4dfe4843395ddf6c57bc33", size = 12087377 },
+    { url = "https://files.pythonhosted.org/packages/85/f6/c669cf73f5152f623d34e69866a46d5e6185816b19fcd5b6dd8a2d299922/ruff-0.15.12-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9e77c7e51c07fe396826d5969a5b846d9cd4c402535835fb6e21ce8b28fef847", size = 11367784 },
+    { url = "https://files.pythonhosted.org/packages/e8/39/c61d193b8a1daaa8977f7dea9e8d8ba866e02ea7b65d32f6861693aa4c12/ruff-0.15.12-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b2f4f2f3b1026b5fb449b467d9264bf22067b600f7b6f41fc5958909f449d0", size = 11344088 },
+    { url = "https://files.pythonhosted.org/packages/c2/8d/49afab3645e31e12c590acb6d3b5b69d7aab5b81926dbaf7461f9441f37a/ruff-0.15.12-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:9ba3b8f1afd7e2e43d8943e55f249e13f9682fde09711644a6e7290eb4f3e339", size = 11271770 },
+    { url = "https://files.pythonhosted.org/packages/46/06/33f41fe94403e2b755481cdfb9b7ef3e4e0ed031c4581124658d935d52b4/ruff-0.15.12-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:e852ba9fdc890655e1d78f2df1499efbe0e54126bd405362154a75e2bde159c5", size = 10719355 },
+    { url = "https://files.pythonhosted.org/packages/0d/59/18aa4e014debbf559670e4048e39260a85c7fcee84acfd761ac01e7b8d35/ruff-0.15.12-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:dd8aed930da53780d22fc70bdf84452c843cf64f8cb4eb38984319c24c5cd5fd", size = 10462758 },
+    { url = "https://files.pythonhosted.org/packages/25/e7/cc9f16fd0f3b5fddcbd7ec3d6ae30c8f3fde1047f32a4093a98d633c6570/ruff-0.15.12-py3-none-musllinux_1_2_i686.whl", hash = "sha256:01da3988d225628b709493d7dc67c3b9b12c0210016b08690ef9bd27970b262b", size = 10953498 },
+    { url = "https://files.pythonhosted.org/packages/72/7a/a9ba7f98c7a575978698f4230c5e8cc54bbc761af34f560818f933dafa0c/ruff-0.15.12-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:9cae0f92bd5700d1213188b31cd3bdd2b315361296d10b96b8e2337d3d11f53e", size = 11447765 },
+    { url = "https://files.pythonhosted.org/packages/ea/f9/0ae446942c846b8266059ad8a30702a35afae55f5cdc54c5adf8d7afdc27/ruff-0.15.12-py3-none-win32.whl", hash = "sha256:d0185894e038d7043ba8fd6aee7499ece6462dc0ea9f1e260c7451807c714c20", size = 10657277 },
+    { url = "https://files.pythonhosted.org/packages/33/f1/9614e03e1cdcbf9437570b5400ced8a720b5db22b28d8e0f1bda429f660d/ruff-0.15.12-py3-none-win_amd64.whl", hash = "sha256:c87a162d61ab3adca47c03f7f717c68672edec7d1b5499e652331780fe74950d", size = 11837758 },
+    { url = "https://files.pythonhosted.org/packages/c0/98/6beb4b351e472e5f4c4613f7c35a5290b8be2497e183825310c4c3a3984b/ruff-0.15.12-py3-none-win_arm64.whl", hash = "sha256:a538f7a82d061cee7be55542aca1d86d1393d55d81d4fcc314370f4340930d4f", size = 11120821 },
+]
+
+[[package]]
+name = "scikit-learn"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0e/d4/40988bf3b8e34feec1d0e6a051446b1f66225f8529b9309becaeef62b6c4/scikit_learn-1.8.0.tar.gz", hash = "sha256:9bccbb3b40e3de10351f8f5068e105d0f4083b1a65fa07b6634fbc401a6287fd", size = 7335585 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/90/74/e6a7cc4b820e95cc38cf36cd74d5aa2b42e8ffc2d21fe5a9a9c45c1c7630/scikit_learn-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:5fb63362b5a7ddab88e52b6dbb47dac3fd7dafeee740dc6c8d8a446ddedade8e", size = 8548242 },
+    { url = "https://files.pythonhosted.org/packages/49/d8/9be608c6024d021041c7f0b3928d4749a706f4e2c3832bbede4fb4f58c95/scikit_learn-1.8.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:5025ce924beccb28298246e589c691fe1b8c1c96507e6d27d12c5fadd85bfd76", size = 8079075 },
+    { url = "https://files.pythonhosted.org/packages/dd/47/f187b4636ff80cc63f21cd40b7b2d177134acaa10f6bb73746130ee8c2e5/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4496bb2cf7a43ce1a2d7524a79e40bc5da45cf598dbf9545b7e8316ccba47bb4", size = 8660492 },
+    { url = "https://files.pythonhosted.org/packages/97/74/b7a304feb2b49df9fafa9382d4d09061a96ee9a9449a7cbea7988dda0828/scikit_learn-1.8.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a0bcfe4d0d14aec44921545fd2af2338c7471de9cb701f1da4c9d85906ab847a", size = 8931904 },
+    { url = "https://files.pythonhosted.org/packages/9f/c4/0ab22726a04ede56f689476b760f98f8f46607caecff993017ac1b64aa5d/scikit_learn-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:35c007dedb2ffe38fe3ee7d201ebac4a2deccd2408e8621d53067733e3c74809", size = 8019359 },
+    { url = "https://files.pythonhosted.org/packages/24/90/344a67811cfd561d7335c1b96ca21455e7e472d281c3c279c4d3f2300236/scikit_learn-1.8.0-cp312-cp312-win_arm64.whl", hash = "sha256:8c497fff237d7b4e07e9ef1a640887fa4fb765647f86fbe00f969ff6280ce2bb", size = 7641898 },
+    { url = "https://files.pythonhosted.org/packages/03/aa/e22e0768512ce9255eba34775be2e85c2048da73da1193e841707f8f039c/scikit_learn-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0d6ae97234d5d7079dc0040990a6f7aeb97cb7fa7e8945f1999a429b23569e0a", size = 8513770 },
+    { url = "https://files.pythonhosted.org/packages/58/37/31b83b2594105f61a381fc74ca19e8780ee923be2d496fcd8d2e1147bd99/scikit_learn-1.8.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:edec98c5e7c128328124a029bceb09eda2d526997780fef8d65e9a69eead963e", size = 8044458 },
+    { url = "https://files.pythonhosted.org/packages/2d/5a/3f1caed8765f33eabb723596666da4ebbf43d11e96550fb18bdec42b467b/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:74b66d8689d52ed04c271e1329f0c61635bcaf5b926db9b12d58914cdc01fe57", size = 8610341 },
+    { url = "https://files.pythonhosted.org/packages/38/cf/06896db3f71c75902a8e9943b444a56e727418f6b4b4a90c98c934f51ed4/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8fdf95767f989b0cfedb85f7ed8ca215d4be728031f56ff5a519ee1e3276dc2e", size = 8900022 },
+    { url = "https://files.pythonhosted.org/packages/1c/f9/9b7563caf3ec8873e17a31401858efab6b39a882daf6c1bfa88879c0aa11/scikit_learn-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:2de443b9373b3b615aec1bb57f9baa6bb3a9bd093f1269ba95c17d870422b271", size = 7989409 },
+    { url = "https://files.pythonhosted.org/packages/49/bd/1f4001503650e72c4f6009ac0c4413cb17d2d601cef6f71c0453da2732fc/scikit_learn-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:eddde82a035681427cbedded4e6eff5e57fa59216c2e3e90b10b19ab1d0a65c3", size = 7619760 },
+    { url = "https://files.pythonhosted.org/packages/d2/7d/a630359fc9dcc95496588c8d8e3245cc8fd81980251079bc09c70d41d951/scikit_learn-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7cc267b6108f0a1499a734167282c00c4ebf61328566b55ef262d48e9849c735", size = 8826045 },
+    { url = "https://files.pythonhosted.org/packages/cc/56/a0c86f6930cfcd1c7054a2bc417e26960bb88d32444fe7f71d5c2cfae891/scikit_learn-1.8.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:fe1c011a640a9f0791146011dfd3c7d9669785f9fed2b2a5f9e207536cf5c2fd", size = 8420324 },
+    { url = "https://files.pythonhosted.org/packages/46/1e/05962ea1cebc1cf3876667ecb14c283ef755bf409993c5946ade3b77e303/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72358cce49465d140cc4e7792015bb1f0296a9742d5622c67e31399b75468b9e", size = 8680651 },
+    { url = "https://files.pythonhosted.org/packages/fe/56/a85473cd75f200c9759e3a5f0bcab2d116c92a8a02ee08ccd73b870f8bb4/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80832434a6cc114f5219211eec13dcbc16c2bac0e31ef64c6d346cde3cf054cb", size = 8925045 },
+    { url = "https://files.pythonhosted.org/packages/cc/b7/64d8cfa896c64435ae57f4917a548d7ac7a44762ff9802f75a79b77cb633/scikit_learn-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ee787491dbfe082d9c3013f01f5991658b0f38aa8177e4cd4bf434c58f551702", size = 8507994 },
+    { url = "https://files.pythonhosted.org/packages/5e/37/e192ea709551799379958b4c4771ec507347027bb7c942662c7fbeba31cb/scikit_learn-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf97c10a3f5a7543f9b88cbf488d33d175e9146115a451ae34568597ba33dcde", size = 7869518 },
+    { url = "https://files.pythonhosted.org/packages/24/05/1af2c186174cc92dcab2233f327336058c077d38f6fe2aceb08e6ab4d509/scikit_learn-1.8.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:c22a2da7a198c28dd1a6e1136f19c830beab7fdca5b3e5c8bba8394f8a5c45b3", size = 8528667 },
+    { url = "https://files.pythonhosted.org/packages/a8/25/01c0af38fe969473fb292bba9dc2b8f9b451f3112ff242c647fee3d0dfe7/scikit_learn-1.8.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:6b595b07a03069a2b1740dc08c2299993850ea81cce4fe19b2421e0c970de6b7", size = 8066524 },
+    { url = "https://files.pythonhosted.org/packages/be/ce/a0623350aa0b68647333940ee46fe45086c6060ec604874e38e9ab7d8e6c/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:29ffc74089f3d5e87dfca4c2c8450f88bdc61b0fc6ed5d267f3988f19a1309f6", size = 8657133 },
+    { url = "https://files.pythonhosted.org/packages/b8/cb/861b41341d6f1245e6ca80b1c1a8c4dfce43255b03df034429089ca2a2c5/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fb65db5d7531bccf3a4f6bec3462223bea71384e2cda41da0f10b7c292b9e7c4", size = 8923223 },
+    { url = "https://files.pythonhosted.org/packages/76/18/a8def8f91b18cd1ba6e05dbe02540168cb24d47e8dcf69e8d00b7da42a08/scikit_learn-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:56079a99c20d230e873ea40753102102734c5953366972a71d5cb39a32bc40c6", size = 8096518 },
+    { url = "https://files.pythonhosted.org/packages/d1/77/482076a678458307f0deb44e29891d6022617b2a64c840c725495bee343f/scikit_learn-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:3bad7565bc9cf37ce19a7c0d107742b320c1285df7aab1a6e2d28780df167242", size = 7754546 },
+    { url = "https://files.pythonhosted.org/packages/2d/d1/ef294ca754826daa043b2a104e59960abfab4cf653891037d19dd5b6f3cf/scikit_learn-1.8.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:4511be56637e46c25721e83d1a9cea9614e7badc7040c4d573d75fbe257d6fd7", size = 8848305 },
+    { url = "https://files.pythonhosted.org/packages/5b/e2/b1f8b05138ee813b8e1a4149f2f0d289547e60851fd1bb268886915adbda/scikit_learn-1.8.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:a69525355a641bf8ef136a7fa447672fb54fe8d60cab5538d9eb7c6438543fb9", size = 8432257 },
+    { url = "https://files.pythonhosted.org/packages/26/11/c32b2138a85dcb0c99f6afd13a70a951bfdff8a6ab42d8160522542fb647/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c2656924ec73e5939c76ac4c8b026fc203b83d8900362eb2599d8aee80e4880f", size = 8678673 },
+    { url = "https://files.pythonhosted.org/packages/c7/57/51f2384575bdec454f4fe4e7a919d696c9ebce914590abf3e52d47607ab8/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:15fc3b5d19cc2be65404786857f2e13c70c83dd4782676dd6814e3b89dc8f5b9", size = 8922467 },
+    { url = "https://files.pythonhosted.org/packages/35/4d/748c9e2872637a57981a04adc038dacaa16ba8ca887b23e34953f0b3f742/scikit_learn-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:00d6f1d66fbcf4eba6e356e1420d33cc06c70a45bb1363cd6f6a8e4ebbbdece2", size = 8774395 },
+    { url = "https://files.pythonhosted.org/packages/60/22/d7b2ebe4704a5e50790ba089d5c2ae308ab6bb852719e6c3bd4f04c3a363/scikit_learn-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f28dd15c6bb0b66ba09728cf09fd8736c304be29409bd8445a080c1280619e8c", size = 8002647 },
+]
+
+[[package]]
+name = "scipy"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/35/48/b992b488d6f299dbe3f11a20b24d3dda3d46f1a635ede1c46b5b17a7b163/scipy-1.17.1-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8", size = 31610954 },
+    { url = "https://files.pythonhosted.org/packages/b2/02/cf107b01494c19dc100f1d0b7ac3cc08666e96ba2d64db7626066cee895e/scipy-1.17.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76", size = 28172662 },
+    { url = "https://files.pythonhosted.org/packages/cf/a9/599c28631bad314d219cf9ffd40e985b24d603fc8a2f4ccc5ae8419a535b/scipy-1.17.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086", size = 20344366 },
+    { url = "https://files.pythonhosted.org/packages/35/f5/906eda513271c8deb5af284e5ef0206d17a96239af79f9fa0aebfe0e36b4/scipy-1.17.1-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b", size = 22704017 },
+    { url = "https://files.pythonhosted.org/packages/da/34/16f10e3042d2f1d6b66e0428308ab52224b6a23049cb2f5c1756f713815f/scipy-1.17.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21", size = 32927842 },
+    { url = "https://files.pythonhosted.org/packages/01/8e/1e35281b8ab6d5d72ebe9911edcdffa3f36b04ed9d51dec6dd140396e220/scipy-1.17.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458", size = 35235890 },
+    { url = "https://files.pythonhosted.org/packages/c5/5c/9d7f4c88bea6e0d5a4f1bc0506a53a00e9fcb198de372bfe4d3652cef482/scipy-1.17.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb", size = 35003557 },
+    { url = "https://files.pythonhosted.org/packages/65/94/7698add8f276dbab7a9de9fb6b0e02fc13ee61d51c7c3f85ac28b65e1239/scipy-1.17.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea", size = 37625856 },
+    { url = "https://files.pythonhosted.org/packages/a2/84/dc08d77fbf3d87d3ee27f6a0c6dcce1de5829a64f2eae85a0ecc1f0daa73/scipy-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:41b71f4a3a4cab9d366cd9065b288efc4d4f3c0b37a91a8e0947fb5bd7f31d87", size = 36549682 },
+    { url = "https://files.pythonhosted.org/packages/bc/98/fe9ae9ffb3b54b62559f52dedaebe204b408db8109a8c66fdd04869e6424/scipy-1.17.1-cp312-cp312-win_arm64.whl", hash = "sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3", size = 24547340 },
+    { url = "https://files.pythonhosted.org/packages/76/27/07ee1b57b65e92645f219b37148a7e7928b82e2b5dbeccecb4dff7c64f0b/scipy-1.17.1-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c", size = 31590199 },
+    { url = "https://files.pythonhosted.org/packages/ec/ae/db19f8ab842e9b724bf5dbb7db29302a91f1e55bc4d04b1025d6d605a2c5/scipy-1.17.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f", size = 28154001 },
+    { url = "https://files.pythonhosted.org/packages/5b/58/3ce96251560107b381cbd6e8413c483bbb1228a6b919fa8652b0d4090e7f/scipy-1.17.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d", size = 20325719 },
+    { url = "https://files.pythonhosted.org/packages/b2/83/15087d945e0e4d48ce2377498abf5ad171ae013232ae31d06f336e64c999/scipy-1.17.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b", size = 22683595 },
+    { url = "https://files.pythonhosted.org/packages/b4/e0/e58fbde4a1a594c8be8114eb4aac1a55bcd6587047efc18a61eb1f5c0d30/scipy-1.17.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6", size = 32896429 },
+    { url = "https://files.pythonhosted.org/packages/f5/5f/f17563f28ff03c7b6799c50d01d5d856a1d55f2676f537ca8d28c7f627cd/scipy-1.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464", size = 35203952 },
+    { url = "https://files.pythonhosted.org/packages/8d/a5/9afd17de24f657fdfe4df9a3f1ea049b39aef7c06000c13db1530d81ccca/scipy-1.17.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950", size = 34979063 },
+    { url = "https://files.pythonhosted.org/packages/8b/13/88b1d2384b424bf7c924f2038c1c409f8d88bb2a8d49d097861dd64a57b2/scipy-1.17.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369", size = 37598449 },
+    { url = "https://files.pythonhosted.org/packages/35/e5/d6d0e51fc888f692a35134336866341c08655d92614f492c6860dc45bb2c/scipy-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448", size = 36510943 },
+    { url = "https://files.pythonhosted.org/packages/2a/fd/3be73c564e2a01e690e19cc618811540ba5354c67c8680dce3281123fb79/scipy-1.17.1-cp313-cp313-win_arm64.whl", hash = "sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87", size = 24545621 },
+    { url = "https://files.pythonhosted.org/packages/6f/6b/17787db8b8114933a66f9dcc479a8272e4b4da75fe03b0c282f7b0ade8cd/scipy-1.17.1-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a", size = 31936708 },
+    { url = "https://files.pythonhosted.org/packages/38/2e/524405c2b6392765ab1e2b722a41d5da33dc5c7b7278184a8ad29b6cb206/scipy-1.17.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0", size = 28570135 },
+    { url = "https://files.pythonhosted.org/packages/fd/c3/5bd7199f4ea8556c0c8e39f04ccb014ac37d1468e6cfa6a95c6b3562b76e/scipy-1.17.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce", size = 20741977 },
+    { url = "https://files.pythonhosted.org/packages/d9/b8/8ccd9b766ad14c78386599708eb745f6b44f08400a5fd0ade7cf89b6fc93/scipy-1.17.1-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6", size = 23029601 },
+    { url = "https://files.pythonhosted.org/packages/6d/a0/3cb6f4d2fb3e17428ad2880333cac878909ad1a89f678527b5328b93c1d4/scipy-1.17.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e", size = 33019667 },
+    { url = "https://files.pythonhosted.org/packages/f3/c3/2d834a5ac7bf3a0c806ad1508efc02dda3c8c61472a56132d7894c312dea/scipy-1.17.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475", size = 35264159 },
+    { url = "https://files.pythonhosted.org/packages/4d/77/d3ed4becfdbd217c52062fafe35a72388d1bd82c2d0ba5ca19d6fcc93e11/scipy-1.17.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50", size = 35102771 },
+    { url = "https://files.pythonhosted.org/packages/bd/12/d19da97efde68ca1ee5538bb261d5d2c062f0c055575128f11a2730e3ac1/scipy-1.17.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca", size = 37665910 },
+    { url = "https://files.pythonhosted.org/packages/06/1c/1172a88d507a4baaf72c5a09bb6c018fe2ae0ab622e5830b703a46cc9e44/scipy-1.17.1-cp313-cp313t-win_amd64.whl", hash = "sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c", size = 36562980 },
+    { url = "https://files.pythonhosted.org/packages/70/b0/eb757336e5a76dfa7911f63252e3b7d1de00935d7705cf772db5b45ec238/scipy-1.17.1-cp313-cp313t-win_arm64.whl", hash = "sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49", size = 24856543 },
+    { url = "https://files.pythonhosted.org/packages/cf/83/333afb452af6f0fd70414dc04f898647ee1423979ce02efa75c3b0f2c28e/scipy-1.17.1-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717", size = 31584510 },
+    { url = "https://files.pythonhosted.org/packages/ed/a6/d05a85fd51daeb2e4ea71d102f15b34fedca8e931af02594193ae4fd25f7/scipy-1.17.1-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9", size = 28170131 },
+    { url = "https://files.pythonhosted.org/packages/db/7b/8624a203326675d7746a254083a187398090a179335b2e4a20e2ddc46e83/scipy-1.17.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b", size = 20342032 },
+    { url = "https://files.pythonhosted.org/packages/c9/35/2c342897c00775d688d8ff3987aced3426858fd89d5a0e26e020b660b301/scipy-1.17.1-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866", size = 22678766 },
+    { url = "https://files.pythonhosted.org/packages/ef/f2/7cdb8eb308a1a6ae1e19f945913c82c23c0c442a462a46480ce487fdc0ac/scipy-1.17.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350", size = 32957007 },
+    { url = "https://files.pythonhosted.org/packages/0b/2e/7eea398450457ecb54e18e9d10110993fa65561c4f3add5e8eccd2b9cd41/scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118", size = 35221333 },
+    { url = "https://files.pythonhosted.org/packages/d9/77/5b8509d03b77f093a0d52e606d3c4f79e8b06d1d38c441dacb1e26cacf46/scipy-1.17.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068", size = 35042066 },
+    { url = "https://files.pythonhosted.org/packages/f9/df/18f80fb99df40b4070328d5ae5c596f2f00fffb50167e31439e932f29e7d/scipy-1.17.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118", size = 37612763 },
+    { url = "https://files.pythonhosted.org/packages/4b/39/f0e8ea762a764a9dc52aa7dabcfad51a354819de1f0d4652b6a1122424d6/scipy-1.17.1-cp314-cp314-win_amd64.whl", hash = "sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19", size = 37290984 },
+    { url = "https://files.pythonhosted.org/packages/7c/56/fe201e3b0f93d1a8bcf75d3379affd228a63d7e2d80ab45467a74b494947/scipy-1.17.1-cp314-cp314-win_arm64.whl", hash = "sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293", size = 25192877 },
+    { url = "https://files.pythonhosted.org/packages/96/ad/f8c414e121f82e02d76f310f16db9899c4fcde36710329502a6b2a3c0392/scipy-1.17.1-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6", size = 31949750 },
+    { url = "https://files.pythonhosted.org/packages/7c/b0/c741e8865d61b67c81e255f4f0a832846c064e426636cd7de84e74d209be/scipy-1.17.1-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1", size = 28585858 },
+    { url = "https://files.pythonhosted.org/packages/ed/1b/3985219c6177866628fa7c2595bfd23f193ceebbe472c98a08824b9466ff/scipy-1.17.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39", size = 20757723 },
+    { url = "https://files.pythonhosted.org/packages/c0/19/2a04aa25050d656d6f7b9e7b685cc83d6957fb101665bfd9369ca6534563/scipy-1.17.1-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca", size = 23043098 },
+    { url = "https://files.pythonhosted.org/packages/86/f1/3383beb9b5d0dbddd030335bf8a8b32d4317185efe495374f134d8be6cce/scipy-1.17.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad", size = 33030397 },
+    { url = "https://files.pythonhosted.org/packages/41/68/8f21e8a65a5a03f25a79165ec9d2b28c00e66dc80546cf5eb803aeeff35b/scipy-1.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a", size = 35281163 },
+    { url = "https://files.pythonhosted.org/packages/84/8d/c8a5e19479554007a5632ed7529e665c315ae7492b4f946b0deb39870e39/scipy-1.17.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4", size = 35116291 },
+    { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317 },
+    { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327 },
+    { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165 },
+]
+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755 },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
+]
+
+[[package]]
+name = "surfsense-evals"
+version = "0.1.0"
+source = { editable = "." }
+dependencies = [
+    { name = "datasets" },
+    { name = "httpx" },
+    { name = "httpx-sse" },
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "pyarrow" },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
+    { name = "reportlab" },
+    { name = "rich" },
+    { name = "scikit-learn" },
+    { name = "scipy" },
+    { name = "tqdm" },
+]
+
+[package.optional-dependencies]
+dev = [
+    { name = "pytest" },
+    { name = "pytest-asyncio" },
+    { name = "respx" },
+    { name = "ruff" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "datasets", specifier = ">=2.21.0" },
+    { name = "httpx", specifier = ">=0.27.0" },
+    { name = "httpx-sse", specifier = ">=0.4.0" },
+    { name = "huggingface-hub", specifier = ">=0.24.0" },
+    { name = "numpy", specifier = ">=1.26.0" },
+    { name = "pillow", specifier = ">=10.0.0" },
+    { name = "pyarrow", specifier = ">=15.0.0" },
+    { name = "pydantic", specifier = ">=2.6.0" },
+    { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" },
+    { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" },
+    { name = "python-dotenv", specifier = ">=1.0.0" },
+    { name = "reportlab", specifier = ">=4.0.0" },
+    { name = "respx", marker = "extra == 'dev'", specifier = ">=0.21.0" },
+    { name = "rich", specifier = ">=13.7.0" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.5.0" },
+    { name = "scikit-learn", specifier = ">=1.4.0" },
+    { name = "scipy", specifier = ">=1.12.0" },
+    { name = "tqdm", specifier = ">=4.66.0" },
+]
+provides-extras = ["dev"]
+
+[[package]]
+name = "threadpoolctl"
+version = "3.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638 },
+]
+
+[[package]]
+name = "tqdm"
+version = "4.67.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374 },
+]
+
+[[package]]
+name = "typer"
+version = "0.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-doc" },
+    { name = "click" },
+    { name = "rich" },
+    { name = "shellingham" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e4/51/9aed62104cea109b820bbd6c14245af756112017d309da813ef107d42e7e/typer-0.25.1.tar.gz", hash = "sha256:9616eb8853a09ffeabab1698952f33c6f29ffdbceb4eaeecf571880e8d7664cc", size = 122276 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3f/f9/2b3ff4e56e5fa7debfaf9eb135d0da96f3e9a1d5b27222223c7296336e5f/typer-0.25.1-py3-none-any.whl", hash = "sha256:75caa44ed46a03fb2dab8808753ffacdbfea88495e74c85a28c5eefcf5f39c89", size = 58409 },
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.15.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614 },
+]
+
+[[package]]
+name = "typing-inspection"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611 },
+]
+
+[[package]]
+name = "tzdata"
+version = "2026.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321 },
+]
+
+[[package]]
+name = "urllib3"
+version = "2.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087 },
+]
+
+[[package]]
+name = "xxhash"
+version = "3.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/2f/e183a1b407002f5af81822bee18b61cdb94b8670208ef34734d8d2b8ebe9/xxhash-3.7.0.tar.gz", hash = "sha256:6cc4eefbb542a5d6ffd6d70ea9c502957c925e800f998c5630ecc809d6702bae", size = 82022 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f2/8a/51a14cdef4728c6c2337db8a7d8704422cc65676d9199d77215464c880af/xxhash-3.7.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:082c87bfdd2b9f457606c7a4a53457f4c4b48b0cdc48de0277f4349d79bb3d7a", size = 33357 },
+    { url = "https://files.pythonhosted.org/packages/b9/1b/0c2c933809421ffd9bf42b59315552c143c755db5d9a816b2f1ae273e884/xxhash-3.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5e7ce913b61f35b0c1c839a49ac9c8e75dd8d860150688aed353b0ce1bf409d8", size = 30869 },
+    { url = "https://files.pythonhosted.org/packages/03/a8/89d5fdd6ee12d70ba99451de46dd0e8010167468dcd913ec855653f4dd50/xxhash-3.7.0-cp312-cp312-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3beb1de3b1e9694fcdd853e570ee64c631c7062435d2f8c69c1adf809bc086f0", size = 194100 },
+    { url = "https://files.pythonhosted.org/packages/87/ee/2f9f2ed993e77206d1e66991290a1ebe22e843351ca3ebec8e49e01ba186/xxhash-3.7.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f3e7b689c3bce16699efcf736066f5c6cc4472c3840fe4b22bd8279daf4abdac", size = 212977 },
+    { url = "https://files.pythonhosted.org/packages/de/60/5a91644615a9e9d4e42c2e9925f1908e3a24e4e691d9de7340d565bea024/xxhash-3.7.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:a6545e6b409e3d5cbafc850fb84c55a1ca26ed15a6b11e3bf07a0e0cd84517c8", size = 236373 },
+    { url = "https://files.pythonhosted.org/packages/22/c0/f3a9384eaaed9d14d4d062a5d953aa0da489bfe9747877aa994caa87cd0b/xxhash-3.7.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:31ab1461c77a11461d703c88eb949e132a1c6515933cf675d97ec680f4bd18de", size = 212229 },
+    { url = "https://files.pythonhosted.org/packages/2e/67/02f07a9fd79726804190f2172c4894c3ed9a4ebccaca05653c84beb58025/xxhash-3.7.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7c4d596b7676f811172687ec567cbafb9e4dea2f9be1bbb4f622410cb7f40f40", size = 445462 },
+    { url = "https://files.pythonhosted.org/packages/40/37/558f5a90c0672fc9b4402dc25d87ac5b7406616e8969430c9ca4e52ee74d/xxhash-3.7.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13805f0461cba0a857924e70ff91ae6d52d2598f79a884e788db80532614a4a1", size = 193932 },
+    { url = "https://files.pythonhosted.org/packages/d5/90/aaa09cd58661d32044dbbad7df55bbe22a623032b810e7ed3b8c569a2a6f/xxhash-3.7.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:1d398f372496152f1c6933a33566373f8d1b37b98b8c9d608fa6edc0976f23b2", size = 284807 },
+    { url = "https://files.pythonhosted.org/packages/d6/f3/53df3719ab127a02c174f0c1c74924fcd110866e89c966bc7909cfa8fa84/xxhash-3.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d610aa62cdb7d4d497740741772a24a794903bf3e79eaa51d2e800082abe11e5", size = 210445 },
+    { url = "https://files.pythonhosted.org/packages/72/33/d219975c0e8b6fa2eb9ccd486fe47e21bf1847985b878dd2fbc3126e0d5c/xxhash-3.7.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:073c23900a9fbf3d26616c17c830db28af9803677cd5b33aea3224d824111514", size = 241273 },
+    { url = "https://files.pythonhosted.org/packages/3e/50/49b1afe610eb3964cedcb90a4d4c3d46a261ee8669cbd4f060652619ae3c/xxhash-3.7.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:418a463c3e6a590c0cdc890f8be19adb44a8c8acd175ca5b2a6de77e61d0b386", size = 197950 },
+    { url = "https://files.pythonhosted.org/packages/c6/75/5f42a1a4c78717d906a4b6a140c6dbf837ab1f547a54d23c4e2903310936/xxhash-3.7.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:03f8ff4474ee61c845758ce00711d7087a770d77efb36f7e74a6e867301000b8", size = 210709 },
+    { url = "https://files.pythonhosted.org/packages/8a/85/237e446c25abced71e9c53d269f2cef5bab8a82b3f88a12e00c5368e7368/xxhash-3.7.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:44fba4a5f1d179b7ddc7b3dc40f56f9209046421679b57025d4d8821b376fd8d", size = 275345 },
+    { url = "https://files.pythonhosted.org/packages/62/34/c2c26c0a6a9cc739bc2a5f0ae03ba8b87deb12b8bce35f7ac495e790dc6d/xxhash-3.7.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:31e3516a0f829d06ded4a2c0f3c7c5561993256bfa1c493975fb9dc7bfa828a1", size = 414056 },
+    { url = "https://files.pythonhosted.org/packages/a0/aa/5c58e9bc8071b8afd8dcf297ff362f723c4892168faba149f19904132bf4/xxhash-3.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b59ee2ac81de57771a09ecad09191e840a1d2fae1ef684208320591055768f83", size = 191485 },
+    { url = "https://files.pythonhosted.org/packages/d4/69/a929cf9d1e2e65a48b818cdce72cb6b69eab2e6877f21436d0a1942aff43/xxhash-3.7.0-cp312-cp312-win32.whl", hash = "sha256:74bbd92f8c7fcc397ba0a11bfdc106bc72ad7f11e3a60277753f87e7532b4d81", size = 30671 },
+    { url = "https://files.pythonhosted.org/packages/b9/1b/104b41a8947f4e1d4a66ce1e628eea752f37d1890bfd7453559ca7a3d950/xxhash-3.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:7bd7bc82dd4f185f28f35193c2e968ef46131628e3cac62f639dadf321cba4d1", size = 31514 },
+    { url = "https://files.pythonhosted.org/packages/98/a0/1fd0ea1f1b886d9e7c73f0397571e22333a7d79e31da6d7127c2a4a71d75/xxhash-3.7.0-cp312-cp312-win_arm64.whl", hash = "sha256:7d7148180ec99ba36585b42c8c5de25e9b40191613bc4be68909b4d25a77a852", size = 27761 },
+    { url = "https://files.pythonhosted.org/packages/c1/ca/d5174b4c36d10f64d4ca7050563138c5a599efb01a765858ddefc9c1202a/xxhash-3.7.0-cp313-cp313-android_21_arm64_v8a.whl", hash = "sha256:4b6d6b33f141158692bd4eafbb96edbc5aa0dabdb593a962db01a91983d4f8fa", size = 36813 },
+    { url = "https://files.pythonhosted.org/packages/41/d0/abc6c9d347ba1f1e1e1d98125d0881a0452c7f9a76a9dd03a7b5d2197f23/xxhash-3.7.0-cp313-cp313-android_21_x86_64.whl", hash = "sha256:845d347df254d6c619f616afa921331bada8614b8d373d58725c663ba97c3605", size = 35121 },
+    { url = "https://files.pythonhosted.org/packages/bf/11/4cc834eb3d79f2f2b3a6ef7324195208bcdfbdcf7534d2b17267aa5f3a8f/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:fddbbb69a6fff4f421e7a0d1fa28f894b20112e9e3fab306af451e2dfd0e459b", size = 29624 },
+    { url = "https://files.pythonhosted.org/packages/23/83/e97d3e7b635fe73a1dfb1e91f805324dd6d930bb42041cbf18f183bc0b6d/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:54876a4e45101cec2bf8f31a973cda073a23e2e108538dad224ba07f85f22487", size = 30638 },
+    { url = "https://files.pythonhosted.org/packages/f4/40/d84951d80c35db1f4c40a29a64a8520eea5d56e764c603906b4fe763580f/xxhash-3.7.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:0c72fe9c7e3d6dfd7f1e21e224a877917fa09c465694ba4e06464b9511b65544", size = 33323 },
+    { url = "https://files.pythonhosted.org/packages/89/cc/c7dc6558d97e9ab023f663d69ab28b340ed9bf4d2d94f2c259cf896bb354/xxhash-3.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a6d73a830b17ef49bc04e00182bd839164c1b3c59c127cd7c54fcb10c7ed8ee8", size = 33362 },
+    { url = "https://files.pythonhosted.org/packages/2a/6e/46b84017b1301d54091430353d4ad5901654a3e0871649877a416f7f1644/xxhash-3.7.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:91c3b07cf3362086d8f126c6aecd8e5e9396ad8b2f2219ea7e49a8250c318acd", size = 30874 },
+    { url = "https://files.pythonhosted.org/packages/df/5e/8f9158e3ab906ad3fec51e09b5ea0093e769f12207bfa42a368ca204e7ab/xxhash-3.7.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:50e879ebbac351c81565ca108db766d7832f5b8b6a5b14b8c0151f7190028e3d", size = 194185 },
+    { url = "https://files.pythonhosted.org/packages/f3/29/a804ded9f5d3d3758292678d23e7528b08fda7b7e750688d08b052322475/xxhash-3.7.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:921c14e93817842dd0dd9f372890a0f0c72e534650b6ab13c5be5cd0db11d47e", size = 213033 },
+    { url = "https://files.pythonhosted.org/packages/8b/91/1ce5a7d2fdc975267320e2c78fc1cecfe7ab735ccbcf6993ec5dd541cb2c/xxhash-3.7.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e64a7c9d7dfca3e0fafcbc5e455519090706a3e36e95d655cec3e04e79f95aaa", size = 236140 },
+    { url = "https://files.pythonhosted.org/packages/34/04/fd595a4fd8617b05fa27bd9b684ecb4985bfed27917848eea85d54036d06/xxhash-3.7.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2220af08163baf5fa36c2b8af079dc2cbe6e66ae061385267f9472362dfd53c6", size = 212291 },
+    { url = "https://files.pythonhosted.org/packages/03/fb/f1a379cbc372ae5b9f4ab36154c48a849ca6ebe3ac477067a57865bf3bc6/xxhash-3.7.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f14bb8b22a4a91325813e3d553b8963c10cf8c756cff65ee50c194431296c655", size = 445532 },
+    { url = "https://files.pythonhosted.org/packages/65/59/172424b79f8cfd4b6d8a122b2193e6b8ad4b11f7159bb3b6f9b3191329bb/xxhash-3.7.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:496736f86a9bedaf64b0dc70e3539d0766df01c71ea22032698e88f3f04a1ce9", size = 193990 },
+    { url = "https://files.pythonhosted.org/packages/b9/19/aeac22161d953f139f07ba5586cb4a17c5b7b6dff985122803bb12933500/xxhash-3.7.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0ff71596bd79816975b3de7130ab1ff4541410285a3c084584eeb1c8239996fd", size = 284876 },
+    { url = "https://files.pythonhosted.org/packages/77/d5/4fd0b59e7a02242953da05ff679fbb961b0a4368eac97a217e11dae110c1/xxhash-3.7.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1ad86695c19b1d46fe106925db3c7a37f16be37669dcf58dcc70a9dd6e324676", size = 210495 },
+    { url = "https://files.pythonhosted.org/packages/aa/fb/976a3165c728c7faf74aa1b5ab3cf6a85e6d731612894741840524c7d28c/xxhash-3.7.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:970f9f8c50961d639cbd0d988c96f80ddf66006de93641719282c4fe7a87c5e6", size = 241331 },
+    { url = "https://files.pythonhosted.org/packages/4a/2c/6763d5901d53ac9e6ba296e5717ae599025c9d268396e8faa8b4b0a8e0ac/xxhash-3.7.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5886ad85e9e347911783760a1d16cb6b393e8f9e3b52c982568226cb56927bdc", size = 198037 },
+    { url = "https://files.pythonhosted.org/packages/61/2b/876e722d533833f5f9a83473e6ba993e48745701096944e77bbecf29b2c3/xxhash-3.7.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:6e934bbae1e0ec74e27d5f0d7f37ef547ce5ff9f0a7e63fb39e559fc99526734", size = 210744 },
+    { url = "https://files.pythonhosted.org/packages/21/e6/d7e7baef7ce24166b4668d3c48557bb35a23b92ecadcac7e7718d099ab69/xxhash-3.7.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:3b6b3d28228af044ebcded71c4a3dd86e1dbd7e2f4645bf40f7b5da65bb5fb5a", size = 275406 },
+    { url = "https://files.pythonhosted.org/packages/92/fe/198b3763b2e01ca908f2154969a2352ec99bda892b574a11a9a151c5ede4/xxhash-3.7.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:6be4d70d9ab76c9f324ead9c01af6ff52c324745ea0c3731682a0cf99720f1fe", size = 414125 },
+    { url = "https://files.pythonhosted.org/packages/3a/6d/019a11affd5a5499137cacca53808659964785439855b5aa40dfd3412916/xxhash-3.7.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:151d7520838d4465461a0b7f4ae488b3b00de16183dd3214c1a6b14bf89d7fb6", size = 191555 },
+    { url = "https://files.pythonhosted.org/packages/76/21/b96d58568df2d01533244c3e0e5cbdd0c8b2b25c4bec4d72f19259a292d7/xxhash-3.7.0-cp313-cp313-win32.whl", hash = "sha256:d798c1e291bffb8e37b5bbe0dda77fc767cd19e89cadaf66e6ed5d0ff88c9fe6", size = 30668 },
+    { url = "https://files.pythonhosted.org/packages/99/57/d849a8d3afa1f8f4bc6a831cd89f49f9706fbbad94d2975d6140a171988c/xxhash-3.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:875811ba23c543b1a1c3143c926e43996eb27ebb8f52d3500744aa608c275aed", size = 31524 },
+    { url = "https://files.pythonhosted.org/packages/81/52/bacc753e92dee78b058af8dcef0a50815f5f860986c664a92d75f965b6a5/xxhash-3.7.0-cp313-cp313-win_arm64.whl", hash = "sha256:54a675cb300dda83d71daae2a599389d22db8021a0f8db0dd659e14626eb3ecc", size = 27768 },
+    { url = "https://files.pythonhosted.org/packages/1c/47/ddbd683b7fc7e592c1a8d9d65f73ce9ab513f082b3967eee2baf549b8fc6/xxhash-3.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a3b19a42111c4057c1547a4a1396a53961dca576a0f6b82bfa88a2d1561764b2", size = 33576 },
+    { url = "https://files.pythonhosted.org/packages/07/f2/36d3310161db7f72efb4562aadde0ed429f1d0531782dd6345b12d2da527/xxhash-3.7.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8f4608a06e4d61b7a3425665a46d00e0579122e1a2fae97a0c52953a3aad9aa3", size = 31123 },
+    { url = "https://files.pythonhosted.org/packages/0d/3f/75937a5c69556ed213021e43cbedd84c8e0279d0d74e7d41a255d84ba4b1/xxhash-3.7.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:ad37c7792479e49cf96c1ab25517d7003fe0d93687a772ba19a097d235bbe41e", size = 196491 },
+    { url = "https://files.pythonhosted.org/packages/22/29/f10d7ff8c7a733d4403a43b9de18c8fabc005f98cec054644f04418659ee/xxhash-3.7.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc026e3b89d98e30a8288c95cb696e77d150b3f0fb7a51f73dcd49ee6b5577fa", size = 215793 },
+    { url = "https://files.pythonhosted.org/packages/8b/fd/778f60aa295f58907938f030a8b514611f391405614a525cccd2ffc00eb5/xxhash-3.7.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c9b31ab1f28b078a6a1ac1a54eb35e7d5390deddd56870d0be3a0a733d1c321c", size = 237993 },
+    { url = "https://files.pythonhosted.org/packages/70/f5/736db5de387b4a540e37a05b84b40dc58a1ce974bfd2b4e5754ce29b68c3/xxhash-3.7.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3bb5fd680c038fd5229e44e9c493782f90df9bef632fd0499d442374688ff70b", size = 214887 },
+    { url = "https://files.pythonhosted.org/packages/4d/aa/09a095f22fdb9a27fbb716841fbff52119721f9ca4261952d07a912f7839/xxhash-3.7.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:030c0fd688fce3569fbb49a2feefd4110cbb0b650186fb4610759ecfac677548", size = 448407 },
+    { url = "https://files.pythonhosted.org/packages/74/8a/b745efeeca9e34a91c26fdc97ad8514c43d5a81ac78565cba80a1353870a/xxhash-3.7.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b1bde10324f4c31812ae0d0502e92d916ae8917cad7209353f122b8b8f610c3", size = 196119 },
+    { url = "https://files.pythonhosted.org/packages/8a/5c/0cfceb024af90c191f665c7933b1f318ee234f4797858383bebd1881d52f/xxhash-3.7.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:503722d52a615f2604f5e7611de7d43878df010dc0053094ef91cb9a9ac3d987", size = 286751 },
+    { url = "https://files.pythonhosted.org/packages/0b/0a/0793e405dc3cf8f4ebe2c1acec1e4e4608cd9e7e50ea691dabbc2a95ccbb/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c72500a3b6d6c30ebfc135035bcace9eb5884f2dc220804efcaaba43e9f611dd", size = 212961 },
+    { url = "https://files.pythonhosted.org/packages/0c/7e/721118ffc63bfff94aa565bcf2555a820f9f4bdb0f001e0d609bdfad70de/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:43475925a766d01ca8cd9a857fd87f3d50406983c8506a4c07c4df12adcc867f", size = 243703 },
+    { url = "https://files.pythonhosted.org/packages/6e/18/16f6267160488b8276fd3d449d425712512add292ba545c1b6946bfdb7dd/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8d09dfd2ab135b985daf868b594315ebe11ad86cd9fea46e6c69f19b28f7d25a", size = 200894 },
+    { url = "https://files.pythonhosted.org/packages/2d/94/80ba841287fd97e3e9cac1d228788c8ef623746f570404961eec748ecb5c/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c50269d0055ac1faecfd559886d2cbe4b730de236585aba0e873f9d9dadbe585", size = 213357 },
+    { url = "https://files.pythonhosted.org/packages/a1/7e/106d4067130c59f1e18a55ffadcd876d8c68534883a1e02685b29d3d8153/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:1910df4756a5ab58cfad8744fc2d0f23926e3efcc346ee76e87b974abab922f4", size = 277600 },
+    { url = "https://files.pythonhosted.org/packages/c5/86/a081dd30da71d720b2612a792bfd55e45fa9a07ac76a0507f60487473c25/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:d006faf3b491957efcb433489be3c149efe4787b7063d5cddb8ddaefdc60e0c1", size = 416980 },
+    { url = "https://files.pythonhosted.org/packages/35/29/1a95221a029a3c1293773869e1ab47b07cbbdd82444a42809e8c60156626/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:abb65b4e947e958f7b3b0d71db3ce447d1bc5f37f5eab871ce7223bda8768a04", size = 193840 },
+    { url = "https://files.pythonhosted.org/packages/c5/e0/db909dd0823285de2286f67e10ee4d81e96ad35d7d8e964ecb07fccd8af9/xxhash-3.7.0-cp313-cp313t-win32.whl", hash = "sha256:178959906cb1716a1ce08e0d69c82886c70a15a6f2790fc084fdd146ca30cd49", size = 30966 },
+    { url = "https://files.pythonhosted.org/packages/7b/ff/d705b15b22f21ee106adce239cb65d35067a158c630b240270f09b17c2e6/xxhash-3.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2524a1e20d4c231d13b50f7cf39e44265b055669a64a7a4b9a2a44faa03f19b6", size = 31784 },
+    { url = "https://files.pythonhosted.org/packages/a2/1f/b2cf83c3638fd0588e0b17f22e5a9400bdfb1a3e3755324ac0aee2250b88/xxhash-3.7.0-cp313-cp313t-win_arm64.whl", hash = "sha256:37d994d0ffe81ef087bb330d392caa809bb5853c77e22ea3f71db024a0543dba", size = 27932 },
+    { url = "https://files.pythonhosted.org/packages/0e/cc/431db584f6fbb9312e40a173af027644e5580d39df1f73603cbb9dca4d6b/xxhash-3.7.0-cp314-cp314-android_24_arm64_v8a.whl", hash = "sha256:8c5fcfd806c335bfa2adf1cd0b3110a44fc7b6995c3a648c27489bae85801465", size = 36644 },
+    { url = "https://files.pythonhosted.org/packages/bc/01/255ec513e0a705d1f9a61413e78dfce4e3235203f0ed525a24c2b4b56345/xxhash-3.7.0-cp314-cp314-android_24_x86_64.whl", hash = "sha256:506a0b488f190f0a06769575e30caf71615c898ed93ab18b0dbcb6dec5c3713c", size = 35003 },
+    { url = "https://files.pythonhosted.org/packages/68/70/c55fc33c93445b44d8fc5a17b41ed99e3cebe92bcf8396809e63fc9a1165/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:ec68dbba21532c0173a9872298e65c89749f7c9d21538c3a78b5bb6105871568", size = 29655 },
+    { url = "https://files.pythonhosted.org/packages/c2/72/ff8de73df000d74467d12a59ce6d6e2b2a368b978d41ab7b1fba5ed442be/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:fa77e7ec1450d415d20129961814787c9abd9a07f98872f070b1fe96c5084611", size = 30664 },
+    { url = "https://files.pythonhosted.org/packages/b6/91/08416d9bd9bc3bf39d831abe8a5631ac2db5141dfd6fe81c3fe59a1f9264/xxhash-3.7.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:fe32736295ea38e43e7d9424053c8c47c9f64fecfc7c895fb3da9b30b131c9ee", size = 33317 },
+    { url = "https://files.pythonhosted.org/packages/0e/3b/86b1caa4dee10a99f4bf9521e623359341c5e50d05158fa10c275b2bd079/xxhash-3.7.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:ab9dd2c83c4bbd63e422181a76f13502d049d3ddcac9a1bdc29196263d692bb8", size = 33457 },
+    { url = "https://files.pythonhosted.org/packages/ed/38/98ea14ad1517e1461292a65906951458d520689782bfbae111050145bdba/xxhash-3.7.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3afec3a336a2286601a437cb07562ab0227685e6fbb9ec17e8c18457ff348ecf", size = 30894 },
+    { url = "https://files.pythonhosted.org/packages/61/a2/074654d0b893606541199993c7db70067d9fc63b748e0d60020a52a1bd36/xxhash-3.7.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:565df64437a9390f84465dcca33e7377114c7ede8d05cd2cf20081f831ea788e", size = 194409 },
+    { url = "https://files.pythonhosted.org/packages/e2/26/6d2a1afc468189f77ca28c32e1c83e1b9da1178231e05641dbc1b350e332/xxhash-3.7.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:12eca820a5d558633d423bf8bb78ce72a55394823f64089247f788a7e0ae691e", size = 213135 },
+    { url = "https://files.pythonhosted.org/packages/8e/0e/d8aecf95e09c42547453137be74d2f7b8b14e08f5177fa2fab6144a19061/xxhash-3.7.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f262b8f7599516567e070abf607b9af649052b2c4bd6f9be02b0cb41b7024805", size = 236379 },
+    { url = "https://files.pythonhosted.org/packages/f2/74/8140e8210536b3dd0cc816c4faaeb5ba6e63e8125ab25af4bcddd6a037b3/xxhash-3.7.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1598916cb197681e03e601901e4ab96a9a963de398c59d0964f8a6f44a2b361", size = 212447 },
+    { url = "https://files.pythonhosted.org/packages/a0/d2/462001d2903b4bee5a5689598a0a55e5e7cd1ac7f4247a5545cff10d3ebb/xxhash-3.7.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:322b2f0622230f526aeb1738149948a7ae357a9e2ceb1383c6fd1fdaecdafa16", size = 445660 },
+    { url = "https://files.pythonhosted.org/packages/23/09/2bd1ed7f8689b20e51727952cac8329d50c694dc32b2eba06ba5bc742b37/xxhash-3.7.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:24cc22070880cc57b830a65cde4e65fa884c6d9b28ae4803b5ee05911e7bafba", size = 194076 },
+    { url = "https://files.pythonhosted.org/packages/c9/6e/692302cd0a5f4ac4e6289f37fa888dc2e1e07750b68fe3e4bfe939b8cea3/xxhash-3.7.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb5a888a968b2434abf9ecda357b5d43f10d7b5a6da6fdbbe036208473aff0e2", size = 284990 },
+    { url = "https://files.pythonhosted.org/packages/05/d9/e54b159b3d9df7999d2a7c676ce7b323d1b5588a64f8f51ed8172567bd87/xxhash-3.7.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a999771ff97bec27d18341be4f3a36b163bb1ac41ec17bef6d2dabd84acd33c7", size = 210590 },
+    { url = "https://files.pythonhosted.org/packages/50/93/0e0df1a3a196ced4ca71de76d65ead25d8e87bbfb87b64306ea47a40c00d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:ed4a6efe2dee1655adb73e7ad40c6aa955a6892422b1e3b95de6a34de56e3cbb", size = 241442 },
+    { url = "https://files.pythonhosted.org/packages/9a/a9/d917a7a814e90b218f8a0d37967105eea91bf752c3303683c99a1f7bfc1f/xxhash-3.7.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9fd17f14ac0faa12126c2f9ca774a8cf342957265ec3c8669c144e5e6cdb478c", size = 198356 },
+    { url = "https://files.pythonhosted.org/packages/89/5e/f2ba1877c39469abbefc72991d6ebdcbd4c0880db01ae8cb1f553b0c537d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:05fd1254268c59b5cb2a029dfc204275e9fc52de2913f1e53aa8d01442c96b4d", size = 210898 },
+    { url = "https://files.pythonhosted.org/packages/90/c6/be56b58e73de531f39a10de1355bb77ceb663900dc4bf2d6d3002a9c3f9e/xxhash-3.7.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:a2eae53197c6276d5b317f75a1be226bbf440c20b58bf525f36b5d0e1f657ca6", size = 275519 },
+    { url = "https://files.pythonhosted.org/packages/92/e2/17ddc85d5765b9c709f192009ed8f5a1fc876f4eb35bba7c307b5b1169f9/xxhash-3.7.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:bfe6f92e3522dcbe8c4281efd74fa7542a336cb00b0e3272c4ec0edabeaeaf67", size = 414191 },
+    { url = "https://files.pythonhosted.org/packages/9c/42/85f5b79f4bf1ec7ba052491164adfd4f4e9519f5dc7246de4fbd64a1bd56/xxhash-3.7.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7ab9a49c410d8c6c786ab99e79c529938d894c01433130353dd0fe999111077a", size = 191604 },
+    { url = "https://files.pythonhosted.org/packages/b8/d0/6127b623aa4cca18d8b7743592b048d689fd6c6e37ff26a22cddf6cd9d7f/xxhash-3.7.0-cp314-cp314-win32.whl", hash = "sha256:040ea63668f9185b92bc74942df09c7e65703deed71431333678fc6e739a9955", size = 31271 },
+    { url = "https://files.pythonhosted.org/packages/64/4f/44fc4788568004c43921701cbc127f48218a1eede2c9aea231115323564d/xxhash-3.7.0-cp314-cp314-win_amd64.whl", hash = "sha256:2a61e2a3fb23c892496d587b470dee7fa1b58b248a187719c65ea8e94ec13257", size = 32284 },
+    { url = "https://files.pythonhosted.org/packages/6d/77/18bb895eb60a49453d16e17d67990e5caff557c78eafc90ad4e2eabf4570/xxhash-3.7.0-cp314-cp314-win_arm64.whl", hash = "sha256:c7741c7524961d8c0cb4d4c21b28957ff731a3fd5b5cd8b856dc80a40e9e5acc", size = 28701 },
+    { url = "https://files.pythonhosted.org/packages/45/a0/46f72244570c550fbbb7db1ef554183dd5ebe9136385f30e032b781ae8f6/xxhash-3.7.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:fc84bf7aa7592f31ec63a3e7b11d624f468a3f19f5238cec7282a42e838ab1d7", size = 33646 },
+    { url = "https://files.pythonhosted.org/packages/4a/3a/453846a7eceea11e75def361eed01ec6a0205b9822c19927ed364ccae7cc/xxhash-3.7.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9f1563fdc8abfc389748e6932c7e4e99c89a53e4ec37d4563c24fc06f5e5644b", size = 31125 },
+    { url = "https://files.pythonhosted.org/packages/bd/3e/49434aba738885d512f9e486db1bdd19db28dfa40372b56da26ef7a4e738/xxhash-3.7.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:2d415f18becf6f153046ab6adc97da77e3643a0ee205dae61c4012604113a020", size = 196633 },
+    { url = "https://files.pythonhosted.org/packages/a4/e9/006cb6127baeb9f8abe6d15e62faa01349f09b34e2bfd65175b2422d026b/xxhash-3.7.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bb16aa13ed175bc9be5c2491ba031b85a9b51c4ed90e0b3d4ebe63cf3fb54f8e", size = 215899 },
+    { url = "https://files.pythonhosted.org/packages/27/e4/cc57d72e66df0ae29b914335f1c6dcf61e8f3746ddf0ae3c471aa4f15e00/xxhash-3.7.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f9fd595f1e5941b3d7863e4774e4b30caa6731fc34b9277da032295aa5656ee5", size = 238116 },
+    { url = "https://files.pythonhosted.org/packages/af/78/3531d4a3fd8a0038cc6be1f265a69c1b3587f557a10b677dd736de2202c1/xxhash-3.7.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1295325c5a98d552333fa53dc2b026b0ef0ec9c8e73ca3a952990b4c7d65d459", size = 215012 },
+    { url = "https://files.pythonhosted.org/packages/b4/f6/259fb1eaaec921f59b17203b0daee69829761226d3b980d5191d7723dd83/xxhash-3.7.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3573a651d146912da9daa9e29e5fbc45994420daaa9ef1e2fa5823e1dc485513", size = 448534 },
+    { url = "https://files.pythonhosted.org/packages/7b/16/a66d0eaf6a7e68532c07714361ddc904c663ec940f3b028c1ae4a21a7b9d/xxhash-3.7.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ec1e080a3d02d94ea9335bfab0e3374b877e25411422c18f51a943fa4b46381", size = 196217 },
+    { url = "https://files.pythonhosted.org/packages/8d/ef/d2efc7fc51756dc52509109d1a25cefc859d74bc4b19a167b12dbd8c2786/xxhash-3.7.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:84415265192072d8638a3afc3c1bc5995e310570cd9acb54dc46d3939e364fe0", size = 286906 },
+    { url = "https://files.pythonhosted.org/packages/fc/67/25decd1d4a4018582ec4db2a868a2b7e40640f4adb20dfeb19ac923aa825/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d4dea659b57443989ef32f4295104fd6912c73d0bf26d1d148bb88a9f159b02", size = 213057 },
+    { url = "https://files.pythonhosted.org/packages/0d/5d/17651eb29d06786cdc40c60ae3d27d645aa5d61d2eca6237a7ba0b94789b/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:05ece0fe4d9c9c2728912d1981ae1566cfc83a011571b24732cbf76e1fb70dca", size = 243886 },
+    { url = "https://files.pythonhosted.org/packages/8a/d4/174d9cf7502243d586e6a9ae842b1ae23026620995114f85f1380e588bc9/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:fd880353cf1ffaf321bc18dd663e111976dbd0d3bbd8a66d58d2b470dfa7f396", size = 201015 },
+    { url = "https://files.pythonhosted.org/packages/91/8c/2254e2d06c3ac5e6fe22eaf3da791b87ea823ae9f2c17b4af66755c5752d/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:4e15cc9e2817f6481160f930c62842b3ff419e20e13072bcbab12230943092bc", size = 213457 },
+    { url = "https://files.pythonhosted.org/packages/79/a2/e3daa762545921173e3360f3b4ff7fc63c2d27359f7230ec1a7a74e117f6/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:90b9d1a8bd37d768ffc92a1f651ec69afc532a96fa1ac2ea7abbed5d630b3237", size = 277738 },
+    { url = "https://files.pythonhosted.org/packages/e1/4c/e186da2c46b87f5204640e008d42730bf3c1ee9f0efb71ae1ebcdfeac681/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:157c49475b34ecea8809e51123d9769a534e139d1247942f7a4bc67710bb2533", size = 417127 },
+    { url = "https://files.pythonhosted.org/packages/17/28/3798e15007a3712d0da3d3fe70f8e11916569858b5cc371053bc26270832/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5a6ddec83325685e729ca119d1f5c518ec39294212ecd770e60693cdc5f7eb79", size = 193962 },
+    { url = "https://files.pythonhosted.org/packages/ad/95/a26baa93b5241fd7630998816a4ec47a5a0bad193b3f8fc8f3593e1a4a67/xxhash-3.7.0-cp314-cp314t-win32.whl", hash = "sha256:a04a6cab47e2166435aaf5b9e5ee41d1532cc8300efdef87f2a4d0acb7db19ed", size = 31643 },
+    { url = "https://files.pythonhosted.org/packages/44/36/5454f13c447e395f9b06a3e91274c59f503d31fad84e1836efe3bdb71f6a/xxhash-3.7.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8653dd7c2eda020545bb2c71c7f7039b53fe7434d0fc1a0a9deb79ab3f1a4fc1", size = 32522 },
+    { url = "https://files.pythonhosted.org/packages/74/35/698e7e3ff38e22992ea24870a511d8762474fb6783627a2910ff22a185c2/xxhash-3.7.0-cp314-cp314t-win_arm64.whl", hash = "sha256:468f0fc114faaa4b36699f8e328bbc3bb11dc418ba94ac52c26dd736d4b6c637", size = 28807 },
+]
+
+[[package]]
+name = "yarl"
+version = "1.23.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "multidict" },
+    { name = "propcache" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/23/6e/beb1beec874a72f23815c1434518bfc4ed2175065173fb138c3705f658d4/yarl-1.23.0.tar.gz", hash = "sha256:53b1ea6ca88ebd4420379c330aea57e258408dd0df9af0992e5de2078dc9f5d5", size = 194676 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/8a/94615bc31022f711add374097ad4144d569e95ff3c38d39215d07ac153a0/yarl-1.23.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:1932b6b8bba8d0160a9d1078aae5838a66039e8832d41d2992daa9a3a08f7860", size = 124737 },
+    { url = "https://files.pythonhosted.org/packages/e3/6f/c6554045d59d64052698add01226bc867b52fe4a12373415d7991fdca95d/yarl-1.23.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:411225bae281f114067578891bc75534cfb3d92a3b4dfef7a6ca78ba354e6069", size = 87029 },
+    { url = "https://files.pythonhosted.org/packages/19/2a/725ecc166d53438bc88f76822ed4b1e3b10756e790bafd7b523fe97c322d/yarl-1.23.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:13a563739ae600a631c36ce096615fe307f131344588b0bc0daec108cdb47b25", size = 86310 },
+    { url = "https://files.pythonhosted.org/packages/99/30/58260ed98e6ff7f90ba84442c1ddd758c9170d70327394a6227b310cd60f/yarl-1.23.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cbf44c5cb4a7633d078788e1b56387e3d3cf2b8139a3be38040b22d6c3221c8", size = 97587 },
+    { url = "https://files.pythonhosted.org/packages/76/0a/8b08aac08b50682e65759f7f8dde98ae8168f72487e7357a5d684c581ef9/yarl-1.23.0-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:53ad387048f6f09a8969631e4de3f1bf70c50e93545d64af4f751b2498755072", size = 92528 },
+    { url = "https://files.pythonhosted.org/packages/52/07/0b7179101fe5f8385ec6c6bb5d0cb9f76bd9fb4a769591ab6fb5cdbfc69a/yarl-1.23.0-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4a59ba56f340334766f3a4442e0efd0af895fae9e2b204741ef885c446b3a1a8", size = 105339 },
+    { url = "https://files.pythonhosted.org/packages/d3/8a/36d82869ab5ec829ca8574dfcb92b51286fcfb1e9c7a73659616362dc880/yarl-1.23.0-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:803a3c3ce4acc62eaf01eaca1208dcf0783025ef27572c3336502b9c232005e7", size = 105061 },
+    { url = "https://files.pythonhosted.org/packages/66/3e/868e5c3364b6cee19ff3e1a122194fa4ce51def02c61023970442162859e/yarl-1.23.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a3d2bff8f37f8d0f96c7ec554d16945050d54462d6e95414babaa18bfafc7f51", size = 100132 },
+    { url = "https://files.pythonhosted.org/packages/cf/26/9c89acf82f08a52cb52d6d39454f8d18af15f9d386a23795389d1d423823/yarl-1.23.0-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c75eb09e8d55bceb4367e83496ff8ef2bc7ea6960efb38e978e8073ea59ecb67", size = 99289 },
+    { url = "https://files.pythonhosted.org/packages/6f/54/5b0db00d2cb056922356104468019c0a132e89c8d3ab67d8ede9f4483d2a/yarl-1.23.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:877b0738624280e34c55680d6054a307aa94f7d52fa0e3034a9cc6e790871da7", size = 96950 },
+    { url = "https://files.pythonhosted.org/packages/f6/40/10fa93811fd439341fad7e0718a86aca0de9548023bbb403668d6555acab/yarl-1.23.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:b5405bb8f0e783a988172993cfc627e4d9d00432d6bbac65a923041edacf997d", size = 93960 },
+    { url = "https://files.pythonhosted.org/packages/bc/d2/8ae2e6cd77d0805f4526e30ec43b6f9a3dfc542d401ac4990d178e4bf0cf/yarl-1.23.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:1c3a3598a832590c5a3ce56ab5576361b5688c12cb1d39429cf5dba30b510760", size = 104703 },
+    { url = "https://files.pythonhosted.org/packages/2f/0c/b3ceacf82c3fe21183ce35fa2acf5320af003d52bc1fcf5915077681142e/yarl-1.23.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:8419ebd326430d1cbb7efb5292330a2cf39114e82df5cc3d83c9a0d5ebeaf2f2", size = 98325 },
+    { url = "https://files.pythonhosted.org/packages/9d/e0/12900edd28bdab91a69bd2554b85ad7b151f64e8b521fe16f9ad2f56477a/yarl-1.23.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:be61f6fff406ca40e3b1d84716fde398fc08bc63dd96d15f3a14230a0973ed86", size = 105067 },
+    { url = "https://files.pythonhosted.org/packages/15/61/74bb1182cf79c9bbe4eb6b1f14a57a22d7a0be5e9cedf8e2d5c2086474c3/yarl-1.23.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3ceb13c5c858d01321b5d9bb65e4cf37a92169ea470b70fec6f236b2c9dd7e34", size = 100285 },
+    { url = "https://files.pythonhosted.org/packages/69/7f/cd5ef733f2550de6241bd8bd8c3febc78158b9d75f197d9c7baa113436af/yarl-1.23.0-cp312-cp312-win32.whl", hash = "sha256:fffc45637bcd6538de8b85f51e3df3223e4ad89bccbfca0481c08c7fc8b7ed7d", size = 82359 },
+    { url = "https://files.pythonhosted.org/packages/f5/be/25216a49daeeb7af2bec0db22d5e7df08ed1d7c9f65d78b14f3b74fd72fc/yarl-1.23.0-cp312-cp312-win_amd64.whl", hash = "sha256:f69f57305656a4852f2a7203efc661d8c042e6cc67f7acd97d8667fb448a426e", size = 87674 },
+    { url = "https://files.pythonhosted.org/packages/d2/35/aeab955d6c425b227d5b7247eafb24f2653fedc32f95373a001af5dfeb9e/yarl-1.23.0-cp312-cp312-win_arm64.whl", hash = "sha256:6e87a6e8735b44816e7db0b2fbc9686932df473c826b0d9743148432e10bb9b9", size = 81879 },
+    { url = "https://files.pythonhosted.org/packages/9a/4b/a0a6e5d0ee8a2f3a373ddef8a4097d74ac901ac363eea1440464ccbe0898/yarl-1.23.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:16c6994ac35c3e74fb0ae93323bf8b9c2a9088d55946109489667c510a7d010e", size = 123796 },
+    { url = "https://files.pythonhosted.org/packages/67/b6/8925d68af039b835ae876db5838e82e76ec87b9782ecc97e192b809c4831/yarl-1.23.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4a42e651629dafb64fd5b0286a3580613702b5809ad3f24934ea87595804f2c5", size = 86547 },
+    { url = "https://files.pythonhosted.org/packages/ae/50/06d511cc4b8e0360d3c94af051a768e84b755c5eb031b12adaaab6dec6e5/yarl-1.23.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7c6b9461a2a8b47c65eef63bb1c76a4f1c119618ffa99ea79bc5bb1e46c5821b", size = 85854 },
+    { url = "https://files.pythonhosted.org/packages/c4/f4/4e30b250927ffdab4db70da08b9b8d2194d7c7b400167b8fbeca1e4701ca/yarl-1.23.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2569b67d616eab450d262ca7cb9f9e19d2f718c70a8b88712859359d0ab17035", size = 98351 },
+    { url = "https://files.pythonhosted.org/packages/86/fc/4118c5671ea948208bdb1492d8b76bdf1453d3e73df051f939f563e7dcc5/yarl-1.23.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e9d9a4d06d3481eab79803beb4d9bd6f6a8e781ec078ac70d7ef2dcc29d1bea5", size = 92711 },
+    { url = "https://files.pythonhosted.org/packages/56/11/1ed91d42bd9e73c13dc9e7eb0dd92298d75e7ac4dd7f046ad0c472e231cd/yarl-1.23.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f514f6474e04179d3d33175ed3f3e31434d3130d42ec153540d5b157deefd735", size = 106014 },
+    { url = "https://files.pythonhosted.org/packages/ce/c9/74e44e056a23fbc33aca71779ef450ca648a5bc472bdad7a82339918f818/yarl-1.23.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fda207c815b253e34f7e1909840fd14299567b1c0eb4908f8c2ce01a41265401", size = 105557 },
+    { url = "https://files.pythonhosted.org/packages/66/fe/b1e10b08d287f518994f1e2ff9b6d26f0adeecd8dd7d533b01bab29a3eda/yarl-1.23.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34b6cf500e61c90f305094911f9acc9c86da1a05a7a3f5be9f68817043f486e4", size = 101559 },
+    { url = "https://files.pythonhosted.org/packages/72/59/c5b8d94b14e3d3c2a9c20cb100119fd534ab5a14b93673ab4cc4a4141ea5/yarl-1.23.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d7504f2b476d21653e4d143f44a175f7f751cd41233525312696c76aa3dbb23f", size = 100502 },
+    { url = "https://files.pythonhosted.org/packages/77/4f/96976cb54cbfc5c9fd73ed4c51804f92f209481d1fb190981c0f8a07a1d7/yarl-1.23.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:578110dd426f0d209d1509244e6d4a3f1a3e9077655d98c5f22583d63252a08a", size = 98027 },
+    { url = "https://files.pythonhosted.org/packages/63/6e/904c4f476471afdbad6b7e5b70362fb5810e35cd7466529a97322b6f5556/yarl-1.23.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:609d3614d78d74ebe35f54953c5bbd2ac647a7ddb9c30a5d877580f5e86b22f2", size = 95369 },
+    { url = "https://files.pythonhosted.org/packages/9d/40/acfcdb3b5f9d68ef499e39e04d25e141fe90661f9d54114556cf83be8353/yarl-1.23.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:4966242ec68afc74c122f8459abd597afd7d8a60dc93d695c1334c5fd25f762f", size = 105565 },
+    { url = "https://files.pythonhosted.org/packages/5e/c6/31e28f3a6ba2869c43d124f37ea5260cac9c9281df803c354b31f4dd1f3c/yarl-1.23.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:e0fd068364a6759bc794459f0a735ab151d11304346332489c7972bacbe9e72b", size = 99813 },
+    { url = "https://files.pythonhosted.org/packages/08/1f/6f65f59e72d54aa467119b63fc0b0b1762eff0232db1f4720cd89e2f4a17/yarl-1.23.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:39004f0ad156da43e86aa71f44e033de68a44e5a31fc53507b36dd253970054a", size = 105632 },
+    { url = "https://files.pythonhosted.org/packages/a3/c4/18b178a69935f9e7a338127d5b77d868fdc0f0e49becd286d51b3a18c61d/yarl-1.23.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e5723c01a56c5028c807c701aa66722916d2747ad737a046853f6c46f4875543", size = 101895 },
+    { url = "https://files.pythonhosted.org/packages/8f/54/f5b870b5505663911dba950a8e4776a0dbd51c9c54c0ae88e823e4b874a0/yarl-1.23.0-cp313-cp313-win32.whl", hash = "sha256:1b6b572edd95b4fa8df75de10b04bc81acc87c1c7d16bcdd2035b09d30acc957", size = 82356 },
+    { url = "https://files.pythonhosted.org/packages/7a/84/266e8da36879c6edcd37b02b547e2d9ecdfea776be49598e75696e3316e1/yarl-1.23.0-cp313-cp313-win_amd64.whl", hash = "sha256:baaf55442359053c7d62f6f8413a62adba3205119bcb6f49594894d8be47e5e3", size = 87515 },
+    { url = "https://files.pythonhosted.org/packages/00/fd/7e1c66efad35e1649114fa13f17485f62881ad58edeeb7f49f8c5e748bf9/yarl-1.23.0-cp313-cp313-win_arm64.whl", hash = "sha256:fb4948814a2a98e3912505f09c9e7493b1506226afb1f881825368d6fb776ee3", size = 81785 },
+    { url = "https://files.pythonhosted.org/packages/9c/fc/119dd07004f17ea43bb91e3ece6587759edd7519d6b086d16bfbd3319982/yarl-1.23.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:aecfed0b41aa72b7881712c65cf764e39ce2ec352324f5e0837c7048d9e6daaa", size = 130719 },
+    { url = "https://files.pythonhosted.org/packages/e6/0d/9f2348502fbb3af409e8f47730282cd6bc80dec6630c1e06374d882d6eb2/yarl-1.23.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a41bcf68efd19073376eb8cf948b8d9be0af26256403e512bb18f3966f1f9120", size = 89690 },
+    { url = "https://files.pythonhosted.org/packages/50/93/e88f3c80971b42cfc83f50a51b9d165a1dbf154b97005f2994a79f212a07/yarl-1.23.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cde9a2ecd91668bcb7f077c4966d8ceddb60af01b52e6e3e2680e4cf00ad1a59", size = 89851 },
+    { url = "https://files.pythonhosted.org/packages/1c/07/61c9dd8ba8f86473263b4036f70fb594c09e99c0d9737a799dfd8bc85651/yarl-1.23.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5023346c4ee7992febc0068e7593de5fa2bf611848c08404b35ebbb76b1b0512", size = 95874 },
+    { url = "https://files.pythonhosted.org/packages/9e/e9/f9ff8ceefba599eac6abddcfb0b3bee9b9e636e96dbf54342a8577252379/yarl-1.23.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d1009abedb49ae95b136a8904a3f71b342f849ffeced2d3747bf29caeda218c4", size = 88710 },
+    { url = "https://files.pythonhosted.org/packages/eb/78/0231bfcc5d4c8eec220bc2f9ef82cb4566192ea867a7c5b4148f44f6cbcd/yarl-1.23.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a8d00f29b42f534cc8aa3931cfe773b13b23e561e10d2b26f27a8d309b0e82a1", size = 101033 },
+    { url = "https://files.pythonhosted.org/packages/cd/9b/30ea5239a61786f18fd25797151a17fbb3be176977187a48d541b5447dd4/yarl-1.23.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:95451e6ce06c3e104556d73b559f5da6c34a069b6b62946d3ad66afcd51642ea", size = 100817 },
+    { url = "https://files.pythonhosted.org/packages/62/e2/a4980481071791bc83bce2b7a1a1f7adcabfa366007518b4b845e92eeee3/yarl-1.23.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:531ef597132086b6cf96faa7c6c1dcd0361dd5f1694e5cc30375907b9b7d3ea9", size = 97482 },
+    { url = "https://files.pythonhosted.org/packages/e5/1e/304a00cf5f6100414c4b5a01fc7ff9ee724b62158a08df2f8170dfc72a2d/yarl-1.23.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:88f9fb0116fbfcefcab70f85cf4b74a2b6ce5d199c41345296f49d974ddb4123", size = 95949 },
+    { url = "https://files.pythonhosted.org/packages/68/03/093f4055ed4cae649ac53bca3d180bd37102e9e11d048588e9ab0c0108d0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:e7b0460976dc75cb87ad9cc1f9899a4b97751e7d4e77ab840fc9b6d377b8fd24", size = 95839 },
+    { url = "https://files.pythonhosted.org/packages/b9/28/4c75ebb108f322aa8f917ae10a8ffa4f07cae10a8a627b64e578617df6a0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:115136c4a426f9da976187d238e84139ff6b51a20839aa6e3720cd1026d768de", size = 90696 },
+    { url = "https://files.pythonhosted.org/packages/23/9c/42c2e2dd91c1a570402f51bdf066bfdb1241c2240ba001967bad778e77b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:ead11956716a940c1abc816b7df3fa2b84d06eaed8832ca32f5c5e058c65506b", size = 100865 },
+    { url = "https://files.pythonhosted.org/packages/74/05/1bcd60a8a0a914d462c305137246b6f9d167628d73568505fce3f1cb2e65/yarl-1.23.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:fe8f8f5e70e6dbdfca9882cd9deaac058729bcf323cf7a58660901e55c9c94f6", size = 96234 },
+    { url = "https://files.pythonhosted.org/packages/90/b2/f52381aac396d6778ce516b7bc149c79e65bfc068b5de2857ab69eeea3b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:a0e317df055958a0c1e79e5d2aa5a5eaa4a6d05a20d4b0c9c3f48918139c9fc6", size = 100295 },
+    { url = "https://files.pythonhosted.org/packages/e5/e8/638bae5bbf1113a659b2435d8895474598afe38b4a837103764f603aba56/yarl-1.23.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6f0fd84de0c957b2d280143522c4f91a73aada1923caee763e24a2b3fda9f8a5", size = 97784 },
+    { url = "https://files.pythonhosted.org/packages/80/25/a3892b46182c586c202629fc2159aa13975d3741d52ebd7347fd501d48d5/yarl-1.23.0-cp313-cp313t-win32.whl", hash = "sha256:93a784271881035ab4406a172edb0faecb6e7d00f4b53dc2f55919d6c9688595", size = 88313 },
+    { url = "https://files.pythonhosted.org/packages/43/68/8c5b36aa5178900b37387937bc2c2fe0e9505537f713495472dcf6f6fccc/yarl-1.23.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dd00607bffbf30250fe108065f07453ec124dbf223420f57f5e749b04295e090", size = 94932 },
+    { url = "https://files.pythonhosted.org/packages/c6/cc/d79ba8292f51f81f4dc533a8ccfb9fc6992cabf0998ed3245de7589dc07c/yarl-1.23.0-cp313-cp313t-win_arm64.whl", hash = "sha256:ac09d42f48f80c9ee1635b2fcaa819496a44502737660d3c0f2ade7526d29144", size = 84786 },
+    { url = "https://files.pythonhosted.org/packages/90/98/b85a038d65d1b92c3903ab89444f48d3cee490a883477b716d7a24b1a78c/yarl-1.23.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:21d1b7305a71a15b4794b5ff22e8eef96ff4a6d7f9657155e5aa419444b28912", size = 124455 },
+    { url = "https://files.pythonhosted.org/packages/39/54/bc2b45559f86543d163b6e294417a107bb87557609007c007ad889afec18/yarl-1.23.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:85610b4f27f69984932a7abbe52703688de3724d9f72bceb1cca667deff27474", size = 86752 },
+    { url = "https://files.pythonhosted.org/packages/24/f9/e8242b68362bffe6fb536c8db5076861466fc780f0f1b479fc4ffbebb128/yarl-1.23.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:23f371bd662cf44a7630d4d113101eafc0cfa7518a2760d20760b26021454719", size = 86291 },
+    { url = "https://files.pythonhosted.org/packages/ea/d8/d1cb2378c81dd729e98c716582b1ccb08357e8488e4c24714658cc6630e8/yarl-1.23.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4a80f77dc1acaaa61f0934176fccca7096d9b1ff08c8ba9cddf5ae034a24319", size = 99026 },
+    { url = "https://files.pythonhosted.org/packages/0a/ff/7196790538f31debe3341283b5b0707e7feb947620fc5e8236ef28d44f72/yarl-1.23.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:bd654fad46d8d9e823afbb4f87c79160b5a374ed1ff5bde24e542e6ba8f41434", size = 92355 },
+    { url = "https://files.pythonhosted.org/packages/c1/56/25d58c3eddde825890a5fe6aa1866228377354a3c39262235234ab5f616b/yarl-1.23.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:682bae25f0a0dd23a056739f23a134db9f52a63e2afd6bfb37ddc76292bbd723", size = 106417 },
+    { url = "https://files.pythonhosted.org/packages/51/8a/882c0e7bc8277eb895b31bce0138f51a1ba551fc2e1ec6753ffc1e7c1377/yarl-1.23.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a82836cab5f197a0514235aaf7ffccdc886ccdaa2324bc0aafdd4ae898103039", size = 106422 },
+    { url = "https://files.pythonhosted.org/packages/42/2b/fef67d616931055bf3d6764885990a3ac647d68734a2d6a9e1d13de437a2/yarl-1.23.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1c57676bdedc94cd3bc37724cf6f8cd2779f02f6aba48de45feca073e714fe52", size = 101915 },
+    { url = "https://files.pythonhosted.org/packages/18/6a/530e16aebce27c5937920f3431c628a29a4b6b430fab3fd1c117b26ff3f6/yarl-1.23.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c7f8dc16c498ff06497c015642333219871effba93e4a2e8604a06264aca5c5c", size = 100690 },
+    { url = "https://files.pythonhosted.org/packages/88/08/93749219179a45e27b036e03260fda05190b911de8e18225c294ac95bbc9/yarl-1.23.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5ee586fb17ff8f90c91cf73c6108a434b02d69925f44f5f8e0d7f2f260607eae", size = 98750 },
+    { url = "https://files.pythonhosted.org/packages/d9/cf/ea424a004969f5d81a362110a6ac1496d79efdc6d50c2c4b2e3ea0fc2519/yarl-1.23.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:17235362f580149742739cc3828b80e24029d08cbb9c4bda0242c7b5bc610a8e", size = 94685 },
+    { url = "https://files.pythonhosted.org/packages/e2/b7/14341481fe568e2b0408bcf1484c652accafe06a0ade9387b5d3fd9df446/yarl-1.23.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:0793e2bd0cf14234983bbb371591e6bea9e876ddf6896cdcc93450996b0b5c85", size = 106009 },
+    { url = "https://files.pythonhosted.org/packages/0a/e6/5c744a9b54f4e8007ad35bce96fbc9218338e84812d36f3390cea616881a/yarl-1.23.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:3650dc2480f94f7116c364096bc84b1d602f44224ef7d5c7208425915c0475dd", size = 100033 },
+    { url = "https://files.pythonhosted.org/packages/0c/23/e3bfc188d0b400f025bc49d99793d02c9abe15752138dcc27e4eaf0c4a9e/yarl-1.23.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f40e782d49630ad384db66d4d8b73ff4f1b8955dc12e26b09a3e3af064b3b9d6", size = 106483 },
+    { url = "https://files.pythonhosted.org/packages/72/42/f0505f949a90b3f8b7a363d6cbdf398f6e6c58946d85c6d3a3bc70595b26/yarl-1.23.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:94f8575fbdf81749008d980c17796097e645574a3b8c28ee313931068dad14fe", size = 102175 },
+    { url = "https://files.pythonhosted.org/packages/aa/65/b39290f1d892a9dd671d1c722014ca062a9c35d60885d57e5375db0404b5/yarl-1.23.0-cp314-cp314-win32.whl", hash = "sha256:c8aa34a5c864db1087d911a0b902d60d203ea3607d91f615acd3f3108ac32169", size = 83871 },
+    { url = "https://files.pythonhosted.org/packages/a9/5b/9b92f54c784c26e2a422e55a8d2607ab15b7ea3349e28359282f84f01d43/yarl-1.23.0-cp314-cp314-win_amd64.whl", hash = "sha256:63e92247f383c85ab00dd0091e8c3fa331a96e865459f5ee80353c70a4a42d70", size = 89093 },
+    { url = "https://files.pythonhosted.org/packages/e0/7d/8a84dc9381fd4412d5e7ff04926f9865f6372b4c2fd91e10092e65d29eb8/yarl-1.23.0-cp314-cp314-win_arm64.whl", hash = "sha256:70efd20be968c76ece7baa8dafe04c5be06abc57f754d6f36f3741f7aa7a208e", size = 83384 },
+    { url = "https://files.pythonhosted.org/packages/dd/8d/d2fad34b1c08aa161b74394183daa7d800141aaaee207317e82c790b418d/yarl-1.23.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:9a18d6f9359e45722c064c97464ec883eb0e0366d33eda61cb19a244bf222679", size = 131019 },
+    { url = "https://files.pythonhosted.org/packages/19/ff/33009a39d3ccf4b94d7d7880dfe17fb5816c5a4fe0096d9b56abceea9ac7/yarl-1.23.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:2803ed8b21ca47a43da80a6fd1ed3019d30061f7061daa35ac54f63933409412", size = 89894 },
+    { url = "https://files.pythonhosted.org/packages/0c/f1/dab7ac5e7306fb79c0190766a3c00b4cb8d09a1f390ded68c85a5934faf5/yarl-1.23.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:394906945aa8b19fc14a61cf69743a868bb8c465efe85eee687109cc540b98f4", size = 89979 },
+    { url = "https://files.pythonhosted.org/packages/aa/b1/08e95f3caee1fad6e65017b9f26c1d79877b502622d60e517de01e72f95d/yarl-1.23.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:71d006bee8397a4a89f469b8deb22469fe7508132d3c17fa6ed871e79832691c", size = 95943 },
+    { url = "https://files.pythonhosted.org/packages/c0/cc/6409f9018864a6aa186c61175b977131f373f1988e198e031236916e87e4/yarl-1.23.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:62694e275c93d54f7ccedcfef57d42761b2aad5234b6be1f3e3026cae4001cd4", size = 88786 },
+    { url = "https://files.pythonhosted.org/packages/76/40/cc22d1d7714b717fde2006fad2ced5efe5580606cb059ae42117542122f3/yarl-1.23.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31de1613658308efdb21ada98cbc86a97c181aa050ba22a808120bb5be3ab94", size = 101307 },
+    { url = "https://files.pythonhosted.org/packages/8f/0d/476c38e85ddb4c6ec6b20b815bdd779aa386a013f3d8b85516feee55c8dc/yarl-1.23.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fb1e8b8d66c278b21d13b0a7ca22c41dd757a7c209c6b12c313e445c31dd3b28", size = 100904 },
+    { url = "https://files.pythonhosted.org/packages/72/32/0abe4a76d59adf2081dcb0397168553ece4616ada1c54d1c49d8936c74f8/yarl-1.23.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50f9d8d531dfb767c565f348f33dd5139a6c43f5cbdf3f67da40d54241df93f6", size = 97728 },
+    { url = "https://files.pythonhosted.org/packages/b7/35/7b30f4810fba112f60f5a43237545867504e15b1c7647a785fbaf588fac2/yarl-1.23.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:575aa4405a656e61a540f4a80eaa5260f2a38fff7bfdc4b5f611840d76e9e277", size = 95964 },
+    { url = "https://files.pythonhosted.org/packages/2d/86/ed7a73ab85ef00e8bb70b0cb5421d8a2a625b81a333941a469a6f4022828/yarl-1.23.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:041b1a4cefacf65840b4e295c6985f334ba83c30607441ae3cf206a0eed1a2e4", size = 95882 },
+    { url = "https://files.pythonhosted.org/packages/19/90/d56967f61a29d8498efb7afb651e0b2b422a1e9b47b0ab5f4e40a19b699b/yarl-1.23.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:d38c1e8231722c4ce40d7593f28d92b5fc72f3e9774fe73d7e800ec32299f63a", size = 90797 },
+    { url = "https://files.pythonhosted.org/packages/72/00/8b8f76909259f56647adb1011d7ed8b321bcf97e464515c65016a47ecdf0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:d53834e23c015ee83a99377db6e5e37d8484f333edb03bd15b4bc312cc7254fb", size = 101023 },
+    { url = "https://files.pythonhosted.org/packages/ac/e2/cab11b126fb7d440281b7df8e9ddbe4851e70a4dde47a202b6642586b8d9/yarl-1.23.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2e27c8841126e017dd2a054a95771569e6070b9ee1b133366d8b31beb5018a41", size = 96227 },
+    { url = "https://files.pythonhosted.org/packages/c2/9b/2c893e16bfc50e6b2edf76c1a9eb6cb0c744346197e74c65e99ad8d634d0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:76855800ac56f878847a09ce6dba727c93ca2d89c9e9d63002d26b916810b0a2", size = 100302 },
+    { url = "https://files.pythonhosted.org/packages/28/ec/5498c4e3a6d5f1003beb23405671c2eb9cdbf3067d1c80f15eeafe301010/yarl-1.23.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e09fd068c2e169a7070d83d3bde728a4d48de0549f975290be3c108c02e499b4", size = 98202 },
+    { url = "https://files.pythonhosted.org/packages/fe/c3/cd737e2d45e70717907f83e146f6949f20cc23cd4bf7b2688727763aa458/yarl-1.23.0-cp314-cp314t-win32.whl", hash = "sha256:73309162a6a571d4cbd3b6a1dcc703c7311843ae0d1578df6f09be4e98df38d4", size = 90558 },
+    { url = "https://files.pythonhosted.org/packages/e1/19/3774d162f6732d1cfb0b47b4140a942a35ca82bb19b6db1f80e9e7bdc8f8/yarl-1.23.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4503053d296bc6e4cbd1fad61cf3b6e33b939886c4f249ba7c78b602214fabe2", size = 97610 },
+    { url = "https://files.pythonhosted.org/packages/51/47/3fa2286c3cb162c71cdb34c4224d5745a1ceceb391b2bd9b19b668a8d724/yarl-1.23.0-cp314-cp314t-win_arm64.whl", hash = "sha256:44bb7bef4ea409384e3f8bc36c063d77ea1b8d4a5b2706956c0d6695f07dcc25", size = 86041 },
+    { url = "https://files.pythonhosted.org/packages/69/68/c8739671f5699c7dc470580a4f821ef37c32c4cb0b047ce223a7f115757f/yarl-1.23.0-py3-none-any.whl", hash = "sha256:a2df6afe50dea8ae15fa34c9f824a3ee958d785fd5d089063d960bae1daa0a3f", size = 48288 },
+]

From 9bcd50164de7c7835090f5e6f0b6e2e0c100ef99 Mon Sep 17 00:00:00 2001
From: "DESKTOP-RTLN3BA\\$punk" <vermarohanfinal@gmail.com>
Date: Thu, 14 May 2026 19:54:41 -0700
Subject: [PATCH 35/36] feat(evals): publish multimodal_doc parser_compare
 benchmark + n=171 report

Adds the full parser_compare experiment for the multimodal_doc suite:
six arms compared on 30 PDFs / 171 questions from MMLongBench-Doc with
anthropic/claude-sonnet-4.5 across the board.

Source code:
- core/parsers/{azure_di,llamacloud,pdf_pages}.py: direct parser SDK
  callers (Azure Document Intelligence prebuilt-read/layout, LlamaParse
  parse_page_with_llm/parse_page_with_agent) used by the LC arms,
  bypassing the SurfSense backend so each (basic/premium) extraction
  is a clean A/B independent of backend ETL routing.
- suites/multimodal_doc/parser_compare/{ingest,runner,prompt}.py:
  six-arm benchmark (native_pdf, azure_basic_lc, azure_premium_lc,
  llamacloud_basic_lc, llamacloud_premium_lc, surfsense_agentic) with
  byte-identical prompts per question, deterministic grader, Wilson
  CIs, and the per-page preprocessing tariff cost overlay.

Reproducibility:
- pyproject.toml + uv.lock pin pypdf, azure-ai-documentintelligence,
  llama-cloud-services as new deps.
- .env.example documents the AZURE_DI_* and LLAMA_CLOUD_API_KEY env
  vars now required for parser_compare.
- 12 analysis scripts under scripts/: retry pass with exponential
  backoff, post-retry accuracy merge, McNemar / latency / per-PDF
  stats, context-overflow hypothesis test, etc. Each produces one
  number cited by the blog report.

Citation surface:
- reports/blog/multimodal_doc_parser_compare_n171_report.md: 1219-line
  technical writeup (16 sections) covering headline accuracy, per-format
  accuracy, McNemar pairwise significance, latency / token / per-PDF
  distributions, error analysis, retry experiment, post-retry final
  accuracy, cost amortization model with closed-form derivation, threats
  to validity, and reproducibility appendix.
- data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/{raw,
  raw_retries,raw_post_retry}.jsonl + run_artifact.json + retry summary
  whitelisted via data/.gitignore as the verifiable numbers source.

Gitignore:
- ignore logs_*.txt + retry_run.log; structured artifacts cover the
  citation surface, debug logs are noise.
- data/.gitignore default-ignores everything, whitelists the n=171 run
  artifacts only (parser manifest left ignored to avoid leaking local
  Windows usernames in absolute paths; manifest is fully regenerable
  via 'ingest multimodal_doc parser_compare').
- reports/.gitignore now whitelists hand-curated reports/blog/.

Also retires the abandoned CRAG Task 3 implementation (download script,
streaming Task 3 ingest, CragTask3Benchmark + tests) and trims the
runner / ingest module APIs to match.

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 surfsense_evals/.env.example                  |   18 +
 surfsense_evals/.gitignore                    |    6 +
 surfsense_evals/data/.gitignore               |   20 +
 .../parser_compare/raw.jsonl                  | 1026 ++++++++++++++
 .../parser_compare/raw_post_retry.jsonl       | 1026 ++++++++++++++
 .../parser_compare/raw_retries.jsonl          |   37 +
 .../parser_compare/raw_retries_summary.json   |  100 ++
 .../parser_compare/run_artifact.json          | 1022 ++++++++++++++
 surfsense_evals/pyproject.toml                |    3 +
 surfsense_evals/reports/.gitignore            |    9 +
 ...ltimodal_doc_parser_compare_n171_report.md | 1219 +++++++++++++++++
 .../scripts/analyze_failure_timing.py         |  125 ++
 surfsense_evals/scripts/analyze_failures.py   |  155 +++
 .../scripts/check_extraction_sizes.py         |   60 +
 .../scripts/check_uploaded_status.py          |   77 ++
 .../scripts/compute_adjusted_accuracy.py      |  112 ++
 .../scripts/compute_blog_extras.py            |  381 ++++++
 .../scripts/compute_post_retry_accuracy.py    |  180 +++
 .../scripts/download_crag_task3.py            |   97 --
 surfsense_evals/scripts/inspect_first30.py    |   59 +
 .../patch_manifest_for_parallel_ingest.py     |  100 ++
 surfsense_evals/scripts/peek_t3_doc_map.py    |   40 -
 .../scripts/retry_failed_questions.py         |  636 +++++++++
 .../scripts/summarise_parser_compare_run.py   |  122 ++
 .../test_context_overflow_hypothesis.py       |  155 +++
 .../surfsense_evals/core/parsers/__init__.py  |   35 +
 .../surfsense_evals/core/parsers/azure_di.py  |  144 ++
 .../core/parsers/llamacloud.py                |  168 +++
 .../surfsense_evals/core/parsers/pdf_pages.py |   35 +
 .../multimodal_doc/parser_compare/__init__.py |   46 +
 .../multimodal_doc/parser_compare/ingest.py   |  356 +++++
 .../multimodal_doc/parser_compare/prompt.py   |  120 ++
 .../multimodal_doc/parser_compare/runner.py   |  797 +++++++++++
 .../suites/research/crag/__init__.py          |   19 +-
 .../suites/research/crag/dataset_task3.py     |  263 ----
 .../suites/research/crag/ingest.py            |   10 +-
 .../suites/research/crag/ingest_task3.py      |  191 ---
 .../suites/research/crag/runner.py            |  126 +-
 .../tests/suites/test_crag_dataset_task3.py   |  259 ----
 surfsense_evals/uv.lock                       |  942 +++++++++++++
 40 files changed, 9303 insertions(+), 993 deletions(-)
 create mode 100644 surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl
 create mode 100644 surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl
 create mode 100644 surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl
 create mode 100644 surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json
 create mode 100644 surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json
 create mode 100644 surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
 create mode 100644 surfsense_evals/scripts/analyze_failure_timing.py
 create mode 100644 surfsense_evals/scripts/analyze_failures.py
 create mode 100644 surfsense_evals/scripts/check_extraction_sizes.py
 create mode 100644 surfsense_evals/scripts/check_uploaded_status.py
 create mode 100644 surfsense_evals/scripts/compute_adjusted_accuracy.py
 create mode 100644 surfsense_evals/scripts/compute_blog_extras.py
 create mode 100644 surfsense_evals/scripts/compute_post_retry_accuracy.py
 delete mode 100644 surfsense_evals/scripts/download_crag_task3.py
 create mode 100644 surfsense_evals/scripts/inspect_first30.py
 create mode 100644 surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py
 delete mode 100644 surfsense_evals/scripts/peek_t3_doc_map.py
 create mode 100644 surfsense_evals/scripts/retry_failed_questions.py
 create mode 100644 surfsense_evals/scripts/summarise_parser_compare_run.py
 create mode 100644 surfsense_evals/scripts/test_context_overflow_hypothesis.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parsers/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py
 create mode 100644 surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py
 create mode 100644 surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py
 delete mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
 delete mode 100644 surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
 delete mode 100644 surfsense_evals/tests/suites/test_crag_dataset_task3.py

diff --git a/surfsense_evals/.env.example b/surfsense_evals/.env.example
index 632e77d8a..aa716063c 100644
--- a/surfsense_evals/.env.example
+++ b/surfsense_evals/.env.example
@@ -63,3 +63,21 @@ OPENROUTER_API_KEY=sk-or-...
 # Where generated reports (summary.md / summary.json) get written.
 # Default: <surfsense_evals>/reports/
 # EVAL_REPORTS_DIR=./reports
+
+# ---------------------------------------------------------------------------
+# 5. Parser SDKs — REQUIRED for the multimodal_doc / parser_compare suite
+# ---------------------------------------------------------------------------
+# parser_compare calls Azure Document Intelligence and LlamaParse SDKs
+# directly from the eval harness so each (basic / premium) extraction
+# is a clean A/B test independent of the SurfSense backend's ETL routing.
+#
+# Azure Document Intelligence — used for the `azure_basic_lc` (prebuilt-read)
+# and `azure_premium_lc` (prebuilt-layout) arms. Get an endpoint + key from
+# https://portal.azure.com (Document Intelligence resource, F0 / S0 tier).
+# AZURE_DI_ENDPOINT=https://<your-resource>.cognitiveservices.azure.com/
+# AZURE_DI_KEY=<your-32-char-key>
+#
+# LlamaCloud (LlamaParse) — used for `llamacloud_basic_lc` (parse_page_with_llm)
+# and `llamacloud_premium_lc` (parse_page_with_agent). Get a key from
+# https://cloud.llamaindex.ai/api-key.
+# LLAMA_CLOUD_API_KEY=llx-...
diff --git a/surfsense_evals/.gitignore b/surfsense_evals/.gitignore
index 0f71d2635..fd6e505c2 100644
--- a/surfsense_evals/.gitignore
+++ b/surfsense_evals/.gitignore
@@ -27,3 +27,9 @@ htmlcov/
 .env.local
 .env.*.local
 !.env.example
+
+# Run / debug logs — keep ephemeral, structured artifacts in
+# `data/.../<run_id>/` are the citation surface, not these.
+*.log
+logs_*.txt
+retry_run.log
diff --git a/surfsense_evals/data/.gitignore b/surfsense_evals/data/.gitignore
index d6b7ef32c..529223f58 100644
--- a/surfsense_evals/data/.gitignore
+++ b/surfsense_evals/data/.gitignore
@@ -1,2 +1,22 @@
+# Default: don't track anything under data/ (large benchmarks, PDFs,
+# extracted markdown, ingestion caches, per-run artifacts can balloon).
 *
+
+# Always keep this gitignore file
 !.gitignore
+
+# Whitelist the artifacts the n=171 multimodal_doc / parser_compare blog
+# report (`reports/blog/multimodal_doc_parser_compare_n171_report.md`)
+# cites by path. These are the verifiable numbers source for the report.
+#
+# Path components have to be unblocked one level at a time because git
+# does not look into a directory whose parent is ignored.
+!multimodal_doc/
+!multimodal_doc/runs/
+!multimodal_doc/runs/2026-05-14T00-53-19Z/
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl
+!multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json
diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl
new file mode 100644
index 000000000..d91e2e530
--- /dev/null
+++ b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl
@@ -0,0 +1,1026 @@
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults\n- 36% rated the ethical standards as \"poor\" (from the table showing \"% who say that the ethical standards of top Trump administration officials are...\")\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 6925, "output_tokens": 125, "cost_micros": 22650, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults (from the Methodology section on page 14)\n- According to the chart on page 2, 36% of the total rated the ethical standards as \"poor\"\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9417, "output_tokens": 128, "cost_micros": 30171, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document, I can find:\n1. The total sample size: 1,503 adults (from the Methodology section)\n2. The percentage who rated ethical standards as \"poor\": 36% (from the table \"Most rate top Trump administration officials' ethics as 'not good' or 'poor'\")\n\nTo calculate the number of adults:\n1,503 \u00d7 0.36 = 541.08\n\nSince we need an integer and this represents people, I should round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 7774, "output_tokens": 158, "cost_micros": 25692, "latency_ms": 6405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump's government ethical standards as poor.\n\nFrom the document:\n- The survey was conducted April 25-May 1, 2018 among 1,503 adults (from the Methodology section on page 14)\n- 36% rated the ethical standards of top Trump administration officials as \"poor\" (from the table on page 2)\n\nCalculation:\n1,503 adults \u00d7 36% = 1,503 \u00d7 0.36 = 541.08\n\nSince we need to report the number of adults, I'll round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9517, "output_tokens": 145, "cost_micros": 30726, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question about the survey conducted April 25 - May 1, 2018.Let me search for the specific data about ethical standards ratings. I can see the document mentions ethical standards. Let me look for the detailed breakdown.I can see from the grep results that there's a table showing ethical standards ratings. Let me read the specific section more carefully:I can see a table showing percentages for ethical standards of Trump administration officials. However, I need to understand the table structure better. The table shows:\n- Total: 58, 36, 9, 39\n- These appear to be percentages for different categories\n\nLet me look at the context more carefully to understand what \"58\" represents:Now I understand the table structure. Looking at the figure:\n- The categories are listed as: Poor, Not good, Good, Excellent\n- For \"Total\" (all adults): The values appear to be 58, 36, 9, 39\n\nHowever, the text says \"58% rate them as not good or poor\" which suggests 58% is the combined total of \"not good\" and \"poor\". Let me look more carefully at how to read this table. Looking at lines 248-249: \"39% say the administration's ethical standards are excellent or good, while 58% rate them as not good or poor.\"\n\nThis indicates:\n- 39% = Excellent + Good (combined)\n- 58% = Not good + Poor (combined)\n\nLooking at the table values for Total: 58, 36, 9, 39\n\nThe layout suggests:\n- Row 1 (Poor): 58\n- Row 2 (Not good): 36  \n- Row 3 (Good): 9\n- Row 4 (Excellent): 39\n\nBut this doesn't make sense because 58 + 36 would be 94%, not matching the text's 58% for \"not good or poor\" combined.\n\nLet me reconsider the table structure. Perhaps it's arranged differently - the numbers might be in columns rather than associating each number with the label above it. Let me look for more context about the survey sample size:The survey was conducted among 1,503 adults. Now, looking back at the text on line 248: \"58% rate them as not good or poor\"\n\nIf 58% of 1,503 adults rated Trump's ethical standards as \"not good or poor\", then:\n58% \u00d7 1,503 = 0.58 \u00d7 1,503 = 871.74 \u2248 872 adults\n\nBut the question specifically asks for those who rated it as \"poor\", not \"not good or poor\" combined.\n\nLet me re-examine the table structure. The table shows categories and then rows for Total, Rep/Lean Rep, and Dem/Lean Dem. Looking at the numbers again:\n\nFor the \"Total\" row, I see: 58, 36, 9, 39\n\nGiven that the text says 58% rated as \"not good or poor\" combined, and the categories listed are Poor, Not good, Good, Excellent - perhaps the table is showing these in a different order or the numbers represent something else.\n\nActually, looking at line 289-295 more carefully, let me reconsider: perhaps the numbers don't align sequentially with the labels above. The figure might have a more complex layout. Let me look at the Republican row: 22, 7, 19, 75. And Democrat row: 86, 61, 2, 12.\n\nFor Republicans: The text (line 257) says \"75% giving the administration high marks\" - this matches the \"75\" in the data.\nFor Democrats: The text (line 258) says \"86% of Democrats rating its ethical standards negatively\" - this matches the \"86\" in the data.\n\nSo for the Total row showing 58, 36, 9, 39:\n- 58 + 36 = 94 (doesn't match the 58% mentioned)\n- 36 + 58 = 94 \n- 9 + 39 = 48 (doesn't match the 39% for excellent/good)\n\nWait - perhaps I'm misreading the structure. Let me think about this differently. The text says:\n- 39% say excellent or good\n- 58% say not good or poor\n\nThese add to 97%, with 3% presumably \"don't know\" (which the note says is not shown).\n\nLooking at the table structure, maybe it's:\n- Poor: 36\n- Not good: 22 (so 36 + 22 = 58% for \"poor or not good\")\n- Good: ? \n- Excellent: ?\n\nActually, rereading the visual structure between lines 273-315, the checkboxes (\u2610) appear before each category label, and then the numbers follow. Let me parse this more carefully as a table where columns might represent the categories.\n\nLooking at the pattern, it seems like the table might have columns for each rating category. If the first number in each row represents \"Poor\":\n- Total: 58 (but this would be just \"Poor\", not \"Poor + Not good\")\n\nBut the text clearly states 58% is the combined \"not good or poor\". So if:\n- Poor alone = X%\n- Not good alone = Y%  \n- X + Y = 58%\n\nAnd looking at potential individual values, perhaps:\n- Poor = 36%\n- Not good = 22%\n- 36 + 22 = 58% \u2713\n\nThis would make sense! So in the Total row (58, 36, 9, 39), these might be arranged as:\n- Combined negative: 58%\n- Poor: 36%\n- Good: 9%\n- Combined positive: 39% (should be Good 9% + Excellent, but listed as 39%)\n\nWait, that still doesn't work cleanly. Let me look for another table or clarification in the document:Perfect! I found a more detailed table starting at line 875. This table clearly shows the structure with labels on lines 880-885:\n- Poor\n- Not good\n- Good\n- Excellent\n\nAnd then for \"Total\" (lines 890-898):\n- 58\n- 36\n- 9\n- 39\n\nBut wait - this arrangement still seems odd. Let me look at the actual layout more carefully. Lines 890-898 show:\n```\nTotal\n58\n36\n9\n39\n```\n\nLooking at the structure and comparing with the text, I think the table columns might be:\n- Column 1: Row label (Total, Rep/Lean Rep, etc.)\n- Column 2: Combined \"Not good or Poor\" (58 for Total)\n- Column 3: Poor specifically (36 for Total)\n- Column 4: Good specifically (9 for Total)\n- Column 5: Combined \"Good or Excellent\" (39 for Total)\n\nThis would make sense because:\n- 58% = \"not good or poor\" combined (matches the text on line 248)\n- 36% = \"poor\" alone\n- 9% = \"good\" alone\n- 39% = \"good or excellent\" combined (matches the text on line 247)\n\nSo the answer to \"how many adults rated Trump's government ethical standards as poor\" would be:\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08 \u2248 541 adults\n\nActually, let me double-check by reading the column headers more carefully in this second table:Looking at the table header (lines 880-885), the categories are listed as:\n- Poor\n- Not good\n- Good  \n- Excellent\n\nAnd for Total (lines 892-898): 58, 36, 9, 39\n\nGiven the table structure and that these are percentages, I now believe the columns represent:\n1. First column after \"Total\" = one rating category\n2. Second column = another rating category\n3. Third column = another rating category\n4. Fourth column = another rating category\n\nBut there are only 4 numbers (58, 36, 9, 39) for 4 categories (Poor, Not good, Good, Excellent).\n\nSo the mapping should be:\n- Poor: 58%? or 36%?\n- Not good: 36%? or 9%?\n- Good: 9%? or 39%?\n- Excellent: 39%? or something else?\n\nLet me check against the text constraints:\n- Text says \"39% say excellent or good\" (line 247-248) \u2192 Good + Excellent = 39%\n- Text says \"58% rate them as not good or poor\" (line 248-249) \u2192 Not good + Poor = 58%\n\nIf the four values are 58, 36, 9, 39:\n- 58 + 36 + 9 + 39 = 142% (impossible for percentages)\n\nSo these must not all be individual category percentages. Some must be combined values. Given the text:\n- 58% = Not good + Poor (combined)\n- 39% = Good + Excellent (combined)\n- 9% = one individual category\n- 36% = one individual category\n\nSince 58% + 39% = 97% (with 3% don't know), and we have two individual values (36 and 9), these must be subcategories within the combined values:\n- 36% = Poor (part of the 58% \"not good or poor\")\n- Therefore Not good = 58% - 36% = 22%\n- 9% = Good (part of the 39% \"good or excellent\")  \n- Therefore Excellent = 39% - 9% = 30%\n\nThis makes sense! So the table is showing:\n- Column 1: Not good or Poor combined (58%)\n- Column 2: Poor alone (36%)\n- Column 3: Good alone (9%)\n- Column 4: Good or Excellent combined (39%)\n\nTherefore, 36% of adults rated Trump's ethical standards as \"poor\".\n\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest integer: 541 adults\n\nAnswer: 541", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94875, "error": null, "extra": {"thread_id": 1904, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7e2e86436614d5c96d6bd9dd3a561c5", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 977, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the domains where adults are \"very confident\" and \"not at all confident\" in Donald Trump, and identify which has the highest percentage for each category.\n\nLooking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the breakdown of confidence levels (Very, Somewhat, Not too, Not at all) for various policy areas.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 19%\n- Make good appointments to the federal courts: 21%\n- Manage the executive branch effectively: 21%\n- Make wise decisions about immigration policy: 16%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 30%\n\nThe highest \"Very confident\" is \"Work effectively with Congress\" at 30%.\n\nFor \"Not at all confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 26%\n- Make good decisions about economic policy: 17%\n- Use military force wisely: 32%\n- Make good appointments to the federal courts: 32%\n- Manage the executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nThe highest \"Not at all confident\" is \"Make wise decisions about immigration policy\" at 39%.\n\nIn alphabetical order:\n1. Make wise decisions about immigration policy\n2. Work effectively with Congress\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 6973, "output_tokens": 374, "cost_micros": 26529, "latency_ms": 9500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to find:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table data:\n\n**Very confident (highest):**\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (HIGHEST)\n\n**Not at all confident (highest):**\n- Make wise decisions about immigration policy: 39% (HIGHEST)\n- Handle an international crisis: 35%\n- Manage the executive branch effectively: 34%\n\nThe two domains are:\n1. \"Make good decisions about economic policy\" (highest very confident at 31%)\n2. \"Make wise decisions about immigration policy\" (highest not at all confident at 39%)\n\nIn alphabetical order:\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9465, "output_tokens": 216, "cost_micros": 31635, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table \"Public's confidence in Trump to handle several policy areas mixed\" on page 4 of the document, I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table:\n\n**Very confident percentages:**\n- Negotiate favorable trade agreements: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 20%\n- Make good appointments to federal courts: 21%\n- Manage executive branch effectively: 24%\n- Make wise decisions about immigration policy: 17%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 21%\n\nHighest \"Very\" confident: **Negotiate favorable trade agreements** and **Manage the executive branch effectively** (both at 24%)\n\n**Not at all confident percentages:**\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: 29%\n- Use military force wisely: 32%\n- Make good appointments to federal courts: 32%\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nHighest \"Not at all\" confident: **Make wise decisions about immigration policy** (39%)\n\nIn alphabetical order:\n1. Manage the executive branch effectively\n2. Make wise decisions about immigration policy\n3. Negotiate favorable trade agreements with other countries\n\nAnswer: Manage the executive branch effectively, Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 7822, "output_tokens": 372, "cost_micros": 29046, "latency_ms": 8530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, manage executive branch effectively, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 3 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table, examining the \"Very\" column:\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (highest)\n- Use military force wisely: 26%\n- Make good appointments to federal courts: 25%\n- Manage executive branch effectively: 21%\n- Make wise decisions about immigration policy: 26%\n- Handle an international crisis: 25%\n- Work effectively with Congress: 13%\n\nFrom the \"Not at all\" column:\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: (not shown separately)\n- Use military force wisely: 32%\n- Make good appointments to federal courts: (not shown separately)\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39% (highest)\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9565, "output_tokens": 285, "cost_micros": 32970, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the survey data from April 25 - May 1, 2018.Let me continue reading to find the specific data about confidence levels in different domains.I need to find the specific data showing \"very confident\" and \"not at all confident\" percentages for different domains. Let me search for that data.Perfect! I found the table with confidence levels. Now I need to identify which columns represent \"Very confident\" and \"Not at all confident\". Let me re-read this section more carefully.Now I can see the table structure clearly. The columns are:\n- Column 1: \"Not at all\" (confidence)\n- Column 2: \"Not too\" (confidence)  \n- Column 3: \"Somewhat\" (confident)\n- Column 4: \"Very\" (confident)\n\nHowever, I notice the last column shows combined percentages (e.g., 54 for trade agreements). Let me look more carefully at the structure. The header says \"Not at all Not too\" (colspan=2) and \"Somewhat Very\" (colspan=2), but then there are 4 data columns per row.\n\nLooking at the data pattern:\n- Row 1 (Trade): 44, 26, 30, 54\n- The 54 appears to be a combined \"Very/Somewhat\" total\n\nLet me reconsider. Looking at the structure more carefully:\n- Column 1: Combined \"Not at all/Not too\" total\n- Column 2: \"Not at all\" alone\n- Column 3: \"Somewhat\" alone  \n- Column 4: Combined \"Somewhat/Very\" total\n\nActually, examining the pattern again with the header \"Not at all Not too\" and \"Somewhat Very\", the most logical interpretation is:\n- Total negative (44) | Not at all (26) | Somewhat (30) | Total positive (54)\n\nBut that doesn't match typical survey formats. Let me look for clarification in the surrounding text or try a different interpretation.\n\nGiven the colspan=2 for each pair and 4 columns of data, the structure should be:\n- Not at all: Column 2 (26, 29, 32, 32, 34, 39, 35, 33)\n- Not too: (would be calculated from total minus \"not at all\")\n- Somewhat: Column 3 (30, 31, 26, 25, 21, 26, 25, 13)\n- Very: (would be calculated from total minus \"somewhat\")\n\nSo \"Not at all confident\" values are: 26, 29, 32, 32, 34, 39, 35, 33\nAnd \"Very confident\" values need to be derived. Looking at the totals in column 4 (54, 53, 46, 46, 45, 43, 43, 43) and subtracting column 3 values would give \"Very\":\n- Trade: 54 - 30 = 24\n- Economic: 53 - 31 = 22\n- Military: 46 - 26 = 20\n- Courts: 46 - 25 = 21\n- Executive branch: 45 - 21 = 24\n- Immigration: 43 - 26 = 17\n- International crisis: 43 - 25 = 18\n- Congress: 43 - 13 = 30\n\nWait, that doesn't seem right either. Let me look at this more systematically. The first column (44, 46, 51, etc.) appears to be totals for \"not confident\" responses.\n\nActually, re-reading the header structure with colspan, I believe:\n- Columns are: [Total Not confident] [Not at all] [Somewhat] [Total confident]\n- So Column 2 is \"Not at all confident\": 26, 29, 32, 32, 34, 39, 35, 33\n- And I need to find \"Very confident\"\n\nThe most likely interpretation given standard survey formats:\nThe rightmost number in each row (54, 53, 46, 46, 45, 43, 43, 43) represents the TOTAL for somewhat + very confident.\nThe second-to-right number (30, 31, 26, 25, 21, 26, 25, 13) represents \"somewhat\" confident.\n\nTherefore \"Very confident\" = Total confident - Somewhat:\n- Trade: 54 - 30 = 24%\n- Economic: 53 - 31 = 22%\n- Military: 46 - 26 = 20%\n- Courts: 46 - 25 = 21%\n- Executive: 45 - 21 = 24%\n- Immigration: 43 - 26 = 17%\n- International: 43 - 25 = 18%\n- Congress: 43 - 13 = 30%\n\nNow finding the highest percentages:\n- \"Very confident\": Congress (30%) is highest\n- \"Not at all confident\": Immigration (39%) is highest\n\nLet me verify by checking if there are ties or other high values:\n- Very confident: 30% (Congress), 24% (Trade and Executive - tied for 2nd)\n- Not at all confident: 39% (Immigration), 35% (International crisis), 34% (Executive)\n\nSo the answers are:\n- Highest \"Very confident\": Work effectively with Congress\n- Highest \"Not at all confident\": Make wise decisions about immigration policy\n\nIn alphabetical order with the format requested:\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61000, "error": null, "extra": {"thread_id": 1905, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_63c71f31681e41ee869fe1c754c29c5b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party holds the highest total percentage of \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news \"very closely.\"\n\nFrom page 11, I can see the chart \"More view GOP as 'too extreme' than say this about Democratic Party\" which shows:\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections \"very closely.\"\n\nFrom page 13, in the chart \"Young people far less likely than older adults to follow news about midterms,\" I can see:\n- Dem/Lean Dem: Very closely = 21%\n\nAnswer: 21", "citations": [], "input_tokens": 33920, "output_tokens": 235, "cost_micros": 105285, "latency_ms": 12219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Good policy ideas:**\n- Republican Party: 50%\n- Democratic Party: 50%\n\n**High ethical standards:**\n- Republican Party: 41%\n- Democratic Party: 42%\n\n**Total percentages:**\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections very closely.\n\nFrom the table on page 12 titled \"Young people far less likely than older adults to follow news about midterms\":\n\nUnder \"Dem/Lean Dem\": 21% followed news very closely\n\nAnswer: 21", "citations": [], "input_tokens": 6951, "output_tokens": 210, "cost_micros": 24003, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest combined percentage for \"good policy ideas\" and \"high ethical standards,\" then determine what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party: Has good policy ideas (50%), Has high ethical standards (41%)\n- Democratic Party: Has good policy ideas (50%), Has high ethical standards (42%)\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**News following by party (page 11):**\nLooking at the table showing who followed news about congressional elections very closely:\n- Rep/Lean Rep: 18%\n- Dem/Lean Dem: 21%\n\nSince the Democratic Party has the highest combined percentage for good policy ideas and high ethical standards (92%), and Democrats/Democratic leaners followed news very closely at 21%, the answer is 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9443, "output_tokens": 235, "cost_micros": 31854, "latency_ms": 6125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage for both \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news very closely.\n\nFrom the document:\n\n**Party traits (page 9-10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\n**Following congressional election news very closely (page 11):**\nLooking at the table showing \"% who say they have followed news about congressional elections in their state or district...\"\n\nFor \"Dem/Lean Dem\" (Democratic Party/Lean Democratic):\n- Very closely: 21%\n\nAnswer: 21", "citations": [], "input_tokens": 7800, "output_tokens": 214, "cost_micros": 26610, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news about congressional elections very closely.\n\nFrom the document:\n\n**Policy Ideas and Ethical Standards (page 9):**\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**Following News About Congressional Elections (page 11):**\nLooking at the table \"Young people far less likely than older adults to follow news about midterms\"\n\nFor \"Dem/Lean Dem\" (Democrats/Democratic leaners):\n- Very closely: 21%\n- Fairly closely: 31%\n- NET: 52%\n\nThe percentage who followed very closely is 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 9543, "output_tokens": 241, "cost_micros": 32244, "latency_ms": 6844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the provided document to answer your question.Let me continue reading to find information about which party holds the highest total percentage of good policy ideas and high ethical standards.Let me search for information about which party has good policy ideas and high ethical standards.Let me search for information about party policy ideas and ethical standards.Perfect! Now I need to find which party has the highest TOTAL percentage of both \"good policy ideas\" and \"high ethical standards\", then find the news following data for that party.\n\nFrom the document:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the higher total (92%). Now I need to find what percentage of Democrats followed congressional election news very closely.Perfect! I found the data. Looking at the table in the document:\n\nFrom lines 1327-1353, I can see:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the highest total percentage (92%).\n\nFrom lines 1600-1604, I can see:\n- Dem/Lean Dem: 21% followed news about congressional elections very closely\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69875, "error": null, "extra": {"thread_id": 1906, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bb4e12b774cf4c398f45bc409de06fef", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 386, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has higher combined ratings for \"good policy ideas\" and \"high ethical standards,\" and who also closely follow congressional elections.\n\nFrom page 11 (page 9 of content):\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal scores:\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nFrom page 14 (page 12 of content), looking at registered voters who support the Democratic candidate:\n- Total registered voters supporting Democratic candidate: 48%\n\nFrom page 13 (page 11 of content), looking at those who follow congressional elections \"very closely\":\n- Among Dem/Lean Dem: 21% follow very closely\n\nHowever, I need to find the intersection of:\n1. Registered voters who support the Democratic candidate\n2. Those who closely follow congressional elections\n\nFrom page 14, among all registered voters: 48% support Democratic candidate\nFrom page 13, among Dem/Lean Dem: 21% follow very closely, and 52% total follow very or fairly closely\n\nThe question asks for those who support the Democratic candidate AND closely follow elections. Looking at page 13, among Dem/Lean Dem, 52% follow closely (very or fairly).\n\nSince the Democratic Party has the higher combined score (92% vs 91%), and 48% of registered voters support the Democratic candidate, but I need those who also follow closely.\n\nFrom the data shown, among Democratic supporters who are registered voters, approximately 52% follow closely. However, the specific cross-tabulation isn't directly provided.\n\nLooking more carefully at page 14, the 48% represents all registered voters supporting Democrats. The closely following percentage among this group would need to be calculated, but the most direct answer from the available data for Democratic-leaning registered voters who follow closely would be derived from the 52% net figure.\n\nAnswer: 48", "citations": [], "input_tokens": 33928, "output_tokens": 480, "cost_micros": 108984, "latency_ms": 27639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher total percentage of \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate AND closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n  - Total: 50% + 41% = 91%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n  - Total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Congressional vote preferences (page 13):**\nThe table shows \"% of registered voters who say they support or lean toward the _ candidate for Congress in their district\"\n- Democratic: 48%\n- Republican: 43%\n\nHowever, I need to find those who BOTH:\n1. Support the Democratic candidate\n2. Closely follow congressional elections\n\nLooking at page 12 for those who follow elections \"very closely\" or \"fairly closely\":\n- The document shows 19% follow \"very closely\" and the NET (very + fairly closely) is shown in various demographic breakdowns\n- For \"Dem/Lean Dem\": 21% follow very closely, 52% follow very or fairly closely\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections. The document doesn't provide a cross-tabulation of vote preference by attention to elections.\n\nWait, let me re-read the question. It asks for \"registered voters who support or lean toward the candidate from the party with the higher total percentage... and closely follow congressional elections.\"\n\nLooking at the data again, I need to find if there's a cross-tab. The document shows on page 13 that 48% of registered voters support the Democratic candidate, but it doesn't break this down by whether they closely follow elections.\n\nThe document provides separate information:\n- 48% of registered voters support Democratic candidate (page 13)\n- Various percentages follow elections closely (page 12)\n\nWithout a cross-tabulation, I cannot determine the exact percentage who meet both criteria. However, re-reading more carefully - the question might be asking simply for the percentage who support the Democratic candidate, which is 48.\n\nAnswer", "citations": [], "input_tokens": 6959, "output_tokens": 512, "cost_micros": 28557, "latency_ms": 10796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher combined percentages for \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate and closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party:\n  - Has good policy ideas: 50%\n  - Has high ethical standards: 41%\n  - Total: 91%\n\n- Democratic Party:\n  - Has good policy ideas: 50%\n  - Has high ethical standards: 42%\n  - Total: 92%\n\nThe Democratic Party has the higher combined total (92% vs 91%).\n\n**Congressional vote preferences (page 12):**\nLooking at registered voters who support the Democratic candidate: 48%\n\nHowever, the question asks specifically for those who \"closely follow congressional elections in their district.\"\n\n**Following congressional elections (page 11):**\nThe question asks about following news \"very closely\" or \"fairly closely\"\n- Total following very closely: 19%\n- Total following fairly closely: 32%\n- NET (very or fairly closely): Not explicitly stated for total, but can be calculated\n\nLooking at the breakdown by party affiliation (page 11):\n- Dem/Lean Dem following very closely: 21%\n- The \"NET\" column shows 52% for Dem/Lean Dem\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections.\n\nLooking more carefully at page 12, the congressional vote preferences show 48% of registered voters favor the Democratic candidate.\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (48% of registered voters)\n2. Follow elections closely\n\nThis specific cross-tabulation is not provided in the document. The document shows these as separate statistics but doesn't provide the intersection.\n\nGiven the data available, if I must provide an answer based on what's shown, the percentage of registered voters who support the Democratic candidate is 48%.\n\nAnswer: 48", "citations": [], "input_tokens": 9451, "output_tokens": 446, "cost_micros": 35043, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find:\n1. Which party has higher total percentage of \"good policy ideas\" and \"high ethical standards\"\n2. The percentage of registered voters who support that party's candidate AND closely follow congressional elections\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91%\nTotal for Democratic Party: 50 + 42 = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Following congressional elections closely (page 11):**\nThe document shows percentages who follow \"very closely\" or \"fairly closely\" by party:\n- Dem/Lean Dem: 21% very closely, NET = 52%\n\n**Congressional vote preference (page 12):**\nThe table shows registered voters who support/lean toward:\n- Democratic candidate: 48%\n- Republican candidate: 43%\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (the party with higher total)\n2. Follow elections closely\n\nThe document doesn't provide a cross-tabulation of vote preference by how closely people follow elections. Looking at page 12, it only shows vote preference by demographics, not by attention to elections.\n\nSince the question asks for registered voters who support the Democratic candidate AND closely follow elections, but this specific cross-tab is not provided in the document, I cannot determine this exact percentage from the available data.\n\nHowever, re-reading the question, it may be asking simply for the percentage who support the Democratic candidate (48%), as that's the party with the higher combined score.\n\nAnswer: 48", "citations": [], "input_tokens": 7808, "output_tokens": 403, "cost_micros": 29469, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has both higher \"good policy ideas\" and \"high ethical standards\" ratings, and who closely follow congressional elections.\n\nFirst, let me identify which party has higher ratings on both metrics:\n\nFrom the document (page 9):\n- Republican Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 41%\n- Democratic Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 42%\n\nThe Democratic Party has a higher percentage on \"high ethical standards\" (42% vs 41%), while both parties tie on \"good policy ideas\" (50% each). Since the question asks for the party with \"higher total percentage,\" I need to add them:\n- Republican Party total: 50% + 41% = 91%\n- Democratic Party total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nNow I need to find registered voters who:\n1. Support or lean toward the Democratic candidate\n2. Closely follow congressional elections in their district\n\nFrom page 12, among all registered voters: 48% support or lean toward the Democratic candidate.\n\nHowever, I need to find those who ALSO closely follow elections. The document doesn't provide a cross-tabulation of vote preference by how closely people follow elections.\n\nLooking at page 11, it shows that 19% of all adults follow \"very closely\" and 31% follow \"fairly closely\" (total 50% follow at least fairly closely).\n\nBut the question asks specifically for those who \"closely follow\" - which appears to mean \"very closely\" (19% of total).\n\nSince the document doesn't provide the specific cross-tabulation of Democratic voters who follow very closely, I cannot calculate this precisely from the data provided. However, re-reading the question, it may be asking simply for the percentage of registered voters who support the Democratic candidate, which is 48%.\n\nBut looking more carefully at page 11, the breakdown shows percentages of people following elections, not broken down by their vote preference.\n\nGiven the data available, the answer appears to be: 48\n\nAnswer: 48", "citations": [], "input_tokens": 9551, "output_tokens": 478, "cost_micros": 35823, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question.Let me continue reading to find information about party policy ideas, ethical standards, and congressional election following:Let me read more sections systematically to find the relevant data:Now let me search for information about congressional elections and following:Now I need to find data specifically about registered voters who closely follow congressional elections broken down by party. Let me search for more detailed tables:Let me look for a more detailed table that might break down voting preferences by how closely they follow elections:Let me search through the appendix or topline data which might contain the detailed crosstabs:Let me search for any tables that combine party support, policy ideas, ethical standards, and following elections. Let me look more carefully at the data I've already read. First, let me identify the key information:\n\n1. From lines 1327-1353, I see that both parties score 50% on \"Has good policy ideas\" and Democratic Party scores 42% on \"Has high ethical standards\" vs Republican Party at 41%.\n\n2. Since both parties tie on policy ideas (50%), I need to determine which party has higher combined score. Democratic Party has 42% ethical standards, Republican has 41%, so Democratic Party has a higher total (50+42=92) vs Republican (50+41=91).\n\n3. Now I need to find data about registered voters who support the Democratic candidate AND closely follow congressional elections.\n\nLet me search for more detailed crosstabs that might show vote preference by attention to elections:I need to look for tables that show voting preference crossed with how closely voters follow congressional elections. Let me search the document more systematically for all tables and figures related to this:Based on my thorough review of the document, I can now solve this step by step:\n\n**Step 1: Determine which party has the higher total percentage of good policy ideas AND high ethical standards**\n\nFrom lines 1327-1353 (chunk id='49127'):\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91\nTotal for Democratic Party: 50 + 42 = 92\n\n**The Democratic Party has the higher total (92 vs 91).**\n\n**Step 2: Find registered voters who support the Democratic candidate**\n\nFrom lines 1638-1643 and 1661-1676 (chunks id='49140' and id='49142'):\n\"Currently, 48% of registered voters say they would vote for the Democratic Party's candidate for Congress in their district\"\n\n**Step 3: Find the percentage who closely follow congressional elections**\n\nFrom lines 1494-1537 and 1582-1604 (chunks id='49135' and id='49138'):\n- Among \"Dem/Lean Dem\": 21% follow \"Very closely\"\n\nHowever, I need to find data specifically about registered voters (not all adults) who support the Democratic candidate AND closely follow congressional elections. The document shows that 21% of Dem/Lean Dem follow very closely (line 1602), and from the congressional vote table, 92% of Dem/Lean Dem registered voters support the Democratic candidate (line 1763).\n\nBut the question asks specifically for registered voters who support the Democratic candidate AND closely follow. The data shows:\n- Line 1602: Among Dem/Lean Dem, 21% follow very closely\n- Line 1763: Among registered voters who are Dem/Lean Dem, 92% support the Democratic candidate\n\nSince 92% of Dem/Lean Dem registered voters support the Democratic candidate, and 21% of Dem/Lean Dem closely follow congressional elections, and these are measuring the same group (Dem/Lean Dem), the answer is **21**.\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 103328, "error": null, "extra": {"thread_id": 1907, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e2603e2724c3411e98fe7264f3325bbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 739, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at page 10, I can see a chart titled \"Wide gender gap in approval of Trump\" which shows approval/disapproval ratings by various demographics.\n\nFor the \"Disapprove\" column (which shows \"Strongly\" and \"Not strongly\" combined):\n\nAges 18-29: 65% total disapproval\nAges 65+: 49% total disapproval\n\nHowever, I need to find specifically the \"Strongly\" disapprove percentages. Looking more carefully at the chart structure, the disapproval is broken down into \"Strongly\" and \"Not strongly\" components.\n\nFrom the chart on page 10:\n- Ages 18-29: The bar shows 65 total, with components that would indicate strongly disapprove\n- Ages 65+: The bar shows 49 total\n\nLet me look more carefully at the specific breakdown. The chart shows:\n- Total disapproval for 18-29: 65%\n- Total disapproval for 65+: 49%\n\nHowever, I need the \"Strongly\" disapprove component specifically. Looking at the total row, it shows 54% total disapproval with 42% strongly disapproving.\n\nWithout being able to see the exact breakdown of \"strongly\" vs \"not strongly\" for each age group in the data provided, I cannot calculate the precise gap for \"strongly disapprove\" specifically.\n\nLet me reconsider - if the overall pattern shows 42% strongly disapprove out of 54% total, and the youngest group (18-29) has 65% total disapproval while the oldest (65+) has 49% total disapproval, the gap in total disapproval is 16 percentage points.\n\nAnswer: 16.0", "citations": [], "input_tokens": 33935, "output_tokens": 416, "cost_micros": 108045, "latency_ms": 24000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "16.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of those who \"strongly disapprove\" of the way Trump is handling his job for the youngest and oldest age groups.\n\nLooking at the table \"Wide gender gap in approval of Trump\" on page 9, I can find the \"Disapprove Strongly\" column for different age groups:\n\nAges 18-29: 46% disapprove strongly\nAges 65+: 39% disapprove strongly\n\nThe gap between the youngest age group (18-29) and the oldest age group (65+) is:\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 6966, "output_tokens": 137, "cost_micros": 22953, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of people who \"strongly disapprove\" of Trump's job performance for the youngest and oldest age groups.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can see the breakdown by age groups with approval/disapproval data.\n\nThe columns show:\n- Disapprove (with \"Strongly\" and \"Not strongly\" subcategories)\n- Approve (with \"Strongly\" and \"Not strongly\" subcategories)\n\nFor age groups:\n- Ages 18-29: The \"Disapprove\" total is 65, with \"Strongly\" at 46\n- Ages 65+: The \"Disapprove\" total is 49, with \"Strongly\" at 39\n\nThe gap between the youngest age group (18-29) strongly disapproving at 46% and the oldest age group (65+) strongly disapproving at 39% is:\n\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 9458, "output_tokens": 227, "cost_micros": 31779, "latency_ms": 6702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap in strong disapproval between the youngest and oldest age groups for Trump's job handling.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 9, which shows \"% who __ of the way Donald Trump is handling his job as president\":\n\nFor age groups:\n- Ages 18-29: 46% strongly disapprove\n- Ages 65+: 39% strongly disapprove\n\nThe gap between the youngest (18-29) and oldest (65+) age groups in strong disapproval is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 7815, "output_tokens": 145, "cost_micros": 25620, "latency_ms": 4266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can find the \"Disapprove Strongly\" column for different age groups:\n\n- Ages 18-29: 46% disapprove strongly\n- Ages 65+: 39% disapprove strongly\n\nThe gap between the youngest (18-29) and oldest (65+) age groups is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 9558, "output_tokens": 137, "cost_micros": 30729, "latency_ms": 4405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the information about the survey conducted April 25-May 1, 2018, regarding approval ratings by age group.Let me continue searching for the age group data on approval ratings. I need to find the specific breakdown by age showing \"strongly disapprove\" percentages.Let me search for \"disapprove\" more broadly:Good! I can see references to approval ratings. Let me read that section more carefully:Perfect! I found the table with age group breakdowns. I can see:\n\n- Ages 18-29 (youngest): Strongly disapprove = 46%\n- Ages 65+ (oldest): Strongly disapprove = 39%\n\nNow I need to calculate the gap:\n46 - 39 = 7\n\nAnswer: 7.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44469, "error": null, "extra": {"thread_id": 1908, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_478b9a7833a5424882dc6822080399bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about a survey conducted April 25-May 1, 2019 regarding Democratic people's views on whether either party has high ethical standards.\n\nHowever, I notice that this document is from a survey conducted April 25-May 1, **2018**, not 2019 as mentioned in the question.\n\nLooking at page 12 (document page 13), I can see a chart titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nThe data shows:\n- Total: 17% say it describes both parties, 47% say one party not the other, 25% say neither party\n- Among Dem/Lean Dem: 18% say both parties, 55% say one party, **18%** say neither party\n\nSo among Democrats and Democratic leaners in this survey, 18% said neither party has high ethical standards.\n\nHowever, the question asks specifically about \"Democratic people\" not \"Dem/Lean Dem\". Let me check if there's more specific data.\n\nThe chart shows the breakdown is for \"Dem/Lean Dem\" which includes both Democrats and Democratic leaners, showing 18% said neither party has high ethical standards.\n\nAnswer: 18", "citations": [], "input_tokens": 33910, "output_tokens": 299, "cost_micros": 106215, "latency_ms": 13266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards.\n\nLooking at the document, I can see a table on page 11 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, describes one party not the other, or describes neither party.\n\nAccording to this table:\n- For \"Democrat\": 18% say it describes neither party\n\nHowever, the question asks for the NUMBER of people, not the percentage. I need to find the actual sample size.\n\nLooking at the methodology section on page 16, the survey was conducted April 25-May 1, 2018 (note: the question asks about 2019, but the document is from 2018). The table shows:\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nSo if 18% of the 710 Democrats/Democratic leaners said neither party has high ethical standards:\n710 \u00d7 0.18 = 127.8\n\nSince we need an integer, this would round to 128.\n\nHowever, I should note that the question asks about 2019, but this document is from a survey conducted in 2018. There is no 2019 data in this document.\n\nBased on the 2018 data available:\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 6941, "output_tokens": 329, "cost_micros": 25758, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the April 25-May 1, 2018 survey (note: the question says 2019 but the document is from 2018).\n\nLooking at the document, I find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes neither party.\n\nFor Democrats, the table shows:\n- Democrat: 18% say \"Describes neither party\"\n\nHowever, I need the actual number of people, not the percentage. \n\nLooking at the methodology section on page 14-15:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe table on page 10 shows that 18% of Democrats said \"high ethical standards\" describes neither party.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents actual people, this would be approximately 128 people.\n\nHowever, I should note that the methodology indicates these are unweighted numbers, and the actual weighted percentages are shown in the table. Without the weighted sample size for Democrats, I can only use the unweighted sample size provided.\n\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9433, "output_tokens": 322, "cost_micros": 33129, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the table titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" on page 10:\n\nThe table shows the percentage who say 'high ethical standards' \"Describes neither party\":\n- Democrat: 18%\n\nThe survey methodology section on page 14 states:\n- Total sample: 1,503 adults\n- The unweighted sample sizes table on page 15 shows:\n  - Dem/Lean Dem: 710\n\nHowever, the table on page 10 shows percentages, not raw numbers. To calculate the number of Democrats who said neither party has high ethical standards:\n\n710 (Dem/Lean Dem sample) \u00d7 0.18 (18% who said neither party has high ethical standards) = 127.8\n\nSince we need an integer and this represents people, this would round to 128.\n\nHowever, I should note that 710 is the unweighted sample size, and the actual weighted number might be different. But based on the information provided and the unweighted sample size given, the calculation would be approximately 128.\n\nAnswer: 128", "citations": [], "input_tokens": 7790, "output_tokens": 304, "cost_micros": 27930, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the document, I can find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nAccording to this table:\n- Democrat: 18% say it describes neither party\n\nHowever, this is a percentage, not the actual number of people. To find the actual number, I need to know how many Democrats were in the survey.\n\nLooking at page 15, the methodology section shows:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe question asks specifically about \"Democratic people\" (which I interpret as Dem/Lean Dem), and from the table on page 10, 18% of Democrats said neither party has high ethical standards.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents people, I should round to 128.\n\nHowever, I need to be careful - the table on page 15 shows \"unweighted\" sample sizes, but the percentages in the analysis would typically be based on weighted data. The actual number would be the unweighted count.\n\nUsing the unweighted sample: 710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9533, "output_tokens": 370, "cost_micros": 34149, "latency_ms": 9297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to find the answer to this question.I need to search for information about the survey conducted April 25-May 1, 2019. Let me continue reading the document to find the relevant data.Let me search for \"ethical standards\" in the document:Good! I found the survey date \"April 25-May 1, 2018\" (not 2019 as stated in the question, but this appears to be the document). Let me read the section about ethical standards of the parties:Perfect! I found the table with the data. Looking at the table in lines 1405-1470, I can see the breakdown by party affiliation. The question asks specifically about \"Democratic people\" (Democrats).\n\nFrom the table:\n- Row \"Democrat\": 18% say neither party has high ethical standards (column \"Describes neither party\")\n\nHowever, I should note that the survey was conducted April 25-May 1, 2018, not 2019 as stated in the question. But this appears to be the relevant document based on the question content.\n\nThe table shows that 18% of Democrats said neither the Republican Party nor the Democratic Party has 'high ethical standards'.\n\nSince the question asks for the number of people (not percentage), I need to find the sample size. Let me search for that information:Let me read more to find the specific sample size for Democrats:Now I need to check if the table shows data for \"Democrat\" or \"Dem/Lean Dem\". Let me reread that section:Perfect! Now I have all the information:\n\nFrom the table:\n- \"Democrat\" row shows 18% said neither party has high ethical standards\n- The note states \"Independents include all those who do not identify as Republican or Democrat\"\n- This means \"Republican\", \"Democrat\", and \"Independent\" are separate categories\n- \"Lean Rep\" and \"Lean Dem\" are subcategories of Independents\n\nLooking at the methodology section, I see:\n- Total sample: 1,503 adults\n- Dem/Lean Dem (combined): 710 people\n\nHowever, the table shows \"Democrat\" separately from \"Lean Dem\". I need to find the sample size for just \"Democrat\" (not including leaners).\n\nLet me search for more details about the Democrat sample size:Unfortunately, the document doesn't provide the specific sample size for \"Democrat\" separate from \"Dem/Lean Dem\". The methodology only provides:\n- Total sample: 1,503\n- Rep/Lean Rep: 644\n- Dem/Lean Dem: 710\n\nGiven that:\n- 18% of Democrats said neither party has high ethical standards\n- The combined \"Dem/Lean Dem\" sample size is 710\n\nHowever, since the table shows separate rows for \"Democrat\" and \"Lean Dem\", and we only have the combined sample size, I cannot calculate the exact number of Democrat respondents (excluding leaners).\n\nBut wait - let me check if there's more information about the sample composition:The document states \"Sample sizes and sampling errors for other subgroups are available upon request\" but doesn't provide the specific breakdown for Democrats vs. Dem/Lean Dem.\n\nLooking back at the data, the question asks for \"Democratic people\" which most likely refers to \"Democrat\" (those who identify as Democrats, not including Democratic leaners who are technically independents).\n\nFrom the table:\n- 18% of Democrats said neither party has high ethical standards\n\nHowever, without the specific sample size for \"Democrat\" (as opposed to \"Dem/Lean Dem\"), I cannot provide the exact number of people.\n\nBut based on typical Pew Research methodology and the fact that the question specifically mentions \"Democratic people in the survey,\" it's most reasonable to interpret this as asking for the actual count rather than the percentage.\n\nSince I don't have the specific Democrat-only sample size, let me check if there's any other information in the document that might help. Let me look at whether the question might be asking about the year 2019 instead:The document I've been examining is from 2018 (April 25-May 1, 2018), not 2019. The question states \"April 25- May 1, 2019\" but based on the priority documents and the content match, this 2018 survey is the relevant document.\n\nGiven the information available:\n- 18% of Democrats said neither party has high ethical standards\n- The document only provides \"Dem/Lean Dem\" combined sample size of 710\n\nWithout the specific Democrat-only sample size, and given that the question asks for an integer representing the number of people (not percentage), I cannot provide a definitive answer based solely on the document provided.\n\nHowever, if I interpret \"Democratic people\" as \"Dem/Lean Dem\" (the broader Democratic coalition), then:\n710 \u00d7 0.18 = 127.8 \u2248 128 people\n\nBut if we look at the note in the table that says \"Independents include all those who do not identify as Republican or Democrat,\" and line 1483 says \"only about two-in-ten Republicans (19%) or Democrats (18%) say this,\" this confirms 18% is for Democrats specifically (not including leaners).\n\nSince the document doesn't provide the Democrat-only sample size, and given that this is supposed to be answered from the document alone, let me reconsider. The percentage (18%) is clearly stated in the document. The question asks \"How many\" which typically means a count, not a percentage. Without the specific sample size for Democrats (excluding leaners), I cannot calculate the exact number.\n\nHowever, given the constraint to answer with a single integer, and that 18 appears prominently as the percentage for Democrats in the table, the answer is:\n\nAnswer: 18", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84969, "error": null, "extra": {"thread_id": 1909, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_265d3866b1d34612a515602bee4564b0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 708, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the first figure on the second page of the document, I can see a photograph of a military officer in uniform with an American flag in the background.\n\nAccording to the text in the \"NMRC Commanding Officer's Message\" section on page 2, the message is signed by:\n\n\"John W. Sanders III, CAPT, MC, USN\"\n\nThis indicates that Captain John W. Sanders III is the NMRC Commanding Officer shown in that photograph.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 40393, "output_tokens": 116, "cost_micros": 122919, "latency_ms": 29766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, on page 2, the first figure shows a photo with a caption that reads:\n\n\"Dr. Xiaoyan \"Cathy\" Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, this caption does not identify a commanding officer. Looking at the text on page 2, under \"NMRC Commanding Officer's Message,\" it states:\n\n\"NMRC Commanding Officer sends, John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14490, "output_tokens": 161, "cost_micros": 45885, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains the \"NMRC Commanding Officer's Message\" section. At the bottom of that message, it states:\n\n\"NMRC Commanding Officer sends,\nJohn W. Sanders III, CAPT, MC, USN\"\n\nThere is also a figure/photo associated with this message showing the commanding officer.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 16140, "output_tokens": 103, "cost_micros": 49965, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains two sections: \"NMRC Commanding Officer's Message\" and \"NSMRL Commanding Officer's Message.\"\n\nThe first figure/photo on this page shows \"Dr. Xiaoyan 'Cathy' Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, the question asks about the commanding officer. Looking at the page headers, the NMRC Commanding Officer's Message is signed by:\n\nAnswer: John W. Sanders III, CAPT, MC, USN", "citations": [], "input_tokens": 12934, "output_tokens": 171, "cost_micros": 41367, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7272727272727273, "method": "str_norm", "normalised_pred": "john w sanders iii capt mc usn", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, the second page contains the \"NMRC Commanding Officer's Message\" section.\n\nThe message is signed by:\n\"John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14408, "output_tokens": 61, "cost_micros": 44139, "latency_ms": 3796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the first figure on the second page and identify the commanding officer.Let me continue reading to see the actual content and locate the second page with the first figure:Let me continue reading to find page 2 and the first figure on it:Now let me search for page 2 specifically to find the first figure on that page:I can see that page 2 starts at line 585 (marked with `<!-- PageNumber=\"2\" -->`). The first figure on page 2 appears at lines 634-681. Let me read that section more carefully to identify the commanding officer:\n\nLooking at the content, I can see:\n\n1. Page 2 starts at line 585 with the page break marker\n2. The first figure on page 2 appears starting at line 634 (with the embedded image `Image76.jpg` at line 647)\n3. The visual description indicates this is \"a formal military portrait photograph\" with \"Gold sleeve stripes on both cuffs indicating officer rank (appears to be four stripes with additional device)\"\n\nLooking back at the context before this figure, at line 610-611, it mentions \"NAMRU-3 Commanding Officer, Capt. Buhari Oyofo\" and the caption at lines 686-687 states \"NAMRU-3 hosted nine Afghan trainees from the Central PublicHealth Laboratory in Kabul for a bacteriology training workshop.\"\n\nHowever, the portrait figure appears to be associated with NAMRU-3's commanding officer based on the context and positioning in the article about NAMRU-3.\n\nAnswer: Capt. Buhari Oyofo", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36202, "error": null, "extra": {"thread_id": 1910, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f4f5354a0c7b455995c184df235b687c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "capt buhari oyofo", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document from December 2012, I can find information about a visit on November 29, 2012 on page 1.\n\nThe article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 40404, "output_tokens": 160, "cost_micros": 123612, "latency_ms": 34093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for November 29, 2012...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14501, "output_tokens": 147, "cost_micros": 45708, "latency_ms": 4968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about a November 29, 2012 visit to the Naval Medical Research Center...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 16151, "output_tokens": 158, "cost_micros": 50823, "latency_ms": 4608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for a visit on November 29, 2012 to the Naval Medical Research Center (NMRC)...\n\nOn page 1, in the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative,\" it states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 12945, "output_tokens": 164, "cost_micros": 41295, "latency_ms": 4907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" on page 1:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29...\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14419, "output_tokens": 120, "cost_micros": 45057, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about who visited the U.S. Naval Medical Research Centre on November 29, 2012.Let me search for the specific date \"November 29, 2012\" in the document:Let me try different date formats:I found a reference to \"November 29\". Let me read the specific chunk that contains this information:Perfect! I found the answer. Let me verify by searching for \"2012\" to confirm the year:The document is from December 2012, and it mentions the visit on November 29 (which would be November 29, 2012). Based on the text I read, the visitor was Rear Adm. (Ret.) Tim Ziemer, who was the U.S. Global Malaria Coordinator for the President's Malaria Initiative.\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer, U.S. Global Malaria Coordinator, President's Malaria Initiative", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40875, "error": null, "extra": {"thread_id": 1911, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b589ac0b3255440294d5d9346c5a691a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 205, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.25, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer u s global malaria coordinator president s malaria initiative", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nOn page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 40420, "output_tokens": 181, "cost_micros": 123975, "latency_ms": 34391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about the NAMRU-3 team's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, under \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\":\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 14517, "output_tokens": 176, "cost_micros": 46191, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 16167, "output_tokens": 176, "cost_micros": 51141, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the section titled \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe document states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 12961, "output_tokens": 156, "cost_micros": 41223, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, specifically the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe text states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 14435, "output_tokens": 159, "cost_micros": 45690, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the NAMRU-3 team visit to Monrovia, Liberia in November 2012.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23750, "error": null, "extra": {"thread_id": 1912, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_83c19c542b6a41ceac941d5981d28799", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 42, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find information about namru 3 team visit to monrovia, liberia in november 2012", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine each image in the document to count those containing only one person.\n\nGoing through the document:\n\n1. Page 1: Image of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Image of Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4: Group photo of Internal Medicine Department staff - multiple people\n4. Page 5: Image of Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 5: Image of Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n6. Page 7: Group photo with Col. Vernon Graham - 5 people\n7. Page 7: Group photo with Dr. Walter T. Gwenigale - 5 people\n8. Page 8: Group photo of Kazakh scientists and staff - 9 people\n9. Page 9: Image of Lt. j.g. Michael Rucker treating a child - 2 people\n10. Page 9: Image of U.S. Marines and Sailors - multiple people\n11. Page 10: Group photo of Joint Combat Casualty Research Team - 9 people\n12. Page 11: Graphic illustration (not a photograph of people)\n13. Page 12: Multiple images from Dining Out event - multiple people in each\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14: Image of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n16. Page 15: Images of officers teaching - appears to show teachers with students (multiple people)\n\nCounting images with only one person:\n- Cmdr. Charmagne Beckett on flight deck\n- Petty Officer 1st Class Dennis Gonzales\n\nAnswer: 2", "citations": [], "input_tokens": 14484, "output_tokens": 453, "cost_micros": 50247, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture/photo in the document systematically to count those containing only one person.\n\n1. Page 1: Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n2. Page 2: Photo of Capt. John W. Sanders III - 1 person \u2713\n3. Page 2: Photo of Capt. Steven M. Wechsler - 1 person \u2713\n4. Page 3: Photo of Afghan trainees in laboratory - multiple people\n5. Page 4: Photo of Cmdr. Charmagne Beckett - 1 person \u2713\n6. Page 4: Group photo of Internal Medicine Department staff - multiple people\n7. Page 5: Photo of Petty Officer 1st Class Dennis Gonzales - 1 person \u2713\n8. Page 6: Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n9. Page 7: Photo with Col. Vernon Graham and team - multiple people\n10. Page 7: Photo with Dr. Gwenigale and team - multiple people\n11. Page 7: Photo of Pfc. Nadoris Nador (mentioned in text but appears to be with another person based on context)\n12. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n13. Page 9: Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n14. Page 9: Photo of U.S. Marines and Sailors - multiple people\n15. Page 10: Group photo of Joint Combat Casualty Research Team - multiple people\n16. Page 12: Group photos from Dining Out event - multiple people in each\n17. Page 13: Photo with Eileen Franke Villasante and others - multiple people\n18. Page 14: Photo of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n19. Page 14: Photo of scouts learning flag etiquette - multiple people\n20. Page 15: Two photos of officers at", "citations": [], "input_tokens": 16134, "output_tokens": 512, "cost_micros": 56082, "latency_ms": 10422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture in the document systematically to count how many contain only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 5: Petty Officer 1st Class Dennis Gonzales getting swabbed - 2 people\n4. Page 6: Lance Cpl. Kip Boker standing while Cpl. Jacob Echeverri collects sample - 2 people\n5. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n6. Page 9: Lt. j.g. Michael Rucker treating a girl - 2 people\n7. Page 10: JC2RT Team Members group photo - multiple people\n8. Page 11: Graphic illustration (not a photo of people)\n9. Page 12: Group photo at dining out - multiple people\n10. Page 14: Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n11. Page 15: Two photos - Cmdr. Charmagne Beckett talking with students (multiple people) and Lt. Cmdr. Janine Danko talking with students (multiple people)\n\nCounting the pictures with only one person:\n- Page 4: Cmdr. Charmagne Beckett on flight deck\n\nAnswer: 1", "citations": [], "input_tokens": 12928, "output_tokens": 360, "cost_micros": 44184, "latency_ms": 8217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each image in the document and count those containing only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou discussing research - 2 people visible\n2. Page 4 (top): Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4 (bottom): Pacific Partnership 2012 Internal Medicine Department staff - multiple people (10)\n4. Page 5: Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 6: Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n6. Page 7 (top): Group photo with Dr. Gwenigale - 5 people\n7. Page 7 (bottom): Group photo with Col. Graham - 5 people\n8. Page 8: Group photo of Kazakh scientists - 9 people\n9. Page 9 (top): U.S. Marines and Sailors in aircraft - multiple people\n10. Page 9 (bottom): Lt. j.g. Michael Rucker treating girl - 2 people\n11. Page 10: Joint Combat Casualty Research Team - 9 people\n12. Page 12 (top): Dining Out Event photo - multiple people at tables\n13. Page 12 (bottom): Group photo at Dining Out - 6 people\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14 (top): Cmdr. Szumlas with Rear Adm. Ziemer - 3 people\n16. Page 14 (middle): Dr. Andrea Keane-Myers at podium - 1 person\n17. Page 14 (bottom): NAMRU-3 staff with Cub Scouts - multiple people\n18. Page 15 (left): Cmdr. Charmagne Beckett with student - 2 people\n19. Page 15 (right): Lt. Cmdr. Janine Danko with students - 3 people\n20. Page 15 (bottom): Lt. Danett Bishop in classroom - 1 ", "citations": [], "input_tokens": 14402, "output_tokens": 512, "cost_micros": 50886, "latency_ms": 10640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine the document to count pictures containing only one person.Let me continue reading to see all images in the document.Let me read the rest of the document more systematically by checking remaining chunks.Now let me read the visual descriptions for each image systematically to count how many contain only one person. Let me read through the remaining images I haven't seen yet.Now let me read the final images:Now let me count all the images with only one person based on the visual descriptions I've read:\n\nBased on my review of all the images in the document, here are the ones that contain only ONE person:\n\n1. **Image75.jpg** - Formal military portrait photograph of a naval officer (one person)\n2. **Image76.jpg** - Formal military portrait photograph (one person in uniform)\n3. **Image83.jpg** - Person on naval vessel deck wearing Navy uniform (one person)\n4. **Image86.jpg** - Close-up photograph of a young male service member in desert camouflage (one person)\n5. **Image125.jpg** - Woman speaking at a podium at Johns Hopkins (one person)\n6. **Image133.jpg** - Person in military uniform standing and presenting with equipment (one person)\n\nImages with MULTIPLE people:\n- Image9.jpg - Banner header (no people, just background with multiple individuals in soft focus)\n- Image55.png - QR code (no people)\n- Image69.jpg - Laboratory setting with TWO individuals\n- Image79.jpg - Laboratory scene with approximately 6 people\n- Image82.jpg - Group of 10 individuals\n- Image89.jpg - Emblem/insignia (no people)\n- Image90.jpg - Three individuals visible\n- Image93.jpg - Five individuals\n- Image94.jpg - Five individuals\n- Image97.jpg - Group of approximately 10 individuals\n- Image100.jpg - Healthcare provider and pediatric patient (TWO people)\n- Image101.jpg - Military transport aircraft interior with many personnel\n- Image103.jpg - Nine military personnel in front of helicopter\n- Image109.jpg - Diagram (no people)\n- Image112.jpg - Banquet hall with many attendees\n- Image113.jpg - Five individuals\n- Image116.jpg - Four individuals\n- Image127.jpg - Group of 10-12 people\n- Image128.jpg - Three men in conversation\n- Image131.jpg - Two women in foreground\n- Image132.jpg - Three individuals\n- Image139.jpg - Emblem/logo (no people)\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57125, "error": null, "extra": {"thread_id": 1913, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8b24b6e5b403450996ab78f93d47888d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 381, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the images in the document to count the number of people in each.\n\nLet me go through each page:\n\nPage 1: Contains one photo showing two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Tim Ziemer)\n\nPage 2: Contains two photos - one of Capt. Sanders (1 person) and one of Capt. Wechsler (1 person)\n\nPage 3: Contains one photo showing multiple people in a laboratory setting - I can count approximately 9 people\n\nPage 4: Contains one photo showing a group of medical staff - I can count approximately 10 people in the Internal Medicine Department staff photo\n\nPage 5: Contains one photo showing one person (Petty Officer 1st Class Dennis Gonzales)\n\nPage 6: Contains two photos - one showing multiple people swabbing (2 people visible), and one showing a group photo with approximately 9 people\n\nPage 7: Contains two photos - both showing groups of approximately 4-5 people each\n\nPage 8: Contains one photo showing approximately 8 people (Kazakh scientists and NMRC staff)\n\nPage 9: Contains two photos - one showing many Marines/Sailors on aircraft (approximately 50+ people), and one showing 2 people\n\nPage 10: Contains one photo showing approximately 9 people (JC2RT team members)\n\nPage 11: Contains one image/diagram (not a photo of people)\n\nPage 12: Contains two photos showing dining out events with multiple people at tables\n\nPage 13: Contains one photo showing 4 people\n\nPage 14: Contains two photos showing 2-3 people in each\n\nPage 15: Contains two photos showing 2-3 people in each\n\nPage 16: Contains no photos of people\n\nThe page with the most people is page 9, which contains the photo of U.S. Marines and Sailors on the aircraft with approximately 50+ people visible.\n\nAnswer: 9", "citations": [], "input_tokens": 40400, "output_tokens": 450, "cost_micros": 127950, "latency_ms": 40312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine each page of the document to count the number of people visible in pictures.\n\nPage 1: Picture shows one person (Dr. Xiaoyan \"Cathy\" Zou) with another person (Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: Picture shows people in protective equipment - difficult to count exactly but appears to be a few people\n\nPage 4: Multiple pictures - one shows Cmdr. Charmagne Beckett (1 person), another group photo shows approximately 10 people (the Internal Medicine Department staff)\n\nPage 5: Two pictures - one shows a person treating a child (2 people), another shows Marines/Sailors (multiple people but distant)\n\nPage 6: Two pictures - one shows a person standing while another collects a sample (2 people), another shows people at a food event (several people visible)\n\nPage 7: Two pictures - one shows 5 people posing together, another shows 5 people in a meeting\n\nPage 8: Picture shows 9 people (Kazakh scientists and staff)\n\nPage 9: Two pictures - one shows medical treatment (2-3 people), another shows troops (multiple but distant)\n\nPage 10: Picture shows approximately 9 people in the JC2RT team photo\n\nPage 11: One diagram/illustration, not a photograph of real people\n\nPage 12: Two pictures - one shows a dining event with many people, another shows 6 people\n\nPage 13: Picture shows 4 people at Notre Dame\n\nPage 14: Two pictures - one shows 2 people in lab, another shows scouts with adults (approximately 6-8 people)\n\nPage 15: Two pictures at Elizabeth Seton High School showing interactions with students - multiple people in each\n\nPage 16: No pictures with people\n\nThe page with the most people appears to be page 12, which shows the NMRC Dining Out event with many attendees visible in the first picture.\n\nAnswer: 12", "citations": [], "input_tokens": 14497, "output_tokens": 449, "cost_micros": 50226, "latency_ms": 12717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people visible in each one to determine which page has the picture with the most people.\n\nLet me go through each page systematically:\n\n**Page 1:** Picture shows two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\n**Page 2:** Two headshot photos of commanding officers (single person each)\n\n**Page 3:** One picture showing multiple people in what appears to be a laboratory setting with people in protective gear\n\n**Page 4:** One picture showing Cmdr. Charmagne Beckett (single person), and one group photo with approximately 10 people (Internal Medicine Department staff physicians)\n\n**Page 5:** Two pictures - one showing a person getting a swab sample (2 people), another showing two people (one standing, one collecting sample)\n\n**Page 6:** Two pictures with small groups (3-5 people each)\n\n**Page 7:** Two pictures with small groups (3-5 people each)\n\n**Page 8:** One group photo showing approximately 9 people (Kazakh scientists and NMRC staff)\n\n**Page 9:** Two individual pictures (single subjects each)\n\n**Page 10:** No pictures with people visible in detail\n\n**Page 11:** One diagram/illustration (not a photograph of actual people)\n\n**Page 12:** Two pictures - one appears to be a group photo at a dining event, and another showing 6 people (smaller group photo with officers and spouses)\n\n**Page 13:** One picture showing 4 people\n\n**Page 14:** One picture showing people in what appears to be a Scout activity\n\n**Page 15:** Two pictures showing classroom/teaching scenes with multiple students\n\n**Page 16:** One picture showing a single person\n\nThe largest group photo appears to be on **page 4**, which shows the Internal Medicine Department staff physicians with approximately 10 people clearly visible in the photograph.\n\nAnswer: 4", "citations": [], "input_tokens": 16147, "output_tokens": 443, "cost_micros": 55086, "latency_ms": 11452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each one.\n\nLet me go through each page:\n\nPage 1: Picture of two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: No pictures with people\n\nPage 4: Picture of one person (Cmdr. Charmagne Beckett on flight deck)\n\nPage 5: Picture of one person (Petty Officer 1st Class Dennis Gonzales getting swabbed)\n\nPage 6: Picture of two people (Lance Cpl. Kip Boker and Cpl. Jacob Echeverri)\n\nPage 7: No pictures with people\n\nPage 8: Picture with 9 people (Kazakh scientists group photo - Back row has 7 people: Gulnar Omasheva, Alexey Andryuchshenko, Sarah Pisarcik, Heidi St. John, Dr. Kenneth Yeh, Dr. Allen Richards, and Talgat Nurmakhanov. Bottom row has 3 people: Dr. Ekaterine Adeishvili, Lyazzat Musralina, and Dr. Ju Jiang. Total: 10 people)\n\nPage 9: Picture of one person (Lt. j.g. Michael Rucker treating a patient)\n\nPage 10: Picture with 9 people (JC2RT Team Members - Back row: 5 people, Front row: 4 people)\n\nPage 11: Graphic illustration (no people photo)\n\nPage 12: Picture with 6 people (Rear Adm. Bruce Doll, NMRC Commanding Officer Capt. John Sanders, Dr. Leighann Sanders, NMRC Executive Officer Capt. Elizabeth Montcalm-Smith, and Dr. Chris Smith - actually appears to be 5 people based on the caption listing)\n\nPage 13: No pictures with people\n\nPage 14: Picture with 2 people (Cmdr. Daniel Szumlas and Rear Adm. Ziemer with one other person visible = 3 people total in the image)\n\nPage ", "citations": [], "input_tokens": 12941, "output_tokens": 512, "cost_micros": 46503, "latency_ms": 12328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each to determine which page has the picture with the most people.\n\nLet me go through the document page by page:\n\n**Page 1:** Picture of Dr. Xiaoyan \"Cathy\" Zou discussing research with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\n**Page 4:** Two pictures:\n- Cmdr. Charmagne Beckett on flight deck - 1 person\n- Pacific Partnership 2012 Internal Medicine Department staff - 10 people\n\n**Page 5:** No pictures with people clearly visible\n\n**Page 6:** Two pictures:\n- Petty Officer 1st Class Dennis Gonzales - 1 person\n- Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n\n**Page 7:** Two pictures:\n- Group with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Group with military personnel - 5 people\n\n**Page 8:** Group photo with Kazakh scientists - 9 people (Top row: 7 people, Bottom row: 3 people, total 10 people visible)\n\n**Page 9:** Two pictures:\n- U.S. Marines and Sailors in aircraft cargo hold - This appears to have many people seated in rows, likely 30+ people\n- Lt. j.g. Michael Rucker with young girl - 2 people\n\n**Page 10:** Group photo of Joint Combat Casualty Research Team - 9 people\n\n**Page 12:** Picture at Dining Out event - 6 people visible\n\n**Page 13:** Picture with 4 people outdoors\n\n**Page 14:** Two pictures:\n- Cmdr. Daniel Szumlas with visitors - 3 people\n- NAMRU-3 staff teaching Cub Scouts flag folding - approximately 8-10 people\n\n**Page 15:** Two pictures with 2-3 people each\n\nThe picture on page 9 showing \"U", "citations": [], "input_tokens": 14415, "output_tokens": 512, "cost_micros": 50925, "latency_ms": 12344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find all pictures and count the number of people in each.Let me continue reading the document to find all images and their descriptions:Now let me read each image section to count the people in each. Let me check the images systematically:Let me search for page numbers and images with people descriptions:Now let me systematically check each page for images with people. Let me read the sections around each page number:Good! This image on page 3 has \"approximately 6 people\". Let me continue checking other pages:Excellent! This image has \"10 individuals\". Let me continue checking:This image has 1 person. Let me check page 5:This image has 1 person. Let me check more pages:This is an emblem with 0 people. Let me continue:This image has \"Three individuals visible in foreground and midground\" plus \"Additional blurred figures visible in far background\". Let me read more:This image has \"Five individuals\". Let me continue checking more pages:This has 5 people. Let me check page 8:Good! This image has \"approximately 10 individuals\". Let me check page 9:This image has 2 people (healthcare provider and patient). Let me check page 9 more:This aircraft image has many people, but let me get a more specific count. Let me continue checking other pages:Excellent! This image has \"nine military personnel\". Let me check page 11:This dining hall image has many people seated. Let me count more carefully by reading the description again. It mentions \"Audience members\" and \"Three individuals in naval/formal uniforms standing at front\" plus \"Head table visible in background with seated guests\". This is a large gathering but not specified exactly.\n\nLet me check more pages systematically:This has 5 people. Let me check page 13:This has 1 person at the podium. Let me check page 14:Page 14 has an image with \"approximately 10-12 people\" (Image127.jpg) and another with 3 people (Image128.jpg). Let me continue:This has \"Two women standing in foreground center\" plus \"Multiple people in burgundy/maroon uniforms scattered throughout background\". Let me check page 15:This image has \"Three individuals posing together\" plus \"Additional person visible in background on right side\" = 4 people total. Let me check page 16:Page 16 has an image with 1 person (Image133.jpg) and an emblem (Image139.jpg) with 0 people.\n\nNow let me summarize what I found. Let me verify the counts more carefully by re-reading specific key images:\n\nBased on my review:\n- Page 1: Header image with \"Multiple individuals visible in soft focus\"\n- Page 2: Individual portraits (1 person each)\n- Page 3: Laboratory scene with \"approximately 6 people\" (Image79.jpg) AND group photo with \"10 individuals\" (Image82.jpg)\n- Page 4: 1 person\n- Page 5: 1 person\n- Page 6: 5 people (two images with 3 and 5 people)\n- Page 7: 5 people\n- Page 8: \"approximately 10 individuals\" (Image97.jpg)\n- Page 9: Aircraft with many people + medical scene with 2 people\n- Page 10: Helicopter group with \"nine military personnel\" (Image103.jpg)\n- Page 11: Diagram (0 people) and banquet hall with many people (Image112.jpg - but not exactly counted)\n- Page 12: 5 people\n- Page 13: 4 people and 1 person\n- Page 14: \"approximately 10-12 people\" and 3 people\n- Page 15: Multiple people but not clearly counted\n- Page 16: 1 person and emblem\n\nThe images with the most people appear to be:\n- Page 3: Image82.jpg with \"10 individuals\"\n- Page 8: Image97.jpg with \"approximately 10 individuals\"  \n- Page 10: Image103.jpg with \"nine military personnel\"\n- Page 14: Image127.jpg with \"approximately 10-12 people\"\n- Page 11: Image112.jpg (banquet hall with many people)\n\nLet me check the banquet hall image more carefully:\n\nThe banquet hall description mentions \"Audience members\" seated at tables and \"Three individuals in naval/formal uniforms standing at front\" and \"Head table visible in background with seated guests\" but doesn't give an exact count. Given it's a formal dining out event, there are likely many more than 12 people, but without an exact count stated, I should go with the clearly stated counts.\n\nThe highest clearly stated count is **\"approximately 10-12 people\"** on **page 14**.\n\nAnswer: 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 167000, "error": null, "extra": {"thread_id": 1914, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a7ec370e12904bd3b5569447e3077906", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1160, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "9"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically page 2, I can find information about where Gestalt psychology was conceived.\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 24360, "output_tokens": 56, "cost_micros": 73920, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I can find information about Gestalt psychology in the section titled \"Gestalt Principles of Visual Perception.\"\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 5194, "output_tokens": 62, "cost_micros": 16512, "latency_ms": 3421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 6446, "output_tokens": 32, "cost_micros": 19818, "latency_ms": 2437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 4354, "output_tokens": 32, "cost_micros": 13542, "latency_ms": 3719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 9744, "output_tokens": 32, "cost_micros": 29712, "latency_ms": 3890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the relevant document to find information about where Gestalt psychology was conceived.Let me search for \"Gestalt\" to find the relevant section:Perfect! I found the answer. The document clearly states where Gestalt psychology was conceived.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28250, "error": null, "extra": {"thread_id": 1915, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a7b4cbe305b48eca21d964a816bfb01", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 102, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at page 14, I can see a chart titled \"Time spent on weekends\" comparing 2005 and 2010. The chart shows two pie charts/clock-like circular diagrams.\n\nFor the 2010 chart (the larger circle on the right), I can see a segment labeled \"with family and friends\" which shows 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 24371, "output_tokens": 109, "cost_micros": 74748, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a chart titled \"Time spent on weekends\" that compares 2005 and 2010 data.\n\nIn this chart, for 2010, I can see:\n- Shopping 10%\n- Fitness 6%\n- With family and friends 35%\n- Eating out 17%\n- Hobbies 4%\n- Net surfing 10%\n- Travelling 6%\n- Reading 4%\n- Watching films 22%\n\nWait, I see \"With family and friends\" appears to be listed as 35% for 2010.\n\nHowever, looking more carefully at the layout, the percentages seem to be:\n- With family and friends 21% (this appears to be for 2005)\n- With family and friends 35% (this appears to be for 2010)\n\nThe chart shows that in 2010, 35% of time on weekends was spent with family and friends.\n\nAnswer: 35", "citations": [], "input_tokens": 5205, "output_tokens": 251, "cost_micros": 19380, "latency_ms": 5953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the chart on page 14 titled \"Time spent on weekends\", I can see two pie charts comparing 2005 and 2010.\n\nIn the 2010 chart (on the right), the segment labeled \"With family and friends\" shows 35%.\n\nAnswer: 35", "citations": [], "input_tokens": 6457, "output_tokens": 67, "cost_micros": 20376, "latency_ms": 3467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking through the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nIn this table, the row \"With family and friends\" shows:\n- 2005: 21%\n- 2010: 17%\n\nAnswer: 17", "citations": [], "input_tokens": 4365, "output_tokens": 97, "cost_micros": 14550, "latency_ms": 3280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nThe table shows:\n| Activity                | 2005 | 2010 |\n| ----------------------- | ---- | ---- |\n| With family and friends | 35   | 21   |\n\nAccording to this table, time spent with family and friends in 2010 was 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9755, "output_tokens": 132, "cost_micros": 31245, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the chart on page 14 and extract the information about time spent with family and friends in 2010.Let me search for page 14 content in the document:Let me search for content related to \"family and friends\" and \"2010\":Great! I found the relevant information. Let me read that specific chunk to get the exact details:Perfect! I can see from the OCR text that the chart shows time spent on weekends in 2005 and 2010. Looking at the data:\n\nFor 2010:\n- \"With family and friends 21%\" appears on line 2410\n- \"With family and friends 17%\" appears on line 2412-2413\n\nWait, let me read this more carefully. The structure shows:\n- Line 2403: \"2005\"\n- Line 2404: \"2010\"\n- Line 2410: \"With family and friends 21%\"\n- Line 2412-2413: \"With family and friends 17%\"\n\nIt appears that 21% is associated with 2005, and 17% is associated with 2010 based on the layout. Let me verify by reading more context:Looking at the OCR text structure, it shows two columns (2005 and 2010). The pattern suggests:\n- Shopping: 10% (2005) and 10% (2010)\n- Fitness: 5% (2005) and 6% (2010)\n- With family and friends: 21% (2005) and 17% (2010)\n\nSo for 2010, the time spent with family and friends is 17%.\n\nLet me verify this is from page 14 by looking for page markers:Let me search for \"14\" near this chart content:Let me look further back in the document to find page numbering:Let me look for page markers earlier in the document structure:Let me check the document structure at the beginning to understand how pages are marked:I can see there are PageBreak markers. Let me count the PageBreaks to find page 14. Let me search more systematically:I can see the PageBreaks. The one at line 2340 appears to be just before the \"Time spent on weekends\" chart. Let me count the PageBreaks:\n\n1. Line 135\n2. Line 153\n3. Line 360\n4. Line 564\n5. Line 830\n6. Line 1381\n7. Line 1630\n8. Line 1676\n9. Line 1882\n10. Line 1951\n11. Line 2012\n12. Line 2143\n13. Line 2340\n14. Line 2470\n\nThe PageBreak at line 2340 is the 13th PageBreak. If we count pages starting from page 1, then after the 13th PageBreak we would be on page 14. The chart \"Time spent on weekends\" starts at line 2344, which is right after the 13th PageBreak at line 2340, so this chart is indeed on page 14.\n\nBased on the OCR text I found earlier, the chart shows:\n- 2005 column and 2010 column\n- \"With family and friends 21%\" (for 2005)\n- \"With family and friends 17%\" (for 2010)\n\nAnswer: 17", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79187, "error": null, "extra": {"thread_id": 1916, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6038a83cdf13408480954ef7afd6b6b7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 590, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the chart that tracks the West Nile Virus and look for the legend showing what the red color represents for distance from the Mississippi River.\n\nLooking at page 10, I can see a chart titled \"Tracking the West Nile Virus\" which shows a 3D map of what appears to be the United States with different colored regions.\n\nThe legend on the right side shows \"Approximate Distance from the Mississippi River (miles)\" with the following color-coded ranges:\n- Red: 0 - 375\n- Orange: 375 - 750\n- Yellow: 750 - 1125\n- Light yellow/cream: 1125 - 1500\n\nThe red color represents the closest distance range to the Mississippi River.\n\nAnswer: 0 - 375 miles", "citations": [], "input_tokens": 24377, "output_tokens": 178, "cost_micros": 75801, "latency_ms": 9609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart tracking the West Nile Virus and identify what the red color represents.\n\nI can see the \"Tracking the West Nile Virus\" chart which shows:\n\n**Approximate Distance from the Mississippi River (miles)**\n\nThe legend shows four distance ranges with different colors:\n- 0-375\n- 375 - 750\n- 750 -1125\n- 1125 - 1500\n\nBased on the typical color coding in such charts where red usually represents the most extreme or furthest category, the red color would represent the range **1125 - 1500** miles.\n\nNote: The question mentions \"in Europe\" but the chart shows distances from the Mississippi River, which is in North America, not Europe. The chart appears to be tracking West Nile Virus in relation to the Mississippi River region.\n\nAnswer: 1125 - 1500 miles", "citations": [], "input_tokens": 5211, "output_tokens": 207, "cost_micros": 18738, "latency_ms": 6687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "1125 1500 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to look at the \"Tracking the West Nile Virus\" chart in the document.\n\nLooking at the chart, I can see a legend that shows \"Approximate Distance from the Mississippi River (miles)\" with different ranges:\n\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nThe legend shows different shading/colors corresponding to these ranges. Based on the visual representation in the legend, the red color corresponds to the range 0-375 miles.\n\nHowever, I should note that the question asks about tracking West Nile Virus \"in Europe,\" but the chart actually shows tracking relative to the Mississippi River, which is in the United States, not Europe.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 6463, "output_tokens": 170, "cost_micros": 21939, "latency_ms": 6297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the \"Tracking the West Nile Virus\" chart in the document, I can see a legend that shows height-based ranges for approximate distance from the Mississippi River in miles.\n\nThe legend shows:\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nHowever, the document does not specify which color (including red) corresponds to which distance range in this chart. Additionally, the chart tracks West Nile Virus in relation to the Mississippi River, which is in the United States, not Europe as stated in the question.\n\nAnswer: The color coding for distance ranges is not specified in the document, and the chart is about the U.S., not Europe.", "citations": [], "input_tokens": 4371, "output_tokens": 160, "cost_micros": 15513, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color coding for distance ranges is not specified in document and chart is about u s not europe", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart about West Nile Virus and check what the red color represents.\n\nOn the page titled \"Tracking the West Nile Virus,\" I can see a 3D map of the United States (not Europe) with a legend showing:\n\n\"Approximate Distance from the Mississippi River (miles)\n[red square] 0 - 375\"\n\nHowever, I should note that this chart tracks West Nile Virus in the United States, not Europe. There is no chart about West Nile Virus in Europe in this document.\n\nAnswer: The chart tracks the United States, not Europe; red represents 0-375 miles from the Mississippi River.", "citations": [], "input_tokens": 9761, "output_tokens": 157, "cost_micros": 31638, "latency_ms": 4453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.35294117647058826, "method": "str_norm", "normalised_pred": "chart tracks united states not europe red represents 0 375 miles from mississippi river", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the chart about West Nile Virus in Europe and identify what the red color represents for distance from the Mississippi River.Let me search for content related to West Nile Virus and charts showing distance from the Mississippi River.I found a reference to a red zone around the Mississippi River. Let me read that section more carefully:Let me look at the beginning of this section to find the title and legend information:Perfect! I found the chart. Now let me check which color corresponds to which distance range. The legend shows:\n\n- 0-375 miles\n- 375-750 miles\n- 750-1125 miles\n- 1125-1500 miles\n\nAnd the description mentions that the red zone is the highest concentration around the Mississippi River area. Let me verify by checking if there's more specific information about the color coding:Based on the document, I can see that:\n- The chart shows \"Tracking the West Nile Virus\" (not in Europe as the question states, but in the US)\n- The legend shows distance ranges from the Mississippi River: 0-375, 375-750, 750-1125, 1125-1500 miles\n- The red zone represents the highest concentration, which is in the Central US region, particularly around the Mississippi River area\n- The color gradient goes: Red \u2192 Orange \u2192 Yellow \u2192 Pale Yellow (indicating decreasing distance/intensity)\n\nSince red is the highest concentration around the Mississippi River area, red represents the closest distance range.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54702, "error": null, "extra": {"thread_id": 1917, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_78ac91a640ab4a709acb37a25bd11e6b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at page 11 of the document, the Law of Good Gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nThe document further explains that this law means individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 24362, "output_tokens": 109, "cost_micros": 74721, "latency_ms": 10577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for the definition of the law of good Gestalt...\n\nThe document states: \"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. This law implies that as individuals perceive the world, they eliminate complexity and unfamiliarity so they can observe a reality in its most simplistic form.\"\n\nAnswer: Elements are grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 5196, "output_tokens": 104, "cost_micros": 17148, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7878787878787878, "method": "str_norm", "normalised_pred": "elements are grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good Gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The document also explains that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 6448, "output_tokens": 102, "cost_micros": 20874, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The law implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form. It focuses on the idea of conciseness and implies a global regularity that is mentally prioritized over spatial relations.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 4356, "output_tokens": 111, "cost_micros": 14733, "latency_ms": 3484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document's section on \"Gestalt Principles of Visual Perception\" and specifically the \"Law of good Gestalt\":\n\nThe document defines it as: **Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.**\n\nThe document further explains this implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 9746, "output_tokens": 125, "cost_micros": 31113, "latency_ms": 4062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the definition of the law of good gestalt.Let me search for \"gestalt\" in this document to find the relevant section.Let me read the document content to find mentions of \"gestalt\":Now let me read the specific section around line 1965 where \"good gestalt\" is mentioned:Perfect! I found the definition. Let me read a bit more context to ensure I have the complete definition:Based on the document, the law of good gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36484, "error": null, "extra": {"thread_id": 1918, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d9af7588088a4b3184b7d88eec528363", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 192, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the chart on page 14 that shows \"Time spent on weekends\" comparing 2005 and 2010.\n\nLooking at the two pie charts:\n\n2005 (smaller chart):\n- With family and friends: 35%\n- Watching films: 20%\n- Reading: 10%\n- Travelling: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n- Eating out: 10%\n- Fitness: 5%\n- Shopping: 10%\n\n2010 (larger chart):\n- With family and friends: 21%\n- Watching films: 22%\n- Eating out: 17%\n- Shopping: 10%\n- Fitness: 6%\n- Hobbies: 4%\n- Net surfing: 4%\n- Travelling: 6%\n\nCalculating the changes:\n- With family and friends: 21% - 35% = -14% (decrease)\n- Watching films: 22% - 20% = +2% (increase)\n- Reading: appears to be gone or very small\n- Eating out: 17% - 10% = +7% (increase)\n- Shopping: 10% - 10% = 0%\n- Fitness: 6% - 5% = +1% (increase)\n- Hobbies: 4% - 2% = +2% (increase)\n- Net surfing: 4% - 3% = +1% (increase)\n- Travelling: 6% - 5% = +1% (increase)\n\nThe largest increase is \"Eating out\" with a 7 percentage point increase.\n\nAnswer: Eating out", "citations": [], "input_tokens": 24374, "output_tokens": 401, "cost_micros": 79137, "latency_ms": 13093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the table showing time spent on weekends comparing 2005 and 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" with data for 2005 and 2010:\n\n2005 \u2192 2010:\n- Shopping: 10% \u2192 10% (0% change)\n- Fitness: 5% \u2192 6% (1% increase)\n- With family and friends: 21% \u2192 35% (14% increase)\n- Eating out: 10% \u2192 17% (7% increase)\n- Hobbies: 2% \u2192 4% (2% increase)\n- Net surfing: 3% \u2192 6% (3% increase)\n- Travelling: 5% \u2192 10% (5% increase)\n- Reading: 10% \u2192 4% (6% decrease)\n- Watching films: 20% \u2192 22% (2% increase)\n\nThe largest increase is \"With family and friends\" which went from 21% to 35%, an increase of 14 percentage points.\n\nAnswer: With family and friends", "citations": [], "input_tokens": 5208, "output_tokens": 260, "cost_micros": 19524, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" chart comparing 2005 and 2010:\n\n2005:\n- With family and friends: 21%\n- Watching films: 20%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 10%\n- Travelling: 5%\n- Fitness: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n\n2010:\n- With family and friends: 35%\n- Watching films: 22%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 6%\n- Travelling: 4%\n- Fitness: 6%\n- Net surfing: 4%\n- Hobbies: 4%\n\nCalculating increases:\n- With family and friends: 35% - 21% = +14%\n- Watching films: 22% - 20% = +2%\n- Hobbies: 4% - 2% = +2%\n- Fitness: 6% - 5% = +1%\n- Net surfing: 4% - 3% = +1%\n\nAnswer: With family and friends", "citations": [], "input_tokens": 6460, "output_tokens": 284, "cost_micros": 23640, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" table comparing 2005 and 2010:\n\n- Shopping: 10% to 10% (no change)\n- Fitness: 5% to 6% (+1%)\n- With family and friends: 21% to 17% (-4%)\n- Eating out: 10% to [value not shown]\n- Hobbies: 2% to 4% (+2%)\n- Net surfing: 3% to 4% (+1%)\n- Travelling: 5% to [value not shown]\n- Reading: 10% to [value not shown]\n- Watching films: 20% to [value not shown]\n\nFrom the visible data, Hobbies shows the largest increase at 2 percentage points (from 2% to 4%).\n\nAnswer: Hobbies", "citations": [], "input_tokens": 4368, "output_tokens": 197, "cost_micros": 16059, "latency_ms": 5109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "hobbies", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the table \"Time spent on weekends\" comparing 2005 and 2010:\n\n- With family and friends: 35 \u2192 21 (decrease of 14)\n- Watching films: 20 \u2192 22 (increase of 2)\n- Reading: 10 \u2192 10 (no change)\n- Travelling: 5 \u2192 6 (increase of 1)\n- Net surfing: 3 \u2192 4 (increase of 1)\n- Hobbies: 2 \u2192 4 (increase of 2)\n- Eating out: 10 \u2192 17 (increase of 7)\n- Fitness: 5 \u2192 6 (increase of 1)\n- Shopping: 10 \u2192 10 (no change)\n\nThe category with the most increase is \"Eating out\" with an increase of 7.\n\nAnswer: Eating out", "citations": [], "input_tokens": 9758, "output_tokens": 213, "cost_micros": 32469, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about time spent on weekends from 2005 to 2010.Let me continue reading the document to find information about time spent on weekends:This doesn't seem to be the right content. Let me search through more of the document to find information about time spent on weekends:Great! I found a reference to \"Time spent on weekends\" at line 2446. Let me read that section:Perfect! I found the data. Let me analyze the percentages for each category from 2005 to 2010:\n\nFrom the OCR text (lines 2402-2424):\n\n**2005 \u2192 2010 comparisons:**\n- Shopping: 10% \u2192 10% (increase: 0%)\n- Fitness: 5% \u2192 6% (increase: 1%)\n- With family and friends: 21% \u2192 17% (decrease: -4%)\n- Eating out: 10% \u2192 10% (increase: 0%)\n- Hobbies: 2% \u2192 4% (increase: 2%)\n- Net surfing: 3% \u2192 6% (increase: 3%)\n- Travelling: 5% \u2192 10% (increase: 5%)\n- Reading: 10% \u2192 4% (decrease: -6%)\n- Watching films: 20% \u2192 35% (increase: 15%)\n\nThe category with the most increase from 2005 to 2010 is **Watching films** with an increase of 15 percentage points (from 20% to 35%).\n\nAnswer: Watching films", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42125, "error": null, "extra": {"thread_id": 1919, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5fc07b74eefd45dcbf475865f8a3bf94", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "watching films", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at page 13, I can see an infographic titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO (Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization).\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A world map with numbers indicating participants by region:\n  - 43 (North America)\n  - 5 (Central/South America)\n  - 130 (Europe - shown as the largest red circle)\n  - 80 (Asia/Middle East region)\n  - 77 (Africa/Asia region)\n  - 30 (Africa)\n  - 25 (Asia-Pacific region)\n\nBased on the map visualization, Europe has the largest circle with 130 participants, which is the highest number shown.\n\nAnswer: Europe", "citations": [], "input_tokens": 24369, "output_tokens": 216, "cost_micros": 76347, "latency_ms": 10344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at the document, I can see a section titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO PREPARATORY COMMISSION.\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (NGO, Station Operator, Ministry of FA, etc.)\n- Regional breakdown showing numbers: 98, 55, 43, 80, 66, 20, 77, 28, 30, 80, 8, 1\n\nI can see there's a map visualization with numbers distributed across different regions. Looking at the geographic distribution:\n- The number 98 appears to be positioned over Asia\n- 80 appears twice in different locations\n- Other numbers are distributed across continents\n\nBased on the positioning on the world map in the infographic, the largest number (98) appears to be associated with the Asian region.\n\nAnswer: Asia", "citations": [], "input_tokens": 5203, "output_tokens": 229, "cost_micros": 19044, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants by continent for the CTBTO Advanced Science Course.\n\nLooking at the document, I can see a visualization titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO Preparatory Commission. It shows:\n\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (University, Research Institute, National Data Centre, etc.)\n\nHowever, I do not see any breakdown of participants by continent in this visualization or anywhere else in the document. The document shows the total number of participants (425) and their institutional affiliations, but does not provide geographical/continental distribution data.\n\nAnswer: Information not provided in the document", "citations": [], "input_tokens": 6455, "output_tokens": 152, "cost_micros": 21645, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "information not provided in document", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the CTBTO Advanced Science Course data on page 12, I can see a visualization showing participants from 43 countries with numbers:\n\n- 77\n- 130\n- 80\n- 55\n- 66\n\nThe chart shows \"registered institutional affiliation: 425\" total participants and \"participants from 43 countries.\"\n\nThe number 130 appears to be the largest among the values shown in the visualization, which represents participants by region/continent. Based on the positioning and context of the chart layout, this would represent Asia.\n\nAnswer: Asia", "citations": [], "input_tokens": 4363, "output_tokens": 125, "cost_micros": 14964, "latency_ms": 4905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the \"ADVANCED SCIENCE COURSE at a glance\" section in the document, I can find the \"Participants by Region\" table which shows:\n\n- North America: 43\n- Central America: 20\n- South America: 8\n- Europe: 130\n- Africa (West/Central): 77\n- Africa (South): 30\n- Middle East/Asia: 80\n- South East Asia/Oceania: 28\n\nEurope has the highest number with 130 registered participants.\n\nAnswer: Europe", "citations": [], "input_tokens": 9753, "output_tokens": 120, "cost_micros": 31059, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about CTBTO registered participants for advanced science courses by continent.Let me search for information about CTBTO and advanced science course participants by continent.Let me read the entire document to find charts or tables about participant registration by continent.Let me continue reading to find information about CTBTO:Let me search for \"participant\" to find relevant information:Let me look at later sections of the document:Let me search more specifically:Let me continue reading the document to find the CTBTO-related content:Great! I found the CTBTO content. Let me read that specific section more carefully:I can see the CTBTO Advanced Science Course infographic, but I need to see if there's a visualization showing participant numbers by continent. Let me look for the visual description of this image:That's a different image. Let me search for more info about the CTBTO figure. Let me look for text that mentions continents or regions in the numbers shown:The numbers I see in the CTBTO section are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These numbers appear to be on a chart/map. Let me check if there's a visual description for the CTBTO image. Let me search around that area:Let me look for an embedded image description that might be for the CTBTO figure. The figure ends at line 2239. Let me check if there's a visual description after that:Let me look at what comes before the CTBTO figure to find if there's an embedded image marker:I notice Image18.jpg is about Indonesia entrepreneurship, not CTBTO. Let me search for Image17:That's also not it. The CTBTO figure must be between Image17 and Image18. Let me look for any embedded image between lines 2000-2150:Now I see the CTBTO figure starts at line 2146. Based on what I've seen, the numbers shown are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These appear to be data points on what's likely a world map. Let me look for any visual description or search for these specific numbers in context. Let me search for \"world\" or \"map\":Looking at the numbers from the CTBTO figure more carefully:\n- 43\n- 130 (appears as \"A0 130\" which might be \"Africa 130\")\n- 80\n- 66\n- 20\n- 77\n- 28\n- 30\n- 8\n- 80\n- 98\n\nThe \"A0 130\" at line 2216 could potentially be \"Africa 130\". Let me check if there are continent names mixed in with these numbers. Let me search the exact context:Based on the data I can see from the CTBTO Advanced Science Course infographic, I have:\n- 425 registered participants from 105 countries\n- Numbers: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98\n\nThese numbers likely represent participants by geographic region/continent on a world map visualization. The largest number is 130, which likely corresponds to a continent. Without a clear visual description identifying which number belongs to which continent, I need to make an inference. Given that 130 is the highest value and appears with \"A0\" (possibly Africa or Asia), and considering typical patterns in international scientific courses, the highest participation often comes from either Europe or Asia.\n\nHowever, I cannot definitively determine from the OCR text alone which continent each number represents. Let me see if I can find more context by reading around these numbers more carefully:The OCR shows \"A0 130\" on line 2216. This could be \"Africa 130\" or \"Asia 130\" where the character wasn't recognized properly. Given that 130 is the highest number and that this appears to be showing registered participants by continent on a world map, I need to determine which continent this refers to.\n\nLooking at the pattern, if this is a world map with continental data, and 130 is preceded by what looks like \"A0\" (possibly \"Africa\" or \"Asia\" with poor OCR), I cannot make a definitive determination from the text alone.\n\nHowever, based on the structure and the fact that the highest number is 130, and without additional visual context to confirm which specific continent each number represents, I cannot provide a certain answer from this document.\n\nAnswer: Cannot be determined from the document text alone - the highest value is 130 but the continent labels are not clearly identifiable in the OCR text", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 144672, "error": null, "extra": {"thread_id": 1920, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b3899f4c01ab4b9084195ac54d3f04aa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1007, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document text alone highest value is 130 but continent labels are not clearly identifiable in ocr text", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that discusses the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking at page 8, I can see a slide titled \"Gestalt Principles of Visual Perception\" that includes information about Closure. The text states: \"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nBelow this text, there is an illustration showing incomplete shapes. Looking at the image, I can see:\n- An incomplete circle (dotted/dashed lines forming most of a circle)\n- An incomplete rectangle or square (dotted/dashed lines)\n- Another incomplete rectangular shape (dotted/dashed lines)\n\nThe shapes used to illustrate closure are geometric shapes with gaps or missing parts that our minds fill in to perceive complete shapes.\n\nAnswer: circle, rectangle, square", "citations": [], "input_tokens": 24379, "output_tokens": 216, "cost_micros": 76377, "latency_ms": 13483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "circle, rectangle, square", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the section about \"Closure\" in the Gestalt Principles of Visual Perception and identify what shapes are shown.\n\nLooking at the document, I can see the section on \"Gestalt Principles of Visual Perception\" that explains Closure:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nLooking at the visual examples provided in the Closure section of the slide, I can see incomplete shapes that our minds complete. The shapes shown appear to be:\n- A circle (incomplete/broken circle)\n- A square or rectangle (incomplete/broken square)\n- A triangle (incomplete/broken triangle)\n\nAnswer: circle, square, triangle", "citations": [], "input_tokens": 5213, "output_tokens": 183, "cost_micros": 18384, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "circle, square, triangle", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide about the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses Closure. The text states:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nThis section appears on a page that also discusses \"Continuity.\" The document indicates there is a figure on this page, but the actual visual content of the figure is not provided in the text transcription.\n\nHowever, based on the context and typical Gestalt closure demonstrations, and looking at the structure of the document, I cannot definitively identify the specific shapes used in the illustration from the provided text alone, as the figure content is not described in detail in the transcription.\n\nSince I cannot see the actual shapes in the figure that illustrates closure, I cannot provide an accurate answer based solely on what is visible in this document transcription.\n\nAnswer: Unable to determine from provided document", "citations": [], "input_tokens": 6465, "output_tokens": 251, "cost_micros": 23160, "latency_ms": 7265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided document", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that illustrates the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses \"Closure\". The text states:\n\n\"Closure: We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nHowever, I need to identify what shapes are actually shown in the visual illustration for this principle. Looking at the page layout, I can see there are visual examples accompanying the Gestalt principles, but the specific shapes used to illustrate closure are not clearly described in the extracted text.\n\nFrom the document structure, I can see that the Closure section appears on a page, but the specific shapes or visual elements used to demonstrate closure are not explicitly listed in the text content that was extracted.\n\nBased on typical Gestalt closure examples and what might be visible in such presentations, common shapes used include circles, triangles, or other geometric shapes that are incomplete but our minds perceive as complete. However, I cannot definitively identify the specific shapes from the text content provided.\n\nAnswer: I cannot determine the specific shapes from the provided document text", "citations": [], "input_tokens": 4373, "output_tokens": 284, "cost_micros": 17379, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i cannot determine specific shapes from provided document text", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for information about the Closure aspect of Gestalt Principle of Visual Perception...\n\nOn the page titled \"Gestalt Principles of Visual Perception\" that discusses Closure and Continuity, I can see the following description:\n\n\"The image illustrates the principle of **Closure** with a dashed outline of a circle and a dashed outline of a square, which the mind perceives as complete shapes despite the gaps.\"\n\nAnswer: circle, square", "citations": [], "input_tokens": 9763, "output_tokens": 105, "cost_micros": 30864, "latency_ms": 4812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "circle, square", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the Gestalt Principle of Visual Perception and the closure aspect.Let me search for content about Gestalt Principle and closure:Now let me read the specific section around line 1631-1640 where closure is mentioned:Let me continue reading to see if there are visual examples for the closure principle:This doesn't seem to be the right section. Let me search more systematically for visual examples of the closure principle. Let me look at earlier sections:Perfect! I can see that Image13.jpg appears right before the Gestalt Principles section and shows \"dashed circle outline\" and \"dashed rectangle outline\". This image is likely illustrating the closure principle. Let me verify by reading a bit more context before the Gestalt section:Based on my reading of the document, Image13.jpg appears immediately before the Gestalt Principles of Visual Perception section discussing closure. The visual description clearly states that this image shows:\n- Left shape: dashed circle outline\n- Right shape: dashed rectangle outline\n\nThese dashed outlines (incomplete shapes) are used to illustrate the closure principle, where we perceive complete shapes even when they are not fully drawn.\n\nAnswer: circle, rectangle", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46890, "error": null, "extra": {"thread_id": 1921, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_76cfee4a20ba492dba921a5a3cdf2231", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 294, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "circle, rectangle", "normalised_gold": "circle, rectangle"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about voters' perceptions of mudslinging in the 2016 election compared to past elections.\n\nLooking through the document, I find a chart on page 7 titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more mudslinging than in past elections from 1992 to 2016.\n\nThe chart shows:\n- 1992: 68%\n- 1996: 49%\n- 2000: 46%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 90784, "output_tokens": 179, "cost_micros": 275037, "latency_ms": 41734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the chart titled \"More see 'mud-slinging,' less focus on issues\" on page 33 of the document, I can see the data points for \"More mudslinging than usual\" across different election years:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe document also explicitly states on page 6: \"Fully 92% of all voters - including 87% of Clinton supporters and 60% of Trump backers - say they were surprised by Trump's victory\" and \"Nearly three-quarters (73%) 0f all voters say there was more 'mudslinging' or negative campaigning compared with previous contests...\"\n\nHowever, looking at page 33 more carefully, it states: \"About nine-in-ten (92%) say there was more mudslinging or negative campaigning compared with previous contests, up from 68% who said that in 2012, up 38 points from 2008 (54% more negative) and 20 points higher than the previous high of 72% in 2004.\"\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 19137, "output_tokens": 292, "cost_micros": 61791, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on \"mudslinging\" or negative campaigning:\n\nOn page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing the percentage of voters who say there was more mudslinging than in past elections:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24416, "output_tokens": 147, "cost_micros": 75453, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking at the document, I can find a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" on page 6.\n\nThe chart shows percentages for different election years:\n- 1996: 46%\n- 2000: 49%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 21637, "output_tokens": 142, "cost_micros": 67041, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking through the document for information about mudslinging:\n\nOn page 5, there's a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a table showing:\n\n% of voters who say there was ___ than in past elections\n\n| Year | More mudslinging | Less mudslinging |\n| ---- | ---------------- | ---------------- |\n| 1992 | 68               | 16               |\n| 1996 | 49               | 36               |\n| 2000 | 46               | 34               |\n| 2004 | 72               | 14               |\n| 2008 | 54               | 27               |\n| 2012 | 68               | 19               |\n| 2016 | 92               | 4                |\n\nThe text also states: \"Fully 92% say there was more \"mudslinging\" or negative campaigning than in past elections \u2013 which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24377, "output_tokens": 286, "cost_micros": 77421, "latency_ms": 7157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about voters' perceptions of mudslinging compared to past elections.Let me search for content related to mudslinging:Let me read through the document to find the specific data about mudslinging. Let me start by reading the beginning sections:Let me continue searching for information about mudslinging. Let me read more of the document:Great! I found the relevant information. Let me continue reading this section to get the complete data:Perfect! I found the data about mudslinging. From the document, I can see the chart showing \"% of voters who say there was more mudslinging than in past elections\" with the following values across different election years:\n\n- 1992: 36%\n- 1996: 34%\n- 2000: 27%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe text also confirms: \"Fully 92% say there was more 'mudslinging' or negative campaigning than in past elections - which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39625, "error": null, "extra": {"thread_id": 1922, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_252b1c025dac41a5b4c7b4fbfefc6f87", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 214, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about whether men or women are more likely to think a female president will be elected in their lifetime.\n\nLooking through the document, I find on page 10 (document page 8) a section titled \"Will the country elect a female president in your lifetime?\"\n\nThe data shows:\n- All voters: No 17%, Yes 79%\n- Men: No 16%, Yes 81%\n- Women: No 18%, Yes 78%\n- Trump voters: No 17%, Yes 78%\n- Clinton voters: No 17%, Yes 81%\n\nComparing men (81% Yes) to women (78% Yes), more men think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 90776, "output_tokens": 163, "cost_micros": 274773, "latency_ms": 43483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on expectations of a female president by gender...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\"\n\nThe table shows:\n- Men who say \"Yes\": 81%\n- Women who say \"Yes\": 78%\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 19129, "output_tokens": 80, "cost_micros": 58587, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the table on page 8 titled \"Will the country elect a female president in your lifetime?\", I can find the breakdown by gender:\n\n- Men: 81% say yes\n- Women: 78% say yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 24408, "output_tokens": 64, "cost_micros": 74184, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on whether men or women think a female president will be elected in their lifetime...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\" showing:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 21629, "output_tokens": 80, "cost_micros": 66087, "latency_ms": 4219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to find the data about whether men or women think a female president will be elected in their lifetime.\n\nOn page 8, there is a table titled \"Will the country elect a female president in your lifetime?\" that shows:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nSince 81% > 78%, more men than women think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 24369, "output_tokens": 102, "cost_micros": 74637, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find information about opinions on whether a female president will be elected in a lifetime.Let me search for content related to female president and lifetime:Let me read the specific section around line 1095 to get the complete data:The text states \"There are no significant differences in these opinions among men and women,\" meaning men and women have similar percentages. Let me search for any table or chart that might have the exact percentages broken down by gender:I can see a table starting. Let me read more to see if it has the gender breakdown:Perfect! I found the table with the gender breakdown. Looking at the \"Yes\" column (indicating those who think a female president will be elected in their lifetime):\n\n- Men: 81%\n- Women: 78%\n\nAnswer: Men (81% vs 78%)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46859, "error": null, "extra": {"thread_id": 1923, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_47a779db7256446daa01071f4e8824a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll carefully examine the document to count all line plots.\n\nGoing through the document page by page:\n\n**Page 3:** Contains a line plot showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages from 1988-2016.\n\n**Page 5:** Contains a line plot showing \"Record share of voters say there was more campaign 'mudslinging'\" with two lines (More mudslinging and Less mudslinging) from 1992-2016.\n\n**Page 9:** Contains a line plot showing \"Half say they are happy Trump was elected president\" with percentages from 1996-2016.\n\n**Page 21:** Contains a line plot showing \"Will relations between Republicans and Democrats improve?\" with three lines from 1992-2016.\n\n**Page 24:** Contains two separate line plots:\n- One for Republican/Rep-leaning voters (More moderate vs More conservative) from 2008-2016\n- One for Democratic/Dem-leaning voters (More moderate vs More liberal) from 2008-2016\n\n**Page 27:** Contains two separate line plots showing grades for:\n- The Republican Party (1988-2016)\n- The Democratic Party (1988-2016)\n\n**Page 29:** Contains two separate line plots showing grades for:\n- The press (1988-2016)\n- The pollsters (1988-2016)\n\n**Page 31:** Contains a line plot showing \"For the first time in eight elections, most are dissatisfied with vote choices\" from 1988-2016.\n\n**Page 32:** Contains a line plot showing \"More see 'mud-slinging,' less focus on issues\" with two sets of lines from 1992-2016.\n\n**Page 33:** Contains two separate line plots showing \"Learning about the candidates and issues\" from 1988-2016.\n\n**Page 36:** Contains a line plot showing \"Most voters are confident own vote, national votes counted accurately\" from 2004-2016.\n\n**Page 37:** Contains a line plot showing \"Similar shares of Trump, Clinton voters very confident in national vote count\" from 2004-2016.\n\nTotal count:\nPage 3: 1\nPage 5: 1\nPage 9: 1\nPage ", "citations": [], "input_tokens": 90760, "output_tokens": 512, "cost_micros": 279960, "latency_ms": 41093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the number of line plots in this document.\n\nLet me go through the document systematically to identify line plots:\n\n1. Page 1: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with two lines (Winning candidate and Losing candidate) from 1988 to 2016.\n\n2. Page 6: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with two lines (More mudslinging and Less mudslinging) from 1992 to 2016.\n\n3. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows a line plot with two lines (Very/Fairly satisfied and Not very/Not at all satisfied) from 1988 to 2016.\n\n4. Page 32: Below the first plot on page 31, there's another line plot showing \"% of voters who say they were very/fairly satisfied with choice of presidential candidates\" with data points from 1988 to 2016 for winning and losing candidates.\n\n5. Page 33: \"More see 'mud-slinging,' less focus on issues\" - This contains TWO separate line plots:\n   - First plot: Shows \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992 to 2016\n   - Second plot: Shows discussion of issues \"Less than usual\" vs \"More than usual\" from 1992 to 2016\n\n6. Page 34: \"Learning about the candidates and issues\" - This contains TWO separate line plots:\n   - First plot: \"Learned enough\" vs \"Did not learn enough\" from 1988 to 2016\n   - Second plot: Presidential debates helpfulness from 1988 to 2016\n\nLet me recount to be sure:\n- Page 1: 1 line plot\n- Page 6: 1 line plot  \n- Page 31: 2 line plots (one for all voters, one comparing winning/losing candidate voters)\n- Page 33: 2 line plots (mudslinging and issues discussion)\n- Page 34: 2 line plots (learning about candidates and debates)\n\nTotal:", "citations": [], "input_tokens": 19113, "output_tokens": 512, "cost_micros": 65019, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document to count all line plots (graphs that show data points connected by lines over time or categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This appears to be a line plot showing grades from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This is a line plot showing data from 1992-2016.\n\n3. Page 9: \"Half say they are happy Trump was elected president\" - This is a line plot showing data from 1996-2016.\n\n4. Page 24: Two separate line plots:\n   - \"Most Republicans continue to say GOP should be more conservative\" (showing data from 2008-2016)\n   - \"As the share of Democrats who want their party to be more liberal grows\" (showing data from 2008-2016)\n\n5. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This is a line plot showing data from 1988-2016.\n\n6. Page 32: Two separate line plots:\n   - One showing \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992-2016\n   - One showing discussion of issues \"Less than usual\" vs \"More than usual\" from 1992-2016\n\n7. Page 33: Two separate line plots:\n   - One showing \"Learned enough\" vs \"Did not learn enough\" from 1988-2016\n   - One showing helpfulness of debates from 1988-2016\n\n8. Page 34: \"More say press had 'too much' influence on outcome of the election\" - This is a line plot showing data from 1992-2016.\n\nTotal count: 1 + 1 + 1 + 2 + 1 + 2 + 2 + 1 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 24392, "output_tokens": 466, "cost_micros": 80166, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades...\" - This appears to be a bar chart showing percentages, not a line plot.\n\n2. Page 4: \"Large share of Trump voters are confident...\" - Bar chart\n   \"How Clinton voters feel about 'giving Trump a chance'\" - Bar chart\n\n3. Page 5: \"% of voters saying Democratic leaders should...\" - Bar charts comparing 2016 and 2008\n\n4. Page 6: \"% of voters saying Donald Trump ___ appoint Democrats...\" - Table format\n   \"% of voters who said Barack Obama ___ appoint Republicans...\" - Table format\n\n5. Page 7: \"Election report card: Barely passing grades\" - Table format\n   \"Record share of voters say there was more campaign 'mudslinging'\" - This appears to be a LINE PLOT showing trend over years 1996-2016\n\n6. Page 8: \"Top reactions to Trump's victory\" - Bar chart\n\n7. Page 9: \"Trump's victory in a word\" - Word cloud/frequency table\n\n8. Page 10: \"Will the country elect a female president in your lifetime?\" - Bar chart\n\n9. Throughout the rest of the document, I see mostly bar charts, tables, and no additional line plots.\n\nLet me recheck page 7 more carefully. The chart showing \"More mudslinging\" and \"Less mudslinging\" from 1992-2016 appears to be displayed as a line plot with two lines tracking these measures over time.\n\nAfter careful review, I can identify 1 line plot in this document on page 7.\n\nAnswer: 1", "citations": [], "input_tokens": 21613, "output_tokens": 393, "cost_micros": 70734, "latency_ms": 11437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines over time or across categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with percentages over years from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with \"More mudslinging\" and \"Less mudslinging\" lines from 1992-2016.\n\n3. Page 9: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" (repeated from earlier) - This is a table showing A or B grades, labeled as \"Note: Percent of 'A' or 'B' grades\" - This appears to be the same as #1.\n\n4. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows two line plots tracking satisfaction levels from 1988-2016.\n\n5. Page 32: \"More see 'mud-slinging,' less focus on issues\" - This shows two separate line plots, one for mudslinging and one for discussion of issues from 1992-2016.\n\nLet me recount more carefully by looking at actual visual line plots:\n\n- Page 3: One line plot showing grades over time\n- Page 5: One line plot showing mudslinging trends\n- Page 31: Two line plots (satisfaction trends)\n- Page 32: Two line plots (mudslinging and issues discussion)\n\nTotal: 1 + 1 + 2 + 2 = 6 line plots\n\nAnswer: 6", "citations": [], "input_tokens": 24353, "output_tokens": 397, "cost_micros": 79014, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the report.Let me continue reading the document to see all the content and identify line plots:Let me continue reading to see if there are more charts/figures:Let me search for all figures/charts in the document to count line plots specifically:Now I need to look at the actual figures to determine which ones are line plots. Let me examine the key figure sections more carefully. Based on what I've already read, I saw one chart around line 798-853 that appears to be about \"mudslinging\" over time. Let me check the visual descriptions and content more carefully:This appears to be a line plot showing data over time (years 92, 96, 00, 04, 08, 12, 16). Let me check another figure:This appears to be another line plot showing data over time from 1996 to 2016. Let me check the first figure to see if it's a line plot:This first figure shows data for winning and losing candidates across years from 1988 to 2016. This appears to be a line plot with two lines (winning candidate and losing candidate). Let me check if there are any other potential line plots by examining more figures. Let me look at the figure around line 2240-2290:This appears to be a bar chart, not a line plot. Let me continue scanning through the document to identify all line plots. Let me check around line 2333:This appears to be a bar chart comparing 2016 and 2008 data, not a line plot. Based on my review of the document so far, I've identified:\n\n1. **Line plot 1** (lines 264-360): Shows grades for winning and losing candidates over time from 1988 to 2016 (two lines)\n2. **Line plot 2** (lines 798-848): Shows \"mudslinging\" over time with two lines (More mudslinging and Less mudslinging) from 1992 to 2016\n3. **Line plot 3** (lines 1209-1241): Shows happiness with election results from 1996 to 2016\n\nThese all show data trends over multiple time points, which is characteristic of line plots. Most other figures in the document appear to be bar charts showing comparisons between groups. Let me verify by checking if there are any other time-series charts I might have missed. Let me do a final check of the document to ensure I haven't missed any line plots.\n\nBased on my thorough review of the document, I found three distinct line plots that show data trends over time:\n\n1. A line plot showing grades for winning and losing candidates from 1988-2016\n2. A line plot showing perceptions of campaign mudslinging from 1992-2016  \n3. A line plot showing voter happiness with election results from 1996-2016\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 65172, "error": null, "extra": {"thread_id": 1924, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dcd15be6dac34256a7ae1a01a0998a5a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 476, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades information...\n\nOn page 3, there is a chart showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages of \"A\" or \"B\" grades on an A, B, C, D, F scale.\n\nThe chart shows:\n- Trump (2016): 30% gave A or B grades\n- Clinton (2016): 43% gave A or B grades\n\nThe document also states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nAdditionally, on page 26, there is a table showing average grades:\n- Trump: C- (average grade)\n- Clinton: C (average grade)\n\nSince C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 90768, "output_tokens": 246, "cost_micros": 275994, "latency_ms": 41342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the campaign grades data in the document:\n\nFor **Trump**:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor **Clinton**:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nTo find the median, I need to determine which grade represents the 50th percentile:\n\nFor Trump: 30% (A/B) + 19% (C) = 49%, then adding the next 15% (D) brings us to 64%. The 50th percentile falls in the D range.\n\nFor Clinton: 43% (A/B) + 20% (C) = 63%. The 50th percentile falls in the C range.\n\nThe document also explicitly states Trump receives a \"C-\" average grade while Clinton receives a \"C\" average grade, which is consistent with this analysis.\n\nAnswer: Clinton", "citations": [], "input_tokens": 19121, "output_tokens": 227, "cost_micros": 60768, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades:\n\nOn page 26, there is a table titled \"Election report card: Barely passing grades\" showing:\n- Trump: 30% gave A or B, Average grade: C-\n- Clinton: 43% gave A or B, Average grade: C\n\nThe document states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nClinton received an average grade of C, while Trump received an average grade of C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24400, "output_tokens": 176, "cost_micros": 75840, "latency_ms": 6358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Let me find the campaign grades for Trump and Clinton from the document.\n\nFrom the section \"Voters give Trump worse grades than they have for any winning candidate in recent decades\":\n\nFor Trump:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor Clinton:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nThe document also shows average grades:\n- Trump: C-\n- Clinton: C\n\nTo determine the median, I need to consider the distribution. With:\n- Trump: 30% (A/B), 19% (C), 15% (D), 35% (F)\n- Clinton: 43% (A/B), 20% (C), 16% (D), 21% (F)\n\nFor Trump, the cumulative percentages are: 30% at B or above, 49% at C or above, 64% at D or above. The median falls around the C grade.\n\nFor Clinton, the cumulative percentages are: 43% at B or above, 63% at C or above, 79% at D or above. The median falls around the C grade.\n\nHowever, since Clinton has a higher percentage (43%) receiving A or B compared to Trump (30%), and a lower percentage receiving F (21% vs 35%), Clinton's distribution is shifted toward higher grades. The average grades confirm this: Clinton received C while Trump received C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 21621, "output_tokens": 349, "cost_micros": 70098, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the campaign grades for Trump and Clinton.\n\nOn page 26-27, there is a table showing \"Voters grade the parties, press and pollsters quite negatively\" with the percentage of voters who give each a grade of A, B, C, D, or F:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n- Average grade: C-\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n- Average grade: C\n\nThe document explicitly states that Trump receives an average grade of C- and Clinton receives an average grade of C. Since C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24361, "output_tokens": 195, "cost_micros": 76008, "latency_ms": 5969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about campaign grades for Trump and Clinton.Let me search for \"campaign grade\" in the document to find relevant information.Let me search for \"grade\" to find relevant sections.Now let me read the specific sections that mention grades for Trump and Clinton. Let me look at the chunks around line 260 and 2800-2900.Good! Now let me look for more specific information about median grades. Let me check the section around line 2800-2900 where grades are discussed in detail.Perfect! I found the information. Let me look at the table that shows the grade distribution to calculate the median. From lines 2820-2838, I can see:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n\nThe document also explicitly states:\n- Trump receives a C- grade on average (line 2801-2802)\n- Clinton's average grade is a C (line 2897)\n\nTo find the median, I need to look at where the 50th percentile falls:\n\n**Trump:**\n- 0-30%: A or B\n- 31-49%: C\n- 50-64%: D (the 50th percentile falls here)\n- 65-100%: F\n\n**Clinton:**\n- 0-43%: A or B\n- 44-63%: C (the 50th percentile falls here)\n- 64-79%: D\n- 80-100%: F\n\nSo Trump's median grade is D, and Clinton's median grade is C.\n\nAnswer: Clinton", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45171, "error": null, "extra": {"thread_id": 1925, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5b7e95c94e72489983c39189c2251166", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 287, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to search through the document for charts related to mudslinging.\n\nLooking through the document, I can find references to mudslinging in the following locations:\n\n1. On page 5 (document page 7), there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more or less mudslinging than in past elections from 1992 to 2016.\n\n2. On page 32 (document page 34), there is another chart with the same title \"More see 'mud-slinging,' less focus on issues\" which shows similar data about mudslinging perceptions.\n\nLet me verify these are distinct charts:\n- The first chart on page 5 shows data points: 68, 49, 34, 72, 54, 68, 92 for \"More mudslinging\" \n- The second chart on page 32 shows the same data\n\nThese appear to be the same chart repeated in different sections of the document.\n\nTherefore, there is 1 unique chart related to mudslinging (though it appears twice in the document).\n\nAnswer: 1", "citations": [], "input_tokens": 90760, "output_tokens": 261, "cost_micros": 276195, "latency_ms": 37187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages from 1992-2016 of voters who say there was more or less mudslinging than in past elections.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows the same data about mudslinging over time (1992-2016) plus a separate section about discussion of issues.\n\nLet me verify if these are two separate charts or if one is a repeat:\n- Page 6 shows the mudslinging chart in the context of the executive summary\n- Page 33 shows the same mudslinging data but combined with another chart about discussion of issues in a single figure\n\nThese appear to be presenting the same mudslinging data, but in different contexts. The page 33 version combines it with the issues discussion data.\n\nCounting distinct charts that contain mudslinging data:\n1. The chart on page 6 (standalone mudslinging chart)\n2. The chart on page 33 (mudslinging combined with issues discussion)\n\nSince the question asks about charts \"related to mudslinging,\" both of these count as they both contain mudslinging data.\n\nAnswer: 2", "citations": [], "input_tokens": 19113, "output_tokens": 323, "cost_micros": 62184, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 5, there is a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a chart showing \"% of voters who say there was ___ than in past elections\" with \"More mudslinging\" and \"Less mudslinging\" data from years 92, 96, 00, 04, 08, 12, 16.\n\n2. On page 32, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" showing \"% of voters who say there was ... More mudslinging than usual\" and \"Less mudslinging than usual\" from years 92, 96, 00, 04, 08, 12, 16.\n\nThese appear to be the same chart repeated in different sections of the document (one in the executive summary and one in the detailed findings).\n\nLet me verify if there are any other mudslinging-related charts... No, I only find these two instances, which appear to be the same chart shown in different parts of the document.\n\nSince the question asks how many charts are related to mudslinging, and there are 2 instances of mudslinging charts in the document (even though they show the same data), the answer would be 2.\n\nHowever, if we're counting unique charts, it would be 1 chart that appears twice.\n\nLooking at the context of the question, it seems to be asking for the count of chart occurrences in the document, which would be 2.\n\nAnswer: 2", "citations": [], "input_tokens": 24392, "output_tokens": 372, "cost_micros": 78756, "latency_ms": 10358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts in the document that are related to \"mudslinging.\"\n\nLet me search through the document for references to mudslinging:\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows percentages of voters who say there was more or less mudslinging than in past elections from 1996-2016.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows percentages of voters who say there was more mudslinging than usual and less mudslinging than usual from 1992-2016.\n\nLet me verify there are no other charts about mudslinging by carefully reviewing the document again.\n\nAfter reviewing the document thoroughly, I can confirm there are 2 charts specifically related to mudslinging:\n- One on page 6 showing the record share saying there was more mudslinging\n- One on page 33 showing mud-slinging trends and comparison with issue discussion\n\nAnswer: 2", "citations": [], "input_tokens": 21613, "output_tokens": 237, "cost_micros": 68394, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/tables in the document that are related to \"mudslinging\" or negative campaigning.\n\nLet me search through the document:\n\n1. On page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" - This shows data about mudslinging from 1992-2016.\n\n2. On page 32, there is a chart titled \"More see 'mud-slinging,' less focus on issues\" - This shows data about mudslinging and discussion of issues from 1992-2016.\n\nThese appear to be the only two charts specifically related to mudslinging in the document. The first chart shows the percentage of voters who say there was more mudslinging than in past elections over different election years. The second chart also shows mudslinging data alongside data about discussion of issues.\n\nAnswer: 2", "citations": [], "input_tokens": 24353, "output_tokens": 194, "cost_micros": 75969, "latency_ms": 8342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find charts related to mudslinging.Let me read the full document to search for content related to mudslinging and identify charts:Let me continue reading to see if there are more charts related to mudslinging:Based on my thorough reading of the document, I found the following charts related to mudslinging:\n\n1. **Lines 788-853 (chunk 49482)**: A chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages of voters who said there was more or less mudslinging than in past elections, with data from years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\n2. **Lines 3933-4048 (chunk 49595)**: A chart in the section \"More see 'mud-slinging,' less focus on issues\" showing percentages of voters who say there was \"More mudslinging than usual\" and \"Less mudslinging than usual\" across election years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\nThese appear to be two representations of the same data - one earlier in the document and one later in a more detailed section about campaign evaluations.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62437, "error": null, "extra": {"thread_id": 1926, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bd535bded05431a9423ba96684e000e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 344, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically looking for percentages who feel \"uneasy\" and \"excited.\"\n\nLooking through the document, I can find on page 6 (document page 8) a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows voter reactions.\n\nThe chart shows:\n- 53% say Trump's election makes them feel \"uneasy\"\n- 51% say it makes them feel \"hopeful\"\n- 41% say \"sad\"\n- 41% say \"scared\"\n- 36% say \"proud\"\n- 31% say \"angry\"\n\nI don't see \"excited\" listed as one of the emotional reactions measured. The document shows \"hopeful,\" \"proud,\" \"uneasy,\" \"sad,\" \"scared,\" and \"angry\" but not \"excited.\"\n\nFor \"uneasy,\" the answer is 53%.\n\nSince \"excited\" is not measured in this survey, I cannot provide a percentage for it.\n\nAnswer: 53% uneasy, excited not measured", "citations": [], "input_tokens": 90783, "output_tokens": 245, "cost_micros": 276024, "latency_ms": 34905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for voters' reactions to Trump's victory:\n\nFrom page 7, there is a table titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n- Uneasy: 53%\n\nFrom page 8, there is a word cloud table showing one-word reactions. Under \"Trump voters,\" I can see:\n- Excited: 16 (this is the actual number of respondents who mentioned this word, not a percentage)\n\nHowever, the question asks for percentages. Looking at page 11, there is a more detailed table showing emotional reactions:\n\"% of voters who say the election of Donald Trump makes them feel ...\"\n- Uneasy: 53%\n\nThe document does not provide \"excited\" as a percentage in the main emotional reactions table on pages 7 and 11. The word \"excited\" only appears in the word cloud with an actual count of 16 mentions from Trump voters, but this is not presented as a percentage of all voters.\n\nBased on the data provided in percentage form:\n- Uneasy: 53%\n- Excited: Not provided as a percentage of all voters\n\nAnswer: 53%, Not provided", "citations": [], "input_tokens": 19136, "output_tokens": 269, "cost_micros": 61443, "latency_ms": 9281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not provided", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, on page 6 (and repeated on page 10), there is a section titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo \"uneasy\" is 53%.\n\nHowever, I don't see \"excited\" listed as one of the reaction options in this chart.\n\nLooking at page 7, there is a word cloud section showing individual words mentioned by Trump and Clinton voters. For Trump voters, \"Excited\" appears with 16 mentions, but this is not a percentage - these are actual counts of how many people mentioned that specific word.\n\nThe main emotional reactions chart on page 6 does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nSince \"excited\" is not measured as a percentage in the same way as \"uneasy,\" I can only provide the percentage for \"uneasy\" which is 53%.\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24415, "output_tokens": 331, "cost_micros": 78210, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel \u2026\n\n| Feeling | % of Voters |\n| ------- | ----------- |\n| Uneasy  | 53          |\n| Hopeful | 51          |\n| Proud   | 36          |\n| Scared  | 41          |\n| Sad     | 41          |\n| Angry   | 31          |\n\nI can see that 53% feel \"uneasy.\"\n\nFor \"excited,\" I need to look at the word cloud section titled \"Trump's victory in a word\" which shows responses from Trump voters and Clinton voters. Under Trump voters, I can see:\n- 16 Excited\n\nHowever, this appears to be a count (16 respondents), not a percentage. The note states \"Figures show actual number of respondents who offered each response; these numbers are not percentages.\"\n\nLooking back at the main emotional reactions table on the previous page, \"excited\" is not listed as one of the specific emotions measured in the survey question about how Trump's election makes them feel.\n\nBased on the available data in the document:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the emotional reactions survey (only appears as a word count of 16 in the open-ended response)\n\nAnswer: 53%, not measured as percentage", "citations": [], "input_tokens": 21636, "output_tokens": 364, "cost_micros": 70368, "latency_ms": 11468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find information about emotional reactions to Trump's election on page 10:\n\n\"Voters express a mix of emotional reactions to the election of Donald Trump. On the positive side, 51% say that Trump's election makes them feel hopeful; somewhat fewer say it makes them feel proud (36%).\n\nOn the negative side, 53% say Trump's election makes them feel uneasy. About four-in-ten say his election makes them feel sad (41%) or scared (41%), and 31% say the election of Trump makes them feel angry.\"\n\nSo 53% felt \"uneasy.\"\n\nLooking at the table on page 6 titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" and the word cloud on page 7, I can see the following reactions listed:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nIn the word cloud on page 7 showing \"Trump's victory in a word,\" I can see \"Excited\" listed among Trump voters with 16 mentions, but this is showing the actual number of respondents, not percentages.\n\nThe main table showing percentage reactions does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nBased on the available data:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the survey\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24376, "output_tokens": 401, "cost_micros": 79143, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the specific percentages for \"uneasy\" and \"excited\" reactions to Trump's victory.Let me read the specific section with the data about voter reactions to see the percentages clearly.Based on the document, I can see the percentages clearly in the figure at lines 890-916:\n\n- **Uneasy: 53%**\n- **Excited: 16** (this appears to be a count in the open-ended word table, not a percentage)\n\nLet me verify if there's a percentage for \"excited\" elsewhere in the document.The document shows a figure with percentages of voter reactions to Trump's victory. The measured reactions with percentages are:\n- Hopeful: 51%\n- Proud: 36%\n- **Uneasy: 53%**\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\n\"Excited\" appears in a different table showing word counts from open-ended responses (16 mentions from Trump voters), not as a percentage measurement of all voters.\n\nBased on the document, the percentage for \"uneasy\" is **53%**. There is no percentage given for \"excited\" as a reaction category - it only appears as a word count (16) in the open-ended responses table.\n\nAnswer: 53%, 16", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44733, "error": null, "extra": {"thread_id": 1927, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5850dcc4518c4b98b48b2eae8c928f95", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 326, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "16, 53", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically the percentage who felt \"angry.\"\n\nLooking through the document, I find a section on page 10 titled \"Emotional reactions to Trump's election\" with a chart showing \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\"\n\nThe chart shows percentages of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nAnswer: 31", "citations": [], "input_tokens": 90769, "output_tokens": 150, "cost_micros": 274557, "latency_ms": 34717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's victory.\n\nLooking through the document for information about emotional reactions to Trump's election:\n\nOn page 7, there is a section titled \"Election reactions: Nearly all Trump supporters feel 'hopeful'\" which includes a chart showing \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\"\n\nThe chart shows the percentage of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states: \"About half of voters (53%) say his election makes them feel 'uneasy,' while nearly as many (51%) say it makes them feel 'hopeful'. Smaller shares say his election triumph makes them feel 'scared', 'sad' (41% each), 'proud' (36%) or 'angry' (31%).\"\n\nAnswer: 31", "citations": [], "input_tokens": 19122, "output_tokens": 243, "cost_micros": 61011, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document for information about voters' reactions to Trump's victory.\n\nOn page 6 (and repeated on page 10), there is a chart titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows the percentage of voters who say the election of Donald Trump makes them feel various emotions.\n\nAccording to this chart:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states that 31% of voters say Trump's election makes them feel \"angry.\"\n\nAnswer: 31", "citations": [], "input_tokens": 24401, "output_tokens": 157, "cost_micros": 75558, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's election.\n\nLooking through the document for information about voters' emotional reactions to Trump's victory...\n\nOn page 12 (in the section \"Voters express a mix of emotional reactions to the election of Donald Trump\"), I find a table showing:\n\n\"% of voters who say the election of Donald Trump makes them feel \u2026\"\n\nThe feelings listed include:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo 31% of voters say the election of Trump makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 21622, "output_tokens": 166, "cost_micros": 67356, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for emotional reactions to Trump's victory...\n\nOn page 6, I find the section \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel ...\n\n| Reaction | %  |\n| -------- | -- |\n| Hopeful  | 51 |\n| Proud    | 36 |\n| Uneasy   | 53 |\n| Sad      | 41 |\n| Scared   | 41 |\n| Angry    | 31 |\n\nThe table clearly shows that 31% of voters say Trump's election makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 24362, "output_tokens": 166, "cost_micros": 75576, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about voter reactions to Trump's victory.Let me search for sections that discuss reactions or emotions about Trump's victory.Great! I found a reference to \"angry\" in the context of reactions. Let me read the specific chunk that contains this information.Perfect! I found the information. According to the document, 31% of voters say Trump's election makes them feel \"angry\".\n\nAnswer: 31", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32359, "error": null, "extra": {"thread_id": 1928, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4e0d0df3d4f749b8a5376414cce75543", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 148, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find data from 2015 about how many people believed the government was doing very/fairly well in reducing the threat of terrorism.\n\nLooking at page 2, I can see a chart titled \"For first time since 9/11, government's job rating on terrorism turns negative\" which shows the percentage who say the government is doing \"Very/Fairly well\" in reducing the threat of terrorism.\n\nFrom the chart on page 2, at the end point in 2015 (specifically December 2015), I can see:\n- Total: 46%\n\nLooking at page 10-11, there's a detailed breakdown by party. The chart on page 10 shows \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" with partisan breakdowns.\n\nFrom the chart on page 10, for 2015 (the rightmost data point):\n- Republican: 27%\n- Democrat: 64%\n- Independent: 44%\n\nSo for the question asking about adults (total), Republicans, and Democrats in 2015:\n- Adults (Total): 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 58172, "output_tokens": 269, "cost_micros": 178551, "latency_ms": 22812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentage of adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section \"Views of how the government is handling the terrorist threat\" on page 10-11.\n\nThe document states:\n- Total (all adults): 46% say the government is doing very/fairly well\n- Democrats: 64% say the government is doing very/fairly well  \n- Republicans: 27% say the government is doing very/fairly well\n\nThis data is from the December 2015 survey (conducted Dec. 8-13, 2015).\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 13406, "output_tokens": 163, "cost_micros": 42663, "latency_ms": 5468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for the specific data on government performance in reducing the threat of terrorism in 2015:\n\nFrom the chart on page 10 titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" showing percentages saying government is doing very/fairly well in reducing the threat of terrorism:\n\nFor December 2015:\n- Total/All adults: 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 19266, "output_tokens": 113, "cost_micros": 59493, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentages of surveyed adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" on page 10.\n\nThe chart shows \"% saying govt is doing very/fairly well in reducing the threat of terrorism\" for December 2015:\n- Total (all adults): 46%\n- Republican: 27%\n- Democrat: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 14501, "output_tokens": 140, "cost_micros": 45603, "latency_ms": 5233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on how well the government is doing in reducing the threat of terrorism in 2015:\n\nFrom the table on page 10 titled \"How well is govt doing reducing threat of terrorism?\" and the section \"Views of how the government is handling the terrorist threat\" on pages 10-11:\n\nIn December 2015:\n- Total: 46% said very/fairly well\n- Republican: 27% said very/fairly well  \n- Democrat: 64% said very/fairly well\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 18633, "output_tokens": 125, "cost_micros": 57774, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the specific data about beliefs regarding government performance in reducing terrorism threats in 2015.Let me search for data about government performance in reducing terrorism threats:I can see a figure showing data about \"How well is govt doing reducing threat of terrorism?\" with percentages for \"Very/Fairly well\" across years. I can see 46 for 2015. Now let me look for the breakdown by party affiliation (Republicans and Democrats):Let me search more specifically for partisan breakdown of government performance on terrorism:I found a reference to partisan data. Let me read that section:The figure I found shows partisan data over time. Let me read more context around this section to understand the 2015 data points:Perfect! I found the data. The text clearly states:\n\n- Overall in 2015: 46% say government is doing very or fairly well\n- Democrats in 2015: 64% say government is doing at least fairly well  \n- Republicans in 2015: 27% say government is doing very or fairly well\n\nThis is also confirmed by the figure showing the data points for year '15' with Democrat at 64, Republican at 27.\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42844, "error": null, "extra": {"thread_id": 1929, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cb2b43cade5c41b98919c13d86621c55", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 281, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nLooking at page 15, I can see a chart titled \"ISIS rises higher atop public's threat list\" showing various threats with their percentages in December 2015 and August 2014, plus the change.\n\nThe threats that increased (positive change) from August 2014 to December 2015 are:\n- ISIS: +16 points (67% to 83%)\n- Iran's nuclear program: +3 points (59% to 62%)\n- North Korea's nuclear program: +2 points (57% to 59%)\n- China's emergence as a world power: +1 point (48% to 49%)\n- Global climate change: +1 point (48% to 49%)\n\nNow I need to find the R-D difference for each of these threats. Looking at page 15, there's another chart showing \"Partisan differences in views of top threats to U.S.\" with R-D diff column:\n\n- Iran's nuclear program: R+27\n- ISIS: R+14\n- China's emergence as a world power: R+18\n- North Korea's nuclear program: R+13\n- Global climate change: D+51 (this is actually a Democratic advantage, so -51 for R-D)\n\nAmong the threats that increased, Iran's nuclear program has the biggest R-D difference at R+27.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 58152, "output_tokens": 342, "cost_micros": 179586, "latency_ms": 35328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find which threats had bigger percentages in December 2015 compared to August 2014, and then determine which has the largest Republican-Democrat difference.\n\nLooking at the table \"ISIS rises higher atop public's threat list\" on page 14:\n\nThreats that increased from August 2014 to December 2015:\n1. ISIS: 67% \u2192 83% (+16)\n2. Iran's nuclear program: 59% \u2192 62% (+3)\n3. North Korea's nuclear program: 57% \u2192 59% (+2)\n4. China's emergence as a world power: 48% \u2192 49% (+1)\n5. Global climate change: 48% \u2192 49% (+1)\n\nNow I need to find the R-D differences for these threats from the table \"Partisan differences in views of top threats to U.S.\" on page 15:\n\n1. ISIS: R-D difference = +14 (93% Republican - 79% Democrat)\n2. Iran's nuclear program: R-D difference = +27 (79% Republican - 52% Democrat)\n3. North Korea's nuclear program: R-D difference = +13 (70% Republican - 57% Democrat)\n4. China's emergence as a world power: R-D difference = +18 (62% Republican - 44% Democrat)\n5. Global climate change: R-D difference = -51 (22% Republican - 73% Democrat = -51, but this is negative)\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 percentage points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 13386, "output_tokens": 380, "cost_micros": 45858, "latency_ms": 8984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nFrom page 14, the table \"ISIS rises higher atop public's threat list\" shows changes from August 2014 to December 2015:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n\nFrom page 15, the table \"Partisan differences in views of top threats to U.S.\" shows R-D differences in December 2015:\n- ISIS: 93% (R) - 79% (D) = +14 R-D difference\n- Iran's nuclear program: 79% (R) - 52% (D) = +27 R-D difference\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13 R-D difference\n- China's emergence as a world power: 62% (R) - 44% (D) = +18 R-D difference\n\nAmong threats that increased from August 2014 to December 2015, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 19246, "output_tokens": 314, "cost_micros": 62448, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the threats that increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" (page 14), the threats that increased are:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence: +1 point\n- Global climate change: +1 point\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" (page 15), the R-D differences are:\n- ISIS: 93% (R) - 79% (D) = +14\n- Iran's nuclear program: 79% (R) - 52% (D) = +27\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13\n- China's emergence: 62% (R) - 44% (D) = +18\n\nAmong the threats that increased from August 2014, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 14481, "output_tokens": 271, "cost_micros": 47508, "latency_ms": 7327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find threats where concern increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" on page 14, I can see the changes from August 2014 to December 2015:\n\n- ISIS: +16 points (83% in Dec 2015 vs 67% in Aug 2014)\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n- Israeli-Palestinian conflict: -5 points (decreased)\n- Growing authoritarianism in Russia: -11 points (decreased)\n\nSo the threats that increased are: ISIS, Iran's nuclear program, North Korea's nuclear program, China's emergence, and global climate change.\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" on page 15, the R-D differences in December 2015 are:\n\n- ISIS: +14 (93% Rep - 79% Dem)\n- Iran's nuclear program: +27 (79% Rep - 52% Dem)\n- North Korea's nuclear program: +13 (70% Rep - 57% Dem)\n- China's emergence as a world power: +18 (62% Rep - 44% Dem)\n- Global climate change: -51 (22% Rep - 73% Dem) - Note: This is negative, meaning Democrats see it as more of a threat\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 18613, "output_tokens": 402, "cost_micros": 61869, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that appears most relevant based on the priority list: the ISIS and terrorism release from December 2015.Let me continue reading to find the data about threats and R-D differences:I can see the document mentions ISIS increased from 67% in August 2014 to 83% as a major threat. Let me continue reading to find data about other threats and their R-D differences:Let me search for data about threats to well-being and R-D differences. I need to find a table or chart comparing different threats:Good! I found references to \"R-D diff\" in the document. Let me read the section around line 1364:This table shows the \"most important problem\" but I need to find the table about \"threats to well-being\" comparing 2015 to August 2014. Let me continue searching:Perfect! I found the section about threats. Let me read the full table around line 1974-2076:Great! Now I need to see the partisan breakdown table with R-D differences. Let me continue reading:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1930, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd9ddaac9b56498ab35c8337203470ec", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 315, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document that appears most relevant based on priority list isis and terrorism release from december 2015 let me continue reading to find data about threats and r d differences i can see document mentions isis increased from 67 in august 2014 to 83 as major threat let me continue reading to find data about other threats and their r d differences let me search for data about threats to well being and r d differences i need to find table or chart comparing different threats good i found references to r d diff in document let me read section around line 1364 this table shows most important problem but i need to find table about threats to well being comparing 2015 to august 2014 let me continue searching perfect i found section about threats let me read full table around line 1974 2076 great now i need to see partisan breakdown table with r d differences let me continue reading", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll go through the document systematically to count all bar charts.\n\nLooking through each page:\n\nPage 1: Title page - no charts\n\nPage 2: \n- One line chart showing \"Views of Government's Handling of Terrorism\"\n- No bar charts\n\nPage 3:\n- One line chart showing partisan gaps\n- One pie chart showing scrutiny of Muslims\n- No bar charts\n\nPage 4:\n- One table showing party preferences on various issues\n- No bar charts\n\nPage 5:\n- One line chart showing views on Islam and violence over time\n- One line chart showing partisan divide\n- One table with demographic data\n- No bar charts\n\nPage 6:\n- Multiple horizontal bar charts showing \"Should Muslims be subject to greater scrutiny\" broken down by different groups\n- Count: approximately 3-4 bar charts on this page\n\nPage 7:\n- Multiple horizontal bar charts continuing the scrutiny question by demographics\n- Count: approximately 3-4 bar charts on this page\n\nPage 8:\n- One table showing \"Most important problem facing nation\"\n- No bar charts\n\nPage 9:\n- One table showing partisan differences\n- No bar charts\n\nPage 10:\n- One line chart showing government ratings over time by party\n- No bar charts\n\nPage 11:\n- One table showing demographic views\n- No bar charts\n\nPage 12:\n- One line chart showing concerns about anti-terrorism policies\n- No bar charts\n\nPage 13:\n- One line chart showing partisan views on anti-terrorism policies\n- One table\n- No bar charts\n\nPage 14:\n- Multiple horizontal bar charts showing ISIS as threat\n- Count: approximately 2 bar charts\n\nPage 15:\n- Multiple horizontal bar charts showing partisan differences in threats\n- Count: approximately 2 bar charts\n\nPage 16:\n- Line charts showing views of U.S. campaign\n- No bar charts\n\nPage 17:\n- Horizontal bar charts showing current assessments\n- Count: approximately 2 bar charts\n\nPage 18:\n- Multiple horizontal bar charts about concerns and ground troops\n- Count: approximately 3-4 bar charts\n\nPage 19:\n- Line chart and horizontal bar charts\n- Count: approximately 2 bar charts\n\nPage 20:\n- Line charts showing views on military force\n-", "citations": [], "input_tokens": 58124, "output_tokens": 512, "cost_micros": 182052, "latency_ms": 17578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with bar-like elements showing percentages, but it's formatted as a comparative table.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table.\n\n8. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n9. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n10. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n11. Page 9: \"More Republicans than Democrats cite immigration, terrorism as top problems\" - This is a table.\n\n12. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n13. Page 11: \"Evaluations of the government's job reducing the threat of terrorism\" - This is a table.\n\n14. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n15. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n16. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n17. Page 14: \"ISIS rises higher atop", "citations": [], "input_tokens": 13358, "output_tokens": 512, "cost_micros": 47754, "latency_ms": 11733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart, not a bar chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart, not a bar chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with horizontal bars showing differences.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - Line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - Line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This shows horizontal bar comparisons.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - Horizontal bars.\n\n9. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - Line chart.\n\n10. Page 12: \"More concern with country's protection than civil liberties\" - Line chart.\n\n11. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - Line chart.\n\n12. Page 14: \"ISIS rises higher atop public's threat list\" - Horizontal bars.\n\n13. Page 15: \"Partisan differences in views of top threats to U.S.\" - Horizontal bars.\n\n14. Page 16: \"Most continue to support U.S. military campaign against ISIS\" - Line chart.\n\n15. Page 17: \"Few say current military effort in Iraq and Syria is going well\" - Horizontal bars.\n\n16. Page 17: \"More long-term optimism in ISIS fight\" - Vertical bars.\n\n17. Page 18: \"Bigger concern: Will U.S. go too far or not far enough in Iraq and Syria?\" - Table format with categories.\n\n18. Page ", "citations": [], "input_tokens": 19218, "output_tokens": 512, "cost_micros": 65334, "latency_ms": 13172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully go through the document to count all bar charts.\n\nLet me examine each chart in the document:\n\n1. Page 2: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line graph, not a bar chart.\n\n2. Page 3: \"Most say more scrutiny of Muslims not warranted\" - This is a bar chart showing percentages for Republicans, Independents, and Democrats.\n\n3. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a bar chart comparing Democratic Party vs Republican Party on various issues.\n\n4. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line graph.\n\n5. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a bar chart showing Republican, Independent, and Democrat responses.\n\n6. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table, not a bar chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a bar chart showing responses by different political groups.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - This is a bar chart showing responses by different demographic groups.\n\n9. Page 8: \"Most important problem facing nation\" - This is a table, not a bar chart.\n\n10. Page 9: \"Most important problem facing nation\" (partisan breakdown) - This is a table, not a bar chart.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a bar chart showing percentages by party affiliation.\n\n12. Page 11: \"Older, less educated give more negative ratings\" - This is a bar chart showing responses by age and education.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line graph.\n\n14. Page 13: Bar chart showing partisan views on anti-terrorism policies - This is a line graph.\n\n15. Page 13: \"Young adults more concerned about civil liberties\" - This is a bar chart showing responses by age group.\n\n16. Page 14: \"", "citations": [], "input_tokens": 14453, "output_tokens": 512, "cost_micros": 51039, "latency_ms": 11922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "16", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This appears to be a simple text display with percentages, not a chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a table, not a bar chart.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n9. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n10. Page 9: \"Most important problem facing nation\" - This is a table.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n12. Page 11: \"How well is govt doing reducing threat of terrorism?\" - This is a table.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n14. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n15. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n16. Page 14: \"ISIS rises higher atop public's threat list\" - This is a table.\n\n17. Page 15: \"Partisan differences in views of top threats to U.S.\" - This is a table.", "citations": [], "input_tokens": 18585, "output_tokens": 512, "cost_micros": 63435, "latency_ms": 12656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the bar charts in the report.Let me continue reading to count all the bar charts in the document.Let me search for \"figure\" tags and examine the visual content descriptions more carefully:I found 31 figures in the document. Now I need to examine each one to determine which are bar charts. Let me read through the document systematically to identify the type of each chart. Based on what I've already read, I can see several line charts and some other visualizations. Let me look for more specific patterns that indicate bar charts.\n\nLooking at the content I've already read, I can identify the following charts:\n\n1. Lines 188-253: Line chart showing government's job rating on terrorism over time\n2. Lines 306-354: Line chart showing partisan gap in views of whether Islam encourages violence  \n3. Lines 372-413: Pie chart showing views on Muslim scrutiny\n4. Lines 436-505: Horizontal bar chart comparing parties on various issues (terrorist threat, gun control, economy, immigration, climate change, environment)\n5. Lines 563-617: Line chart about Islam and violence over time\n6. Lines 618-657: Line chart showing partisan differences over time\n7. Lines 937-1006: Horizontal bar chart showing Muslim scrutiny views by demographic groups\n8. Lines 1055-1193: Horizontal bar chart showing scrutiny views by various demographics\n9. Lines 1508-1563: Line chart showing government ratings on terrorism by party\n10. Lines 1738-1802: Line chart about civil liberties concerns\n11. Lines 1850-1905: Line chart showing partisan views on anti-terror policies\n12. Lines 2004-2072: Vertical bar chart comparing threat levels (ISIS, Iran, North Korea, etc.)\n13. Lines 2117-2210: Horizontal bar chart showing partisan differences in threat views\n14. Lines 2239-2304: Line chart about military campaign approval\n15. Lines 2353-2425: Two separate charts about military campaign assessment\n\nLet me carefully review the entire document to count specifically the bar charts (not line charts or pie charts).\n\nBased on my thorough reading of the document, I can identify the following **bar charts**:\n\n1. **Page 4** (lines 436-505): Horizontal bar chart - \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - comparing Democratic Party vs Republican Party on 6 issues\n2. **Page 6** (lines 937-1006): Horizontal bar chart - \"Should Muslims be subject to greater scrutiny because of their religion?\" - showing responses by party/ideology\n3. **Page 7** (lines 1055-1193): Horizontal bar chart - \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - showing responses by demographics\n4. **Page 14** (lines 2004-2072): Mixed chart showing ISIS and other threats with numerical comparisons\n5. **Page 15** (lines 2117-2210): Horizontal bar chart - \"Partisan differences in views of top threats to U.S.\"\n\nAfter careful examination, the document contains **5 bar charts**.\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46078, "error": null, "extra": {"thread_id": 1931, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6dc6e43bf0ee4e80af05e0ae2c870d11", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 404, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically page 17 (document page 18), I can find information about public attitudes regarding the ISIS fight.\n\nThe chart titled \"More long-term optimism in ISIS fight\" shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria.\n\nThe text confirms: \"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nAnswer: Two-thirds believe the U.S. and allies will succeed", "citations": [], "input_tokens": 58160, "output_tokens": 159, "cost_micros": 176865, "latency_ms": 22375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Let me search the document for information about people's attitudes regarding the ultimate success of the U.S. and its allies in the fight against ISIS.\n\nI found the relevant section on page 17, which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also notes this represents an increase: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Long-term optimism that U.S. and allies will succeed", "citations": [], "input_tokens": 13394, "output_tokens": 182, "cost_micros": 42912, "latency_ms": 6734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "long term optimism that u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, on page 17, there is a section titled \"More long-term optimism in ISIS fight\" which shows:\n\n\"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail. The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Will definitely or probably succeed", "citations": [], "input_tokens": 19254, "output_tokens": 118, "cost_micros": 59532, "latency_ms": 4703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "will definitely or probably succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically the section titled \"More long-term optimism in ISIS fight\" on page 16, I can find the answer.\n\nThe document shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria, compared to only 27% who said they will \"Definitely/Probably fail.\"\n\nThe text states: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: They will succeed/be successful", "citations": [], "input_tokens": 14489, "output_tokens": 141, "cost_micros": 45582, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4444444444444445, "method": "str_norm", "normalised_pred": "they will succeed be successful", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document for information about public attitudes on the ultimate success of the U.S. and its allies against ISIS in 2015...\n\nFrom page 17, I can see the section \"Current assessments of U.S. military campaign, prospects for success\" which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also shows that \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Two-thirds believe U.S. and allies will succeed", "citations": [], "input_tokens": 18621, "output_tokens": 191, "cost_micros": 58728, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about public attitudes toward the U.S. and its allies' efforts against ISIS in 2015.Let me continue reading to find the specific information about public attitudes:Let me search for information about attitudes toward the fight against ISIS and whether the U.S. and allies will succeed:Perfect! I found the information. Let me read a bit more to confirm the complete data:Based on the document, I found the answer. The document states that although few say the current military effort in Iraq and Syria is going well (only 35% in December 2015 say it's going very/fairly well), when it comes to long-term outlook, two-thirds (66%) believe the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria.\n\nAnswer: Two-thirds (66%) believe the U.S. and its allies will succeed", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43858, "error": null, "extra": {"thread_id": 1932, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a011d8590eae4d86a7883730ba9de008", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.26666666666666666, "method": "str_norm", "normalised_pred": "two thirds 66 believe u s and its allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1 on page 26:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nand\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 59872, "output_tokens": 134, "cost_micros": 181626, "latency_ms": 31250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 14:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 32, the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 19062, "output_tokens": 151, "cost_micros": 59451, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 18:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 31, we can see that the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22767, "output_tokens": 155, "cost_micros": 70626, "latency_ms": 4282, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the output dimension of the linear_class layer in the DETR PyTorch inference code.\n\nLooking at the code in Listing 1:\n\n```python\ndef __init__(self, num_classes, hidden_dim, nheads,\n             num_encoder_layers, num_decoder_layers):\n    super().__init__()\n    # ... other layers ...\n    self.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe linear_class layer is defined as `nn.Linear(hidden_dim, num_classes + 1)`, which means it takes `hidden_dim` as input and outputs `num_classes + 1` dimensions.\n\nThen looking at line 33:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe model is instantiated with `num_classes=91`.\n\nTherefore, the output dimension of the linear_class layer is:\nnum_classes + 1 = 91 + 1 = 92\n\nAnswer: 92", "citations": [], "input_tokens": 19551, "output_tokens": 270, "cost_micros": 62703, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 15:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 33:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe `linear_class` layer has an output dimension of `num_classes + 1`, where `num_classes=91`.\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22903, "output_tokens": 155, "cost_micros": 71034, "latency_ms": 4562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the DETR document to find the output dimension of the linear_class layer in the PyTorch inference code.Let me search for \"linear_class\" in the document to find the PyTorch inference code:Let me search for PyTorch code or inference code in the document:Now let me read the section with the PyTorch inference code around line 2864:Perfect! I found the PyTorch inference code. Looking at line 2898, I can see:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 2915, the model is instantiated with:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nSo the output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39343, "error": null, "extra": {"thread_id": 1933, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e081bc008e9a4c03bbbaf39b68e10bd7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 162, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about missed instances when the number of visible instances is 60.\n\nLooking at Figure 12 on page 25, I can see a graph titled \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nThe graph shows three colored lines representing different categories:\n- Dog (blue line)\n- Person (orange line)\n- Apple (green line)\n\nThe x-axis shows \"Number of visible instances\" and the y-axis shows \"% of missed instances.\"\n\nAt the point where the number of visible instances = 60, I need to compare the values for each category:\n\nLooking at the graph at x=60:\n- The dog (blue) line appears to be at approximately 20-25%\n- The person (orange) line appears to be at approximately 15-20%\n- The apple (green) line appears to be at approximately 10-15%\n\nAt 60 visible instances, the dog category shows the highest percentage of missed instances.\n\nAnswer: dog", "citations": [], "input_tokens": 59873, "output_tokens": 231, "cost_micros": 183084, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, I can see a graph plotting \"% of missed instances\" (y-axis) against \"Number of visible instances\" (x-axis) for three categories: dog, person, and apple.\n\nAt 60 visible instances, I need to identify which line/category has the highest percentage of missed instances:\n\n- The dog line (appears to be one color)\n- The person line (appears to be another color)  \n- The apple line (appears to be a third color)\n\nFrom the graph, at the 60 visible instances mark, the \"apple\" category appears to have the highest percentage of missed instances, showing approximately 20-25% missed instances, while \"dog\" and \"person\" show lower percentages (around 10-15%).\n\nAnswer: apple", "citations": [], "input_tokens": 19063, "output_tokens": 203, "cost_micros": 60234, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nLooking at the graph in Figure 12, I can see three lines representing different categories: dog, person, and apple.\n\nAt 60 visible instances (x-axis), I need to compare the percentage of missed instances (y-axis) for each category:\n\n- Dog (appears to be around 20-25%)\n- Person (appears to be around 15-20%)\n- Apple (appears to be the highest, around 30-35%)\n\nThe apple category clearly shows the highest percentage of missed instances at 60 visible instances.\n\nAnswer: apple", "citations": [], "input_tokens": 22768, "output_tokens": 160, "cost_micros": 70704, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, it shows a graph with \"Number of visible instances\" on the x-axis (from 0 to 100) and \"% of missed instances\" on the y-axis (from 0 to 70). The graph shows three lines for different categories: dog, apple, and one other category.\n\nAt the point where the number of visible instances = 60, I need to identify which category line is highest (meaning the largest proportion of missed instances).\n\nFrom the graph, at 60 visible instances:\n- The \"dog\" line (appears to be one line)\n- The \"apple\" line (appears to be another line)\n- There appears to be a third line\n\nLooking at the vertical position at x=60, the \"dog\" line appears to be the highest among the three categories shown, indicating the largest proportion of missed instances at that point.\n\nAnswer: dog", "citations": [], "input_tokens": 19552, "output_tokens": 226, "cost_micros": 62046, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 12 (the table on page 25), I need to find the number of missed instances for each category when there are 60 visible instances.\n\nFrom the table:\n- dog: 5 missed instances\n- person: 22 missed instances  \n- apple: 15 missed instances\n\nOut of 60 visible instances:\n- dog: 5/60 = 8.3%\n- person: 22/60 = 36.7%\n- apple: 15/60 = 25%\n\nThe person category has the largest proportion of missed instances.\n\nAnswer: person", "citations": [], "input_tokens": 22904, "output_tokens": 139, "cost_micros": 70797, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "person", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about visible instances and missed instances by category.Let me search for content about \"missed instances\" and look for figures or charts.Let me read the section with Figure 12 which discusses missed instances:Perfect! I found Figure 12 which shows the chart. Let me read the data more carefully. The chart shows:\n- Y-axis: % of missed instances (0 to 70)\n- X-axis: Number of visible instances (20 to 100)\n- Three categories plotted: dog, person, and apple\n\nAt 60 visible instances, I need to identify which category has the largest proportion of missed instances. From the text data shown:\n- Lines 2795-2796: dog\n- Lines 2797-2798: person  \n- Lines 2801-2802: apple\n\nThe chart shows these three categories, and I need to determine which has the highest value at x=60. Based on the chart layout and the typical pattern where lines are listed in the legend in order of their values, and given that \"dog\" is listed first in the legend (line 2795), followed by \"person\" (line 2797), and then \"apple\" (line 2801), this typically indicates their relative positions on the chart.\n\nLet me look for any additional data that might give exact values:Based on the chart data from Figure 12, I can see:\n- Y-axis values listed: 70, 60, 50, 40, 30, 20, 10, 0\n- X-axis values: 20, 40, 60, 80, 100\n- Three categories in the legend: dog, person, apple\n\nThe legend positioning typically indicates the order of lines on the chart. In this case, at x=60 (60 visible instances), \"dog\" appears to be listed first in the legend (lines 2795-2796), suggesting it has the highest proportion of missed instances at that point, followed by \"person\" and then \"apple\".\n\nGiven the layout where:\n- \"dog\" is listed at the top of the legend\n- \"person\" is in the middle\n- \"apple\" is at the bottom\n\nAnd the typical convention where legend items are ordered by their y-values at key points or overall prominence, \"dog\" would have the largest proportion of missed instances when the number of visible instances equals 60.\n\nAnswer: dog", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47125, "error": null, "extra": {"thread_id": 1934, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39684984e24b48b0a29495ffed5edb23", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 304, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 on page 13, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP50, \u0394, APS, APM, APL\n\nThe rows show different combinations:\n- Row 1: X (class), X (\u21131), blank (no GIoU): AP=35.8, AP50=57.3\n- Row 2: X (class), blank (no \u21131), X (GIoU): AP=39.9, AP50=61.6\n- Row 3: X (class), X (\u21131), X (GIoU): AP=40.6, AP50=61.6\n\nThe question asks for DETR with L1 loss and WITHOUT GIoU loss, which corresponds to the first row where class=X, \u21131=X, and GIoU is blank.\n\nAnswer: 57.3", "citations": [], "input_tokens": 59883, "output_tokens": 274, "cost_micros": 183759, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three different configurations:\n\n1. First row: class \u2713, L1 \u2717, GIoU \u2713 \u2192 AP: 35.8, AP50: 57.3\n2. Second row: class \u2713, L1 \u2713, GIoU \u2717 \u2192 AP: 39.9, AP50: 61.6\n3. Third row (baseline): class \u2713, L1 \u2713, GIoU \u2713 \u2192 AP: 40.6, AP50: 61.6\n\nThe question asks for the configuration with L1 loss and without GIoU loss, which corresponds to the second row.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19073, "output_tokens": 223, "cost_micros": 60564, "latency_ms": 5608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three rows with different loss combinations:\n\n1. Row 1: class \u2612, l1 \u2612, GIoU \u2610 (class and L1, but no GIoU)\n   - AP: 35.8\n   - AP50: 57.3\n\n2. Row 2: class \u2612, l1 \u2610, GIoU \u2612 (class and GIoU, but no L1)\n   - AP: 39.9\n   - AP50: 61.6\n\n3. Row 3 (baseline): class \u2612, l1 \u2612, GIoU \u2612 (all three losses)\n   - AP: 40.6\n   - AP50: 61.6\n\nThe question asks for DETR with L1 loss and without GIoU loss, which corresponds to the first row in Table 4.\n\nAnswer: 57.3", "citations": [], "input_tokens": 22778, "output_tokens": 278, "cost_micros": 72504, "latency_ms": 7140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see the ablation study for different loss combinations:\n\nThe table shows three rows:\n1. class \u2713, L1 \u2717, GIoU \u2713: AP = 35.8, AP50 = 57.3\n2. class \u2713, L1 \u2713, GIoU \u2717: AP = 39.9, AP50 = 61.6\n3. class \u2713, L1 \u2713, GIoU \u2713: AP = 40.6, AP50 = 61.6 (baseline)\n\nThe second row shows the model with L1 loss but without GIoU loss (class \u2713, L1 \u2713, GIoU \u2717).\n\nThe AP50 value for this configuration is 61.6.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19562, "output_tokens": 256, "cost_micros": 62526, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP\u2085\u2080, \u0394, AP\u209b, AP\u2098, AP\u2097\n\nThe rows show different combinations:\n1. Row with \u2713 for class and \u21131, but no GIoU: AP = 35.8, AP\u2085\u2080 = 57.3\n2. Row with \u2713 for class and GIoU, but no \u21131: AP = 39.9, AP\u2085\u2080 = 61.6\n3. Row with \u2713 for all three (class, \u21131, GIoU): AP = 40.6, AP\u2085\u2080 = 61.6\n\nThe first row corresponds to the model with L1 loss but without GIoU loss, which shows AP\u2085\u2080 = 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 22914, "output_tokens": 285, "cost_micros": 73017, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for the AP50 value of DETR with L1 loss and without GIoU loss in the provided document.Let me read the table that shows the effect of loss components:Perfect! I found Table 4 which shows the effect of loss components on AP. Looking at the table:\n\n- The first row shows: class=\u2612, l1=\u2612, GIoU=(empty), which means L1 loss is used but GIoU loss is not used\n- For this configuration, the AP50 value is 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32109, "error": null, "extra": {"thread_id": 1935, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_984a5f35790043fbba33381ecf31e0b6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 on page 22, which shows the \"Architecture of DETR's transformer,\" I can see the Multi-Head Self-Attention layers are shown in the diagram.\n\nThe Multi-Head Self-Attention boxes in the diagram are colored pink/salmon.\n\nAnswer: Pink (or salmon)", "citations": [], "input_tokens": 59883, "output_tokens": 71, "cost_micros": 180714, "latency_ms": 31750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "pink or salmon", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 \"Architecture of DETR's transformer\" in the document, I need to identify the color of the Multi-Head Self-Attention layer.\n\nIn the diagram, the Multi-Head Self-Attention layer appears in both the Encoder and Decoder sections. These layers are shown in a light orange or peach color.\n\nAnswer: Orange (or peach/light orange)", "citations": [], "input_tokens": 19073, "output_tokens": 89, "cost_micros": 58554, "latency_ms": 3953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange or peach light orange", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I can see the architecture diagram of DETR's transformer.\n\nThe Multi-Head Self-Attention layer boxes in the diagram are colored blue.\n\nAnswer: Blue", "citations": [], "input_tokens": 22778, "output_tokens": 48, "cost_micros": 69054, "latency_ms": 4203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I need to identify the color of the Multi-Head Self-Attention layer in the architecture diagram.\n\nIn the figure, the Multi-Head Self-Attention layer appears in the Encoder section of the architecture.\n\nAnswer: Orange", "citations": [], "input_tokens": 19562, "output_tokens": 62, "cost_micros": 59616, "latency_ms": 3406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, which shows the architecture of DETR's transformer:\n\nThe Multi-Head Self-Attention layers are shown in the diagram boxes in the Encoder and Decoder sections.\n\nAnswer: The diagram does not specify colors for the layers in Fig 10", "citations": [], "input_tokens": 22914, "output_tokens": 65, "cost_micros": 69717, "latency_ms": 3734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "diagram does not specify colors for layers in fig 10", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 10 and identify the color of the Multi-Head Self-Attention layer in DETR's transformer architecture.Let me search for \"Fig 10\" or \"Figure 10\" in the document:Let me search for references to transformer architecture figures:Great! I found that Figure 10 shows the \"Architecture of DETR's transformer\" at line 2409. Let me read that section:I can see the text content of Figure 10, but I need to look for color information. The document mentions it's embedded as an image. Let me search for more detailed description of the figure or any color information:The visual description I found was for a different image (zebras). Let me go back and look more carefully at the Figure 10 architecture diagram. Let me search around that area more carefully:I see the text extracted from Figure 10, but the visual description that follows (zebras) is clearly for a different image (Im7.png). Let me search for visual descriptions that might be associated with the transformer architecture diagram. Let me look before Figure 10:Let me search for where there might be an embedded image description for Figure 10. Let me check if there's an image description before line 2400:Let me search for image descriptions or embedded images related to Figure 10. Let me check what comes right before the figure starts:Let me look more carefully. The embedded image Im7.png comes AFTER Figure 10. Let me check if there's an embedded image BEFORE Figure 10 that might contain the actual architecture diagram:The document doesn't seem to have a visual description embedded for Figure 10's architecture diagram itself within the accessible text. However, looking at the structure, it appears Figure 10 is presented as a figure with text labels but the actual image with colors might not have been described in the OCR/visual description section.\n\nLet me search more broadly for any mentions of colors in the architecture or in figures:Let me search for other color terms that might be in Figure 10:These color mentions don't seem to be about Figure 10. Let me try to find if there's an image file reference specifically for the transformer architecture. Let me look for \"Im\" images around Figure 10:Image Im8.png is not Figure 10 either. Let me look between Figure 10 text and Im7.png more carefully. Actually, let me check if there's an embedded image right after the </figure> tag for Figure 10. Let me look at the exact area around the figure closing:So Figure 10 ends at line 2485, and then Im7.png starts at line 2489, which is clearly about zebras, not the transformer architecture. The Figure 10 text content doesn't include a visual description with colors. Let me search for if there's any embedded image metadata that might describe colors for transformer diagrams. Let me try searching for \"green\" or \"red\" along with terms near Figure 10 context:Let me try a different approach. Perhaps the PDF was processed and the image containing Figure 10 wasn't given a separate visual description. Let me look for if there's any image file reference or visual annotation BEFORE Figure 10 that might be the diagram itself:It seems the document processing didn't include a detailed visual description with colors for Figure 10's architecture diagram. This could mean the image was processed but only the text labels were extracted, not the visual styling information like colors of the boxes.\n\nLet me try searching the entire document for any reference to colors of \"Self-Attention\" layers or transformer layers:Based on my thorough search through the document, I cannot find any visual description that specifies the color of the Multi-Head Self-Attention layer in Figure 10. The document contains the text content and labels from Figure 10, but the visual styling information (colors of boxes/components) was not captured in the OCR or visual description process.\n\nWithout access to the actual image file or a visual description that includes color information, I cannot determine from this document what color the Multi-Head Self-Attention layer is depicted as in Figure 10.\n\nAnswer: Color information not available in the provided document text.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 122281, "error": null, "extra": {"thread_id": 1936, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_be29632a506941cfa603de6bebd1c2eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 844, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color information not available in provided document text", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the number of multi-head self-attention layers in DETR's default structure.\n\nLooking at the document, I need to identify the default configuration of DETR.\n\nFrom Section 4 (Experiments), specifically the \"Technical details\" subsection:\n\"we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nHowever, I need to understand the architecture better. Looking at Section 3.2 and Figure 10:\n\n1. The **encoder** has 6 layers, and each encoder layer contains one multi-head self-attention module (as stated: \"Each encoder layer has a standard architecture and consists of a multi-head self-attention module and a feed forward network (FFN)\")\n\n2. The **decoder** has 6 layers. Looking at the description and Figure 10, each decoder layer contains:\n   - Multi-head self-attention\n   - Multi-head attention (encoder-decoder attention)\n\nSo each decoder layer has one multi-head self-attention layer.\n\nFrom the default configuration mentioned in the experiments:\n- 6 encoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n- 6 decoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19056, "output_tokens": 334, "cost_micros": 62178, "latency_ms": 8546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about DETR's default structure regarding multi-head self-attention layers.\n\nFrom Section 4.1 \"Comparison with Faster R-CNN\":\n\"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nFrom Section 4.2 \"Ablations\":\n\"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\nFrom the architecture description in Section 3.2:\n- The transformer encoder has multiple layers, each containing a multi-head self-attention module\n- The transformer decoder has multiple layers, each containing multi-head self-attention and encoder-decoder attention mechanisms\n\nSo the default DETR structure has:\n- 6 encoder layers (each with 1 multi-head self-attention layer) = 6 layers\n- 6 decoder layers (each with 1 multi-head self-attention layer) = 6 layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22761, "output_tokens": 271, "cost_micros": 72348, "latency_ms": 7187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and the number of multi-head self-attention layers.\n\nLooking through the document:\n\n1. In Section 4 \"Experiments\" under \"Technical details\", it states:\n\"We use scale augmentation... we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\n2. In Section A.3 \"Detailed architecture\", the document describes the transformer architecture with both encoder and decoder layers.\n\n3. From the architecture description and Figure 10:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing:\n  - Multi-head self-attention\n  - Multi-head attention (encoder-decoder attention)\n\n4. From the technical details in Section 4:\n- 6 encoder layers (each with multi-head self-attention)\n- 6 decoder layers (each with multi-head self-attention)\n\nSo the total number of multi-head self-attention layers would be:\n- 6 encoder layers \u00d7 1 self-attention layer each = 6\n- 6 decoder layers \u00d7 1 self-attention layer each = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19545, "output_tokens": 287, "cost_micros": 62940, "latency_ms": 7234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and count the multi-head self-attention layers.\n\nFrom the document, I can find the architecture details:\n\n1. In Section 4.2 (Ablations), it states: \"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\n2. Looking at Figure 10 and Section A.3 (Detailed architecture), the structure shows:\n   - Encoder has N layers, each with Multi-Head Self-Attention\n   - Decoder has M layers, each with Multi-Head Self-Attention AND Multi-Head Attention (cross-attention)\n\n3. From the baseline model description: \"we choose a model with 6 transformer and 6 decoder layers\"\n\n4. In the encoder: Each of the 6 encoder layers contains 1 multi-head self-attention layer = 6 layers\n\n5. In the decoder: Each of the 6 decoder layers contains 1 multi-head self-attention layer = 6 layers\n   (Note: The decoder also has multi-head cross-attention, but that's different from self-attention)\n\nTotal multi-head self-attention layers = 6 (encoder) + 6 (decoder) = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22897, "output_tokens": 303, "cost_micros": 73236, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll search for information about DETR's structure in the provided document.Now I found the key information. Let me search for more details about the architecture to confirm the total number of self-attention layers.Let me check the decoder structure to understand how many self-attention layers are in the decoder:Now let me look at the supplementary material section that describes the architecture in detail:Based on the document, I can see that:\n\n1. The default DETR model has **6 encoder layers** (line 1054, 1073: \"6 transformer and 6 decoder layers\" and \"6 encoder, 6 decoder layers\")\n\n2. Each encoder layer has a **multi-head self-attention module** (line 624)\n\n3. The default DETR model has **6 decoder layers** (line 1054, 1073)\n\n4. Each decoder layer has **multi-head self-attention** (line 2403-2404: \"multiple multi-head self-attention and decoder-encoder attention\")\n\nSo the total number of multi-head self-attention layers in DETR's default structure is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61719, "error": null, "extra": {"thread_id": 1937, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_12c4837a12434eeca194f2ee2772f40e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 320, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this InVue 2021 Product Catalog.\n\nLooking through the document systematically:\n\n1. **Series 2865** (page 9) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - Multi-position security system with sensors\n\n2. **OnePOD** (page 10) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - POD display solution\n\n3. **Series 960** (page 12) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - POD with exposed cable\n\n4. **iPad + Keyboard Solutions** (pages 17-19):\n   - Standard Solution for iPad Pro + Keyboards (page 18)\n   - High Security Solution for iPad Pro + Keyboards (page 19)\n\n5. **iPad Digital Display** (pages 15-16) - Bracketed pedestal solution\n\nSo counting the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. iPad + Keyboard Standard Solution\n5. iPad + Keyboard High Security Solution\n6. iPad Digital Display\n\nAnswer: 6", "citations": [], "input_tokens": 96851, "output_tokens": 271, "cost_micros": 294618, "latency_ms": 54405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this Apple catalog document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** (pages 3-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 6-9) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (pages 10-12) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 13) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 14) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 15-16) - A bracketed pedestal solution for iPads as digital displays\n\n7. **iPad + Keyboard Solutions** (pages 17-19):\n   - iPad Pro + Keyboards Standard Solution (page 18)\n   - iPad Pro + Keyboards High Security Solution (page 19)\n\nLet me count the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nAnswer: 8", "citations": [], "input_tokens": 11786, "output_tokens": 335, "cost_micros": 40383, "latency_ms": 6532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays\n\n7. **iPad Pro + Keyboards Standard Solution** (page 11) - For iPad Pro with Magic Keyboard and Smart Keyboard\n\n8. **iPad Pro + Keyboards High Security Solution** (page 12) - For iPad Pro with keyboards\n\nSo there are **8** different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 18388, "output_tokens": 271, "cost_micros": 59229, "latency_ms": 6733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions mentioned in the document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without Keyboard) on page 38\n\n2. **OnePOD** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 40\n\n3. **Series 960** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 41\n\n4. **iPad Digital Display** - mentioned as a \"Bracketed pedestal for utilizing iPads as digital displays on Apple Fixtures\" on pages 11-12\n\n5. **iPad Pro + Keyboards Standard Solution** - mentioned on page 11 for \"iPad Pro, Magic Keyboard, Smart Keyboard and Apple Pencil\"\n\n6. **iPad Pro + Keyboards High Security Solution** - mentioned on page 12\n\nSo I can identify 6 different iPad solutions in the document:\n- Series 2865 (for standard iPads)\n- OnePOD (for standard iPads)\n- Series 960 (for standard iPads)\n- iPad Digital Display\n- iPad Pro + Keyboards Standard Solution\n- iPad Pro + Keyboards High Security Solution\n\nAnswer: 6", "citations": [], "input_tokens": 12620, "output_tokens": 289, "cost_micros": 42195, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in the document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays on Apple Fixtures\n\n7. **iPad + Keyboard Solutions - Standard Solution** (page 11) - For iPad Pro with Magic Keyboard, Smart Keyboard and Apple Pencil\n\n8. **iPad + Keyboard Solutions - High Security Solution** (page 12) - For iPad Pro with Magic and Smart Folio keyboards\n\nSo there are 8 different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 16954, "output_tokens": 288, "cost_micros": 55182, "latency_ms": 7030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for iPad solutions in the provided document.Let me search for \"solution\" to find where iPad solutions are mentioned:Now let me read the table of contents section more carefully to see the iPad solutions listed:I can see there are two main sections: \"iPhone & iPad Solutions\" and \"iPad + Keyboard Solutions\". Let me now read through these sections to count the individual iPad solutions. Let me start with the \"iPhone & iPad Solutions\" section:Let me read the document more systematically. Let me check the pages 1-10 which should cover the \"iPhone & iPad Solutions\" section:Let me search for specific product series names to count the solutions:Let me look more systematically at the document. Let me read pages that should list individual iPad solutions:I can see Series 2865, OnePOD, and Series 960 mentioned. Let me continue reading to find all the different solutions and then look at the iPad + Keyboard Solutions section:Let me search for more product names. Let me look for what comes after Series 960:Good, I found \"One60 Tethered\". Let me continue:Good, I found \"One90QR\". Let me continue searching through the document:Good, I found \"iPad Digital Display\". Now let me check if there are more iPhone & iPad solutions and then look at the iPad + Keyboard Solutions section. Let me look at page 10 where iPad + Keyboard Solutions should start:Good! I found \"iPad Pro + Keyboards Standard Solution\". Let me continue to find the next one:Good! I found \"iPad Pro + Keyboards High Security Solution\". Now let me continue to see if there are more iPad solutions. Let me check what comes on page 12 and beyond, which should start MacBook Solutions (page 13 according to the table of contents):Perfect! Now I can see that page 13 starts MacBook Solutions, which means the iPad + Keyboard Solutions section ends at page 12. Let me now compile all the iPad solutions I found. Let me verify by checking if there are any other iPad product names I might have missed. Let me look at pages 10-11 more carefully:Now let me look more carefully at page 9 to see if there are more solutions listed on that introductory page:Good. Now let me compile the complete list. I need to be systematic. Let me re-examine the section headers carefully. Let me look at what page 9 shows at the bottom to clarify:\n\nActually, looking back at my findings, page 9 seems to be a transition page showing the \"iPad + Keyboard Solutions\" section header with compatibility information. Pages 10-11 then have two specific solutions.\n\nLet me now count all the different iPad solutions I found in the document:\n\n**From \"iPhone & iPad Solutions\" section (pages 1-8):**\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n\n**From \"iPad + Keyboard Solutions\" section (pages 10-11):**\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nThat gives me a total of **8** different iPad solutions.\n\nBut wait, let me verify this more carefully. OnePOD was mentioned on page 2 in the product comparisons - let me verify if it's a distinct solution:Yes, OnePOD is a distinct solution. Let me also verify the others are all distinct iPad solutions and not just iPhones. Let me check the compatibility sections more carefully. Looking back at my readings, all of these products list iPad compatibility:\n\n- Series 2865: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- OnePOD: There's a specific OnePOD section for iPad ordering\n- Series 960: Compatible with iPads\n- One60 Tethered: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- One90QR: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- iPad Digital Display: Specifically for iPads on Apple Fixtures\n- iPad Pro + Keyboards Standard Solution: For iPad Pro with keyboards\n- iPad Pro + Keyboards High Security Solution: For iPad Pro with keyboards\n\nAll 8 are indeed iPad solutions. \n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 126703, "error": null, "extra": {"thread_id": 1938, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_18872fac42c746e097bc8836213e95a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 870, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features are listed but not shown in the illustration.\n\nLooking at page 15 (document page 22), I can see the \"MacBook High Security Solution\" section.\n\nThe \"FEATURES AND BENEFITS\" lists 6 numbered features:\n\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow looking at the illustrations on the same page, I can see:\n- Image showing features 3, 6, 1, 6, and 2 labeled\n- Image showing feature 5 labeled\n\nLet me verify which features are NOT shown in the illustrations:\n- Feature 1: Shown (labeled in bottom left image)\n- Feature 2: Shown (labeled in bottom left image)\n- Feature 3: Shown (labeled in bottom left image)\n- Feature 4: NOT shown in any illustration\n- Feature 5: Shown (labeled in top right image)\n- Feature 6: Shown (labeled in bottom left image)\n\nAnswer: 1", "citations": [], "input_tokens": 96859, "output_tokens": 327, "cost_micros": 295482, "latency_ms": 47640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which feature numbers are mentioned in the text but not shown in the illustration.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section.\n\nThe FEATURES AND BENEFITS list shows:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to look at the illustration to see which numbers are labeled in the image.\n\nFrom the illustration, I can see the following numbers are clearly marked:\n- 1 (pointing to the rear anchor area)\n- 2 (pointing to the cable/tether area)\n- 3 (pointing to bracket/arm area)\n- 5 (pointing to the metal tabs)\n- 6 (appears twice, pointing to the molded feet)\n\nThe numbers NOT shown in the illustration are:\n- 4 (Ability to power/alarm MacBook with USB-C Port)\n\nAnswer: 4", "citations": [], "input_tokens": 11794, "output_tokens": 307, "cost_micros": 39987, "latency_ms": 8657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed in text with what's shown in the illustration.\n\nLooking at pages 14-17, I find the MacBook High Security Solution on page 15.\n\nThe text lists these FEATURES AND BENEFITS:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration to see which features are labeled. The illustration shows numbered callouts (1, 2, 3, 6) pointing to different parts of the MacBook security solution.\n\nFrom the visible callouts in the illustration:\n- Feature 1 is shown\n- Feature 2 is shown\n- Feature 3 is shown\n- Feature 6 is shown\n\nFeatures NOT shown in the illustration:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nTherefore, 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 18396, "output_tokens": 323, "cost_micros": 60033, "latency_ms": 9530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed with what's shown in the illustration.\n\nLooking at page 15, I can see the \"HIGH SECURITY EXCEPTION PROGRAM - MACBOOK\" section titled \"MacBook High Security Solution\".\n\nThe FEATURES AND BENEFITS section lists 6 numbered features:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration on that page to see which features are NOT shown visually. Looking at the image description \"delete alarm\" and \"return\" text visible, this appears to be showing a MacBook with the security solution.\n\nFrom the numbered features:\n- Feature 4 (USB-C Port) - this is an internal capability, not visually shown in an illustration\n- Feature 5 (Metal tabs with rubber pads preventing closing) - this would be visible if shown\n- Feature 6 (Stable, molded feet) - these would be underneath and likely not visible in a standard product photo\n\nWithout being able to see the exact details of the illustration clearly, but based on typical product photography, features that are functional/internal capabilities (like feature 4 - power/alarm ability) or underneath components (like feature 6 - molded feet) are typically not shown in product illustrations.\n\nBased on the context and typical product documentation, features 4 and 6 would most likely not be presented in the illustration, giving us 2 features.\n\nAnswer: 2", "citations": [], "input_tokens": 12628, "output_tokens": 418, "cost_micros": 44154, "latency_ms": 12765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features listed are NOT shown in the illustrations.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section with:\n\n**FEATURES AND BENEFITS** (numbered 1-6):\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe page shows 4 images with numbered callouts:\n- Image 1 (Top Left): Shows callout 5\n- Image 2 (Top Right): Shows callout 5 (close-up)\n- Image 3 (Bottom Left): Shows callouts 1, 2, 3, and 6\n- Image 4 (Bottom Right): Shows callout 2 (close-up)\n\nFeatures shown in illustrations:\n- Feature 1: YES (shown in Image 3)\n- Feature 2: YES (shown in Images 3 and 4)\n- Feature 3: YES (shown in Image 3)\n- Feature 4: NO (not shown in any image)\n- Feature 5: YES (shown in Images 1 and 2)\n- Feature 6: YES (shown in Image 3)\n\nOnly Feature 4 (Ability to power/alarm MacBook with USB-C Port) is NOT presented in the illustrations.\n\nAnswer: 4", "citations": [], "input_tokens": 16962, "output_tokens": 396, "cost_micros": 56826, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about MacBook high security solution and identify which features are not presented in the illustration.Let me search for \"security\" in the document to find the relevant section:Let me search for \"MacBook\" and \"high security\" together to find the specific section:Now let me read the specific section about \"MacBook High Security Solution\":Perfect! I found the MacBook High Security Solution section. Now let me read it more carefully to see the full list of features and the illustration. Let me go back to line 3132:I can see the features listed are numbered 1-6:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe illustration shows numbers 1, 2, 3, and 6 marked on the MacBook keyboard image in the figure. The OCR text shows these numbers visible in the illustration. However, I notice the visual description after the figure is describing a smartwatch, which seems incorrect. Let me check if there's a better description of the MacBook illustration by reading a bit more context around this section.\n\nLooking at the figure content (lines 3159-3225), I can see the illustration has:\n- Number 1 appears twice (lines 3185, 3189, 3223)\n- Number 2 appears twice (lines 3137, 3179, 3191, 3217)\n- Number 3 appears twice (lines 3173, 3215)\n- Number 6 appears three times (lines 3213, 3221)\n\nFrom the text, the 6 features are listed, and from the illustration markers I can identify which numbers are shown:\n- 1 is shown (rigid anchor design)\n- 2 is shown (steel cable)\n- 3 is shown (bracket bar arms)\n- 6 is shown (stable molded feet)\n\nThe features that are NOT shown in the illustration are:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nSo 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46969, "error": null, "extra": {"thread_id": 1939, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_abe777eda57d4187b59395c83dff7b8b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 346, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about \"One40\"...\n\nI cannot find any mention of \"One40\" in this Apple Solutions 2021 Product Catalog from InVue. The document discusses various security solutions for Apple products including:\n\n- Series 2865\n- OnePOD (One55, One60, One65)\n- Series 960\n- W2000\n- WS2\n- HSW100\n- One60 Wearable\n- OnePOD Wearable\n\nBut there is no product called \"One40\" mentioned in this catalog.\n\nAnswer: Cannot determine - product not found in document", "citations": [], "input_tokens": 96873, "output_tokens": 141, "cost_micros": 292734, "latency_ms": 38656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine product not found in document", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and whether it can only be used for Apple Watch.\n\nOn page 21, in the \"OnePOD Wearable / Apple Watch Solutions\" section, under \"FEATURES AND BENEFITS,\" it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is mentioned in the context of Apple Watch solutions. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nSearching through the rest of the document, One40 is only mentioned in this one location in relation to Apple Watch compatibility. There is no indication that One40 is used for any other products in this catalog.\n\nHowever, the question asks if it can \"only\" be used for Apple Watch. The document doesn't explicitly state that One40 cannot be used for other purposes - it simply doesn't mention One40 being used for anything else in this particular catalog.\n\nGiven that this is specifically an Apple catalog and One40 is only mentioned in the Apple Watch section without any other applications shown, and based solely on what is presented in this document:\n\nAnswer: yes", "citations": [], "input_tokens": 11808, "output_tokens": 265, "cost_micros": 39399, "latency_ms": 8577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and what products it can be used with.\n\nOn page 21, under \"OnePOD Wearable\" section for Apple Watch Solutions, it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is compatible with the OnePOD Wearable system for Apple Watch. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nLooking through the rest of the document, I can see that One40 is only mentioned in the context of Apple Watch solutions. There is no mention of One40 being used for iPhones, iPads, or other Apple products in their respective sections.\n\nAnswer: No", "citations": [], "input_tokens": 18410, "output_tokens": 179, "cost_micros": 57915, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I need to search the document for information about \"One40\" and what products it can be used with.\n\nLooking through the document, I can find mentions of \"One40\" on page 21 in the OnePOD Wearable section:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis appears in the context of Apple Watch solutions, but it only states compatibility with these stands. It doesn't state that One40 can ONLY be used for Apple Watch or that it's exclusive to Apple Watch.\n\nThe document doesn't provide enough information to definitively say whether One40 can only be used for Apple Watch or if it has other uses beyond what's mentioned in this catalog.\n\nHowever, since the question asks for a direct yes/no answer based on what's in the document, and the document only shows One40 mentioned in the Apple Watch context without explicitly stating it's exclusive to Apple Watch, I cannot confirm the statement is true.\n\nAnswer: No", "citations": [], "input_tokens": 12642, "output_tokens": 221, "cost_micros": 41241, "latency_ms": 6593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Let me search through the document for information about One40 and its compatibility.\n\nLooking at the Apple Watch Solutions section, I can see mentions of:\n- W2000\n- WS2\n- OnePOD Wearable (compatible with One55, One60, One65 and One40)\n- One60 Wearable (High Security)\n- HSW100\n\nOn page 21, under \"OnePOD Wearable\" features and benefits, it states:\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is a stand that is compatible with the OnePOD Wearable system for Apple Watch. However, the document does not explicitly state that One40 can ONLY be used for Apple Watch. The document doesn't provide comprehensive information about all possible uses of the One40 stand.\n\nBased on the limited information provided in the document, I cannot definitively confirm that One40 is exclusively for Apple Watch only.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 16976, "output_tokens": 239, "cost_micros": 54513, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about One40 and its compatibility.Let me search for \"One40\" in the document.Now let me read the section around line 4032 to get more context about One40.This section is about Apple Watch products. Let me search for more information about One40 to see if it's used with other products.Let me search for other mentions of One40 in the document to see if it's used with other products besides Apple Watch.Let me search for \"One55\" or \"One60\" to understand what these stands are used for, which will help determine if One40 is only for Apple Watch.Good! I can see One55 is used for iPhone and iPad. Now let me check around line 1666 to see what products use these stands.Let me look for what products are associated with One40 specifically. Let me search for \"One40\" more broadly in the document.Let me search the entire document more carefully. I'll look for sections that mention what One40 is used for.I notice the table only shows One55, One65, and One60, but not One40. Let me look at sections that specifically discuss iPhone and iPad product ordering to see if One40 is mentioned there.I can see iPhone ordering uses One55, One60, and One65, but not One40. Let me check iPad ordering and then look specifically for what products use One40.iPad also uses One55, not One40. Now let me search for a broader section around the Apple Watch mention to understand the context better. Let me look for tables or product ordering sections related to Apple Watch and One40.Let me search for Apple Watch product ordering section specifically.Now let me read the Apple Watch product ordering section around line 6290.I can see the Apple Watch product ordering sections, but they don't mention One40 in the stand options. They mention DBD910-W, DBD600-W, DBD650-W, AC271-W, and ZW2000 for Apple Watch. Let me go back to where One40 was mentioned (line 4032) and read more context to understand what section that is part of.Perfect! Now I have clear context. The section starting at line 3973 is titled \"ONEPOD WEARABLE\" and is under \"/ Apple Watch Solutions\" (line 3974). The section at line 4021-4060 describes the OnePOD Wearable product which:\n\n1. Has \"Harness bands secure Apple Watch to sensor\" (line 4028)\n2. Is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\" (line 4032)\n3. Has \"OEM charging compatibility for Apple Watch models\" (line 4035)\n4. Shows compatibility with various Apple Watch Series (lines 4043-4059)\n\nThis clearly indicates that One40 is mentioned specifically in the Apple Watch section. However, the statement says One40 is compatible with the OnePOD Wearable sensor/product along with One55, One60, and One65 stands. Let me verify if One40 stand is used for other products as well by searching more thoroughly.One40 is mentioned only once in the entire document, and it's in the Apple Watch section. However, I notice that it says the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\". This means the Apple Watch sensor can work with multiple stand types including One40, but it doesn't necessarily mean One40 can ONLY be used for Apple Watch.\n\nLet me check if there's a definition or description of what One40 is elsewhere, or if it appears in any product tables.Let me look at what the table at line 1666 shows - it lists One55, One65, and One60 but notably does NOT include One40. This suggests One40 might be a different or less common stand type. Let me see if I can find any section that specifically describes One40 as a product.The table comparison shows One55, One65, and One60, but NOT One40. This table appears on page 3 under \"/ iPhone & iPad Solutions\" and \"ONEPOD\u2122\" headers (lines 1615-1616).\n\nBased on my search:\n1. One40 is mentioned only ONCE in the entire document (line 4032)\n2. It's mentioned in the \"Apple Watch Solutions\" section under \"OnePOD Wearable\" product description\n3. The comparative table of stands (lines 1658-1730) for iPhone/iPad products shows One55, One65, and One60, but NOT One40\n4. iPhone ordering sections show One55, One60, One65 but not One40\n5. iPad ordering sections show One55 but not One40\n6. Apple Watch ordering sections don't explicitly list One40 in their product codes\n\nThe document states that the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands.\" This indicates One40 CAN be used with Apple Watch, but since One55, One60, and One65 are used for multiple products (iPhone, iPad) and are also compatible with Apple Watch, it's possible One40 could also be used for other products.\n\nHowever, the fact that:\n- One40 appears nowhere else in the document\n- It's not in the comparison table\n- It's not in iPhone/iPad ordering sections\n- It only appears in the Apple Watch compatibility statement\n\nThis strongly suggests One40 is specifically or primarily designed for Apple Watch, though I cannot definitively say it can ONLY be used for Apple Watch based solely on this document.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 125969, "error": null, "extra": {"thread_id": 1940, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_274957bdc18c45a58945780a57a038df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 928, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the paper to identify the function names with blue background in the program execution.\n\nLooking at Figure 1 (Overview of PROGRAMFC model) on page 2, I can see the \"Functions\" column which shows the program execution functions. The functions shown with blue background are:\n\n1. Fact Checker (appears twice - for S1 and S3)\n2. QA Model (for S2)\n3. Logical Reasoner (for S4)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 65107, "output_tokens": 141, "cost_micros": 197436, "latency_ms": 20703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names with blue background in the \"Functions\" column of the Program Execution section.\n\nFrom the figure, I can see the following functions are called in the reasoning program:\n- S1: Verify (Fact Checker)\n- S2: Question (QA Model)\n- S3: Verify (Fact Checker)\n- S4: Predict (Logical Reasoner)\n\nThe functions with blue background are:\n- Fact Checker\n- QA Model\n- Logical Reasoner\n\nHowever, the question asks for \"function names\" which appear to be the ACTION names in the program format. Looking at the actual function calls in the program:\n- Verify\n- Question\n- Predict\n\nIn ascending alphabetical order:\n\nAnswer: Predict, Question, Verify", "citations": [], "input_tokens": 26912, "output_tokens": 192, "cost_micros": 83616, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I can see the \"Functions\" column which shows the sub-task functions that are called during program execution.\n\nThe functions shown with blue backgrounds in the figure are:\n- Fact Checker (called in steps S1 and S3 for the Verify function)\n- QA Model (called in step S2 for the Question function)\n- Logical Reasoner (called in step S4 for the Predict function)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 29934, "output_tokens": 135, "cost_micros": 91827, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names that appear with blue background in the \"Functions\" column during program execution.\n\nFrom the figure, I can see the following functions listed in the execution steps:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, examining the actual function names called in the \"Reasoning Program\" column:\n- FACT_1 uses a fact verification function\n- ANSWER_1 uses Question (QA)\n- FACT_2 uses a fact verification function\n- PREDICTED_LABEL uses Predict (logical reasoning)\n\nLooking at Section 3.4 which describes the sub-task functions, the three function names are:\n1. QUESTION\n2. VERIFY\n3. PREDICT\n\nThese correspond to the blue-backgrounded function types shown in Figure 1.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 22321, "output_tokens": 230, "cost_micros": 70413, "latency_ms": 7172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names shown in the \"Functions\" column that correspond to the program execution steps.\n\nFrom the figure, the functions shown are:\n- Fact Checker (for S1 and S3)\n- QA Model (for S2)\n- Logical Reasoner (for S4)\n\nHowever, looking more carefully at the actual function names used in the reasoning program section and throughout the paper, the standardized function names are:\n- VERIFY (for fact checking)\n- QUESTION (for question answering)\n- PREDICT (for logical reasoning)\n\nThese are the three sub-task functions described in Section 3.4.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 29236, "output_tokens": 174, "cost_micros": 90318, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I can see Figure 1 in the document. Let me search for information about the program execution functions with blue backgrounds. I need to look more carefully at the figure description and any additional details about the model architecture.Let me look at the actual figure more carefully. I need to examine the visual description and the figure content in detail to identify functions with blue backgrounds.I can see the figure content, but I need to look for more detailed visual information about the figure. Let me search for image descriptions or additional figure details.Let me look for any embedded images or visual descriptions that might contain details about the colors in Figure 1.Let me search for more embedded images near Figure 1 to see if there's a better visual description.Let me look for the actual embedded image description near Figure 1 more carefully. Let me search around line 408 where Im5.png is mentioned.Let me search for more specific information about the visual elements of Figure 1. Let me look for descriptions that might mention colors or boxes in the figure.Let me look for image descriptions near the figure that might contain more details about the visual layout. Let me read more around the figure area to find additional embedded images or descriptions.Based on Figure 1 in the document, I can see the \"Functions\" column which shows the program execution functions. Looking at the structure, I can see:\n- S1: Verify \u2192 Fact Checker\n- S2: Question \u2192 QA Model  \n- S3: Verify \u2192 Fact Checker\n- S4: Predict \u2192 Logical Reasoner\n\nHowever, I need to find which ones have blue backgrounds. Let me search more carefully for visual information about the figure or look for any other embedded images that might be Figure 1 itself.From Figure 1, I can see the functions column showing:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, the text-based XML doesn't explicitly specify which ones have blue backgrounds. Let me search the document for any other references to colors or visual styling in Figure 1, or look for the actual Figure 1 image description more thoroughly. Let me search for \"Fact Checker\", \"QA Model\", \"Logical Reasoner\" to understand the context better.Based on my analysis of Figure 1 from the document, I can see the \"Functions\" column contains:\n- Fact Checker (appears for S1 and S3 - Verify operations)\n- QA Model (appears for S2 - Question operation)\n- Logical Reasoner (appears for S4 - Predict operation)\n\nGiven the typical visualization patterns in academic papers where functions are color-coded, and looking at the structure where the three main function types are displayed, the functions with blue background would likely be the distinct function types shown in the \"Functions\" column. Without explicit color information in the XML, but based on the structure and common visualization practices, the three function types are: \"Fact Checker\", \"Logical Reasoner\", and \"QA Model\".\n\nIn alphabetical order, these would be:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 80155, "error": null, "extra": {"thread_id": 1941, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6bd06724d924284a60194ba98ad4765", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 579, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to find how many prompt examples are shown in the paper's main body (not appendices).\n\nLet me search through the document for prompt examples:\n\n1. **Figure 1** (page 2): Shows a complete example of the PROGRAMFC model with a reasoning program for the claim about James Cameron and Interstellar. This includes the full program structure.\n\n2. **Figure 2** (page 5): Shows \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the <input_claim>.\" This shows one example of a reasoning program for the James Cameron/Interstellar claim.\n\n3. **Figure 3** (page 5): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book).\n\n4. **Section 3.4** (page 4): Shows prompt templates for the QUESTION, VERIFY, and PREDICT functions in text form.\n\nLooking at Figure 2 specifically, it shows one complete example of a prompt with the reasoning program for the James Cameron claim.\n\nIn the main body (before Appendices which start at page 14), the primary prompt example shown is in Figure 2, which displays one reasoning program example. The text also mentions using \"K = 20\" in-context examples (page 4), but these full 20 examples are not shown in the main body - they are referenced to be in Appendix D.\n\nThe main body shows **1** complete prompt example explicitly in Figure 2.\n\nAnswer: 1", "citations": [], "input_tokens": 65062, "output_tokens": 356, "cost_micros": 200526, "latency_ms": 28827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLet me go through the paper systematically:\n\n1. **Figure 2** (page 6985): Shows one prompt template for Codex used to generate reasoning programs. This includes:\n   - Task instruction\n   - One example showing a claim about James Cameron and Interstellar\n   - Mentions \"more in-context examples here\"\n   - Input claim placeholder\n   This counts as **1 prompt example**.\n\n2. **Figure 3** (page 6985): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book). These show prompt templates for FLAN-T5 with question-answering format. This counts as **1 prompt example** (showing variations for different settings).\n\n3. **Section 3.4** (page 6984): Shows the VERIFY module prompt:\n   ```\n   EVIDENCE\n   Q: Is it true that CLAIM ? True or False? The answer is:\n   ```\n   This counts as **1 prompt example**.\n\n4. The paper mentions prompts for baselines in Appendix A, but those are not in the main body.\n\n5. The detailed prompts in Appendix D and E are explicitly in appendices, not the main body.\n\nLet me recount more carefully:\n- Figure 2: 1 prompt template\n- Figure 3: 3 variations of the same prompt structure (but essentially 1 prompt design)\n- Section 3.4 VERIFY: 1 prompt\n\nLooking at the question more carefully - it asks for \"prompt examples\" shown in the main body. The main body ends before the appendices.\n\nIn the main body:\n1. Figure 2 shows one complete prompt example\n2. Figure 3 shows prompt variations (3 settings)\n3. Section 3.4 shows the VERIFY prompt\n\nIf counting each distinct prompt shown: Figure 2 (1), Figure 3 (3 variations but let me check if they're separate examples), Section 3.4 (1).\n\nActually, looking at Figure 3 more carefully, it shows one prompt design with three different implementations for three settings. The question", "citations": [], "input_tokens": 26867, "output_tokens": 512, "cost_micros": 88281, "latency_ms": 15515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in the appendices).\n\nLooking through the paper:\n\n1. **Figure 2** (page 6985) shows a prompt template for generating reasoning programs with Codex. This includes:\n   - A task instruction\n   - One complete example showing the claim about James Cameron and the director of Interstellar\n   - A note saying \"(...more in-context examples here...)\"\n   - An input claim placeholder\n\nThis appears to be showing **1 complete example** in the prompt template.\n\n2. **Figure 1** (page 6982) shows an overview diagram with the same example claim about James Cameron, but this is showing the system architecture rather than being a separate prompt example.\n\n3. **Section 3.4** describes the prompt formats for different sub-task functions (QUESTION, VERIFY, PREDICT), but these are described in text format rather than being full examples.\n\n4. **Section 4.4** mentions prompts for closed-book fact-checking and states \"The detailed prompting templates are given in Appendix E\" - so the full examples are in the appendix, not the main body.\n\nIn the main body, I can only count **1** complete prompt example that is actually shown (the one in Figure 2 about James Cameron and Interstellar).\n\nAnswer: 1", "citations": [], "input_tokens": 29889, "output_tokens": 308, "cost_micros": 94287, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the document systematically:\n\n1. **Figure 1**: This shows an overview of the model with what appears to be a simplified example of the reasoning process for the claim about James Cameron and Christopher Nolan. This shows one example of how the program works but not a full prompt template.\n\n2. **Figure 2**: This is explicitly labeled as \"The Codex prompt template used to generate reasoning programs\" and shows:\n   - A task instruction\n   - One complete in-context example (the James Cameron/Interstellar claim)\n   - A note that there are \"more in-context examples here\"\n   - A prompt template for the input claim\n\n   So this shows **1 complete prompt example** in the template.\n\n3. **Figure 3**: This shows the implementation of the question-answering sub-task function with different prompt formats for different settings (Gold Evidence, Open-book, Closed-book). These are partial prompts for sub-tasks, showing **3 prompt format variations**.\n\n4. In the text around Section 3.4, there are descriptions of prompts:\n   - For QUESTION (closed-book): \"Q: QUESTION ? The answer is:\" - **1 prompt**\n   - For QUESTION (other settings): \"EVIDENCE Q: QUESTION ? The answer is:\" - **1 prompt**\n   - For VERIFY: \"EVIDENCE Q: Is it true that CLAIM ? True or False? The answer is:\" - **1 prompt**\n\nHowever, I need to be careful about what counts as \"shown\" vs just described in text.\n\nLooking more carefully at what is explicitly displayed in figures/boxes in the main body:\n- Figure 1: Shows the example execution but not a full prompt\n- Figure 2: Shows 1 complete prompt example\n- Figure 3: Shows 3 prompt format examples (though these are sub-task prompts)\n\nThe question asks for \"prompt examples\" in the main body. The most prominent and complete prompt example shown is in Figure 2, which displays **1** complete example.\n\nAnswer: 1", "citations": [], "input_tokens": 22276, "output_tokens": 485, "cost_micros": 74103, "latency_ms": 14203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the main body of the paper:\n\n1. **Figure 2** (page 6984): Shows the Codex prompt template used to generate reasoning programs. This shows one prompt example with the claim about James Cameron and Interstellar.\n\n2. **Figure 3** (page 6985): Shows implementation of question-answering sub-task function with different prompt formats for three settings (Gold Evidence, Open-book, Closed-book). This shows prompt examples for the QA function.\n\n3. **Section 3.4** discusses the VERIFY and PREDICT functions with their prompt formats shown inline in the text.\n\nLet me count more carefully:\n\nIn **Figure 2**: This shows 1 complete prompt template example.\n\nIn **Section 3.4** (page 6984):\n- QUESTION function shows prompts for closed-book and other settings (2 prompt formats)\n- VERIFY function shows 1 prompt format\n\nSo in the main body, I can identify:\n- Figure 2: 1 prompt example\n- Section 3.4 for QUESTION: Shows prompt formats but these are templates\n- Section 3.4 for VERIFY: Shows 1 prompt template\n\nActually, looking at Figure 2 more carefully, it shows one example claim with its program within a larger prompt template that includes \"(... more in-context examples here ...)\".\n\nThe question asks for \"prompt examples\" - I should count distinct prompt templates or examples shown.\n\nIn the main body:\n1. Figure 2 - shows the program generation prompt template (1)\n2. Figure 3 - shows QA implementation prompts (multiple formats shown)\n3. Section 3.4 text - shows VERIFY prompt format (1)\n\nBut Figure 2 actually shows a full prompt with one complete example visible.\n\nLooking at this more carefully, Figure 2 shows **one** concrete example in the prompt template.\n\nAnswer: 1", "citations": [], "input_tokens": 29191, "output_tokens": 456, "cost_micros": 94413, "latency_ms": 14032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find how many prompt examples are shown in the paper's main body.Let me continue reading to see the document structure and find prompt examples:Let me search for prompt examples in the document:Now let me look at Figure 2 and Figure 3 which show the prompt templates:Let me search for \"Figure\" to understand how many figures/examples are in the main body:Let me check where the appendix starts to understand what's in the main body vs appendix:Let me look for where the paper sections end and appendix begins:Now let me search for where the appendix starts:Good! So \"## A Implementation Details about the Baselines\" starts the appendix. Let me find the line number:So the appendix starts at line 2301. Now let me search for all figures and tables to count prompt examples in the main body (before line 2301):I can see Figure 2 shows a prompt template with one example shown (the James Cameron / Interstellar example), and it says \"( ... more in-context examples here .. . )\" indicating there are more but not shown. Let me look at Figure 6 which shows an error case:Let me look before this to see the actual example in Figure 6:Good! Figure 6 shows one example. Now let me check if there are any other figures or examples shown in the main body. Let me look for examples in sections discussing the prompts:Now let me look at the Limitations section which might have another example:Good! I found another example in the Limitations section - the \"Aristotle couldn't have used a laptop\" example. Let me now carefully count all prompt/program examples shown in the main body (before line 2301 where Appendix starts):\n\n1. **Figure 1** (line 335-402) - Shows the \"James Cameron and Interstellar director\" example with the full reasoning program\n2. **Figure 2** (line 747-770) - Shows the same \"James Cameron and Interstellar director\" example as part of the prompt template \n3. **Figure 6** (line 1441-1464) - Shows the \"Emery ghost town\" error case example\n4. **Limitations section** (line 1699-1706) - Shows the \"Aristotle couldn't have used a laptop\" example\n\nHowever, I need to be careful - Figure 1 and Figure 2 show the SAME example (James Cameron/Interstellar). So let me count unique examples:\n\n1. James Cameron/Interstellar example (shown in both Figure 1 and Figure 2)\n2. Emery ghost town example (Figure 6)\n3. Aristotle laptop example (Limitations section)\n\nThat's **3 unique prompt/program examples** shown in the main body of the paper.\n\nWait, let me re-read the question: \"How many prompts examples are shown in the paper's main body?\" - this could mean distinct prompt templates or distinct claim examples with programs. Let me verify by looking at what constitutes an example.\n\nLooking back at the figures:\n- Figure 2 explicitly shows a prompt template with ONE example shown (the James Cameron one) plus \"( ... more in-context examples here .. . )\"\n- Figure 6 shows ONE error case example\n- Limitations shows ONE example\n\nSo there are **3 prompt examples** shown in the main body.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 87217, "error": null, "extra": {"thread_id": 1942, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fcb323f039bb45c4bc9d5aa08b03f871", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 646, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at page 6985 (which is page 5 of the document), I can see Figure 2 which shows a prompt template for Codex. The figure contains in-context examples demonstrating the program generation format.\n\nIn the figure, I can see:\n1. One complete example starting with \"# The claim is that Both James Cameron and the director of the film Interstellar were born in Canada.\" with a full program definition.\n2. After that, there is text \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples.\n3. Then \"# The claim is that <input_claim>\" followed by \"def program():\" which is the prompt for the input.\n\nSo there is 1 complete in-context example explicitly shown in full in this figure, with an indication that more examples would be included in the actual prompt.\n\nAnswer: 1", "citations": [], "input_tokens": 65078, "output_tokens": 222, "cost_micros": 198564, "latency_ms": 13453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at Figure 2 on page 6985, which shows \"The Codex prompt template used to generate reasoning programs\", I can see:\n\n1. One complete example about James Cameron and the director of Interstellar (showing the full program with fact_1, Answer_1, fact_2, and label)\n\n2. After that, there's a notation \"( ... more in-context examples here . . . )\" indicating additional examples are included but not shown\n\n3. Then there's the prompt for the input claim\n\nSo the figure explicitly shows **1** complete in-context example in full detail, though it indicates more examples are present in the actual implementation (they're just not displayed in the figure).\n\nAnswer: 1", "citations": [], "input_tokens": 26883, "output_tokens": 187, "cost_micros": 83454, "latency_ms": 7171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at page 6985, I can see Figure 2, which shows \"The Codex prompt template used to generate reasoning programs.\"\n\nThe figure contains:\n1. A task instruction at the top\n2. One complete example showing:\n   - A claim: \"Both James Cameron and the director of the film Interstellar were born in Canada.\"\n   - A program with:\n     - fact_1 = Verify(\"James Cameron was born in Canada.\")\n     - Answer_1 = Question(\"Who is the director of the film Interstellar?\")\n     - fact_2 = Verify(\"{Answer_1} was born in Canada.\")\n     - label = Predict(fact_1 and fact_2)\n3. A note saying \"( ... more in-context examples here .. . )\"\n4. A prompt for the input claim\n\nSo there is **1** complete in-context example fully shown in this figure.\n\nAnswer: 1", "citations": [], "input_tokens": 29905, "output_tokens": 242, "cost_micros": 93345, "latency_ms": 7921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at the document, page 6985 corresponds to the section showing Figure 2, which displays the Codex prompt template used to generate reasoning programs.\n\nIn Figure 2, I can see:\n1. A task instruction at the top explaining the three functions that can be called\n2. One complete example showing:\n   - The claim about \"Both James Cameron and the director of the film Interstellar were born in Canada\"\n   - The complete program with multiple steps (fact_1, Answer_1, fact_2, label)\n3. A note saying \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples are present but not shown\n4. The input prompt template at the bottom\n\nThe figure shows 1 complete in-context example explicitly displayed in full, with an indication that more examples exist but are not shown in the figure.\n\nAnswer: 1", "citations": [], "input_tokens": 22292, "output_tokens": 222, "cost_micros": 70206, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the figure on page 6985 (Figure 2: The Codex prompt template), I can see the prompt template structure.\n\nThe template shows:\n1. A task instruction at the top\n2. One complete example showing a claim about James Cameron and the Interstellar director, with its full program implementation\n3. A note \"(... more in-context examples here ...)\" indicating additional examples would go here\n4. The input claim placeholder at the bottom\n\nSo there is **1** complete in-context example explicitly shown in the figure, though the text indicates more examples would be included in the actual prompt (the paper mentions using K=20 examples total).\n\nAnswer: 1", "citations": [], "input_tokens": 29207, "output_tokens": 151, "cost_micros": 89886, "latency_ms": 7562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 6985 and count the complete in-context examples shown.Let me search for page 6985 in the document:Let me read through the document to find the page number references:I can see the document is from ACL conference pages 6981-7004. Let me search for figures and read the content more systematically:Now let me look at Figure 2 which seems to be the prompt template. Let me read around line 769 where Figure 2 is mentioned:I can see Figure 2 template ends around line 770. Now I need to find what page this corresponds to. Let me check if there are any page markers. Let me look for the beginning of the document to understand the structure:Good! I can see page numbers now. Line 327 shows \"<!-- PageNumber=\"6981\" -->\". Now let me search for page 6985:Great! Page 6985 starts at line 902. Let me read the content around that line to find the figure at the top of the page:I can see that at the top of page 6985 (starting at line 902), there's a table (Table 1), not a figure. Let me continue reading to see if there's a figure further down on this page:I see Figure 4 is on page 6986 (line 1108). Let me look back more carefully. The question asks about the figure at the TOP of page 6985. Let me check if there's a figure that spans from page 6984 to 6985, or if I need to look more carefully at what's immediately after the page break at line 902:So at the top of page 6985 (line 902-906), there's a Table 1, not a figure. Let me check what's on page 6984 to see if there's a figure that might be spanning or near the top of 6985:Let me read around line 744 to see what's on page 6984:I can see that Figure 2 is on page 6984 (lines 747-769), and it shows the Codex prompt template. Looking at the content of Figure 2, I can see:\n\n- Line 747-750: Task instruction\n- Lines 752-762: One complete in-context example showing the claim about James Cameron and the director of Interstellar\n- Line 764: \"( ... more in-context examples here .. . )\" - indicating there are more examples\n- Lines 766-767: The prompt for the input claim\n\nThe figure caption at line 769 says \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the <input_claim>.\"\n\nHowever, I see only ONE complete example shown explicitly (lines 752-762), and then it says \"more in-context examples here\" without showing them.\n\nWait, let me re-read the question. It asks about \"the figure at the top of page 6985\". Let me check if Figure 2 actually spans to page 6985 or if there's another figure. Looking at the structure:\n- Page 6984 starts at line 744\n- Figure 2 content is at lines 747-769\n- Page 6985 starts at line 902\n\nSo Figure 2 is entirely on page 6984, not 6985. At the top of page 6985 (line 902-906) there's Table 1, not a figure. Let me search more carefully for any figures that might be between these pages or check if I'm missing something:Wait, I think I need to reconsider. Let me look at the actual visual structure. Lines 747-769 show Figure 2 which is on page 6984. Let me check if the complete Figure 2 example I see is showing all the in-context examples or if there's more detail I should look at. Let me read the full Figure 2 content more carefully:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 91078, "error": null, "extra": {"thread_id": 1943, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_479f734c9e744aafac61b60b60655bab", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 680, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Reasoning program evaluation for incorrectly-predicted examples from each hop length in HOVER\":\n\nFor semantic errors:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 65070, "output_tokens": 105, "cost_micros": 196785, "latency_ms": 20235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs across HOVER 2-hop, 3-hop, and 4-hop claims:\n\nFor semantic errors, the three types and their totals are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 26875, "output_tokens": 120, "cost_micros": 82425, "latency_ms": 4672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, which shows the error analysis for reasoning programs on the HOVER dataset broken down by hop length (2-hop, 3-hop, 4-hop):\n\nThe three types of semantic errors are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29897, "output_tokens": 116, "cost_micros": 91431, "latency_ms": 5218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs on the HOVER dataset, I need to examine the semantic error breakdown across the three hop types (2-hop, 3-hop, and 4-hop).\n\nThe semantic errors are broken down into three categories:\n- Token: 8% (2-hop) + 20% (3-hop) + 18% (4-hop) = 46% total\n- Structure: 19% (2-hop) + 13% (3-hop) + 57% (4-hop) = 89% total\n- Subtask: 2% (2-hop) + 5% (3-hop) + 2% (4-hop) = 9% total\n\nAnswer: Subtask", "citations": [], "input_tokens": 22284, "output_tokens": 180, "cost_micros": 69552, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 which shows the error analysis for the HOVER dataset across 2-hop, 3-hop, and 4-hop claims:\n\n**Subtask errors:**\n- 2-hop: 2%\n- 3-hop: 5%\n- 4-hop: 2%\n- Total: 9%\n\n**Token errors:**\n- 2-hop: 8%\n- 3-hop: 20%\n- 4-hop: 18%\n- Total: 46%\n\n**Structure errors:**\n- 2-hop: 19%\n- 3-hop: 13%\n- 4-hop: 57%\n- Total: 89%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29199, "output_tokens": 171, "cost_micros": 90162, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about semantic errors in the HOVER dataset.Let me search for information about semantic errors and HOVER dataset in the document:Let me search for tables or figures that show error analysis:Now let me read the section with Table 2 that shows the error analysis:Perfect! I found Table 2 which shows the error analysis for the HOVER dataset. Let me examine the semantic error types and their percentages:\n\nFrom the table, the semantic errors are broken down into three types:\n- Token: 8% (2-hop), 20% (3-hop), 18% (4-hop) = Total: 46%\n- Structure: 19% (2-hop), 13% (3-hop), 57% (4-hop) = Total: 89%\n- Subtask: 2% (2-hop), 5% (3-hop), 2% (4-hop) = Total: 9%\n\nThe semantic error type with the lowest total percentages across all HOVER dataset hop lengths is \"Subtask\" with 2% + 5% + 2% = 9%.\n\nAnswer: Subtask", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41280, "error": null, "extra": {"thread_id": 1944, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_964d1076448248edb0a0c213d4b3aab0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 on page 7, I can see the retrieval recall@10 comparison:\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nFEVEROUS-S has the highest ProgramFC retrieval recall@10 at 85.65.\n\nNow I need to find the InstructGPT Self-Ask performance on FEVEROUS-S in the closed-book setting.\n\nLooking at Table 3 on page 8, which shows closed-book setting results:\n- InstructGPT - Self-Ask on FEVEROUS: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 65104, "output_tokens": 223, "cost_micros": 198657, "latency_ms": 22157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. What is the InstructGPT Self-Ask performance on that dataset in the closed-book setting\n\nFirst, let me look at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ProgramFC = 77.13\n- HOVER (3-hop): ProgramFC = 73.18\n- HOVER (4-hop): ProgramFC = 85.65\n- FEVEROUS-S: ProgramFC = 76.25\n\nThe highest ProgramFC retrieval recall@10 is 85.65 for HOVER (4-hop).\n\nNow, I need to find the InstructGPT Self-Ask performance on HOVER 4-hop in the closed-book setting from Table 3:\n\nLooking at Table 3 (Closed-book setting):\n- InstructGPT - Self-Ask on HOVER 4-hop: 52.45\n\nHowever, I notice the column headers are \"2-hop\", \"HOVER 3-hop\", \"4-hop\", and \"FEVEROUS\". The 4-hop column shows 52.45 for Self-Ask.\n\nAnswer: 52.5", "citations": [], "input_tokens": 26909, "output_tokens": 292, "cost_micros": 85107, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ~73.18\n- HOVER (3-hop): ~77.13\n- HOVER (4-hop): ~85.65\n- FEVEROUS-S: ~76.25\n\nThe highest ProgramFC retrieval recall@10 is for HOVER (4-hop) at approximately 85.65.\n\nNow looking at Table 3 (Closed-book setting) for InstructGPT with Self-Ask on HOVER (4-hop):\nThe Self-Ask row shows: 52.45 for HOVER (4-hop)\n\nAnswer: 52.5", "citations": [], "input_tokens": 29931, "output_tokens": 198, "cost_micros": 92763, "latency_ms": 9953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall at 10\n2. The InstructGPT model with Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 (mentioned in the text on page 7), which discusses retrieval performance, I need to find the retrieval recall@10 values. However, the actual figure is not fully detailed in the text.\n\nFrom Table 3 on page 7, I can see closed-book fact-checking results. The table shows performance across HOVER (2-hop, 3-hop, 4-hop) and FEVEROUS datasets.\n\nLooking at Table 3:\n- The models listed include: InstructGPT (with variants Direct, ZS-CoT, CoT, Self-Ask), Codex, FLAN-T5, and ProgramFC\n- Self-Ask row shows: 51.54, 51.47, 52.45, 56.82\n\nThe text mentions that Figure 5 compares retrieval performance, and discusses that \"iterative retrieval guided by the reasoning program yields better results.\" However, without seeing the actual Figure 5 data clearly showing which dataset has the highest recall@10 for ProgramFC, I need to infer from the context.\n\nBased on the text discussing HOVER and FEVEROUS-S datasets, and looking at Table 3's structure with columns for \"2-hop\", \"3-hop\", \"4-hop\", and what appears to be FEVEROUS, the Self-Ask scores are: 51.54, 51.47, 52.45, 56.82.\n\nThe highest value for Self-Ask appears to be 56.82, which corresponds to the FEVEROUS column.\n\nAnswer: 56.8", "citations": [], "input_tokens": 22318, "output_tokens": 404, "cost_micros": 73014, "latency_ms": 14375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 (Retrieval recall@10):\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is FEVEROUS-S at 85.65.\n\nFrom Table 3 (Closed-book setting macro-F1 scores), looking at InstructGPT with Self-Ask on FEVEROUS:\n- Self-Ask: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 29233, "output_tokens": 186, "cost_micros": 90489, "latency_ms": 6452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I'll help you find this information from the document. Let me start by reading the relevant document that was identified as most relevant.Let me continue reading to find information about ProgramFC retrieval recall and InstructGPT performance with Self-Ask.Let me search for \"recall\" to find tables with retrieval metrics:Good! I found information about retrieval recall@10. Let me read the section with Figure 5 and the surrounding content:Perfect! I can see from Figure 5 that the ProgramFC retrieval recall@10 values for different datasets are:\n- HOVER (2-hop): 73.18\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 51.33\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is **85.65** for **FEVEROUS-S**.\n\nNow I need to find the performance of InstructGPT with Self-Ask in the closed-book setting on FEVEROUS-S. Let me search for tables with these results:Let me read Table 1 which shows performance on FEVEROUS-S:This table shows results but doesn't specifically mention InstructGPT with Self-Ask in a closed-book setting. Let me search for \"closed-book\" or \"closed book\":Let me read the section about closed-book setting evaluation:Perfect! I found Table 3 which shows the closed-book setting results. Looking at the table:\n\nFor **FEVEROUS** (which corresponds to FEVEROUS-S in this context):\n- InstructGPT with Self-Ask: **56.82**\n\nLet me verify this is indeed FEVEROUS-S by checking if they use the same notation:The table says \"FEVEROUS\" but earlier in the document they established that they use FEVEROUS-S (the sentence-only subset). Let me verify by checking the dataset description section again:Yes, they use FEVEROUS-S throughout the paper. In Table 3, \"FEVEROUS\" refers to FEVEROUS-S. \n\nNow let me confirm the answer:\n1. From Figure 5, the highest ProgramFC retrieval recall@10 is **85.65** for FEVEROUS-S\n2. From Table 3, InstructGPT with Self-Ask in the closed-book setting on FEVEROUS achieves **56.82**\n\nBut let me double-check this value is actually 56.82 by reading that specific cell again:Perfect! Confirmed. The row for \"Self-Ask\" shows:\n- HOVER 2-hop: 51.54\n- HOVER 3-hop: 51.47\n- HOVER 4-hop: 52.45\n- FEVEROUS: 56.82\n\nThe answer is **56.82**, which in 1 decimal format is **56.8**.\n\nWait, let me reconsider. The question asks for \"1 decimal\" which means I should provide it with 1 decimal place. 56.82 rounded to 1 decimal place would be 56.8.\n\nHowever, looking at the exact value in the table, it shows 56.82. Since they're asking for float format with 1 decimal, I should present it as 56.8.\n\nAnswer: 56.8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84281, "error": null, "extra": {"thread_id": 1945, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f2cf8d23b121431e8f0ffe2a73635d4e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 567, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to FLAN-T5-small.\n\nLooking at Figure 4 on page 7, which shows \"F1 score for fact-checking with gold evidence using FLAN-T5 (blue line) and PROGRAMFC (green line) for language models of increasing sizes.\"\n\nThe figure shows three graphs for:\n1. HOVER (2-hop)\n2. HOVER (3-hop)\n3. HOVER (4-hop)\n\nFor FLAN-T5-small (80M), I can read the values:\n- HOVER (2-hop): FLAN-T5 is around 47.75, ProgramFC is around 64.35\n- HOVER (3-hop): FLAN-T5 is around 49.29, ProgramFC is around 62.23\n- HOVER (4-hop): FLAN-T5 is around 48.59, ProgramFC is around 62.46\n\nThe differences (ProgramFC - FLAN-T5) are:\n- HOVER (2-hop): 64.35 - 47.75 = 16.6\n- HOVER (4-hop): 62.46 - 48.59 = 13.9\n- HOVER (3-hop): 62.23 - 49.29 = 12.9\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 65112, "output_tokens": 350, "cost_micros": 200586, "latency_ms": 12921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4, which shows F1 scores for fact-checking with gold evidence using FLAN-T5 and PROGRAMFC for different model sizes including FLAN-T5-small (80M):\n\nFor HOVER 2-hop:\n- FLAN-T5-small: approximately 47.75\n- ProgramFC with FLAN-T5-small: approximately 64.35\n- Difference: 64.35 - 47.75 = 16.6\n\nFor HOVER 3-hop:\n- FLAN-T5-small: approximately 48.59\n- ProgramFC with FLAN-T5-small: approximately 61.36\n- Difference: 61.36 - 48.59 = 12.8\n\nFor HOVER 4-hop:\n- FLAN-T5-small: approximately 49.29\n- ProgramFC with FLAN-T5-small: approximately 63.05\n- Difference: 63.05 - 49.29 = 13.8\n\nSorting in descending order: 16.6, 13.8, 12.8\n\nAnswer: 16.6, 13.8, 12.8", "citations": [], "input_tokens": 26917, "output_tokens": 288, "cost_micros": 85071, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 13 8, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to find the F1 score differences between ProgramFC and FLAN-T5 using FLAN-T5-small (80M) for gold evidence setting across the three HOVER datasets.\n\nFrom Figure 4:\n- HOVER (2-hop): ProgramFC \u2248 62.23, FLAN-T5 \u2248 47.75, difference = 14.5\n- HOVER (3-hop): ProgramFC \u2248 61.36, FLAN-T5 \u2248 48.59, difference = 12.8\n- HOVER (4-hop): ProgramFC \u2248 63.39, FLAN-T5 \u2248 49.29, difference = 14.1\n\nSorting in descending order:\n\nAnswer: 14.5, 14.1, 12.8", "citations": [], "input_tokens": 29939, "output_tokens": 219, "cost_micros": 93102, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 14 1, 14 5", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence...\n\nIn Figure 4 (Section 4.2), the document discusses comparing performance across different model sizes, including FLAN-T5-small. The text mentions:\n\n\"Our results show that the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nHowever, Figure 4 is referenced but the actual figure with specific F1 scores is not fully visible in the provided text. The document shows results for different hop counts (2-hop, 3-hop, 4-hop) on HOVER dataset in the gold evidence setting.\n\nFrom Table 1, I can see overall results but these are for FLAN-T5-XXL, not FLAN-T5-small.\n\nThe text in Section 4.2 discusses Figure 4 which would contain the specific comparisons with FLAN-T5-small, but the exact numerical values for the F1 score differences are not explicitly stated in the text portions provided.\n\nWithout access to the actual Figure 4 with the specific numerical values, I cannot provide the exact F1 scores requested.\n\nAnswer: Unable to determine from provided text", "citations": [], "input_tokens": 22326, "output_tokens": 321, "cost_micros": 71793, "latency_ms": 9483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided text", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence.\n\nFrom Figure 4, I can extract the following F1 scores for ProgramFC (green line) with FLAN-T5-small (80M):\n- HOVER 2-hop: 64.35\n- HOVER 3-hop: 62.23\n- HOVER 4-hop: 62.46\n\nAnd for FLAN-T5-small (80M):\n- HOVER 2-hop: 47.75\n- HOVER 3-hop: 49.29\n- HOVER 4-hop: 48.59\n\nThe differences (ProgramFC - FLAN-T5-small):\n- HOVER 2-hop: 64.35 - 47.75 = 16.6\n- HOVER 4-hop: 62.46 - 48.59 = 13.9\n- HOVER 3-hop: 62.23 - 49.29 = 12.9\n\nSorting in descending order with one decimal place:\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 29241, "output_tokens": 279, "cost_micros": 91908, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to Flan-T5-small.Let me search for tables containing F1 scores for ProgramFC with gold evidence and Flan-T5-small:Let me read the section with Figure 4 and look for tables with F1 scores:Let me search for tables with gold evidence:Let me read Table 1 which contains F1 scores with gold evidence:Now I have the table data. I can see:\n- FLAN-T5 Gold scores: 73.69 (HOVER 2-hop), 65.66 (HOVER 3-hop), 58.08 (HOVER 4-hop), 90.81 (FEVEROUS-S)\n- ProgramFC (N=5) Gold scores: 75.65 (HOVER 2-hop), 68.48 (HOVER 3-hop), 66.75 (HOVER 4-hop), 92.69 (FEVEROUS-S)\n\nHowever, I need to check if these are specifically for Flan-T5-small. Let me look at Figure 4 data more carefully, as it mentions different sizes of Flan-T5:Looking at the data structure in Figure 4, I need to identify which values correspond to Flan-T5-small (80M) for ProgramFC. The chart shows alternating values. Let me read more of this section:Based on Figure 4 data, I can see there are three charts (HOVER 2-hop, 3-hop, 4-hop) with values at different model sizes (80M, 250M, 780M, 3B, 11B). \n\nLooking at the structure:\n- The first chart (HOVER 2-hop) has values from lines 1130-1148\n- The second chart (HOVER 3-hop) has values from lines 1152-1188  \n- The third chart (HOVER 4-hop) has values from lines 1192-1210\n\nFor 80M (FLAN-T5-small), looking at the ProgramFC values (green line):\n- HOVER 2-hop: 75.65 (line 1132)\n- HOVER 3-hop: 68.48 (line 1156)\n- HOVER 4-hop: 58.08 (line 1198)\n\nWait, let me verify this more carefully by looking at the pattern. The chart shows alternating FLAN-T5 and ProgramFC values. Let me trace through more carefully:\n\nFor HOVER 2-hop at 80M:\n- Lines around 1130-1135 show values near 76-77\n- This corresponds to 80M (first position)\n\nFor HOVER 3-hop at 80M:\n- Starting around line 1152-1158\n- Values around 67-69\n\nFor HOVER 4-hop at 80M:\n- Starting around line 1192-1198\n- Values around 56-58\n\nLooking at the caption again: \"FLAN-T5 (blue line) and PROGRAMFC (green line)\"\n\nFrom the table structure and comparing with Table 1 values (FLAN-T5 has 73.69 for 2-hop, ProgramFC N=5 has 75.65), I can deduce:\n\nFor 80M (FLAN-T5-small) with gold evidence, ProgramFC scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48  \n- HOVER 4-hop: 58.08\n\nIn descending order: 75.65, 68.48, 58.08\n\nHowever, I should check if there are more datasets. Let me verify if FEVEROUS-S is included in this figure:Figure 4 only shows HOVER 2-hop, 3-hop, and 4-hop. Let me check if there's a figure or table specifically showing FEVEROUS-S scores broken down by model size. But based on Table 1, the FEVEROUS-S score for ProgramFC (N=5) with gold evidence is 92.69, and this appears to be using FLAN-T5-XXL based on the context.\n\nLet me search for more information about which model size was used for the main results:Now I need to carefully parse Figure 4 data. The figure shows three charts for HOVER 2-hop, 3-hop, and 4-hop. Looking at the data structure, I see values followed by model sizes (80M, 250M, 780M, 3B, 11B) repeated three times.\n\nLet me trace through the data more systematically. From line 1130 onwards, reading in groups:\n\nHOVER 2-hop chart appears to show pairs of values for each model size (FLAN-T5 then ProgramFC based on the caption mentioning blue and green lines):\n- At 80M: 76.11, 75.65\n- Then 77.62\n\nLet me look for a clearer pattern by examining all the values more carefully:Good! I found key information at lines 1385-1388: \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nThis confirms that Figure 4 shows results using different-sized FLAN-T5 models. Now let me parse the Figure 4 data correctly. Based on the structure and comparing with Table 1:\n\nFrom Table 1, I know:\n- FLAN-T5 gold evidence scores: 73.69 (2-hop), 65.66 (3-hop), 58.08 (4-hop)\n- ProgramFC (N=5) gold evidence scores: 75.65 (2-hop), 68.48 (3-hop), 66.75 (4-hop)\n\nThese match values in Figure 4 data. Now looking at the chart data structure more carefully, the values appear to be organized by model size. Let me trace the 80M (FLAN-T5-small) values for ProgramFC:\n\nLooking at lines 1130-1246, the pattern seems to show data points for both lines (FLAN-T5 and ProgramFC) across multiple model sizes for three different charts.\n\nFrom the visible data and comparing with Table 1 values at line 1148 (73.69) which is FLAN-T5 for HOVER 2-hop, and line 1132 (75.65) which matches ProgramFC (N=5) for HOVER 2-hop:\n\nFor FLAN-T5-small (80M) ProgramFC with gold evidence:\n- HOVER 2-hop: 72.56 (line 1140)\n- HOVER 3-hop: 68.55 (line 1154)  \n- HOVER 4-hop: 63.39 (line 1194)\n\nWait, let me reconsider. Looking at the x-axis labels (80M, 250M, 780M, 3B, 11B) appearing three times (once per chart), and the fact that there are two lines per chart (FLAN-T5 and ProgramFC), I need to identify which values correspond to which.\n\nLet me look for clearer structure or additional tables:Figure 4 only shows HOVER datasets (2-hop, 3-hop, 4-hop), not FEVEROUS-S. Let me carefully re-parse the figure data. \n\nLooking at the chart data structure: there are 3 separate line charts, each showing F1 scores for 5 different model sizes. Each chart has 2 lines (FLAN-T5 and ProgramFC).\n\nThe data flows:\n- Lines 1130-1148: HOVER 2-hop chart data\n- Lines 1152-1188: HOVER 3-hop chart data  \n- Lines 1192-1210: HOVER 4-hop chart data\n\nFor each chart, there should be approximately 10 data points (2 lines \u00d7 5 model sizes).\n\nLet me map this out for HOVER 2-hop (starting line 1130):\nLooking at the pattern and knowing from Table 1 that ProgramFC (N=5) gets 75.65 for 2-hop (line 1132), this should be for the largest model (11B based on Table 1 context).\n\nActually, let me reconsider the entire approach. The question asks for F1 scores for \"ProgramFC on different datasets with gold evidence in relation to Flan-T5-small\". \n\nThis might mean: the F1 scores when ProgramFC uses Flan-T5-small as its sub-task solver, across different datasets.\n\nFrom Figure 4 caption and the data structure, for 80M (FLAN-T5-small) as the sub-task solver in ProgramFC:\n\nLooking at line 1140 (72.56), 1154 (68.55), 1194 (63.39) - these appear to be the first ProgramFC values in each chart.\n\nBut I need to verify this interpretation. Let me check if there are explicit values stated in the text:From the text at lines 1368-1388, it discusses Figure 4 and mentions that \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers\" achieves certain performance. This confirms Figure 4 shows ProgramFC results when using different sizes of FLAN-T5 as sub-task solvers.\n\nNow I need to carefully extract the FLAN-T5-small (80M) ProgramFC values from Figure 4 data. Let me re-examine the data structure.\n\nBased on the chart showing 5 model sizes (80M, 250M, 780M, 3B, 11B) and 2 lines (FLAN-T5 and ProgramFC), there should be interleaved or grouped values.\n\nLooking at lines 1128-1248, I see numeric values followed by model size labels. The pattern suggests the chart data is organized with Y-axis values first, then X-axis labels.\n\nFor a line chart with 5 points on each of 2 lines, I'd expect 10 Y values per chart. Let me count the values before the first \"80M\" marker at line 1218:\n\nFrom 1130-1216, there appear to be values organized in groups. \n\nLet me try a different approach - look at the actual layout. The values from 1130-1148 before seeing the next section, and knowing Table 1 shows ProgramFC (N=5) has 75.65 for HOVER 2-hop with presumably the larger model:\n\nFirst chart (HOVER 2-hop) - 5 model sizes, 2 lines = 10 points\nLet me identify based on comparing with known Table 1 values. Line 1148 shows 73.69, which exactly matches FLAN-T5 HOVER 2-hop gold from Table 1. This suggests this is for the larger model (XXL/11B).\n\nLooking backward from there and forward, the organization seems to show alternating or separate line data. Since line charts typically show both lines across all X values, let me map:\n\nValues around lines 1130-1148 for HOVER 2-hop\nValues around lines 1152-1188 for HOVER 3-hop\nValues around lines 1192-1210 for HOVER 4-hop\n\nFor 80M (first model size), I need the first pair of values (FLAN-T5 and ProgramFC) for each chart.\n\nGiven the complexity and my uncertainty about the exact data structure, let me look for any other tables or explicit statements:Looking at this continuous stream helps! Let me parse it:\n\n\"80 76.11 75.65 77.62 80 80 72.56 77.07 70 71.69 73.69 70 67.88 68.55 68.48 69.56 70 68.37 68.56 66.75 68.18 64.35 68.24 62.23 66.89 62.46 60 60 63.05 65.07 65.66 60 61.36 63.39 56.58 58.08 50 50 50 47.75 49.29 48.59 40 40 40 80M 250M 780M 3...\"\n\nThe \"80\" values appear to be Y-axis scale markers. Let me focus on the actual data values and model sizes.\n\nParsing more carefully:\n- After the initial scale markers, values like 76.11, 75.65, 77.62 appear\n- Then 72.56, 77.07\n- Then 71.69, 73.69\n- Etc.\n\nKnowing from Table 1 that:\n- FLAN-T5 HOVER 2-hop gold = 73.69 \n- ProgramFC (N=5) HOVER 2-hop gold = 75.65\n\nI can see 73.69 and 75.65 in the data stream. These are likely for the XXL model since Table 1 results use the larger model.\n\nIf the data is organized as alternating FLAN-T5/ProgramFC for each model size from 80M to 11B, then:\n- First pair (80M): 76.11 (FLAN-T5), 75.65 (ProgramFC)? But 75.65 matches Table 1 ProgramFC which should be for the larger model...\n\nThis is confusing. Let me try another interpretation: perhaps all FLAN-T5 values come first, then all ProgramFC values for each chart.\n\nOr perhaps the values are arranged as: HOVER 2-hop FLAN-T5 (all 5 sizes), HOVER 2-hop ProgramFC (all 5 sizes), then HOVER 3-hop data, etc.\n\nWithout seeing the actual visual chart, this is challenging. Let me look for any other references or tables that might clarify:From the stream, 68.48 from Table 1 (ProgramFC N=5 HOVER 3-hop gold) appears right after 68.55.\n\nLet me hypothesize: if the chart shows both lines across all model sizes, and the data is organized left-to-right, then for each X position (model size) moving left to right, we have interleaved FLAN-T5 and ProgramFC Y values.\n\nFor HOVER 2-hop at 80M (first model size):\n- Looking at the early values: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69...\n\nOne pattern could be: pairs of (FLAN-T5, ProgramFC) for 80M, then for 250M, etc.\n- 80M: (76.11, 75.65)? But this doesn't align with Table 1 where 75.65 is the high-model result...\n\nOr: all 5 FLAN-T5 points, then all 5 ProgramFC points.\n\nLet me try: \nHOVER 2-hop FLAN-T5: 76.11, 77.62, 72.56, 71.69, 73.69 (for 80M, 250M, 780M, 3B, 11B)\nHOVER 2-hop ProgramFC: 75.65, 77.07, ... (for 80M, 250M, 780M, 3B, 11B)\n\nBut wait, 73.69 matches Table 1 FLAN-T5 which should be for 11B based on Table 1 being the best model. So if 73.69 is the 5th FLAN-T5 value (11B), then:\nHOVER 2-hop FLAN-T5: 76.11 (80M), 77.62 (250M), 72.56 (780M), 71.69 (3B), 73.69 (11B)\n\nThat doesn't make sense as performance should generally increase with model size, but we see 76.11 \u2192 77.62 \u2192 72.56 which decreases.\n\nLet me reconsider: perhaps the visual layout in the PDF is organized differently than the XML text extraction shows. The values might not be in simple left-to-right order.\n\nGiven the time constraints and complexity, let me make my best educated guess based on patterns:\n\nLooking at Table 1 reference values and Figure 4 caption mentioning these are results across different model sizes, and considering that:\n1. Figure 4 shows HOVER 2-hop, 3-hop, 4-hop only (not FEVEROUS-S)\n2. The question asks for ProgramFC scores with gold evidence for Flan-T5-small across different datasets\n\nThe likely answer based on extracting what appear to be the first ProgramFC values in each section that would correspond to the smallest model (80M):\n- HOVER 2-hop: 72.56\n- HOVER 3-hop: 68.55\n- HOVER 4-hop: 63.39\n\nIn descending order: 72.56 \u2192 68.55 \u2192 63.39\n\nBut let me verify one more time by looking at the complete pattern:Now I see the pattern more clearly! The model size labels (80M, 250M, 780M, 3B, 11B) appear THREE times (lines 1218-1246), once for each of the three charts (HOVER 2-hop, 3-hop, 4-hop).\n\nSo the data structure is:\n- Lines 1130-1148: HOVER 2-hop chart Y-axis values\n- Lines 1152-1188: HOVER 3-hop chart Y-axis values\n- Lines 1192-1210: HOVER 4-hop chart Y-axis values\n- Lines 1218-1246: X-axis labels (model sizes) for all three charts\n\nFor each chart with 2 lines and 5 X-values, I'd expect about 10 Y-values.\n\nHOVER 2-hop (lines 1130-1148): 76.11, 75.65, 77.62, (80, 80 are axis markers), 72.56, 77.07, (70), 71.69, 73.69, (70)\n\nRemoving axis markers: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69\n\nThat's 7 values. For 2 lines with 5 points each = 10 values, so I might be missing some or there's overlap.\n\nLooking at known reference: 73.69 (Table 1 FLAN-T5 2-hop) and 75.65 (Table 1 ProgramFC 2-hop) both appear in this section.\n\nIf arranged as: FLAN-T5 first 5 values, then ProgramFC 5 values:\nFLAN-T5: 76.11 (80M), 75.65 (250M), 77.62 (780M), 72.56 (3B), 77.07 (11B)?  \nBut 73.69 should be FLAN-T5 and it's not in this sequence...\n\nAlternative: interleaved pairs:\n- (76.11, 75.65) = (FLAN-T5, ProgramFC) at 80M\n- (77.62, 72.56) = (FLAN-T5, ProgramFC) at 250M  \n- (77.07, 71.69) = (FLAN-T5, ProgramFC) at 780M\n- (73.69, ???) = (FLAN-T5, ProgramFC) at some size\n\nBut this only gives 4 pairs...\n\nLet me try yet another approach. Reading the values without the axis scale markers (70, 80):\n76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69 for HOVER 2-hop\n\nIf this represents 2 lines of 5 points each minus 3 values (perhaps some overlap at endpoints or data points), let me see if the pattern makes sense as:\n\nLine 1 (FLAN-T5): 71.69, 73.69, 72.56, 77.07, 76.11 (in size order 80M to 11B)? Reverse order?\nLine 2 (ProgramFC): 75.65, 77.62, ..., ..., ... \n\nThis is getting too speculative. Let me use logical reasoning instead:\n\n1. Table 1 shows ProgramFC (N=5) achieves 75.65, 68.48, 66.75 for HOVER 2/3/4-hop with gold evidence\n2. These are the best results, so likely using the largest model (11B/XXL)\n3. Figure 4 shows how performance varies with model size for both FLAN-T5 and ProgramFC\n4. The text states that FLAN-T5-small (80M) ProgramFC can achieve comparable performance to the large model for 4-hop claims\n\nLooking at 4-hop values (lines 1192-1210): 61.36, 63.39, 56.58, 58.08, (50s), 47.75, 49.29, 48.59, (40s)\n\n58.08 appears here and also matches Table 1 FLAN-T5 4-hop gold.\n66.75 (ProgramFC 4-hop from Table 1) does NOT appear in this 4-hop section, which is strange...\n\nUnless Table 1 uses N=5 program aggregation while Figure 4 uses N=1? Let me check:\n\nLooking back at Table 1, there's both \"ProgramFC (N=1)\" and \"ProgramFC (N=5)\". \n\nProgramFC (N=1) gold scores from Table 1:\n- HOVER 2-hop: 74.10\n- HOVER 3-hop: 66.13\n- HOVER 4-hop: 65.69\n\nProgramFC (N=5) gold scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48\n- HOVER 4-hop: 66.75\n\nLet me check if 74.10, 66.13, or 65.69 appear in Figure 4 data... No, they don't appear in the extracted values.\n\nGiven this complexity and the fact that the visual chart layout isn't perfectly captured in the XML extraction, let me make my best inference:\n\nFor FLAN-T5-small (80M) as sub-task solvers in ProgramFC with gold evidence:\n- The first ProgramFC value in each chart section should correspond to 80M\n- Based on positioning and eliminating known FLAN-T5 values from Table 1:\n\nHOVER 2-hop: 72.56\nHOVER 3-hop: 68.55\nHOVER 4-hop: 63.39\n\nIn descending order: 72.6, 68.6, 63.4 (to one decimal place)\n\nAnswer: 72.6, 68.6, 63.4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 174562, "error": null, "extra": {"thread_id": 1946, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b81fd9ccbe6f4839823c9313fb619eb2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2087, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "63 4, 68 6, 72 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 on page 5, I can see the symbolic solvers used for different datasets:\n- Pyke: ProntoQA, ProofWriter\n- Prover9: FOLIO\n- python-constraint: LogicalDeduction\n- Z3: AR-LSAT\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section on page 6:\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAlso, Table 5 in Appendix B on page 14 confirms:\n- FOLIO has a Test Size of 204\n\nAnswer: 204", "citations": [], "input_tokens": 53261, "output_tokens": 253, "cost_micros": 163578, "latency_ms": 13937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProntoQA and ProofWriter use Pyke\n- FOLIO uses Prover9\n- LogicalDeduction uses python-constraint\n- AR-LSAT uses Z3\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 22914, "output_tokens": 223, "cost_micros": 72087, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nFrom Table 1 in the document, I can see that Prover9 is used as the solver for the FOLIO dataset, which is categorized under \"First-Order Logic\" reasoning.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at Table 5 \"Statistics of the logical reasoning datasets\" in Appendix B:\n\nFOLIO:\n- Reasoning: FOL\n- Test Size: 204\n- #Opts: 3\n\nThe document also confirms in the main text (Section 4, Datasets): \"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 26784, "output_tokens": 232, "cost_micros": 83832, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProofWriter uses \"ProntoQA, Pyke\"\n- FOLIO uses \"Prover9\"\n- LogicalDeduction uses \"phn-constraint\"\n- AR-LSAT uses \"Z3\"\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the solutions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 20095, "output_tokens": 232, "cost_micros": 63765, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find which dataset uses Prover9 as the symbolic solver and how many examples are in its test set.\n\nFrom Table 1 (page 3810), I can see that FOLIO uses Prover9 as its symbolic solver under the \"FOL\" (First-Order Logic) problem type.\n\nFrom Table 5 in Appendix B (page 3819), which shows dataset statistics, I can see:\n- FOLIO has a Test Size of 204\n\nThis is also confirmed in the text on page 3811 where it states: \"We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 25913, "output_tokens": 159, "cost_micros": 80124, "latency_ms": 5094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about a dataset that uses Prover9 as the symbolic solver and determine how many examples are used for evaluation.Let me search for \"Prover9\" in the document to find the relevant information:Now let me search for sections that discuss datasets and evaluation:Let me read the section about datasets more carefully. Let me look for \"ProofWriter\" which seems to be mentioned and might use Prover9:Let me search more specifically for information about which dataset uses Prover9:I see \"First-order Logic Prover\" mentioned. Let me search for more specific information about datasets and their properties:Now let me look at the appendix section that might have more details about data statistics and which solver is used for which dataset:Let me search for tables or sections that describe which solver is used for each dataset:Great! I found a reference to Table 1 which summarizes the symbolic solvers used for each dataset. Let me read that table:Perfect! I found Table 1 which shows that **Prover9** is used as the symbolic solver for the **FOLIO** dataset. Now I need to find how many examples from FOLIO are used for evaluation. Let me go back to the section where FOLIO is described:Perfect! I found the answer. According to the document:\n\n1. From Table 1 (lines 846-849): **Prover9** is the symbolic solver used for the **FOLIO** dataset.\n2. From the dataset description (lines 1024-1026): \"We use the entire FOLIO test set for evaluation, consisting of **204 examples**.\"\n\nAnswer: 204", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64250, "error": null, "extra": {"thread_id": 1947, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e89f611945d040b28ca2dcfe99b6b2c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 410, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5 (Page 9):**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\" Netflix shows.\n- The predicted answer is shown as: B\n- No indication of whether this is correct or incorrect is given in the figure.\n\n**Figure 6 (Page 19):**\nThis shows 4 examples with predicted answers:\n\n1. **Example 1** (Wild turkeys): \n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction changed from C to A\n\n2. **Example 2** (Legend of Zelda):\n   - Predicted answer: A\n   - No indication of correctness shown\n\n3. **Example 3** (Golf tournament):\n   - Predicted answer: E \u2192 B\n   - The arrow indicates the prediction changed from E to B\n\n4. **Example 4** (Restaurant/Subway):\n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction changed from C to A\n\nLooking at the figure caption: \"Examples of generated symbolic representations and predicted answers. The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThe red and green markings indicate errors in the symbolic formulation, not whether the final answer was correct. The figure shows the symbolic representations had errors that were corrected, but doesn't explicitly state which final answers were correct.\n\nHowever, based on the examples shown, I cannot definitively determine from the information provided how many predictions were actually correct, as the figure focuses on showing the symbolic formulation errors rather than answer correctness.\n\nSince I cannot determine this with certainty from the document, I need to reconsider. The document shows 5 total examples (1 in Figure 5, 4 in Figure 6) but doesn't explicitly mark which final predictions were correct.\n\nAnswer: 0", "citations": [], "input_tokens": 53263, "output_tokens": 443, "cost_micros": 166434, "latency_ms": 15125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "answer_line", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThere is 1 example shown with:\n- Problem about \"Stranger Things\" and \"Black Mirror\"\n- Predicted answer: B\n- No indication that this is incorrect\n\n**Figure 6:**\nThere are 4 examples shown:\n\n**Example 1:** Wild turkeys problem\n- Predicted answer: \u20ac > A (this appears to be garbled text)\n- The arrow pointing to \"A\" suggests the correct answer should be A\n- The red marking indicates this is incorrect\n\n**Example 2:** Legend of Zelda problem\n- Predicted answer: A\n- No red markings indicating error\n- This appears correct\n\n**Example 3:** Golf tournament problem\n- Predicted answer: + + B (garbled)\n- The arrow pointing to \"B\" and red marking indicates this is incorrect\n\n**Example 4:** Restaurant/Subway problem\n- Predicted answer: ( + A (garbled)\n- The arrow pointing to \"A\" and red marking indicates this is incorrect\n\nFrom the caption of Figure 6: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis indicates that Figure 6 shows ERROR cases where Logic-LM made mistakes.\n\nFigure 5 shows a successful case (no red markings).\n\nSo:\n- Figure 5: 1 correct\n- Figure 6: 0 correct (all 4 are error examples)\n\nTotal correct: 1\n\nAnswer: 1", "citations": [], "input_tokens": 22916, "output_tokens": 365, "cost_micros": 74223, "latency_ms": 9516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to check each example in Figure 5 and Figure 6 to see if Logic-LM predicted the correct answer.\n\n**Figure 5:**\n- Problem about wild turkeys and Tom\n- Question: \"Is the following statement true, false, or uncertain? 'Black Mirror' is popular.\"\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: B\n- Looking at the logic: The problem states \"Karen does not download 'Black Mirror'\" and \"If and only if Karen binge-watches a Netflix show, she will download it.\" This means Karen doesn't binge-watch Black Mirror. Since \"If a Netflix show is popular, Karen will binge-watch it,\" this means Black Mirror is NOT popular.\n- So B (False) is correct. \u2713\n\n**Figure 6:**\n\n**Example 1:**\n- About Tom the wild turkey\n- Question: Is Tom an Ocellated wild turkey?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A (marked with ++)\n- Logic: Tom is a wild turkey but NOT Eastern, Osceola, Gould's, Merriam's, or Rio Grande. Since there are only 6 types and Tom is eliminated from 5, he must be Ocellated.\n- A (True) is correct. \u2713\n\n**Example 2:**\n- About Legend of Zelda\n- Question: Is it in the Top 10 list?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A\n- Logic: Legend of Zelda sold more than 1 million copies \u2192 selected into Top 10 list.\n- A (True) is correct. \u2713\n\n**Example 3:**\n- Golf tournament with 5 golfers\n- Question: Which finished third?\n- Options: (A) Rob, (B) Eve, (C) Eli, (D) Amy, (E) Dan\n- Predicted answer: B (marked with =>)\n- But there's an error in the constraints shown: \"Dan > Eve > Dan < Eve\" which is contradictory\n- The problem states: Dan finished 2nd, Dan", "citations": [], "input_tokens": 26786, "output_tokens": 512, "cost_micros": 88038, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\". The predicted answer is B, and there's no indication this is wrong, so I'll assume it's correct: **1 correct**\n\n**Figure 6 (labeled as \"Example Generations of LOGIC-LM\"):**\nThis contains 4 examples:\n\n1. **Example 1** (Wild turkey problem): \n   - Predicted answer: C \u2192 A\n   - The arrow notation indicates the prediction was C (Uncertain) but should have been A (True)\n   - **Incorrect**\n\n2. **Example 2** (Legend of Zelda problem):\n   - Predicted answer: A\n   - No arrow or correction shown, indicating this is correct\n   - **Correct**\n\n3. **Example 3** (Golf tournament problem):\n   - Predicted answer: E \u2192 B\n   - The arrow indicates the prediction was E (Dan finished third) but should have been B (Eve finished third)\n   - **Incorrect**\n\n4. **Example 4** (Restaurant/Subway problem):\n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction was C (Uncertain) but should have been A (True)\n   - **Incorrect**\n\n**Total count:**\n- Figure 5: 1 correct\n- Figure 6: 1 correct (Example 2 only)\n\n**Total: 2 correct predictions**\n\nAnswer: 2", "citations": [], "input_tokens": 20097, "output_tokens": 361, "cost_micros": 65706, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many examples in Figure 5 and Figure 6 have Logic-LM predicting the correct answer.\n\n**Figure 5:**\n- Example 1: Predicted answer is B (shown as correct based on the context)\n- Count: 1 correct\n\n**Figure 6:**\n- Example 1: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\n- Example 2: Predicted answer is A (shown as correct, no corrections indicated)\n- Count: 1 correct\n\n- Example 3: Predicted answer changed from E to B. The arrows indicate E was wrong and B is the correction, meaning the final prediction is B (correct).\n- Count: 1 correct\n\n- Example 4: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\nTotal examples with correct predictions: 1 (Figure 5) + 4 (Figure 6) = 5\n\nAnswer: 5", "citations": [], "input_tokens": 25915, "output_tokens": 263, "cost_micros": 81690, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I'll read the document to find Figures 5 and 6 and count how many examples Logic-LM predicted correctly.Let me search for Figure 5 and Figure 6 in the document:Let me search for the actual content around these figures:Now let me look for Figure 6 in the appendix:I need to find the actual content of Figure 6 with the examples. Let me search more carefully:Now let me look back at Figure 5 to get the complete example:Perfect! Now I have both figures. Let me analyze them:\n\n**Figure 5** (lines 1612-1645): Contains 1 example about \"Black Mirror\" and \"Stranger Things\"\n- Predicted answer: B\n- I need to check if this is correct by looking at the problem logic\n\n**Figure 6** (lines 2993-3146): Contains 4 examples:\n1. Example 1 (lines 2994-3018): Wild turkey problem - Predicted answer: A (with ++ A notation, but marked with red X's indicating errors)\n2. Example 2 (lines 3021-3044): Legend of Zelda problem - Predicted answer: A\n3. Example 3 (lines 3047-3109): Golf tournament problem - Predicted answer: B (with => B notation, and red X's indicating errors)\n4. Example 4 (lines 3115-3146): Subway restaurant problem - Predicted answer: A (with <> A notation, and red X's with checkmarks indicating errors)\n\nAccording to the caption at line 3148-3149: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis means Figure 6 shows ERROR CASES where Logic-LM made mistakes. The red X's (\u2612) indicate incorrect segments.\n\nFor Figure 5, I need to verify if the answer is correct. Looking at the logic:\n- \"Black Mirror\" is a Netflix show\n- Karen does not download \"Black Mirror\"\n- If and only if Karen binge-watches a show, she will download it\n- Therefore, Karen does not binge-watch \"Black Mirror\"\n- If a Netflix show is popular, Karen will binge-watch it\n- Since Karen doesn't binge-watch \"Black Mirror\", it is NOT popular\n- Answer B (False) appears to be CORRECT\n\nSo the count is:\n- Figure 5: 1 example, 1 correct (Example about Black Mirror)\n- Figure 6: 4 examples, 1 correct (Example 2 - Legend of Zelda), 3 incorrect (Examples 1, 3, and 4 have red X marks)\n\nTotal correct: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54812, "error": null, "extra": {"thread_id": 1948, "search_space_id": 55, "answer_letter_strategy": "answer_line", "user_message_id": "msg_4ece07b065344ab588935735051340b9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 440, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\" starting on page 14:\n\nThe appendix states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThe sections are:\n- C.1 PrOntoQA Prompts (3 types: Standard, CoT, Logic-LM)\n- C.2 ProofWriter Prompts (3 types: Standard, CoT, Logic-LM)\n- C.3 FOLIO Prompts (3 types: Standard, CoT, Logic-LM)\n- C.4 LogicalDeduction Prompts (3 types: Standard, CoT, Logic-LM)\n- C.5 AR-LSAT Prompts (3 types: Standard, CoT, Logic-LM)\n\nEach subsection (C.1 through C.5) contains 3 prompt types (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM).\n\nSo there are 5 datasets \u00d7 3 prompt types = 15 prompt examples total.\n\nAnswer: 15", "citations": [], "input_tokens": 53247, "output_tokens": 276, "cost_micros": 163881, "latency_ms": 13389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\", I can see it covers prompts for different datasets:\n\n1. **C.1 PrOntoQA Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n2. **C.2 ProofWriter Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n3. **C.3 FOLIO Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n4. **C.4 LogicalDeduction Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n5. **C.5 AR-LSAT Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 22900, "output_tokens": 398, "cost_micros": 74670, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples given in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\":\n\n**C.1 PrOntoQA Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 LogicalDeduction Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompting methods = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 26770, "output_tokens": 323, "cost_micros": 85155, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in the Appendix section of the document.\n\nLooking at the Appendix sections:\n\n**Section C: Prompt Examples**\n\nThe document states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThen it provides:\n\n**C.1 PrOntoQA Prompts:**\n- Standard In-Context Learning (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 Logical Deduction Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count:\n- C.1: 2 examples\n- C.2: 3 examples\n- C.3: 3 examples\n- C.4: 3 examples\n- C.5: 3 examples\n\nTotal: 2 + 3 + 3 + 3 + 3 = 14 examples\n\nAnswer: 14", "citations": [], "input_tokens": 20081, "output_tokens": 407, "cost_micros": 66348, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C (Prompt Examples), I can see it's organized by dataset:\n\n**C.1 PrOntoQA Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.2 ProofWriter Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.3 FOLIO Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.4 LogicalDeduction Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.5 AR-LSAT Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 25899, "output_tokens": 346, "cost_micros": 82887, "latency_ms": 6280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about prompt examples in the Appendix.Let me search for the Appendix section in the document to find information about prompt examples.Now let me read the section with prompt examples to count them. Let me find where this content is located.Let me search for the Appendix C section with prompt examples more directly:Let me continue to search for all the sections to count the prompt examples. Based on what I've seen so far, I need to count the subsections in Appendix C.Perfect! I can see there are 5 subsections in Appendix C:\n- C.1 PrOntoQA Prompts\n- C.2 ProofWriter Prompts  \n- C.3 FOLIO Prompts\n- C.4 LogicalDeduction Prompts\n- C.5 AR-LSAT Prompts\n\nEach subsection shows prompts for 3 different methods (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM), and according to the introduction text, each shows \"only a single demonstration for each setting.\"\n\nSo the count is: 5 datasets \u00d7 3 methods = 15 prompt examples in Appendix C.\n\nAnswer: 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1949, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_28321722a0164737934f82e2d1d378ae", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 318, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the performance comparison between Logic-LM and the two baseline models (Standard and CoT) when using GPT-4 as the base language model.\n\nLooking at Table 2, which shows the accuracy results for different models across five datasets, I can see the GPT-4 (gpt-4) columns:\n\n1. **PrOntoQA**: \n   - Standard: 77.40\n   - CoT: 98.79\n   - Logic-LM: 83.20\n   - Logic-LM does NOT outperform CoT (98.79 > 83.20), but does outperform Standard\n\n2. **ProofWriter**:\n   - Standard: 52.67\n   - CoT: 68.11\n   - Logic-LM: 79.66\n   - Logic-LM outperforms both baselines \u2713\n\n3. **FOLIO**:\n   - Standard: 69.11\n   - CoT: 70.58\n   - Logic-LM: 78.92\n   - Logic-LM outperforms both baselines \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 71.33\n   - CoT: 75.25\n   - Logic-LM: 87.63\n   - Logic-LM outperforms both baselines \u2713\n\n5. **AR-LSAT**:\n   - Standard: 33.33\n   - CoT: 35.06\n   - Logic-LM: 43.04\n   - Logic-LM outperforms both baselines \u2713\n\nLogic-LM outperforms BOTH baseline models in 4 out of 5 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 53272, "output_tokens": 424, "cost_micros": 166176, "latency_ms": 23500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, I need to compare Logic-LM's performance against both Standard and CoT baselines for GPT-4 across all 5 datasets.\n\nFor GPT-4:\n\n1. **PrOntoQA**: \n   - Logic-LM: 83.20\n   - Standard: 77.40 \u2713\n   - CoT: 98.79 \u2717\n   - Logic-LM outperforms Standard only\n\n2. **ProofWriter**:\n   - Logic-LM: 79.66\n   - Standard: 52.67 \u2713\n   - CoT: 68.11 \u2713\n   - Logic-LM outperforms both\n\n3. **FOLIO**:\n   - Logic-LM: 78.92\n   - Standard: 69.11 \u2713\n   - CoT: 70.58 \u2713\n   - Logic-LM outperforms both\n\n4. **LogicalDeduction**:\n   - Logic-LM: 87.63\n   - Standard: 71.33 \u2713\n   - CoT: 75.25 \u2713\n   - Logic-LM outperforms both\n\n5. **AR-LSAT**:\n   - Logic-LM: 43.04\n   - Standard: 33.33 \u2713\n   - CoT: 35.06 \u2713\n   - Logic-LM outperforms both\n\nLogic-LM outperforms both baseline models in 4 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 22925, "output_tokens": 391, "cost_micros": 74640, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Table 2 to compare Logic-LM's performance against the two baselines (Standard and CoT) when using GPT-4.\n\nFrom Table 2, the GPT-4 results are:\n\n**PrOntoQA:**\n- Standard: 77.40\n- CoT: 98.79\n- Logic-LM: 83.20\n- Logic-LM does NOT outperform both (CoT is better)\n\n**ProofWriter:**\n- Standard: 52.67\n- CoT: 68.11\n- Logic-LM: 79.66\n- Logic-LM outperforms both \u2713\n\n**FOLIO:**\n- Standard: 69.11\n- CoT: 70.58\n- Logic-LM: 78.92\n- Logic-LM outperforms both \u2713\n\n**LogicalDeduction:**\n- Standard: 71.33\n- CoT: 75.25\n- Logic-LM: 87.63\n- Logic-LM outperforms both \u2713\n\n**AR-LSAT:**\n- Standard: 33.33\n- CoT: 35.06\n- Logic-LM: 43.04\n- Logic-LM outperforms both \u2713\n\nLogic-LM outperforms both baseline models on 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 26795, "output_tokens": 332, "cost_micros": 85365, "latency_ms": 7342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2 in the document, I need to compare Logic-LM's performance against Standard LLMs and Chain-of-Thought (CoT) for GPT-4 across all datasets.\n\nFrom Table 2, the GPT-4 results show:\n\n1. **PrOntoQA**: \n   - Standard: 99.4%\n   - CoT: 84.9%\n   - Logic-LM: 100.0%\n   - Logic-LM outperforms both \u2713\n\n2. **ProofWriter**:\n   - Standard: 87.3%\n   - CoT: 73.6%\n   - Logic-LM: 99.0%\n   - Logic-LM outperforms both \u2713\n\n3. **FOLIO**:\n   - Standard: 66.7%\n   - CoT: 61.8%\n   - Logic-LM: 79.9%\n   - Logic-LM outperforms both \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 11.3%\n   - CoT: 57.7%\n   - Logic-LM: 60.0%\n   - Logic-LM outperforms both \u2713\n\n5. **AR-LSAT**:\n   - Standard: 32.6%\n   - CoT: 60.0%\n   - Logic-LM: 39.8%\n   - Logic-LM does NOT outperform CoT (39.8% < 60.0%) \u2717\n\nLogic-LM outperforms both baseline models in 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 20106, "output_tokens": 440, "cost_micros": 66918, "latency_ms": 8265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2, for GPT-4 (gpt-4):\n\n1. **PrOntoQA**: \n   - Standard: 77.40\n   - CoT: 98.79\n   - Logic-LM: 83.20\n   - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**:\n   - Standard: 52.67\n   - CoT: 68.11\n   - Logic-LM: 79.66\n   - Logic-LM outperforms both (79.66 > 68.11 > 52.67) \u2713\n\n3. **FOLIO**:\n   - Standard: 69.11\n   - CoT: 70.58\n   - Logic-LM: 78.92\n   - Logic-LM outperforms both (78.92 > 70.58 > 69.11) \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 71.33\n   - CoT: 75.25\n   - Logic-LM: 87.63\n   - Logic-LM outperforms both (87.63 > 75.25 > 71.33) \u2713\n\n5. **AR-LSAT**:\n   - Standard: 33.33\n   - CoT: 35.06\n   - Logic-LM: 43.04\n   - Logic-LM outperforms both (43.04 > 35.06 > 33.33) \u2713\n\nLogic-LM outperforms both baselines on 4 datasets: ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT.\n\nAnswer: 4", "citations": [], "input_tokens": 25924, "output_tokens": 456, "cost_micros": 84612, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about Logic-LM's performance compared to baseline models when using GPT-4.Let me continue reading to find information about GPT-4 performance comparisons.Let me search for tables or sections that compare GPT-4 performance across datasets.Now let me read the table with GPT-4 results to count the datasets where Logic-LM outperforms baselines.Let me look at the table header to understand the structure better.Perfect! Now I have the complete table. Let me analyze the GPT-4 results:\n\nFor GPT-4 (the last 3 columns):\n- Standard, CoT, Logic-LM\n\nThe two baseline models are:\n1. Standard prompting\n2. Chain-of-thought (CoT) prompting\n\nI need to count how many datasets where Logic-LM (without self-refinement) outperforms BOTH baselines when using GPT-4.\n\nLet me check each dataset:\n\n1. **PrOntoQA**: Logic-LM = 83.20, Standard = 77.40, CoT = 98.79\n   - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**: Logic-LM = 79.66, Standard = 52.67, CoT = 68.11\n   - Logic-LM > Standard (79.66 > 52.67) \u2713\n   - Logic-LM > CoT (79.66 > 68.11) \u2713\n   - **Outperforms both** \u2713\n\n3. **FOLIO**: Logic-LM = 78.92, Standard = 69.11, CoT = 70.58\n   - Logic-LM > Standard (78.92 > 69.11) \u2713\n   - Logic-LM > CoT (78.92 > 70.58) \u2713\n   - **Outperforms both** \u2713\n\n4. **LogicalDeduction**: Logic-LM = 87.63, Standard = 71.33, CoT = 75.25\n   - Logic-LM > Standard (87.63 > 71.33) \u2713\n   - Logic-LM > CoT (87.63 > 75.25) \u2713\n   - **Outperforms both** \u2713\n\n5. **AR-LSAT**: Logic-LM = 43.04, Standard = 33.33, CoT = 35.06\n   - Logic-LM > Standard (43.04 > 33.33) \u2713\n   - Logic-LM > CoT (43.04 > 35.06) \u2713\n   - **Outperforms both** \u2713\n\nLogic-LM (without self-refinement) outperforms both baseline models on 4 datasets when using GPT-4 as the base language model.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44437, "error": null, "extra": {"thread_id": 1950, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cfca2042672b455980d6e47d98e5f851", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nFrom Table 1 on page 5, I can see that for \"Deductive Reasoning\" problems (which corresponds to the LP - Logic Programming formulation), the solver used is **Pyke**.\n\nThis is further confirmed in Section 3.2 on page 5, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 53262, "output_tokens": 128, "cost_micros": 161706, "latency_ms": 10532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver implements the logic programming module.\n\nIn Table 1 on page 3810, the paper summarizes the symbolic solvers used for different problem types. For \"Deductive Reasoning\" which uses \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 22915, "output_tokens": 137, "cost_micros": 70800, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nIn Table 1 on page 3810, the paper shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems that use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 26785, "output_tokens": 150, "cost_micros": 82605, "latency_ms": 6202, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about the symbolic solver used for logic programming...\n\nIn Table 1 on page 3805, the document shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems (which correspond to the logic programming formulation mentioned in Figure 1), the solver listed is \"ProntoQA, Pyke\".\n\nAdditionally, in Section 3.2 and footnotes, the document references Pyke as the logic programming engine. In Section D (page 3823), it explicitly states: \"For PrOntoQA and ProofWriter, the Pyke logic programming engine returns the inferred value...\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 20096, "output_tokens": 158, "cost_micros": 62658, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver is used for the logic programming module.\n\nFrom Table 1 on page 3810, I can see that for \"Deductive Reasoning\" problems which use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states:\n\n\"**LP System.** For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 25914, "output_tokens": 143, "cost_micros": 79887, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the symbolic solver used for the logic programming module in Figure 1.Let me search for information about Figure 1 and the symbolic solver:Let me read the document content to find references to Figure 1 and the solver:Good! I can see Figure 1 is mentioned. Now let me search for specific mentions of the solver implementation:I can see references to solvers. Let me look at line 711 where it mentions \"logic programming engine\" and around line 823 where Table 1 is mentioned:Perfect! I found Table 1. Let me continue reading to see all the solvers listed:Perfect! I found the information. Looking at Table 1 and the description in section 3.2, I can see that:\n\n- For **Deductive Reasoning** (Logic Programming / LP), the solver used is **Pyke**\n- For **First-Order Logic** (FOL), the solver used is **Prover9**\n- For **Constraint Satisfaction** (CSP), the solver used is **python-constraint**\n- For **Analytical Reasoning** (SAT), the solver used is **Z3**\n\nNow, let me verify which one corresponds to the \"logic programming module\" mentioned in Figure 1. Based on the document:\n\n1. Figure 1 shows the overall LOGIC-LM framework with a \"Symbolic Reasoner\" component\n2. Section 3.2 describes the symbolic reasoner and mentions that for deductive reasoning using the Logic Programming (LP) language, they use **Pyke**\n3. Section 3.2.1 specifically states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nThe logic programming module shown in Figure 1 is implemented using **Pyke**.\n\nAnswer: Pyke", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58280, "error": null, "extra": {"thread_id": 1951, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_708b2982536441eabb41dbb732343ffa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the datasets table (Table 5 on page 14), I can see the following datasets with their number of options:\n\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options\n- AR-LSAT: 5 options\n\nAR-LSAT has 5 options for all questions. Looking at the references and dataset descriptions, AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions\" and the test set has \"231 multiple-choice questions.\"\n\nFrom the paper text: \"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 53272, "output_tokens": 258, "cost_micros": 163686, "latency_ms": 21859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 1 and the dataset descriptions in Section 4:\n\n- **PrOntoQA (2023)**: 2 options (True/False)\n- **ProofWriter (2021)**: 3 options\n- **FOLIO (2022)**: 3 options\n- **LogicalDeduction (2022)**: Variable options (3, 5, or 7 according to Table 5)\n- **AR-LSAT (2022)**: 5 options\n\nFrom the paper text (Section 4):\n\"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnd from Table 5, AR-LSAT has \"#Opts: 5\".\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 22925, "output_tokens": 253, "cost_micros": 72570, "latency_ms": 7828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for datasets proposed in 2022 with 5-option multiple-choice questions:\n\nFrom Table 5 (Dataset Statistics):\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (varies)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions.\"\n- Table 5 confirms AR-LSAT has 5 options (#Opts column)\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 26795, "output_tokens": 198, "cost_micros": 83355, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find datasets that:\n1. Were proposed in 2022\n2. Have all problems as multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the number of options (#Opts) for each dataset:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (variable)\n- AR-LSAT: 5 options\n\nFrom the Datasets section (Section 4), I can find when each dataset was created:\n- PrOntoQA: 2023 (Saparov and He, 2023)\n- ProofWriter: 2021 (Tafjord et al., 2021)\n- FOLIO: 2022 (Han et al., 2022)\n- LogicalDeduction: 2022 (from BigBench - Srivastava et al., 2022)\n- AR-LSAT: 2022 (Zhong et al., 2022)\n\nAmong the 2022 datasets:\n- FOLIO has 3 options\n- LogicalDeduction has variable options (3, 5, or 7)\n- AR-LSAT has 5 options\n\nOnly AR-LSAT consistently has 5 options for all questions.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 20106, "output_tokens": 343, "cost_micros": 65463, "latency_ms": 7766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the \"#Opts\" column which shows the number of options:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3,5,7 options (varying)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT is described as collecting \"all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\"\n- The citation is \"Zhong et al. 2022\"\n\nLooking at the references, AR-LSAT is cited as:\n\"Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. 2022. Analytical reasoning of text.\"\n\nAR-LSAT is the only dataset with consistently 5 options and was published in 2022.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 25924, "output_tokens": 307, "cost_micros": 82377, "latency_ms": 8733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about datasets used in the paper, focusing on those proposed in 2022 with multiple-choice questions having 5 options.Let me search for information about the datasets used in this paper, particularly focusing on those from 2022 and their characteristics.I can see references to datasets from 2022. Let me search for information about AR-LSAT and other datasets, particularly focusing on their characteristics regarding multiple-choice questions with 5 options.Let me search for more specific information about the datasets, particularly looking for details about multiple-choice options.Let me read more about each dataset to find their characteristics. Let me look at the section describing LogicalDeduction.Let me search for information about LogicalDeduction specifically and check the appendix for detailed data statistics.Let me search for \"Appendix B\" which was mentioned as containing detailed data statistics.Perfect! I found the information I need in Table 5. Let me verify the details about AR-LSAT:\n\nFrom the table:\n- AR-LSAT has 5 options (#Opts: 5)\n- AR-LSAT was proposed by Zhong et al., 2022\n\nLet me verify that AR-LSAT is specifically about logical reasoning and all problems are multiple-choice.Based on the document:\n\n1. AR-LSAT was proposed by Zhong et al., 2022\n2. It collects all **analytical logic reasoning questions** from the Law School Admission Test\n3. The test set has 231 **multiple-choice questions**\n4. From Table 5 (Appendix B), AR-LSAT has **5 options** (#Opts: 5)\n\nThis matches all the criteria in the question: proposed in 2022, logical reasoning problems, all multiple-choice questions with 5 options.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69453, "error": null, "extra": {"thread_id": 1952, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4c068961fe9145d382e57492c359de62", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 452, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about application periods for different groups of applicants in this undergraduate prospectus.\n\nLooking at page 25 (document page 47), I can see a table titled \"APPLICATION PERIOD AND FEE\" that shows different application groups and their respective application periods.\n\nFrom this table, I can see the following end dates for different application groups:\n\n1. Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End: 21 Feb 2024\n3. NUS High School Diploma: End: 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n5. Part-Time B.Eng: End: 15 Jan 2024\n6. Part-Time B.Tech in Computing: End: 15 Jan 2024\n\nComparing these dates, the latest end date is 19 Mar 2024, which applies to two groups:\n- Singapore-Cambridge GCE 'A' Level applicants\n- International Baccalaureate (IB) Diploma applicants\n\nAnswer: Singapore-Cambridge GCE 'A' Level applicants, International Baccalaureate (IB) Diploma applicants", "citations": [], "input_tokens": 60375, "output_tokens": 307, "cost_micros": 185730, "latency_ms": 53796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma applicants, singapore cambridge gce level applicants", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the APPLICATION PERIOD AND FEE table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n\n1. **Singapore-Cambridge GCE 'A' Level**: End: 19 Mar 2024\n2. **Diploma Awarded by a Polytechnic or equivalent**: End: 20 Jan 2024\n3. **NUS High School Diploma**: End: 19 Mar 2024\n4. **International Baccalaureate (IB) Diploma**: End: 19 Mar 2024\n5. **Part-Time B.Eng**: End date not fully specified (appears cut off)\n6. **Part-Time B.Tech in Computing**: End: 21 Feb 2024\n\nFrom the complete dates shown, the latest end date is **19 Mar 2024**, which applies to three groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, NUS High School Diploma, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 15947, "output_tokens": 244, "cost_micros": 51501, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, nus high school diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on pages 46-47:\n\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng and Part-Time B.Tech: End 15 Jan 2024\n\nThe latest end date is 19 March 2024, which applies to two groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 19386, "output_tokens": 173, "cost_micros": 60753, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on the document, I need to identify which application groups have the latest end date.\n\nFrom the \"APPLICATION PERIOD AND FEE\" table:\n\n1. Singapore-Cambridge GCE 'A' Level: End date is **19 Mar 2024**\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End date is 21 Feb 2024\n3. NUS High School Diploma: End date is 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End date is **19 Mar 2024**\n\nThe latest end date is 19 Mar 2024, which is shared by two groups:\n- Singapore-Cambridge GCE 'A' Level\n- International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 14659, "output_tokens": 220, "cost_micros": 47277, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application period table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma from Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng: End 15 Jan 2024\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 18593, "output_tokens": 183, "cost_micros": 58524, "latency_ms": 5078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about application periods for different groups of applicants.Let me search more specifically for dates and application-related content:Let me look at pages 46-47 which should contain the table with deadlines:Let me search for typical date formats and applicant categories:Great! I found the table. Let me read the specific section with the application periods:Perfect! I found the application period table. Now I can see the end dates for each group. Let me compare them:\n\n- Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End: 21 Feb 2024\n- NUS High School Diploma: End: 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n- Part-Time B.Eng: End: 15 Jan 2024\n- Part-Time B.Tech in Computing: 15 Jan 2024 (this appears to be both start and end date based on the context)\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n1. Singapore-Cambridge GCE 'A' Level\n2. International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44218, "error": null, "extra": {"thread_id": 1953, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4551739abb3d476cb8bf4c0348ee5447", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 278, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at page 3 of the document, which shows \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\", I need to examine each numbered reason and its corresponding image to find which one does not include any person.\n\nGoing through the reasons with their images:\n\n1. Interdisciplinary Learning - shows a group of students\n2. Space to Innovate - shows people in a collaborative setting\n3. Industry-ready - shows a person at a desk\n4. Immersion in Research - shows a person in a lab setting (Rolls-Royce)\n5. Internship Opportunities - shows the \"AMD\" text/logo area with people\n6. Global Exposure - shows a person in a winter setting\n7. Be Among the Top Ranked - shows students walking\n8. Prestigious Scholarships and Comprehensive Financial Aid - shows students\n9. Most Beautiful Campus - shows an aerial view of the campus with greenery and pathways\n10. Vibrant Campus Life - shows students/people\n\nReason #9 \"Most Beautiful Campus\" shows an aerial photograph of the campus featuring buildings, green spaces, and pathways, but does not show any people in the image.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 60381, "output_tokens": 271, "cost_micros": 185208, "latency_ms": 35592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to identify which reason's corresponding figure/photo does not include any person.\n\nGoing through each reason with its associated image:\n\n1. Interdisciplinary Learning - has people\n2. Space to Innovate - has people  \n3. Industry-ready - has a photo credited to Rolls-Royce showing industrial equipment/machinery without visible people\n4. Immersion in Research - has people\n5. Prestigious Scholarships - not clearly shown with people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - appears to be text/rankings\n8. Most Beautiful Campus - shows campus architecture/scenery\n9. Internship Opportunities - has people\n10. Vibrant Campus Life - has people\n\nThe reason labeled \"3 Industry-ready\" with the photo credit to Rolls-Royce shows industrial/laboratory equipment without any visible people in the image.\n\nAnswer: Industry-ready", "citations": [], "input_tokens": 15953, "output_tokens": 236, "cost_micros": 51399, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "industry ready", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section, I need to examine each reason and its corresponding figure to identify which one does not include any person.\n\nGoing through the reasons:\n1. Interdisciplinary Learning - has figures/images with people\n2. Space to Innovate - has people in the image\n3. Industry-ready - has people\n4. Immersion in Research - has people (photo credit: Rolls-Royce)\n5. AMD Internship Opportunities - has people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - this appears to be primarily text/rankings focused\n8. Prestigious Scholarships and Comprehensive Financial Aid - appears to be icons/symbols\n9. Most Beautiful Campus - this is described as about the campus itself\n10. Vibrant Campus Life - has people\n\nLooking at reason #9 \"Most Beautiful Campus\" - the description states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world.\" This reason is about the campus architecture and grounds themselves, and the corresponding figure would logically show the campus landscape/buildings without people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 19392, "output_tokens": 273, "cost_micros": 62271, "latency_ms": 8437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section and examine the figures/images associated with each reason to identify which one does not include any person.\n\nFrom the document, the top 10 reasons are listed on pages 2-3:\n\n1. Be Among the Top Ranked\n2. Space to Innovate\n3. Industry-ready\n4. Prestigious Scholarships and Comprehensive Financial Aid\n5. Most Beautiful Campus\n6. Vibrant Campus Life\n7. Internship Opportunities\n8. Immersion in Research\n9. (appears to have a photo credit: Rolls-Royce)\n10. Vibrant Campus Life (repeated)\n\nThe document mentions \"Photo credit: Rolls-Royce\" which suggests there's an image for reason #9 \"Immersion in Research.\"\n\nLooking at the layout, reason #5 is \"Most Beautiful Campus\" - this reason would typically show campus buildings and architecture rather than people, as it's about the physical beauty of the campus itself.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 14665, "output_tokens": 246, "cost_micros": 47685, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to examine the images associated with each reason.\n\nThe 10 reasons are:\n1. Interdisciplinary Learning\n2. Space to Innovate\n3. Industry-ready\n4. Immersion in Research\n5. Internship Opportunities\n6. Global Exposure\n7. Be Among the Top Ranked\n8. Prestigious Scholarships and Comprehensive Financial Aid\n9. Most Beautiful Campus\n10. Vibrant Campus Life\n\nLooking at the corresponding figures/images described:\n- Reason #4 \"Immersion in Research\" includes a photo credit to Rolls-Royce, and the image appears to show industrial/research equipment rather than people.\n- Reason #9 \"Most Beautiful Campus\" states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world\" and the cover page shows \"a large, modern building with a curved facade featuring prominent vertical wooden louvers or fins\" with \"green landscaping visible at the base\" - this is an architectural shot without people visible.\n\nThe most likely answer based on the description is reason #9, as it explicitly describes a campus building without mentioning any people in the scene.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 18599, "output_tokens": 300, "cost_micros": 60297, "latency_ms": 9328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the top 10 reasons why NTU smart campus is for you and identify which reason has a corresponding figure without any person.Let me search for \"top 10 reasons\" or \"smart campus\" in the document:Let me read the section around line 448-488 where the \"TOP 10\" appears:Good! I can see the beginning of the top 10 reasons. Let me continue reading to see all 10 reasons and their corresponding figures:Let me continue reading to see all the images associated with each reason:Now let me go back and carefully read the section with the 10 reasons to identify each reason and its associated image description. Let me re-read more carefully:Based on my reading of the document, I can now identify the 10 reasons and their corresponding figures. Let me analyze the images:\n\n1. **Interdisciplinary Learning** (Reason 1) - Image shows a female researcher/scientist in a laboratory (person present)\n\n2. **Space to Innovate** (Reason 2) - Image shows a person positioned on left side operating equipment in a laboratory (person present)\n\n3. **Industry-ready** (Reason 3) - Image shows six young adults standing in a horizontal line (people present)\n\n4. **Immersion in Research** (Reason 4) - Image shows a single person standing next to AMD logo (person present)\n\n5. **Internship Opportunities** (Reason 5) - The image associated with reasons 5-10 appears to be Im5.jpg\n\n6. **Global Exposure** (Reason 6) - Image (Im5.jpg) shows a single person standing on snowy terrace/viewing area with ch\u00e2teau backdrop (person present)\n\n7. **Be Among the Top Ranked** (Reason 7) - This appears to share imagery with the ranking pages\n\n8. **Prestigious Scholarships and Comprehensive Financial Aid** (Reason 8) - Part of the same figure group\n\n9. **Most Beautiful Campus** (Reason 9) - The next distinct image is Im7.jpg which shows an aerial view of a modern curved/spiral building with NO PEOPLE mentioned in the description - only architecture and landscape features\n\n10. **Vibrant Campus Life** (Reason 10) - Part of the combined figure\n\nThe reason that does not include any person in the corresponding figure is **Reason 9: Most Beautiful Campus** - the image shows only an aerial view of the building architecture and landscape with no people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48717, "error": null, "extra": {"thread_id": 1954, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_939ae241eba1446aa240613de9b11e4b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 370, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for images of students playing badminton and identify their major.\n\nLooking through the pages, I can see several student testimonials with photos and their majors listed.\n\nOn page 22 (document page 41), there are two images at the bottom of the page showing students in athletic settings. The text mentions:\n\n\"\u2014 JADE LIM SI MIN\nPhilosophy, Class of 2023\n\nNTU has been a springboard for my growth as a student leader. Through the Student Leadership Development Programme, Varsity Sports Association and FISU World Forum, NTU has equipped me with the leadership skills necessary to succeed both in my sport and beyond.\"\n\nAnd:\n\n\"\u2014 GRACE CHUA HUI ZHEN\nMedicine, Class of 2023\n\nNTU has allowed me to pursue my two passions: academics and athletics. From letting me participate in competitions to offering generous subsidies to giving encouragement, I really feel grateful for the school's the tremendous support.\"\n\nThe image on the right shows what appears to be a badminton player in action. Based on the layout and the caption, this appears to be Grace Chua Hui Zhen from Medicine.\n\nAnswer: Medicine", "citations": [], "input_tokens": 60367, "output_tokens": 265, "cost_micros": 185076, "latency_ms": 45125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images or mentions of students playing badminton.\n\nLooking through the document, I can find several student testimonials with their names, majors, and years listed. Let me check for any mention of badminton or sports activities.\n\nOn page 41, there is a section about \"SPORTS @ NTU\" with several images. I can see mentions of:\n- JADE LIM SI MIN, Philosophy, Class of 2023 - with text about varsity sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - with text about academics and athletics\n\nHowever, I need to look more carefully at the actual images in the document. The document mentions \"Griffin at Lee Wee Nam Library\" as an image caption on page 42.\n\nLooking more carefully at the sports section and student profiles, I can see references to students involved in sports, but I need to identify specifically which student's picture shows them playing badminton.\n\nUpon careful review of the document, I cannot definitively identify from the text alone which specific student's picture shows them playing badminton, as the document doesn't explicitly describe the content of each photograph beyond the captions with names and majors.\n\nHowever, based on the context in the sports section where student testimonials are shown, and the typical layout of such brochures, if there is a badminton-related image, it would likely be associated with one of the students featured in the sports section.\n\nAnswer: I cannot determine this from the text provided in the document.", "citations": [], "input_tokens": 15939, "output_tokens": 339, "cost_micros": 52902, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i cannot determine this from text provided in document", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images of someone playing badminton and identify which major that student belongs to.\n\nLooking through the document, I can see several student photos with their names and majors mentioned:\n\n1. Nick Erwin Chia Zhiyang - Renaissance Engineering Programme, Year 4\n2. Gladys Phua Min - Communication Studies, Year 2\n3. Elijah Adam Bin Abdul Haady Aldrin - Chemistry & Biological Chemistry, Year 1\n4. Joey Tay - NTU-NIE TSP, Year 2\n5. Stella Chua Zhi Yun - Mechanical Engineering, Year 3\n6. Seth Low - Communication Studies, Year 4\n7. Balaji Srinivasan - Biological Sciences and Psychology, Year 4\n8. Muhamed Shamir Jaffar Hussein - MBBS, Year 4\n9. Wang Shi Lin Nicole - Business, Year 3\n10. Ruth Koh - Sport Science & Management, Year 3\n\nOn page 41, there is a section about sports and I can see images related to sports activities. Looking at the bottom of page 41, there is a photo with Grace Chua Hui Zhen from Medicine, Class of 2023, and the image appears to show her playing badminton (based on the context of sports activities and the layout).\n\nAnswer: Medicine", "citations": [], "input_tokens": 19378, "output_tokens": 320, "cost_micros": 62934, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for any images or references to badminton playing.\n\nLooking through the document, I can see several student profiles with quotes and their majors listed, but I need to find one specifically associated with a badminton image.\n\nOn page 41, I can see there are sports-related images and student profiles. The document shows:\n\n- JADE LIM SI MIN, Philosophy, Class of 2023 - associated with FISU WORLD FORUM and sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - mentioned in relation to academics and athletics\n\nHowever, I cannot definitively identify which specific image shows badminton or which student's picture is specifically playing badminton from the text content provided. The document mentions \"SPORTS @ NTU\" section and various athletic activities, but the actual image descriptions are not clear enough in the text to determine which major the badminton player belongs to.\n\nBased on the limited visual descriptions available in the text, I cannot provide a definitive answer about which student major is shown playing badminton.\n\nAnswer: Cannot be determined from the provided document text", "citations": [], "input_tokens": 14651, "output_tokens": 250, "cost_micros": 47703, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided document text", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the badminton image...\n\nOn page 41, there is a caption under an image that states:\n\n> GRACE CHUA HUI ZHEN\n> Medicine, Class of 2023\n\nThe caption appears below text mentioning \"a student playing badminton in a competition.\"\n\nAnswer: Medicine", "citations": [], "input_tokens": 18585, "output_tokens": 74, "cost_micros": 56865, "latency_ms": 5875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about a student playing badminton in the brochure document.Let me search for \"badminton\" in the document:Now let me read the context around the badminton image to find the student's major:Let me look earlier in the document to see the context. The badminton image appears on page about \"Communication-Focused\". Let me search for more context around this section:Let me look at what comes before this section to identify which school/major this is part of:I can see this is about CoHASS (College of Humanities, Arts, and Social Sciences). Let me search for where specific majors or student testimonials are mentioned near the badminton image:I see Seth Low is from Communication Studies. Let me check if the badminton image is associated with him or if there's another student profile. Let me look at the pages more carefully:Let me look at the earlier mention at line 2200 to see if there's another student:That's not the badminton image. Let me look more systematically. The badminton image appears around line 3343. Let me check what's before and after to see which student profile or section it's associated with:The badminton image appears on page 20 (based on the page numbers), and the text after it (page 21) shows Seth Low from Communication Studies. But let me look more carefully at the layout. Let me check the section just before the badminton image to see which school section it belongs to:I see on page 19 there's a quote from Seth Low, Communication Studies. Now let me look at page 20 where the badminton image is:Let me look at the structure better. The Im1.jpg is on page 19 (Seth Low's image), then page 20 starts with CoHASS section. The badminton image Im0.jpg should be on page 20. Let me check the page markers more carefully:So page 19 ends at line 3127, then the next page marker is page 21 at line 3387. So the badminton image (around line 3343) is on page 20 which doesn't have an explicit marker. Let me look at the entire section more carefully to see which school this belongs to:Now I see WKWSCI (Wee Kim Wee School of Communication and Information) section starts around line 3548, but the badminton image is earlier around line 3343. Let me trace back to see which section the badminton image actually belongs to. Let me read backwards from line 3320:Perfect! Now I can see the structure. The badminton image (Im0.jpg) appears under the \"Communication-Focused\" section which is part of the CoHASS (College of Humanities, Arts and Social Sciences) overview on page 20. This section appears before the individual school pages. Then on page 21, we see Seth Low from Communication Studies.\n\nSo the badminton image is associated with Communication Studies, which is evident from:\n1. It's on the CoHASS overview page (page 20)\n2. The \"Communication-Focused\" heading right before the badminton image\n3. Seth Low, Communication Studies student, appears on the next page (21)\n4. WKWSCI (Wee Kim Wee School of Communication and Information) is listed as one of the schools under CoHASS\n\nAnswer: Communication Studies", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 83500, "error": null, "extra": {"thread_id": 1955, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2258a5b949624b49af1a1b98a8b2e791", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 658, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "communication studies", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document to identify which colleges/schools/institutes feature Year 3 students on their cover pages or promotional sections.\n\nLooking through the document:\n\n1. Page 10 (College of Engineering section) - Features **Stella Chua Zhi Yun, Mechanical Engineering, Year 3**\n\n2. Page 12 (College of Humanities, Arts and Social Sciences) - Features **Seth Low, Communication Studies, Year 4** (not Year 3)\n\n3. Page 14 (College of Science) - Features **Balaji Srinivasan, Biological Sciences and Psychology, Year 4** (not Year 3)\n\n4. Page 16 (Lee Kong Chian School of Medicine) - Features **Muhamed Shamir Jaffar Hussein, MBBS, Year 4** (not Year 3)\n\n5. Page 17 (Nanyang Business School) - Features **Wang Shi Lin Nicole, Business, Year 3**\n\n6. Page 18 (National Institute of Education) - Features **Ruth Koh, Sport Science & Management, Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 60377, "output_tokens": 272, "cost_micros": 185211, "latency_ms": 42937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to look through the document to identify which colleges/schools/institutes feature Year 3 students on their cover/featured pages.\n\nLet me examine each section:\n\n1. **College of Engineering** (page 16-17): Features STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21): Features SETH LOW, Communication Studies, Year 4 (not Year 3)\n\n3. **College of Science** (page 24-25): Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not Year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28-29): Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not Year 3)\n\n5. **Nanyang Business School** (page 30-31): Features WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33): Features RUTH KOH, Sport Science & Management, Year 3\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 15949, "output_tokens": 282, "cost_micros": 52077, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the document to identify which colleges/schools/institutes feature year 3 students on their cover pages.\n\nLooking through the document:\n\n1. **College of Engineering** (page 16) - Features STELLA CHUA ZHI YUN, Mechanical Engineering, **Year 3**\n\n2. **College of Humanities, Arts and Social Sciences** (page 20) - Features SETH LOW, Communication Studies, Year 4 (not year 3)\n\n3. **College of Science** (page 24) - Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28) - Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not year 3)\n\n5. **Nanyang Business School** (page 30) - Features WANG SHI LIN NICOLE, Business, **Year 3**\n\n6. **National Institute of Education** (page 32) - Features RUTH KOH, Sport Science & Management, **Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 19388, "output_tokens": 278, "cost_micros": 62334, "latency_ms": 6905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document for references to Year 3 students who appear as student models on cover pages or in promotional materials.\n\nLooking through the document, I can find the following Year 3 students quoted/featured:\n\n1. Page 16 - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3 - This is from the **College of Engineering** section\n\n2. Page 30 - WANG SHI LIN NICOLE, Business, Year 3 - This is from the **Nanyang Business School** section (she appears twice on this page)\n\n3. Page 33 - RUTH KOH, Sport Science & Management, Year 3 - This is from the **National Institute of Education** section (she appears twice on this page)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 14661, "output_tokens": 193, "cost_micros": 46878, "latency_ms": 6233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify which colleges/schools/institutes feature Year 3 students on their cover/promotional pages in this prospectus.\n\nLet me search through the document for mentions of students with their year level and their associated colleges/schools:\n\n1. **College of Engineering** (page 16-17):\n   - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21):\n   - SETH LOW, Communication Studies, Year 4\n\n3. **College of Science** (page 24-25):\n   - BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4\n\n4. **Lee Kong Chian School of Medicine** (page 28-29):\n   - MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4\n\n5. **Nanyang Business School** (page 30-31):\n   - WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33):\n   - RUTH KOH, Sport Science & Management, Year 3\n\nFrom this analysis, the colleges/schools/institutes that use Year 3 students as their cover page student models are:\n- College of Engineering (Stella Chua)\n- Nanyang Business School (Wang Shi Lin Nicole)\n- National Institute of Education (Ruth Koh)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 18595, "output_tokens": 352, "cost_micros": 61065, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find which colleges/schools/institutes from NTU use year 3 students as cover page student models.Let me search for information about cover page student models and year 3 students.Let me search for \"Year 3\" with capital letters and look at the beginning of the document which might contain information about cover pages or student models.Now let me search for information about cover pages and models. Let me read the beginning of the document more carefully:Let me search more specifically for information about models or photos. Let me look for text that mentions students being models or featured on covers:Let me look at the end of the document, which often contains credits or acknowledgments about models or photos:Let me search for the total lines in this document and read from the end:Let me read closer to the end of the document where credits or acknowledgments are typically found:Let me continue reading towards the very end where photo credits or acknowledgments might be:Let me search near the very end of the document:The document ended without showing photo credits. Let me search more specifically for mentions of colleges and Year 3 together. Let me look at those specific line numbers from the grep results:Good! I found one: Stella Chua Zhi Yun is from Mechanical Engineering, Year 3. Now let me check the other instances:Good! Wang Shi Lin Nicole is from Business (Nanyang Business School), Year 3. Now let me check the other instances:Good! Ruth Koh is from Sport Science & Management (National Institute of Education), Year 3. Now let me check the next instance:This is another mention of Ruth Koh. Now let me look more carefully at the context. I need to understand which of these students are featured on \"cover pages\" of their respective schools/colleges. Let me search for any section that lists or describes cover page models:None of these matches refer to \"cover page\" in the context I'm looking for. Let me look at the actual pages where these students appear to see if they are featured as cover page models. Let me check around the Mechanical Engineering section:Now let me look before this section to see if there's a cover page for College of Engineering:Perfect! I can see that Stella Chua Zhi Yun (Mechanical Engineering, Year 3) is featured on the cover page for the College of Engineering section. Now let me check the other sections:Great! I can see Wang Shi Lin Nicole (Business, Year 3) is featured on the cover page for Nanyang Business School. Now let me check the NIE section:Perfect! I can see Ruth Koh (Sport Science & Management, Year 3) is featured on the cover page for the National Institute of Education (NIE) section.\n\nNow let me check if there are any other colleges/schools that I might have missed. Let me look at the table of contents and see what major sections exist:Let me check if there's a cover page model for College of Humanities, Arts and Social Sciences (CoHASS):I can see Seth Low (Communication Studies, Year 4) is mentioned but not Year 3. Let me check the College of Science:I can see Balaji Srinivasan (Biological Sciences and Psychology, Year 4) is mentioned for College of Science, but not Year 3. Let me check if there's a cover page before the College of Science section:So for College of Science, the cover page model is Balaji Srinivasan (Year 4), not Year 3. Let me also check Lee Kong Chian School of Medicine:Let me check the cover page before Lee Kong Chian School of Medicine:So for Lee Kong Chian School of Medicine, the cover page model is Muhamed Shamir Jaffar Hussein (MBBS, Year 4), not Year 3.\n\nNow let me systematically check all the major schools/colleges. Based on my review:\n\n**Year 3 students as cover page models:**\n1. **College of Engineering** - Stella Chua Zhi Yun (Mechanical Engineering, Year 3)\n2. **Nanyang Business School** - Wang Shi Lin Nicole (Business, Year 3)\n3. **National Institute of Education** - Ruth Koh (Sport Science & Management, Year 3)\n\nLet me verify there are no other colleges I missed by checking the table of contents more carefully:Based on the table of contents, the main colleges/schools are:\n1. College of Engineering (has Year 3 student - Stella Chua)\n2. Colleges of Humanities, Arts and Social Sciences (has Year 4 student - Seth Low)\n3. Colleges of Science (has Year 4 student - Balaji Srinivasan)\n4. Lee Kong Chian School of Medicine (has Year 4 student - Muhamed Shamir)\n5. Nanyang Business School (has Year 3 student - Wang Shi Lin Nicole)\n6. National Institute of Education (has Year 3 student - Ruth Koh)\n\nSo the colleges/schools/institutes that use Year 3 students as cover page models are:\n1. College of Engineering\n2. Nanyang Business School\n3. National Institute of Education\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 154578, "error": null, "extra": {"thread_id": 1956, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_071293dfad4941b99ec3b52bc671c7da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1092, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the College of Humanities, Arts, and Social Sciences (CoHASS) and count how many schools are listed but not individually introduced in detail.\n\nFrom page 12, I can see the College of Humanities, Arts and Social Sciences section lists \"OUR SCHOOLS\":\n- School of Art, Design and Media\n- School of Humanities\n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 7 entities listed under \"OUR SCHOOLS\".\n\nLooking at pages 13-14, I can see detailed introductions for:\n1. School of Art, Design and Media (ADM) - page 13\n2. School of Humanities (SoH) - page 13\n3. School of Social Sciences (SSS@NTU) - page 13\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - page 13\n\nThese 4 schools have detailed \"ABOUT\" sections with descriptions of their programs and advantages on pages 22-23.\n\nThe entities that are listed but NOT individually introduced in detail are:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 3 entities.\n\nAnswer: 3", "citations": [], "input_tokens": 60364, "output_tokens": 324, "cost_micros": 185952, "latency_ms": 38015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools in the College of Humanities, Arts, and Social Sciences (CoHASS) and determine which ones are NOT introduced in detail.\n\nFrom page 20-21, the CoHASS section lists \"OUR SCHOOLS\":\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n\nIt also mentions:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow, let me check which schools get detailed individual introductions on pages 22-23:\n\n1. **School of Art, Design and Media (ADM)** - YES, has detailed section on page 22\n2. **School of Humanities (SoH)** - YES, has detailed section on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, has detailed section on page 23\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, has detailed section on page 23\n\nThe three centers/institutes mentioned (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences) are listed under \"OUR SCHOOLS\" but do NOT have individual detailed introductions.\n\nTherefore, 3 entities listed under CoHASS schools do not have individual detailed introductions.\n\nAnswer: 3", "citations": [], "input_tokens": 15936, "output_tokens": 340, "cost_micros": 52908, "latency_ms": 8296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools that belong to the College of Humanities, Arts and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFrom page 20, the document lists the schools under CoHASS:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow I need to check which schools have individual detailed introductions on pages 22-23 (the \"LEARN MORE ABOUT OUR SCHOOLS\" section):\n\nPage 22:\n- School of Art, Design and Media (ADM) - YES, has detailed introduction\n- School of Humanities (SoH) - YES, has detailed introduction\n\nPage 23:\n- School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n- Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nSchools that are NOT individually introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 19375, "output_tokens": 302, "cost_micros": 62655, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools within the College of Humanities, Arts, and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFirst, let me find the list of schools under CoHASS from page 20:\n\n**OUR SCHOOLS:**\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nThat's 7 schools total listed under CoHASS.\n\nNow let me check which schools have detailed individual introductions in the document (pages 21-23):\n\n1. **School of Art, Design and Media (ADM)** - YES, detailed introduction on page 22\n2. **School of Humanities (SoH)** - YES, detailed introduction on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, detailed introduction on page 22\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, detailed introduction on page 23\n\nThe following schools are listed but do NOT have individual detailed introductions:\n5. Nanyang Centre for Public Administration - NO detailed introduction\n6. NTU Centre for Contemporary Art Singapore - NO detailed introduction\n7. Centre for Liberal Arts and Social Sciences - NO detailed introduction\n\nTherefore, 3 schools are not individually introduced in detail.\n\nAnswer: 3", "citations": [], "input_tokens": 14648, "output_tokens": 358, "cost_micros": 49314, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify which schools from the College of Humanities, Arts, and Social Sciences (CoHASS) are listed, and then determine which ones are NOT introduced in detail.\n\nFrom the document, the CoHASS schools listed on page 20 are:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow, looking at the detailed introductions on pages 22-23 under \"LEARN MORE ABOUT OUR SCHOOLS\":\n1. School of Art, Design and Media (ADM) - YES, has detailed introduction\n2. School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n3. School of Humanities (SoH) - YES, has detailed introduction\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nThe schools that are NOT introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 18582, "output_tokens": 292, "cost_micros": 60126, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the College of Humanities, Arts, and Social Sciences and count which schools are not individually introduced in detail.Let me search for \"College of Humanities, Arts, and Social Sciences\" in the document:Let me try searching for variations:Let me read the section about the College of Humanities:Let me search for more information about the college structure. Let me look for \"COLLEGE OF HUMANITIES\" in caps:Found it! Let me read that section:Good! I can see the College of Humanities, Arts and Social Sciences has 4 schools listed:\n1. School of Art, Design and Media (page 22)\n2. School of Humanities (page 23)\n3. School of Social Sciences (page 23)\n4. Wee Kim Wee School of Communication and Information (page 24)\n\nNow let me check which ones are introduced in detail. Let me read pages 22-24:Let me search for where each school is detailed. Let me look at the actual page content around lines 3200-3300 where I saw \"School of Humanities\" mentioned:Good! I can see the CoHASS section lists their schools. Now let me find where individual schools are detailed. Let me search for detailed sections of each school:Let me read the ADM section:Great! Now let me check for Wee Kim Wee School:Perfect! Now let me also check if there are any other schools in CoHASS that I might have missed. Let me go back to the list to verify:I see that the College of Humanities, Arts and Social Sciences has the following units listed:\n- School of Art, Design and Media\n- School of Humanities  \n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow let me check if the last three (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) have detailed introductions. Let me search:So the three centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) are only listed in the OUR SCHOOLS section but don't have detailed individual introductions like the four main schools do.\n\nNow let me verify the total count by checking what the document considers as \"schools\" in CoHASS. The question asks specifically about \"schools\" - let me see if these centres are considered schools:\n\nLooking at the structure:\n- In the table of contents (lines 398-413), only 4 schools are listed under \"COLLEGES OF HUMANITIES, ARTS AND SOCIAL SCIENCES\":\n  1. School of Art, Design and Media (page 22)\n  2. School of Humanities (page 23)\n  3. School of Social Sciences (page 23)\n  4. Wee Kim Wee School of Communication and Information (page 24)\n\nAll 4 of these have detailed individual introductions as I found above.\n\nBut in the \"OUR SCHOOLS\" section (lines 3216-3236), it lists 7 entities:\n- 4 Schools (the ones mentioned above)\n- 3 Centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences)\n\nThe question asks about \"schools\" specifically. The centres are NOT called \"schools\" - they are called \"centres\". So the question is asking only about the 4 schools.\n\nSince all 4 schools (School of Art, Design and Media, School of Humanities, School of Social Sciences, and Wee Kim Wee School of Communication and Information) ARE individually introduced in detail, the answer is:\n\nAnswer: 0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97500, "error": null, "extra": {"thread_id": 1957, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_84cc170418784e7aaee4655dd9c795d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 722, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to analyze Figure 1 and find other figures that convey similar messages.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" across four datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10). The key message is that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries, especially in the low budget regime.\n\nNow let me look through the document for figures with similar messages:\n\n**Figure 6** (page 16): Shows \"Label diversity yields more performant and robust active querying strategies\" on PathMNIST, OrganAMNIST, and BloodMNIST. This compares strategies with and without label diversity, not the cold start problem specifically.\n\n**Figure 9** (page 20): Shows \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, focuses on label diversity rather than cold start.\n\n**Figure 4** (page 7): Shows \"Quantitative comparison of map-based querying strategies\" comparing easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast with random selection as baseline. This shows performance at low budgets and demonstrates the cold start problem.\n\n**Figure 5** (page 8): Shows \"On the importance of selecting a superior initial query\" with performance across active learning cycles starting from initial queries. This demonstrates how different initial query strategies perform, related to the cold start problem.\n\n**Figure 13** (page 24): Shows performance of active learning strategies with different initial query strategies on BloodMNIST, demonstrating the importance of initial query selection.\n\n**Figure 14** (page 24): Same as Figure 13 but for PathMNIST.\n\nFigures that convey the same core message as Figure 1 (that active learning fails at initial selection/cold start):\n- Figure 4: Shows the cold start problem through comparison with random\n- Figure 5: Shows importance of initial query selection\n- Figure 13: Shows initial query performance across cycles\n- Figure 14:", "citations": [], "input_tokens": 58482, "output_tokens": 512, "cost_micros": 183126, "latency_ms": 44218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" where most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. The figure has 4 subplots (a-d) for different datasets showing AUC performance vs. number of images.\n\nLet me search for other figures with similar messages:\n\n**Figure 6**: \"Label diversity yields more performant and robust active querying strategies\" - Shows 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) with 6 subplots each comparing strategies with/without diversity. This is about diversity improvement, not the cold start problem itself.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10 and CIFAR-10-LT. Also about diversity improvement rather than demonstrating the cold start problem.\n\n**Figure 4**: \"Quantitative comparison of map-based querying strategies\" - Shows random selection as a baseline and compares different strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across 4 datasets. This demonstrates that random selection is competitive and that hard-to-contrast performs best, which is related to the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST\" - This is about subsequent cycles after initial query.\n\n**Figure 14**: Similar to Figure 13 but for PathMNIST - Also about subsequent cycles.\n\n**Figure 5**: \"On the importance of selecting a superior initial query\" on OrganAMNIST - Shows active learning cycles but focuses on the importance of initial query selection.\n\nThe figures that most clearly convey the same core message as Figure 1 (that existing active querying strategies fail to outperform random selection at the start) are:\n\n- **Figure 4**: Directly compares strategies at low budgets and shows random selection as competitive baseline\n- Possibly Figure 6 and 9, but these focus more on the solution (diversity) rather than the", "citations": [], "input_tokens": 21463, "output_tokens": 512, "cost_micros": 72069, "latency_ms": 12625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1's main message is about the \"Cold start problem in vision active learning\" - showing that most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. It displays AUC performance curves across different numbers of images for multiple datasets.\n\nLet me go through the other figures to find similar messages:\n\n**Figure 2**: Shows label diversity of querying criteria - different message (about class distribution bias)\n\n**Figure 3**: Shows Dataset Maps and easy/hard-to-learn vs easy/hard-to-contrast data - different message (about data selection strategies)\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies, comparing hard-to-contrast with other strategies against random selection - **similar message** (comparing active learning strategies with random selection)\n\n**Figure 5**: Shows importance of selecting superior initial query on OrganAMNIST - **similar message** (about initial query performance across active learning cycles)\n\n**Figure 6**: Extended from Table 1, shows label diversity yields more performant strategies - different message (about adding label diversity)\n\n**Figure 7**: Continued from Figure 2, about label diversity - different message\n\n**Figure 8**: Visualization of K-means clustering - different message\n\n**Figure 9**: Similar to Figure 6, about diversity and performance - different message (about label diversity effect)\n\n**Figure 10**: Similar to Figure 2, about label diversity - different message\n\n**Figure 11**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 12**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 13**: Performance across active learning cycles on BloodMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\n**Figure 14**: Performance across active learning cycles on PathMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\nFigures that convey similar messages to Figure 1 (comparing active learning strategies, showing cold start problem):\n- Figure 4\n- Figure 5\n- Figure 13\n- Figure 14\n\nAnswer: 4", "citations": [], "input_tokens": 26468, "output_tokens": 506, "cost_micros": 86994, "latency_ms": 11765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey similar messages to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" - it demonstrates that most existing active querying strategies (like BALD, Consistency, etc.) are outperformed by random selection when selecting initial queries, particularly in low budget regimes.\n\nLet me go through the document to find figures with similar messages:\n\n**Figure 4**: Shows comparison of different querying strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across different datasets. This shows performance comparisons but focuses on the proposed method rather than demonstrating the cold start problem itself.\n\n**Figure 6**: Explicitly states it is \"Extended from Table 1\" and shows \"Label diversity yields more performant and robust active querying strategies.\" It compares active querying strategies with and without label diversity on PathMNIST, OrganAMNIST, and BloodMNIST. This is about the solution (label diversity) rather than demonstrating the cold start problem.\n\n**Figure 9**: States \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, this focuses on the solution rather than demonstrating the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST.\" This compares different strategies across active learning cycles but focuses on the full active learning procedure rather than specifically demonstrating the cold start problem.\n\n**Figure 14**: Similar to Figure 13, shows \"Performance of each active learning querying strategies with different initial query strategies on PathMNIST.\"\n\nLooking back at the document more carefully, none of the other figures directly replicate Figure 1's specific message about the cold start problem where active learning strategies fail to outperform random selection at the first choice. The other figures either:\n- Show solutions to the problem (Figures 6, 9)\n- Show active learning trajectories (Figures 13, 14)\n- Show other aspects like dataset maps or visualizations (Figures 3, 7, 8, 10, 11, 12)\n\nAnswer: 0", "citations": [], "input_tokens": 23168, "output_tokens": 504, "cost_micros": 77064, "latency_ms": 11578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1, which shows the \"Cold start problem in vision active learning\" - demonstrating that most existing active querying strategies are outperformed by random selection when selecting initial queries.\n\nLet me examine each figure:\n\n**Figure 1**: Shows cold start problem - active learning strategies perform worse than random selection at initial queries across multiple datasets.\n\n**Figure 2**: Shows label diversity of querying criteria - demonstrates class distribution bias in active querying strategies.\n\n**Figure 3**: Shows active querying based on Dataset Maps - compares easy/hard-to-learn vs easy/hard-to-contrast data.\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies - compares different strategies including hard-to-contrast vs random selection.\n\n**Figure 5**: Shows importance of selecting superior initial query - correlation analysis showing hard-to-contrast outperforms others across active learning cycles.\n\n**Figure 6**: Extended from Table 1 - shows label diversity yields more performant strategies, comparing with/without label diversity across budgets.\n\n**Figure 7**: Continued from Figure 2 - shows label diversity for OrganAMNIST and BloodMNIST.\n\n**Figure 8**: Visualization of K-means clustering and active selection using UMAP.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10-LT - shows diversity yields more performant strategies.\n\n**Figure 10**: Similar to Figure 2 but for CIFAR-10 and CIFAR-10-LT - shows label diversity.\n\n**Figure 11**: Similar to Figure 3 - Active querying based on Dataset Maps for PathMNIST and OrganAMNIST.\n\n**Figure 12**: Similar to Figure 3 - Active querying based on Dataset Maps for CIFAR-10-LT.\n\n**Figure 13**: Performance of active learning strategies with different initial queries on BloodMNIST - shows correlation across cycles.\n\n**Figure 14**: Performance of active learning strategies with different initial queries on PathMNIST - shows correlation across cycles.\n\nFigures conveying a similar message to Figure 1 (showing the cold start problem where active learning strategies underperform random selection at the start):\n- **Figure ", "citations": [], "input_tokens": 37261, "output_tokens": 512, "cost_micros": 119463, "latency_ms": 12672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to understand the first figure and then identify how many other figures convey similar messages.Let me continue reading to find all figures in the document and understand what the first figure conveys.Now let me read more sections to understand all the figures and their messages better.Let me continue reading to see more figures.Let me continue reading to identify all figures.Let me search for more figures to get a complete count.Now let me understand what message Figure 1 conveys and identify similar figures. Based on my reading, let me analyze:\n\n**Figure 1** (lines 490-584): Shows four subplots (a-d) of line graphs plotting AUC vs Number of images, demonstrating the \"cold start problem\" where active querying strategies are outperformed by random selection in selecting initial queries. The message is: **existing active learning strategies perform worse than random selection at the beginning (cold start problem)**.\n\nNow let me identify which other figures convey similar messages:\n\n1. **Figure 4** (lines 1346-1455): Shows four subplots comparing map-based querying strategies with random selection as baseline, showing that random selection is \"a highly competitive baseline in cold start because it outperforms six popular active querying strategies as shown in Figure 1.\" This conveys the **same message about cold start problem**.\n\n2. **Figure 6** (lines 2545-2805): Shows plots about label diversity yielding more performant strategies - this is about **solutions**, not about demonstrating the cold start problem itself.\n\n3. **Figure 7** (lines 2840-2920): Shows bar charts about label diversity - this is about **class distribution bias**, related but different message.\n\n4. **Figure 9** (lines 3454-3640): Similar to Figure 6, about diversity yielding better strategies - this is about **solutions**.\n\n5. **Figure 10** (lines 3689-3800): Bar charts about label diversity - about **class distribution bias**.\n\nLet me verify by reading more carefully about what each figure demonstrates:Based on my thorough analysis of the document, Figure 1's main message is: **Active learning strategies are outperformed by random selection at the cold start (initial query selection) due to the cold start problem.**\n\nFigures that convey this same or very similar message:\n- **Figure 4**: Explicitly shows random selection as a competitive baseline that outperforms popular active querying strategies in the cold start problem, directly referencing Figure 1.\n\nAll other figures (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14) convey different messages:\n- Figures 2, 7, 10: Show class distribution bias\n- Figures 3, 11, 12: Show dataset maps for selecting data\n- Figure 5, 13, 14: Show importance of superior initial query across learning cycles\n- Figures 6, 9: Show that diversity improves querying strategies\n- Figure 8: Shows visualization of clustering\n\nTherefore, **1 figure** (Figure 4) conveys a similar message as Figure 1.\n\nAnswer: 1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78500, "error": null, "extra": {"thread_id": 1958, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cace0f3b35134ef58541b082bc26d8e7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 533, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1, which shows the \"Cold start problem in vision active learning,\" I can see performance comparisons on different datasets including CIFAR-10 (subplot d). The figure shows that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries.\n\nLooking at Table 1, which compares different querying strategies on CIFAR-10-LT at 10% and 20% budgets:\n- At 10% budget: Random achieves 91.6\u00b11.1\n- At 20% budget: Random achieves 93.1\u00b10.6\n\nComparing with other strategies at these budgets:\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%, strategies that outperform random (91.6): VAAL (92.6), Entropy (92.0), Margin (91.7), BALD (92.8) = 4 strategies\n\nAt 20%, strategies that outperform random (93.1): Consistency (93.4), VAAL (93.7), Margin (93.2), Coreset (93.6) = 4 strategies\n\nHowever, the question asks generally how many strategies outperform random selection. Looking at the paper's proposed \"Ours\" strategy (hard-to-contrast), the results show it significantly outperforms random", "citations": [], "input_tokens": 58490, "output_tokens": 512, "cost_micros": 183150, "latency_ms": 41641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10, I can see performance curves for different strategies. The document mentions CIFAR-10-LT specifically in several places.\n\nFrom Figure 4(d), I can see a comparison of different strategies on CIFAR-10-LT including:\n- Random (baseline, shown as dotted line)\n- Easy-to-learn\n- Hard-to-learn\n- Easy-to-contrast\n- Hard-to-contrast\n\nLooking at the quantitative results in Figure 4(d) for CIFAR-10-LT at different budget levels (20.0% and 30.0% with 2481 and 3721 images), I can observe which strategies perform above the random baseline.\n\nFrom the text in section 3.2: \"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nLooking at Figure 4(d) more carefully, I can see that at the budget levels shown:\n1. Hard-to-contrast (performs best, clearly above random)\n2. Easy-to-learn (appears to perform above random)\n3. Hard-to-learn (appears close to or slightly above random)\n4. Easy-to-contrast (appears below random)\n\nFrom the visual inspection of Figure 4(d), it appears that 2-3 strategies outperform random selection, with hard-to-contrast being the clear winner, and easy-to-learn also performing above the baseline.\n\nBased on the figure, I count: Hard-to-contrast and Easy-to-learn as clearly outperforming random selection.\n\nAnswer: 2", "citations": [], "input_tokens": 21471, "output_tokens": 478, "cost_micros": 71583, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows the cold start problem on CIFAR-10, I can see performance curves for different strategies compared to random selection. However, the question asks specifically about CIFAR-10-LT.\n\nLet me check the text more carefully. In Section 3.2, it states:\n\n\"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nThis indicates that the \"hard-to-contrast\" strategy (which is the authors' proposed method) outperforms random selection.\n\nLooking at Figure 1(d) which shows CIFAR-10 (not CIFAR-10-LT, but related), most of the active learning strategies shown (BALD, Consistency, Coreset, Margin, VAAL, Entropy) appear to perform worse than or similar to random selection at the initial stages (the \"cold start\" problem that the paper discusses).\n\nThe main point of the paper is that existing active learning strategies fail to outperform random selection at the cold start (initial query selection), which is why they propose their hard-to-contrast method.\n\nLooking at Table 1 and Table 7, which compare different strategies on CIFAR-10-LT with different budget levels, at low budgets (10%, 20%), the proposed method with diversity enforcement shows the best performance. However, most existing strategies WITHOUT diversity enforcement perform poorly or fail to select all classes (marked as \"-\").\n\nBased on the paper's main finding that existing active querying strategies are \"outperformed by random selection\" at the cold start (as stated in Figure 1 caption and throughout the introduction), and that only their proposed \"hard-to-contrast\" method significantly outperforms random selection on CIFAR-10-LT, the answer appears to be:\n\nAnswer: 1", "citations": [], "input_tokens": 26476, "output_tokens": 501, "cost_micros": 86943, "latency_ms": 12703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many active learning strategies outperform random selection on CIFAR-10-LT in the cold start problem (initial query selection).\n\nLooking at Figure 1 in the document, which illustrates the \"Cold start problem in vision active learning,\" I can see panel (d) shows results for CIFAR-10-LT.\n\nThe document states in the caption of Figure 1: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries... However, some classes are not selected by active querying strategies due to selection bias, so their results are not presented in the low budget regime.\"\n\nThe document consistently emphasizes that existing active learning strategies fail to outperform random selection at the cold start (initial query stage). The main contribution of the paper is proposing a new strategy (hard-to-contrast) that does outperform random selection.\n\nLooking at Table 1, for CIFAR-10-LT at 10% and 20% budgets:\n- Random: 91.6\u00b11.1 (10%), 93.1\u00b10.6 (20%)\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%: VAAL (92.6), Entropy (92.0), BALD (92.8), and Margin (91.7) appear close or slightly better than Random (91.6)\nAt 20%: VAAL (93.7), Consistency (93.4), Coreset (93.", "citations": [], "input_tokens": 23176, "output_tokens": 512, "cost_micros": 77208, "latency_ms": 12390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10-LT:\n\nThe figure shows performance curves for different strategies:\n- Random (baseline)\n- BALD (Kirsch et al., 2019)\n- Consistency (Gao et al., 2020)\n- Coreset (Sener et al., 2017)\n- Margin (Balcan et al., 2007)\n- VAAL (Sinha et al., 2019)\n- Entropy (Wang et al., 2014)\n\nThe text states: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries, since random selection is i.i.d. to the entire dataset.\"\n\nFrom Figure 1(d), I can see that at the initial query stage (low budget regime), the random selection baseline appears to outperform most other strategies. The figure caption states \"Active learning fails to select data as effectively as random selection at the first choice.\"\n\nHowever, looking more carefully at the results, I need to determine which strategies actually outperform random at the cold start (initial query selection).\n\nAccording to the paper's main findings and Figure 4, the proposed \"hard-to-contrast\" strategy significantly outperforms random selection on CIFAR-10-LT. The paper states this is their contribution - addressing the cold start problem where existing strategies fail to beat random.\n\nFrom the various results shown, only the authors' proposed strategy (hard-to-contrast) consistently outperforms random selection at the initial query stage on CIFAR-10-LT.\n\nAnswer: 1", "citations": [], "input_tokens": 37269, "output_tokens": 407, "cost_micros": 117912, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about strategies that outperform the random selection baseline on CIFAR-10-LT.Let me search for content related to CIFAR-10-LT and performance comparisons:Let me read through the document more systematically to find tables or figures with results:Let me search for tables that compare strategies on CIFAR-10-LT:Perfect! I found Table 1. Let me continue reading to see the complete table for CIFAR-10-LT results:Now I can see Table 1 clearly. Let me analyze the CIFAR-10-LT columns (10% and 20% budget). The table shows performance with and without enforcing label diversity. Looking at the CIFAR-10-LT columns:\n\nFor the 10% budget (column 9), I need to compare each strategy's score with Random baseline (91.6\u00b11.1):\n- Random (baseline): 91.6\u00b11.1\n- Consistency with diversity (\u2713): 91.4\u00b11.1 - DOES NOT outperform\n- Consistency without diversity (\u2717): 67.1\u00b117.1 - DOES NOT outperform\n- VAAL with diversity (\u2713): 92.6\u00b10.2 - OUTPERFORMS\n- VAAL without diversity (\u2717): - (missing)\n- Margin with diversity (\u2713): 91.7\u00b10.9 - OUTPERFORMS (slightly)\n- Margin without diversity (\u2717): 81.9\u00b10.8 - DOES NOT outperform\n- Entropy with diversity (\u2713): 92.0\u00b11.2 - OUTPERFORMS (slightly)\n- Entropy without diversity (\u2717): 65.6\u00b115.6 - DOES NOT outperform\n- Coreset with diversity (\u2713): 91.5\u00b10.4 - DOES NOT outperform\n- Coreset without diversity (\u2717): 65.9\u00b115.9 - DOES NOT outperform\n- BALD with diversity (\u2713): 92.8\u00b10.1 - OUTPERFORMS\n- BALD without diversity (\u2717): 64.9\u00b114.9 - DOES NOT outperform\n\nLooking at 10% budget, strategies that outperform random (91.6):\n1. VAAL with diversity: 92.6\n2. Margin with diversity: 91.7\n3. Entropy with diversity: 92.0\n4. BALD with diversity: 92.8\n\nThat's 4 strategies at 10% budget.\n\nFor the 20% budget (column 10), comparing with Random baseline (93.1\u00b10.6):\n- Random: 93.1\u00b10.6\n- Consistency with diversity: 93.4\u00b10.2 - OUTPERFORMS\n- Consistency without diversity: 88.6\u00b10.3 - DOES NOT outperform\n- VAAL with diversity: 93.7\u00b10.4 - OUTPERFORMS\n- VAAL without diversity: - (missing)\n- Margin with diversity: 93.2\u00b10.2 - OUTPERFORMS (slightly)\n- Margin without diversity: 86.3\u00b10.3 - DOES NOT outperform\n- Entropy with diversity: 91.9\u00b11.3 - DOES NOT outperform\n- Entropy without diversity: 86.4\u00b10.2 - DOES NOT outperform\n- Coreset with diversity: 93.6\u00b10.2 - OUTPERFORMS\n- Coreset without diversity: 86.9\u00b10.1 - DOES NOT outperform\n- BALD with diversity: 90.8\u00b12.4 - DOES NOT outperform\n- BALD without diversity: 84.7\u00b10.6 - DOES NOT outperform\n\nAt 20% budget, strategies that outperform random (93.1):\n1. Consistency with diversity: 93.4\n2. VAAL with diversity: 93.7\n3. Margin with diversity: 93.2\n4. Coreset with diversity: 93.6\n\nThat's 4 strategies at 20% budget.\n\nBut the question asks about \"CIFAR-10-LT\" in general. Let me count unique strategies (considering both budgets):\n\nStrategies that outperform at 10%: VAAL, Margin, Entropy, BALD (all with diversity)\nStrategies that outperform at 20%: Consistency, VAAL, Margin, Coreset (all with diversity)\n\nCombined unique strategies: VAAL, Margin, Entropy, BALD, Consistency, Coreset = 6 strategies\n\nBut I should check if the question means at ANY budget level or at BOTH budget levels. Looking at Figure 1d in the document might also help clarify. Let me check that figure.I notice Figure 1(d) is labeled \"CIFAR-10\" not \"CIFAR-10-LT\". But the question specifically asks about CIFAR-10-LT. Let me look more carefully at Table 1. The table clearly has a column header \"CIFAR-10-LT\" with two sub-columns (10% and 20%).\n\nBased on Table 1, I need to count how many strategies (considering different variations - with/without diversity) outperform the random baseline on CIFAR-10-LT. Let me be more systematic:\n\nAt 10% budget, Random = 91.6\u00b11.1:\n1. Consistency (\u2713): 91.4 - NO\n2. Consistency (\u2717): 67.1 - NO  \n3. VAAL (\u2713): 92.6 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 91.7 - YES\n6. Margin (\u2717): 81.9 - NO\n7. Entropy (\u2713): 92.0 - YES\n8. Entropy (\u2717): 65.6 - NO\n9. Coreset (\u2713): 91.5 - NO\n10. Coreset (\u2717): 65.9 - NO\n11. BALD (\u2713): 92.8 - YES\n12. BALD (\u2717): 64.9 - NO\n\nAt 10%: 4 strategies outperform random\n\nAt 20% budget, Random = 93.1\u00b10.6:\n1. Consistency (\u2713): 93.4 - YES\n2. Consistency (\u2717): 88.6 - NO\n3. VAAL (\u2713): 93.7 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 93.2 - YES\n6. Margin (\u2717): 86.3 - NO\n7. Entropy (\u2713): 91.9 - NO\n8. Entropy (\u2717): 86.4 - NO\n9. Coreset (\u2713): 93.6 - YES\n10. Coreset (\u2717): 86.9 - NO\n11. BALD (\u2713): 90.8 - NO\n12. BALD (\u2717): 84.7 - NO\n\nAt 20%: 4 strategies outperform random\n\nThe question asks \"How many strategies outperform the random selection baseline on CIFAR-10-LT?\" \n\nLooking at both budget levels (10% and 20%), and counting each unique strategy configuration:\n- At 10%: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713) = 4 \n- At 20%: Consistency(\u2713), VAAL(\u2713), Margin(\u2713), Coreset(\u2713) = 4\n\nIf we count unique strategy configurations across both budgets: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713), Consistency(\u2713), Coreset(\u2713) = 6 total\n\nBut the most straightforward interpretation is: at a given budget level. Since the table shows two budget levels and the question doesn't specify which, I should look at what makes most sense. Given that 4 strategies outperform at each budget level separately, and 6 unique strategy variations outperform across at least one budget level, the answer is likely **4** (at each specific budget) or **6** (total unique strategies that ever outperform).\n\nHowever, re-reading the question more carefully - it asks how many strategies outperform, not how many strategy-variants. If we count base strategies (ignoring the diversity modifier):\n- VAAL outperforms (with diversity at both budgets)\n- Margin outperforms (with diversity at both budgets)  \n- Entropy outperforms (with diversity at 10%)\n- BALD outperforms (with diversity at 10%)\n- Consistency outperforms (with diversity at 20%)\n- Coreset outperforms (with diversity at 20%)\n\nThat would be 6 unique base strategies.\n\nBut looking at the data more carefully at the initial query (lowest budget), which is what the paper focuses on for \"cold start\", the answer is most likely **4** for the 10% budget.\n\nActually, let me reconsider - in Table 1, the \"Unif.\" column indicates whether uniform/diversity enforcement is applied. Each strategy has 2 rows (with and without diversity). So we have 7 base strategies tested: Random, Consistency, VAAL, Margin, Entropy, Coreset, BALD.\n\nIf I count how many of these 7 base strategies (in any configuration) outperform random at either budget:\n- Consistency: NO at 10%, YES at 20% (with diversity)\n- VAAL: YES at 10%, YES at 20% (with diversity)\n- Margin: YES at 10%, YES at 20% (with diversity)\n- Entropy: YES at 10%, NO at 20%\n- Coreset: NO at 10%, YES at 20% (with diversity)\n- BALD: YES at 10%, NO at 20%\n\nCount = 6 base strategies outperform random in at least one configuration at at least one budget level.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 81906, "error": null, "extra": {"thread_id": 1959, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7833996f4234f1db0fa9af29b8ebd6c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 805, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7, which show label diversity comparisons across different datasets (PathMNIST, OrganAMNIST, BloodMNIST), I can see the class distributions for different querying strategies.\n\nFrom these figures, VAAL consistently shows the poorest label diversity across datasets:\n- In PathMNIST (Figure 2), VAAL heavily selects from the \"muscle\" class while largely ignoring \"mucus\" and \"mucosa\" classes\n- In OrganAMNIST (Figure 7), VAAL prefers \"femur-right\" and \"platelet\" classes while largely ignoring lung, liver and monocyte classes\n- In BloodMNIST (Figure 7), VAAL shows similarly poor label diversity with heavy bias toward specific classes\n\nThis is further supported by Table 1, where VAAL frequently has missing results (marked as \"-\") because it fails to sample at least one data point for each class.\n\nAnswer: VAAL", "citations": [], "input_tokens": 58491, "output_tokens": 229, "cost_micros": 178908, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7 in the document, which show label diversity comparisons across different datasets, I can observe the class distribution patterns for various active querying strategies.\n\nFrom the figures, VAAL consistently shows highly imbalanced distributions with strong bias towards specific classes:\n- In PathMNIST (Figure 2): VAAL heavily favors muscle class while largely ignoring mucus and mucosa\n- In OrganAMNIST (Figure 7a): VAAL prefers femur-right while ignoring lung and liver\n- In BloodMNIST (Figure 7b): VAAL favors platelet while ignoring other classes\n\nThe document also states: \"For example, VAAL prefers selecting data in the muscle class, but largely ignores data in the mucus and mucosa classes.\"\n\nTable 2 further confirms this, showing VAAL has very poor class coverage (0.11-0.30) compared to other methods at low budgets.\n\nAnswer: VAAL", "citations": [], "input_tokens": 21472, "output_tokens": 230, "cost_micros": 67866, "latency_ms": 25187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity figures and tables in the document:\n\nIn Figure 2 (PathMNIST), VAAL shows extremely poor label diversity with an entropy of only 2.800, heavily biased toward the muscle class while largely ignoring mucus and mucosa classes.\n\nIn Figure 7 (OrganAMNIST and BloodMNIST), VAAL again shows the poorest label diversity, preferring specific classes like femur-right and platelet while ignoring others.\n\nIn Table 2 (class coverage), VAAL consistently has the lowest class coverage across datasets:\n- PathMNIST: 0.11 (lowest)\n- OrganAMNIST: 0.18 (lowest)\n- BloodMNIST: 0.13 (lowest)\n- CIFAR-10-LT: 0.30 (tied for lowest)\n\nThe document also notes that VAAL results are often missing (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 26477, "output_tokens": 247, "cost_micros": 83136, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Class coverage of selected data\" across different datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10-LT), I can see the class coverage values for different querying strategies.\n\nVAAL consistently shows the lowest class coverage values across the datasets:\n- PathMNIST: 0.11 at 0.00015%, 0.11 at 0.00030%, 0.18 at 0.001%, 0.18 at 0.002%\n- OrganAMNIST: 0.13 at 0.001%, 0.13 at 0.002%\n- BloodMNIST: values not explicitly shown but indicated as poor\n- CIFAR-10-LT: 0.30 at 0.2%, 0.30 at 0.3%\n\nAdditionally, Table 1 shows that VAAL has many missing results (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 23177, "output_tokens": 263, "cost_micros": 73476, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity analysis in the document, I need to examine Figure 2, Table 2, Figure 7, and Figure 10 which show class distributions and coverage across different querying strategies.\n\nFrom Figure 2 (PathMNIST), VAAL shows extremely poor label diversity - it selects 25% from muscle class but only 1% and 2% from mucosa and mucus classes respectively, with an entropy of 2.800 (lowest among all strategies).\n\nFrom Table 2 (class coverage):\n- VAAL consistently shows the poorest class coverage across datasets\n- PathMNIST (0.00015%): VAAL covers only 0.11 (11% of classes)\n- OrganAMNIST (0.001%): VAAL covers only 0.18 (18% of classes)  \n- BloodMNIST (0.001%): VAAL covers only 0.13 (13% of classes)\n- CIFAR-10-LT (0.2%): VAAL covers only 0.30 (30% of classes)\n\nFigure 7 confirms VAAL selects 100% from femur-right and platelet while selecting 0% from most other classes.\n\nFigure 10 shows VAAL fails to select any samples from 9 out of 10 classes in CIFAR-10-LT.\n\nAnswer: VAAL", "citations": [], "input_tokens": 37270, "output_tokens": 330, "cost_micros": 116760, "latency_ms": 9437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find information about querying criteria and label diversity across datasets.Let me continue reading the document to find information about querying criteria and label diversity:Let me search for information about label diversity across different querying criteria in the document:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28125, "error": null, "extra": {"thread_id": 1960, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9f3482197c3247b0aae1aed4062d9a6a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 96, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read priority document to find information about querying criteria and label diversity across datasets let me continue reading document to find information about querying criteria and label diversity let me search for information about label diversity across different querying criteria in document", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each page systematically.\n\nPage 1 (Figure 1): Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. This appears to be 4 figures with multiple lines each.\n\nPage 2 (Figure 1 continued): Same figure from page 1.\n\nPage 4 (Figure 2): Shows a bar chart comparing class distributions, not line plots.\n\nPage 5 (Figure 3): Shows three panels with scatter plots (Dataset Maps), not line plots.\n\nPage 7 (Figure 4): Shows 4 subplots (a-d), each containing line plots with error bars comparing different strategies. This is 4 figures with multiple lines each.\n\nPage 8 (Figure 5): Shows 10 subplots arranged in 2 rows, each containing multiple line plots. This is 10 figures with multiple lines each.\n\nPage 16 (Figure 6): Shows 18 subplots (6 strategies \u00d7 3 datasets), each containing line plots. This is 18 figures with multiple lines each.\n\nPage 17 (Figure 7): Shows bar charts, not line plots.\n\nPage 18 (Figure 8): Shows UMAP visualizations (scatter plots), not line plots.\n\nPage 20 (Figure 9): Shows 12 subplots (6 strategies \u00d7 2 datasets), each containing line plots. This is 12 figures with multiple lines each.\n\nPage 21 (Figure 10): Shows bar charts, not line plots.\n\nPage 22 (Figure 11): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 23 (Figure 12): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 24 (Figure 13 and 14): Each figure shows 10 subplots with line plots. This is 20 figures with multiple lines each.\n\nCounting the figures that contain line plots:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal: 4", "citations": [], "input_tokens": 58479, "output_tokens": 512, "cost_micros": 183117, "latency_ms": 36375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure systematically.\n\n**Figure 1** (page 2): Shows 4 subplots (a-d), each containing multiple line plots comparing different methods. Each subplot has multiple lines.\n\n**Figure 2** (page 4): This is a bar chart showing class distributions, not line plots.\n\n**Figure 3** (page 5): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 4** (page 8): Shows 4 subplots (a-d), each containing multiple line plots (4 different methods plotted).\n\n**Figure 5** (page 9): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 6** (page 16): Shows 3 rows \u00d7 6 columns = 18 subplots, each containing line plots (with and without diversity).\n\n**Figure 7** (page 17): These are bar charts showing class distributions, not line plots.\n\n**Figure 8** (page 18): These are scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9** (page 20): Shows 2 rows \u00d7 6 columns = 12 subplots, each containing line plots.\n\n**Figure 10** (page 21): These are bar charts showing class distributions, not line plots.\n\n**Figure 11** (page 22): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 12** (page 23): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 13** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 14** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\nNow let me count the total number of individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 4 panels  \n- Figure 5: 10 panels\n- Figure 6: 18 panels\n- Figure 9: 12 panels\n- Figure 13: 10 panels", "citations": [], "input_tokens": 21460, "output_tokens": 512, "cost_micros": 72060, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document page by page:\n\n**Figure 1 (page 2)**: Contains 4 line plots (a, b, c, d) showing cold start problem comparisons.\n\n**Figure 2 (page 3)**: This is a bar chart showing label diversity, not line plots.\n\n**Figure 3 (page 5)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 4 (page 7)**: Contains 4 line plots (a, b, c, d) comparing map-based querying strategies.\n\n**Figure 5 (page 8)**: Contains 10 line plots (2 rows \u00d7 5 columns) showing training from scratch and fine-tuning results.\n\n**Figure 6 (page 16)**: Contains 18 line plots (3 rows \u00d7 6 columns) showing extended results from Table 1.\n\n**Figure 7 (page 17)**: Contains bar charts, not line plots.\n\n**Figure 8 (page 18)**: Contains scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9 (page 20)**: Contains 12 line plots (2 rows \u00d7 6 columns) showing results on CIFAR-10 and CIFAR-10-LT.\n\n**Figure 10 (page 21)**: Contains bar charts, not line plots.\n\n**Figure 11 (page 22)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 12 (page 23)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 13 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for BloodMNIST results.\n\n**Figure 14 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for PathMNIST results.\n\nTotal count:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal =", "citations": [], "input_tokens": 26465, "output_tokens": 512, "cost_micros": 87075, "latency_ms": 8219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document:\n\n**Figure 1**: Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. Each subplot has multiple lines representing different strategies.\n\n**Figure 4**: Shows 4 subplots for different datasets (PathMNIST, OrganAMNIST, BloodMNIST, CIFAR-10-LT), each with multiple line plots.\n\n**Figure 6**: Shows 6 subplots (one for each strategy: BALD, Consistency, Coreset, Margin, VAAL, Entropy) across 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) = 18 subplots total, each containing 2 line plots (with and without diversity).\n\n**Figure 9**: Shows 6 subplots (one for each strategy) for 2 datasets (CIFAR-10 and CIFAR-10-LT) = 12 subplots total, each containing 2 line plots.\n\n**Figure 13**: Shows performance plots for BloodMNIST with two parts (a) and (b). Each part shows 6 subplots (Random, Entropy, Margin, BALD, Coreset, and appears to show multiple strategies). Each subplot contains multiple line plots showing different initial query strategies.\n\n**Figure 14**: Shows performance plots for PathMNIST with two parts (a) and (b). Similar structure to Figure 13, with 6 subplots in each part, each containing multiple line plots.\n\nLet me count more carefully by looking at each figure:\n\n- Figure 1: 4 subplots \u00d7 ~7 lines each = 28 line plots\n- Figure 4: 4 subplots \u00d7 4 lines each = 16 line plots\n- Figure 6: 18 subplots \u00d7 2 lines each = 36 line plots\n- Figure 9: 12 subplots \u00d7 2 lines each = 24 line plots\n- Figure 13: 2 parts \u00d7 6 subplots \u00d7 ~4 lines = ", "citations": [], "input_tokens": 23165, "output_tokens": 512, "cost_micros": 77175, "latency_ms": 10296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure.\n\n**Figure 1:** Shows 4 subplots (a-d) for PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10. Each subplot contains multiple line plots comparing different strategies. This appears to be 4 plots with multiple lines each.\n\n**Figure 2:** This is a table showing label diversity, not line plots.\n\n**Figure 3:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 4:** Shows 4 sets of comparisons (a-d) with 2 budget levels each. Each comparison shows line plots. This gives us 8 line plot panels.\n\n**Figure 5:** Shows 2 subplots (a-b) for \"Training from scratch\" and \"Fine-tuning from self-supervised pre-training\". Each contains multiple line plots.\n\n**Figure 6:** Shows a 3\u00d76 grid (3 datasets \u00d7 6 methods) = 18 line plot panels.\n\n**Figure 7:** This is a table, not line plots.\n\n**Figure 8:** Shows UMAP visualizations (scatter plots), not line plots.\n\n**Figure 9:** Shows 2 rows \u00d7 6 columns = 12 line plot panels.\n\n**Figure 10:** This is a table, not line plots.\n\n**Figure 11:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 12:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 13:** Shows 2 subplots (a-b) with multiple line plots each.\n\n**Figure 14:** Shows 2 subplots (a-b) with multiple line plots each.\n\nNow let me count more carefully by counting individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 8 panels (4 datasets \u00d7 2 budgets)\n- Figure 5: 2 panels\n- Figure 6: 18 panels (3 datasets \u00d7 6 methods)\n- Figure 9: 12 panels (2 datasets \u00d7 6 methods)\n- Figure 13: 2 panels\n- Figure 14: 2 panels", "citations": [], "input_tokens": 37258, "output_tokens": 512, "cost_micros": 119454, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the paper.Let me continue reading to see the full document and search for figures/plots:Now let me search for mentions of plots, charts, or examine the figures more carefully:I can see Figure 1 has 4 subfigures (1a, 1b, 1c, 1d) and they appear to be line plots. Let me continue examining other figures:Figure 2 appears to be a bar chart/heatmap showing class distributions. Let me check Figure 3:Figure 3 appears to be scatter", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46530, "error": null, "extra": {"thread_id": 1961, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5cc38c9bf2aa4c2ba5f55758d03cf5bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 228, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the optimizers used in this research by searching through the document.\n\nLooking at the implementation configurations section (Appendix A), specifically:\n\n1. In Table 3a (MedMNIST pre-training), the optimizer is listed as \"SGD\"\n2. In Table 3b (CIFAR-10-LT pre-training), the optimizer is listed as \"SGD\"\n3. In Table 6 (Benchmark settings), the optimizer is listed as \"SGD\"\n\nThroughout all the experimental settings described in the document, SGD (Stochastic Gradient Descent) is consistently used as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 58486, "output_tokens": 152, "cost_micros": 177738, "latency_ms": 32796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the optimizers used in the research.\n\nIn Table 3 (page 14), which shows \"Contrastive learning settings on MedMNIST and CIFAR-10-LT,\" I can see:\n\nFor both MedMNIST pre-training (Table 3a) and CIFAR-10-LT pre-training (Table 3b):\n- optimizer: SGD\n\nIn Table 6 (page 15), which shows \"Benchmark settings,\" the optimizer listed is also:\n- optimizer: SGD\n\nThroughout the document, only SGD (Stochastic Gradient Descent) is mentioned as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 21467, "output_tokens": 164, "cost_micros": 66861, "latency_ms": 6155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (pages 13-14), which shows contrastive learning settings:\n- For MedMNIST pre-training: optimizer = SGD\n- For CIFAR-10-LT pre-training: optimizer = SGD\n\nIn Table 6 (page 15), which shows benchmark settings:\n- For training MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 26472, "output_tokens": 120, "cost_micros": 81216, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (page 12), the contrastive learning settings show:\n- optimizer: SGD\n- optimizer momentum: 0.9\n\nIn Table 6 (page 13), the benchmark settings show:\n- optimizer: SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 23172, "output_tokens": 72, "cost_micros": 70596, "latency_ms": 3764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (Contrastive learning settings), the optimizer is listed as **SGD** for both MedMNIST and CIFAR-10-LT pre-training.\n\nIn Table 6 (Benchmark settings), the optimizer is also listed as **SGD** for training MedMNIST, CIFAR-10, and CIFAR-10-LT.\n\nAnswer: SGD", "citations": [], "input_tokens": 37265, "output_tokens": 106, "cost_micros": 113385, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the optimizers used in the research.Let me search for more specific terms related to optimizers:Good! I found SGD. Let me read the sections that mention optimizer settings to get a complete picture:Let me check the benchmark settings table as well:Perfect! I found the optimizer information. Based on the document, I can see that SGD (Stochastic Gradient Descent) is consistently used as the optimizer across different experimental settings:\n\n1. Table 3(a) - MedMNIST pre-training: optimizer = SGD\n2. Table 3(b) - CIFAR-10-LT pre-training: optimizer = SGD  \n3. Table 6 - Benchmark settings for MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD (Stochastic Gradient Descent)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42718, "error": null, "extra": {"thread_id": 1962, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eae58ea384884f52967fd1489c25db07", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 204, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "sgd stochastic gradient descent", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 which shows the Dataset Maps for PathMNIST:\n\nThe Data Map by ground truth (panel b) shows data points that are clearly stratified by classes, with distinct vertical bands representing different classes across the confidence axis.\n\nThe Data Map by pseudo-labels (panel c) shows data points that are more continuously distributed across the confidence range, without the same degree of class stratification.\n\nFrom the visual representation, the Data Map by ground truth appears to have data points spread more widely across different confidence levels (from approximately 0.0 to 1.0), while the pseudo-labels map shows a somewhat more compressed distribution in terms of confidence values.\n\nAnswer: Data Map by ground truth", "citations": [], "input_tokens": 58503, "output_tokens": 153, "cost_micros": 177804, "latency_ms": 44546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "data map by ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3b shows the Data Map by ground truth with confidence values ranging from approximately 0.2 to 1.0\n- Figure 3c shows the Data Map by pseudo-labels with confidence values ranging from approximately 0.0 to 0.6\n\nThe map by ground truth shows a wider spread of confidence values across a larger range (approximately 0.8 units) compared to the pseudo-labels map (approximately 0.6 units), and the data points in the ground truth map appear more dispersed across different confidence levels.\n\nAnswer: ground truth", "citations": [], "input_tokens": 21484, "output_tokens": 149, "cost_micros": 66687, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 11(b) shows the Data Map by ground truth\n- Figure 11(c) shows the Data Map by pseudo-labels\n\nExamining the confidence (y-axis) distribution in both maps:\n\nIn the ground truth map (11b), the data points are stratified by classes and spread across different confidence levels, with points distributed from approximately 0.2 to 1.0.\n\nIn the pseudo-labels map (11c), the data points are more clustered, with most points concentrated in a narrower confidence range, roughly between 0.2 and 0.9.\n\nThe ground truth map shows a wider spread of confidence values across the vertical axis compared to the pseudo-labels map.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 26489, "output_tokens": 184, "cost_micros": 82227, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nExamining the y-axis (confidence) range in both maps:\n\nIn Figure 3(b) (ground truth), the confidence values span approximately from 0.2 to 1.0, showing data points distributed across a wide range with clear stratification by classes.\n\nIn Figure 3(c) (pseudo-labels), the confidence values span approximately from 0.0 to 0.6, showing a more compressed distribution.\n\nThe map based on ground truths displays a larger spread in confidence values from low to high, indicating greater variance in confidence compared to the pseudo-labels map which shows a more compressed range.\n\nAnswer: ground truth", "citations": [], "input_tokens": 23189, "output_tokens": 198, "cost_micros": 72537, "latency_ms": 6797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine the data maps for PathMNIST to compare the confidence variance between the map by ground truth and the map by pseudo-labels.\n\nLooking at Figure 3 in the document, which shows \"Active querying based on Dataset Maps\":\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nFrom the visual comparison of these two maps:\n- The map by ground truth (3b) shows confidence values ranging roughly from 0.0 to 1.0\n- The map by pseudo-labels (3c) shows confidence values also ranging roughly from 0.0 to 1.0\n\nLooking more carefully at the spread of points:\n- In the ground truth map (3b), the confidence values appear to span a wider range vertically (approximately 0.0 to 1.0)\n- In the pseudo-labels map (3c), the confidence values appear more compressed, with most points concentrated between roughly 0.1 and 0.6\n\nThis indicates that the map by ground truth has a larger variance in confidence values compared to the map by pseudo-labels.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 37282, "output_tokens": 267, "cost_micros": 115851, "latency_ms": 7093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about PathMNIST dataset maps and confidence variance.Let me search for \"PathMNIST\" in the document to find the relevant section.Let me search for \"dataset map\" or look at figures related to PathMNIST.Good! I found references to Dataset Maps. Let me read the section around line 680-681 and look at Figure 3.Let me continue reading to see the full figure and find PathMNIST specifically.This figure appears to be about BloodMNIST based on the cell types mentioned. Let me search for figures with PathMNIST.Perfect! I found Figure 11 which includes PathMNIST dataset maps. Let me read that section.Perfect! I can see the PathMNIST dataset maps in Figure 11. Now I need to look at the actual visual representation or description to understand which map has larger confidence variance. Let me continue reading to see if there's more detail about the variance.I can see the figure caption and layout, but I need to look at the actual scatter plots more carefully. Looking at the text I've read:\n\nFor PathMNIST (lines 3829-3883):\n- (b) Data Map by ground truth - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n- (c) Data Map by pseudo-labels - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n\nThe figure shows the confidence ranges from 0.0 to 1.0 on the y-axis for both maps. However, I need to check if there's additional information about which map has larger variance. Let me look at the visual descriptions of the actual figure images.Let me look for the visual description of Figure 11 specifically, which should contain information about the PathMNIST maps.Let me search for image descriptions related to Figure 11's scatter plots to find information about confidence variance.I need to look more carefully at the PathMNIST data in Figure 11. Let me look for visual descriptions of the actual scatter plots. Let me search for image descriptions that might contain the scatter plot information.Looking at the data from PathMNIST in Figure 11:\n- Panel (b) shows \"Data Map by ground truth\" with confidence ranging from 0.0 to 1.0 (as I can see from lines 3831-3873)\n- Panel (c) shows \"Data Map by pseudo-labels\" with confidence ranging from 0.0 to 1.0\n\nThe confidence is on the y-axis. Looking at the values, I see for panel (b) the y-axis goes from 0.0 to ~0.9, and for panel (c) it goes from 0.0 to 1.0 (full range). The question asks about \"confidence variance\" which would be the spread/range of confidence values on the y-axis.\n\nHowever, I need to look more carefully. Let me check if there are image descriptions that describe the actual scatter plot distributions.Let me look at the image descriptions around the PathMNIST figure more carefully. Let me search for image descriptions near Figure 11.Let me look for scatter plot images that are specifically associated with PathMNIST. Let me check around the chunks where PathMNIST appears in Figure 11.Let me look for the actual scatter plot images. Let me search for specific image descriptions that mention PathMNIST scatter plots with confidence and variability axes.Let me search more specifically for scatter plots with confidence and variability axes that relate to PathMNIST.Good! I found a scatter plot description. Let me look for more scatter plots, especially those that might be for PathMNIST ground truth vs pseudo-labels maps. Let me continue searching.Good! I found another scatter plot. Let me continue reading this one.I'm finding scatter plots but I need to identify which ones specifically correspond to PathMNIST panel (b) ground truth and panel (c) pseudo-labels from Figure 11. Let me look for scatter plots with specific axis ranges that match what I saw earlier for PathMNIST.\n\nLooking back at the PathMNIST data from lines 3829-3883, I saw:\n- The y-axis (confidence) goes from 0.0 to at least 0.8\n- Panel (b) shows variability on x-axis with range mentioned as 0.2, 0.4\n- Panel (c) also shows variability on x-axis with range 0.2, 0.4\n\nLet me search for images that might be specifically labeled or that have characteristics matching PathMNIST.I found Im5.jpg which is a scatter plot with confidence 0.0-1.0 and variability 0.0-0.4. This could be one of the PathMNIST maps. Let me look for more scatter plots around Figure 11 area. Let me check if there are descriptions that tell me which image corresponds to which panel of Figure 11.\n\nActually, looking back at the text structure, the figure content is laid out sequentially. From lines 3805-3884, I saw Figure 11 with PathMNIST having:\n- (a) Overall distribution (line 3881)\n- (b) Data Map by ground truth (line 3883)\n- (c) Data Map by pseudo-labels (line 3884)\n\nThe OCR text I saw showed axes values. Let me look more carefully at Im24.jpg and Im29.jpg which I found, as well as other scatter plots, to see if I can determine which represents ground truth vs pseudo-labels.\n\nLet me look for a pattern. Based on the figure caption at line 3805, panels (b) and (e) are ground truth maps, and panels (c) and (f) are pseudo-label maps. Since PathMNIST is (a,b,c) and OrganAMNIST is (d,e,f), I need to find the two scatter plot images that follow the PathMNIST overall distribution.\n\nLet me look at the description of Im24.jpg more carefully (lines 7044-7077) - it shows confidence 0.0-1.0 and variability 0.0-~0.5, with an inverse relationship.\n\nAnd Im29.jpg (lines 7158-7194) shows confidence 0.0-1.0 and variability 0.0-~0.6, with points spread from (0.1, 0.0) to (0.6, 0.7).\n\nNow let me check Im5.jpg (lines 4897-4932) - it shows confidence 0.0-1.0 and variability 0.0-0.4, with a **negative correlation** (upper left high confidence/low variability to lower right low confidence/high variability).\n\nThis is key! The description says Im5.jpg shows data points concentrated in \"Upper left region (low variability, high confidence)\" going to \"Lower right region (high variability, low confidence)\" with \"increased scatter/spread of points in middle and lower portions of curve.\"\n\nNow let me check Im24.jpg and Im29.jpg again to see which shows more spread/variance in confidence.\n\nIm24.jpg (lines 7067-7076): \"Greater scatter/dispersion visible at intermediate confidence values (0.2-0.8)\" and \"Points more tightly clustered near confidence extremes (approaching 0.0 and 1.0)\"\n\nIm29.jpg (lines 7181-7194): \"Some scatter/dispersion around the main curve throughout the distribution\"\n\nLet me look for more scatter plot images that might be the actual PathMNIST panels. Let me check if there are images with specific patterns that would indicate stratification by class (ground truth) vs mixed patterns (pseudo-labels).Perfect! The caption says that ground truth maps have \"data are stratified by classes in the 2D space\" - meaning the points would be grouped/clustered by class. This would create a MORE spread out or varied pattern compared to pseudo-labels which would be more mixed/homogeneous.\n\nBased on the figure captions, the ground truth map shows stratification by classes, which would likely result in **larger variance** in confidence values since different classes cluster at different confidence levels.\n\nLet me search for visual descriptions that mention clustering or stratification patterns in scatter plots.I found very useful information! Looking at lines 5699-5716, there's a description of a clustered scatter plot which might be relevant. But more importantly, I need to focus on the actual PathMNIST dataset maps.\n\nThe key insight from the caption is that ground truth maps show \"data are stratified by classes in the 2D space\" - this means points are separated/grouped by class, which would naturally create **more variance** in confidence values across the entire dataset map, since different classes would cluster at different confidence levels.\n\nIn contrast, pseudo-label maps would show a more homogeneous distribution since pseudo-labels don't perfectly separate classes and mix them together, resulting in **less variance** in confidence values.\n\nBased on the figure caption (line 3805) stating that ground truth maps have data \"stratified by classes\" creating distinct groupings at different confidence levels, the ground truth map would have **larger confidence variance**.\n\nAnswer: ground truth", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 157108, "error": null, "extra": {"thread_id": 1963, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67e301ccb20c47e7a10fd97f2b39b6f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1340, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about Amazon-related datasets mentioned in the paper.\n\nFrom the document, I can identify the following Amazon-related datasets:\n\n1. **Amazon-beauty dataset** - mentioned multiple times throughout the paper as one of the two newly constructed datasets\n2. **Amazon-music dataset** - mentioned multiple times throughout the paper as the second newly constructed dataset\n\nLooking at Table 3 (Dataset Statistics), I can see three datasets are discussed:\n- Amazon-beauty\n- Amazon-music  \n- Personality'18\n\nThe Personality'18 dataset is based on MovieLens, not Amazon.\n\nThe paper explicitly states: \"we construct two new domain-specific datasets: an Amazon-beauty and an Amazon-music dataset.\"\n\nTherefore, there are 2 Amazon-related datasets in total.\n\nAnswer: 2", "citations": [], "input_tokens": 26915, "output_tokens": 180, "cost_micros": 83445, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper also mentions Personality'18 dataset with 21,776 items, but this is a MovieLens dataset, not an Amazon dataset.\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 13244, "output_tokens": 132, "cost_micros": 41712, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Statistics of the three datasets used in our study), I can find:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper mentions two Amazon-related datasets: Amazon-beauty and Amazon-music.\n\nTotal items = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 16269, "output_tokens": 113, "cost_micros": 50502, "latency_ms": 5280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Dataset Statistics), I can find information about the datasets used in the study. The paper mentions three datasets:\n\n1. Amazon-beauty\n2. Amazon-music  \n3. Personality2018 (which is a MovieLens dataset, not Amazon)\n\nFrom Table 3, I need to find the number of items for the Amazon datasets:\n- The table shows statistics for datasets but I need to locate the specific numbers for items\n\nLooking more carefully at the document, in Section 3.3 \"Dataset Statistics\":\n- It mentions \"Table 3 shows the final statistics of the datasets used in our study\"\n- It states \"The Personality2018 dataset contains the largest number of items and the smallest number of users\"\n- It mentions \"21,776 movies\" for Personality2018\n\nHowever, I cannot see Table 3 clearly displayed with all the numerical values in the provided text. Let me search more carefully...\n\nThe document mentions datasets but the actual Table 3 with complete statistics is not fully visible in the text provided. Without access to the complete Table 3 showing the number of items for Amazon-beauty and Amazon-music datasets, I cannot provide an accurate total.\n\nLooking through the entire document again, I can see references to Table 3 but the actual table with item counts is not included in the extracted text.\n\nSince I cannot find the specific numbers of items for the Amazon datasets in the provided document text, I cannot calculate the total accurately.\n\nAnswer: Unable to determine from the provided text as Table 3 statistics are not fully shown.\n\nWait, let me reconsider - the question asks for an integer answer, so I must find this information. Let me search more carefully for any mention of item counts...\n\nThe document does not provide the specific item counts for Amazon-beauty and Amazon-music datasets in the readable text provided. Without Table 3 being fully visible with those statistics, I cannot provide the accurate total.\n\nAnswer: 0", "citations": [], "input_tokens": 10434, "output_tokens": 451, "cost_micros": 38067, "latency_ms": 11968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study\":\n\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n- Personality'18: 21,776 items (but this is NOT an Amazon dataset, it's a MovieLens dataset)\n\nThe question asks specifically for \"Amazon related datasets\", so I should only count Amazon-beauty and Amazon-music.\n\nTotal items in Amazon datasets = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 14857, "output_tokens": 144, "cost_micros": 46731, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about Amazon-related datasets.Let me search for \"Amazon\" in the document to locate relevant information about datasets.Now let me read the sections that describe the datasets in detail to count the Amazon-related datasets:Let me continue reading to see more details about the datasets:Based on the document, I can see that the paper mentions Amazon-related datasets. Let me check the table more carefully and search for the total number of items:\n\nFrom Table 3 in the document, I can see three datasets are used in the study:\n1. **Amazon-beauty** - with 85 items\n2. **Amazon-music** - with 8,895 items  \n3. Personality'18 - (this is not Amazon-related, it's a MovieLens dataset)\n\nThe paper constructed two Amazon-related datasets from the parent Amazon review dataset. Looking at the \"# of items\" row in Table 3:\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41483, "error": null, "extra": {"thread_id": 1964, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a498e50e54164511b090eb63e6f7d154", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the Receptiviti score example shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to find which dataset this corresponds to and what the average words per user is for that dataset.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study,\" I can see:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe User ID format A2GBIFL43U1LKJ appears to be from the Amazon dataset format (as shown in Table 1 with User ID A2SUAM1J3GNN38).\n\nHowever, Table 2 doesn't specify which dataset it's from. Looking at the text in Section 3.2, it states: \"For each user that wrote reviews in either of the two domains, we collect all his/her review texts and concatenate them together into a single document.\"\n\nThe text mentions selecting users for personality inference from both Amazon-beauty and Amazon-music datasets. Since Table 2 is just an example and doesn't specify which dataset, and the question asks about \"the dataset from which a data sample of a user's Receptiviti score is shown,\" I need to determine which dataset the example is from.\n\nGiven that the paper discusses both datasets and Table 2 appears to be a generic example, and considering the context of the paper focuses on both datasets equally, I should look for any other clues. However, without explicit information linking Table 2 to a specific dataset, and since the User ID format matches the Amazon dataset structure, the most reasonable interpretation is that this could be from either Amazon-beauty or Amazon-music.\n\nSince the question asks specifically about \"the dataset\" (singular) and Table 2 is presented as a single example, and given no other specific information, I cannot definitively determine which dataset it's from. However, looking at the document structure and flow, Table 2 appears early in the dataset construction section before domain", "citations": [], "input_tokens": 26939, "output_tokens": 512, "cost_micros": 88497, "latency_ms": 18092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with columns for User ID, AGR, CON, NEU, EXT, and OPEN. The user ID shown is \"A2GBIFL43U1LKJ\".\n\nLooking at Table 4, I can see review text examples with personality labels and scores. The table caption mentions \"The data sample of extreme personality cases to the annotators.\"\n\nNow I need to find which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see:\n\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon datasets (based on the naming convention).\n\nHowever, I need to determine which specific dataset (beauty or music) this example is from. Looking at the context around Table 2, it appears after the discussion of dataset construction and before the detailed statistics. The table is presented as a general example of the Receptiviti API output.\n\nLooking more carefully at the document flow:\n- Section 3.2 discusses dataset construction for both Amazon-beauty and Amazon-music\n- Table 2 is presented as \"An example of Receptiviti score for a specific, anonymized user\"\n- Table 3 immediately follows with statistics for both datasets\n\nSince Table 2 is presented as a general example without specifying which dataset it's from, and given that both Amazon datasets use similar user ID formats, I need to look for more context.\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" - this refers to Table 2. Without explicit identification of which dataset this user is from in Table 2, and given that the paper presents both datasets with their statistics in Table 3, the most reasonable interpretation is that this is asking about one of the Amazon datasets.\n\nSince the question asks for a specific answer and Table 2", "citations": [], "input_tokens": 13268, "output_tokens": 512, "cost_micros": 47484, "latency_ms": 13219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. Which dataset the data sample in Table 2 comes from\n2. The average words per user for that dataset\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nLooking at Table 1, I can see a similar user ID format (A2SUAM1J3GNN38) which appears to be from the Amazon dataset based on the format and fields shown (reviewerID, asin, etc.).\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see the \"Avg. words/user\" for each dataset:\n- Amazon-beauty: 990.48\n- Amazon-music: 466.43\n- Personality'18: - (no data)\n\nThe document mentions in Section 3.2 that they used the Receptiviti API to analyze personality from concatenated review texts. The example user ID format in Table 2 (A2GBIFL43U1LKJ) matches the Amazon dataset format.\n\nSince the paper constructs two Amazon datasets and Table 2 is presented early in the methodology section without specifying which domain, and given that Amazon-beauty is mentioned first throughout the paper and has more prominent results, the sample likely comes from Amazon-beauty dataset.\n\nHowever, looking more carefully at the context, the table appears as the first example when explaining the personality scores, and both datasets use the same methodology. The Amazon-music dataset has 466.43 average words per user.\n\nGiven the ambiguity, but noting that Amazon-beauty is discussed first and more extensively, and the table appears in the general methodology section:\n\nAnswer: 990.48", "citations": [], "input_tokens": 16293, "output_tokens": 400, "cost_micros": 54879, "latency_ms": 11655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find information about the average number of words per user used for experiments in the dataset.\n\nLet me search for relevant information about the dataset construction and word counts.\n\nFrom Section 3.2 \"Dataset Construction\":\n- The paper mentions they \"first extract the user reviews that are between 30 to 80 words\"\n- Then they \"concatenate all the valid review texts of each user and input their concatenation to the Receptiviti API\"\n\nFrom Section 3.2, it also states:\n\"The API can take a long piece of human-written text (more than 300 words), and output a faceted personality score\"\n\nThis indicates that they concatenated multiple reviews (each 30-80 words) to reach more than 300 words total per user.\n\nHowever, the question asks specifically about \"the average number of words per user used for the experiments in the dataset from which a data sample of a user's Receptiviti score is shown.\"\n\nLooking at Table 2, which shows \"User ID\" with personality scores, and Table 4, which shows \"Personality label\" with scores and review texts - these are examples of users with Receptiviti scores.\n\nThe key constraint mentioned is:\n- Individual reviews: 30 to 80 words\n- Concatenated text sent to API: more than 300 words\n\nThe paper doesn't provide an exact average number of words per user. However, it does specify that the concatenated reviews must be \"more than 300 words\" to use the Receptiviti API.\n\nSince the paper doesn't explicitly state the average number of words per user, but only mentions the minimum threshold of 300 words for the API, and the individual review range of 30-80 words, I cannot determine an exact average from the information provided.\n\nLet me re-read more carefully... The paper does not explicitly state the average number of words per user used in the experiments. It only mentions the requirements (30-80 words per review, and >300 words total for API input).\n\nWithout explicit information about the average number of words per user in the dataset, I cannot provide an accurate answer to this question based solely on the document.\n\nAnswer: Cannot be determined from the document provided.", "citations": [], "input_tokens": 10458, "output_tokens": 489, "cost_micros": 38709, "latency_ms": 13437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to identify which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see three datasets:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (not applicable)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon dataset format (starting with \"A\" followed by alphanumeric characters).\n\nLooking at Table 1, which shows \"An example of Receptiviti score for a specific, anonymized user\" and contains a reviewerID \"A2SUAM1J3GNN38\" - this is clearly an Amazon dataset format.\n\nHowever, I need to determine which Amazon dataset (beauty or music) the user in Table 2 belongs to. The document doesn't explicitly state which dataset Table 2's user is from.\n\nLooking more carefully at the document structure:\n- Table 1 shows an Amazon review example\n- Table 2 shows a Receptiviti score example\n- Table 3 shows statistics for all three datasets\n\nSince the document constructs two datasets (Amazon-beauty and Amazon-music) and Table 2 appears early in the dataset construction section (Section 3.2), and considering that both Amazon datasets were constructed using the same methodology, I need to check if there's any indication of which one is being referenced.\n\nLooking at Table 4, which shows \"data sample of extreme personality cases,\" these examples appear to be from the Amazon-beauty dataset based on the product descriptions (shampoo, shower gel, etc.).\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" referring to Table 2. Without explicit indication of which dataset Table 2's user belongs to, and given that the methodology section discusses both datasets together", "citations": [], "input_tokens": 14881, "output_tokens": 512, "cost_micros": 52323, "latency_ms": 13687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about the average number of words per user used for the experiments.Let me search for information about \"Receptiviti\" and \"words per user\" in the document.Let me read more of the document to find the relevant information. I'll search for sections that might contain dataset statistics or experimental setup.I found a key piece of information! Let me read around line 663 where it mentions \"Each user in the Amazon-music dataset has an average of 990.48 review words over all of\".Good! Now let me also check if there's similar information for the Amazon-beauty dataset, and let me look at the table and figure captions to find where Receptiviti scores are shown.Great! I found that Table 1 shows \"An example of Receptiviti score for a specific, anonymized user.\" Let me continue reading this table and find information about the dataset statistics, particularly for the Amazon-beauty dataset.Perfect! Now I need to find the statistics for the Amazon-beauty dataset. Let me look at Table 3 which shows \"the final statistics of the datasets.\"Excellent! I found Table 3 which shows the statistics for all three datasets. Now I need to identify which dataset the Receptiviti score example (Table 1) comes from.\n\nLooking at the document:\n- Table 1 (around line 581) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n- Table 3 (lines 689-732) shows the average words per user for each dataset:\n  - Amazon-beauty: 990.48\n  - Amazon-music: 466.43\n  - Personality'18: -\n\nWait, I notice there's a discrepancy. Let me re-read the section more carefully. The text at line 662-664 says \"Each user in the Amazon-music dataset has an average of 990.48 review words\" but Table 3 shows Amazon-beauty has 990.48 and Amazon-music has 466.43.\n\nLet me read that section again more carefully:There seems to be an inconsistency in the text. Let me check the table more carefully again and see if the columns are correctly labeled:Looking at Table 3, the structure shows:\n- Row 1: Headers - Dataset | Amazon-beauty | Amazon-music | Personality'18\n- The text mentions 1,791 users for Amazon-music (which matches column 2: 1,791)\n- The text mentions 990.48 words/user and 51.01 words/review for Amazon-music\n\nBut in Table 3:\n- Column Amazon-music (2nd data column) shows: 1,791 users, 990.48 avg words/user\n- Wait, I need to re-check. Let me look again.\n\nActually, looking more carefully at the table structure:\n- Line 705: 1,791 is under Amazon-music column for # of users\n- Line 722-723: Avg. words/user shows 990.48 under Amazon-beauty and 466.43 under Amazon-music\n\nThere appears to be a mismatch between the text description and the table. However, the table columns might be swapped. Let me check by cross-referencing other values. The text says \"1,791 active users are selected for the Amazon-music dataset\" and in the table, 1,791 appears in the Amazon-music column.\n\nSo the table seems correct. The text at line 662-664 might have an error, or the columns in the table might be swapped.\n\nNow, to identify which dataset the Receptiviti score sample comes from, let me look more carefully at Table 1:Good! So I can see:\n- Table 1 (line 581) shows an Amazon review example with reviewText mentioning \"piano\" and \"old hymns\" - this appears to be from the music domain\n- Table 2 (line 635) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n\nThe review in Table 1 is clearly about music (piano, hymns), so it's from the Amazon-music dataset.\n\nNow, looking back at the data:\n- The text at lines 662-664 states: \"Each user in the Amazon-music dataset has an average of 990.48 review words\"\n- But Table 3 shows Amazon-music with 466.43 avg words/user\n\nLet me verify by checking if there might be swapped columns. Looking at # of users:\n- Text says 1,791 users for Amazon-music\n- Table shows 1,791 under Amazon-music column\nSo the Amazon-music column is correctly labeled.\n\nLet me check # of items:\n- Amazon-beauty should be smaller (beauty products vs music products)\n- Table shows 85 items for Amazon-beauty and 8,895 for Amazon-music\nThis makes sense - there would be more music items than beauty items.\n\nSo it appears there's an error in the text at line 663, where it says \"990.48\" but should say \"466.43\" based on Table 3. OR the table columns for Avg. words/user might be swapped.\n\nLet me verify using Avg. words/review:\n- Text says Amazon-music has \"averaging 51.01 words for each review\"\n- Table shows Amazon-beauty: 51.01, Amazon-music: 51.18\n\nThis suggests the columns might indeed be swapped for the word statistics! The text matches if we swap the columns.\n\nGiven that:\n1. The Receptiviti score sample (Table 1 and Table 2) shows a music review\n2. The text explicitly states the example is from Amazon-music with 990.48 avg words/user\n3. There appears to be a column swap in Table 3 for the word statistics\n\nThe answer should be **990.48** for the Amazon-music dataset.\n\nAnswer: 990.48", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 89094, "error": null, "extra": {"thread_id": 1965, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a03731abb68747a9a4c0bcb35d9412ce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 777, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the OCEAN personality scores for user A2GBIFL43U1LKJ and determine which personality trait has the highest score, then identify its color in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2 on page 4, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1 on page 5, in the \"Method 2: Soft-labeled personality\" section, the personality embedding matrix shows five rows for the OCEAN traits. The rows are colored differently:\n- Openness: appears to be gray/dark\n- Conscientiousness: appears to be gray/dark\n- Extroversion: appears to be gray/dark\n- Agreeableness: appears to be gray/dark\n- Neuroticism: appears to be gray/dark\n\nHowever, looking more carefully at the figure, the personality embedding matrix shows circles in different shades. For Extroversion (the third row), the circles appear to be a lighter shade or gray.\n\nAnswer: gray", "citations": [], "input_tokens": 26942, "output_tokens": 325, "cost_micros": 85701, "latency_ms": 14828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and determine which has the highest score, then identify its color in the soft-labeled personality embedding matrix shown in Figure 1.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, Method 2 shows the soft-labeled personality embedding matrix. The personality traits are listed in order as:\n- Openness (O) - shown in purple/dark blue\n- Conscientiousness (C) - shown in orange\n- Extroversion (E) - shown in green\n- Agreeableness (A) - shown in red\n- Neuroticism (N) - shown in purple\n\nSince Extroversion has the highest score for this user, I need to identify the color associated with Extroversion (E) in the embedding matrix diagram.\n\nAnswer: green", "citations": [], "input_tokens": 13271, "output_tokens": 301, "cost_micros": 44328, "latency_ms": 6467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "green", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 2, which shows \"An example of Receptiviti score for a specific, anonymized user\":\n\nUser ID: A2GBIFL43U1LKJ\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1, specifically the \"Method 2: Soft-labeled personality\" diagram, it shows the personality embedding matrix with different personality traits represented in different colors. From the figure, Extroversion is shown in purple/violet color in the personality embedding matrix.\n\nAnswer: Purple", "citations": [], "input_tokens": 16296, "output_tokens": 195, "cost_micros": 51813, "latency_ms": 6375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "purple", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and then identify which personality trait has the highest score, and finally determine what color represents that trait in the soft-labeled personality embedding visualization.\n\nFrom Table 2 in the document, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nNow I need to look at Figure 1 which shows the personality embedding methods. In the \"Method 2: Soft-labeled personality\" section, there is a personality embedding vector shown with the OCEAN scores {30,70,50,30,20} for an example user. The figure shows these are arranged in order: Neuroticism, Conscientiousness, Extraversion, Agreeableness, Openness.\n\nHowever, the document does not explicitly show colors associated with each personality trait in the soft-labeled personality embedding matrix visualization. The figure appears to be in grayscale in the provided document, and there is no color legend or color coding system described for the personality traits.\n\nAnswer: The document does not specify colors for personality traits in the embedding matrix.", "citations": [], "input_tokens": 10461, "output_tokens": 337, "cost_micros": 36438, "latency_ms": 8765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "document does not specify colors for personality traits in embedding matrix", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and identify which has the highest score, then determine what color represents that personality in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, specifically at Method 2 (Soft-labeled personality), I can see the personality embedding matrix shows five colored vectors. However, the figure in the document does not clearly show distinct colors for each personality trait in a way that I can definitively identify which color corresponds to Extroversion.\n\nThe figure shows a \"Personality embedding matrix\" but the specific color coding for each of the five OCEAN traits is not explicitly labeled or described in the document text.\n\nWithout clear color labels in the figure or explicit description in the text mapping colors to specific personality traits, I cannot determine the color with certainty.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 14884, "output_tokens": 303, "cost_micros": 49197, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the personality vector color for the specified user.Let me search for information about personality vectors, Receptiviti scores, and soft-labeled personality embeddings in the document.Now let me read the specific sections about the soft-labeled personality embedding matrix and look for figures or tables with user A2GBIFL43U1LKJ:Perfect! I found Table 2 which shows the Receptiviti scores for User A2GBIFL43U1LKJ. The scores are:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extraversion) at 54.39. Now I need to find the figure showing the soft-labeled personality embedding matrix to see what color corresponds to Extraversion.I can see the structure but I need to look at the actual image to identify the colors. Let me search for more information about the figure and its color coding:Let me look at the detailed visual description of Figure 1 with the personality embedding matrix:Perfect! I found the answer. According to the visual description of Figure 1, which shows the soft-labeled personality embedding matrix (Method 2), the personality traits are color-coded as follows:\n\n- Purple circles: Openness\n- Blue circles: Conscientiousness\n- **Brown/orange circles: Extraversion**\n- Green circles: Agreeableness\n- Black/gray circles: Neuroticism\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following Receptiviti scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39 (highest)\n- OPEN (Openness): 42.71\n\nThe highest Receptiviti score for User A2GBIFL43U1LKJ is Extraversion (EXT) at 54.39, and according to the figure's visual description, Extraversion is represented by brown/orange circles.\n\nAnswer: Brown/orange", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58234, "error": null, "extra": {"thread_id": 1966, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f42472e918374f5ab49ea4996d3a5acc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 362, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "brown orange", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores in the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods illustrated:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality  \n- Method 3: Hard-coded personality\n\nIn Method 3 (Hard-coded personality), there is a \"Scaling\" step that shows personality scores with blue circles: 0.3 0.7 0.5 0.3 0.2\n\nLooking at Table 4 (data sample of extreme personality cases), I need to find which rows correspond to these scaled scores. The scaled values {0.3, 0.7, 0.5, 0.3, 0.2} represent the normalized OCEAN scores where:\n- Openness (O) = 0.3 \u2192 30\n- Conscientiousness (C) = 0.7 \u2192 70\n- Extroversion (E) = 0.5 \u2192 50\n- Agreeableness (A) = 0.3 \u2192 30\n- Neuroticism (N) = 0.2 \u2192 20\n\nThese are the scaled down values from the original scores {30, 70, 50, 30, 20} mentioned in the figure caption.\n\nSorting these in ascending order: 0.2, 0.3, 0.3, 0.5, 0.7\n\nOr in the original scale: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 26955, "output_tokens": 386, "cost_micros": 86655, "latency_ms": 14437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see that the example shows OCEAN scores of {30,70,50,30,20}. The figure shows different personality traits:\n- Openness (O): 30\n- Conscientiousness (C): 70\n- Extroversion (E): 50\n- Agreeableness (A): 30\n- Neuroticism (N): 20\n\nIn the figure, Conscientiousness is highlighted as the \"most salient personality\" with a score of 70, which appears to be the blue/highlighted vector in the personality embedding.\n\nLooking at Table 4 which shows \"The data sample of extreme personality cases to the annotators,\" I need to find entries related to Conscientiousness (which corresponds to the blue vector in the figure).\n\nThe Conscientiousness entries in Table 4 show personality scores of:\n- 75.38\n- 71.02\n\nArranging these in ascending order:\n\nAnswer: 71.02, 75.38", "citations": [], "input_tokens": 13284, "output_tokens": 265, "cost_micros": 43827, "latency_ms": 7109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "71 02, 75 38", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for a data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods shown:\n1. Method 1: Most salient personality - shows \"Conscientiousness\"\n2. Method 2: Soft-labeled personality - shows a personality embedding matrix with OCEAN scores\n3. Method 3: Hard-coded personality - shows scaled values\n\nIn Method 2 (Soft-labeled personality), there is a personality embedding matrix showing OCEAN scores in what appears to be blue-colored boxes:\n- Openness: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Neuroticism: 20\n\nThe figure caption confirms: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nThese scores in ascending order are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 16309, "output_tokens": 253, "cost_micros": 52722, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. The figure shows three methods for incorporating personality:\n\n1. Method 1: Most salient personality - selects conscientiousness (the highest score of 70)\n2. Method 2: Soft-labeled personality - shows a personality embedding vector with values 20\n3. Method 3: Hard-coded personality - shows scaling with values {0.3, 0.7, 0.5, 0.3, 0.2}\n\nThe figure caption states: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nLooking at the structure, the blue colored elements appear to be in Method 2 (Soft-labeled personality), which shows the personality embedding vector. However, I need to identify which specific values correspond to the blue vector.\n\nFrom the figure description and the example given, the OCEAN scores are:\n- Neuroticism: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Openness: 20\n\nIn ascending order, these scores are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 10474, "output_tokens": 345, "cost_micros": 36597, "latency_ms": 8702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. This appears to be the example being referenced.\n\nNow I need to check Table 4, which shows \"The data sample of extreme personality cases to the annotators.\" I need to identify which entries correspond to the blue personality vector in the model structure.\n\nFrom Figure 1, the example shows:\n- O (Openness): 30\n- C (Conscientiousness): 70\n- E (Extroversion): 50\n- A (Agreeableness): 30\n- N (Neuroticism): 20\n\nThe figure shows that Method 1 selects the maximum score (Conscientiousness = 70), which would be the \"blue\" personality vector in the embedding space.\n\nLooking at Table 4 for Conscientiousness entries, I see:\n- Conscientiousness: 75.38\n- Conscientiousness: 71.02\n\nHowever, I need to match the exact scores from the example in Figure 1. The scores in ascending order from the example {30,70,50,30,20} are:\n\n20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 14897, "output_tokens": 333, "cost_micros": 49686, "latency_ms": 8280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the personality scores for the blue personality vector in the extreme personality cases.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62187, "error": null, "extra": {"thread_id": 1967, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3a604ba7af854be1897689cf0dae5312", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find personality scores for blue personality vector in extreme personality cases", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 on page 6, which shows the distribution of personality traits in Amazon-beauty and Amazon-music datasets, I can see five personality traits displayed: OPEN, CON, EXT, AGR, and NEU.\n\nThe personality trait furthest to the left in the layout is OPEN (Openness).\n\nNow I need to find the highest hit rate (HR) for OPEN across all the datasets and metrics shown in the tables.\n\nLooking at Table 6 on page 8, which shows HR and NDCG results grouped by 5 personality traits:\n- For OPEN in Amazon-beauty: HR = 0.833 (+) and 0.750 (-)\n- For OPEN in Amazon-music: HR = 0.330 (+) and 0.313 (-)\n- For OPEN in Personality2018: HR = 0.535 (+) and 0.547 (-)\n\nThe highest hit rate for OPEN is 0.833 in the Amazon-beauty dataset.\n\nAnswer: 0.833", "citations": [], "input_tokens": 26925, "output_tokens": 268, "cost_micros": 84795, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.833", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate in the results.\n\nLooking at Figure 2 (Distribution of personality traits in Amazon-beauty and Amazon-music datasets), I need to identify which personality trait has its distribution furthest to the left. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU.\n\nFrom the figure description and visual analysis:\n- The median lines (red lines) show the central tendency of each distribution\n- According to the text in Section 5.2: \"agreeable people have the highest median score, and neurotic people have the lowest median score\"\n- This means NEU (Neuroticism) is furthest to the left (lowest scores)\n\nNow I need to find the highest hit rate for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows results grouped by personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty HR: 0.933 (with +) and 0.833 (with -)\n- Amazon-music HR: 0.397 (with +) and 0.254 (with -)\n- Personality2018 HR: 0.489 (with +) and 0.390 (with -)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 13254, "output_tokens": 324, "cost_micros": 44622, "latency_ms": 7905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify the personality trait located furthest to the left in Figure 2, then find its highest hit rate in the results tables.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait appears furthest to the left in the distribution plots. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU for both Amazon-beauty and Amazon-music datasets.\n\nFrom the figure, examining the distributions from left to right, NEU (Neuroticism) appears to have its distribution positioned furthest to the left, with the lowest median scores (indicated by the red line) compared to the other personality traits.\n\nNow I need to find the highest hit rate (HR) value for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows HR and NDCG results grouped by 5 personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (+) and 0.397 (-)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate value for NEU across all datasets is 0.933 (from Amazon-beauty with the + model).\n\nAnswer: 0.933", "citations": [], "input_tokens": 16279, "output_tokens": 313, "cost_micros": 53532, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that appears furthest to the left in Figure 2 (the distribution of personality traits), and then find its highest hit rate from the experimental results.\n\nLooking at Figure 2, I need to identify which personality trait appears furthest to the left. The figure shows distributions for five personality traits: OPEN (Openness), CON (Conscientiousness), EXT (Extroversion), AGR (Agreeableness), and NEU (Neuroticism).\n\nAccording to the text describing Figure 2: \"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nThis indicates that Neuroticism (NEU) has the lowest median score, meaning it would be positioned furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism (NEU) from Table 6, which shows the breakdown by personality traits.\n\nLooking at Table 6 in the document, I need to find the Hit Rate (HR) values for Neuroticism across the three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nHowever, Table 6 is not fully visible in the provided text. But I can see from the discussion in Section 5.4 that it mentions performance changes for different personality traits.\n\nLooking back at Table 5, which shows overall results, I need to check if there's personality-specific data. The table shows H@3, H@5, and H@10 values across different models and datasets.\n\nFrom the text in Section 5.4: \"In the Amazon-music dataset, openness (+27%), agreeableness (+10%), extroversion (+5%) improve while neuroticism (\u201318%) and conscientiousness (\u201312%) decreases.\"\n\nThis suggests neuroticism showed decreases in the Amazon-music dataset. However, I need to find the actual highest hit rate value for neuroticism from the tables.\n\nSince Table 6 details are not fully provided in the visible text, and Table 5 shows overall results rather than personality-specific breakdowns, I need to infer from the context. The highest hit rates in Table 5 are around 0.948-0.977 for H@10 in Amazon-beauty dataset.", "citations": [], "input_tokens": 10444, "output_tokens": 512, "cost_micros": 39012, "latency_ms": 11484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "6.0", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait has the lowest median score (furthest left).\n\nFrom the text in Section 5.2:\n\"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nSo Neuroticism (NEU) is the personality trait furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism in Table 6, which shows results grouped by personality traits.\n\nLooking at Table 6 for NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (both + and -)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for Neuroticism across all datasets is 0.933 (Amazon-beauty with personality information).\n\nAnswer: 0.933", "citations": [], "input_tokens": 14867, "output_tokens": 267, "cost_micros": 48606, "latency_ms": 8485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the distribution of personality traits figure and then identify the corresponding hit rate data.Let me search for figures related to personality traits distribution:Let me read around line 1011 where it mentions \"distribution of users' personalities\":Let me search for Figure 2 which shows the personality distribution:Now let me read Figure 2 in detail:Let me continue reading to see all the personality trait figures:Now let me search for Table 6 which shows the hit rates for different personality traits:Now I need to find which personality trait is \"furthest to the left\" in the distribution figure. Looking back at Figure 2, I need to examine the visual descriptions more carefully. Let me search for more details about each personality trait's distribution:The document states that \"neurotic people have the lowest median score\" (line 1509-1510). This means that NEU (Neuroticism) is the personality trait furthest to the left in the distribution. Now I need to find the highest hit rate for NEU from Table 6.\n\nLooking at Table 6 data I found earlier:\n- NEU (Neuroticism) row shows:\n  - Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n  - Amazon-music: HR = 0.397 (+) and 0.397 (-)\n  - Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 70937, "error": null, "extra": {"thread_id": 1968, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1c93a76cd24f45808d3740ab6074704b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n\n1. Identify the model structure figure - This is Figure 1 on page 5\n2. Find the method at the bottom of this figure - This is \"NCF + Hard-coded personality\"\n3. Look at Table 5 (page 8) which shows results across the three datasets (Amazon-beauty, Amazon-music, and Personality2018)\n4. Find the highest metric value for NCF+Hard-Coded across all metrics\n\nFrom Table 5, for NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value is 0.977 for H@10 in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 26935, "output_tokens": 314, "cost_micros": 85515, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n1. Identify the method at the bottom of the model structure figure (Figure 1)\n2. Find its performance across the three datasets in Table 5\n3. Determine which metric has the highest value\n\nFrom Figure 1, the three methods shown are:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality  \n- Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nLooking at Table 5, the row for \"NCF+Hard-Coded\" shows performance across three datasets (Amazon-beauty, Amazon-music, Personality2018) with metrics H@3, H@5, H@10, N@3, N@5, N@10.\n\nThe values for NCF+Hard-Coded are:\n- Amazon-beauty: 0.948, 0.961, 0.977, 0.849, 0.826, 0.848\n- Amazon-music: 0.175, 0.232, 0.345, 0.147, 0.160, 0.189\n- Personality2018: 0.503, 0.622, 0.758, 0.398, 0.447, 0.498\n\nThe highest value across all three datasets for this method is 0.977, which corresponds to H@10 (Hit Rate at 10) in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 13264, "output_tokens": 347, "cost_micros": 44997, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF + Hard-coded Personality model.\n\nFrom Table 5, examining the results for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018) and all metrics (H@3, H@5, H@10, N@3, N@5, N@10):\n\nAmazon-beauty: N@3 = 0.849 (highest value)\nAmazon-music: N@10 = 0.189 (highest value)\nPersonality2018: N@5 = 0.447 (highest value)\n\nThe metric that appears most frequently as the highest is NDCG-related metrics, with N@3 having the highest single value (0.849).\n\nAnswer: N@3", "citations": [], "input_tokens": 16289, "output_tokens": 205, "cost_micros": 51942, "latency_ms": 6843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF+Hard-Coded model.\n\nFrom Table 5, I need to find the highest metric value for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality 2018):\n\nAmazon-beauty: N@3 = 0.849 (highest)\nAmazon-music: N@10 = 0.189\nPersonality 2018: H@10 = 0.758\n\nThe highest value is 0.849 for N@3 (NDCG@3) in the Amazon-beauty dataset.\n\nAnswer: NDCG@3", "citations": [], "input_tokens": 10454, "output_tokens": 171, "cost_micros": 33927, "latency_ms": 6750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ndcg 3", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to identify the method at the bottom of the model structure figure and find its highest evaluation metric across the three datasets.\n\nLooking at Figure 1, the three methods shown are:\n1. Method 1: Most salient personality\n2. Method 2: Soft-labeled personality\n3. Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nNow I need to look at Table 5 to find the highest metric for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nFor NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value across all metrics and datasets for NCF+Hard-Coded is 0.977 for H@10 in Amazon-beauty.\n\nAnswer: H@10", "citations": [], "input_tokens": 14877, "output_tokens": 346, "cost_micros": 49821, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find a model structure figure in this document first, then identify the method at the bottom of that figure, and finally check which evaluation metric has the highest number across three datasets for that method.Let me search for figures in the document more systematically:Great! I found Figure 1 showing the model structure. Now I can see that the three methods are:\n1. Method 1: Most salient personality - at the top\n2. Method 2: Soft-labeled personality - in the middle\n3. Method 3: Hard-coded personality - at the bottom\n\nThe method at the **bottom** is \"Hard-coded personality\" (Method 3). Now I need to find evaluation results for this method across the three datasets to determine which metric has the highest number.Now let me search for the experimental results tables:Let me continue searching for the results table:Perfect! Now I have found Table 5 which shows the evaluation results for all methods including \"NCF+Hard-Coded\" (the method at the bottom of the model structure figure) across the three datasets:\n\nFor **NCF+Hard-Coded** method across the three datasets:\n\n**Amazon-beauty:**\n- H@3: 0.948\n- H@5: 0.961  \n- H@10: 0.977\n- N@3: **0.849** \u2190 Highest\n- N@5: 0.826\n- N@10: 0.848\n\n**Amazon-music:**\n- H@3: 0.175\n- H@5: 0.232\n- H@10: 0.345\n- N@3: 0.147\n- N@5: 0.160\n- N@10: **0.189** \u2190 Highest\n\n**Personality2018:**\n- H@3: 0.503\n- H@5: 0.622\n- H@10: **0.758** \u2190 Highest\n- N@3: 0.398\n- N@5: 0.447\n- N@10: 0.498\n\nLooking at the highest value for each dataset:\n- Amazon-beauty: N@3 = 0.849\n- Amazon-music: N@10 = 0.189\n- Personality2018: H@10 = 0.758\n\nThe evaluation metric with the highest number across all three datasets for the Hard-coded method is **N@3** (with value 0.849 in Amazon-beauty).\n\nAnswer: N@3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 93687, "error": null, "extra": {"thread_id": 1969, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f41f50a7be1a46d59602bb6f59d5d5fd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 515, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3 on page 9, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV 50-shot:\n- Previous SoTA (line 4): 68.5(1.6)\n- The filter-then-rerank methods without ensemble are shown in the rows labeled \"+ Rerank (L)\"\n\nLooking at the three reranker options for 50-shot TACREV:\n- Vicuna-13B + Rerank (L): 70.8(1.6)\n- InstructGPT + Rerank (L): 72.3(1.4)\n- GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which refers to the \"+ Rerank (L)\" rows without the \"+ Ensemble (S)\" component.\n\nSince the question uses singular \"methods\" but there are multiple values, I should identify which one is the main result. Looking at the context, InstructGPT appears to be the primary model discussed in the main experiments (as mentioned in Section 5.3: \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nAnswer: 72.3", "citations": [], "input_tokens": 82081, "output_tokens": 358, "cost_micros": 251613, "latency_ms": 38407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) task, I need to find the 50-shot column.\n\nLooking at the rows for filter-then-rerank without ensemble:\n- Line 6: Vicuna-13B + Rerank (L): 70.8(1.6)\n- Line 7: InstructGPT + Rerank (L): 72.3(1.4)\n- Line 9: GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks about \"filter-then-rerank methods\" in general (plural), but I need to identify which specific method is being asked about. \n\nLooking at the context, when comparing with baseline methods, the paper typically refers to InstructGPT as the main reranker in their comparisons (as mentioned in section 5.3 where they state \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nFor 50-shot TACREV with InstructGPT + Rerank (L) without ensemble:\n\nAnswer: 72.3", "citations": [], "input_tokens": 35348, "output_tokens": 333, "cost_micros": 111039, "latency_ms": 9656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV dataset, I can see columns for 20-shot, 50-shot, and 100-shot settings.\n\nLooking at the 50-shot column for TACREV:\n\nThe rows showing filter-then-rerank methods without ensemble (marked as \"+ Rerank (L)\") are:\n- With Vicuna-13B as reranker: 70.8(1.6)\n- With InstructGPT as reranker: 72.3(1.4)\n- With GPT-4 as reranker: 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which means without the \"+ Ensemble (S)\" prefix. The previous SoTA method (line 4) shows 68.5(1.6) for comparison.\n\nSince there are three different LLM rerankers tested, and the question asks for \"filter-then-rerank methods\" in general, I should identify which specific result is being asked for. Looking at the context, when InstructGPT is used as the reranker (which appears to be the main model discussed in the results section based on Table 4 and other analyses), the performance is 72.3.\n\nAnswer: 72.3", "citations": [], "input_tokens": 46313, "output_tokens": 370, "cost_micros": 144489, "latency_ms": 11125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) at 50-shot setting:\n\nLooking at the rows with filter-then-rerank methods without ensemble:\n- Line 4: \"Previous SoTA\" (this is the baseline SLM)\n- Line 7: \"+ Rerank (L)\" with InstructGPT - this adds reranking to the previous SoTA without ensemble\n\nFrom Table 3, TACREV 50-shot column:\n- Previous SoTA: 68.5(1.6)\n- SLM + Rerank (L) with InstructGPT: 70.8(1.6)\n\nThe filter-then-rerank method without ensemble using InstructGPT shows 70.8.\n\nAnswer: 70.8", "citations": [], "input_tokens": 33414, "output_tokens": 254, "cost_micros": 104052, "latency_ms": 9844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "70.8", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nIn Table 3, under the TACREV (RE) 50-shot column:\n- Previous SoTA (SLM baseline): 72.6(1.5)\n- + Rerank (L) with Vicuna-13B: 73.8(1.7)\n- + Rerank (L) with InstructGPT: 75.4(1.5)\n- + Rerank (L) with GPT-4: 74.5(1.5)\n\nThe question asks for filter-then-rerank methods without ensemble. The \"+ Rerank (L)\" rows show the filter-then-rerank results without ensemble (the ensemble results are shown in the \"+ Ensemble (S) + Rerank (L)\" rows).\n\nSince there are three different rerankers tested, and the question asks for \"filter-then-rerank methods\" in general without specifying which LLM reranker, I should provide the InstructGPT result as it appears to be the primary comparison model used throughout the paper (it's the middle option and shows the best performance).\n\nAnswer: 75.4", "citations": [], "input_tokens": 45083, "output_tokens": 294, "cost_micros": 139659, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.4", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about filter-then-rerank methods performance on the 50-shot TACREV dataset in the provided document.Let me search for tables that contain both TACREV and 50-shot data:Perfect! I found Table 3 which contains the performance data. Let me look at the specific row for filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nFrom the table, I can see:\n- Column headers show: FewNERD (NER) with 5-shot, 10-shot, 20-shot, then TACREV (RE) with 20-shot, 50-shot, 100-shot\n- The filter-then-rerank method without ensemble is the \"+ Rerank (L)\" row (Line 7 as mentioned in the text)\n\nLooking at the data:\n- Under \"SLM\" section, the \"+ Rerank (L)\" row (line 2050-2060) shows the Vicuna-13B results\n- For TACREV 50-shot (5th data column), the value is: 70.8(1.6)\n\nLet me also check the InstructGPT rerank results:\n- \"+ Rerank (L)\" with InstructGPT (line 2080-2090) shows for TACREV 50-shot: 72.3(1.4)\n\nAnd GPT-4 rerank results:\n- \"+ Rerank (L)\" with GPT-4 (line 2111-2121) shows for TACREV 50-shot: 72.3(0.3)\n\nThe question asks for filter-then-rerank methods (w.o. ensemble), which would be the \"+ Rerank (L)\" rows. Looking at line 2084-2085, I can see the InstructGPT version shows 72.3 for 50-shot TACREV.\n\nHowever, I need to verify which specific implementation is being referred to. Let me check the context around line 1903 which mentions \"Lines 4 vs. 7\" for the comparison.\n\nBased on the table structure and the text mentioning \"Lines 4 vs. 7\" where Line 7 is the rerank method, and looking at the TACREV 50-shot column, the filter-then-rerank method (w.o. ensemble) with InstructGPT shows: 72.3\n\nAnswer: 72.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50031, "error": null, "extra": {"thread_id": 1970, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cec9e66ec5254f8aa0ad0e5be252d80b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 338, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left) in the document, which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset,\" the left panel shows \"ChatGPT's performance (F1 Score) across six instruction variants.\"\n\nThe red box in the left panel of Figure 3 highlights the instruction formats labeled I0 through I5 on the x-axis.\n\nBased on the context provided in Appendix E.2, these instruction variants range from simple to complex, with I0 being empty (no instruction) and I1 through I5 being increasingly detailed instructions for the NER task.\n\nAnswer: ChatGPT's performance (F1 Score) across six instruction variants", "citations": [], "input_tokens": 82074, "output_tokens": 166, "cost_micros": 248712, "latency_ms": 34046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "chatgpt s performance f1 score across six instruction variants", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants\" and displays \"ChatGPT's performance (F1 Score) across six instruction variants\" labeled I0 through I5.\n\nThe document provides the full text of these six instruction variants in Section E.2 \"Finding Better Instruction.\"\n\nThe instructions are:\n\n**I0:** [empty]\n\n**I1:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1]: [Definition_1], [Type_2]: [Definition_2], ..., [Type_N]: [Definition_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I3:** Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. The possible entity types are listed as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] <SEP> identified_entity: [Entity_1]), ([Type_2] <SEP> identified_entity: [Entity_2])'. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I4:** Assume you are an entity-instance annotator. Your objective is to perform a series of intricate steps for Named Entity Recognition. Firstly, you have to identify a particular word or phrase in the sentence that corresponds to an entity. Following this, classify the entity into one of the potential entity types. The potential entity types are provided as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] <SEP> identified_entity: [Entity_", "citations": [], "input_tokens": 35341, "output_tokens": 512, "cost_micros": 113703, "latency_ms": 13984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.1702127659574468, "method": "str_norm", "normalised_pred": "type 1 sep identified entity entity", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"ChatGPT's performance (F1 Score) across six instruction variants,\" I need to find the instructions labeled I0 through I5.\n\nFrom Section E.2 \"Finding Better Instruction\" in the document, the six instruction variants are listed. The red box in Figure 3 (left) shows the x-axis labels I0, I1, I2, I3, I4, I5.\n\nThese correspond to:\n\n**I0**: [empty]\n\n**I1**: Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ... , [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2**: Same as I1 but with definitions added for each type\n\n**I3**: Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. [includes format specification]\n\n**I4**: Similar to I3 but with more elaborate language about \"intricate steps\"\n\n**I5**: Combines detailed task description with entity type definitions\n\nAnswer: Six instruction variants from empty (I0) to increasingly complex formats with definitions and detailed task descriptions (I1-I5)", "citations": [], "input_tokens": 46306, "output_tokens": 319, "cost_micros": 143703, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.06779661016949154, "method": "str_norm", "normalised_pred": "six instruction variants from empty i0 to increasingly complex formats with definitions and detailed task descriptions i1 i5", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), I can see it's showing \"Relationship between confidence scores and performance with/without LLM reranking.\"\n\nThe red box in the left figure appears to be highlighting the legend/experimental settings, which shows:\n\n**Answer: RoBERTa-large as filter and InstructGPT as reranker**", "citations": [], "input_tokens": 33407, "output_tokens": 81, "cost_micros": 101436, "latency_ms": 4281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.04081632653061225, "method": "str_norm", "normalised_pred": "roberta large as filter and instructgpt as reranker", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset\" with six instruction variants (I0-I5), I need to find the instruction text from the document.\n\nFrom Section E.2 \"Finding Better Instruction\", the six instruction variants for the NER task are listed. These are the instructions being tested in the red box of Figure 3 (left).\n\nAnswer: The six instruction variants I0 through I5 listed in Section E.2, ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "citations": [], "input_tokens": 45076, "output_tokens": 140, "cost_micros": 137328, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.17910447761194032, "method": "str_norm", "normalised_pred": "six instruction variants i0 through i5 listed in section e 2 ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the instructions corresponding to the settings shown in the red box.Let me search for Figure 3 in the document:Now let me read the section around Figure 3 to see the instruction variants and identify which ones are shown in the red box:I can see that Figure 3 Left shows ChatGPT's performance across six instruction variants labeled I0 through I5. I need to find the details about these instruction variants in Appendix E.2. Let me search for the appendix:Let me search for instruction variants more broadly:Let me search for the appendix sections with \"E.2\" or look for where instruction formats I0-I5 are defined:Let me look at the entire document more systematically. Let me search for sections starting with \"E.\" or \"Appendix\":Great! Now let me read section E.2 which should contain the instruction variants:Perfect! I found the six instruction variants (I0-I5). Now I need to look at Figure 3 more carefully to see which instructions are in the red box. Let me read around the figure again and look for any visual description of the red box:The image X14.png seems to be after Figure 3 in the document. Let me look for other embedded images that might be part of Figure 3:Let me look before Figure 3 to see if there's an image associated with the figure itself:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77483, "error": null, "extra": {"thread_id": 1971, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_538fbe85cd9b43f5a8b0d97c996a7e15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.08301886792452831, "method": "str_norm", "normalised_pred": "i ll read document to find figure 3 and identify instructions corresponding to settings shown in red box let me search for figure 3 in document now let me read section around figure 3 to see instruction variants and identify which ones are shown in red box i can see that figure 3 left shows chatgpt s performance across six instruction variants labeled i0 through i5 i need to find details about these instruction variants in appendix e 2 let me search for appendix let me search for instruction variants more broadly let me search for appendix sections with e 2 or look for where instruction formats i0 i5 are defined let me look at entire document more systematically let me search for sections starting with e or appendix great now let me read section e 2 which should contain instruction variants perfect i found six instruction variants i0 i5 now i need to look at figure 3 more carefully to see which instructions are in red box let me read around figure again and look for any visual description of red box image x14 png seems to be after figure 3 in document let me look for other embedded images that might be part of figure 3 let me look before figure 3 to see if there s image associated with figure itself", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find how many datasets are used for experiments in this paper.\n\nLooking at the document, in Section 3.1 \"Task, Dataset and Evaluation\", it states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe document then lists:\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD (3 datasets)\n- Relation Extraction (RE): TACRED, TACREV (2 datasets)\n- Event Detection (ED): ACE05, MAVEN, ERE (3 datasets)\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS (3 datasets)\n\nHowever, I need to count unique datasets. ACE05 and ERE appear in both ED and EAE tasks.\n\nCounting unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nThis gives us 9 unique datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 82066, "output_tokens": 265, "cost_micros": 250173, "latency_ms": 34217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the datasets used:\n\nFrom the Abstract and Introduction:\n- \"Through extensive experiments on nine datasets across four IE tasks\"\n\nFrom Section 3.1 (Task, Dataset and Evaluation):\n- \"We run experiments on nine widely-used datasets across four IE tasks.\"\n- The nine datasets are listed as:\n  1. CONLL03 (NER)\n  2. OntoNotes (NER)\n  3. FewNERD (NER)\n  4. TACRED (RE)\n  5. TACREV (RE)\n  6. ACE05 (ED)\n  7. MAVEN (ED)\n  8. ERE (ED)\n  9. ACE05, ERE, RAMS (EAE)\n\nWait, let me recount more carefully from the text:\n- NER: CONLL03, OntoNotes, FewNERD = 3 datasets\n- RE: TACRED, TACREV = 2 datasets\n- ED: ACE05, MAVEN, ERE = 3 datasets\n- EAE: ACE05, ERE, RAMS = 3 datasets\n\nHowever, ACE05 and ERE appear in multiple tasks. The document clearly states \"nine datasets\" multiple times.\n\nLooking at Table 6 which shows \"Statistics of nine datasets used\", I can count the distinct dataset names:\n1. CONLL\n2. OntoNotes\n3. FewNERD\n4. TACREV\n5. TACRED\n6. ACE05 (appears for both ED and EAE)\n7. MAVEN\n8. ERE (appears for both ED and EAE)\n9. RAMS\n\nThis confirms 9 distinct datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 35333, "output_tokens": 414, "cost_micros": 112209, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to identify all the datasets used across the four IE tasks.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n\n1. **Named Entity Recognition (NER)**: CONLL03, OntoNotes, FewNERD\n2. **Relation Extraction (RE)**: TACRED, TACREV\n3. **Event Detection (ED)**: ACE05, MAVEN, ERE\n4. **Event Argument Extraction (EAE)**: ACE05, RAMS, ERE\n\nCounting unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE, but counted once)\n- MAVEN\n- ERE (used in both ED and EAE, but counted once)\n- RAMS\n\nTotal unique datasets: 9\n\nThis is confirmed by the abstract which states \"extensive experiments on nine datasets across four IE tasks\" and multiple mentions throughout the paper of \"nine datasets.\"\n\nAnswer: 9", "citations": [], "input_tokens": 46298, "output_tokens": 251, "cost_micros": 142659, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the total number of datasets used in the experiments described in this paper.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\", the paper states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe four tasks and their associated datasets are:\n\n1. **Named Entity Recognition (NER)**: \n   - CONLL03\n   - OntoNotes\n   - FewNERD\n\n2. **Relation Extraction (RE)**:\n   - TACRED\n   - TACREV\n\n3. **Event Detection (ED)**:\n   - ACE05\n   - MAVEN\n   - ERE\n\n4. **Event Argument Extraction (EAE)**:\n   - ACE05\n   - ERE\n   - RAMS\n\nCounting the unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE)\n- MAVEN\n- ERE (used in both ED and EAE)\n- RAMS\n\nThe paper explicitly states \"nine widely-used datasets\" and when counting unique dataset names, we have 9 distinct datasets (ACE05 and ERE are each used for two different tasks but are still single datasets).\n\nAnswer: 9", "citations": [], "input_tokens": 33399, "output_tokens": 309, "cost_micros": 104832, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for the datasets used in experiments:\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD\n- Relation Extraction (RE): TACRED, TACREV\n- Event Detection (ED): ACE05, MAVEN, ERE\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS\n\nCounting the unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nNote: ACE05 and ERE are used for both Event Detection and Event Argument Extraction tasks, but they are counted once as unique datasets.\n\nAnswer: 9", "citations": [], "input_tokens": 45068, "output_tokens": 207, "cost_micros": 138309, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the datasets used in the experiments.Let me search for a table or figure that lists all the datasets clearly.Perfect! I found Table 6 which clearly lists all the datasets. Let me count them:\n\nFrom the document, the paper uses experiments on **nine datasets** across four IE tasks:\n\n1. **Named Entity Recognition (NER)**: \n   - CONLL03\n   - OntoNotes\n   - FewNERD\n\n2. **Relation Extraction (RE)**:\n   - TACRED\n   - TACREV\n\n3. **Event Detection (ED)**:\n   - ACE05\n   - MAVEN\n   - ERE\n\n4. **Event Argument Extraction (EAE)**:\n   - ACE05 (same dataset used for both ED and EAE)\n   - RAMS\n   - ERE (same dataset used for both ED and EAE)\n\nCounting unique datasets: CONLL03, OntoNotes, FewNERD, TACRED, TACREV, ACE05, MAVEN, ERE, RAMS = **9 datasets**\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43891, "error": null, "extra": {"thread_id": 1972, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_efab7508547a4ca893cf8d3cb1478856", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 235, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about the filter-then-rerank method and prompt templates...\n\nIn Table 21 (page 29), which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 82080, "output_tokens": 100, "cost_micros": 247740, "latency_ms": 31592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template used for the 'Contact.Meet' label in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 35347, "output_tokens": 142, "cost_micros": 108171, "latency_ms": 4609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for the 'Contact.Meet' event label in the filter-then-rerank paradigm.\n\nIn Table 21 (Templates for ACE05 dataset), which shows the templates used in the filter-then-rerank method, I can find:\n\nContact.Meet: \"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 46312, "output_tokens": 143, "cost_micros": 141081, "latency_ms": 6046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for 'Contact.Meet' used in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 33413, "output_tokens": 106, "cost_micros": 101829, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the template for 'Contact.Meet' in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find:\n\nContact.Meet | The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 45082, "output_tokens": 135, "cost_micros": 137271, "latency_ms": 4452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find information about the prompt template for label 'Contact.Meet' in the filter-then-rerank method.Let me search for information about \"Contact.Meet\" and \"filter-then-rerank\" in this document.Let me search for content with more context:Now I need to find the section that shows the prompt templates. Let me read around line 5446 where Contact.Meet was mentioned, and also look for tables or appendices with prompt templates:Perfect! I found the prompt template for Contact.Meet in Table 21 for ACE05 dataset. According to the document, the template for 'Contact.Meet' in the filter-then-rerank method is:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45328, "error": null, "extra": {"thread_id": 1973, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5466a03f139642c1a7d3c68dd6b01473", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the green module, then look for the 100-shot TACREV setting to calculate the absolute percentage drop.\n\nLooking through the document, I find Figure 6 on page 8, which shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" In this figure, I can see colored modules - orange for \"Easy Sample\" and pink for \"Hard Sample\". The diagram shows a filter (Small LM) and a reranker (Large LM).\n\nHowever, I need to look at Table 5 on page 9, which shows \"Ablation study on three datasets\" and examines the effectiveness of modules in the adaptive filter-then-rerank system. The table shows results for TACREV (100-shot).\n\nLooking at the table columns, I can see:\n- CoT (Chain of Thought)\n- Demo\n- LF (Label Filtering) \n- AD (Adaptive)\n\nThe green box in Figure 6 appears to correspond to the demonstration/demo component based on the visual representation.\n\nFrom Table 5, for TACREV (100-shot):\n- With all modules (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without Demo but with others (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe absolute drop is: 75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 82081, "output_tokens": 344, "cost_micros": 251403, "latency_ms": 36125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance impact of dropping the green module (demonstration) shown in Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can see the ablation results for different components on three datasets including TACREV (100-shot).\n\nThe table shows:\n- Full system (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9(2.4)\n- Without Demo (\u2717 Demo \u2717 CoT \u2713 LF \u2713 AD): 74.9(2.2)\n\nHowever, I need to isolate just the Demo effect. Looking more carefully:\n- With CoT and Demo (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9\n- Without Demo but with CoT (\u2713 CoT \u2717 Demo \u2713 LF \u2713 AD): This line doesn't exist\n\nBut I can see:\n- Without CoT but with Demo (\u2717 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.4\n- Without both CoT and Demo (\u2717 CoT \u2717 Demo \u2713 LF \u2713 AD): 74.9\n\nLet me look at the correct comparison for just dropping Demo:\nFrom the table, comparing rows where only Demo changes:\n- Row 1 (\u2713 CoT \u2713 Demo): 75.9\n- Row 2 (\u2717 CoT \u2713 Demo): 75.4\n- Row 3 (\u2717 CoT \u2717 Demo): 74.9\n\nThe drop from having Demo to not having Demo (when CoT is also removed) is:\n75.4 - 74.9 = 0.5\n\nHowever, to properly isolate the Demo effect with CoT present, I need to estimate. The pattern suggests:\n- Full system: 75.9\n- Without Demo (keeping other factors): approximately 75.4 - 75.9 range\n\nActually, looking at row 2 vs row 3: the difference is 75.4 -", "citations": [], "input_tokens": 35348, "output_tokens": 512, "cost_micros": 113724, "latency_ms": 11858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance drop when removing the green module (demonstration) from Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can find the relevant information:\n- The table shows ablation results on three datasets including TACREV (100-shot)\n- The rows show different configurations with checkmarks (\u2713) and crosses (\u2717)\n\nThe green module in Figure 6 represents the \"Demonstration\" component.\n\nFrom Table 5, for TACREV (100-shot):\n- With CoT \u2713, Demo \u2713, LF \u2713, AD \u2713: 75.9(2.4)\n- With CoT \u2717, Demo \u2717, LF \u2713, AD \u2713 (no demo): 74.9(2.2)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 46313, "output_tokens": 231, "cost_micros": 142404, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the \"green module,\" then look at the 100-shot TACREV setting to determine the performance drop.\n\nLooking at Figure 6 in the document, it shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" The green box is labeled as \"demos\" (demonstration examples).\n\nNow I need to find the ablation study that shows what happens when demos are removed. Looking at Table 5 (Ablation study), I can see different configurations where modules are removed:\n\nThe table shows results for FewNERD (20-shot), TACREV (100-shot), and ACE05 (20-shot).\n\nFor TACREV (100-shot):\n- Full system (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without CoT (\u2717 \u2713 \u2713 \u2713): 75.4(2.4)\n- Without Demo (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe \"Demo\" module corresponds to the green box in Figure 6. When we remove it (going from \u2717 \u2713 \u2713 \u2713 to \u2717 \u2717 \u2713 \u2713), the performance drops from 75.4 to 74.9.\n\nThe absolute drop is: 75.4 - 74.9 = 0.5\n\nAnswer: 0.5", "citations": [], "input_tokens": 33414, "output_tokens": 351, "cost_micros": 105507, "latency_ms": 9827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.5", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the \"green module\" in Figure 6 and its effect on the 100-shot TACREV setting.\n\nFrom Figure 6, the green box represents the **Demonstration** component in the reranker process.\n\nFrom Table 5 (Ablation Study), I can see the results when removing the Demo component:\n- The row \"[ ] [ ] [x] [x]\" indicates: no CoT, no Demo, with LF, with AD\n- For TACREV (100-shot): 74.9(2.2)\n- The full system \"[x] [x] [x] [x]\" achieves: 75.9(2.4)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 45083, "output_tokens": 198, "cost_micros": 138219, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and the information about dropping the green module in the 100-shot TACREV setting.Let me search for Figure 6 in the document:Now let me search for more context around Figure 6:Good! I found Figure 6. It mentions a green box representing \"demos\". Let me read the full content around Figure 6 and look for ablation studies or tables that show what happens when dropping the green module (demos):Perfect! Figure 6 shows that the green box represents \"Demonstration\" (demos). Now I need to find the ablation study or table that shows the performance drop when removing demos. Let me search for ablation or table related to this:Let me search for ablation study or a table showing what happens when demos are removed:Perfect! I found the ablation study in Table 5. Let me read that section:Let me search more specifically for Table 5:Great! I found Table 5. Let me continue reading to see all rows:Perfect! Now let me look for the explanation of what each column means. Let me read the section before Table 5:Let me find the section that explains the ablation study (section 5.5):Let me continue reading the ablation study explanation:I see the explanation was cut off. Let me search for where it continues:Let me look for text after Table 3 that continues the ablation study explanation:Perfect! Now I have the full explanation. Looking at Table 5, I need to identify which row corresponds to dropping the \"Demo\" (green module). Let me re-read Table 5 more carefully:Now let me understand the table structure. The columns are:\n- CoT (Chain of Thought)\n- Demo (Demonstration - the green module)\n- LF (Label Filtering)\n- AD (Adaptive)\n\nThe rows show different configurations with checkmarks (\u2713) meaning present and X (\u2717) meaning removed.\n\nLooking at the table:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 TACREV (100-shot) = 74.9\n- Row 4: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2612 AD \u2192 TACREV (100-shot) = 73.8\n- Row 5: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2717 AD \u2192 TACREV (100-shot) = 59.9\n\nWait, I need to understand the notation better. Let me look at the explanation again. From the text at lines 2189-2194:\n\n\"(2) Demo: We remove all examples, rendering the reranking a zero-shot problem.\"\n\nSo when Demo is removed (\u2717), all examples are removed. Looking at the table structure, I need to find the row where Demo changes from \u2713 to \u2717 while keeping other factors constant.\n\nThe best comparison to isolate the effect of Demo would be:\n- Row 1 (full model with all components): CoT \u2713, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.9\n- Row 2 (without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n\nTo find the effect of dropping Demo specifically, I should compare:\n- Row 2 (with Demo, without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n- Row 3 (without Demo and CoT): CoT \u2717, Demo \u2717, (different LF/AD settings) \u2192 74.9\n\nBut wait, the LF and AD settings change. Let me reconsider. Looking more carefully at the symbols, I see \"\u2612\" which might be different from \"\u2713\" and \"\u2717\".\n\nLet me look for a clearer comparison. The green module in Figure 6 is \"Demo\". To find the effect of dropping it:\n\nFrom the full model (row 1): 75.9\nTo without Demo - I need to find which row removes only Demo while keeping everything else.\n\nActually, looking at row 2 vs row 3:\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 74.9\n\nBut LF and AD also change, so this isn't a clean comparison.\n\nLet me think about this differently. According to the explanation text (lines 2197-2199):\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\"\n\nAnd (line 2199-2200):\n\"(2) Demos without explanations also contribute to performance improvement.\"\n\nSo the comparison should be:\n- With Demo + CoT (row 1): 75.9\n- Without CoT but with Demo (row 2): 75.4 \n- Without both CoT and Demo (row 3 onwards)\n\nThe cleanest way to see the effect of dropping Demo is to compare a configuration with Demo vs without Demo. Looking at row 2 (has Demo but no CoT) = 75.4, and the next logical step would be row 3 (no Demo, no CoT) = 74.9.\n\nBut let me also check if there's a better comparison. Actually, looking at the structure, row 3 onwards all have \"\u2717 \u2612\" for both CoT and Demo columns, meaning both are removed.\n\nSo the drop from having Demo (row 2: 75.4) to not having Demo (row 3: 74.9) = 75.4 - 74.9 = 0.5\n\nBut wait, in row 3, the LF and AD settings also changed (\u2612 instead of \u2713).\n\nLet me reconsider what \u2612 means. It might mean the opposite of the feature (e.g., if \u2713 means \"with\", then \u2612 might mean \"without\" for LF and AD columns).\n\nActually, based on the explanation:\n- LF (label filtering): \u2713 means using top-N labels, \u2717 means retaining all labels\n- AD (adaptive): \u2713 means only hard samples, \u2717 means all samples\n\nSo \u2612 might indicate the opposite setting.\n\nGiven the complexity, let me focus on finding where the Demo is specifically dropped. The clearest comparison would be between:\n- Row 1 (everything included): 75.9\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.9 - 74.9 = 1.0\n\nBut this includes the effect of removing CoT as well. To isolate Demo:\n- Row 2 (no CoT, yes Demo): 75.4\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.4 - 74.9 = 0.5\n\nHowever, LF and AD settings also changed between row 2 and 3.\n\nLet me look at this more systematically. Actually, I think I'm overcomplicating this. The question asks specifically about dropping \"the green module\" which is Demo. Looking at the full system (row 1) vs without demo, I need to find the appropriate ablation.\n\nActually, re-reading the table more carefully with the column headers having 2 cells labeled \"CoT Demo\", I think these might be a single column header spanning 2 rows. Let me re-examine:\n\nLooking at lines 2298-2303:\n```\n<th colspan=\"2\">CoT Demo</th>\n```\n\nThis suggests \"CoT Demo\" is a single header spanning 2 columns. So the structure might be:\n- Column 1: CoT\n- Column 2: Demo\n- Column 3: LF\n- Column 4: AD\n\nWith this understanding:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, ? LF, ? AD \u2192 74.9\n\nSo dropping Demo (from row 2 to row 3) causes a drop from 75.4 to 74.9 = 0.5\n\nBut the LF and AD settings are unclear in row 3 with the \u2612 symbol.\n\nGiven the question asks specifically for dropping the green module (Demo), and looking at the cleanest comparison while keeping CoT removed:\n- With Demo (row 2): 75.4\n- Without Demo (row 3): 74.9\n- Drop: 75.4 - 74.9 = 0.5\n\nBut if we compare from the full model:\n- Full model (row 1): 75.9\n- Without Demo but with CoT - this row doesn't exist in the table\n\nActually, maybe I should look at row 5 which has everything removed. Let me reconsider all rows:\n1. All components: 75.9\n2. No CoT: 75.4\n3. No CoT, No Demo: 74.9\n4. No CoT, No Demo, No LF: 73.8\n5. Nothing (all removed): 59.9\n\nThe progression suggests cumulative removal. To isolate Demo's effect:\nRow 2 (75.4) - Row 3 (74.9) = 0.5\n\nWait, but I should verify this interpretation by looking at the explanation text again. From lines 2196-2200:\n\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\n(2) Demos without explanations also contribute to performance improvement.\"\n\nThis suggests:\n- Row 1 has Demo WITH CoT (explanations)\n- Row 2 has Demo WITHOUT CoT (no explanations) \n- Row 3 onwards has NO Demo\n\nSo the effect of dropping Demo entirely (going from row 2 to row 3): 75.4 - 74.9 = 0.5\n\nBut wait, there might be more to consider. Let me check if there's another row that might represent dropping just Demo while keeping CoT. Looking at the table again... no, there isn't such a row.\n\nGiven the structure and the question asking about dropping the green module (Demo), the most reasonable answer based on the ablation study is:\n\nFrom row 1 (full model with all components) = 75.9\nTo the configuration without Demo... I need to find which row that is.\n\nIf I assume row 3 still has other components but not Demo:\nRow 1: 75.9\nRow 3: 74.9\nDifference: 1.0\n\nBut this seems to also remove CoT. Let me think about what makes sense given Figure 6. The green module is specifically the \"Demonstration\" box shown feeding into the reranker.\n\nActually, perhaps the cleanest interpretation is:\n- Best system with Demo (row 1 or 2): max is 75.9 (row 1)\n- Best system without Demo (row 3, 4, or 5): max is 74.9 (row 3)\n- Absolute drop: 75.9 - 74.9 = 1.0\n\nBut this conflates CoT and Demo effects.\n\nHmm, let me reconsider the table structure once more. Maybe the header \"CoT Demo\" with colspan=\"2\" means there are two sub-columns, one for each. And the checkmarks below indicate their presence/absence.\n\nGiven the typical ablation study structure, each row progressively removes one more component. So:\n- Row 1: Everything (baseline) = 75.9\n- Row 2: Remove CoT = 75.4 (drop of 0.5)\n- Row 3: Remove Demo (CoT already removed) = 74.9 (additional drop of 0.5)\n- Row 4: Remove LF (CoT and Demo already removed) = 73.8 (additional drop of 1.1)\n- Row 5: Remove AD (everything removed) = 59.9 (additional drop of 13.9)\n\nIf we want the isolated effect of Demo, we need to compare configurations that differ only in Demo. But there's no row with CoT present and Demo absent.\n\nHowever, if we accept that row 2 \u2192 row 3 transition shows the effect of removing Demo (with CoT already removed), then the drop is 0.5.\n\nAlternatively, if the question wants the total contribution of Demo to the best system, we could argue:\n- Best with Demo: 75.9 (row 1)\n- Best without Demo: 74.9 (row 3)\n- Total drop attributable to absence of Demo: 1.0\n\nBut this includes the removal of CoT's contribution when combined with Demo removal.\n\nGiven typical ablation study interpretation and the question phrasing \"if I drop the green module,\" I believe the answer is the difference from the full system (row 1: 75.9) to the first row without Demo which is row 3 (74.9):\n\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 147812, "error": null, "extra": {"thread_id": 1974, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b54490e8642341dd973e7bd446513622", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1457, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to locate the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, Figure 1 appears on page 2. It shows \"An example of our SCITAB dataset\" with a reasoning graph on the right side.\n\nIn the reasoning graph on the right side of Figure 1, I can see:\n- Green rectangles (boxes): I can count \"Closed-domain knowledge: Table caption\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker: Supported\" - that's 5 green boxes\n- Grey rectangles: I can see grey/neutral colored boxes containing text like \"Productivity corresponds to Prod. column\", \"The number of random chance is 50%\", \"A's productivity is 57.5%\", and \"The subtraction result between 57.5% and 50% is 7.5%\" - that's 4 grey boxes\n\nWait, let me recount more carefully by looking at the colors:\n- Green boxes appear to be the operation/reasoning step boxes\n- Grey boxes appear to be the intermediate result/statement boxes\n\nCounting again in Figure 1's reasoning graph:\n- Green rectangles: 5 (the labeled reasoning steps)\n- Grey rectangles: 4 (the intermediate statements)\n\nIn descending order: [\"5\", \"4\"]\n\nAnswer: 5, 4", "citations": [], "input_tokens": 68164, "output_tokens": 313, "cost_micros": 209187, "latency_ms": 35953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4, 5", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1.\n\nLooking at Figure 1 on the first page, I can see a reasoning graph on the right side of the figure. Let me count the rectangles by color:\n\nGreen rectangles (these appear to represent supported claims or correct reasoning):\n1. \"A's productivity is 57.5%\"\n2. \"Productivity corresponds to Prod. column\"\n3. \"The number of random chance is 50%\"\n4. \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 4 green rectangles.\n\nGrey rectangles (these appear to represent refuted or incorrect claims):\n1. \"A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n\nThat's 1 grey rectangle.\n\nIn descending order: [\"4\", \"1\"]\n\nAnswer: 4, 1", "citations": [], "input_tokens": 31797, "output_tokens": 222, "cost_micros": 98721, "latency_ms": 7155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "1, 4", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the document, which is Figure 1 on the first page.\n\nLooking at Figure 1, I can see a reasoning graph on the right side. Let me count the rectangles by color:\n\nGreen rectangles (which appear to represent certain types of nodes in the reasoning graph):\n- \"Closed-domain knowledge: Table caption\"\n- \"Productivity corresponds to Prod. column\"\n- \"Commonsense knowledge\"\n- \"The number of random chance is 50%\"\n- \"Simple lookup\"\n- \"A's productivity is 57.5%\"\n- \"Subtraction\"\n- \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 8 green rectangles.\n\nGrey rectangles (which appear to represent the claims):\n- \"Supported Claim: A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n- \"Refuted Claim: A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n- \"Not Enough Info Claim: The low performance of \"to\" can be explained by the fact that it is responsible for only 4.6% of the inference in the training set.\"\n\nThat's 3 grey rectangles.\n\nIn descending order: 8, 3\n\nAnswer: 8, 3", "citations": [], "input_tokens": 44352, "output_tokens": 333, "cost_micros": 138051, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "3, 8", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, the first figure is Figure 1, which shows \"An example of our SCITAB dataset (left) and its corresponding reasoning graph (right).\"\n\nIn the reasoning graph (right side of Figure 1), I can see:\n- The graph contains nodes connected by arrows\n- There are rectangular boxes of different colors\n- Looking at the reasoning graph structure, I can identify green rectangles and grey rectangles\n\nCounting the rectangles in the reasoning graph:\n- Green rectangles: These appear to be the operation/function nodes in the graph. I can count: \"Lookup\", \"Background knowledge\", \"Commonsense knowledge\", \"Subtract\" - that's 4 green rectangles\n- Grey rectangles: These appear to be the value/result nodes. I can count: \"57.5%\", \"Prod.\", \"50%\", \"7.5%\" - that's 4 grey rectangles\n\nWait, let me recount more carefully by examining the figure description and structure:\n- Green rectangles (operations): 4\n- Grey rectangles (values): 4\n\nIn descending order: [\"4\", \"4\"]\n\nAnswer: 4, 4", "citations": [], "input_tokens": 28779, "output_tokens": 281, "cost_micros": 90552, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1 on page 1.\n\nLooking at Figure 1, I can see it contains:\n- A table on the left side\n- A reasoning graph on the right side with various nodes and connections\n\nIn the reasoning graph (right side), I can identify:\n- Grey rectangles: These appear to be nodes like \"Closed-domain knowledge\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker\"\n- Green rectangles: I need to look for any green-colored rectangles\n\nLet me count carefully:\n- Grey rectangles in the reasoning graph: I can see multiple grey nodes representing different types of operations and knowledge\n- Green rectangles: Looking at the figure, I don't see distinctly green rectangles in the traditional sense, but there appear to be some lighter/different colored elements\n\nActually, examining the figure more carefully:\n- There appear to be approximately 5 grey rectangular nodes in the reasoning graph\n- I don't clearly see green rectangles that are distinctly separate from the grey ones\n\nHowever, looking more carefully at the color scheme and the structure, it appears there are:\n- Grey rounded rectangles: approximately 5\n- There don't appear to be clearly distinct green rectangles\n\nBased on my examination, I count approximately 5 grey rectangles and 0 distinctly green rectangles in Figure 1.\n\nAnswer: 5, 0", "citations": [], "input_tokens": 36689, "output_tokens": 320, "cost_micros": 114867, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "0, 5", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper. Let me continue reading to locate Figure 1.Perfect! I found Figure 1. Now I need to examine this figure carefully to count the green and grey rectangles. Based on the content I can see, Figure 1 shows a reasoning graph with different colored rectangles representing different steps in the reasoning process. Let me read more carefully to identify all the rectangles and their colors.\n\nFrom what I can see in the figure description:\n- There's a table on the left\n- A reasoning graph on the right\n- The reasoning graph contains several boxes/rectangles with different steps\n\nLooking at the text content that describes the figure structure:\n1. \"Closed-domain knowledge: Table caption\" - appears to be one rectangle\n2. \"Commonsense knowledge\" - appears to be another rectangle\n3. \"Productivity corresponds to Prod. column\" - part of the reasoning\n4. \"Simple lookup\" - appears to be a step\n5. \"The number of random chance is 50%\" - another element\n6. \"Supported Claim\" - labeled element\n7. \"A's productivity is 57.5%\" - text element\n8. Various reasoning steps with operators (+, -, =, \u00d7, ?)\n9. \"Refuted Claim\" - labeled element\n10. \"Not Enough Info Claim\" - labeled element\n\nLet me search for more details about the actual visual representation or read the next section to see if there's a clearer description.Let me go back and read the figure description more carefully to understand the visual elements:It seems the content around line 632-646 is describing a green checkmark icon, which is not Figure 1. Let me look more carefully at the actual Figure 1 content. Let me re-read around the figure caption area:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55655, "error": null, "extra": {"thread_id": 1975, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b73a2e9dd2a74761aa49d98419f10801", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 323, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me search for more details about actual visual representation or read next section to see if there s clearer description let me go back and read figure description more carefully to understand visual elements it seems content around line 632 646 is describing green checkmark icon, which is not figure 1 let me look more carefully at actual figure 1 content let me re read around figure caption area", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to count the reasoning steps shown in the reasoning graph on the right side of the figure.\n\nThe reasoning graph shows the following steps:\n1. Closed-domain knowledge: Table caption\n2. Commonsense knowledge\n3. Simple lookup\n4. Subtraction\n\nThese are the four distinct reasoning steps required to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nAnswer: 4", "citations": [], "input_tokens": 68131, "output_tokens": 119, "cost_micros": 206178, "latency_ms": 30733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see a reasoning graph on the right side that shows the steps needed to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nThe reasoning graph shows the following steps:\n\n1. \"Closed-domain knowledge: Productivity corresponds to Prod. column\" (from table caption)\n2. \"Commonsense knowledge: The number of random chance is 50%\"\n3. \"Simple lookup: A's productivity is 57.5%\"\n4. \"Subtraction: The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese steps lead to verifying whether the claim is \"Supported\" or \"Refuted\".\n\nCounting the reasoning steps in the graph, I can identify 4 distinct reasoning steps.\n\nAnswer: 4", "citations": [], "input_tokens": 31764, "output_tokens": 225, "cost_micros": 98667, "latency_ms": 8390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the reasoning steps shown in the reasoning graph in Figure 1.\n\nLooking at Figure 1's reasoning graph (on the right side), I can identify the following reasoning steps:\n\n1. \"Closed-domain knowledge: Table caption\" - Productivity corresponds to Prod. column\n2. \"Commonsense knowledge\" - The number of random chance is 50%\n3. \"Simple lookup\" - A's productivity is 57.5%\n4. \"Subtraction\" - The subtraction result between 57.5% and 50% is 7.5%\n5. Final verification step - comparing the claim with the calculated result\n\nThe reasoning graph shows 5 distinct reasoning steps from the initial knowledge extraction to the final verification of the claim.\n\nAnswer: 5", "citations": [], "input_tokens": 44319, "output_tokens": 174, "cost_micros": 135567, "latency_ms": 6969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see it contains:\n- A table with columns: Cue, App., Prod., Cov.\n- A claim about \"A's productivity of 57.5%\"\n- A reasoning graph on the right side\n\nThe reasoning graph shows the verification process with several nodes connected by arrows. Let me count the reasoning steps by tracing through the graph:\n\n1. Start with the claim about A's productivity of 57.5%\n2. Look up \"A's productivity\" \u2192 refers to cell at last row and \"Prod.\" column\n3. Background knowledge: \"productivity\" corresponds to \"Prod.\" column\n4. Simple lookup: Get value 57.5% from the table\n5. Commonsense knowledge: \"random chance\" means 50% accuracy\n6. Numerical reasoning: Calculate difference 57.5% - 50% = 7.5%\n7. Compare calculated result with claimed value (7.5%)\n\nCounting the nodes in the reasoning graph shown in Figure 1, I can see there are 7 distinct reasoning steps involved in verifying this claim.\n\nAnswer: 7", "citations": [], "input_tokens": 28746, "output_tokens": 277, "cost_micros": 90393, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I need to count the reasoning steps shown in the reasoning graph on the right side.\n\nThe reasoning graph shows the following steps to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\":\n\n1. **Closed-domain knowledge**: Productivity corresponds to Prod. column (from table caption)\n2. **Commonsense knowledge**: The number of random chance is 50%\n3. **Simple lookup**: Finding the value 57.5% in the table (A's productivity)\n4. **Subtraction**: The subtraction result between 57.5% and 50% is 7.5%\n\nAnswer: 4", "citations": [], "input_tokens": 36656, "output_tokens": 169, "cost_micros": 112503, "latency_ms": 7671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned as most relevant to find Figure 1 and count the reasoning steps.Let me continue reading to find Figure 1:Now let me read the section around line 562 where Figure 1 is described:Let me read more to see the complete figure with all reasoning steps:Based on the document content, I can see Figure 1 shows a reasoning graph with the following steps:\n\n1. **Closed-domain knowledge: Table caption** - \"Productivity corresponds to Prod. column\"\n2. **Simple lookup** - \"A's productivity is 57.5%\"\n3. **Commonsense knowledge** - \"The number of random chance is 50%\"\n4. **Subtraction** - \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese 4 reasoning steps lead to verifying the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\" which is marked as \"Supported\".\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37062, "error": null, "extra": {"thread_id": 1976, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6471c23a58a448bd8c1bac8c5ba5da64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 190, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from the document.\n\nFrom Table 1 on page 4, I can see:\n\n**Wiki Table datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Table claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 68140, "output_tokens": 175, "cost_micros": 207045, "latency_ms": 33358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the number of claims in Wiki Table datasets versus Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 31773, "output_tokens": 182, "cost_micros": 98049, "latency_ms": 6016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 44328, "output_tokens": 174, "cost_micros": 135594, "latency_ms": 6858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 28755, "output_tokens": 183, "cost_micros": 89010, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 36665, "output_tokens": 174, "cost_micros": 112605, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about claims in Wiki Table datasets compared to scientific articles datasets.Perfect! I found the table with the statistics. Let me extract the specific information:\n\nFrom the table:\n- **Wiki Tables datasets**: \n  - TabFact: 117,854 claims\n  - FEVEROUS: 87,026 claims\n  - Total: 117,854 + 87,026 = 204,880 claims\n\n- **Scientific Articles datasets**:\n  - SEM-TAB-FACTS: 5,715 claims\n  - SCITAB: 1,225 claims\n  - Total: 5,715 + 1,225 = 6,940 claims\n\nDifference: 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36890, "error": null, "extra": {"thread_id": 1977, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eedb3672a2cb45bf898fdd6ba6d9a71c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 152, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the total number of claims in the dataset and the payment per claim annotation.\n\nFrom the document:\n\n1. Total number of claims in SCITAB: 1,225 claims (mentioned in the abstract and Table 1)\n\n2. Payment per claim annotation: 0.37 USD (mentioned in Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\")\n\nHowever, I need to consider that the annotation process involves multiple rounds and different types of claims:\n\nFrom Section 2.3, the manual verification process involves:\n- Verifying 872 false claims (counter-claims)\n- Verifying 900 NEI claims\n- Reviewing 872 real-world scientific claims\n\nThis suggests a two-round annotation process where each claim is annotated by two different annotators.\n\nLooking more carefully at the construction process:\n- 872 original claims were extracted\n- For each original claim, a counter-claim was generated (872 counter-claims)\n- 900 NEI claims were generated\n- After verification, the final dataset has 1,225 claims total\n\nThe annotation process in Section 2.3 states: \"We assign two different annotators to perform a two-round annotation for each claim\"\n\nSo the total annotations would be:\n- For supported/refuted claim pairs: 872 pairs need verification (2 rounds each)\n- For NEI claims: 900 claims need verification (2 rounds each)\n\nTotal annotations = (872 \u00d7 2) + (900 \u00d7 2) = 1,744 + 1,800 = 3,544 annotations\n\nTotal cost = 3,544 \u00d7 0.37 = 1,311.28 USD\n\nAnswer: 1311.28", "citations": [], "input_tokens": 68152, "output_tokens": 408, "cost_micros": 210576, "latency_ms": 38546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1311.28", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the annotation cost and the total number of claims in the dataset.\n\nFrom the document:\n\n1. **Annotation cost**: \"Each claim annotation is reimbursed at a rate of 0.37 USD\" (Section 2.3)\n\n2. **Total number of claims in SCITAB**: Looking at Table 1, the total number of claims in SCITAB is 1,225.\n\nHowever, I need to determine how many claims actually needed annotation. According to Section 2:\n\n- Section 2.1: 872 real-world scientific claims were filtered from SciGen\n- Section 2.2: They generated 872 false claims (counter-claims) and 900 NEI claims\n- Section 2.3: Manual verification was done on all these claims\n\nFrom Section 2.3 and Appendix D:\n- 872 false claims were verified\n- 900 NEI claims were verified (which resulted in 355 final NEI claims)\n- 872 original claims were also reviewed\n\nSo the total annotations performed:\n- False claim verification: 872 claims\n- NEI claim verification: 900 claims\n- Total annotations: 872 + 900 = 1,772 claims\n\nTotal cost = 1,772 claims \u00d7 0.37 USD per claim = 655.64 USD\n\nAnswer: 655.64", "citations": [], "input_tokens": 31785, "output_tokens": 318, "cost_micros": 100125, "latency_ms": 9562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "655.64", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. The total number of claims in the SCITAB dataset\n2. The payment rate per claim annotation\n\nFrom the document:\n\n**Total number of claims:**\nFrom Table 1 and Section 2.3: \"Total # of Claims: 1,225\"\n\n**Payment per claim:**\nFrom Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n**Calculation:**\nTotal cost = 1,225 claims \u00d7 0.37 USD per claim = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 44340, "output_tokens": 145, "cost_micros": 135195, "latency_ms": 5389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost for annotating all claims in the SCITAB dataset.\n\nFrom the document, I can find:\n\n1. **Total number of claims in SCITAB**: According to the abstract and Section 2, SCITAB contains **1,225 claims** (also written as \"1.2K expert-verified scientific claims\").\n\n2. **Cost per claim annotation**: According to Section 2.3 under \"Annotator Recruitment\":\n   - \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n   Total cost = Number of claims \u00d7 Cost per claim\n   Total cost = 1,225 \u00d7 0.37\n   Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 28767, "output_tokens": 183, "cost_micros": 89046, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost of annotating all claims in the SCITAB dataset.\n\nFrom the document:\n\n1. **Total number of claims in SCITAB**: According to Table 1, SCITAB has 1,225 total claims.\n\n2. **Payment per claim annotation**: From Section 2.3, it states \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n   Total cost = Number of claims \u00d7 Cost per claim\n   Total cost = 1,225 \u00d7 0.37\n   Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 36677, "output_tokens": 152, "cost_micros": 112311, "latency_ms": 5469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant to find information about annotation costs for the proposed dataset.Let me search for information about annotation costs, dataset size, and pricing in the document.Let me read through the document more systematically. I'll start with sections that typically discuss dataset details and costs.Let me search for sections that discuss the dataset size and annotation costs. Let me look for keywords like \"price\", \"annotator\", \"pay\", etc.Let me search for numbers that might relate to costs:Let me read sections that might contain appendix or details about annotation costs:Let me look in later sections of the document where appendix or cost information might be:Let me search for \"Appendix\" section which might contain cost details:Let me try searching with content mode to find where these terms appear:Great! I found a reference to annotation cost. Let me read around line 974:Perfect! I found the annotation cost information. Now I need to find the total number of claims in the dataset. Let me search for that information:Perfect! Now I have all the information I need:\n- Total number of claims: 1,225\n- Cost per claim annotation: 0.37 USD\n\nLet me calculate the total cost:\nTotal cost = 1,225 \u00d7 0.37 = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97266, "error": null, "extra": {"thread_id": 1978, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77659594d74045e99872a87f698ab753", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 502, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about the distribution of reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3 on page 5, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset\":\n\nThe figure shows the percentage of claims for each number of reasoning steps (1-11).\n\nFrom the histogram, I can read the following percentages:\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20% (highest)\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1 on page 4, the total number of claims in SCITAB is 1,225.\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 68134, "output_tokens": 299, "cost_micros": 208887, "latency_ms": 34843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of claims with the highest reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the distribution of reasoning steps.\n\nThe x-axis shows reasoning steps from 1 to 11, and the y-axis shows the percentage.\n\nFrom the figure, I can read the following percentages:\n- 1 step: 1%\n- 2 steps: 7%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 6%\n- 9 steps: 5%\n- 10 steps: 3%\n- 11 steps: 2%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nThe document states there are 1,225 total claims in SCITAB (from Table 1).\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 31767, "output_tokens": 277, "cost_micros": 99456, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step percentage distribution in the SCITAB dataset from Figure 3.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 2%\n- 2 steps: 3%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 7%\n- 9 steps: 6%\n- 10 steps: 5%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nSince the dataset contains 1,225 claims total (as stated in the abstract and throughout the document), I need to calculate 20% of 1,225:\n\n1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 44322, "output_tokens": 243, "cost_micros": 136611, "latency_ms": 6812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the highest percentage of reasoning steps in the SCITAB dataset and determine how many claims fall into that category.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see:\n\n- The x-axis represents the reasoning steps in each claim\n- The y-axis represents the frequency for each reasoning step\n- The figure distinguishes between shallow claims (1-2 reasoning steps, highlighted in red) and deep claims (3+ reasoning steps, highlighted in blue)\n\nFrom the histogram in Figure 3, I need to identify which reasoning step has the highest frequency (highest bar).\n\nExamining the histogram carefully:\n- The highest bar appears to be at 4 reasoning steps\n- The frequency at 4 reasoning steps appears to be approximately 350-400 claims\n\nLooking more carefully at the visual representation, the bar at 4 reasoning steps is the tallest in the histogram, with a frequency of approximately 350 claims.\n\nAnswer: 350", "citations": [], "input_tokens": 28749, "output_tokens": 223, "cost_micros": 89592, "latency_ms": 7078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "350", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step with the highest percentage in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20%\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1, the total number of claims in SCITAB is 1,225.\n\nNumber of claims with 5 reasoning steps = 1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 36659, "output_tokens": 266, "cost_micros": 113967, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the most relevant document to find information about claims and reasoning steps in the author's proposed dataset.Let me continue reading to find information about the dataset and reasoning steps distribution.Great! I found a reference to a distribution histogram of reasoning steps. Let me read that specific section.Let me look at the earlier part where the figure caption was mentioned to understand the chart better.Perfect! Now I can see the histogram data. Let me read more to see all the percentage values clearly:From the histogram data I can see, the percentages are listed vertically and the reasoning steps go from 2 to 11. Looking at the data:\n\n- The percentages shown are: 20, 18, 20, 15, 15, 15, 6, 8, 7, 5, 3, 2, 1, 1\n\nThe highest percentages appear to be 20% which occurs twice. Let me look for more context to understand the exact mapping between reasoning steps and percentages. Let me search for more information about the total number of claims.Good! So SCITAB has 1,225 total claims. Now I need to figure out which reasoning step number has the highest percentage (20%), and calculate how many claims that represents. Looking at the histogram data again, I need to understand which reasoning steps correspond to the 20% values. Let me look for more details about the distribution.Now let me carefully re-examine the histogram data. The data shows reasoning steps 2-11 and their corresponding percentages. Looking at the visual structure again:I see the structure now. Lines 1182-1198 show \"2 3 4 5 6 7 8 9 10 11\" which are the reasoning steps on the x-axis. The percentages must be reading from the bars. Based on the structure, it appears:\n\nFrom the data, I can see percentages: 20, 18, 20, 15, 15, 15, 10, 6, 8, 7, 5, 5, 3, 2, 1, 1\n\nThe highest percentage is 20%, which appears twice in the data. With 1,225 total claims and 20% being the highest:\n- 20% of 1,225 = 0.20 \u00d7 1,225 = 245 claims\n\nHowever, I see 20% appears twice, which could mean there are two different reasoning step counts that each have 20%. Let me verify by checking if there's a table or more precise description.Based on the figure caption, the y-axis shows \"frequency\" which is shown as \"Percentages (%)\". The highest percentage is 20%, and since this appears to occur for specific reasoning step(s), I need to calculate the number of claims.\n\nWith 1,225 total claims in SCITAB and the highest percentage being 20%:\n20% \u00d7 1,225 = 245 claims\n\nAnswer: 245", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77500, "error": null, "extra": {"thread_id": 1979, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0b34f104b1a04155b78b4d63f4b337dd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the number of figures in the Appendix section of the paper.\n\nLooking through the document, I can see the Appendix starts at section A on page 13. Let me go through each appendix section and count the figures:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task (page 14)\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task (page 15)\n- Figure 7: The user interface for the refuted claim verification task (page 16)\n- Figure 8: Examples of post-annotation survey questions (page 17)\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: Refuted claims cases A to C (page 22)\n- Figure 10: Refuted claims cases D and E (page 23)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT (page 24)\n- Figure 12: Error Cases 4-6 for InstructGPT (page 25)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts (page 26)\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts (page 27)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures in the Appendix.\n\nAnswer: 10", "citations": [], "input_tokens": 68138, "output_tokens": 472, "cost_micros": 211494, "latency_ms": 35156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the paper.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- A.2 Claim Extraction Interface: Figure 5\n\n**Appendix B (Manual Claim Verification Procedure):**\n- B.2 NEI Claim Verification Interface: Figure 6\n- B.3 Refuted Claim Verification Interface: Figure 7\n- B.4 Annotation Post-Survey: Figure 8\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9 (refuted cases A to C)\n- Figure 10 (refuted cases D and E)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11 (Error Cases 1-3)\n- Figure 12 (Error Cases 4-6)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13 (Error Cases 1-3)\n- Figure 14 (Error Cases 4 and 5)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 31771, "output_tokens": 373, "cost_micros": 100908, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 44326, "output_tokens": 415, "cost_micros": 139203, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure)**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure)**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset)**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration)**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims)**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT)**\n- Figure 11: Error Cases 1-3 for InstructGPT\n- Figure 12: Error Cases 4-6 for InstructGPT\n\n**Appendix G (Error Cases for Program-of-Thoughts)**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts)**\n- No figures\n\nTotal figures in Appendix:\nFigure 5, 6, 7, 8, 9, 10, 11, 12, 13, 14\n\nAnswer: 10", "citations": [], "input_tokens": 28753, "output_tokens": 380, "cost_micros": 91959, "latency_ms": 8592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLet me go through the Appendix sections:\n\n**Appendix A** (Claim Extraction Procedure):\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B** (Manual Claim Verification Procedure):\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C** (Analysis of Refuted Reasons in the Sci-Fact dataset):\n- No figures\n\n**Appendix D** (Discussions on Human-Machine Collaboration):\n- No figures\n\n**Appendix E** (Case Study for Refuted Claims):\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F** (Error Cases for InstructGPT):\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G** (Error Cases for Program-of-Thoughts):\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H** (Prompts):\n- No figures\n\nTotal figures in Appendix:\n- Figure 5\n- Figure 6\n- Figure 7\n- Figure 8\n- Figure 9\n- Figure 10\n- Figure 11\n- Figure 12\n- Figure 13\n- Figure 14\n\nAnswer: 10", "citations": [], "input_tokens": 36663, "output_tokens": 427, "cost_micros": 116394, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find out how many figures are in the Appendix.Let me search for the Appendix section in the document.Now let me search for figures in the Appendix sections. Let me read the end of the document where appendices are typically located.Let me continue reading to find the Appendix section.Let me search for all figures in the appendix sections. Let me look for \"Figure\" in the context of Appendix.Let me read the document more systematically to find all appendix sections and count the figures. Let me search for specific appendix sections.Now I can see the appendix sections starting. Let me continue reading to find all figures in the appendices.Let me continue reading to find more figures in the appendix.Let me continue to look for all figures in the appendix. Let me read further.Let me continue reading to find all the figures.Let me continue reading to find more figures.Now let me search for the end of the document to make sure I've found all figures.Perfect! Now let me count all the figures I found in the Appendix sections. Based on my reading, I found the following figures in the appendices:\n\n**Appendix A:**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B:**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix E:**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F:**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G:**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\nTotal figures in the Appendix: 10\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 85187, "error": null, "extra": {"thread_id": 1980, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0161632b18b34ebfa7ee0421186ab320", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 490, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly at Figures 3, 7, and 9 which show the relative sizes of Swp (information flow from text part to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\n- In shallow layers, Swp is high while Spq is low\n- In deep layers, Spq becomes dominant and exceeds Swp\n\nThe document explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis means that Swp is NOT always greater than Spq - it depends on the layer depth.\n\nAnswer: no", "citations": [], "input_tokens": 41233, "output_tokens": 158, "cost_micros": 126069, "latency_ms": 32250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the relative sizes of Swp (information flow from text to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\nIn shallow layers (early layers), Swp is greater than Spq.\nIn deep layers (later layers), Spq becomes dominant and is greater than Swp.\n\nThe document explicitly states: \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince Swp is not always greater than Spq across all layers (it depends on which layer), the answer is no.\n\nAnswer: no", "citations": [], "input_tokens": 17763, "output_tokens": 200, "cost_micros": 56289, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 and Figure 7 in the document, which show the relative sizes of S_wp (information flow from text to label words), S_pq (information flow from label words to target position), and S_ww across different layers:\n\nIn shallow layers (layers close to input), S_wp is high while S_pq is low.\n\nIn deep layers (layers close to output), S_pq becomes dominant and exceeds S_wp.\n\nThe document explicitly states: \"In shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low, while S_wp, the information flow from the text part to label words is high; (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince S_wp is greater in shallow layers but S_pq becomes greater in deep layers, S_wp is NOT always greater than S_pq.\n\nAnswer: no", "citations": [], "input_tokens": 20121, "output_tokens": 222, "cost_micros": 63693, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly Section 2.1 and Figure 3, the metrics Swp (information flow from text part to label words) and Spq (information flow from label words to target position) are compared across different layers.\n\nThe text states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Swp is larger in shallow layers but Spq becomes larger in deep layers. Therefore, Swp is not always greater than Spq across all layers.\n\nAnswer: No", "citations": [], "input_tokens": 13762, "output_tokens": 132, "cost_micros": 43266, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the data presented in the document, particularly in Figure 3 and related figures showing $S_{wp}$, $S_{pq}$, and $S_{ww}$ values across different layers:\n\nIn shallow layers (e.g., layers 0-10), $S_{wp}$ (information flow from text to label words) is greater than $S_{pq}$ (information flow from label words to target position).\n\nHowever, in deep layers (e.g., layers 15-48), $S_{pq}$ becomes much larger than $S_{wp}$.\n\nFor example, in Figure 3(a) for SST-2:\n- Layer 0: $S_{wp}$ = 0.72, $S_{pq}$ = 0.15\n- Layer 20: $S_{wp}$ = 0.10, $S_{pq}$ = 0.90\n\nSince the relationship changes across layers, $S_{wp}$ is not **always** greater than $S_{pq}$.\n\nAnswer: no", "citations": [], "input_tokens": 23820, "output_tokens": 243, "cost_micros": 75105, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about information flow between text parts, label words, and target positions.Let me search for information about information flow between text parts, label words, and target positions.Let me search for specific terms related to the comparison:Let me read through the document more systematically to find tables or figures that might contain this information:Let me read Figure 3 and related sections since it mentions Swp, Spq, and Sww which seem to be related to information flow:Good! I found relevant information. Let me read more to understand the definitions and comparisons better:Perfect! Now I have the key information. Let me look at Figure 3 more carefully to see the actual values:Based on my reading of the document, I can now answer the question. The document clearly states:\n\nFrom the text (lines 842-851):\n- \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while **Swp, the information flow from the text part to label words is high**\"\n- \"in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one\"\n\nThe document also states in the figure caption (line 943): \"Initially, **Swp occupies a significant proportion**, but it gradually decays over layers, while **Spq becomes the dominant one**.\"\n\nThis indicates that:\n- In shallow layers: Swp (text part to label words) > Spq (label words to target position)\n- In deep layers: Spq (label words to target position) > Swp (text part to label words)\n\nSince Swp starts high and decays while Spq starts low and increases, across ALL layers (taking the mean), Swp is NOT always greater than Spq. In fact, Spq becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59108, "error": null, "extra": {"thread_id": 1981, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_31954715cb0b491eade4006731d43b22", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the related discussion in Section 2.1:\n\nThe text states: \"Figure 3 reveals that: (1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 41220, "output_tokens": 149, "cost_micros": 125895, "latency_ms": 31437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe information flow from label words to the target position (S_pq) is LOW in shallow layers, but becomes DOMINANT in deep layers. The document states: \"(1) in shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 17750, "output_tokens": 119, "cost_micros": 55035, "latency_ms": 4890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe text states: \"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 20108, "output_tokens": 140, "cost_micros": 62424, "latency_ms": 5203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and Section 2.1, I need to examine when the information flow from label words to the target position (Spq) dominates.\n\nFrom Figure 3 and the text in Section 2.1:\n- Initially (in shallow layers), Swp (information flow from text to label words) \"occupies a significant proportion\"\n- Spq (information flow from label words to target position) \"gradually\" becomes dominant\n- Spq becomes \"the dominant one\" but this happens over layers, not in all layers\n\nThe text explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Spq does NOT dominate in all layers - only in the deeper layers after the information aggregation phase in shallow layers is complete.\n\nAnswer: No", "citations": [], "input_tokens": 13749, "output_tokens": 198, "cost_micros": 44217, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the paper's findings in Section 2.1 and the results shown in Figures 3, 7, and 9:\n\nIn shallow layers, $S_{wp}$ (information flow from text to label words) is high while $S_{pq}$ (information flow from label words to target position) is low.\n\nIn deep layers, $S_{pq}$ becomes dominant while $S_{wp}$ decreases.\n\nThe paper explicitly states: \"in shallow layers, $S_{pq}$, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, $S_{pq}$, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 23807, "output_tokens": 171, "cost_micros": 73986, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to answer your question.Let me search for content related to \"information flow\" and \"label words\" in the document.Now let me read the specific sections that discuss whether information flow from label words dominates in all layers.Perfect! I found the answer. Let me read a bit more to confirm the complete picture.Based on the document, the answer is clear. The text states:\n\n\"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis shows that information flow from label words to the target position does NOT dominate in all layers - it is low in shallow layers and only becomes dominant in deep layers.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38452, "error": null, "extra": {"thread_id": 1982, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_91e767a2b44d476aa25ee429c43c2924", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 4, which shows the impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers:\n\nThe figure shows that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - has minimal impact on both Label Loyalty and Word Loyalty metrics for both GPT2-XL and GPT-J models. The bars for \"Random (Last)\" show loyalty values close to 100%, similar to the \"No Isolation\" baseline.\n\nThis is further confirmed in the text on page 4 which states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 41222, "output_tokens": 170, "cost_micros": 126216, "latency_ms": 33032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - shows loyalty values very close to the \"No Isolation\" baseline (around 100%). This indicates almost no impact on the model's behavior.\n\nAnswer: Yes", "citations": [], "input_tokens": 17752, "output_tokens": 101, "cost_micros": 54771, "latency_ms": 3967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that:\n\n- \"Random (Last)\" (randomly isolating non-label words in the last 5 layers) shows very high loyalty scores (close to 100%), nearly identical to \"No Isolation\"\n- This indicates minimal impact on the model's predictions\n\nThe text also confirms: \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 20110, "output_tokens": 134, "cost_micros": 62340, "latency_ms": 6030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 and the related text in section 2.2, the document shows that isolating label words within the first 5 layers has substantial impact, but isolation within the last 5 layers has negligible impact. The text also states that \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nFigure 4 and Figure 8 show that randomly isolating non-label words (represented by lighter colored lines) in the last layers results in very high loyalty scores (close to 100%), indicating almost no impact on the model's behavior.\n\nAnswer: yes", "citations": [], "input_tokens": 13751, "output_tokens": 139, "cost_micros": 43338, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see the results for \"Random (Last)\".\n\nThe table shows:\n- Label Loyalty (GPT2-XL) with Random (Last): 98\n- Word Loyalty (GPT2-XL) with Random (Last): 98\n- Label Loyalty (GPT-J) with Random (Last): 98\n- Word Loyalty (GPT-J) with Random (Last): 98\n\nThese values are nearly identical to the \"No Isolation\" baseline (100), indicating minimal impact.\n\nThe document states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 23809, "output_tokens": 191, "cost_micros": 74292, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about randomly isolating non-label words within the last 5 layers.Perfect! I found the answer. The document states that randomly isolating non-label words within the last 5 layers has \"inconsequential\" impact, which means \"almost no impact.\"\n\nAnswer: Yes", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40125, "error": null, "extra": {"thread_id": 1983, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c2144cf872eb43e78cc21f804b3432de", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with Vanilla In-Context Learning (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 41209, "output_tokens": 115, "cost_micros": 125352, "latency_ms": 29484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find the average improvement from the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 on the page discussing Anchor Re-weighting results:\n\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nThis is also confirmed in the text which states: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.7", "citations": [], "input_tokens": 17739, "output_tokens": 149, "cost_micros": 55452, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.7", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with the baseline vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract that mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 20097, "output_tokens": 141, "cost_micros": 62406, "latency_ms": 5483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the average improvement of the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 in the document, I can see the following results:\n\n**Vanilla In-Context Learning (1-shot per class):**\n- SST-2: 61.28\n- TREC: 57.56\n- AGNews: 73.32\n- EmoC: 15.44\n- Average: 51.90\n\n**Anchor Re-weighting (1-shot per class):**\n- SST-2: 90.07\n- TREC: 60.92\n- AGNews: 81.94\n- EmoC: 41.64\n- Average: 68.64\n\nThe improvement is calculated as:\n68.64 - 51.90 = 16.74\n\nThe document also states in the text: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 13738, "output_tokens": 229, "cost_micros": 44649, "latency_ms": 5717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to find the performance improvement of Anchor Re-weighting over vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract and Section 3.1 which mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 23796, "output_tokens": 148, "cost_micros": 73608, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the Anchor Re-weighting method and its performance improvement over vanilla ICL.Let me search for \"Anchor Re-weighting\" in the document to find the relevant information.Let me read the section with the results table to find the specific improvement values.Perfect! I found Table 1 which shows the performance comparison. Let me calculate the improvement:\n\nFrom the table:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement = 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35969, "error": null, "extra": {"thread_id": 1984, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25b28dfa8e5c4fbeb8c80f7d7650cb08", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 144, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I need to find information about anchor-only context compression speedup for GPT2-XL on AGNews.\n\nLooking at Table 3 on page 8, which shows \"Acceleration ratios of the Hiddenanchor method\":\n\nThe table shows:\n- Model: GPT2-XL\n- AGNews: 2.5\u00d7\n\nThis indicates that using the anchor-only context compression method (Hiddenanchor), GPT2-XL achieves a 2.5\u00d7 speedup on the AGNews dataset.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 41212, "output_tokens": 129, "cost_micros": 125571, "latency_ms": 31171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews using anchor-only context compression (Hiddenanchor method):\n\nIn Table 3 on the topic of \"Acceleration ratios of the Hiddenanchor method,\" the speedup ratio for GPT2-XL on AGNews is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 17742, "output_tokens": 90, "cost_micros": 54576, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup for GPT2-XL on AGNews...\n\nIn Table 3 (page 7), the acceleration ratios of the Hiddenanchor method are provided:\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 20100, "output_tokens": 86, "cost_micros": 61590, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews with anchor-only context compression...\n\nIn Table 3 (on page 8), which shows \"The speed-up ratio compared to the original ICL\", I can find the speedup values for different models and datasets.\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 13741, "output_tokens": 100, "cost_micros": 42723, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup on AGNews with GPT2-XL...\n\nIn Table 3 on page 11, the acceleration ratios of the Hidden_anchor method are presented:\n\n| Model   | SST-2 | TREC | AGNews | EmoC |\n| ------- | ----- | ---- | ------ | ---- |\n| GPT2-XL | 1.1\u00d7  | 1.5\u00d7 | 2.5\u00d7   | 1.4\u00d7 |\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 23799, "output_tokens": 134, "cost_micros": 73407, "latency_ms": 5157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I'll search for information about anchor-only context compression and GPT2-XL speed up on AGNews in the provided document.Let me search for \"anchor\" and \"AGNews\" in the document to find the relevant information.Now let me look at Table 3 which shows acceleration ratios for the Hiddenanchor method:Perfect! I found the answer in Table 3. The table shows acceleration ratios of the Hiddenanchor method, and for GPT2-XL on AGNews, the speedup is 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36062, "error": null, "extra": {"thread_id": 1985, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c1692b1f5bfe4ec6aa79ada899da40bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 139, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the most confusing category of Abbreviation in the document.\n\nLooking at the confusion matrices in the document, particularly in Section 3.3 (Anchor Distances for Error Diagnosis) and the appendices, I can see confusion matrices for the TREC dataset.\n\nIn Figure 6 on page 8, there are two confusion matrices shown:\n- (a) Confusion matrix of Confusion^pred_ij\n- (b) Confusion matrix of Confusion_ij\n\nLooking at the Abbreviation row in both matrices, I need to identify which category Abbreviation is most confused with (the lowest confusion score, or highest confusion).\n\nFrom Figure 6(b), looking at the Abbreviation row:\n- Abbreviation-Abbreviation: 1 (diagonal, set to 1)\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n- Abbreviation-Person: 1\n- Abbreviation-Location: 1\n- Abbreviation-Number: 0.87\n\nThe lowest value (indicating highest confusion) in the Abbreviation row is with Description (0.76), followed by Entity (0.84).\n\nThis is also confirmed in the text on page 8 which states: \"Figure 6 shows that the proposed approximation metric, Confusion^pred_ij, can identify the most confusing case (Description-Entity) and performs reasonably well for highly confusing categories (Entity-Abbreviation, Description-Abbreviation).\"\n\nAnswer: Description", "citations": [], "input_tokens": 41202, "output_tokens": 351, "cost_micros": 128871, "latency_ms": 26937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the confusion matrix of Confusion_ij on the TREC dataset, I need to find which category has the highest confusion score with Abbreviation.\n\nFrom the confusion matrix in Figure 6(b), the values for Abbreviation's confusion with other categories are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nThe lowest value (highest confusion) is with Entity at 0.84.\n\nAnswer: Entity", "citations": [], "input_tokens": 17732, "output_tokens": 125, "cost_micros": 55071, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the actual confusion matrix (Confusionij) on the TREC dataset, I need to find which category Abbreviation is most confused with.\n\nFrom the confusion matrix in Figure 6(b), the Abbreviation row shows the following confusion scores with other categories:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nLower confusion scores indicate higher confusion between categories (as stated in the paper). The lowest score is 0.76 with Description, followed by 0.84 with Entity.\n\nAnswer: Description", "citations": [], "input_tokens": 20090, "output_tokens": 147, "cost_micros": 62475, "latency_ms": 5234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices in Figure 14, I need to identify which category is most confused with \"Abbreviation.\"\n\nIn the confusion matrices, lower values indicate higher confusion (as stated in the document: \"The closer Confusion_{ij} approximates 1, the less pronounced the confusion\").\n\nLooking at the \"Abbreviation\" row across the different matrices (M=5, 10, 20, 50, 100, 200), the lowest values (highest confusion) are:\n- Description: 0.31, 0.45, 0.52\n- Entity: 0.44, 0.49, 0.51\n\nThe \"Description\" category consistently shows the lowest values when compared with Abbreviation, indicating the highest degree of confusion.\n\nAnswer: Description", "citations": [], "input_tokens": 13731, "output_tokens": 174, "cost_micros": 43803, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices provided in the document, I need to find which category is most confused with \"Abbreviation.\"\n\nIn Figure 6(a), which shows the predicted confusion matrix ($\\text{Confusion}_{ij}^{\\text{pred}}$), the values for Abbreviation with other categories are:\n- Entity: 0.49\n- Description: 0.45\n- Person: 0.90\n- Location: 1.00\n- Number: 0.83\n\nLower values indicate higher confusion (as stated in the document: \"0 indicating the highest degree of category confusion\").\n\nIn Figure 6(b), which shows the actual confusion matrix ($\\text{Confusion}_{ij}$), the values for Abbreviation are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1.00\n- Location: 1.00\n- Number: 1.00\n\nIn both matrices, the lowest value (highest confusion) for Abbreviation is with \"Description\" (0.45 predicted, 0.76 actual), followed closely by \"Entity\" (0.49 predicted, 0.84 actual).\n\nAnswer: Description", "citations": [], "input_tokens": 23789, "output_tokens": 270, "cost_micros": 75417, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the most confusing category of abbreviation.Let me search for information about \"confusing\" and \"abbreviation\" categories in the document.Let me read the specific chunk that mentions confusing categories and abbreviations:Perfect! I found the answer. Let me read a bit more context to confirm:Based on the document, the text clearly states that \"the most confusing case (Description-Entity)\" was identified. However, the question asks specifically about \"the most confusing category of Abbreviation.\"\n\nLooking at the confusion matrix data and the text, it mentions that the highly confusing categories involving Abbreviation are:\n- Entity-Abbreviation\n- Description-Abbreviation\n\nLet me check if there's more specific information about which is THE most confusing for Abbreviation:\n\nLooking at the confusion matrix values shown:\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n\nThe lower the value in a confusion matrix, the more confusing the pair. So Entity (0.84) is more confusing with Abbreviation than Description (0.76), but actually looking at this as a similarity matrix (as the caption says \"heatmaps display similarity\"), higher values mean more similar/confusing.\n\nBased on the text stating \"highly confusing categories (Entity-Abbreviation, Description-Abbreviation)\" with Entity-Abbreviation listed first, and the confusion matrix showing 0.84 for Abbreviation-Entity vs 0.76 for Abbreviation-Description:\n\nAnswer: Entity", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40344, "error": null, "extra": {"thread_id": 1986, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_052d25ab92e746d681f311675087569d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 227, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2, which shows the MIMIC-IT overview tree diagram, I can see three main branches: Perception, Reasoning, and Planning.\n\nAt the top of the Planning branch (shown in red/pink on the right side), the task labeled is \"Indoor Event Planning\".\n\nAnswer: Indoor Event Planning", "citations": [], "input_tokens": 55843, "output_tokens": 76, "cost_micros": 168669, "latency_ms": 49328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "indoor event planning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the MIMIC-IT overview with different branches representing fundamental capabilities, I need to identify the task at the top of the planning branch.\n\nIn the planning section (rightmost branch of the tree diagram), examining from top to bottom, the topmost task shown is \"Commonsense Reasoning\" with the example question \"What might this young boy learn from this experience? (LA)\"\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 21188, "output_tokens": 104, "cost_micros": 65124, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, which shows the MIMIC-IT overview with three main branches (Perception, Reasoning, and Planning), I can see the Planning branch on the right side.\n\nAt the top of the Planning branch, the task shown is \"Indoor Event Planning (IEP)\" with the question \"Can you suggest any recreational pursuits that can be enjoyed within this room?\"\n\nAnswer: Indoor Event Planning (IEP)", "citations": [], "input_tokens": 23513, "output_tokens": 95, "cost_micros": 71964, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "indoor event planning iep", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a planning branch or tree structure with tasks.\n\nIn Figure 1 (MIMIC-IT overview), I can see different capabilities organized into three main branches: Perception, Reasoning, and Planning.\n\nUnder the Planning branch, I can see two tasks listed:\n1. Egocentric Planning (E4D)\n2. Episode Memory (DC)\n\nLooking at the visual layout in Figure 1, \"Egocentric Planning\" appears to be positioned at the top of the Planning branch.\n\nAnswer: Egocentric Planning", "citations": [], "input_tokens": 20245, "output_tokens": 129, "cost_micros": 62670, "latency_ms": 5657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "egocentric planning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the task at the top of the Planning branch.\n\nThe Planning section shows three items:\n1. At the top: \"Is it safe to walk on the floor while the woman is cleaning? E4D\" \u2192 EVR [Egocentric Video Reasoning]\n2. In the middle: \"Can you suggest any ways to brighten up the room I'm in? E4D\" \u2192 EP [Egocentric Planning]\n3. At the bottom: \"Can you suggest any recreational pursuits that can be enjoyed within this room? IEP\" \u2192 IEPP [Indoor Event Planning]\n\nThe topmost task in the Planning branch is Egocentric Video Reasoning (EVR).\n\nAnswer: Egocentric Video Reasoning", "citations": [], "input_tokens": 24261, "output_tokens": 176, "cost_micros": 75423, "latency_ms": 6139, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "egocentric video reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the planning branch of the tree and identify the task at the top.Let me search for content related to \"planning branch\" or \"tree\" to find the relevant figure or diagram:Let me search more specifically for \"tree\" and look at figures:Let me look at Figure 1 which mentions perception, reasoning, and planning:Good! I can see \"Planning\" section. Let me continue reading to see what tasks are under the Planning branch:Perfect! I can see the Planning section with its tasks. Looking at the structure, under \"Planning\" I can see:\n\n1. \"Commonsense Reasoning\" - \"What might this young boy learn from this experience? (LA)\"\n2. \"General Scene Difference\" \n3. \"Egocentric Planning\"\n4. \"Indoor Event Planning\"\n\nBut I need to see the visual tree structure more carefully. Let me look at the earlier part to understand the tree layout better:Now I can see the structure clearly. Looking at Figure 1, there's a tree structure with three main branches:\n1. Perception\n2. Reasoning\n3. Planning\n\nUnder the \"Planning\" branch, the first task/question that appears is: \"What might this young boy learn from this experience? (LA)\" which is labeled as \"Commonsense Reasoning\".\n\nSo the task at the top of the Planning branch is \"Commonsense Reasoning\".\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51937, "error": null, "extra": {"thread_id": 1987, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f3cc3b28e854493c927ab87e43d7d1d0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 285, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for bar plots.\n\nLooking through the figures:\n\n**Figure 1** (page 2): This shows example images and questions from the MIMIC-IT dataset. No bar plots here.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text. No bar plots here.\n\n**Figure 3** (page 5): This shows the Syphus pipeline overview. No bar plots here.\n\n**Figure 4** (page 7): This figure shows \"The data statistics of multi-modal in-context instruction-response pairs\" with:\n- (a) Instructions - shown as a sunburst/radial chart (not a bar plot)\n- (b) Responses - shown as a sunburst/radial chart (not a bar plot)\n- (c) Statistics of instructions and responses - This contains **4 bar plots**:\n  1. Instruction Length distribution\n  2. Response Length distribution\n  3. # Images distribution\n  4. # Related Instructions distribution\n\n**Figure 5** (page 8): This shows example responses from Otter with images and text. No bar plots here.\n\n**Figure 6** (page 10): This shows three subplots:\n- (a) Video understanding - **2 bar plots** (MSVD 0-shot and MSRVTT 0-shot)\n- (b) Vision-language model alignment - **1 bar plot** (Elo Rating)\n- (c) COCO caption - This is a line graph, not a bar plot\n\nTotal bar plots: 4 (from Figure 4c) + 2 (from Figure 6a) + 1 (from Figure 6b) = **7 bar plots**\n\nAnswer: 7", "citations": [], "input_tokens": 55838, "output_tokens": 405, "cost_micros": 173589, "latency_ms": 40905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots, including any subplots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text - no bar plots.\n\n**Figure 2**: Shows example images and text comparisons - no bar plots.\n\n**Figure 3**: Shows a pipeline diagram - no bar plots.\n\n**Figure 4**: This figure contains multiple subplots showing data statistics:\n- (a) Shows two circular/sunburst plots for instructions - not bar plots\n- (b) Shows two circular/sunburst plots for responses - not bar plots\n- (c) Shows four plots with statistics:\n  1. \"Instruction Length\" - appears to be a histogram/bar plot\n  2. \"Response Length\" - appears to be a histogram/bar plot\n  3. \"# Images\" - appears to be a histogram/bar plot\n  4. \"# Related Instructions\" - appears to be a histogram/bar plot\n\nSo Figure 4(c) contains 4 bar plots.\n\n**Figure 5**: Shows example images and responses - no bar plots.\n\n**Figure 6**: This figure contains three subfigures:\n- (a) \"Video understanding\" - shows a grouped bar chart with multiple bars\n- (b) \"Vision-language model alignment\" - shows a bar chart\n- (c) \"COCO caption\" - shows a line plot with points, not a bar plot\n\nSo Figure 6 has 2 bar plots (a and b).\n\nTotal count: 4 (from Figure 4c) + 2 (from Figure 6) = 6 bar plots.\n\nAnswer: 6", "citations": [], "input_tokens": 21183, "output_tokens": 367, "cost_micros": 69054, "latency_ms": 11281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count the bar plots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text describing different capabilities - no bar plots.\n\n**Figure 2**: Shows data format comparison with images and text - no bar plots.\n\n**Figure 3**: Shows the Sythus pipeline overview - no bar plots.\n\n**Figure 4**: This is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst plot showing root verb-noun pairs of instructions\n- (b) A circular/sunburst plot showing root verb-noun pairs of responses  \n- (c) Four separate plots labeled \"Statistics of instructions and responses\":\n  - Top left: A histogram/bar plot showing \"# Instructions\" vs \"Instruction Length\"\n  - Top right: A histogram/bar plot showing \"# Responses\" vs \"Response Length\"\n  - Bottom left: A histogram/bar plot showing \"# Instructions\" vs \"# Images\"\n  - Bottom right: A histogram/bar plot showing \"# Instructions\" vs \"# Related Instructions\"\n\nSo in Figure 4(c), there are **4 bar plots**.\n\n**Figure 5**: Shows example responses with images and text - no bar plots.\n\n**Figure 6**: Contains three subplots:\n- (a) \"Video understanding\" - shows bar plots comparing VideoChatGPT and Otter on MSVD and MSRVTT for QA and Captioning. This contains **4 grouped bar comparisons** (which I count as bar plots).\n- (b) \"Vision-language model alignment\" - shows an Elo rating comparison with bars for different models - this is **1 bar plot**.\n- (c) \"COCO caption\" - shows a line plot, not a bar plot.\n\nSo Figure 6 has: 4 (from 6a) + 1 (from 6b) = **5 bar plots**.\n\n**Figure 7**: Shows text examples in multiple languages - no bar plots.\n\nTotal bar plots: 4 (from Figure 4c) + 5 (from Figure 6) = **9 bar plots**.\n\nAnswer: 9", "citations": [], "input_tokens": 23508, "output_tokens": 506, "cost_micros": 78114, "latency_ms": 14187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count bar plots.\n\nLooking through the figures:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with three main branches (Perception, Reasoning, Planning). Not a bar plot.\n\n**Figure 2**: This shows data format comparisons with images and text examples. Not a bar plot.\n\n**Figure 3**: This is a flowchart showing the Sythus overview. Not a bar plot.\n\n**Figure 4**: This figure is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst chart showing instructions (verb-noun pairs)\n- (b) A circular/sunburst chart showing responses (verb-noun pairs)\n- (c) \"Statistics of instructions and responses\" which contains 4 separate bar plots:\n  1. Instruction Length vs # Instructions (bar plot)\n  2. Response Length vs # Responses (bar plot)\n  3. # Images vs # Instructions (bar plot)\n  4. # Related Instructions vs # Instructions (bar plot)\n\nSo Figure 4(c) contains **4 bar plots**.\n\n**Figure 5**: This shows example responses from Otter with images and text. Not a bar plot.\n\n**Figure 6**: This contains three parts:\n- (a) A table showing video understanding results. Not a bar plot.\n- (b) A table showing vision-language model alignment. Not a bar plot.\n- (c) A table showing COCO caption results. Not a bar plot.\n\nTotal count: 4 bar plots (all from Figure 4c)\n\nAnswer: 4", "citations": [], "input_tokens": 24256, "output_tokens": 385, "cost_micros": 78543, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count all bar plots (including subplots).Let me search for figures and plots in the document:Now let me read the actual content around the figures to see the descriptions of plots:Let me read more sections to find all the figures:Now let me look at Figure 4 more carefully to see if it contains bar plots:Good! I can see Figure 4 (c) has bar plots. Let me continue looking at Figure 6:Now I need to look at the actual visual description to understand what kind of plots Figure 6 has. Looking at the data in lines 1693-1823, I can see Figure 6 has three subplots (a), (b), and (c). Let me check if these are bar plots by examining the structure:\n\n- Figure 6(a): Shows \"MSVD 0-shot\" and \"MSRVTT 0-shot\" with \"QA\" and \"Captioning\" categories, comparing VideoChatGPT and Otter - this appears to be bar plots\n- Figure 6(b): Shows \"Elo Rating\" comparison among MM-GPT, Inst. BLIP, LLaVA, MiniGPT, and Otter - this appears to be a bar plot\n- Figure 6(c): Shows \"COCO caption\" with \"0-shot 4-shot 8-shot 16-shot\" comparing Otter and OpenFlamingo - this appears to be a line or bar plot\n\nNow let me search for more figures to make sure I don't miss any:Figure 7 is mentioned but let me check if it's a plot or just examples. From line 2292, Figure 7 seems to be just text examples for multi-lingual translation, not a plot.\n\nLet me search for any appendix figures or additional figures:Great! I found references to bar charts. Let me read those sections more carefully:These descriptions refer to the charts/bar plots that appear in Figure 4(c). Let me now carefully read to understand which images correspond to which figures. The Im1.png through Im4.png appear to be the four subplots in Figure 4(c).\n\nLet me search for Figure 6 visual descriptions:Let me look for the visual description of Figure 6:Let me search for visual descriptions that mention bar charts or plots more systematically by searching around the figure areas:Now let me check if there are any additional figures by searching for all figure captions:Good! So we have:\n- Figure 1: Overview (not a bar plot)\n- Figure 2: Data format comparison (not a bar plot)\n- Figure 3: Sythus overview (not a bar plot)\n- Figure 4: Data statistics with (a) and (b) showing verb-noun pairs, and (c) showing statistics with multiple subplots\n- Figure 5: Response examples (not a bar plot)\n- Figure 6: With (a), (b), and (c) subplots\n- Figure 7: Multi-lingual translation examples (not a bar plot)\n\nNow I need to count the bar plots more carefully. Let me re-examine Figure 4(c) and Figure 6:\n\nFrom my reading:\n- Figure 4(a): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles\n- Figure 4(b): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles  \n- Figure 4(c): Contains 4 bar plot histograms based on the visual descriptions I found:\n  1. Instructions vs Instruction Length (Im1.png) - bar plot\n  2. Responses vs Response Length (Im2.png) - bar plot\n  3. Instructions vs # Images (Im3.png) - bar plot\n  4. Instructions vs # Related Instructions (Im4.png) - bar plot\n\nFor Figure 6, I need to examine what type of plots they are. Let me look at the text around Figure 6 more carefully. From the data I saw (lines 1693-1823), Figure 6 appears to have:\n- Figure 6(a): Shows QA and Captioning comparisons for MSVD and MSRVTT - likely bar plots (2 groups x 2 bars = 4 bars total, but this counts as 2 bar plots for the two datasets)\n- Figure 6(b): Shows Elo Rating comparison - likely a bar plot (1 bar plot)\n- Figure 6(c): Shows COCO caption with 0-shot, 4-shot, 8-shot, 16-shot - this could be a line plot or bar plot\n\nLet me search for more specific information about these plots. Let me check if there are visual descriptions for Figure 6 plots:The file ends at line 5199. Let me search systematically for any remaining bar plot descriptions:Based on my thorough analysis of the document, let me now count all the bar plots:\n\n**Figure 4(c)** contains 4 bar plot histograms:\n1. Instructions vs Instruction Length\n2. Responses vs Response Length  \n3. Instructions vs # Images\n4. Instructions vs # Related Instructions\n\n**Figure 6** - I need to determine how many bar plots are in each subplot. Looking at the data structure:\n- Figure 6(a): Video understanding evaluation with MSVD 0-shot and MSRVTT 0-shot, each showing QA and Captioning - this appears to be 2 grouped bar charts (one for MSVD, one for MSRVTT)\n- Figure 6(b): Elo Rating comparison showing multiple models - this is 1 bar chart\n- Figure 6(c): COCO caption showing 0-shot through 16-shot results comparing Otter and OpenFlamingo - this could be either a grouped bar chart or line chart\n\nLooking at the structure in the text (lines 1816-1817: \"0-shot 4-shot 8-shot 16-shot\"), this suggests it's showing a progression, which is more typical of a line chart. However, I need to be careful.\n\nLet me reconsider the counting. The question asks for \"bar plots (including subplots)\". This means I should count each separate bar plot panel, not individual bars.\n\nFrom the evidence:\n- **Figure 4(c)**: 4 bar plot histograms (clearly described as histograms/bar charts in the visual descriptions)\n- **Figure 6(a)**: 2 bar plots (one for MSVD 0-shot, one for MSRVTT 0-shot, each comparing models on QA and Captioning)\n- **Figure 6(b)**: 1 bar plot (Elo Rating comparison)\n- **Figure 6(c)**: Likely 1 chart but unclear if it's a bar plot or line plot based on the \"0-shot 4-shot 8-shot 16-shot\" progression\n\nTotal count depends on whether Figure 6(c) is a bar plot. Given the context of comparing few-shot performance across different numbers of shots, and looking at typical machine learning papers, this is often shown as either a line chart or grouped bar chart.\n\nHowever, examining the OCR text more carefully from chunk 152307 (lines 1816-1822), the layout shows:\n- (a) Video understanding\n- (b) Vision-language model alignment  \n- (c) COCO caption\n\nAnd earlier in the caption it mentions \"CIDEr\" metric with values like \"60.8\", \"55\", etc. with labels for different shot counts.\n\nGiven the uncertainty, let me make my best assessment: Figure 6(c) showing few-shot learning progression is more likely to be a line plot given it shows a progression/trend across shot counts.\n\n**Final count:**\n- Figure 4(c): 4 bar plots\n- Figure 6(a): 2 bar plots\n- Figure 6(b): 1 bar plot\n- Figure 6(c): Not a bar plot (likely line plot)\n\n**Total: 7 bar plots**\n\nWait, let me reconsider Figure 6(a). Looking at the text structure again (lines 1693-1806), it shows:\n- MSVD 0-shot with QA and Captioning\n- MSRVTT 0-shot with QA and Captioning\n- These are comparing VideoChatGPT vs Otter\n\nThis could be:\n- 2 separate bar plots (one for MSVD, one for MSRVTT), OR\n- 1 bar plot with 2 groups\n\nTypically in papers, when multiple datasets are shown side-by-side in subfigure (a), they would be considered separate bar plots. So I'll count them as 2.\n\n**Final answer: 7**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 164109, "error": null, "extra": {"thread_id": 1988, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_98fcc3ce4d2f4d98b3c04b41720c35ca", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1170, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 titled \"Sythus overview\" on page 5, I can see the cold start process.\n\nThe figure shows a \"Cold Start\" stage that comes before the main Sythus pipeline. The cold start stage shows:\n- In-context examples (document icon at top)\n- An arrow pointing down to \"Cold Start\"\n- ChatGPT being queried during this stage\n- This then leads to \"Step 1: System Message + visual annotation\"\n\nThe cold start stage is described in the text as being used to \"identify the optimal system message and in-context example for querying instruction-response pairs in a given dataset.\"\n\nAnswer: In-context examples", "citations": [], "input_tokens": 55843, "output_tokens": 153, "cost_micros": 169824, "latency_ms": 41358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "in context examples", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage appears before the main pipeline steps (Step 1-4).\n\nThe cold start stage shows interactions with ChatGPT to identify the optimal system message and in-context examples. The figure shows arrows connecting to \"System Message\" and \"In-context examples\" components.\n\nAnswer: Identify optimal system message and in-context example", "citations": [], "input_tokens": 21188, "output_tokens": 91, "cost_micros": 64929, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "identify optimal system message and in context example", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage shows a flow diagram where the first step involves \"System Message\" with an arrow pointing to ChatGPT.\n\nAnswer: System Message", "citations": [], "input_tokens": 23513, "output_tokens": 50, "cost_micros": 71289, "latency_ms": 4468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 (Sythus overview), I can see the pipeline stages.\n\nThe cold start stage is shown at the top, and the first step within it involves prompting ChatGPT to generate system messages and in-context examples.\n\nAnswer: System Message", "citations": [], "input_tokens": 20245, "output_tokens": 62, "cost_micros": 61665, "latency_ms": 4858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, I can see the cold start process and the subsequent steps.\n\nThe cold start section shows \"In-context examples\" which feeds into Step 1.\n\nStep 1 is labeled as \"System Message + visual annotation\" which leads to the \"Prompt\".\n\nAnswer: System Message + visual annotation", "citations": [], "input_tokens": 24261, "output_tokens": 80, "cost_micros": 73983, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message visual annotation", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure about cold start and identify the first step.Let me search for \"cold start\" in the document to locate the relevant figure.Good! I found a reference to \"Cold Start\" with steps. Let me read the section around line 759-798 (chunk 152250) where the figure appears.Perfect! I can see the figure clearly shows the steps of the cold start process. According to the figure, the steps are:\n\n- Step 1: System Message\n- Step 2: Generate instruction-response pairs\n- Step 3: Filtering\n- Step 4: Translation\n\nThe first step of cold start is \"System Message\".\n\nAnswer: System Message", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 29859, "error": null, "extra": {"thread_id": 1989, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39c9f325d0c8483fb88a1b48780b1918", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figures in the document, particularly Figure 1 and Figure 5 which show examples from the MIMIC-IT dataset and Otter's capabilities:\n\nIn Figure 1, there are examples showing egocentric video reasoning with what appears to be soccer/football scenes.\n\nIn Figure 5, the egocentric visual assistant examples at the bottom show scenes from what appears to be soccer/football, including references to passing the ball, wearing jerseys, and the goalkeeper saving the ball.\n\nAnswer: Soccer/Football", "citations": [], "input_tokens": 55848, "output_tokens": 115, "cost_micros": 169269, "latency_ms": 35250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples showing sports.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an \"Egocentric Visual Assistant\" example with text that says \"Hey Otter, what should I do?\" and the response mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\"\n\nThis clearly refers to soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 21193, "output_tokens": 102, "cost_micros": 65109, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, particularly in Figure 5 under \"Egocentric Visual Assistant,\" I can see an image showing a soccer/football scene from a first-person perspective.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 23518, "output_tokens": 52, "cost_micros": 71334, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, I need to identify what sport is shown in the MIMIC-IT dataset examples and Otter's response examples.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an example labeled \"Egocentric Visual Assistant\" at the bottom. The text mentions \"guiding users to land a small aircraft.\"\n\nAdditionally, in the Ego4D (E4D) section's in-context examples (Table 5), there are questions about:\n- \"What color of jersey does my teammates wear?\" with the response \"Black, as you pass the ball to a player wearing black jersey\"\n- \"Does our team goal?\" with the response about a goalkeeper saving the ball\n\nThis indicates soccer/football is being discussed.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 20250, "output_tokens": 180, "cost_micros": 63450, "latency_ms": 7734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples that show sports.\n\nIn Figure 5, under the \"Egocentric Visual Assistant\" section, there are two screenshots. The right screenshot shows \"A person playing soccer with a chat overlay\" and includes the text \"User: Hey Otter, what should I do? OTTER-E: Pass the ball *to the middle* and run to the gate, looking for a shooting opportunity.\"\n\nAnswer: Soccer", "citations": [], "input_tokens": 24266, "output_tokens": 102, "cost_micros": 74328, "latency_ms": 6890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "soccer", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document you mentioned to find information about the visual examples in MIMIC-IT and Otter.Let me search for visual examples and figures in the document to find information about sports shown in MIMIC-IT and Otter examples.Let me read more of the document to find figures and visual examples:Let me continue reading to find specific visual examples that show sports:Found \"tennis\"! Let me search for the context around this mention:Perfect! Let me read that section to get more context about the tennis visual example:Now let me search for where this image appears in relation to MIMIC-IT or Otter examples. Let me look earlier in the document to understand the context of these figures:Let me search backwards to find where these images are part of a figure showing examples:Let me search for \"Otter\" in the document to find where these visual examples are shown:Now let me look at Figure 5, which shows \"Otter's response examples\":Great! I found it. In the Egocentric Visual Assistant section of Figure 5, there's a conversation where Otter-E says \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\" This clearly refers to soccer/football. Let me verify by looking at the visual description of the image that accompanies this text:Perfect! I found the answer. The visual description clearly states this is an \"Outdoor soccer/football field with green grass\" with players wearing blue athletic shirts. The Otter-E response also mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity,\" which confirms this is soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 123078, "error": null, "extra": {"thread_id": 1990, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e493d9b5d4804d67aac4ae04786b3ef7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at Figure 7 on page 16, which shows \"In-context examples for multi-lingual translation query,\" I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 55842, "output_tokens": 108, "cost_micros": 169146, "latency_ms": 37125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nAnswer: 8", "citations": [], "input_tokens": 21187, "output_tokens": 113, "cost_micros": 65256, "latency_ms": 4327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 23512, "output_tokens": 102, "cost_micros": 72066, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 7 which shows \"In-context examples for multi-lingual translation query\", I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 20244, "output_tokens": 108, "cost_micros": 62352, "latency_ms": 4578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7 on page 16, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 24260, "output_tokens": 107, "cost_micros": 74385, "latency_ms": 4422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about multi-lingual translation query and count the languages in the in-context examples.Now I can see references to the multi-lingual translation query. Let me read the specific section that mentions Figure 7 and the languages.Perfect! I found the information. Let me now look at Figure 7 to see the actual in-context examples and count the languages.Perfect! I can now see Figure 7 which shows the in-context examples for multi-lingual translation query. Let me count the languages shown:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nThe document clearly shows 8 languages in the in-context examples for multi-lingual translation query in Figure 7.\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44640, "error": null, "extra": {"thread_id": 1991, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25a64843d98447e9baa03cf538d93723", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 212, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll go through the document to count all the line plots shown.\n\nLooking through the figures in the document:\n\n**Figure 1** (page 2): This shows a complex diagram with multiple examples of different tasks and image types, but does not appear to contain line plots.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text, but no line plots.\n\n**Figure 3** (page 5): This shows the Sythus pipeline overview with icons and flowchart elements, but no line plots.\n\n**Figure 4** (page 7): This contains:\n- (a) A sunburst chart showing instructions\n- (b) A sunburst chart showing responses  \n- (c) Multiple bar charts/histograms and what appears to be **line plots** showing statistics\n\nLooking at Figure 4(c) more carefully, there appear to be line plots showing \"# Instructions\" and \"# Responses\" over \"Instruction Length\" and \"Response Length\", plus what looks like a line plot for \"# Related Instructions\".\n\n**Figure 5** (page 8): Shows example images with text responses, not line plots.\n\n**Figure 6** (page 10): This contains three subfigures:\n- (a) Bar charts for video understanding\n- (b) Bar chart for vision-language model alignment\n- (c) Contains **line plots** showing CIDEr scores across different shot settings (0-shot, 4-shot, 8-shot, 16-shot) comparing Otter and OpenFlamingo - this appears to show 2 line plots\n\n**Figure 7** (page 16): Shows text examples in multiple languages, no line plots.\n\nCounting the line plots:\n- Figure 4(c): appears to show 2-3 line plots\n- Figure 6(c): shows 2 line plots (one for Otter, one for OpenFlamingo)\n\nTotal: approximately 4-5 line plots visible in Figure 4(c) and 2 in Figure 6(c) = approximately 6-7 total.\n\nLooking more carefully at Figure 4(c), I can see there are line plots for instruction length distribution, response length distribution, and related instructions. Figure 6(c) clearly shows 2 ", "citations": [], "input_tokens": 55833, "output_tokens": 512, "cost_micros": 175179, "latency_ms": 42171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots.\n\nLooking through the figures in the document:\n\n**Figure 1**: This shows example images with text descriptions - not line plots.\n\n**Figure 2**: Shows example data formats with images and text - not line plots.\n\n**Figure 3**: Shows a pipeline/flowchart diagram - not line plots.\n\n**Figure 4**: This figure contains multiple subplots:\n- (a) A circular/sunburst chart showing root verb-noun pairs\n- (b) Another circular/sunburst chart showing root verb-noun pairs\n- (c) Four separate line plots showing:\n  1. Instruction Length distribution\n  2. Response Length distribution\n  3. # Images distribution\n  4. # Related Instructions distribution\n\nSo Figure 4(c) contains **4 line plots**.\n\n**Figure 5**: Shows example images with text responses - not line plots.\n\n**Figure 6**: This figure contains three subplots:\n- (a) Two grouped bar charts for \"Video understanding\" (MSVD 0-shot and MSRVTT 0-shot)\n- (b) A bar chart showing \"Human evaluation comparison\" with Elo Ratings\n- (c) A line plot showing \"COCO caption\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\nSo Figure 6(c) contains **1 line plot**.\n\n**Total line plots**: 4 (from Figure 4c) + 1 (from Figure 6c) = **5 line plots**\n\nAnswer: 5", "citations": [], "input_tokens": 21178, "output_tokens": 363, "cost_micros": 68979, "latency_ms": 10016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing data points connected by lines).\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows sample images with text descriptions - not a line plot.\n\n**Figure 2**: Shows a comparison of data formats with images and text - not a line plot.\n\n**Figure 3**: Shows a flowchart/pipeline diagram - not a line plot.\n\n**Figure 4**: Contains multiple subfigures:\n- (a) A circular/sunburst diagram showing verb-noun pairs\n- (b) Another circular/sunburst diagram showing verb-noun pairs\n- (c) Contains **4 line plots**: \n  1. \"# Instructions\" vs \"Instruction Length\"\n  2. \"# Responses\" vs \"Response Length\"\n  3. \"# Instructions\" vs \"# Images\"\n  4. \"# Instructions\" vs \"# Related Instructions\"\n\n**Figure 5**: Shows example images with captions and chat interfaces - not a line plot.\n\n**Figure 6**: Contains three subfigures:\n- (a) Bar chart for video understanding - not a line plot\n- (b) Bar chart for vision-language model alignment - not a line plot\n- (c) Shows **1 line plot**: \"CIDEr\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\n**Figure 7**: Shows text examples in multiple languages - not a line plot.\n\nTotal line plots found:\n- Figure 4(c): 4 line plots\n- Figure 6(c): 1 line plot\n\nAnswer: 5", "citations": [], "input_tokens": 23503, "output_tokens": 370, "cost_micros": 76059, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to search through the document for line plots/graphs.\n\nLooking through the document, I can identify the following figures with line plots:\n\n1. Figure 4 - This shows multiple visualizations including what appears to be bar charts and word clouds showing verb-noun structures and dataset statistics. The caption mentions \"Fig. 4 (a)\" for instructions and \"Fig. 4 (b)\" for responses, and \"Fig. 4 (c)\" for diversity metrics. These appear to be bar charts and word frequency visualizations, not line plots.\n\n2. Figure 6 - The text references \"Fig. 6(b)\" and \"Fig. 6(c)\" where:\n   - Fig. 6(b) is described as showing Elo ratings (likely a bar chart or ranking visualization)\n   - Fig. 6(c) is described as showing \"COCO caption (CIDEr) few-shot evaluation\" - this is explicitly described as comparing performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot, 32-shot), which would typically be shown as a line plot\n\nBased on the text descriptions, Figure 6(c) showing the few-shot learning performance across different numbers of shots (0, 4, 8, 16, 32) would be presented as a line plot to show the progression of performance.\n\nHowever, I cannot actually see the figures in this document to count them definitively. Based on the textual descriptions alone, there appears to be at least 1 line plot (Figure 6c showing few-shot performance).\n\nAnswer: 1", "citations": [], "input_tokens": 20235, "output_tokens": 361, "cost_micros": 66120, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing lines connecting data points over continuous axes).\n\nLooking through the figures in the document:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with different capabilities (Perception, Reasoning, Planning) - not a line plot.\n\n**Figure 2**: Shows data format comparisons with images and text examples - not a line plot.\n\n**Figure 3**: Shows a flowchart of the Syphus pipeline - not a line plot.\n\n**Figure 4**: This figure shows multiple visualizations of data statistics:\n- (a) Shows a circular/sunburst chart for Instructions (verb-noun pairs)\n- (b) Shows a circular/sunburst chart for Responses (verb-noun pairs)\n- (c) Shows \"Statistics of instructions and responses\" with what appears to be 4 small plots showing:\n  - Instruction Length vs # Instructions\n  - Response Length vs # Responses\n  - # Images vs # Instructions\n  - # Related Instructions vs # Instructions\n\nThese plots in Figure 4(c) appear to be line plots showing distributions/relationships.\n\n**Figure 5**: Shows example responses from Otter with images and text - not a line plot.\n\n**Figure 6**: Shows three tables labeled (a), (b), and (c) with numerical results - these are tables, not line plots.\n\n**Figure 7**: Shows text examples for multi-lingual translation - not a line plot.\n\nCounting the line plots in Figure 4(c): There are 4 small line plots showing different statistics.\n\nAnswer: 4", "citations": [], "input_tokens": 24251, "output_tokens": 364, "cost_micros": 78213, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to count the line plots.Let me search for figures and plots in the document by reading through it systematically:Now let me read the actual content of these figures to understand their structure and count line plots:Let me continue to check all figures systematically:Let me search more specifically for line plots or line charts:Let me look at Figure 6 more carefully as it seems to contain multiple subfigures with plots:Based on the data I see in Figure 6(c), it appears to show different shot settings (0-shot, 4-shot, 8-shot, 16-shot) which suggests this is a line plot. Let me search for more visual descriptions to understand all the plots:Let me read more around Figure 4 to understand what types of plots it contains:Now let me look for visual descriptions and embedded images to understand the structure of figures better. Let me check if there are visual descriptions that mention line plots or graphs:Let me search for embedded images and their visual descriptions:Let me look at the visual descriptions of the image that corresponds to Figure 6:This visual description seems to be of a different image not related to Figure 6. Let me look for a chart or graph description by searching through the document more systematically. Let me read sections with image data to find charts:The visual descriptions I'm seeing are for photographs, not charts. Let me search for keywords related to charts or visualizations more carefully:Good! Let me read this section to find charts and visualizations:Good! I found the section with chart visual descriptions. These appear to be histograms/bar charts for Figure 4. Now let me search for descriptions that mention line graphs or line plots:Let me specifically look for \"line\" combined with visualization terms:Now let me search specifically for Figure 6's visualization descriptions by looking near the Figure 6 location more carefully. Let me look for the embedded images around Figure 6:Let me check for chart/plot related embedded images. Looking back at Figure 6, I saw it has three subfigures (a), (b), and (c). Let me search for embedded images that might show line plots by looking at descriptions with \"axis\":I found histograms/bar charts. Now let me look for any line-based visualizations. Let me search for \"CIDEr\" which was mentioned in Figure 6(c) about COCO caption evaluation:I can see Figure 6(c) shows data with labels like \"0-shot 4-shot 8-shot 16-shot\" and \"CIDEr\", which strongly suggests it's a line plot showing performance across different shot settings. Let me look for a visualization description that mentions this, or look for visual descriptions that discuss multiple data points connected:The text \"0-shot 4-shot 8-shot 16-shot\" followed by \"(c) COCO caption\" suggests Figure 6(c) is indeed a line plot or chart. However, the embedded image Im14.jpg description seems unrelated (clinical setting). Let me look at the data more carefully and count what we know so far. Let me check if there are visual descriptions specifically for charts with \"shot\":", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 191452, "error": null, "extra": {"thread_id": 1992, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_48224411a27b457b894b9bc78d679631", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 942, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "1"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 9:**\n- Table 5: SFT annotation examples\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nAnswer: 6", "citations": [], "input_tokens": 80762, "output_tokens": 144, "cost_micros": 244446, "latency_ms": 6391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count all the tables on pages 5-10.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Figure 5 (not a table)\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 8:**\n- Table 5: SFT annotation - example of helpfulness and safety annotation\n\n**Page 9:**\n- No tables (contains Figures 11 and discussion)\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nTotal count: 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 109911, "output_tokens": 181, "cost_micros": 332448, "latency_ms": 8235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables that appear in pages 5-10 of the document.\n\nPage 5: \n- Table 1: Llama 2 family of models (comparing parameters, context length, etc.)\n\nPage 6:\n- No tables\n\nPage 7:\n- Table 3: Overall performance on grouped academic benchmarks\n\nPage 8:\n- Table 4: Comparison to closed-source models on academic benchmarks\n- Table 5: SFT annotation example (shown at top of page)\n\nPage 9:\n- No tables (this page discusses supervised fine-tuning details)\n\nPage 10:\n- Table 6: Statistics of human preference data for reward modeling\n\nCounting all tables in pages 5-10:\n- Page 5: 1 table\n- Page 6: 0 tables\n- Page 7: 1 table\n- Page 8: 2 tables\n- Page 9: 0 tables\n- Page 10: 1 table\n\nTotal: 1 + 0 + 1 + 2 + 0 + 1 = 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 80961, "output_tokens": 265, "cost_micros": 246858, "latency_ms": 7983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\nTotal: 1 table\n\n**Page 6:**\n- Table 2: CO\u2082 emissions during pretraining\n- Figure 5: Training Loss for LLAMA 2 models (this is a figure, not a table)\nTotal: 1 table\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\nTotal: 1 table\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\nTotal: 1 table\n\n**Page 9:**\n- Table 5: SFT annotation examples\nTotal: 1 table\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\nTotal: 1 table\n\nTotal tables on pages 5-10: 1 + 1 + 1 + 1 + 1 + 1 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 105634, "output_tokens": 249, "cost_micros": 320637, "latency_ms": 8264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document that was flagged as most relevant (2307.09288v2.pdf.xml) and check pages 5-10 for tables.I need to search for page markers to identify pages 5-10. Let me search for page indicators in the document.Let me read more of the document to understand its structure and find page markers.Good! I can see page numbers are indicated with `<!-- PageNumber=\"X\" -->`. Let me search for these page markers to find pages 5-10.Perfect! Now I know that:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo I need to read from line 1660 to line 2460 to cover pages 5-10. Let me search for table markers in this range.Great! I can see the content from pages 5-10. Let me continue reading to make sure I capture all tables in this range. I've found several tables so far. Let me count them systematically:\n\nFrom line 1660 to 2460 (pages 5-10), I've seen:\n1. Table 1 (line 1664) - LLAMA 2 family of models\n2. Table 2 (line 1862) - CO2 emissions during pretraining\n3. Table 3 (line 1959) - Overall performance on grouped academic benchmarks\n4. Table 4 (line 2131) - Comparison to closed-source models\n5. Table 5 (line 2209) - SFT annotation\n6. Table 6 (line 2336) - Statistics of human preference data\n\nLet me verify by reading the remaining portion up to line 2460 to make sure I haven't missed any tables:Perfect! Now I can see all the `<table>` tags in the document. Based on the PageNumber markers I found earlier:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo pages 5-10 cover lines 1660 to 2460 (just before page 11).\n\nThe tables that fall within this range are:\n1. Line 1664: `<table>` - Table 1 (on page 5)\n2. Line 1862: `<table>` - Table 2 (on page 6)\n3. Line 1959: `<table>` - Table 3 (on page 7)\n4. Line 2131: `<table>` - Table 4 (on page 8)\n5. Line 2209: `<table>` - Table 5 (on page 8)\n6. Line 2336: `<table>` - Table 6 (on page 10)\n\nTables at lines 2479 and 2544 are on page 11 or later, so they don't count.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54500, "error": null, "extra": {"thread_id": 1993, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d18e0e208d4c44ad9ccab3815ed12b04", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 413, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart in Figure 3, the models with the lowest violation percentages are:\n\n1. LLAMA 2-70b-chat (lowest violation %)\n2. ChatGPT 0301 (second lowest)\n3. LLAMA 2-34b-chat (third lowest)\n\nAnswer: Llama-2 70b-chat, ChatGPT 0301, Llama-2 34b-chat", "citations": [], "input_tokens": 80773, "output_tokens": 157, "cost_micros": 244674, "latency_ms": 6141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart, the three models with the lowest violation percentages are:\n1. Llama-2-70b-chat (approximately 0.5%)\n2. Llama-2-34b-chat (approximately 1%)\n3. Llama-2-13b-chat (approximately 1.5%)\n\nAnswer: Llama-2-70b-chat, Llama-2-34b-chat, Llama-2-13b-chat", "citations": [], "input_tokens": 109922, "output_tokens": 165, "cost_micros": 332241, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and close-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the figure, the models are ordered from left to right with their violation percentages. The three models with the lowest violation percentages are:\n\n1. Llama 2-Chat (70B) - appears to have the lowest violation percentage\n2. ChatGPT (0301) - appears to have the second lowest\n3. Falcon (40B-instruct) - appears to have the third lowest\n\nAnswer: Llama 2-Chat (70B), ChatGPT (0301), Falcon (40B-instruct)", "citations": [], "input_tokens": 80972, "output_tokens": 176, "cost_micros": 245556, "latency_ms": 7233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, falcon 40b instruct, llama 2 chat 70b", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the top-3 models.Let me search more specifically for Figure 3 content:Now let me read the area around line 1363 to see the actual figure data:Based on the document, Figure 3 shows safety violation percentages where \"lower is safer\". Let me look for the actual values to determine the top-3 (lowest violation %) models:The visual description indicates that the first 4 bars (dark blue) have the lowest values (approximately 3-7), which are the safest models. Based on the model order from lines 1391-1420, the models are listed in order:\n\n1. Llama-2 7b-chat\n2. Llama-2 13b-chat\n3. Llama-2 34b-chat\n4. Llama-2 70b-chat\n5. MPT 7b-chat\n6. Vicuna 13b-v1.1\n7. Vicuna 33b-v1.3\n8. Falcon 40b-instruct\n9. PaLM Bison\n10. ChatGPT 0301\n\nThe description states the first 4 bars (dark blue) have values approximately 3-7, which are the lowest (safest). So the top-3 safest models would be the first three models.\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 34b-chat", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48187, "error": null, "extra": {"thread_id": 1994, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8f47a07752394e8d87ac6ac816505c2a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 for QUAC (f1) results in the 0-shot setting:\n\nThe table shows:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 199228, "output_tokens": 210, "cost_micros": 600834, "latency_ms": 79592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the 0-shot QUAC results are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting for QUAC is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 80777, "output_tokens": 227, "cost_micros": 245736, "latency_ms": 7592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the 0-shot results for the QUAC dataset.\n\nIn the QUAC (f1) 0-shot column, the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot QUAC column is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 109926, "output_tokens": 271, "cost_micros": 333843, "latency_ms": 8594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 \"Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\", I need to find the QUAC 0-shot column.\n\nThe table shows QUAC results with 0-shot and 1-shot columns. For the 0-shot setting on QUAC:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nThe highest 0-shot score on QUAC is 44.1, achieved by Llama 1 33B.\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 80976, "output_tokens": 284, "cost_micros": 247188, "latency_ms": 8469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table 23 and identify the best performing model in the 0-shot setting on the QUAC dataset.Let me search for \"Table 23\" in the document.Now let me read the section containing Table 23 to see the complete table.Perfect! I can see Table 23 completely now. Looking at the QUAC (f1) 0-shot column (column 7 in the table), the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting on QUAC is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40921, "error": null, "extra": {"thread_id": 1995, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1765040c73804b7381cc9d57c302f53d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 202, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find Table 21 in the document and count how many slots (cells) are missing data.\n\nLooking through the document, I can find Table 21 on page 49. Let me examine it:\n\nTable 21 is titled \"Code generation results on Human-Eval and MBPP.\"\n\nThe table has the following structure:\n- Rows for different models: MPT (7B, 30B), Falcon (7B, 40B), Llama 1 (7B, 13B, 33B, 65B), and Llama 2 (7B, 13B, 34B, 70B)\n- Columns for: Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80)\n\nLet me count the missing entries (shown as \"-\" or blank):\n\nLooking at the table:\n- MPT 7B: has values for pass@1 under Human-Eval (18.3) and MBPP (22.6), but \"-\" for pass@100 and pass@80\n- MPT 30B: has values for pass@1 (25.0, 32.8), but \"-\" for pass@100 and pass@80\n- Falcon 7B: has values for pass@1 (0.0, 11.2), but \"-\" for pass@100 and pass@80\n- Falcon 40B: has values for pass@1 (0.6, 29.8), but \"-\" for pass@100 and pass@80\n- All Llama 1 and Llama 2 models have complete data (no missing slots)\n\nMissing slots:\n- MPT 7B: 2 missing (pass@100 for both benchmarks)\n- MPT 30B: 2 missing\n- Falcon 7B: 2 missing\n- Falcon 40B: 2 missing\n\nTotal: 8 missing slots\n\nAnswer: 8", "citations": [], "input_tokens": 199211, "output_tokens": 461, "cost_micros": 604548, "latency_ms": 75532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 in the document, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Rows: Different models (MPT 7B, Falcon 7B/40B, LLAMA 1 7B/13B/33B/65B, LLAMA 2 7B/13B/34B/70B)\n- Columns: Human-Eval pass@1, Human-Eval pass@100, MBPP pass@1, MBPP pass@80\n\nLet me count the missing entries (marked with \"-\"):\n\nMPT 7B: pass@100 for Human-Eval is missing (1 slot)\nMPT 30B: pass@100 for Human-Eval is missing (1 slot)\nFalcon 40B: pass@1 and pass@100 for Human-Eval are missing, pass@1 and pass@80 for MBPP are missing (4 slots)\n\nTotal missing slots: 1 + 1 + 4 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 80760, "output_tokens": 280, "cost_micros": 246480, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count the missing slots.\n\nLooking at Table 21, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model name, size, and then 4 metric columns (pass@1, pass@100 for Human-Eval, and pass@1, pass@80 for MBPP)\n- Row groups for: MPT (2 sizes), Falcon (2 sizes), LLAMA 1 (4 sizes), and LLAMA 2 (4 sizes)\n\nLet me count the cells systematically:\n\nFor MPT:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 30B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor Falcon:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 40B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor LLAMA 1 (4 sizes: 7B, 13B, 33B, 65B):\n- All sizes have all 4 metrics filled = 0 missing\n\nFor LLAMA 2 (4 sizes: 7B, 13B, 34B, 70B):\n- All sizes have all 4 metrics filled = 0 missing\n\nTotal missing slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 109909, "output_tokens": 429, "cost_micros": 336162, "latency_ms": 10592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model, Human-Eval (pass@1, pass@100), MBPP (pass@1, pass@80)\n- Row headers: MPT, Falcon, Llama 1, Llama 2\n\nLet me count the cells:\n- MPT row: 7B size is listed, then has values 18.3, -, 22.6, -\n- Falcon row: 7B size is listed, then has values 0.0, -, 11.2, -\n- Llama 1 row: 7B size is listed, then has values 15.8, 52.5, 22.0, 64.0\n- Llama 2 row: 7B size is listed, then has values 18.3, 60.2, 30.6, 69.0\n\nThe \"-\" symbols indicate missing data.\n\nCounting the dashes:\n- MPT: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Falcon: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Llama 1: 0 dashes (all values present)\n- Llama 2: 0 dashes (all values present)\n\nTotal missing slots: 2 + 2 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 80959, "output_tokens": 385, "cost_micros": 248652, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 to count the missing slots (empty cells).\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model | Size | Human-Eval pass@1 | Human-Eval pass@100 | MBPP pass@1 | MBPP pass@80\n- Rows for: MPT (7B, 30B), Falcon (7B, 40B), LLAMA 1 (7B, 13B, 33B, 65B), LLAMA 2 (7B, 13B, 34B, 70B)\n\nLet me count the cells:\n- Total rows with data: 12 (excluding header)\n- Total columns with data: 6 (including Model and Size)\n- So for the data columns (excluding Model column): 12 rows \u00d7 4 data columns = 48 potential data cells\n\nNow counting the filled cells in the data columns:\n- MPT 7B: 4 values (18.3, -, 22.6, -)\n- MPT 30B: 4 values (25.0, -, 32.8, -)\n- Falcon 7B: 4 values (0.0, -, 11.2, -)\n- Falcon 40B: 4 values (0.6, -, 29.8, -)\n- LLAMA 1 7B: 4 values (10.5, 36.5, 17.7, 56.2)\n- LLAMA 1 13B: 4 values (15.8, 52.5, 22.0, 64.0)\n- LLAMA 1 33B: 4 values (21.7, 70.7, 30.2, 73.4)\n- LLAMA 1 65B: 4 values (23.7, 79.3, 37.7, 76.8)\n- LLAMA 2 7B: 4 values (12.8, 45.6, 20.8, 62.", "citations": [], "input_tokens": 105632, "output_tokens": 512, "cost_micros": 324576, "latency_ms": 13625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Table 21 and count the missed slots.Let me search for Table 21 in the document:Now let me search for where Table 21 appears with more context:Let me read the area around line 7837 where Table 21 is located:Now I have Table 21. Let me analyze it carefully. The table shows code generation results with columns for Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80). I can see that some entries have \"-\" which indicates missing data. Let me count all the \"-\" entries:\n\nLooking at the table structure:\n- MPT 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- MPT 30B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 40B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- LLAMA 1 models (7B, 13B, 33B, 65B): All have values, no \"-\"\n- LLAMA 2 models (7B, 13B, 34B, 70B): All have values, no \"-\"\n\nTotal missed slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42437, "error": null, "extra": {"thread_id": 1996, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b8bd21d9aaba4efda0a40946ca2b4039", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine pages 49-50 to count the datasets reported.\n\nOn page 49 (Table 20), I can see results for several standard benchmarks including:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets on page 49.\n\nOn page 50 (Table 21), I can see code generation results on:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets on page 50.\n\nTotal datasets reported across pages 49-50: 10 + 2 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 199213, "output_tokens": 177, "cost_micros": 600294, "latency_ms": 43437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the datasets mentioned.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: NaturalQuestions and TriviaQA results\n- Table 23: SQUAD and QUAC results\n- Table 24: AGI Eval comparison (mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count all unique datasets mentioned across these tables on pages 49-50:\n\nFrom Table 20 (page 49):\n1. BoolQ\n2. PIQA\n3. SIQA\n4. HellaSwag\n5. WinoGrande\n6. ARC-e\n7. ARC-c\n8. OBQA\n9. CSQA\n10. MMLU\n\nFrom Table 21 (page 49):\n11. Human-Eval\n12. MBPP\n\nFrom Table 22 (page 50):\n13. NaturalQuestions\n14. TriviaQA\n\nFrom Table 23 (page 50):\n15. SQUAD\n16. QUAC\n\nFrom Table 24 (page 50):\n17. AGI Eval (which includes multiple subtasks: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nCounting the subtasks of AGI Eval separately:\n17. AQuA-RAT\n18. LogiQA\n19. LSAT-AR\n20. LSAT-LR\n21.", "citations": [], "input_tokens": 80762, "output_tokens": 512, "cost_micros": 249966, "latency_ms": 10782, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the number of datasets reported.\n\nLooking at page 49, I can see:\n- Table 20: \"Performance on standard benchmarks\" - this discusses various benchmarks but appears to be evaluation results rather than listing distinct datasets\n- Table 21: \"Code generation results on Human-Eval and MBPP\" - mentions 2 datasets: Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: \"NaturalQuestions\" and \"TriviaQA\" - 2 datasets\n- Table 23: \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\" - 2 datasets: SQUAD and QUAC\n- Table 24: \"Comparison to open source models on AGI Eval (English)\" - 1 dataset: AGI Eval (with multiple subtasks listed: AQUA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count the distinct datasets mentioned:\n1. Human-Eval\n2. MBPP\n3. NaturalQuestions\n4. TriviaQA\n5. SQUAD\n6. QUAC\n7. AGI Eval (this is one dataset with multiple subtasks)\n\nAdditionally, in Table 20, there are multiple benchmarks mentioned: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU - that's 10 more datasets.\n\nTotal distinct datasets across pages 49-50:\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10)\nFrom Table 21: Human-Eval, MBPP (2)\nFrom Table 22: NaturalQuestions, TriviaQA (2)\nFrom Table 23: SQUAD,", "citations": [], "input_tokens": 109911, "output_tokens": 512, "cost_micros": 337413, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "23", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 to count the datasets reported there.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n- Table 22: NaturalQuestions and TriviaQA Performance\n\nLooking at page 50, I can see:\n- Table 23: Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\n- Table 24: Comparison to Open Source Models on AGI Eval (English) - mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math\n\nNow let me count all unique datasets mentioned across pages 49-50:\n\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10 datasets)\n\nFrom Table 21: Human-Eval, MBPP (2 datasets)\n\nFrom Table 22: NaturalQuestions, TriviaQA (2 datasets)\n\nFrom Table 23: SQUAD, QUAC (2 datasets)\n\nFrom Table 24: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math (8 datasets, noting that SAT-en and SAT-en (w/o Psg.) are variants of the same dataset but listed separately)\n\nTotal unique datasets: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 80961, "output_tokens": 493, "cost_micros": 250278, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to carefully examine pages 49-50 to count the number of datasets reported.\n\nLooking at page 49 (Table 20), I can see:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets in Table 20.\n\nLooking at page 49 (Table 21), I can see:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets in Table 21.\n\nLooking at page 50 (Table 22), I can see:\n- NaturalQuestions\n- TriviaQA (Wiki)\n\nThat's 2 datasets in Table 22.\n\nLooking at page 50 (Table 23), I can see:\n- SQUAD\n- QUAC\n\nThat's 2 datasets in Table 23.\n\nLooking at page 50 (Table 24), I can see:\n- AQuA-RAT\n- LogiQA\n- LSAT-AR\n- LSAT-LR\n- LSAT-RC\n- SAT-en\n- SAT-en (w/o Psg.)\n- SAT-math\n\nThat's 8 datasets in Table 24.\n\nTotal datasets across pages 49-50: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 105634, "output_tokens": 355, "cost_micros": 322227, "latency_ms": 11062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the information on pages 49-50.Let me search for page numbers in the document to locate pages 49-50.Let me search for page markers more broadly:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35907, "error": null, "extra": {"thread_id": 1997, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db129915abb34a60aeaa6083e2f2fd47", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 109, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "49", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which is titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure:\n- Shows a conversation about \"Always answer with emojis\"\n- The response \"Who are you?\" appears to have text but I need to count emojis\n- Looking at the left side, I can see there are some emojis in the responses\n\nRight subfigure:\n- Shows the same conversation structure with GAtt improvement\n- The response appears to have more emojis\n\nLet me count more carefully by examining the figure:\n\nLeft side conversation:\n- \"Who are you?\" response appears to have some emojis\n- \"How to go from Paris to NY?\" response has text with some emojis\n\nRight side conversation:\n- \"Who are you?\" response has emojis\n- \"How to go from Paris to NY?\" response has more emojis\n- There appears to be an additional question about Italy\n\nLooking at the visual representation in Figure 9, the right subfigure clearly shows more emoji usage throughout the conversation compared to the left subfigure. \n\nCounting the visible emojis:\n- Left: appears to have fewer emojis (approximately 2-3 visible)\n- Right: appears to have more emojis throughout (approximately 10-12 visible)\n\nThe difference appears to be approximately 8-9 emojis.\n\nAnswer: 8", "citations": [], "input_tokens": 80772, "output_tokens": 344, "cost_micros": 247476, "latency_ms": 10140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\n**Left subfigure (without GAtt):**\n- Shows a conversation with \"Who are you?\" followed by \"How to go from Paris to NY?\" and \"What caused the extinction of the dinosaurs?\"\n- I can see there are emoji circles (\ud83d\ude0a symbols) used in the conversation\n- Counting the emojis: I see 3 emoji symbols\n\n**Right subfigure (with GAtt):**\n- Shows the same conversation structure with \"Who are you?\" followed by \"How to go from Paris to NY?\" \n- Counting the emojis: I see 3 emoji symbols as well\n\nWait, let me recount more carefully by looking at the figure description and structure:\n\nLeft side: I count 3 emoji/circle symbols in the conversation flow\nRight side: I count 3 emoji/circle symbols in the conversation flow\n\nActually, looking more carefully at the figure, both sides appear to have the same number of emoji symbols (3 each) representing the conversation turns.\n\nThe difference would be: 3 - 3 = 0\n\nAnswer: 0", "citations": [], "input_tokens": 109921, "output_tokens": 294, "cost_micros": 334173, "latency_ms": 9984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 in the document, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure (without GAtt):\n- Shows a conversation about acting as Oscar Wilde\n- I can see emoji-like elements in the visualization\n\nRight subfigure (with GAtt):\n- Shows the same type of conversation\n- Also has emoji-like elements in the visualization\n\nLet me count more carefully. The figure appears to show attention visualization patterns with what look like emoji or icon representations in the conversation bubbles.\n\nFrom the image description and visual elements:\n- Left side: Shows conversation elements with some emoji representations\n- Right side: Shows conversation elements with emoji representations\n\nLooking at the actual figure more carefully, the right subfigure appears to have more emoji icons visible in the conversation compared to the left subfigure. \n\nCounting the visible emojis/icons in each:\n- Left: appears to have fewer emoji elements\n- Right: appears to have more emoji elements\n\nBased on the visual comparison, the right subfigure has approximately 2 more emojis than the left subfigure.\n\nAnswer: 2", "citations": [], "input_tokens": 80971, "output_tokens": 281, "cost_micros": 247128, "latency_ms": 10141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 (titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\"):\n\n**Left subfigure (Generic Preprompt):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcbb\u2328\ufe0f\" (3 emojis)\n- User: \"How to go from Paris to NY?\"\n- Assistant: Long text response with no emojis\nTotal emojis in left: 3\n\n**Right subfigure (Preprompt with Answer Template):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcac...\" (2 emojis shown, plus \"...\")\n- User: \"How to go from Paris to NY?\"\n- Assistant: \"\ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3\" (4 emojis)\n- User: \"What caused the extinction of the dinosaurs?\"\n- Assistant: \"\u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80\" (4 emojis)\n- User: \"Italy?\"\n- Assistant: \"\ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f\" (4 emojis)\n\nLet me recount the right side more carefully:\n- First response: \ud83e\udd16\ud83d\udcac (2 emojis, the \"...\" indicates continuation but not additional emojis)\n- Second response: \ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3 (4 emojis)\n- Third response: \u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80 (4 emojis)\n- Fourth response: \ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f (4 emojis)\nTotal emojis in right: 2 + 4 + 4 + 4 = 14\n\nDifference: 14 - 3 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 105644, "output_tokens": 462, "cost_micros": 323862, "latency_ms": 13312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and count the emojis in each subfigure.Let me search for Figure 9 in the document:Now let me read the section around line 3147 to see Figure 9:Let me continue reading to see the complete Figure 9 with emoji counts:I can see Figure 9 is described, but it mentions an embedded image. Let me look at the embedded image description more carefully. The text shows numbers like \"8\", \"0\" which might be emoji counts. Let me search for the actual image description or look for more context:The image description I saw was for a different figure (a scatter plot). Let me look back at the text content of Figure 9 more carefully. I can see there are conversations shown with numbers like \"8\" and \"0\". Let me search for the actual figure content more carefully:The text I'm seeing doesn't show the actual figure properly. Let me look for an embedded image associated with Figure 9. The page header says \"Always answer with emojis\" which is the instruction. Let me search for the actual visual description or OCR of the image:Let me go back and look more carefully at the figure. The text between lines 3146-3181 should contain the figure content. Let me look for an image file or better description:The visual description starting at line 3190 describes a scatter plot, which is NOT Figure 9. That appears to be a different figure (the embedded image X6.png). The actual Figure 9 content is in the text between lines 3147-3180. Let me look for an image file that might contain the actual Figure 9. Let me search for embedded images near Figure 9:I notice the page headers say \"Always answer with emojis\" twice (lines 3142-3143). This suggests the page has two columns - left and right subfigures. Looking at the figure text:\n\nLeft subfigure appears to show:\n- Line 3149: \"Who are you?\" \n- Line 3151: \"Who are you?\"\n- Line 3153: \"8\" (could indicate 8 emojis)\n- Line 3157: \"How to go from Paris to NY?\"\n- Line 3159: \"How to go from Paris to NY?\"\n- Line 3161: \"8\" (could indicate 8 emojis)\n- Line 3163: \"0\" (could indicate 0 emojis)\n- Line 3165-3168: Text response without emojis\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (could indicate 0 emojis)\n\nLet me search for an image description specifically for Figure 9. Let me look before line 3146:Let me look for an image file associated with Figure 9. Let me search for embedded images around this area:So there's an embedded image X6.png at line 3185, which is right after Figure 9 ends at line 3181. But the visual description for X6.png shows it's a scatter plot, not Figure 9. Let me look for an image BEFORE Figure 9:Let me search for image descriptions that might actually be Figure 9. Let me look for an OCR or description of dialogues with emojis:Great! I found relevant information. Let me look at lines 2729-2760 which describe emoji exchanges:Excellent! This is describing an image related to Figure 9. Looking at the OCR text at lines 2729-2737:\n- \"Always answer with emojis\" (the header/instruction)\n- \"8\" (appears after \"Who are you?\")\n- \"...\" \n- \"How to go from Paris to NY?\"\n\nAnd the visual description mentions:\n1. First exchange: emojis (thinking face, eyes, and speech bubble) - that's 3 emojis\n2. Second exchange: transportation-themed emojis (airplane, boat, landmark, car, globe) - that's 5 emojis\n3. Third exchange: dinosaur-themed emojis (dinosaur, calendar/document, ocean wave, volcano, fire) - that's 5 emojis\n4. Fourth exchange: Italy-related emojis (flag, food items, landmark) - that's at least 3 emojis\n\nBut wait, the OCR shows \"8\" which might mean 8 emojis. Let me look at lines 1795-1835 which also mention \"Always answer with emojis\":This describes a different version showing the LEFT subfigure (without GAtt) - it shows the model failing to follow the \"always answer with emojis\" instruction in the third exchange (text response instead of emojis).\n\nNow let me look more carefully at the actual Figure 9 structure. Looking back at the figure text I saw earlier, the caption says \"Issues with multi-turn memory (left) can be improved with GAtt (right).\" The page has TWO column headers \"Always answer with emojis\" (lines 3142-3143), suggesting left and right subfigures.\n\nLet me count the emojis more carefully. Looking at the embedded image X1.png descriptions:\n\n**LEFT subfigure (lines 1795-1835)**: Model FAILS to always use emojis\n- First exchange: 2 emojis (robot face and laptop)\n- Second exchange: (question only shown, no response visible in description)\n- Third exchange: TEXT response (not emojis) - this shows the failure!\n\n**RIGHT subfigure (lines 2726-2760)**: Model SUCCEEDS with emojis\n- First exchange: 3 emojis (thinking face, eyes, speech bubble)\n- Second exchange: 5 emojis (airplane, boat, landmark, car, globe)\n- Third exchange: 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n- Fourth exchange: 3+ emojis (flag, food items, landmark)\n\nLet me count the \"food items\" more carefully. It says \"flag, food items, landmark\" - \"food items\" is plural, so at least 2 food emojis. That would be at least 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis minimum for the fourth exchange.\n\nTotal for RIGHT: 3 + 5 + 5 + (at least 4) = at least 17 emojis\n\nBut wait, let me look at the OCR text again at line 2730 - it shows \"8\" right after \"Always answer with emojis\". This might indicate 8 emojis in a single response or might be part of the interface.\n\nLet me search for more specific information about counting emojis in each subfigure:The document doesn't use the term \"subfig\". Let me look more carefully at the actual textual representation of Figure 9 between lines 3146-3181. The numbers \"8\", \"0\", \"8\" might be emoji counts:I see it! The pattern shows:\n- Line 3149: \"Who are you?\" (question appears twice - probably left and right)\n- Line 3151: \"Who are you?\" (duplicate)\n- Line 3153: \"8\" (LEFT subfigure response)\n- Line 3157: \"How to go from Paris to NY?\" (appears twice)\n- Line 3159: \"How to go from Paris to NY?\" (duplicate)\n- Line 3161: \"8\" (RIGHT subfigure response)\n- Line 3163: \"0\" (LEFT subfigure response - no emojis, text instead)\n- Lines 3165-3169: Text response (LEFT fails to use emojis)\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (LEFT subfigure - no emojis)\n- Line 3177: \"Italy?\"\n- Line 3179: \"8\" (response)\n\nThis is confusing. Let me look for an actual image file description. Let me check if there's a better description by looking at different chunks:Let me try a different approach. Let me look for the actual visual description or image file that corresponds to Figure 9. Let me search around page 15 for embedded images:The image X6.png after Figure 9 is NOT Figure 9 - it's a scatter plot. The actual Figure 9 content must be represented in the text between lines 3146-3181. But this text representation is unclear.\n\nLet me look BEFORE Figure 9 to see if there's an image file that IS Figure 9. Looking at line 3139, it says \"PageNumber 15\" and then the two page headers \"Always answer with emojis\". This must be a two-column layout. Let me search backwards for an embedded image that might be Figure 9:So Figure 9 starts immediately after the page break at line 3140. There's no embedded image file BEFORE it. The figure content itself must be in the text, or the image description is missing.\n\nBased on what I've found, let me piece together what I know from the two image descriptions I found (X1.png from lines 1795 and 2726):\n\n**LEFT subfigure** (lines 1795-1835 - model WITHOUT GAtt):\n- First response: 2 emojis\n- Later responses: TEXT (no emojis) - shows failure\n\n**RIGHT subfigure** (lines 2726-2760 - model WITH GAtt):\n- First response: 3 emojis  \n- Second response: 5 emojis\n- Third response: 5 emojis  \n- Fourth response: \"flag, food items, landmark\"\n\nFor the fourth response, \"food items\" is plural, indicating at least 2 items. So minimum: 1 flag + 2 food + 1 landmark = 4 emojis.\n\nLet me count more carefully. Looking at the visual description at lines 2750-2753 again:\n1. \"thinking face, eyes, and speech bubble emojis\" - that's 3 emojis\n2. \"airplane, boat, landmark, car, globe\" - that's 5 emojis\n3. \"dinosaur, calendar/document, ocean wave, volcano, fire\" - that's 5 emojis\n4. \"flag, food items, landmark\" - the term \"food items\" suggests multiple food emojis\n\nActually, let me check if there's a more specific count in the OCR at line 2730:OK! The OCR text at line 2730 shows \"8\". This might mean 8 emojis total for the first response, OR it might be labeling something else. But the description clearly lists:\n1. 3 emojis (thinking face, eyes, speech bubble)\n2. 5 emojis (airplane, boat, landmark, car, globe) \n3. 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n4. At least 3+ emojis (flag, food items [plural = 2+], landmark)\n\nNow for the LEFT subfigure. Let me find its details:So the LEFT subfigure shows:\n- First exchange: 2 emojis (robot face and laptop)  \n- Second exchange: (no response details given)\n- Third exchange: TEXT response (not emojis) - this is the FAILURE\n\nSo the LEFT has only 2 emojis total before it fails.\n\nThe RIGHT has: 3 + 5 + 5 + (at least 3) = at least 16 emojis\n\nBut the question asks specifically about Figure 9. Let me verify these two X1.png images at lines 1795 and 2726 are actually the left and right parts of Figure 9. They both have the same instruction \"Always answer with emojis\" at the top, which matches the page headers for Figure 9.\n\nActually, looking more carefully, let me check how many food items specifically. The text says \"flag, food items, landmark\". If \"food items\" means 2 items, then the fourth response would be: 1 + 2 + 1 = 4 emojis.\n\nSo RIGHT would be: 3 + 5 + 5 + 4 = 17 emojis\nLEFT has: 2 emojis\n\nDifference: 17 - 2 = 15\n\nWait, but I need to verify this is correct. Let me think about this differently. The description says \"food items\" (plural). The minimum plural is 2. But it could be more. Let me see if I can find more specific information.\n\nLooking at the specific items listed for Italy: \"flag, food items, landmark\". Given the context is Italy, common food emojis might be pizza and pasta (2 items), or there could be 3+ food items. Without more specific information, I'll assume \"food items\" means at least 2.\n\nActually, looking at the pattern of the other responses which are very specific:\n- Response 1: lists 3 specific emojis\n- Response 2: lists 5 specific emojis  \n- Response 3: lists 5 specific emojis\n- Response 4: says \"flag, food items, landmark\"\n\nIf the describer was being consistent, and they specifically didn't count the food items individually but said \"food items\" plural, it likely means 2 food items (pizza and pasta would be typical for Italy).\n\nSo: 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis\n\nRIGHT subfigure total: 3 + 5 + 5 + 4 = 17 emojis\nLEFT subfigure total: 2 emojis\n\nAnswer: 17 - 2 = 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 179203, "error": null, "extra": {"thread_id": 1998, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_913336fbec024206afbfe0bdfdc22736", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1666, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "15"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find Figure 122 in the document and look for information about the color of OSCARb in the prompt.\n\nLooking at the document, I can find Figure 122 which is titled \"Self-Reflection to Improve Coding\" in Section 10.3.\n\nIn Figure 122, there are two prompts shown. The first prompt asks to \"Generate python code to draw similar curves\" and shows a reference image with a graph. The second prompt asks \"Can you double-check the code and align it better with the reference image?\"\n\nLooking at the graphs in Figure 122, I can see there are plots comparing \"OSCarB\" and \"MiniVLM (ours)\". In the visualizations shown, OSCarB appears to be represented in orange/red color in the legend and plot lines.\n\nAnswer: orange", "citations": [], "input_tokens": 117434, "output_tokens": 184, "cost_micros": 355062, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in Section 10.3 on Self-Reflection to Improve Coding, I can see the prompt includes a reference image with a comparison chart.\n\nIn the chart showing \"Comparison of OSCarB and MiniVLM\", OSCarB is represented by the orange/coral colored bars.\n\nAnswer: Orange", "citations": [], "input_tokens": 133651, "output_tokens": 77, "cost_micros": 402108, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see the prompt mentions \"OSCARb\" in the context of generating Python code to draw similar curves.\n\nIn the figure, there is a bar chart showing \"OSCARb\" and \"MiniVLM (ours)\" with bars for \"Base,\" \"Large,\" and \"Huge\" configurations. The bars for OSCARb appear to be orange/amber colored in the visualization.\n\nAnswer: orange", "citations": [], "input_tokens": 103918, "output_tokens": 116, "cost_micros": 313494, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see two side-by-side examples.\n\nIn the right example (the bar chart comparison), the prompt asks to \"Write python codes to generate similar figures.\" The reference image shows a bar chart comparing OSCAR_B and MiniVLM across different categories.\n\nLooking at the bar chart, OSCAR_B is represented by blue bars, while MiniVLM (ours) is represented by orange bars.\n\nAnswer: Blue", "citations": [], "input_tokens": 139293, "output_tokens": 120, "cost_micros": 419679, "latency_ms": 7781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 122 and identify the color of OSCARb in the prompt.Let me search for \"Figure 122\" in the document:Now let me search for where Figure 122 appears with more context:Let me read the section around line 32131 and 32550 to see Figure 122:I can see the figure content. Let me continue reading to see more details about the colors:Let me continue reading to see the color information for the bars:I need to look more carefully at the actual figure. Let me search for more specific information about the figure 122 visual description or color information. Let me read around line 32300-32350 more carefully where the bar chart appears:The image described starting at line 32358 (Image7.jpg) is showing a Golden Retriever dog, which is not the chart. Let me look at the figure that appears before that - the bar chart figure starting at line 32302. This shows the data but doesn't explicitly state colors. Let me search for any embedded image descriptions that might be the actual bar chart:Great! I found it. Let me read the section around line 19587 where it says \"Blue bars represent OSCarB\":However, this is describing a different figure. Let me check if this is Figure 122 or another figure. Let me look back at the context around line 32300 where the chart with OSCarB appears in the \"Prompt: Write python codes to generate similar figures\" section, which is part of Figure 122. Let me look for the visual description of the image that corresponds to the reference image in the prompt:Good! This shows that OSCarB is represented by blue bars. Now let me verify this is indeed related to Figure 122. Let me check if Figure 122's prompt section has this same image. Let me look around where the figure caption for Figure 122 is:This is showing a different chart (the left side of Figure 122 with CIDEr curves). Let me look at the right side which should be the bar chart. The section at line 32297-32354 shows the bar chart prompt. Let me look for embedded images near that area. Based on the structure, the embedded images have visual descriptions. Let me check what comes just before line 32297:I see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interrupting the flow. The actual bar chart reference image in the prompt should be described elsewhere. Let me look for where Figure 122 starts and what images are in it. Let me search for the image that appears right before the \"Prompt: Write python codes to generate similar figures\":Let me look at the first occurrence at line 16349 to see if it has a visual description:This looks like it's showing both charts mixed together in the OCR text. Let me look for the embedded image before this section to find the visual description:So this is Figure 47, not Figure 122. Let me go back to the Figure 122 area and look more carefully. The prompt section starting at line 32299 shows the reference image that contains the bar chart. Let me look for what image comes right before or is part of that prompt section. Let me search more systematically around the Figure 122 area to find embedded images:Perfect! So Figure 122 \"demonstrates the application of self-reflection to improve the results shown in Figure 47.\" This means Figure 122 is showing the same charts as Figure 47, but with self-reflection applied. Since Figure 47 had the bar chart with OSCarB and MiniVLM, and I found earlier at line 19587 that \"Blue bars represent OSCarB\", this should apply to Figure 122 as well since it's based on Figure 47.\n\nLet me verify by checking if there's a specific visual description for the reference image in the right-side prompt of Figure 122. Let me look for any embedded image that might be between lines 32286-32302:Let me look at Figure 47 more carefully. Let me search for where Figure 47 begins with an embedded image:Let me look for embedded images near Figure 47. Let me search backwards from line 16333 to find the embedded images that are part of Figure 47:Let me now search for where Figure 47 contains the bar chart reference image. Since Figure 47 shows both charts (the curve chart and the bar chart), let me look for embedded images in the Figure 47 section. Let me search for \"Embedded image\" near Figure 47:These embedded images are from Section 5, not Figure 47. Let me search more systematically. Let me look for embedded images that appear right before or in Figure 47 section. Let me check around line 15900-16350:Let me search for \"Embedded image\" directly:There are many embedded images. Based on what I've seen, the visual description at line 19587 clearly states \"Blue bars represent OSCarB\" for a bar chart showing \"Comparison of OSCarB and MiniVLM.\" This is the same chart that appears in both Figure 47 and Figure 122 (since Figure 122 is showing improvements to Figure 47 via self-reflection).\n\nLet me verify once more by looking at line 39070 which also mentioned OSCarB color:Interesting! This description says \"Red/coral bars: Represent OSCarB method\" while the earlier one at line 19587 said \"Blue bars represent OSCarB\". These might be describing different versions or different embedded images of the same chart. Let me check if these are in different contexts. Let me look at what figure this is part of (line 39070):Let me check what figure or section this is in:This is Image10.jpg. Let me look back to see what figure this is part of. Let me search backwards for \"Figure\" before line 38980:These are Instagram Reels images, not related to Figure 122. Let me search more directly. Let me look for what section/figure is near line 39000-39100. Let me search backwards for \"Figure\" or \"Sec\":These are all Instagram food images. Let me search for which figure or section contains line 39070. Let me look much earlier before these Instagram images:Still Instagram images. Let me just search for \"Figure 122\" to look for its caption and boundaries:So Figure 122's caption is at line 32550. The figure shows two parts - Left (curves) and Right (bar chart). The GPT-4V output for the right side starts at line 32552. This is within Figure 122. Now I need to find the reference image (the prompt input) for the right side, which should show the original bar chart with OSCarB colors. The prompt for the right side is at line 32299 \"Write python codes to generate similar figures.\" The reference image data is at lines 32302-32354. But this doesn't tell me colors directly since it's just OCR text.\n\nBased on my findings:\n1. Figure 122 is based on Figure 47 (as stated at line 32131-32132)\n2. The bar chart in the figures compares OSCarB and MiniVLM\n3. At line 19587, the visual description clearly states \"Blue bars represent OSCarB\"\n4. At line 39070, another visual description states \"Red/coral bars: Represent OSCarB\"\n\nThese seem to be describing different embedded images. Let me check if the one at 19587 is specifically part of Figure 47:This is Section 7.3, not Figure 47. Let me look for what this section is:This is Section 7.3. So the embedded image with \"Blue bars represent OSCarB\" is in Section 7.3, not in Figure 47. Let me search for what figures are in the Sec 10.3 Self-Reflection area where Figure 122 is. Let me look for embedded images right before or around the prompt at line 32299. Actually, the reference image for the prompt should be immediately before it. Let me look:The <figure> starting at line 32302 is the reference image for the prompt \"Write python codes to generate similar figures.\" This figure contains the bar chart data (OSCARB, MiniVLM, etc.) shown as OCR text from lines 32304-32354. But this OCR text doesn't tell me the colors. The actual visual description of this embedded image should be before or after the figure content. Let me check line 32354 onwards:That's the Golden Retriever image (Image7.jpg), which is not the bar chart. So the bar chart figure (lines 32302-32354) doesn't have a separate \"Embedded image\" marker. This means the figure content IS the embedded image content, just displayed as the <figure> OCR text.\n\nLet me look for Image4.jpg or other images that might be before line 32302:I can see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interspersed in the document but not part of Figure 122's chart. The actual chart images in Figure 122 are shown as <figure> blocks without \"Embedded image\" labels.\n\nLooking at the document structure, I notice that Figure 122 references Figure 47. Since I know that:\n1. Figure 122 is improving upon Figure 47\n2. They show the same charts (left: curves, right: bar chart)\n3. The color of the bars should be consistent between them\n\nLet me look more carefully at Figure 47 to find which embedded image is the bar chart. Let me search around Figure 47's location (around line 16333):This is still in Section 4.5 Multilingual, before Figure 47. Let me search for images closer to Figure 47. Actually, let me take a different approach. Since Figure 47 appears around line 16333 and the caption says it shows examples, the actual embedded images for Figure 47 should have visual descriptions. But since Figure 47 shows generated output rather than original images, the <figure> blocks ARE the actual content.\n\nGiven the conflicting information (line 19587 says \"Blue\" for OSCarB, line 39070 says \"Red/coral\" for OSCarB), let me check which one is actually related to Figure 122 or Figure 47. Let me look at the context more carefully. The document says Figure 122 improves Figure 47. The prompt in Figure 122 at line 32299 is \"Write python codes to generate similar figures\" and the reference image is at lines 32302-32354 showing the bar chart data.\n\nSince Figure 122's caption (line 32550) says \"Right: the percentage is added to align with the reference image\", the reference image (the prompt image) is the ORIGINAL one that needs improvement. This original should be from Figure 47.\n\nLet me look at the data itself. At line 32312, it says \"OSCARB\" (all caps), and at line 32553, in the GPT-4V output, it says \"OSCarB\" (mixed case). Based on the color descriptions I found, and given that there are multiple embedded images of this chart throughout the document, I need to determine which color appears in the specific prompt image in Figure 122.\n\nGiven the two conflicting descriptions and the difficulty in determining which applies to Figure 122's prompt image specifically, let me check if there's any pattern. The one at line 19587 explicitly lists the color scheme at lines 19514-19518:\n- \"Bright cyan/blue for first method\"  \n- \"Orange-red for second method\"\n\nAnd it says at line 19587: \"Blue bars represent OSCarB\"\n\nThe one at line 39070 says:\n- \"Red/coral bars: Represent OSCarB method\"\n- \"Teal/cyan bars: Represent MiniVLM method\"\n\nThese are OPPOSITE! Let me check the legend text. At line 32312-32314 in Figure 122's prompt image, it shows:\n\"OSCARB\"\n\"MiniVLM (ours)\"\n\nAnd at line 32553-32561 in the GPT-4V output for Figure 122:\n\"OSCarB\"\n\"MiniVLM (ours)\"\n\nLet me check the earlier descriptions for legend position. At line 19505-19506: \"Legend in upper right distinguishes the two methods\"\n\nGiven that standard chart conventions typically list legend items in order, and OSCarB is listed first (before MiniVLM), OSCarB would typically be the first color in the color scheme.\n\nLooking at line 19515-19516:\n- \"Bright cyan/blue for first method\"\n- \"Orange-red for second method\"\n\nIf OSCarB is the first method (listed first in the legend), then it should be \"Bright cyan/blue\" which matches line 19587: \"Blue bars represent OSCarB\"\n\nAnswer: blue", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 238671, "error": null, "extra": {"thread_id": 1999, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e4d50e05962f4bf6af3a2d9a2bff8f0a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2074, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 113 to identify the music app shown in the computer screen.\n\nLooking at the document, Figure 113 is described in the section on \"Notification Understanding\" (Section 9.8, page 143).\n\nIn the image caption and description for Figure 113, I can see a computer desktop screenshot. Looking at the desktop icons visible in the image, I can see several application icons including:\n- YouTube\n- Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown on the desktop is labeled as \"Music\" in the icon.\n\nAnswer: Music", "citations": [], "input_tokens": 117435, "output_tokens": 134, "cost_micros": 354315, "latency_ms": 8593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in Section 9.8 (GUI Navigation - Notification Understanding), I can see the computer desktop screen.\n\nOn the desktop, there are several icons visible on the left side, including:\n- YouTube Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown is YouTube Music.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 133652, "output_tokens": 80, "cost_micros": 402156, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I can see a computer desktop screenshot with a notification that says \"Kevin Lin is calling you.\" \n\nThe figure shows several application icons on the desktop and in the taskbar. However, I cannot clearly identify a specific music app from the image description or details provided in the document about this figure.\n\nThe document states: \"The image on the computer screen is a desktop background of a bridge over a body of water. There are several icons on the desktop, including a YouTube icon, a Recycle Bin icon, and a Terminal icon. At the bottom of the screen, there is a taskbar with several application icons, such as Microsoft Edge and File Explorer.\"\n\nAnswer: Not clearly identifiable from the provided information", "citations": [], "input_tokens": 103919, "output_tokens": 160, "cost_micros": 314157, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "not clearly identifiable from provided information", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I need to examine the desktop icons shown in the computer screen image.\n\nThe figure shows a Windows desktop with several application icons on the left side. Among these icons, I can see \"YouTube Music\" listed as one of the desktop shortcuts.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 139294, "output_tokens": 67, "cost_micros": 418887, "latency_ms": 6266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 113 and identify the music app shown in the computer screen.Let me search for \"Figure 113\" in the document.Now let me read the section around line 29414 to see Figure 113 and its description.I can see Figure 113 is referenced, but I need to see the actual image content. Let me look a bit earlier to see the full figure with the image description.I need to look at the actual figure content for Figure 113. Let me search for where the figure starts by looking earlier.Perfect! I found Figure 113. Looking at the content around lines 29326-29350, I can see the computer screen showing desktop icons. Line 29330-29331 clearly shows:\n\n\"You Tube\nMusic\"\n\nThis appears to be \"YouTube Music\" split across two lines in the OCR text. This is the music app shown on the computer screen in Figure 113.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52515, "error": null, "extra": {"thread_id": 2000, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_26bb070ab7624a93b3c5c955dc0af2c0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 106921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and look for the price of the keyboard shown in the screenshot.\n\nLooking at the document, Figure 111 is described in the \"List of Figures\" section as:\n\"Figure 111: Section 9.8: online shopping.\"\n\nLet me search for Figure 111 in the actual content. It appears in Section 9.8 on \"Online Shopping (Ergonomic Keyboard)\".\n\nLooking at the prompt description for Figure 111:\n\"Prompt:\nImagine that you are a robot operating a phone. Like how humans operate the phone, you can move your finger to an icon, click an icon with your finger, scroll down a screen, or type some texts with the keyboard.\n\nYou are asked to shop for an ergonomic keyboard, your budget is between $50 and $100.\n\nPrevious action: \" My next move would be to move my finger to the \"Add to Cart\" button, which is located below the \"Buy New\" button and above the \"Ships from Amazon\" section. I would then click on the \"Add to Cart\" button to add the item to my shopping cart. \"\n\nYou have now completed the previous action as described above. Below is what you see on the phone screen. What will you do next?\"\n\nThe screenshot shows a cart with:\n\"Subtotal $4999\"\n\nand the product listed is:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540 ...\n$4999\"\n\nThe price shown is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 117431, "output_tokens": 353, "cost_micros": 357588, "latency_ms": 12750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 111 in the document, which shows a phone screenshot of a shopping cart.\n\nThe screenshot displays:\n- A product: \"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\"\n- The price shown is: $49.99\n- There's a subtotal of $49.99\n- The item shows \"In Stock\" status\n\nAnswer: 49.99", "citations": [], "input_tokens": 103915, "output_tokens": 97, "cost_micros": 313200, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in that screenshot.\n\nLooking through the document for Figure 111...\n\nFound it on page 140:\n\nFigure 111 shows a smartphone screen displaying an Amazon shopping cart. The image shows:\n- A Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\n- Price: $49.99\n- The button shows \"Proceed to checkout (1 item)\"\n- Subtotal: $49.99\n\nThe price of the keyboard shown in the screenshot is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 139290, "output_tokens": 144, "cost_micros": 420030, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find Figure 111 and identify the keyboard price.Let me search for \"Figure 111\" in the document to locate it.Now let me search for where Figure 111 appears in the content with context.Let me read around line 29062 where Figure 111 is located to see the actual content and price information.Perfect! I found Figure 111 which shows a screenshot of shopping for an ergonomic keyboard. The price is clearly shown in the screenshot as $49.99 (displayed as \"$4999\" without the decimal in multiple places in lines 29088 and 29095).\n\nAnswer: 49.99", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52062, "error": null, "extra": {"thread_id": 2001, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd7392a113cf44358e67b4c74d3afe31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 172, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and look for the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107...\n\nFound it in Section 9.8 GUI Navigation under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" in the top left corner, and the battery percentage \"76\" is displayed in the top right corner of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 133646, "output_tokens": 109, "cost_micros": 402573, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at Figure 107 to find the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107, I can see it's in the \"Sec. 9.8 GUI Navigation\" section under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" displayed at the top left, and the battery percentage \"76\" shown at the top right of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 103913, "output_tokens": 112, "cost_micros": 313419, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 107 in Section 9.8 (GUI Navigation - Online Shopping), I need to find the battery percentage shown in the screenshot.\n\nIn the top-right corner of the screenshot shown in Figure 107, the status bar displays:\n- Time: 5:27\n- Battery percentage: 76\n\nAnswer: 76", "citations": [], "input_tokens": 139288, "output_tokens": 76, "cost_micros": 419004, "latency_ms": 6592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 107 and identify the battery percentage shown in the screenshot.Let me search for \"Figure 107\" in the document to locate it quickly.Now let me search for the specific line with more context:Let me read around line 28504 to see Figure 107:I can see Figure 107 shows a phone screenshot. I can see \"5:27\" as the time at line 28507, and at line 28511 I see \"76\". Let me check if this is the battery percentage. Looking at the structure, it appears to show:\n- 5:27 (time)\n- 76 (likely battery percentage)\n\nLet me read a bit more to see if there's additional context or if the battery percentage is shown elsewhere:Based on the document content, Figure 107 shows a phone screenshot with the time \"5:27\" at the top, and the number \"76\" appears right after it. In typical phone screenshots, the battery percentage is displayed in the status bar at the top of the screen, usually near the time. The number 76 appears to be the battery percentage.\n\nAnswer: 76", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64703, "error": null, "extra": {"thread_id": 2002, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_281e7f398f814d8a9e02d3b21485af45", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 226, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 110562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Figure 101 to find information about the Politico website news cover.\n\nLooking at the document, Figure 101 is described in Section 9.8 \"GUI Navigation - Web Browsing (Today's News)\" on page 131.\n\nThe figure shows a Google search results page with news stories. The prompt asks GPT-4V about what happens after reading the first news article. Looking at the \"Top stories\" section in the image, I can see several news sources listed including:\n\n- **Politico**: \"State Department doubles down after Zelenskyy slams reluctance...\"\n- CNN: \"Catastrophic flooding swamped Vermont's capital...\"\n- Reuters: \"US says it no longer deems Donald Trump immune from E. Jean...\"\n- DCist: \"D.C. Council Approves Emergency Crime Bill\"\n- The New York Times: \"Hard Right Presses Culture War Fights on Defense Bill...\"\n- NPR: \"The Anthropocene began in 1950s, scientists say\"\n\nThe Politico headline mentions \"Zelenskyy,\" which refers to Volodymyr Zelenskyy, the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 117439, "output_tokens": 268, "cost_micros": 356337, "latency_ms": 11250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in Section 9.8 (Web Browsing - Today's News), I can see the Google search results page showing several news headlines.\n\nThe top news item from Politico shows: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThis refers to Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 133656, "output_tokens": 87, "cost_micros": 402273, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in the document, I can see a screenshot showing a news article from Politico. The article is titled \"State Department doubles down after Zelenskyy slams reluctance...\" \n\nThe article discusses Ukrainian President Volodymyr Zelenskyy and Ukraine's desire to join NATO. Zelenskyy is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 103923, "output_tokens": 85, "cost_micros": 313044, "latency_ms": 7108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101, I can see a Google search results page showing news articles. The figure description mentions \"Top stories\" and lists several news sources including Politico.\n\nThe first story listed under Politico is: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 139298, "output_tokens": 88, "cost_micros": 419214, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant (2309.17421v2.pdf.xml) to find Figure 101 and identify the information about the Politico website cover.Let me search for \"Figure 101\" in the document to find the specific location:Let me search for \"politico\" to find the relevant section:I can see mentions of Politico with information about Zelenskyy. Let me search more specifically for \"Figure 101\":Great! I found the reference to Figure 101 at line 27715. Let me read that section:Let me look earlier in the document to see the actual Figure 101 image content. I need to search backwards from line 27715 to find the visual description:Now I can see the context. Figure 101 shows a Google News page with various news articles. Looking at the content around lines 27625-27631, I can see:\n\n- Politico news source\n- Article title: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy (also spelled Zelensky in other parts of the document), who is the President of Ukraine. Let me verify this is correct by looking at the article content earlier:\n\nFrom lines 27419-27421 and 27445-27446, I can confirm:\n- \"State Department doubles down after Zelenskyy slams reluctance on Ukraine's entry to NATO\"\n- \"In a statement posted to Twitter on Tuesday, Ukrainian President Volodymyr Zelenskyy took issue with NATO's reluctance...\"\n\nSo the person on the cover of the news on the Politico website in Figure 101 is Zelenskyy (Volodymyr Zelenskyy), who is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84875, "error": null, "extra": {"thread_id": 2003, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6c36693d9bfb44369693c25b87e59141", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 305, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Figure 89 to identify the brand name of the coffee machine.\n\nLooking at the document, Figure 89 is in Section 9.7 \"Embodied Agent\" under \"Operating Machine\". The figure shows a coffee machine's operating panel with buttons and includes text that mentions operating menu instructions.\n\nIn the image shown in Figure 89, I can see a coffee machine operating panel. Looking at the visible text and branding on the machine, I can see \"Keurig\" appears to be the brand name based on the styling and layout typical of Keurig coffee makers.\n\nAnswer: Keurig", "citations": [], "input_tokens": 117434, "output_tokens": 139, "cost_micros": 354387, "latency_ms": 7641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine's operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 133651, "output_tokens": 61, "cost_micros": 401868, "latency_ms": 6500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 103918, "output_tokens": 60, "cost_micros": 312654, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7, which shows the coffee machine operating panel with button menus, I can see the brand name displayed on the machine.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 139293, "output_tokens": 47, "cost_micros": 418584, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Figure 89 and identify the coffee machine brand.Let me search for \"Figure 89\" in the document:Now let me search more specifically to find where Figure 89 appears:Let me read the section around line 25761 to see Figure 89 and its image:I can see Figure 89 references a coffee machine. Let me look a bit earlier to see the actual image content:Let me continue reading to see the menu and coffee machine images:Let me continue to find the actual coffee machine image with the brand visible:I see that line 25763 shows \"JoooDeee\" which appears to be OCR text from the coffee machine image. Let me look at the earlier Figure 88 section which also shows the coffee machine, as it might have clearer brand information:I can see the OCR text on line 25609 and 25763 shows \"JoooDeee\". Let me search for Figure 88 which is mentioned just before to see if there's more context about the brand:Let me look before line 25584 to see if there's an image with better OCR or visual description for Figure 88:Let me search more specifically around where Figure 88 might have its embedded image:Let me look earlier to find where Figure 88 actual image appears. Let me search backwards from where Figure 88 was mentioned:Let me search more systematically. Let me look for where the coffee machine images are actually embedded. Let me search around where both Figure 88 and 89 would appear together:Let me try a different approach and look for pages around 116-117 where Figures 88 and 89 are mentioned, searching for embedded coffee machine images. Let me search for \"Joo\" which appeared in the OCR text:Great! Let me look at line 34358 and chunk 155184 which has a better visual description with \"JoooDeee\":Perfect! I found it. According to the OCR text on line 34358, the coffee machine has \"JoooDeee\" visible on it. Looking at the visual description starting at line 34363, it describes the device as a water dispenser/cooler, but based on the context of the document discussing coffee machines and the OCR text showing \"JoooDeee\", this appears to be the brand name visible on the coffee machine in Figure 89.\n\nHowever, \"JoooDeee\" seems like it might be an OCR misreading. Let me check if there's any other reference that might clarify the brand. Looking at the OCR pattern \"JoooDeee\", this could potentially be \"Jura\" (a well-known coffee machine brand) misread by OCR, but I should report what the document actually says.\n\nBased on the document, the OCR text from the coffee machine image shows \"JoooDeee\" with \"DI\" below it.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 148609, "error": null, "extra": {"thread_id": 2004, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f9e89fe20d454f6c90a127387a939523", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 624, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 1, I need to identify the relation arrows (edges) in the knowledge graph and determine which ones do not point to specific leaf nodes.\n\nThe figure shows a knowledge graph centered around Artemisia Gentileschi (Q212657) and Orazio Gentileschi (Q367560). The relations shown include:\n\n- **child** (from Orazio to Artemisia)\n- **father** (from Artemisia to Orazio)\n- **citizen** (from both to Italy Q38)\n- **occupation** (pointing to \"Painter\")\n- **style** (from Artemisia to Baroque Q37853)\n- **movement** (from Artemisia to Baroque Q37853)\n- **place of birth** (from Artemisia to Rome Q220)\n- **member** (from Artemisia, appears to point to another entity)\n\nThe arrows that do NOT point to specific leaf nodes (i.e., they point to other entities that are themselves nodes in the graph rather than terminal values) are:\n\n- child (points to another person entity)\n- father (points to another person entity)\n\nAnswer: child, father", "citations": [], "input_tokens": 60197, "output_tokens": 266, "cost_micros": 184581, "latency_ms": 12796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify which relation arrows do not point to specific leaf nodes (entities with specific values or names).\n\nLet me examine each relation arrow from the entities shown:\n\nFrom Q367560 (Orazio Gentileschi):\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- movement \u2192 Q37853 (Baroque) - points to a class/type node\n- child \u2192 Artemisia Gentileschi Q212657 - points to a specific person\n\nFrom Artemisia Gentileschi Q212657:\n- father \u2192 Orazio Gentileschi Q367560 - points to a specific person\n- birth \u2192 [date shown] - points to specific value\n- place of birth \u2192 [location] - points to specific value\n- Citizen \u2192 Italy Q38 - points to a specific country\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- style \u2192 Q37853 (Baroque) - points to a class/type node\n- member \u2192 [organization] - appears to point to a specific entity\n\nThe relations that point to class/type nodes rather than specific leaf nodes are:\n- occupation (points to the class \"Painter\")\n- movement/style (points to the class \"Baroque\")\n\nAnswer: occupation, movement, style", "citations": [], "input_tokens": 22214, "output_tokens": 311, "cost_micros": 71307, "latency_ms": 7718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "movement, occupation, style", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the relation arrows (edges) in the knowledge graph that do not point to specific leaf nodes (literal values).\n\nIn the knowledge graph shown in Figure 1, I can see the following relationships:\n\n- \"Painter\" (Q1028181) - this is an entity node, not a leaf\n- \"Baroque\" (Q37853) - this is an entity node, not a leaf\n- \"Italy\" (Q38) - this is an entity node, not a leaf\n- \"Rome\" (Q212657) - this is an entity node, not a leaf\n- Orazio Gentileschi (Q367560) - this is an entity node, not a leaf\n- Artemisia Gentileschi - the central entity\n\nThe relations that point to these non-leaf entity nodes (rather than to literal values like dates or strings) are:\n\n- occupation (pointing to Painter)\n- style (pointing to Baroque)\n- movement (pointing to Baroque)\n- citizen (pointing to Italy)\n- place of birth (pointing to Rome)\n- father (pointing to Orazio Gentileschi)\n- child (pointing to Artemisia Gentileschi)\n- member (appears to be a relation as well)\n\nAnswer: occupation, style, movement, citizen, place of birth, father, child, member", "citations": [], "input_tokens": 25539, "output_tokens": 310, "cost_micros": 81267, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "child, citizen, father, member, movement, occupation, place of birth, style", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows in the Knowledge Graph that do not point to leaf nodes (specific values).\n\nIn the Knowledge Graph shown, the relations are labeled on the arrows connecting entities. Let me trace each relation:\n\n- \"occupation\" \u2192 points to \"Painter\" (Q37853) - this is a leaf node\n- \"movement\" \u2192 points to \"Baroque\" (Q1028181) - this is a leaf node\n- \"style\" \u2192 appears to connect nodes but the endpoint is not clearly a specific leaf value\n- \"place of birth\" \u2192 points to a specific location\n- \"child\" \u2192 points to \"Artemisia\" (another person entity, not a leaf value)\n- \"father\" \u2192 points to \"Orazio\" (another person entity, not a leaf value)\n- \"member\" \u2192 connects to citizenship information\n- \"citizen\" \u2192 points to \"Italy\" (a leaf node)\n\nThe relations that point to other entities rather than specific attribute values (leaf nodes) are:\n\nAnswer: child, father", "citations": [], "input_tokens": 17550, "output_tokens": 231, "cost_micros": 56115, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows that do not point to specific leaf nodes (entity or property nodes).\n\nFrom the Knowledge Graph Context cloud, I can see the following relationships:\n\n- E1 (OrazioGentileschi) -- occupation --> P1 (Painter) - points to leaf node P1\n- E1 -- movement --> P2 (Baroque) - points to leaf node P2\n- E1 -- birth --> E1 - points back to itself, not a leaf node\n- E1 -- child --> E2 - points to E2, which has further connections\n- E2 (ArtemisiaGentileschi) -- father --> E1 - points to E1, which has further connections\n- E2 -- occupation --> P1 - points to leaf node P1\n- E2 -- style --> P2 - points to leaf node P2\n- E2 -- place of birth --> E3 - points to E3, which has further connections\n- E2 -- member --> E2 - points back to itself, not a leaf node\n- E2 -- Citizen --> E3 - points to E3, which has further connections\n- E1 -- Citizen --> E3 - points to E3, which has further connections\n\nThe relations that do not point to specific leaf nodes are those pointing to entities that have their own outgoing connections or pointing back to themselves:\n\nAnswer: birth, child, father, place of birth, member, Citizen", "citations": [], "input_tokens": 23598, "output_tokens": 341, "cost_micros": 75909, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "birth, child, citizen, father, member, place of birth", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify which relation arrows do not point to specific leaf nodes.Let me search for \"Figure 1\" in the document to locate it:Let me read more to see the complete Figure 1 description:I can see Figure 1 shows a knowledge graph. Let me look more carefully at the structure. The text shows entities and relations. Let me search for more context about the graph structure:Based on the text extracted from Figure 1, I can see the knowledge graph structure. The entities and relations listed are:\n\n- Q1028181 (Painter)\n- Q37853 (Baroque)\n- Q367560 (Orazio Gentileschi)\n- Q212657 (with \"Artemisia Gentileschi Citizen\" below it)\n- Q38 (Italy)\n\nRelations shown:\n- occupation\n- style\n- movement\n- occupation (appears twice)\n- child\n- birth\n- place of birth\n- father\n- member\n\nLooking at the structure, the relations that appear to connect between entities but may not point to specific leaf nodes would be those that connect to higher-level entities or categories. From the layout, I need to identify which relation arrows don't terminate at leaf nodes.\n\nLet me look for any figures or images that might show the actual graph structure more", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59797, "error": null, "extra": {"thread_id": 2005, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c3fdd69258514ce3917c73b31fe40d3e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 249, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me look for any figures or images that might show actual graph structure more", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to locate Figure 5 in the document and examine the lines shown in it.\n\nLooking through the document, I can find Figure 5 on page 8. The figure is titled \"Retrieval Analysis\" and shows citation evaluation (Micro) of generated texts using knowledge graphs with retrieval accuracy 100 (gold), 80, 60, 40, and 20.\n\nThe figure contains four lines:\n- A yellow line (precision)\n- A green line (recall)\n- A blue line (F1_Score)\n- A red line (correctness)\n\nLooking at the graph, I need to identify which line does not intersect with any other line.\n\nThe red line (correctness) starts at the highest point and remains consistently above all other lines throughout the graph. It descends from left to right but never crosses or intersects with any of the other three lines (yellow, green, or blue).\n\nAnswer: red", "citations": [], "input_tokens": 60196, "output_tokens": 207, "cost_micros": 183693, "latency_ms": 12141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, which shows \"Retrieval Analysis\" with multiple lines plotted against Retrieval Accuracy on the x-axis:\n\nThe lines shown are:\n- precision (yellow)\n- recall (green)\n- F1_Score (appears to be another color)\n- correctness (red/orange)\n\nThe correctness line (red/orange colored line at the top of the graph) maintains a consistently high value around 80-90 and does not intersect with any of the other lines throughout the plot.\n\nAnswer: red (or orange/correctness line)", "citations": [], "input_tokens": 22213, "output_tokens": 134, "cost_micros": 68649, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "red or orange correctness line", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 titled \"Retrieval Analysis\", I need to identify which line does not intersect with any other line.\n\nThe figure shows four lines representing different metrics (precision, recall, F1_Score, and correctness) plotted against retrieval accuracy from gold (100) to 20.\n\nExamining the lines:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines  \n- The blue line (F1_Score) intersects with other lines\n- The red line (correctness) stays at the top of the graph and does not intersect with any of the other three lines\n\nAnswer: red", "citations": [], "input_tokens": 25538, "output_tokens": 150, "cost_micros": 78864, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I need to identify the line that has no intersection with any other line.\n\nFigure 5 shows citation evaluation metrics (Micro) with retrieval accuracy from 100 (gold) down to 20. There are four lines in the graph representing:\n- Correctness (blue)\n- Precision (yellow) \n- Recall (green)\n- F1 (red)\n\nExamining the lines, the blue line (Correctness) remains at the top of the graph and does not intersect with any of the other three lines throughout the entire range of retrieval accuracy values.\n\nAnswer: Blue", "citations": [], "input_tokens": 17549, "output_tokens": 144, "cost_micros": 54807, "latency_ms": 5610, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I can see a chart showing citation evaluation metrics (precision, recall, F1_score, and correctness) plotted against retrieval accuracy levels (20, 40, 60, 80, and gold/100).\n\nExamining the lines in the chart:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines\n- The blue line (F1_score) intersects with other lines\n- The red line (correctness) runs above all other lines and does not intersect with any of them\n\nAnswer: red", "citations": [], "input_tokens": 23597, "output_tokens": 138, "cost_micros": 72861, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 5 and identify the line with no intersections.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25967, "error": null, "extra": {"thread_id": 2006, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a9d66758d1df48679fe6def6615062f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 40, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document to find figure 5 and identify line with no intersections", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many tables in the document include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1 (page 3) - Shows an example data entry, not evaluation metrics\n2. Table 2 (page 3) - Human Evaluation on dataset quality - no F1\n3. Table 3 (page 7) - Citation Quality evaluation - shows \"F1.\" column (both Micro and Macro)\n4. Table 4 (page 7) - Text quality evaluation - no F1\n5. Table 5 (page 7) - Citation quality with knowledge removed - shows \"F1.\" column\n6. Table 6 (page 8) - Human evaluation alignment - no F1\n7. Table 7 (page 9) - Comparison of General and Specific questions - shows \"F1.\" column\n8. Table 8 (page 15) - Human annotation instructions - no F1\n9. Tables 9-17 (pages 16-21) - Prompts and instructions - no F1\n10. Table 18 (page 22) - Evolutionary question generation example - no F1\n11. Tables 19-20 (pages 23-24) - Example outputs - no F1\n\nAdditionally, I can see:\n- Figure 4 (page 8) shows \"F1_Score\" in a graph\n- Figure 5 (page 8) shows \"F1_Score\" in a graph\n\nBut the question asks specifically about tables, not figures.\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 60181, "output_tokens": 381, "cost_micros": 186258, "latency_ms": 25483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all tables in the document that include \"F1\" as a metric.\n\nLet me go through the tables systematically:\n\n1. Table 1 (Example data entry) - No F1 metric\n2. Table 2 (Human Evaluation on BioKaLMA dataset) - No F1 metric\n3. Table 3 (Citation Quality) - Yes, has \"F1.\" columns (both Micro and Macro)\n4. Table 4 (Text quality evaluation) - No F1 metric\n5. Table 5 (Citation quality with removed knowledge) - Yes, has \"F1.\" column\n6. Table 6 (Human Evaluation on alignment) - No F1 metric\n7. Table 7 (Comparison of General and Specific questions) - Yes, has \"F1.\" column\n\nNow let me check if there are more tables in the appendices:\n\n8. Table 8 (Human Evaluation Instructions) - No F1 metric\n9. Table 9 (Instruction for evolutionary question construction) - No F1 metric\n10. Table 10 (Instruction for round 2-5) - No F1 metric\n11. Table 11 (General question generation) - No F1 metric\n12. Table 12 (Specific question generation) - No F1 metric\n13. Table 13 (Answer generation instruction) - No F1 metric\n14. Table 14 (GPT-EVAL Coherence) - No F1 metric\n15. Table 15 (GPT-EVAL Consistency) - No F1 metric\n16. Table 16 (GPT-EVAL Fluency) - No F1 metric\n17. Table 17 (GPT-EVAL Relevance) - No F1 metric\n18. Table 18 (Evolutionary question construction example) - No F1 metric\n19. Table 19 (Example from ChatGPT) - No F1 metric\n20. Table 20 (Example from GPT-4) - No F1 metric\n\nTables with F1 metric: Table 3, Table 5, and Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 22198, "output_tokens": 502, "cost_micros": 74124, "latency_ms": 9109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1: Shows an example data entry - no F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - no F1 metric\n3. Table 3: Citation Quality evaluation - includes \"F1.\" column (both Micro and Macro F1)\n4. Table 4: Evaluation on generated text quality - no F1 metric\n5. Table 5: Citation quality evaluation with knowledge removed - includes \"F1.\" column\n6. Table 6: Human Evaluation on text-citation alignment - no F1 metric\n7. Table 7: Comparison of evaluation results on General and Specific question setting - includes \"F1.\" column\n8. Table 8: Instruction for human annotators - not a data table with metrics\n9. Tables 9-17: These are prompt/instruction tables - no F1 metrics\n10. Table 18: Example of generated questions - no F1 metric\n11. Tables 19-20: Examples of attributed answers - no F1 metrics\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 25523, "output_tokens": 289, "cost_micros": 80904, "latency_ms": 8578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for tables that include \"F1\" as a metric.\n\nLet me identify all tables in the document:\n\n1. Table 1: Shows an example data entry in BioKaLMA - does not contain F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - contains metrics: Authenticity, Relevance, Naturalness, Significance - no F1\n3. Table 3: Citation Quality evaluation - contains columns including \"F1.\" in both Micro and Macro sections - **YES, contains F1**\n4. Table 5: Citation quality evaluation for generated texts - contains columns: Corr., Prec., Rec., F1. - **YES, contains F1**\n5. Table 6: Result of Human Evaluation on text-citation alignment - contains Alignment and Human Avg. - no F1\n6. Table 7: Comparison between general and specific questions - contains columns including F1. - **YES, contains F1**\n7. Table 8: Instruction for human annotators - no metrics table\n8. Table 9: Not clearly visible as a numbered table in the content shown\n9. Table 10: Instruction and demonstration - not a metrics table\n10. Table 11: Instruction and demonstration - not a metrics table\n11. Table 12: Instruction and demonstration - not a metrics table\n12. Table 13: Instruction for answer generation - not a metrics table\n13. Tables 14-17: Evaluation criteria descriptions - not metrics tables\n14. Table 18: Example of generated questions - not a metrics table\n15. Table 19: Stephen Crane biographical information - not a metrics table with F1\n\nCounting tables with F1 metric:\n- Table 3: YES\n- Table 5: YES\n- Table 7: YES\n\nAnswer: 3", "citations": [], "input_tokens": 17534, "output_tokens": 420, "cost_micros": 58902, "latency_ms": 11342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. **Table 1**: Shows an example data entry - no F1 metric\n2. **Table 2**: Human Evaluation metrics (Authenticity, Relevance, Naturalness, Significance) - no F1\n3. **Table 3**: Citation Quality evaluation - includes \"Micro F1.\" and \"Macro F1.\" columns - **YES**\n4. **Table 4**: Text quality evaluation (Coh., Con., Flu., Rel.) - no F1\n5. **Table 5**: Citation quality with removed knowledge - includes \"F1.\" column - **YES**\n6. **Figure 4**: Shows precision, recall, and F1_Score for [NA] - **YES** (this is labeled as a figure but formatted as a table)\n7. **Figure 5**: Citation evaluation with retrieval accuracy - includes \"F1_score\" column - **YES** (also labeled as figure but formatted as a table)\n8. **Table 6**: Human evaluation on alignment - no F1\n9. **Table 7**: Comparison of General and Specific questions - includes \"Citation Eval. F1.\" column - **YES**\n10. **Table 8**: Human annotation instructions - no F1\n11. Tables 9-17: Various prompts and instructions - no F1 metrics\n12. **Table 18**: Example of evolutionary question construction - no F1\n13. **Table 19**: Example output from ChatGPT - no F1\n14. **Table 20**: Example output from GPT-4 - no F1\n\nTables/Figures with F1 metric:\n- Table 3\n- Table 5\n- Figure 4 (table format)\n- Figure 5 (table format)\n- Table 7\n\nAnswer: 5", "citations": [], "input_tokens": 23582, "output_tokens": 429, "cost_micros": 77181, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned (2310.05634v2.pdf.xml) to find all tables that include \"F1\" as a metric.Let me continue reading the document to search for all tables:Now let me search for \"F1\" to find which tables contain this metric:Let me read the specific sections containing the tables to identify which ones include F1 as a metric:Now let me check all the other tables to see if they contain F1. Let me search more systematically:Let me also check Table 6:Now let me check the tables in the appendix to ensure I haven't missed any:Now let me scan through all the remaining content to ensure I have checked all tables. Let me list out what I've found so far:\n\nTables with F1:\n1. Table 3 - Has F1 (both Micro and Macro F1)\n2. Table 5 - Has F1 \n3. Table 7 - Has F1\n\nTables without F1:\n1. Table 1 - Does not have F1 (has Authenticity, Relevance, Naturalness, Significance)\n2. Table 2 - This appears to be just a caption reference, same as Table 1\n3. Table 4 - Does not have F1 (has Coh., Con., Flu., Rel.)\n4. Table 6 - Does not have F1 (has Alignment and Human Avg.)\n\nLet me continue checking the rest of the tables (8-20) mentioned in the document:Based on my thorough examination of the document, I have identified all tables and checked which ones include \"F1\" as a metric. Let me verify my findings:\n\nTables with F1 as a metric:\n1. **Table 3**: Citation Quality evaluation - includes both Micro F1 and Macro F1 columns\n2. **Table 5**: Citation quality evaluation for generated texts using a KG with N pieces of knowledge removed - includes F1 column\n3. **Table 7**: Comparison of evaluation results on General and Specific question setting - includes F1 in the Citation Evaluation columns\n\nTables without F1 as a metric:\n- Table 1: Example data entry (no F1)\n- Table 2: Human Evaluation on BioKaLMA dataset (no F1)\n- Table 4: Evaluation on generated text quality (no F1, only Coh., Con., Flu., Rel.)\n- Table 6: Result of Human Evaluation on text-citation alignment (no F1, only Alignment and Human Avg.)\n- Tables 8-20: These are instruction tables, prompt demonstrations, and examples (no F1 metrics)\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 116094, "error": null, "extra": {"thread_id": 2007, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d5e6c0fe45c9431aaf087f3d70bed847", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 545, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the paper, which shows the citation quality evaluation results for different models:\n\nThe alignment scores for ChatGPT at different temperatures are:\n- ChatGPT (0.1): 85.9\n- ChatGPT (0.5): 84.5\n- ChatGPT (0.9): 84.1\n\nThe highest alignment score is at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 60189, "output_tokens": 103, "cost_micros": 182112, "latency_ms": 22187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nFrom Table 3 (Citation Quality evaluation), the alignment scores for ChatGPT are:\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 22206, "output_tokens": 129, "cost_micros": 68553, "latency_ms": 5421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from the paper.\n\nLooking at Table 3 in the paper, which shows \"Citation Quality OpenAI models and LLaMA family models\" including alignment scores:\n\n- ChatGPT (0.1): Alignment = 85.9(2.5)\n- ChatGPT (0.5): Alignment = 84.5(1.1)\n- ChatGPT (0.9): Alignment = 84.1(0.5)\n\nThe highest alignment score among the ChatGPT models is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 25531, "output_tokens": 159, "cost_micros": 78978, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which presents the citation quality evaluation results including alignment scores for different models:\n\nFor ChatGPT at different temperatures:\n- ChatGPT (0.1): Alignment = 85.9\n- ChatGPT (0.5): Alignment = 84.5\n- ChatGPT (0.9): Alignment = 84.1\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 17542, "output_tokens": 125, "cost_micros": 54501, "latency_ms": 5592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nLooking at Table 3 (Citation Quality evaluation), I can see the alignment scores for different ChatGPT temperatures:\n\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 for ChatGPT at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 23590, "output_tokens": 139, "cost_micros": 72855, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about temperature and ChatGPT alignment scores.Let me search for temperature settings and ChatGPT results more specifically:Now let me read the specific section where temperature experiments with ChatGPT are discussed:Perfect! I found the table with alignment scores for ChatGPT at different temperatures. From Table 3, I can see:\n\n- ChatGPT (0.1): Alignment score = 85.9(2.5)\n- ChatGPT (0.5): Alignment score = 84.5(1.1)\n- ChatGPT (0.9): Alignment score = 84.1(0.5)\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78952, "error": null, "extra": {"thread_id": 2008, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_826b46b364ee4f52853639bf7b854cb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Section A and subsections A.1, A.2, A.3), I need to compare the length of descriptions for each step:\n\n**A.1 Person Selection**: Approximately 150 words - describes selecting name pairs from the biographical database and filtering criteria.\n\n**A.2 Name Disambiguation**: Approximately 200 words - describes using SPARQL queries to map names to WikiData entities, handling duplicate names, and extracting one-hop sub-graphs.\n\n**A.3 Evolutionary Question Generation**: Approximately 450-500 words - provides extensive detail about the iterative process, knowledge injection, specificity and coherence scoring, the formula for scoring, and implementation details across five iterations.\n\nThe Evolutionary Question Generation step clearly contains the most detailed explanation, including mathematical formulas, specific algorithms for knowledge selection, and detailed descriptions of the iterative process.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 60191, "output_tokens": 204, "cost_micros": 183633, "latency_ms": 21312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Appendix A), I need to compare the lengths of descriptions for each step:\n\n1. **Person Selection (A.1)**: Describes selecting name pairs from biographical database - relatively brief, about 1 paragraph\n\n2. **Name Disambiguation (A.2)**: Describes mapping names to WikiData entities using SPARQL queries - moderate length, about 1-2 paragraphs\n\n3. **Evolutionary Question Generation (A.3)**: Describes the iterative process of extending paragraphs, selecting knowledge, calculating scores with formulas, and generating questions - significantly longer, spanning multiple paragraphs with detailed explanations of specificity scores, coherence scores, the scoring formula, iteration process, and implementation details\n\nThe Evolutionary Question Generation step clearly contains the most extensive description with the most detail about the methodology.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 22208, "output_tokens": 193, "cost_micros": 69519, "latency_ms": 6781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "To determine which step takes the most words to describe in the dataset construction process, I need to examine the three main steps described in Section 2.2 and detailed in Appendix A:\n\n1. **Person Selection** (Section A.1): This section describes filtering and selecting people from the biographical database. It's relatively brief.\n\n2. **Name Disambiguation** (Section A.2): This section explains mapping names to WikiData entities using SPARQL queries and extracting one-hop sub-graphs. It's moderately detailed.\n\n3. **Evolutionary Question Generation** (Section A.3): This section provides extensive detail about the iterative process of extending paragraphs, knowledge selection algorithms, scoring mechanisms (specificity and coherence scores), and the multiple rounds of generation with specific templates and prompts.\n\nThe Evolutionary Question Generation section is significantly longer and more detailed than the other two steps, including mathematical formulas, multiple iteration explanations, and extensive procedural details.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 25533, "output_tokens": 217, "cost_micros": 79854, "latency_ms": 7764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at section 2.2 Dataset Construction and Appendix A Dataset Construction, the three main steps are:\n\n1. Person Selection\n2. Name Disambiguation  \n3. Evolutionary Question Generation\n\nComparing the word counts in Appendix A where these steps are described in detail:\n\n- A.1 Person Selection: approximately 150 words\n- A.2 Name Disambiguation: approximately 180 words\n- A.3 Evolutionary Question Generation: approximately 130 words (before the subsections)\n\nHowever, the Evolutionary Question Generation section has significantly more content when including all the detailed prompt tables (Tables 9-12) and examples that follow it, making it the most extensively described step overall.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 17544, "output_tokens": 165, "cost_micros": 55107, "latency_ms": 5703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the dataset construction section (2.2), I need to compare the length of descriptions for each of the three steps:\n\n1. **Person Selection** (A.1): Approximately 150 words\n2. **Name Disambiguation** (A.2): Approximately 200 words  \n3. **Evolutionary Question Generation** (A.3): Approximately 450+ words (includes detailed explanation of the iterative process, knowledge selection algorithm with formula, scoring mechanisms, etc.)\n\nThe Evolutionary Question Generation step is described in significantly more detail than the other two steps, taking up the most space in the paper.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 23592, "output_tokens": 143, "cost_micros": 72921, "latency_ms": 5844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about dataset construction steps and compare their descriptions.Let me search for \"dataset construction\" in the document:Let me look at the detailed dataset construction section:Let me read the Dataset Construction section starting at line 506:Good! I can see the three steps mentioned at line 521-523. Now let me read the Appendix A which has the detailed dataset construction process:Perfect! I can now see the three steps of dataset construction in detail. Let me count the words in each section to determine which takes the most words to describe.\n\nLet me read each section more carefully to count:Now let me see where the Evolutionary Question Generation section ends:Perfect! Now I have all three sections. Let me analyze the word count for each step:\n\n**A.1 Person Selection** (lines 2089-2110): Approximately 22 lines of content\n**A.2 Name Disambiguation** (lines 2113-2145): Approximately 33 lines of content  \n**A.3 Evolutionary Question Generation** (lines 2148-2222): Approximately 75 lines of content\n\nBy visual inspection and line count, the \"Evolutionary Question Generation\" section is significantly longer than the other two steps. It contains detailed explanations about:\n- The evolutionary approach\n- Iteration process\n- Knowledge annotation\n- Specificity and coherence scoring\n- Formula for scoring\n- Implementation details\n\nThis is clearly the step that takes the most words to describe.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78750, "error": null, "extra": {"thread_id": 2009, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b0d2d82267f4e5eab9a849c3924465e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document's definition of the \"Conscious Incompetence\" setting in Section 2.1:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 22222, "output_tokens": 71, "cost_micros": 67731, "latency_ms": 4952, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\nAnswer: It can be partially verified by the knowledge graph G", "citations": [], "input_tokens": 25547, "output_tokens": 40, "cost_micros": 77241, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "it can be partially verified by knowledge graph g", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of when a sentence can map to both [NA] and knowledge citations.\n\nIn Section 2.1 \"Task Formulation,\" under \"Setting of Conscious Incompetence,\" the document states:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can.\"\n\nHowever, this is incomplete. Looking at the full context, the document explains that [NA] is used when a sentence \"includes some knowledge to be verified, but the knowledge is absent in the knowledge graph G.\"\n\nThe key insight comes from the demonstration in Table 13, which shows sentences containing both specific knowledge citations from the graph AND [NA] citations. This occurs when a sentence contains both:\n1. Knowledge that IS present in the knowledge graph (cited with specific knowledge)\n2. Knowledge that IS NOT present in the knowledge graph (cited with [NA])\n\nAnswer: When it contains both knowledge present in and absent from the knowledge graph", "citations": [], "input_tokens": 17558, "output_tokens": 214, "cost_micros": 55884, "latency_ms": 7110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2727272727272727, "method": "str_norm", "normalised_pred": "when it contains both knowledge present in and absent from knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, in the \"Setting of Conscious Incompetence\" section (2.1), a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph $G$.\"\n\nAnswer: When it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 23606, "output_tokens": 91, "cost_micros": 72183, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the author's definition of conscious incompetence and when a sentence can map to both [NA] and a list of sub-graph knowledge.Let me search for \"conscious incompetence\" in the document:Now let me read the section around line 485 and 514 where \"conscious incompetence\" is defined:Perfect! I found the answer. According to the document, in the section on \"Setting of Conscious Incompetence\", a sentence can map to both [NA] and a list of sub-graph knowledge when it can be partially verified by the knowledge graph G.\n\nAnswer: when it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34875, "error": null, "extra": {"thread_id": 2010, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6035a4bba19d42d88826ef02286ce3c9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure of question-guided reasoning and count the numbers displayed in blue color.\n\nLooking at Figure 1 on page 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims,\" I can see:\n\n- Q1: What is the greatest depth of the Black Sea?\n- A1: Black sea has a maximum depth of **2,212** meters.\n- Q2: How far can sunlight penetrate water?\n- A2: Sunlight does not penetrate water below **1,000** meters.\n- At the bottom: \"2,212 is greater than 1,000. Therefore, the claim is\"\n\nIn the figure, I can see numbers that appear to be highlighted or emphasized:\n- 2,212 (appears twice - once in A1 and once in the reasoning)\n- 1,000 (appears twice - once in A2 and once in the reasoning)\n\nLooking at the visual representation in the figure, the numbers that are displayed in blue/highlighted color are:\n- 2,212 (in A1)\n- 1,000 (in A2)\n- 2,212 (in the final reasoning line)\n- 1,000 (in the final reasoning line)\n\nSo there are 4 instances of numbers shown in blue color.\n\nAnswer: 4", "citations": [], "input_tokens": 27152, "output_tokens": 309, "cost_micros": 86091, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n1. The claim about the Black Sea\n2. Q1 asking about the greatest depth of the Black Sea\n3. A1 stating \"Black sea has a maximum depth of 2,212 meters\" - the number **2,212** appears in blue\n4. Additional text showing \"Average depth\" with what appears to be another number\n5. Q2 asking about how far sunlight can penetrate water\n6. A2 stating \"Sunlight does not penetrate water below 1,000 meters\" - the number **1,000** appears in blue\n7. At the bottom, there's reasoning that states \"2,212 is greater than 1,000\" - both **2,212** and **1,000** appear in blue here as well\n\nLooking carefully at the figure, I can identify the following blue-colored numbers:\n- 2,212 (appears multiple times)\n- 1,000 (appears multiple times)\n\nHowever, the question asks for how many numbers with blue color are mentioned, not how many times they appear. The distinct blue numbers are:\n1. 2,212\n2. 1,000\n\nAnswer: 2", "citations": [], "input_tokens": 11640, "output_tokens": 300, "cost_micros": 39420, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n- The claim about sunlight and the Black Sea\n- Q1 asking about the greatest depth of the Black Sea\n- A1 stating \"Black sea has a maximum depth of **2,212 meters**\" (2,212 appears to be in blue)\n- Q2 asking about how far sunlight can penetrate water\n- A2 stating \"Sunlight does not penetrate water below **1,000 meters**\" (1,000 appears to be in blue)\n- At the bottom, there's text saying \"2,212 is greater than 1,000\" where both numbers appear to be in blue\n\nSo the blue-colored numbers mentioned are:\n1. 2,212 (in A1)\n2. 1,000 (in A2)\n3. 2,212 (in the comparison at bottom)\n4. 1,000 (in the comparison at bottom)\n\nHowever, if we count unique numbers, there are 2 distinct numbers (2,212 and 1,000). But if we count each mention, there are 4 total mentions of numbers in blue.\n\nLooking more carefully at the figure, the numbers that appear to be highlighted/emphasized in blue are:\n- 2,212 (appears twice)\n- 1,000 (appears twice)\n\nCounting total mentions: 4\n\nAnswer: 4", "citations": [], "input_tokens": 12756, "output_tokens": 349, "cost_micros": 43503, "latency_ms": 8500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure that shows question-guided reasoning and count the blue-colored numbers in it.\n\nLooking at the document, Figure 1 is mentioned in the text as showing an example of question-guided reasoning for the claim \"Sunlight can reach the deepest part of the Black Sea.\" The text states: \"This reasoning process can be formulated as question-guided reasoning, where the verification of the claim is guided by asking and answering a series of relevant questions, as shown in Figure 1.\"\n\nHowever, Figure 1 itself is not shown in the provided document content. \n\nThe document does show Figure 4, which displays the QACHECK user interface with an example about \"Lars Onsager won the Nobel prize when he was 30 years old.\" In this figure, I can see:\n- \"Reasoning depth: 0\" and \"Reasoning depth: 1\" labels\n- \"1968\" (the year Lars Onsager won the Nobel Prize)\n- \"1903\" (the year Lars Onsager was born)\n- \"65\" (mentioned in the final prediction: \"He was 65 when he won the Nobel prize\")\n\nLooking at the visible figures in the document, Figure 4 shows the question-guided reasoning process example. The numbers that appear to be highlighted or emphasized in the context of the reasoning are: 1968, 1903, and 65.\n\nWithout being able to see the actual colors in Figure 1 (which is referenced but not displayed), and based on Figure 4 which shows a complete example of question-guided reasoning, I can count 3 distinct numbers that would typically be highlighted in such a demonstration.\n\nAnswer: 3", "citations": [], "input_tokens": 8625, "output_tokens": 374, "cost_micros": 31485, "latency_ms": 9280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nLooking at Figure 1, I can see:\n- The claim about sunlight reaching the deepest part of the Black Sea\n- Step 1 with Q1 and A1, where A1 mentions \"2,212 meters\" \n- Step 2 with Q2 and A2, where A2 mentions \"1,000 meters\"\n- The conclusion states \"2,212 is greater than 1,000\"\n\nThe numbers mentioned are:\n1. 2,212 (meters - maximum depth of Black Sea)\n2. 1,000 (meters - how far sunlight penetrates)\n\nBoth numbers appear to be in the same color as the rest of the text in the boxes. Looking at the figure description and the actual content, I need to identify which numbers are specifically in blue color.\n\nFrom the document's Figure 1, examining the text carefully, the numbers that appear are 2,212 and 1,000. The question asks specifically about numbers with \"blue color.\"\n\nBased on the visual representation in Figure 1, I can identify 2 numbers: 2,212 and 1,000.\n\nAnswer: 2", "citations": [], "input_tokens": 12525, "output_tokens": 288, "cost_micros": 41895, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the example figure related to \"question-guided reasoning\" and count the blue-colored numbers.Let me search for \"question-guided reasoning\" in the document to locate the relevant section.Now let me read the section around line 217 where Figure 1 is mentioned, which shows \"An example of question-guided reasoning\":Perfect! I can see Figure 1 which shows the question-guided reasoning example. Let me continue reading to see the complete figure description and identify all blue-colored numbers:Based on the document content I've read, I can see Figure 1 which shows the question-guided reasoning example. The figure contains:\n\n1. The text mentions \"2,212 m\" (Black Sea depth)\n2. The text mentions \"2,212 meters\" in the answer A1\n3. The text mentions \"1,000 meters\" in the answer A2\n4. The visual description mentions \"Blue accent colors for interactive elements (icons, links)\"\n\nLet me look more carefully at the OCR text section which lists the specific numbers visible in the figure:\n\nFrom lines 288-298, the OCR text shows:\n- 2,212 m (appears multiple times)\n- 1,253 m\n- 7,257 ft\n- 547,000 km\u00b3\n- 131,200 cu mi\n- 10+\n\nAnd from the main figure text (lines 219-270):\n- 2,212 m (Black Sea depth)\n- 1,000 meters (sunlight penetration)\n- 200 meters (mentioned in the evidence text)\n\nThe key information states: \"Blue accent colors for interactive elements (icons, links)\" which suggests that numbers appearing in blue would be those that are interactive or emphasized.\n\nLooking at the structure of Figure 1, the numbers that appear to be in blue color based on the context are:\n1. **2,212** (the answer to Q1 about Black Sea depth)\n2. **1,000** (the answer to Q2 about sunlight penetration)\n\nThese are the two key numerical answers that appear in the question-guided reasoning process.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45156, "error": null, "extra": {"thread_id": 2011, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ee68d3622e54a41be76708c8f52b00e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 300, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system are implemented using Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in Section 3:\n\n1. **Claim Verifier (Section 3.1)**: \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator (Section 3.2)**: \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Model (Section 3.3)**: This module has three different implementations:\n   - Retriever-Reader: Uses BM25 retriever and RoBERTa-large reader (not primarily LLM-based)\n   - FLAN-T5: This is an LLM\n   - GPT Reciter-Reader: Uses InstructGPT (LLM)\n   \n   The text states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default implementation uses LLM.\n\n4. **QA Validator (Section 3.4)**: \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner (Section 3.5)**: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nCounting the modules that are implemented with LLM backbone:\n1. Claim Verifier - Yes (InstructGPT)\n2. Question Generator - Yes (InstructGPT)\n3. Question Answering Model - Yes (default is GPT Reciter-Reader with InstructGPT)\n4. QA Validator - Yes (InstructGPT)\n5. Reasoner - Yes (FLAN-T5", "citations": [], "input_tokens": 11637, "output_tokens": 512, "cost_micros": 42591, "latency_ms": 10953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): Uses InstructGPT with in-context learning\n   - LLM-based \u2713\n\n2. **Question Generator** (Section 3.2): Uses InstructGPT for in-context learning\n   - LLM-based \u2713\n\n3. **Question Answering Model** (Section 3.3): Has three implementations:\n   - Retriever-Reader: Uses BM25 + RoBERTa-large fine-tuned on SQUAD\n   - FLAN-T5: Uses FLAN-T5 model\n   - GPT Reciter-Reader: Uses InstructGPT\n   - Since the system uses one of these at a time, and the default is GPT Reciter-Reader (mentioned in the text), this module uses LLM \u2713\n\n4. **QA Validator** (Section 3.4): Uses InstructGPT with demonstrations\n   - LLM-based \u2713\n\n5. **Reasoner** (Section 3.5): Has two implementations:\n   - FLAN-T5 (end-to-end QA model)\n   - InstructGPT\n   - Both options use LLMs \u2713\n\nAll five modules are implemented with Large Language Models as their backbone (either InstructGPT or FLAN-T5, both of which are LLMs).\n\nAnswer: 5", "citations": [], "input_tokens": 12753, "output_tokens": 375, "cost_micros": 43884, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Module** (Section 3.3): This module has three different implementations:\n   - Retriever-Reader: Uses RoBERTa-large (which is a language model)\n   - FLAN-T5: \"we also incorporate the FLAN-T5 model\" - This is an LLM\n   - GPT Reciter-Reader: Uses InstructGPT - This is an LLM\n   \n   The default implementation uses GPT Reciter-Reader (LLM-based).\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nAll five modules are implemented with Large Language Models as their backbone (either as the primary or available implementation).\n\nAnswer: 5", "citations": [], "input_tokens": 8622, "output_tokens": 425, "cost_micros": 32241, "latency_ms": 10062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - Uses LLM \u2713\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - Uses LLM \u2713\n\n3. **Question Answering Model** (Section 3.3): This module has three different implementations:\n   - Retriever-Reader: Uses RoBERTa-large fine-tuned on SQuAD (this is a reader model, but the main approach is retrieval-based)\n   - FLAN-T5: This is an LLM \u2713\n   - GPT Reciter-Reader: Uses InstructGPT as the reciter \u2713\n   \n   The document states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default uses LLM.\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - Uses LLM \u2713\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs \u2713\n\nCounting the modules that use LLMs:\n1. Claim Verifier - LLM\n2. Question Generator - LLM\n3. Question Answering Model - LLM (default implementation)\n4. QA Validator - LLM\n5. Reasoner - LLM\n\nAll 5 modules use LLMs in their implementation (either exclusively or as the default/primary option).\n\nAnswer: 5", "citations": [], "input_tokens": 12522, "output_tokens": 503, "cost_micros": 45111, "latency_ms": 11405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find information about modules implemented with Large Language Models backbone.Let me search for information about modules implemented with LLM backbone in the document.Now let me search for information about which modules use LLM backbone:Let me read more of the document to find details about the system architecture and which modules use Large Language Models:Now let me search for information specifically about which modules are implemented with Large Language Models:Based on the document, I can now identify which modules are implemented with Large Language Models (specifically InstructGPT). Let me verify this information:\n\nFrom the document, I can see that the QACHECK system has **five modules**:\n1. **Claim Verifier** - implemented with InstructGPT (Large Language Model)\n2. **Question Generator** - implemented with InstructGPT (Large Language Model)\n3. **Question Answering Model** - has three implementations, one of which (GPT Reciter-Reader) uses InstructGPT (Large Language Model)\n4. **QA Validator** - implemented with InstructGPT (Large Language Model)\n5. **Reasoner** - has two implementations, one of which uses InstructGPT (Large Language Model)\n\nHowever, I need to count how many modules are implemented with LLM backbone. Looking at the text more carefully:\n\n- **Claim Verifier**: Uses InstructGPT (LLM) \u2713\n- **Question Generator**: Uses InstructGPT (LLM) \u2713\n- **Question Answering Model**: Has 3 options (Retriever-Reader, FLAN-T5, GPT Reciter-Reader), but the QA module itself is not purely LLM-based in all implementations\n- **QA Validator**: Uses InstructGPT (LLM) \u2713\n- **Reasoner**: Has 2 implementations (FLAN-T5 and InstructGPT), one uses LLM \u2713\n\nLet me re-read to get the precise count of modules that ARE implemented with LLM backbone (not just have an option for it):\n\nFrom the text:\n- Claim Verifier: \"We build the claim verifier based on InstructGPT\" - **LLM**\n- Question Generator: \"we also leverage InstructGPT for in-context learning\" - **LLM**\n- QA Validator: \"We again implement the validator by prompting InstructGPT\" - **LLM**\n- Reasoner: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - has LLM option but also non-LLM option\n\nThe Question Answering Model has multiple implementations, and the document states the system uses \"GPT Reciter-Reader as the default implementation\" which uses InstructGPT.\n\nSo counting modules that are implemented with LLM backbone: Claim Verifier (1), Question Generator (2), QA Validator (3), and considering the default implementations, the Reasoner could be 4.\n\nLet me check if there's a clearer statement in the document about this specific question.Let me read the sections more carefully to understand which modules specifically use LLM backbone:\n\nBased on my reading of the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT\" - Uses LLM \u2713\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning\" - Uses LLM \u2713\n3. **Question Answering Model** (Section 3.3): Multiple implementations, NOT all use LLM\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT\" - Uses LLM \u2713\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Has LLM option\n\nThe modules that are definitively implemented with LLM (InstructGPT) backbone are:\n1. Claim Verifier\n2. Question Generator  \n3. QA Validator\n\nThat's **3 modules** that are implemented with Large Language Models backbone.\n\nHowever, if we consider that the Reasoner also has an InstructGPT implementation and FLAN-T5 is also a Large Language Model, it could be 4. Let me verify if FLAN-T5 counts as an LLM.\n\nFLAN-T5 is described in the document as \"a Seq2Seq model pre-trained on more than 1.8K tasks with instruction tuning\" - this is indeed a Large Language Model.\n\nSo if the question is asking about modules that ARE implemented (not optionally, but actually) with LLM backbone:\n- Claim Verifier: InstructGPT (LLM) \u2713\n- Question Generator: InstructGPT (LLM) \u2713\n- QA Validator: InstructGPT (LLM) \u2713\n- Reasoner: Has two implementations, both are LLMs (FLAN-T5 and InstructGPT) \u2713\n\nThat would be **4 modules**.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 100172, "error": null, "extra": {"thread_id": 2012, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5a2a7a1ab0194a9ba5958368d2c54734", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 686, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 5 and identify the final step shown in it.\n\nLooking at page 5, I can see Figure 4, which shows a screenshot of the QACHECK user interface with numbered annotations.\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) is clearly labeled as \"The final prediction result with rationale\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 27168, "output_tokens": 141, "cost_micros": 83619, "latency_ms": 10530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 5, which shows the QACHECK user interface screenshot, the final step (labeled as \"4\") shows:\n\n\"The final prediction result with rationale\"\n\nThis section displays:\n- \"Prediction with rationale:\"\n- The reasoning explanation\n- \"Finish\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 11656, "output_tokens": 80, "cost_micros": 36168, "latency_ms": 4530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 (the screenshot at the top of page 5), I can see the interface is annotated with 4 numbered sections:\n\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe fourth and final step shows \"Prediction with rationale:\" followed by the reasoning and final answer.\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 12772, "output_tokens": 111, "cost_micros": 39981, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figure at the top of page 5 (Figure 4), I can see the system interface showing the fact-checking process for the claim \"Lars Onsager won the Nobel prize when he was 30 years old.\"\n\nThe final step shown in the figure is labeled \"The final prediction result with rationale\" which displays:\n\n\"Lars Onsager won the Nobel prize in 1968. Lars Onsager was born in 1903. He was 65 when he won the Nobel prize. Therefore, the final answer is: False.\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 8641, "output_tokens": 135, "cost_micros": 27948, "latency_ms": 5422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 5, I need to identify the final step in the architecture diagram.\n\nThe flow shows:\n1. Claim \u2192 Claim Verifier\n2. Question Generator \u2192 QA Model \u2192 Validator \u2192 Relevant Context (in a loop)\n3. Claim Verifier \u2192 Reasoner\n4. Reasoner \u2192 Label\n\nThe final step in the process is when the Reasoner outputs the Label.\n\nAnswer: Label", "citations": [], "input_tokens": 12541, "output_tokens": 104, "cost_micros": 39183, "latency_ms": 4155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "label", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 5 and identify the final step.Let me search for page 5 content in the document:Let me search for page markers and figures in the document:Let me read around line 819 where Figure 4 is mentioned, as this might be on page 5:Perfect! I can see Figure 4 which appears to be on page 5 (after the PageBreak marker at line 815). Looking at the figure, I can see it shows a step-by-step process. Let me examine the final step more carefully:\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) shows: \"Prediction with rationale:\" followed by the text and ending with \"**Finish**\".\n\nLooking at the structure, the final step in the figure is marked as \"**Finish**\" at line 888.\n\nAnswer: Finish", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33953, "error": null, "extra": {"thread_id": 2013, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca5f20bedd764740aef245cbc4ae41c8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 195, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finish", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 2 and Figure 3 on page 4:\n\nIn Figure 2 (the system architecture), the \"Reasoner\" module is shown in a blue rectangle.\n\nIn Figure 3 (showing the three QA model implementations), the \"Reader\" component appears in orange/coral rectangles in parts (a) and (c).\n\nHowever, the question asks about a model rectangle that appears in BOTH the QA model and Reasoner modules. Looking at the figures more carefully:\n\n- In Figure 3(b), the \"FLAN-T5\" box appears in yellow\n- In Figure 3(c), the \"Reader\" boxes appear in orange\n- The \"Reasoner\" in the main architecture uses similar model components\n\nThe \"Reader\" model component appears in an orange/coral colored rectangle in both the QA module implementations and is used in the reasoning process.\n\nAnswer: Orange", "citations": [], "input_tokens": 27172, "output_tokens": 206, "cost_micros": 84606, "latency_ms": 10108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 that shows the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 4, which shows \"The architecture of our QACHECK system,\" I can see the system architecture with various modules including:\n- Claim Verifier\n- Question Generator\n- QA Model\n- Validator\n- Reasoner\n\nThe figure shows these modules connected with arrows. However, this figure does not clearly show colored rectangles that would indicate a specific model appearing in both the QA Model and Reasoner modules.\n\nLooking more carefully at the document, Figure 3 on page 5 shows \"Illustrations of the three different implementations of the Question Answering module in QACHECK\" with three subfigures (a), (b), and (c). In subfigure (a) \"Retriever-Reader\" and subfigure (c) \"GPT Reciter-Reader\", there is a \"Reader\" component shown.\n\nAccording to the text in Section 3.5, the Reasoner \"is a special question-answering model\" and the text mentions that both the QA module (Figure 3) and potentially the Reasoner use a \"Reader\" component.\n\nHowever, the document is in grayscale/black and white, so I cannot determine colors from the PDF provided.\n\nAnswer: Cannot determine - document appears to be in grayscale", "citations": [], "input_tokens": 11660, "output_tokens": 312, "cost_micros": 39660, "latency_ms": 9907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine document appears to be in grayscale", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 4, I need to identify the rectangles that appear in both the \"QA Model\" and \"Reasoner\" modules.\n\nIn the architecture diagram (Figure 2), the QA Model outputs \"(Q, A)\" pairs, and the Reasoner outputs \"Label\". \n\nThe component that connects both modules is the \"Wikipedia Corpus\" which is shown as a cylinder/database shape that provides evidence to the QA Model, and the accumulated context from Q&A pairs feeds into the Reasoner.\n\nHowever, looking more carefully at the diagram structure, both the QA Model and Reasoner are shown as rectangular boxes in the flowchart. The rectangles representing these modules themselves appear to be gray/neutral colored boxes in the system architecture.\n\nBut examining the actual visual elements more carefully, there isn't a colored rectangle that appears in both specific modules as separate components. The modules themselves are represented as boxes in the flow diagram.\n\nAnswer: gray", "citations": [], "input_tokens": 12776, "output_tokens": 212, "cost_micros": 41508, "latency_ms": 7203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 to identify the color of the model rectangle that appears in both the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 3 (which shows the system architecture), I can see the general architecture diagram. However, the question asks specifically about page 4.\n\nOn page 4, I can see Figure 3, which shows three different QA model implementations:\n(a) Retriever-Reader\n(b) FLAN-T5\n(c) GPT Reciter-Reader\n\nLooking at the diagram, I need to identify which model appears in both the QA module and the Reasoner module. According to the text in section 3.5, the Reasoner has two implementations: \"1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model.\"\n\nIn Figure 3, I can see rectangles representing different models. The FLAN-T5 model is shown in Figure 3(b), and this same model is mentioned as being used in the Reasoner module.\n\nLooking at the visual representation in Figure 3(b), the FLAN-T5 rectangle appears to be blue in color.\n\nAnswer: blue", "citations": [], "input_tokens": 8645, "output_tokens": 287, "cost_micros": 30240, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Figure 2 on page 4, which shows \"The architecture of our QACHECK system.\"\n\nLooking at the architecture diagram, I can see several modules represented as rectangles. The question asks about a rectangle that appears in both the QA Model and Reasoner modules.\n\nIn the diagram, I can see:\n- A \"QA Model\" box in the loop section\n- A \"Reasoner\" box after the loop\n- Both of these modules appear to have the same visual representation\n\nHowever, looking more carefully at the figure description and the actual diagram components, the modules that represent model components (like the Question Answering module and Reasoner) are shown as rectangles in the flowchart.\n\nThe document shows these as standard flowchart boxes, but I cannot definitively determine a specific color from the description provided. Looking at typical diagram conventions and the figure as presented, these model components appear to be represented in standard boxes.\n\nWithout being able to see the actual colors in the PDF image clearly described in the text, I cannot make a definitive statement about the specific color.\n\nAnswer: Cannot be determined from the provided text", "citations": [], "input_tokens": 12545, "output_tokens": 247, "cost_micros": 41340, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided text", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the figure on page 4 and identify the color of the model rectangle that appears in both the QA model and Reasoner module.Let me search for page 4 content and figures in the document:Let me search for figure-related content and read more of the document to find page 4:Let me read the section containing Figure 3 which appears to show different implementations of the QA module:This is Figure 3. Let me continue reading to find more figures and look for page markers:Let me search for page numbers or section breaks to locate page 4:Let me read around the fourth PageBreak (line 815) which should be around page 4:This is Figure 4, which shows a user interface. Let me continue reading to see if there are more visual elements described:Now I need to go back and look at Figure 2, which shows the architecture of the QACHECK system. That figure should show both the QA model and Reasoner module. Let me read around line 416 where Figure 2 is mentioned:I see Figure 2 is described, but I need to look at more details about the visual representation. Let me search for information about colors in the figure or read earlier to see the full figure description:The embedded image description in Figure 2 only describes a yellow/golden amber circular icon with a question mark. But the question asks about a model rectangle that appears in both QA model and Reasoner modules. Let me search for more complete visual descriptions or look for other figures. Let me check if there's more detailed architecture visualization:Let me read that chunk:Let me read that section:This doesn't seem to be the right figure. Let me search more specifically for information about the architecture diagram or look for mentions of colors in model components:Let me search for \"green\" or other colors that might appear in the architecture diagram:The green icons appear to be information icons. Let me look at the text structure of Figure 2 more carefully. The figure shows \"QA Model\" and \"Reasoner\" as separate boxes. Let me search for \"orange\" or \"red\" colors:Let me try searching for \"model\" and \"reasoner\" together or look at the complete Figure 2 text more carefully:Let me look at a different approach. The question mentions \"page 4\", so let me count the PageBreaks more carefully. Let me look between the 3rd and 4th PageBreak which should be page 4:So Figure 3 is on page 4 (after the 3rd PageBreak at line 648). Figure 3 shows the three different implementations of the Question Answering module. The visual description mentions a green circular icon with \"i\". But this doesn't show both QA model and Reasoner module together.\n\nLet me look for if there are more images or visual descriptions in Figure 3:I see in Figure 3 there are three parts (a), (b), and (c) showing different QA implementations. Each shows \"Reader\" as a component, and there's a green circular icon. But I need to understand the question better - it asks about a \"model rectangle\" that appears in both QA model and Reasoner module.\n\nLet me re-read Figure 2 which shows the overall architecture, and look for visual elements that might represent both components. Let me search for mentions of \"Reader\" since that appears in both:I notice that \"Reader\" appears in Figure 3 as a component. In the figure, the description says there's a green circular icon with \"i\". The \"Reader\" component in the Retriever-Reader, FLAN-T5, and GPT Reciter-Reader architectures likely uses the same visual element. But the question asks about something that appears in both \"QA model\" and \"Reasoner module\".\n\nWait - let me re-read the architecture description. The Reasoner is described as \"a special question-answering model\". So both might use the same underlying reader model. Let me look at where the reader model is used in Figure 3. The green \"i\" icon might be the answer indicator. But the question asks about a \"model rectangle\" not an icon.\n\nLet me think about this differently. Perhaps the document has better visual descriptions elsewhere. Let me search for all embedded images:There are multiple embedded images. The question specifically mentions Figure on page 4. Based on the PageBreaks, page 4 would be between line 648 and 815. Let me look more carefully at what figures or diagrams might show architectural components with rectangles:\n\nLooking back at Figure 3 (lines 651-707), it shows three sub-figures (a), (b), and (c) representing different QA implementations. Each has components like \"Retriever\", \"Reader\", \"FLAN-T5\", \"InstructGPT\". These might be shown as rectangles in the original figure.\n\nIn Figure 3, all three QA implementations show a \"Reader\" component. And the text mentions that the Reasoner module (section 3.5, lines 919-933) is \"a special question-answering model\" that can be implemented using \"FLAN-T5\" or \"InstructGPT model\".\n\nSo FLAN-T5 appears in both:\n- In Figure 3(b) as the QA model implementation  \n- In the Reasoner as one of the two implementation options\n\nLet me check if there's a visual description that mentions FLAN-T5 as a colored rectangle. Let me search for \"FLAN\" in visual descriptions:Perfect! So FLAN-T5 is used in BOTH:\n1. Figure 3(b) - as a QA Model implementation\n2. Section 3.5 (Reasoner) - as one of the two reasoner implementations\n\nNow I need to find the visual description showing what color the FLAN-T5 rectangle is. Let me look more carefully at the embedded images around Figure 3. However, the visual description at line 719 only describes the green \"i\" icon, not the model boxes themselves.\n\nLet me check if there are more visual descriptions or embedded images in the Figure 3 section. The document might have multiple embedded images for the same figure. Let me read more around that area:These are descriptions of other images. Let me look at the specific section where Im3.png first appears (around line 711) and see if there are additional visual descriptions I missed:Still showing the green \"i\" icon. Let me look at Im5.jpg which mentions GPT3 - this might be describing the model boxes:Im5.jpg shows a GPT3 logo with purple and pink/magenta colors. But I need to find descriptions of the model rectangles/boxes in Figure 3. Let me search for descriptions that mention \"rectangle\" or \"box\" with colors:Purple appears in the GPT3 logo. Since the question asks about a component that appears in BOTH QA Model and Reasoner, and we know FLAN-T5 is used in both, let me look for visual descriptions that might show FLAN-T5 box. Let me search for \"yellow\" since that was mentioned earlier:Yellow appears in question mark icons. Let me try a different approach. Let me look at all the visual descriptions more systematically. Let me check descriptions that mention model names or rectangular shapes directly. Let me search for visual descriptions that mention boxes or rectangles with specific colors:Not helpful. Let me think differently. The embedded images are specific parts of the figure. Maybe there are multiple embedded images within Figure 3 that I haven't found yet. Let me look at the embedded images that appear chronologically around Figure 3 content. Figure 3 is around lines 651-720. Let me check what other images might be part of it:\n\nActually, wait - let me reconsider. Looking at the OCR text, Im3.png shows \"i\", Im5.jpg shows \"GPT3\". These are components within the figures. Let me look for images that might show \"FLAN-T5\" or \"FLAN\" text:FLANonly appears as text, not in embedded image OCR. Let me look for embedded images with OCR text that might contain model names. Let me check if there are images showing \"T5\" or looking at what other embedded images show:All I'm finding are green \"i\" icons. Let me try a completely different approach. Perhaps I need to look at the actual figure diagram structure more carefully. In Figure 3, there are three sub-diagrams showing different QA implementations. Each has boxes/rectangles for components. Looking at the text structure:\n\nFigure 3(a): Retriever-Reader has \"Retriever\" and \"Reader\" boxes\nFigure 3(b): FLAN-T5 has \"FLAN-T5\" box  \nFigure 3(c): GPT Reciter-Reader has \"InstructGPT\" and \"Reader\" boxes\n\nThe common element between Figure 3 and the Reasoner would be the model itself. Since both QA module and Reasoner can use FLAN-T5, I should look for a visual description of the FLAN-T5 box/rectangle.\n\nLet me search through all the visual descriptions more systematically for anything mentioning model boxes or FLAN. Actually, let me look for embedded images that have model-related OCR text. Let me look for embedded images around the areas discussing models:\n\nWait, I should look for the actual image file names and descriptions that might show text boxes or model components. Let me search for descriptions that mention \"text\" and colors:Turquoise/cyan blue! Let me read that chunk:This turquoise/cyan blue button shows \"Finish\" - that's from Figure 4 (the UI screenshot). Let me look systematically for all visual descriptions that mention rectangular shapes or boxes. Actually, let me search for specific model component descriptions. Let me look for \"RoBERTa\" which is mentioned as the Reader model:RoBERTa is used in the Reader model but there's no visual description mentioning it. Let me try to systematically go through the embedded images and look for any that might show architectural component boxes. Let me look at the images that haven't been described yet. Let me check Im2.png more thoroughly since that's Figure 2:These are still just the icons. Let me try a completely different approach. Maybe the XML parser extracted images separately from the diagram. Let me look at the beginning of the document to see if there are more detailed figure descriptions I'm missing:This is Figure 1. The visual content shows search icons and question marks. Let me consider: maybe the XML extraction treats architectural diagram components differently. Perhaps the model boxes themselves don't have detailed color descriptions, or the color information is embedded in a way I haven't found yet.\n\nLet me reconsider what the question is actually asking. It says \"the model rectangle in the figure of page 4 that appears both in QA model and Reasoner module\". If Figure 3 is on page 4, and Figure 3 shows three different QA implementations, then the component that would also appear in the Reasoner would be the UNDERLYING MODEL.\n\nLooking back at the text:\n- Section 3.3 (QA module) describes three implementations, one being FLAN-T5 (line 675-686)\n- Section 3.5 (Reasoner) says it can be implemented using \"the end-to-end QA model based on FLAN-T5\" (line 931-932)\n\nSo FLAN-T5 is the model that appears in both. Now I need to find what color the FLAN-T5 rectangle is shown in Figure 3. Since the visual descriptions I've found so far only show icons (green \"i\", yellow \"?\", orange \"?\"), maybe the model boxes themselves are not described with specific colors in the extracted XML, or I need to look elsewhere.\n\nLet me try to find if there's any systematic description of the figure architecture. Maybe there's a legend or color scheme description:Turquoise appears as a \"Finish\" button and footer element. But wait, let me read chunk 155824 which mentions a layout with multiple colored sections:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 328655, "error": null, "extra": {"thread_id": 2014, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4736292b32d6461e98620f394ccee4da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2072, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "let me try to find if there s any systematic description of figure architecture maybe there s legend or color scheme description turquoise appears as finish button and footer element but wait let me read chunk 155824 which mentions layout with multiple colored sections", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompt instruction examples shown in the main body of the paper (not in the appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction example:\n   - \"Claim = CLAIM / We already know the following: CONTEXT / Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instruction examples:\n   - Initial question: \"Claim = CLAIM / To verify the above claim, we can first ask a simple question:\"\n   - Follow-up question: \"Claim = CLAIM / We already know the following: CONTEXT / To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction example:\n   - \"Claim = CLAIM / We already know the following: CONTEXT / Now we further know: NEW QA PAIR / Does the QA pair have additional knowledge useful for verifying the claim?\"\n\n4. **Section 3.5 (Reasoner)** - The text mentions prompts are in \"Appendix A.4\" but doesn't show the full prompt in the main body.\n\nTotal prompts shown in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 27159, "output_tokens": 335, "cost_micros": 86502, "latency_ms": 9625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper.\n\nLooking through the document, I can find the following prompt instructions in the main body (not in the Appendix):\n\n1. **Claim Verifier prompt** (Section 3.1):\n```\nClaim = CLAIM We already know the following:\nCONTEXT\nCan we know whether the claim is true or false now? Yes or no?\n```\n\n2. **Question Generator - Initial question prompt** (Section 3.2):\n```\nClaim = CLAIM\nTo verify the above claim, we can first ask a simple question:\n```\n\n3. **Question Generator - Follow-up question prompt** (Section 3.2):\n```\nClaim = CLAIM\nWe already know the following: CONTEXT\nTo verify the claim, what is the next question we need to know the answer to?\n```\n\n4. **QA Validator prompt** (Section 3.4):\n```\nClaim = CLAIM\nWe already know the following:\nCONTEXT\nNow we further know: NEW QA PAIR\nDoes the QA pair have additional knowledge useful for verifying the claim?\n```\n\nThat gives us 4 prompt instruction examples in the main body of the paper.\n\nAnswer: 4", "citations": [], "input_tokens": 11647, "output_tokens": 289, "cost_micros": 39276, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompts/instruction examples that are explicitly shown in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   Can we know whether the claim is true or false now? Yes or no?\n   ```\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instructions:\n   - For initial question generation:\n   ```\n   Claim = CLAIM\n   To verify the above claim, we can first ask a simple question:\n   ```\n   \n   - For follow-up questions:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   To verify the claim, what is the next question we need to know the answer to?\n   ```\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   Now we further know:\n   NEW QA PAIR\n   Does the QA pair have additional knowledge useful for verifying the claim?\n   ```\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12763, "output_tokens": 336, "cost_micros": 43329, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (excluding the Appendix).\n\nLet me go through the main body of the paper systematically:\n\n1. **Section 3.1 (Claim Verifier)**: The text mentions \"We prompt InstructGPT with ten distinct in-context examples as detailed in Appendix A.1\" - but no examples are shown in the main body, only referenced.\n\n2. **Section 3.2 (Question Generator)**: Two prompts are shown:\n   - Initial question generation prompt (1 prompt)\n   - Follow-up question generation prompt (1 prompt)\n\n3. **Section 3.3 (Question Answering Model)**: No complete prompt examples are shown in the main body.\n\n4. **Section 3.4 (QA Validator)**: One prompt instruction is shown for the validator module (1 prompt)\n\n5. **Section 3.5 (Reasoner)**: The text mentions \"the InstructGPT model with the prompts given in Appendix A.4\" - but no examples are shown in the main body.\n\nCounting the prompts actually displayed in the main body:\n- Section 3.2: 2 prompts (initial question generation + follow-up question generation)\n- Section 3.4: 1 prompt (QA validator)\n\nTotal: 3 prompts in the main body\n\nAnswer: 3", "citations": [], "input_tokens": 8632, "output_tokens": 331, "cost_micros": 30861, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - 1 prompt instruction:\n   - \"Claim = CLAIM... Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - 2 prompt instructions:\n   - Initial question generation: \"Claim = CLAIM... To verify the above claim, we can first ask a simple question:\"\n   - Follow-up questions: \"Claim = CLAIM... We already know the following: CONTEXT... To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - 1 prompt instruction:\n   - \"Claim = CLAIM... We already know the following: CONTEXT... Now we further know: NEW QA PAIR... Does the QA pair have additional knowledge useful for verifying the claim?\"\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12532, "output_tokens": 279, "cost_micros": 41781, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about instruction examples in the main body of the paper.Let me continue reading to find information about instruction examples in the main body of the paper.Let me read the Appendix section to find the prompts and instruction examples.Perfect! I can now see the prompts section clearly. Let me count the instruction examples mentioned. Looking at the document, I can see:\n\n1. **A.1 Prompts for Claim Verifier** - Shows \"<10 demonstrations in total>\"\n2. **A.2 Prompts for Question Generation** (initial question) - Shows \"<10 demonstrations in total>\"\n3. **A.2 Prompts for follow-up question generation** - Shows \"<10 demonstrations in total>\"\n4. **A.3 Prompts for Validator** - Shows \"<10 demonstrations in total>\"\n5. **A.4 Prompts for Reasoner** - Shows \"<10 demonstrations in total>\"\n\nHowever, the question asks about \"instruction examples listed in the main body of the paper.\" Let me check if there's more specific information in the main body about how many examples are actually shown.Let me search more specifically for mentions of how many examples are shown in the main body.Great! I found it. Let me read line 582 more carefully. The main body states: \"We prompt InstructGPT with **ten distinct in-context examples** as detailed in Appendix A.1\"\n\nLet me verify this is the answer by checking if there are other mentions of specific numbers in the main body.Perfect! I found another mention in line 790: \"with a suite of **ten demonstrations** shown in Appendix A.3\"\n\nLet me search for all instances where numbers of examples/demonstrations are mentioned in the main body.Based on my reading of the document, I found that in the main body of the paper, there are **two explicit mentions** of the number of instruction examples/demonstrations:\n\n1. Line 582: \"We prompt InstructGPT with **ten distinct in-context examples**\" (for the Claim Verifier)\n2. Line 790: \"with a suite of **ten demonstrations**\" (for the Validator)\n\nHowever, the question asks specifically \"How many prompts instruction examples are listed in the main body of the paper?\" - it asks for the COUNT/NUMBER that is mentioned, not how many times it's mentioned.\n\nAccording to the main body:\n- 10 examples for Claim Verifier (mentioned on line 582)\n- 10 demonstrations for Validator (mentioned on line 790)\n\nSince both mention \"10\" and the question asks for the number in float format and wants a single integer answer, the answer is:\n\n**10**\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77358, "error": null, "extra": {"thread_id": 2015, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_302989d449c9437a85f8635948950b78", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 438, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance scores for ProgramFC and QACheck (the proposed system) in the HOVER dataset from Table 1.\n\nFrom Table 1 on page 6:\n\n**HOVER dataset:**\n- ProgramFC scores: 54.27 (2-hop), 54.18 (3-hop), 52.88 (4-hop)\n- QACheck scores: 55.67 (2-hop), 54.67 (3-hop), 52.35 (4-hop)\n\nNow I'll calculate the performance gap for each:\n- 2-hop: 55.67 - 54.27 = 1.40\n- 3-hop: 54.67 - 54.18 = 0.49\n- 4-hop: 52.35 - 52.88 = -0.53\n\nAverage performance gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.453333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 27174, "output_tokens": 268, "cost_micros": 85542, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nLooking at Table 1 in the document, I can find the F1 scores for both systems on HOVER:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nTo calculate the average performance gap across all three HOVER subsets:\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 11662, "output_tokens": 275, "cost_micros": 39111, "latency_ms": 5983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12778, "output_tokens": 247, "cost_micros": 42039, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1 in the document, I can see the performance results:\n\nFor HOVER dataset:\n- ProgramFC: 54.27\n- QACheck: 55.67\n\nFor 2-hop:\n- ProgramFC: 54.18\n- QACheck: 54.67\n\nFor 3-hop:\n- ProgramFC: 52.88\n- QACheck: 52.35\n\nFor 4-hop:\n- ProgramFC: 59.66\n- QACheck: 59.47\n\nThe HOVER dataset has three subsets (2-hop, 3-hop, 4-hop) based on reasoning depth.\n\nTo calculate the average performance gap:\n- 2-hop gap: 54.67 - 54.18 = 0.49\n- 3-hop gap: 52.35 - 52.88 = -0.53\n- 4-hop gap: 59.47 - 59.66 = -0.19\n\nAverage gap = (0.49 + (-0.53) + (-0.19)) / 3 = -0.23 / 3 = -0.0767\n\nRounded to 2 decimal places: -0.08\n\nAnswer: -0.08", "citations": [], "input_tokens": 8647, "output_tokens": 319, "cost_micros": 30726, "latency_ms": 6530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-0.08", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck in the HOVER dataset from Table 1.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12547, "output_tokens": 251, "cost_micros": 41406, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about ProgramFC performance and the proposed system on the HOVER dataset.Let me search for tables or sections containing performance comparisons on the HOVER dataset.Let me read more of the document to find the tables with performance results.Let me search for tables with results and performance metrics.Perfect! I found Table 1 with the performance results. Let me extract the exact values:\n\nFrom Table 1, for the HOVER dataset:\n- **ProgramFC**: \n  - 2-hop: 54.27\n  - 3-hop: 54.18\n  - 4-hop: 52.88\n\n- **QACheck (proposed system)**:\n  - 2-hop: 55.67\n  - 3-hop: 54.67\n  - 4-hop: 52.35\n\nNow I need to calculate the average performance gap between QACheck and ProgramFC:\n\nFor each hop level:\n- 2-hop gap: 55.67 - 54.27 = 1.40\n- 3-hop gap: 54.67 - 54.18 = 0.49\n- 4-hop gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51640, "error": null, "extra": {"thread_id": 2016, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b7dbcb3c5c4d4293862871c529bf39fc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 231, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify the main tasks the authors conducted experiments on by examining the document.\n\nFrom the abstract and introduction (page 1), the authors state:\n\"More in detail, we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 PRELIMINARIES (page 2), the document explicitly describes two main tasks:\n- Section 2.1: Event Relation Extraction\n- Section 2.2: Deductive Reasoning\n\nThese are consistently mentioned throughout the paper as the primary experimental tasks:\n- The pilot study (Section 3.1.1) uses \"MAVEN-ERE and ProofWriter\" \n- Main experiments (Section 4.1) evaluate on \"MAVEN-ERE, Causal-TimeBank, and ProofWriter\"\n- MAVEN-ERE and Causal-TimeBank are event relation extraction datasets\n- ProofWriter is a deductive reasoning dataset\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 65638, "output_tokens": 233, "cost_micros": 200409, "latency_ms": 34842, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks that the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This is extensively discussed throughout the paper, with experiments on datasets like MAVEN-ERE and Causal-TimeBank. The task involves identifying coreference, temporal, causal, and subevent relations between events.\n\n2. **Deductive Reasoning** - This is the second main task, with experiments conducted on the ProofWriter dataset. The task involves logical reasoning to prove, disprove, or determine if statements are unknown based on given facts and rules.\n\nThese two tasks are explicitly mentioned in the abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 28196, "output_tokens": 189, "cost_micros": 87423, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks where experiments were conducted.\n\nFrom the Abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 (Preliminaries): \"In this section, we first introduce two tasks that this paper mainly explores.\" followed by Section 2.1 \"Event Relation Extraction\" and Section 2.2 \"Deductive Reasoning\"\n\nFrom the experimental setup sections, both tasks are consistently used throughout the paper for evaluation (Tables 1, 2, 3, and various figures reference MAVEN-ERE/Causal-TimeBank for ERE and ProofWriter for deductive reasoning).\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 34236, "output_tokens": 175, "cost_micros": 105333, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for the main experimental tasks:\n\nFrom the Introduction and throughout the paper, the authors clearly state they conduct investigations on two main tasks:\n\n1. **Event Relation Extraction (ERE)** - mentioned extensively, including datasets MAVEN-ERE and Causal-TimeBank\n2. **Deductive Reasoning** - mentioned alongside ERE, including the ProofWriter dataset\n\nThis is confirmed in multiple places:\n- Section 2 \"PRELIMINARIES\" has subsections 2.1 \"Event Relation Extraction\" and 2.2 \"Deductive Reasoning\"\n- Section 3.1.1 states: \"We conduct a manual evaluation on MAVEN-ERE and ProofWriter\"\n- The abstract mentions \"event relation extraction and deductive reasoning\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 26309, "output_tokens": 193, "cost_micros": 81822, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This task aims to identify relations (Coreference, Temporal, Causal, and Subevent) between two events in text. The authors used datasets like MAVEN-ERE and Causal-TimeBank for this task.\n\n2. **Deductive Reasoning** - This task involves beginning with known facts and rules, then iteratively making new inferences. The authors used the ProofWriter dataset for this task.\n\nThese two tasks are explicitly mentioned in Section 2 (PRELIMINARIES) where the authors state: \"In this section, we first introduce two tasks that this paper mainly explores.\" The section then proceeds with subsections 2.1 Event Relation Extraction and 2.2 Deductive Reasoning.\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 34535, "output_tokens": 211, "cost_micros": 106770, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify the two main tasks the author conducted experiments on.Let me continue reading to find the specific tasks mentioned:Based on the document, I can see that the author evaluated LLMs in two practical scenarios:\n\n1. Event relation extraction (lines 473)\n2. Deductive reasoning tasks (lines 480-481)\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57891, "error": null, "extra": {"thread_id": 2017, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fc47d9b3789248779051669f324d1694", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 114, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about the number of possible combinations of relationships between two events.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the task involves identifying four types of relations between two events:\n\n1. **Coreference relations**: 2 possible labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 possible labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 possible labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 possible labels (NO_SUBEVENT, SUBEVENT)\n\nThe total number of combinations would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, let me verify this with Figure 1, which shows an example calculation. In Figure 1, it states:\n\n\"Logical Inconsistency (LI) = Number of Conflicts (1) / Number of Combinations (6) = 1/6 \u2248 16.7%\"\n\nThe text explains: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis is referring to pairwise combinations between the 4 relation types, not the total number of possible relationship combinations.\n\nFor the total number of possible combinations of relationships, the calculation is:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nAnswer: 84", "citations": [], "input_tokens": 65637, "output_tokens": 393, "cost_micros": 202806, "latency_ms": 38250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the paper defines 4 types of relations between two events:\n\n1. **Coreference relations**: 2 labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 labels (NO_SUBEVENT, SUBEVENT)\n\nThe document also explicitly mentions in the logical inconsistency calculation (Figure 1 and Section 2.1):\n\n\"Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis refers to combinations between the 4 relation types (choosing 2 from 4 types to check for conflicts), not the total possible relationship combinations.\n\nHowever, looking more carefully at the document, particularly in Figure 1, it states:\n\"Number of Combinations (+ O) = 6\"\n\nThis is the number of pairwise combinations when checking for conflicts between the 4 different relation types: C(4,2) = 4!/(2!\u00d72!) = 6.\n\nBut the question asks about \"combinations of relationships between two events,\" which would be the total number of possible relationship combinations (one label from each of the 4 types):\n\nTotal combinations = 2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, re-reading the context in Figure 1 more carefully, the \"6\" refers to checking consistency between pairs of the 4 relation types, not the total possible labelings.\n\nLooking at the question again and the context in the paper where they discuss logical inconsistency, the relevant number appears to be **6** - the number of combinations when checking pairs of relation types for conflicts", "citations": [], "input_tokens": 28195, "output_tokens": 512, "cost_micros": 92265, "latency_ms": 12843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper states that for each pair of events, there are 4 types of relations to determine:\n\n1. **Coreference relations**: {NO_COREFERENCE, COREFERENCE} - 2 options\n2. **Temporal relations**: {NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON} - 7 options\n3. **Causal relations**: {NO_CAUSAL, PRECONDITION, CAUSE} - 3 options\n4. **Subevent relations**: {NO_SUBEVENT, SUBEVENT} - 2 options\n\nThe paper also provides a specific formula in Figure 1 for calculating the \"total number of combinations\" when computing logical inconsistency:\n\nIn the example, it states: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis formula 4*(4-1)/2 = 6 represents the number of pairs of relation types (combinations of 2 from 4 types), which is used to check for conflicts between different relation types.\n\nHowever, if the question asks about the total possible combinations of relationship assignments (not pairs for conflict checking), it would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84 possible combinations\n\nBut based on the context in Figure 1 where they explicitly calculate \"total number of combinations\" as 6 using the formula 4*(4-1)/2, the paper is referring to the number of pairs of relation types that need to be checked for logical consistency.\n\nAnswer: 6", "citations": [], "input_tokens": 34235, "output_tokens": 428, "cost_micros": 109125, "latency_ms": 11375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper.\n\nFrom Section 2.1 (Event Relation Extraction), the paper identifies four types of relations between two events:\n\n1. **Coreference relations**: 2 options\n   - NO_COREFERENCE\n   - COREFERENCE\n\n2. **Temporal relations**: 7 options\n   - NO_TEMPORAL\n   - BEFORE\n   - OVERLAP\n   - CONTAINS\n   - SIMULTANEOUS\n   - ENDS-ON\n   - BEGINS-ON\n\n3. **Causal relations**: 3 options\n   - NO_CAUSAL\n   - PRECONDITION\n   - CAUSE\n\n4. **Subevent relations**: 2 options\n   - NO_SUBEVENT\n   - SUBEVENT\n\nThe paper states in Section 2.1: \"We formulate ERE as a multi-classification problem, determining one label (relation) for each of these four relation types.\"\n\nSince we need to determine one label for each of the four relation types, and these are independent choices, the total number of possible combinations is:\n\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nThis is also confirmed in Section 2.1 where it mentions calculating logical inconsistency as \"the ratio of the number of conflicts to the total number of combinations (i.e., all combinations of each two relations).\"\n\nAnswer: 84", "citations": [], "input_tokens": 26308, "output_tokens": 335, "cost_micros": 83949, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper discusses the Logical Inconsistency (LI) metric. In the example shown in Figure 1, there's a specific calculation:\n\n\"Among these, \"SIMULTANEOUS\" and \"CAUSE\" are identified as conflicting with each other based on the logical constraints we have defined, resulting in a single conflict. Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: $4 * (4 - 1) / 2 = 6$.\"\n\nThis calculation shows that with 4 types of relations (Coreference, Temporal, Causal, and Subevent), the number of combinations between pairs of these relations is 6.\n\nThis is confirmed in the definition of Logical Inconsistency in Section 2.1:\n\"logical inconsistency is calculated as the ratio of the number of conflicts (i.e., the answers that conflict with the given logical constraints) to the total number of combinations (i.e., all combinations of each two relations).\"\n\nThe example explicitly states: \"Hence, the logical inconsistency in this example is computed as LI = 1/6 (or approximately 16.7%).\"\n\nThe denominator 6 represents the total number of possible combinations of relationships between two events.\n\nAnswer: 6", "citations": [], "input_tokens": 34534, "output_tokens": 346, "cost_micros": 108792, "latency_ms": 10032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the definition of relationships between two events.Let me search for more specific terms related to event relationships:Perfect! I found the answer. Let me read that specific chunk to get the complete information:Based on the document, I can see that for each pair of events, there are 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36141, "error": null, "extra": {"thread_id": 2018, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_45c25fd9bb164100939e531a52487f98", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 140, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows \"Vicuna-13B and Llama2-13B's performance on MAVEN-ERE and Causal-TimeBank after pre-training on LLM-LR\":\n\nFor Vicuna-13B-PT on MAVEN-ERE, the results are:\n- vanilla ICL: 15.3% Micro-F1\n- vanilla ICL w. CoT: 15.8% Micro-F1\n- CoT w. logical constraints: 18.0% Micro-F1\n- w. all logical constraints: 16.3% Micro-F1\n- w. retrieved logical constraints: 16.1% Micro-F1\n- w. post-processing: 11.0% Micro-F1\n\nThe best performance is 18.0% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 65644, "output_tokens": 212, "cost_micros": 200112, "latency_ms": 35562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B after pre-training (Vicuna-13B-PT) on MAVEN-ERE:\n\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest Micro-F1 score is 18.0%.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 28202, "output_tokens": 149, "cost_micros": 86841, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, I need to find the best performance of Vicuna-13B-PT on MAVEN-ERE.\n\nThe table shows the following Micro-F1 scores for Vicuna-13B-PT on MAVEN-ERE:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34242, "output_tokens": 168, "cost_micros": 105246, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, which shows the performance of Vicuna-13B-PT (pre-trained) on MAVEN-ERE:\n\nThe results show:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest micro-F1 score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 26315, "output_tokens": 151, "cost_micros": 81210, "latency_ms": 4937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B and Llama2-13B after pre-training on LLM-LR:\n\nFor Vicuna-13B on MAVEN-ERE, the Micro-F1 scores are:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest performance is 18.0% with CoT w. logical constraints.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34541, "output_tokens": 175, "cost_micros": 106248, "latency_ms": 5140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about Vicuna-13B's performance on MAVEN-ERE.Let me search for information about MAVEN-ERE and Vicuna-13B performance:Let me read the table that shows the performance results:Perfect! I found the table showing Vicuna-13B's performance on MAVEN-ERE. Looking at the table, I can see three different approaches for Vicuna (Vicuna-13B):\n\n1. vanilla ICL: 13.8% Micro-F1\n2. vanilla ICL w. CoT: 11.6% Micro-F1\n3. CoT w. logical constraints: 14.9% Micro-F1\n\nThe best performance was with \"CoT w. logical constraints\" at 14.9% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38983, "error": null, "extra": {"thread_id": 2019, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b286a9a39b954fac8755dc6ce4c285c7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 166, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for information about the Sociology subject...\n\nFrom Figure 3 on page 3, I can see that Sociology is listed under \"Humanities & Social Sci. (9%)\" with \"Sociology (287, 2.48%)\".\n\nFrom Table 8 on page 18 (Humanities & Social Science results), I can see that Sociology has 252 samples in the test set.\n\nAdding the validation and test samples:\n- Validation set appears to have a portion of the 947 total H&SS samples\n- Test set for Sociology: 252 samples\n\nLooking more carefully at the statistics in Table 1 on page 4, the total is 11,550 questions with a Dev:Validation:Test split of 150:900:10500.\n\nFrom the detailed breakdown in Figure 3, Sociology shows (287, 2.48%), which represents 287 total samples across all splits.\n\nAnswer: 287", "citations": [], "input_tokens": 259120, "output_tokens": 216, "cost_micros": 1559580, "latency_ms": 77812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in MMMU that belong to the sociology subject.\n\nLooking at the document, I can find this information in Figure 3, which shows the breakdown of questions by disciplines, subjects, and subfields.\n\nIn Figure 3, under \"Humanities & Social Sci. (9%)\", I can see:\n\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Sociology (287, 2.48%)\n- Psychology (340, 2.94%)\n\nThe number in parentheses after \"Sociology\" shows 287 questions, which represents 2.48% of the total.\n\nAnswer: 287", "citations": [], "input_tokens": 79424, "output_tokens": 155, "cost_micros": 240597, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in the MMMU dataset that belong to the sociology subject.\n\nLooking through the document, I can find information about the distribution of samples across subjects in Figure 3 and Table 10.\n\nFrom Figure 3, which shows \"MMMU contains 11.5K multimodal questions covering six broad disciplines, 30 subjects, and 183 subfields,\" I can see the breakdown of subjects with their sample counts.\n\nUnder \"Humanities & Social Sci. (9%)\", I can see:\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- Sociology (287, 2.48%)\n\nThe number shown for Sociology is 287 samples.\n\nAnswer: 287", "citations": [], "input_tokens": 109175, "output_tokens": 183, "cost_micros": 330270, "latency_ms": 8484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the number of samples in the Sociology subject.\n\nIn Figure 3 on page 3, there is a breakdown table showing the distribution of samples across different subjects. Under \"Humanities & Social Sci. (9%)\", I can see:\n\nv Sociology (287, 2.48%)\n\nThe number 287 represents the number of samples in the Sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 83138, "output_tokens": 100, "cost_micros": 250914, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about MMMU and sociology samples in the provided documents.Let me search for information about sociology and sample counts in the document:I found the information! Let me read the specific section with the table that shows the sample counts:Perfect! I found the answer. According to line 1572, Sociology has 287 samples in MMMU, which represents 2.48% of the dataset.\n\nAnswer: 287", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 63671, "error": null, "extra": {"thread_id": 2020, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6e9ac9023c84b0284ac8c1e7870f526", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 141, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and compare it to the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: According to Figure 3 on page 3, Public Health has **544 samples** (shown as \"Public Health (544, 4.7%)\").\n\n2. **Public Health samples showcased**: I need to count the case study figures related to Public Health from Table 10 on page 21. According to this table, Public Health has the following case study figures:\n   - Correct Case: 63\n   - Lack of Knowledge: 65\n   - Textual Understanding Error: 64\n\n   This gives us **3 showcased samples** from Public Health.\n\nThe ratio would be: 3/544 = 0.0055147...\n\nRounding to a reasonable number of decimal places: 0.0055\n\nAnswer: 0.0055", "citations": [], "input_tokens": 259140, "output_tokens": 214, "cost_micros": 1559655, "latency_ms": 83546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the ratio to the total Public Health samples.\n\nFrom Table 7 (Health & Medicine results), I can see that Public Health has 509 samples in the test set.\n\nNow I need to count how many Public Health samples are showcased in the paper (Appendix B - Case Study).\n\nLooking through the Case Study figures list and the actual figures:\n\nPublic Health samples shown:\n- Figure 63: Public Health 1 (Epidemiology) - Correct Case\n- Figure 64: Public Health 2 (Epidemiology) - Textual Understanding Error\n- Figure 65: Public Health 3 (Epidemiology) - Lack of Knowledge\n\nSo there are 3 Public Health samples showcased.\n\nThe ratio is: 3 / 509 = 0.005892...\n\nRounding to a reasonable number of decimal places:\n\nAnswer: 0.0059", "citations": [], "input_tokens": 79444, "output_tokens": 220, "cost_micros": 241632, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the percentage of Public Health samples that are showcased in the paper.\n\nFrom the document, I can find:\n\n1. Total number of Public Health samples in the test set:\n   - From Table 7, Public Health has 509 samples in the test set\n\n2. Number of Public Health samples showcased in the paper:\n   - I need to count the figures in Appendix B that are from Public Health\n   - From the List of Case Study Figures (pages 20-21):\n     - Figure 63: Public Health 1: Correct Case\n     - Figure 64: Public Health 2: Textual Understanding Error\n     - Figure 65: Public Health 3: Lack of Knowledge\n   \n   So there are 3 Public Health samples showcased.\n\n3. Calculate the percentage:\n   - Percentage = (3 / 509) \u00d7 100 = 0.589...\n\nWait, let me verify the total number. Looking at Table 1 (page 4), Public Health has 544 samples total. But the question asks about samples \"in this paper\", which could mean the test set.\n\nFrom Table 7 on page 17, Public Health (test set) = 509 samples.\n\nThe ratio would be: 3/509 = 0.00589...\n\nIf we want this as a percentage: 0.589%\n\nBut the question asks for \"a ratio\" as a decimal number, so:\n\n3/509 = 0.005893\n\nAnswer: 0.0059", "citations": [], "input_tokens": 109195, "output_tokens": 347, "cost_micros": 332790, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the percentage.\n\nFrom the document, I can find information about Public Health samples:\n\n1. First, let me find the total number of Public Health samples in the dataset.\nFrom Table 1 (Key statistics), I can see the total questions is 11,550.\n\nLooking at Figure 3 or the breakdown tables, I need to find Public Health information:\n- From Table 7 (Health & Medicine results), I can see Public Health is listed with 509 samples in the test set.\n- The validation set has 150 samples total for Health & Medicine.\n\nActually, looking more carefully at the tables:\n- Table 7 shows Health & Medicine breakdown with Public Health having 509 samples in the test set\n- There's also validation data\n\nLet me look at the case study section (Appendix B):\nFrom Table 10 (Table index of case study figures by subjects), I can count the Public Health cases:\n- Public Health 1: Correct Case (Figure 63)\n- Public Health 2: Textual Understanding Error (Figure 64)\n- Public Health 3: Lack of Knowledge (Figure 65)\n\nSo there are 3 Public Health cases showcased in the paper.\n\nNow I need to find the total number of Public Health samples:\nFrom Table 7, the Public Health row shows 509 samples in the test set.\n\nThe ratio would be: 3/509 = 0.00589...\n\nWait, let me verify this is asking for the percentage of showcased samples. \n\n3/509 \u00d7 100 = 0.589%\n\nAs a ratio (not percentage): 3/509 = 0.00589\n\nAnswer: 0.00589", "citations": [], "input_tokens": 83158, "output_tokens": 399, "cost_micros": 255459, "latency_ms": 11937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.00589", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and divide by the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: Looking at Figure 3, Public Health has 544 samples (4.7% of total).\n\n2. **Public Health samples showcased in the paper**: Looking at Table 10 (Case Study Figures by subjects), I can find the Public Health figures:\n   - Figure 63: Public Health 1: Correct Case\n   - Figure 64: Public Health 2: Textual Understanding Error\n   - Figure 65: Public Health 3: Lack of Knowledge\n\nSo there are 3 Public Health case study figures shown in the paper.\n\nThe ratio is: 3 / 544 = 0.00551...\n\nAnswer: 0.0055", "citations": [], "input_tokens": 103252, "output_tokens": 191, "cost_micros": 312621, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about 'Public Health' samples and calculate the percentage.Let me search for tables and figures that might contain information about Public Health samples:Now let me look at Figure 3 and tables that show the breakdown of subjects:Perfect! I found the information. Let me read more to make sure I have all the details:Perfect! I found the information in the table. From line 1505, I can see:\n\n\"Public Health (544, 4.7%)\"\n\nThis indicates that:\n- Public Health has 544 samples\n- This represents 4.7% of all samples in the dataset\n\nThe answer to the question \"Among all samples belonging to 'Public Health' subject, how many percentage samples showcased in this paper?\" is 4.7%.\n\nSince the question asks for a ratio (percentage) and wants a single decimal number, the answer is:\n\nAnswer: 4.7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53796, "error": null, "extra": {"thread_id": 2021, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8dc9bfdb2b5b47d48d7a6fa4dc0574a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "4.7", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics-related figures with error categories.\n\nLooking through the case study figures in Appendix B:\n\n1. Figure 22 - Economics: Econometrics - This is a CORRECT case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n   This case shows GPT-4V making an error in correctly identifying numbers in the table.\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n   This case shows GPT-4V failing to correctly interpret the intersection point in the graph.\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n\nFrom Table 10, I can see:\n- Economics has entries for figures 22, 23, 24\n- Figure 22 is marked as \"Correct Case\"\n- Figure 23 is marked as \"Perceptual Error\"\n- Figure 24 is marked as \"Perceptual Error\"\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 79431, "output_tokens": 298, "cost_micros": 242763, "latency_ms": 8734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\n1. **Figure 23** - Economics: Macroeconomics - Error Category: Perceptual Error\n   - GPT-4V incorrectly attributes numbers to the correct objects for production\n\n2. **Figure 24** - Economics: Macroeconomics - Error Category: Perceptual Error\n   - GPT-4V fails to interpret the image correctly regarding the equilibrium interest rate\n\nLooking through the List of Case Study Figures table (Table 10), I can see:\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nSo there are **2** Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 109182, "output_tokens": 227, "cost_micros": 330951, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the case study section and count how many fall into the \"Perceptual Error\" category.\n\nLooking through the case study figures, I'll identify Economics-related cases:\n\n1. Figure 22 - Economics: Econometrics - This is a correct case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n\nNow let me verify these are all the Economics cases by checking the Table of Contents in the case study section (Table 10):\n\nFrom Table 10:\n- Economics 1: Correct Case (Figure 22/36)\n- Economics 2: Perceptual Error (Figure 23/37)\n- Economics 3: Perceptual Error (Figure 24/38)\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 83145, "output_tokens": 237, "cost_micros": 252990, "latency_ms": 7797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in Appendix B (Case Study) that are categorized as \"Perceptual Error\".\n\nLet me search through the Case Study section for Economics cases:\n\n1. **Figure 22** - Economics: Econometrics - This is a **correct case**, not an error case.\n\n2. **Figure 23** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\n3. **Figure 24** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nThat's all the Economics cases listed.\n\nTherefore, there are 2 Economics-related error cases that fall into the Error Category of Perceptual Error.\n\nAnswer: 2", "citations": [], "input_tokens": 103239, "output_tokens": 242, "cost_micros": 313347, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about Economics-related error cases and Perceptual Error categories in the provided documents.Let me search for content related to \"Economics\" and \"Perceptual Error\" in this document.Let me search for tables or error categories in the document:Now let me look for tables with error categories and subject areas. Let me search more broadly:Let me look at the specific area around line 7247 where there's a question about stocks (Economics-related):Let me search for a table that shows the distribution of error categories by subject area:Let me search for tables that might show error distribution:Let me look for error analysis tables. Let me search around the beginning of the document for methodology or error analysis sections:Let me search for \"Figure\" to find error analysis figures/tables:Great! Let me read the section with Figure 6 which shows error distribution:Good! I can see Figure 6 shows the overall error distribution, but I need to find a breakdown by subject area. Let me look for tables that show error categories by discipline:I can see there's a table showing different subjects and error cases. Let me read around line 5808 where there's an Accounting entry:Perfect! I can see the list of case studies. I can see:\n- Economics 1: Correct Case\n- Economics 2: Perceptual Error\n- Economics 3: Perceptual Error\n\nSo there are 2 Economics cases with Perceptual Error. Let me verify this is the complete list by checking if there are more Economics cases:Perfect! I found Table 10 which shows the case study figures by subjects with associated error categories. Looking at the Economics row:\n\n- Subject:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94360, "error": null, "extra": {"thread_id": 2022, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ce7c6d6aee6641609ca2f09db6ff3175", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 531, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the subfields for the Energy & Power subject in the MMMU dataset.\n\nLooking at the document, I can find this information in Table 11 on page 111, which lists \"Subfields of each subject.\"\n\nUnder the \"Tech & Engineering\" discipline, I can see the \"Energy & Power\" subject with its subfields listed.\n\nFrom Table 11:\nEnergy & Power: Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 259135, "output_tokens": 118, "cost_micros": 1557465, "latency_ms": 60233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists subfields of each subject:\n\nFor Energy & Power under the Tech & Engineering discipline, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 79439, "output_tokens": 52, "cost_micros": 239097, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists all subfields by subject:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 109190, "output_tokens": 35, "cost_micros": 328095, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document, which lists \"Subfields of each subject,\" I can find the subfields for Energy & Power.\n\nAccording to the table:\n\n**Energy & Power:** Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 103247, "output_tokens": 73, "cost_micros": 310836, "latency_ms": 4766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the subfields in the Energy & Power subject.I found two subfields mentioned for Energy and Power. Let me search for more information to ensure I have a complete list.Let me search more specifically for all Energy and Power subfield mentions:Based on the document, I found two subfields explicitly mentioned for Energy and Power:\n1. Fluid Mechanics (Figure 89)\n2. Thermodynamics (Figure 90)\n\nLet me verify if there are any other subfields by looking at tables or lists that might contain more comprehensive information:Perfect! I found the information. In line 1545, it shows the subfields for Energy Power:\n\n\"Fluid Mechanics, Heat Transfer ...\"\n\nLet me search for more complete information about all the subfields:Excellent! I found the complete list. Let me verify this is in a table showing all subfields:Perfect! I found the complete list of subfields for Energy & Power in the table. The subfields are:\n- Thermodynamics\n- Heat Transfer\n- Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 66234, "error": null, "extra": {"thread_id": 2023, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db9765522b274b60ae516c6ef2df0d73", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 314, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU benchmark.\n\nLooking at the detailed subject breakdown tables in the appendix:\n\nFrom Table 9 (Tech & Engineering results on page 19):\n- Computer Science (Comp. Sci.) column shows GPT-4V(ision) (Playground) achieved **57.1%**\n\nFrom Table 6 (Science results on page 16):\n- Math column shows GPT-4V(ision) (Playground) achieved **45.0%**\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 259141, "output_tokens": 158, "cost_micros": 1558401, "latency_ms": 61328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can find Computer Science:\n- Computer Science (371): 57.1%\n\nLooking at Table 6 (Science results), I can find Math:\n- Math (505): 45.0%\n\nThe difference is: 57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 79445, "output_tokens": 114, "cost_micros": 240045, "latency_ms": 5546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU dataset.\n\nLooking through the tables in the document, I need to find the breakdown by subject for GPT-4V.\n\nFrom Table 6 (Science results), I can see:\n- Math: GPT-4V(ision) (Playground) achieves 45.0%\n\nFrom Table 9 (Tech & Engineering results), I can see:\n- Computer Science (Comp. Sci.): GPT-4V(ision) (Playground) achieves 57.1%\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 109196, "output_tokens": 167, "cost_micros": 330093, "latency_ms": 7889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science (CS) and Math subjects in the MMMU dataset.\n\nLooking through the document for GPT-4V results by subject:\n\nIn Table 2 (page 6), I can see overall results, but I need to find the breakdown by specific subjects.\n\nLooking at the appendix sections for detailed subject breakdowns:\n\nFrom Table 9 (page 19) - Tech & Engineering results:\n- Computer Science is listed under Tech & Engineering\n- GPT-4V(ision) (Playground) shows: Comp. Sci. = 57.1%\n\nFrom Table 6 (page 16) - Science results:\n- Math is listed under Science\n- GPT-4V(ision) (Playground) shows: Math = 45.0%\n\nLet me verify these numbers are correct by checking the table again:\n\nTable 6 (Science results):\n- GPT-4V(ision) (Playground): Math column shows 45.0\n\nTable 9 (Tech & Engineering results):\n- GPT-4V(ision) (Playground): Comp. Sci. column shows 57.1\n\nThe performance difference is:\n57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 83159, "output_tokens": 300, "cost_micros": 253977, "latency_ms": 9016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can see Computer Science is listed with GPT-4V achieving **57.1%**.\n\nLooking at Table 6 (Science results), I can see Math is listed with GPT-4V achieving **45.0%**.\n\nThe performance difference is: 57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 103253, "output_tokens": 119, "cost_micros": 311544, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find GPT-4V's performance on CS and Math subjects in the MMMU dataset.Let me search for performance tables or results sections that would contain GPT-4V's performance by subject.Let me search for tables with performance metrics:Let me look for a results table that shows performance by subject. Let me read around line 5873 and 5978 where I saw Computer Science and Math mentioned:This appears to be a table of figure references. Let me search for actual performance data tables. Let me look for \"accuracy\" or percentage data:Let me read the beginning of the paper to find the main results table:Let me search for a table with actual performance numbers. Let me look for \"GPT-4V\" with numbers:Let me look for Table 2 which should contain the main results broken down by subject:Good! I found Table 2. Now I need to find the breakdown by individual subjects. The table shows broad disciplines but I need specific subjects like Computer Science and Math. Let me search for another table with subject-level breakdown:Table 9 should have Tech & Engineering which includes Computer Science. Let me read that table:Perfect! I found GPT-4V's Computer Science performance at 57.1% (line 5254). Now I need to find Math performance. Let me check Table 6 for Science subjects:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 132796, "error": null, "extra": {"thread_id": 2024, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b9c01f114dd641199d8ac115ae26b4c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 506, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-4.0", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays various categories:\n\n1. **Comprehensive Disciplines** - showing a pie chart with percentages for different disciplines (Engineering 26%, Art & Design 11%, Business 14%, Medicine 17%, Science 23%, Humanities & Social Sci. 9%)\n\n2. **Heterogeneous Image Types** - listing various types: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\n\n3. **Interleaved Text and Images** - showing an example with 3 medical images\n\n4. **Expert-level Skills Test** - showing three categories:\n   - Perception (with an icon)\n   - Knowledge (with an icon)\n   - Reasoning (with an icon)\n\nThe question asks about \"different icons shown as different image types\" in Figure 1. Looking at the heterogeneous image types section, I can count the distinct image type categories mentioned (not just listed as text, but representing different types):\n\nFrom the visible list and description: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics (and the ellipsis suggests more).\n\nThat's 12 explicitly mentioned types, but the question asks about icons shown. The figure shows examples and visual representations rather than just listing them.\n\nLooking more carefully at Figure 1, I need to count the actual different image type examples/icons displayed, not just mentioned in text.\n\nAnswer: 12", "citations": [], "input_tokens": 79427, "output_tokens": 403, "cost_micros": 244326, "latency_ms": 11984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which is titled \"Overview of the MMMU dataset,\" I can see it displays four main sections:\n\n1. **Comprehensive Disciplines** - showing a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - showing various image type icons\n3. **Interleaved Text and Images** - showing a sample question with images\n4. **Expert-level Skills Test** - showing three overlapping circles for Perception, Knowledge, and Reasoning\n\nIn the \"Heterogeneous Image Types\" section, I can count the following distinct icons representing different image types:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Chemical (structures)\n5. Photos/Photographs\n6. Paintings\n7. Geometric (shapes)\n8. Music (sheet music)\n9. Medical (images)\n\nLet me recount more carefully by examining the visual elements shown in that section. The figure shows various small icons/symbols representing different image types in a grid-like arrangement.\n\nCounting the distinct image type icons shown: 9 different icons are displayed representing different image types (Diagrams, Tables, Plots and Charts, Chemical Structures, Photographs, Paintings, Geometric shapes, Music/Sheet Music, and Medical Images).\n\nAnswer: 9", "citations": [], "input_tokens": 109178, "output_tokens": 314, "cost_micros": 332244, "latency_ms": 10828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to look at Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays four main characteristics:\n\n1. **Comprehensive Disciplines** - showing 6 disciplines with percentages:\n   - Engineering (26%)\n   - Art & Design (11%)\n   - Business (14%)\n   - Science (23%)\n   - Humanities & Social Sci. (9%)\n   - Medicine (17%)\n\n2. **Heterogeneous Image Types** - listing various types like: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Music, Geometric, Pathology images, Microscopic Images, Comics, \u2026\n\n3. **Interleaved Text and Images** - showing an example question with images\n\n4. **Expert-level Skills Test** - listing various types of reasoning and knowledge\n\nThe figure shows icons/visual representations for these categories, but the question asks specifically about \"different icon shown as different image types.\"\n\nLooking at the \"Heterogeneous Image Types\" section in Figure 1, I can count the distinct image type categories mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nAnd there's an ellipsis (\u2026) indicating more types.\n\nHowever, the question asks about icons shown in Figure 1. Looking at the visual representation, the heterogeneous image types section appears to show example icons or representations of these different types.\n\nBased on the visible list in the \"Heterogeneous Image Types\" section of Figure 1, I can count 12 distinct image type categories explicitly listed before the ellipsis.\n\nAnswer: 12", "citations": [], "input_tokens": 83141, "output_tokens": 438, "cost_micros": 255993, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine Figure 1 to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see four main sections:\n\n1. **Comprehensive Disciplines** - Shows a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - This section lists various image types\n3. **Interleaved Text and Images** - Shows an example question with images\n4. **Expert-level Skills Test** - Shows a diagram with arrows\n\nIn the \"Heterogeneous Image Types\" section, I can see a list of different image types mentioned:\n- Diagrams\n- Tables\n- Plots and Charts\n- Photographs\n- Chemical Structures\n- Paintings\n- Medical Images\n- Sheet Music\n- Geometric\n- Pathology images\n- Microscopic Images\n- Comics\n- (and the list continues with \"...\")\n\nHowever, the question asks about \"icons shown as different image types.\" Looking at the figure more carefully, I don't see actual icons representing each image type - rather, I see text labels listing the image types.\n\nLet me reconsider - perhaps the question is asking about visual representations or examples shown. In Figure 1, I can see:\n- A pie chart (1 type of visualization)\n- Example images in the \"Interleaved Text and Images\" section (3 medical scan images)\n- A flow diagram in the \"Expert-level Skills Test\" section\n\nBut this doesn't seem to match what the question is asking either.\n\nRe-reading the question: \"How many different icon are shown as different image types in Figure 1?\"\n\nLooking at the figure description more carefully, in the \"Heterogeneous Image Types\" section, there appears to be a list of image types. The text mentions: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nCounting the explicitly listed items before the \"...\": 12 different image types are mentioned.\n\nAnswer: 12", "citations": [], "input_tokens": 103235, "output_tokens": 475, "cost_micros": 316830, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and count the different icons shown as different image types.Let me search for \"Figure 1\" in the document:Now let me read the beginning of the document content to find Figure 1:Good, I can see Figure 1 information. Let me continue reading to see the complete description of the heterogeneous image types shown in Figure 1:Perfect! I can see the \"Heterogeneous Image Types\" section in Figure 1. The document lists the following image types:\n\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThe text shows \"...\" after Comics, but I need to verify if there are icons shown for each type. Let me search for more information about Figure 1 to see if there's a visual description that tells us how many icons are actually displayed:Looking back at the text from Figure 1, I can see it lists these image types under \"Heterogeneous Image Types\":\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nLet me count the distinct types mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts (this appears to be one category)\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nHowever, I need to verify if the figure actually shows icons for each. Let me look for embedded images or visual descriptions around Figure 1 to see if there are actual icon images:I see that Im1.png is an embedded image showing a hierarchical flowchart. That's not the heterogeneous image types. Let me search more carefully through the document. Let me look for more embedded images around Figure 1:Let me look more carefully at the beginning of the document. Im1.png appeared right after the figure caption for Figure 1. Let me look at Figure 1 more systematically. Let me check if there are multiple images that are part of Figure 1:I see. The Figure 1 section ends at line 1160 with `</figure>`, and then there are embedded images. The text shows <image 1>, <image 2>, <image 3> within the figure. Let me search more systematically. The figure lists:\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nBut I need to see if there are icons displayed. Let me search for where the actual icons might be described. Let me look for \"icon\" in the document:Perfect! I found it. At line 1265, there's a reference to a camera icon. Let me read that section more carefully since it's right after Figure 1:Good! So now I can see there are embedded images that are icons representing different image types. Let me systematically go through and look for all the icons that are part of Figure 1. The document structure shows Im11.png is a camera icon and Im12.png is a geometric icon. Let me look back at the text of Figure 1 to understand the layout better. Looking at the document, it seems the figure starts at line 1097 and the text lists image types. Let me search for more icon descriptions nearby:I see that after the icon descriptions (Im11.png - camera, Im12.png - geometric), the document moves to Figure 3 (a table). So the icons are between Figure 1 and Figure 3. Let me go back and look more carefully at the images embedded right after Figure 1 ends. Let me look for all embedded images between line 1160 (where Figure 1 ends) and line 1455 (where Figure 3 starts):I see Im13.png is a germ/pathogen icon. That's another icon in the series. Let me systematically look at all embedded images between Figure 1 ending and where we transition to other figures. Let me search systematically from line 1160 to 1860:\n\nBased on what I've seen:\n- Im1.png (line 1164): Flowchart diagram\n- Im11.png (line 1256): Camera icon (Photographs)\n- Im12.png (line 1428): Geometric icon\n- Im13.png (line 1863): Germ/pathogen icon\n\nLet me continue looking for more icons. Let me search for the next embedded images:Good! Im14.png is a microscope icon. Let me continue to find more icons. Let me look at the next embedded images. Let me search more systematically. Based on what the abstract says: \"comprising 30 highly heterogeneous image types\" - but Figure 1 specifically lists these types. Let me look at the section with the image types more carefully. I need to look back at line 2501 which says \"Music\" and \"Medical\" - these might be labels for icons:Ah! Figure 5 shows different types of images: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical. That's 9 types. But this is Figure 5, not Figure 1. Let me go back and look very carefully at what exactly is in Figure 1. Let me re-read the text in Figure 1:\n\nFrom lines 1134-1138, the text explicitly states under \"Heterogeneous Image Types\":\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nSo let me count these carefully:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nBut the question asks about \"different icons shown as different image types in Figure 1\". I need to look back at Figure 1 itself more carefully. Let me check if there are actually icons displayed IN Figure 1, not just listed. Let me re-examine the structure. The figure tag starts at 1097 and ends at 1160. Let me look at what images are referenced within that figure:I see! Within the figure (lines 1097-1160), there are references to `<image 1>`, `<image 2>`, and `<image 3>` (lines 1113-1115 as part of a question, and again at lines 1154-1158). But the key section is \"Heterogeneous Image Types\" which lists the types. However, the question asks about icons shown as different image types. \n\nThe text lists image types but doesn't specify how many icons are actually displayed visually. Based on the embedded images that come right after Figure 1:\n- Im1.png (flowchart - but this appears to be an example diagram, not an icon representing a type)\n- Im11.png (camera icon - represents Photographs)\n- Im12.png (geometric shapes icon - represents Geometric)\n- Im13.png (germ icon - could represent Medical/Pathology/Microscopic)\n- Im14.png (microscope icon - represents Microscopic/Medical)\n\nBut I need to find ALL the icons that are specifically showing different image types in Figure 1. Let me look more systematically at what comes after Figure 1 but before Figure 2 (which starts at line 1289). All embedded images between line 1160 and line 1289 might be icons for Figure 1.\n\nFrom my reading:\n- Line 1164: Im1.png (flowchart diagram - this might be part of the figure illustration)\n- Line 1256: Im11.png (camera icon)\n- Line 1428: Im12.png (geometric shapes)\n\nLet me check if there are other embedded images I missed before Figure 2:\n\nLooking at the embedded image list I got earlier, between lines 1160 and 1289 I have:\n- Im1.png at line 1164\n- Im11.png at line 1256\n- Im12.png at line 1428 (but line 1289 is Figure 2, so this is after Figure 2 starts)\n\nWait, let me recheck. Figure 2 caption is at line 1289. So Im12.png at line 1428 is after Figure 2. Let me look more carefully:So between Figure 1 (ending at line 1160) and Figure 2 (starting at line 1288), there are only 2 embedded images:\n- Im1.png at line 1164\n- Im11.png at line 1256\n\nBut actually, looking at the visual descriptions, Im1.png is described as a \"Hierarchical flowchart/organizational diagram\" which seems to be an example diagram that's PART of Figure 1, not an icon representing an image type.\n\nIm11.png is clearly described as a \"Stylized camera icon\" which represents the Photographs image type.\n\nLet me think about this differently. Maybe the icons are all embedded within the actual Figure 1 structure, but the XML parsing has extracted them as separate images. Looking at the text in Figure 1, it lists types under \"Heterogeneous Image Types\". Perhaps in the original PDF, there are visual icons shown alongside or above these text labels, and those icons have been extracted as separate embedded images.\n\nGiven the timeframe and what I can see, let me search for a clearer pattern. Looking at Figure 5 (line 2475) which shows \"Performance of models on different types of images\" and lists: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical - that's 9 types.\n\nBut I need to answer specifically about Figure 1. Let me think about the structure: Figure 1 has a section called \"Heterogeneous Image Types\" that lists multiple types. The question asks how many different icons are shown. \n\nLooking at the abstract again (line 1212): \"comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures.\"\n\nBut in Figure 1's \"Heterogeneous Image Types\" section, it explicitly lists: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nIf I count unique types (treating \"Plots and Charts\" as one type):\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThat's 12 distinct types listed (plus \"...\" suggesting more).\n\nHowever, Figure 5 later in the paper shows icons for 9 types. Perhaps Figure 1 shows icons for these 9 main types as well. Let me verify Figure 5 shows exactly 9 by re-reading:\n\nFrom line 2487-2503 in Figure 5:\n1. Diagrams\n2. Tables\n3. Charts\n4. Chemical\n5. Photos\n6. Paintings\n7. Geometric\n8. Music\n9. Medical\n\nThat's exactly 9 types in Figure 5.\n\nGiven the typical structure of such figures and the pattern I see, my best answer is that Figure 1 shows icons for **9** different image types.\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 192280, "error": null, "extra": {"thread_id": 2025, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f360703be6ba4537aef7760373d79d2f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1273, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts included in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Contains text and references but no charts visible\n\n**Page 6:**\n- Table 2: \"Overall results of different models on the MMMU validation and test set\" - This is a table, not a chart\n\n**Page 7:**\n- Figure 5: \"Performance of models on different types of images\" - This is a bar chart\n- Table 3: \"Result decomposition across question difficulty levels\" - This is a table, not a chart\n\n**Page 8:**\n- Figure 6: \"Error distribution over 150 annotated GPT-4V errors\" - This is a pie chart\n- Figure 7: Shows an example case with images and text - Not a chart\n\n**Page 9:**\n- Contains text discussion, no charts\n\n**Page 10:**\n- Contains text and references, no charts\n\nCounting the charts:\n- Page 7: Figure 5 (bar chart) = 1 chart\n- Page 8: Figure 6 (pie chart) = 1 chart\n\nTotal charts in pages 5-10: 2\n\nAnswer: 2", "citations": [], "input_tokens": 79424, "output_tokens": 276, "cost_micros": 242412, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll carefully count all charts in Pages 5-10.\n\n**Page 5:**\n- Figure 2: This shows sample MMMU examples in a table format, not a chart\n- No charts on this page\n\n**Page 6:**\n- Table 2: This is a table showing model results, not a chart\n- No charts on this page\n\n**Page 7:**\n- Figure 5: This is a bar chart showing \"Performance of models on different types of images\"\n- Table 3: This is a table, not a chart\n- Count: 1 chart\n\n**Page 8:**\n- Figure 6: This is a pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\n- Figure 7: This shows an example with illustrations but is not a chart\n- Count: 1 chart\n\n**Page 9:**\n- No figures or charts on this page (only references text)\n\n**Page 10:**\n- No figures or charts on this page (only references text)\n\nTotal charts in Pages 5-10: 1 (Figure 5) + 1 (Figure 6) = 2\n\nAnswer: 2", "citations": [], "input_tokens": 109175, "output_tokens": 264, "cost_micros": 331485, "latency_ms": 9110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts (plots, graphs, tables, diagrams) that appear in pages 5-10 of the document.\n\nLet me go through pages 5-10:\n\n**Page 5:**\n- Table 2: Overall results of different models (this is a large table)\n- Count: 1\n\n**Page 6:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a bar chart)\n- Count: 2\n\n**Page 7:**\n- Figure 6: Error analysis pie chart showing distribution of error types\n- Figure 7: Example error case with text and an image (not a chart)\n- Count: 1\n\n**Page 8:**\n- Continuation of references, no charts\n\n**Page 9:**\n- Continuation of references, no charts\n\n**Page 10:**\n- References continue, no charts\n\nTotal charts in pages 5-10:\n- Page 5: 1 (Table 2)\n- Page 6: 2 (Table 3, Figure 5)\n- Page 7: 1 (Figure 6)\n- Pages 8-10: 0\n\nTotal: 1 + 2 + 1 + 0 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 300, "cost_micros": 253914, "latency_ms": 8781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count all charts appearing in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Table 1: Key statistics of the MMMU benchmark\n\n**Page 6:**\n- Table 2: Overall results of different models on the MMMU validation and test set\n\n**Page 7:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a table/chart)\n\n**Page 8:**\n- Figure 6: Error distribution over 150 annotated GPT-4V errors (this is a table/chart)\n- Figure 7: A sample error case (this is an example, not a chart)\n\n**Page 9:**\n- No charts (this page discusses conclusion and references begin)\n\n**Page 10:**\n- No charts (continues references section)\n\nLet me recount more carefully by looking at what constitutes a \"chart\":\n\nPages 5-10:\n- Page 5: Table 1 (1 chart)\n- Page 6: Table 2 (1 chart)\n- Page 7: Table 3 (1 chart), Figure 5 which shows a table of performance data (1 chart)\n- Page 8: Figure 6 showing error distribution as a table (1 chart), Figure 7 is an example case study, not a chart\n- Pages 9-10: References, no charts\n\nTotal charts: 5\n\nAnswer: 5", "citations": [], "input_tokens": 103232, "output_tokens": 346, "cost_micros": 314886, "latency_ms": 10516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll read the document to count the charts in pages 5-10.Let me search for page markers to identify pages 5-10:Let me read through the document to find page markers:I can see page markers. Let me continue searching for pages 5-10:Perfect! Now I know the line numbers for pages 5-10. Let me read those pages to count the charts:Now let me search for charts/figures systematically in pages 5-10. Let me look for figure markers:Now let me carefully count the charts in pages 5-10. Let me search for \"Figure\" markers in this range:\n\nBased on my reading of pages 5-10 (lines 2071-3184), I can identify the following charts:\n\n**Page 5** (starts at line 2071):\n- No charts (only text and Table 2)\n\n**Page 6** (starts at line 2462):\n- **Figure 5**: \"Performance of models on different types of images\" - this is a bar chart showing performance metrics\n\n**Page 7** (starts at line 2687):\n- **Figure 6**: \"Error distribution over 150 annotated GPT-4V errors\" - this is a pie chart showing error percentages\n- **Figure 7**: Shows a perceptual error example (not a chart, but a diagram/illustration)\n\n**Page 8** (starts at line 2912):\n- No charts (only text starting with Conclusion section)\n\nLet me continue reading pages 9 and 10:**Page 9** (starts at line 3045): References section - no charts\n\n**Page 10** (starts at line 3184): References section - no charts\n\nNow let me verify my count by carefully reviewing what I found:\n\n**Summary of charts in Pages 5-10:**\n\n- **Page 5**: 0 charts (contains Table 2, which is a table, not a chart)\n- **Page 6**: 1 chart (Figure 5 - bar chart)\n- **Page 7**: 1 chart (Figure 6 - pie chart) + Figure 7 (which is an illustration/example, not a chart)\n- **Page 8**: 0 charts (text only)\n- **Page 9**: 0 charts (references)\n- **Page 10**: 0 charts (references)\n\nTotal charts in pages 5-10: **2 charts**\n\n(Figure 5 is a bar chart and Figure 6 is a pie chart. Figure 7 is not a chart but an illustrative example showing a perceptual error case.)\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79750, "error": null, "extra": {"thread_id": 2026, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8497190a9f7e4c40b3088d1ce4cbc759", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 453, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across two dimensions - Breadth (Knowledge) and Depth (Reasoning).\n\nThe figure shows various datasets plotted on these two axes:\n- VQA, GQA, VisWiz (basic breadth and depth)\n- TextVQA, MMBench, SEED (moderate breadth)\n- MM-Vet, ScienceQA (moderate breadth)\n- OKVQA (moderate breadth)\n- MMMU (positioned highest on both breadth and depth)\n\nAccording to the figure and the accompanying table, MMMU covers:\n- 11.5K questions\n- 30 Types of images\n- 6 disciplines, 30 subjects, and 183 subfields\n\nThis is explicitly described in the caption as excelling \"in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nAnswer: MMMU", "citations": [], "input_tokens": 259139, "output_tokens": 228, "cost_micros": 1559964, "latency_ms": 60530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the paper, I need to examine which dataset covers the broadest range of disciplines and knowledge areas.\n\nFrom the figure, the datasets compared are:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nThe figure shows that MMMU covers:\n- 6 disciplines\n- 30 subjects\n- 183 subfields\n- 11.5K questions\n\nIn comparison, other datasets are more limited in scope. For example, ScienceQA covers specific subjects but is noted as being at elementary to middle school level. The other datasets (VQA, GQA, etc.) focus on specific task types rather than broad disciplinary coverage.\n\nThe paper explicitly states that MMMU \"covers 30 subjects across 6 disciplines\" and emphasizes its \"breadth\" across multiple disciplines including Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.\n\nAnswer: MMMU", "citations": [], "input_tokens": 79443, "output_tokens": 256, "cost_micros": 242169, "latency_ms": 8655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across breadth (knowledge) and depth (reasoning) dimensions:\n\nThe figure shows various datasets plotted, with MMMU positioned to excel in both breadth and depth. Among the nine different datasets shown (VQA, GQA, VizWiz, TextVQA, OKVQA, SEED, MMBench, MM-Vet, ScienceQA, and MMMU), MMMU is positioned highest on the breadth axis.\n\nAnswer: MMMU", "citations": [], "input_tokens": 109194, "output_tokens": 132, "cost_micros": 329562, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset covers the most breadth of knowledge.\n\nThe figure shows a comparison of different datasets along two axes:\n- Breadth (Knowledge) - horizontal axis\n- Depth (Reasoning) - vertical axis\n\nFrom the figure, the datasets shown include:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nLooking at the horizontal positioning (breadth), MMMU is positioned furthest to the right, indicating it has the greatest breadth of knowledge coverage among the datasets shown. This is also supported by the text which states MMMU covers \"30 Types\" of images and spans across 6 disciplines, 30 subjects, and 183 subfields.\n\nAnswer: MMMU", "citations": [], "input_tokens": 83157, "output_tokens": 210, "cost_micros": 252621, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset represents the most breadth (knowledge) on the x-axis.\n\nThe scatter plot shows various datasets plotted with \"Breadth (Knowledge)\" on the x-axis and \"Depth (Reasoning)\" on the y-axis. MMMU is shown as a star at the highest position for both breadth and depth.\n\nAmong the datasets shown in Figure 4, MMMU is positioned furthest to the right on the Breadth axis, indicating it has the most breadth of knowledge coverage.\n\nAnswer: MMMU", "citations": [], "input_tokens": 103251, "output_tokens": 132, "cost_micros": 311733, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which dataset reflects the most breadth of knowledge.Let me search for Figure 4 in the document:Good! I found the caption for Figure 4. Now let me read the actual content around Figure 4 to see the details about which dataset reflects the most breadth of knowledge:Now let me look at the actual Figure 4 to see the visual comparison. Let me search for more context about breadth of knowledge in the figure:Let me read the section that shows the actual Figure 4 visualization:Perfect! I can see Figure 4 shows a scatter plot with \"Breadth (Knowledge)\" on one axis and \"Depth (Reasoning)\" on the other axis. The figure shows the following datasets plotted:\n- MMMU\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- MMBench\n- SEED\n- MM-Vet\n- ScienceQA\n- OKVQA\n\nFrom the figure structure (lines 1834-1858), I can see \"Breadth (Knowledge)\" is labeled as one of the axes. The caption states \"MMMU excels in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nLooking at the text in chunk 156205 (lines 2022-2029), it explicitly states: \"From the breadth perspective, the prior benchmarks are heavily focused on daily knowledge and common sense. The covered image format is also limited. Our benchmark aims to cover college-level knowledge with 30 image formats...\"\n\nThis indicates that **MMMU** reflects the most breadth of knowledge among the nine datasets shown in Figure 4.\n\nAnswer: MMMU", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57452, "error": null, "extra": {"thread_id": 2027, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e1415f7fe2384e1ba85595cd09d85b89", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 282, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I need to look at Figure 6, which shows the error distribution over 150 annotated GPT-4V errors.\n\nFrom Figure 6, I can see the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 79448, "output_tokens": 174, "cost_micros": 240954, "latency_ms": 5937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 in the document, which shows the error analysis of 150 GPT-4V error cases:\n\nThe distribution shows:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding Error: 6%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 83162, "output_tokens": 122, "cost_micros": 251316, "latency_ms": 5827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 which shows the error distribution over 150 annotated GPT-4V errors:\n\n1. Perceptual Error: 35%\n2. Lack of Knowledge: 29%\n3. Reasoning Error: 26%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 103256, "output_tokens": 77, "cost_micros": 310923, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and identify the top-3 error types over 150 annotated GPT-4V errors.Let me search for Figure 6 in the document:Now let me read the section around line 2692 where Figure 6 is located:Perfect! I found Figure 6 which shows the error distribution over 150 annotated GPT-4V errors. Looking at the data:\n\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33125, "error": null, "extra": {"thread_id": 2028, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c246c6154fa7469892ccf49cfee50145", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 158, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 (Figures 82-87) to identify all error types mentioned in the case studies.\n\nLooking at each figure in the specified range:\n\n**Figure 82** (Architecture and Engineering: Civil Engineering) - Page 96\n- Error Category: Reasoning Error\n\n**Figure 83** (Computer Science: Data Structure and Algorithm) - Page 97\n- This is a correct case, no error category\n\n**Figure 84** (Computer Science: Compiler Principle) - Page 98\n- Error Category: Perceptual Error, Lack of Knowledge\n\n**Figure 85** (Computer Science: Operating System) - Page 99\n- Error Category: Perceptual Error\n\n**Figure 86** (Computer Science: Compiler Principle) - Page 100\n- Error Category: Perceptual Error\n\n**Figure 87** (Electronics: Signal Processing) - Page 101\n- This is a correct case, no error category\n\nSo the error types mentioned in pages 95-100 are:\n- Reasoning Error\n- Perceptual Error\n- Lack of Knowledge\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 79440, "output_tokens": 265, "cost_micros": 242295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 of the document to identify all error types mentioned in the case studies.\n\nLooking through the figures on these pages:\n\n**Page 95 (Figure 81):** Architecture and Engineering - Correct case (no error type)\n\n**Page 96 (Figure 82):** Architecture and Engineering - **Reasoning Error**\n\n**Page 97 (Figure 83):** Computer Science - Correct case (no error type)\n\n**Page 98 (Figure 84):** Computer Science - **Perceptual Error, Lack of Knowledge**\n\n**Page 99 (Figure 85):** Computer Science - **Perceptual Error**\n\n**Page 100 (Figure 86):** Computer Science - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 109191, "output_tokens": 192, "cost_micros": 330453, "latency_ms": 8281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 of the document for error types mentioned in case studies.\n\nLooking at the case studies in this range:\n\nPage 95 (Figure 81 - Architecture and Engineering): No error type specified (correct case)\n\nPage 96 (Figure 82 - Architecture and Engineering): **Reasoning Error**\n\nPage 97 (Figure 83 - Computer Science): No error type specified (correct case)\n\nPage 98 (Figure 84 - Computer Science): **Perceptual Error, Lack of Knowledge**\n\nPage 99 (Figure 85 - Computer Science): **Perceptual Error**\n\nPage 100 (Figure 86 - Computer Science): **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 83154, "output_tokens": 184, "cost_micros": 252222, "latency_ms": 6655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "Looking through pages 95-100 of the document (Figures 82-86 in the case studies):\n\nFigure 82 (Architecture and Engineering: Civil Engineering) - **Reasoning Error**\n\nFigure 83 (Computer Science: Data Structure and Algorithm) - No error type mentioned (this is a correct case)\n\nFigure 84 (Computer Science: Compiler Principle) - **Perceptual Error, Lack of Knowledge**\n\nFigure 85 (Computer Science: Operating System) - **Perceptual Error**\n\nFigure 86 (Computer Science: Compiler Principle) - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 103248, "output_tokens": 158, "cost_micros": 312114, "latency_ms": 11889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll read the document to find the error types mentioned in the case studies on pages 95-100.Let me search for page numbers to locate pages 95-100:Let me search for \"case stud\" to find the case studies section:Let me read around line 5765 where Table 10 mentions case study figures with error categories:Good! I found Table 10 which shows the error categories. The columns are: Correct Case, Perception, Lack of Knowledge, Reasoning, and Other. Let me continue reading to see the complete table:Good! I found that the error categories are: Perception, Lack of Knowledge, Reasoning, and Other (plus \"Correct Case\" which is not an error). Now I need to find the actual case study pages 95-100. Let me search for figure 95 or case 95:Let me search for the case studies section more specifically. Let me look for the pattern of case numbers 95-100:Figure 96 is something different. Let me check the structure - from the earlier search, I saw \"90 Energy and Power 2: Reasoning Error 104\" and \"95 Mechanical Engineering 3: Re\". Let me search for these patterns directly:Good! So these are Figure numbers 90-95. Let me read that section where it lists figures 90-100:I can see figures 90-95 here. Now I need to find what comes after figure 95. Let me search earlier in the document to see the complete list:This appears to be a table of contents or index. I need to understand better - these numbers (95-100) might refer to page numbers or figure numbers. Let me look at this list more carefully. I can see:\n\n- Page 95: Figure 81 Architecture and Engineering 1: Correct Case\n- Page 96: Figure 83 Computer Science 1: Correct Case  \n- Page 97: ?\n- Page 98: Figure 84 Computer Science 2: Perceptual Error, Lack of Knowledge\n- Page 99: Figure 85 Computer Science 3: Perceptual Error\n- Page 100: Figure 86 Computer Science 4: Perceptual Error\n\nBut I need to verify this interpretation. Let me search for where these actual figures appear in the document:Perfect! Now let me read from page 95 to page 100. Let me find line numbers around page 95:Good, I'm at page 95. Now let me continue reading through pages 95-100:Good! I'm now at page 97. Let me continue reading to get to page 100:Good! I'm at page 99. Let me continue to page 100:Good! Now let me continue to see the rest of page 100:Perfect! Now I have reviewed pages 95-100. Let me compile all the error types I found in the case studies on these pages:\n\nFrom the case studies I read on pages 95-100:\n\n**Page 95:**\n- Figure 81: Correct Case (not an error)\n\n**Page 96:**\n- Figure 82: Reasoning Error\n\n**Page 97:**\n- Figure 83: Correct Case (not an error)\n\n**Page 98:**\n- Figure 84: Perceptual Error, Lack of Knowledge\n\n**Page 99:**\n- Figure 85: Perceptual Error\n\n**Page 100:**\n- Figure 86: Perceptual Error\n- Figure 87: Correct Case (not an error)\n\nNow let me verify by looking back at the table of contents I saw earlier to ensure I have the right mapping. Let me check once more:Perfect! This confirms my interpretation. The table shows:\n- Figure 81 is on pages 94-95\n- Figure 82 is on page 96 (spanning from before)\n- Figure 83 is on pages 96-97\n- Figure 84 is on page 98\n- Figure 85 is on page 99\n- Figure 86 is on page 100\n- Figure 87 is on page 101 (starting at page 100)\n\nSo the error types mentioned in case studies on pages 95-100 are:\n\n1. **Reasoning Error** (Figure 82 - Architecture and Engineering)\n2. **Perceptual Error** (Figure 84, 85, 86 - Computer Science)\n3. **Lack of Knowledge** (Figure 84 - Computer Science)\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 114937, "error": null, "extra": {"thread_id": 2029, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77fb85ac92354e66b7280742dc24bcdb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 755, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to examine pages 65-70 to count the successful cases (marked with a checkmark \u2713).\n\n**Page 65** (Figure 51): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 66** (Figure 52): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 67** (Figure 53): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 68** (Figure 54): Clinical Medicine: Pathology - **Error case** \u2717 (Perceptual Error)\n\n**Page 69** (Figure 55): Clinical Medicine: Heart Disease - **Error case** \u2717 (Lack of Knowledge)\n\n**Page 70** (Figure 56): Diagnostics and Laboratory Medicine: Radiology - **Correct case** \u2713\n\nCounting the successful cases (those with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 259120, "output_tokens": 295, "cost_micros": 1561358, "latency_ms": 64484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful (correct) cases in pages 65-70 of the document.\n\nLet me go through each figure in this range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine: Pathology - \"A sample **error** case\" \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine: Heart Disease - \"A sample **error** case\" \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Laboratory Medicine: Radiology - \"A sample **correct** case\" \u2713\n\nCounting the correct cases: Pages 65, 66, 67, and 70 each have one correct case.\n\nTotal successful cases = 4\n\nAnswer: 4", "citations": [], "input_tokens": 79424, "output_tokens": 257, "cost_micros": 242127, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (marked with \u2713 or \"V\") in Pages 65-70.\n\nLet me go through each figure in this page range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine 1: Correct Case \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine 2: Correct Case \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine 3: Correct Case \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine 4: Perceptual Error \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine 5: Lack of Knowledge \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Lab Medicine 1: Correct Case \u2713\n\nCounting the successful cases (marked with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 109175, "output_tokens": 280, "cost_micros": 331725, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases in Pages 65-70 of the document.\n\nLet me go through each page:\n\n**Page 65 (Clinical Medicine 1):**\n- Figure 51: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 66 (Clinical Medicine 2):**\n- Figure 52: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 67 (Clinical Medicine 3):**\n- Figure 53: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 68 (Clinical Medicine 4):**\n- Figure 54: \"A sample error case of Clinical Medicine (subfield: Pathology). Error category: Perceptual Error\"\n- This is an ERROR case \u2717\n\n**Page 69 (Clinical Medicine 5):**\n- Figure 55: \"A sample error case of Clinical Medicine (subfield: Heart Disease). Error category: Lack of Knowledge\"\n- This is an ERROR case \u2717\n\n**Page 70 (Diagnostics and Laboratory Medicine 1):**\n- Figure 56: \"A sample correct case of Diagnostics and Laboratory Medicine (subfield: Radiology)\"\n- This is a CORRECT case \u2713\n\nTotal successful (correct) cases in pages 65-70: 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 348, "cost_micros": 254634, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list and look at pages 65-70 to find information about successful cases.I need to search for content related to pages 65-70. Let me search for page markers in the document.Let me search for page numbers in a different format:Let me search for the word \"successful\" or \"cases\" to understand the document structure:Let me search for \"cases\" or look for page numbers differently. Let me read more of the document to understand its structure:This is not helpful. Let me search for the actual page numbers in the document. PDF documents often have page numbers embedded. Let me try different patterns:I can see there's a \"Case Study\" section. Let me look for the appendix section that might have pages 65-70. Let me read the entire document more systematically. First, let me check how long the document is:Great! I found a list of case studies with page numbers. I can see this is a table mapping case numbers to their descriptions and page numbers. Let me continue reading to see pages 65-70:Perfect! I found the information. Looking at pages 65-70, I can see:\n\n- Page 65: Case 51 - Clinical Medicine 1: **Correct Case**\n- Page 66: Case 52 - Clinical Medicine 2: **Correct Case**\n- Page 67: Case 53 - Clinical Medicine 3: **Correct Case**\n- Page 68: Case 54 - Clinical Medicine 4: Perceptual Error\n- Page 69: Case 55 - Clinical Medicine 5: Lack of Knowledge\n- Page 70: Case 56 - Diagnostics and Lab Medicine 1: **Correct Case**\n\nCounting the \"Correct Case\" entries in pages 65-70:\n1. Page 65: Clinical Medicine 1: Correct Case\n2. Page 66: Clinical Medicine 2: Correct Case\n3. Page 67: Clinical Medicine 3: Correct Case\n4. Page 70: Diagnostics and Lab Medicine 1: Correct Case\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54718, "error": null, "extra": {"thread_id": 2030, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77d82c91b9624d83aeec575d8461194f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 347, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many key steps the authors divided causal inference into.\n\nLooking through the document, I can see in Section 3.2 \"Generating the Explanations\" on page 6, there is a discussion of step-by-step explanations and subskills:\n\n\"Apart from the question-answer pairs, we also generate the step-by-step explanations. Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis clearly states that they identified **six subskills** for causal inference.\n\nAdditionally, in Figure 1 on page 2, I can see these six steps are illustrated in the example.\n\nIn Section 4 \"Our CAUSALCOT Model\" on page 7, it also mentions \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: \u2460 identifying the causal graph structure; \u2461 determining the causal query type; \u2462 formulating the query symbolically precisely; and \u2463 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: \u2464 correctly deducing the estimand using causal inference techniques; and finally \u2465 evaluating the estimand to answer the question.\"\n\nThis confirms the six steps again.\n\nAnswer: 6", "citations": [], "input_tokens": 75261, "output_tokens": 410, "cost_micros": 231933, "latency_ms": 16531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Figure 1, there is a section titled \"Correct steps to lead to the ground-truth answer:\" which shows 6 numbered steps:\n\n1) Parse the causal graph\n2) Classify the query type\n3) Formulate the query to its symbolic form\n4) Collect the available data\n5) Derive the estimand using causal inference\n6) Solve for the estimand by plugging in the relevant data\n\nThis is also confirmed in Section 3.2 where they discuss generating explanations: \"We identify the following six subskills: 1 causal graph extraction; 2 correct query type interpretation; 3 symbolic formalization of the query; 4 semantic parsing to compile the available data; 5 estimand derivation; and 6 arithmetic calculation to solve the estimand\"\n\nAdditionally, in Section 4 describing their CAUSALCOT model, they state: \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps... Then... the formal solution: 5 correctly deducing the estimand... and finally 6 evaluating the estimand\"\n\nAnswer: 6", "citations": [], "input_tokens": 31749, "output_tokens": 291, "cost_micros": 99612, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Section 4, \"Our CAUSALCOT Model,\" the authors describe their approach based on the causal inference engine. On page 7, they explicitly state:\n\n\"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nThis clearly describes 6 steps:\n1. Identifying the causal graph structure\n2. Determining the causal query type\n3. Formulating the query symbolically precisely\n4. Extracting relevant data from the prompt\n5. Correctly deducing the estimand using causal inference techniques\n6. Evaluating the estimand to answer the question\n\nThis is also shown in Figure 1 and Figure 4, where 6 steps are illustrated.\n\nAnswer: 6", "citations": [], "input_tokens": 38818, "output_tokens": 294, "cost_micros": 120864, "latency_ms": 8828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the key steps of causal inference.\n\nIn Section 3.2 \"Generating the Explanations,\" the document states:\n\n\"Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis is also reflected in Figure 4 which shows the CAUSALCOT model with 6 steps, and in Section 4 where they describe their prompting strategy that \"breaks down a causal reasoning problem into multiple symbolically-grounded, simpler steps.\"\n\nAnswer: 6", "citations": [], "input_tokens": 33630, "output_tokens": 227, "cost_micros": 104295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can identify the key steps that the authors divided causal inference into.\n\nThe figure clearly shows 6 numbered steps:\n\n1. Parse the causal graph (Causal Relation Extraction)\n2. Classify the query type (Causal Question Classification)\n3. Formulate the query to its symbolic form (Formalization)\n4. Collect the available data (Semantic Parsing)\n5. Derive the estimand using causal inference (Formal Causal Inference)\n6. Solve for the estimand by plugging in the relevant data (Arithmetics)\n\nThis is further confirmed in Section 3.2 where the explanation generation is described, and in Section 4 where the CAUSALCOT model is introduced, which follows these same 6 steps.\n\nAnswer: 6", "citations": [], "input_tokens": 39832, "output_tokens": 211, "cost_micros": 122661, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the provided document to find information about the key steps the author divided causal inference into.Let me search for information about how the author divided the process or steps:Perfect! I found the answer. The text clearly states that the author divided causal inference into 6 steps. Let me verify this by reading a bit more context:Perfect! The document clearly shows that the author divided causal inference into **6 steps**:\n\n1. Extract the causal graph\n2. Determine the query type\n3. Formalize the query\n4. Gather all relevant data\n5. Deduce the estimand using causal inference\n6. Calculate the estimand\n\nThe text explicitly states: \"first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then... 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44937, "error": null, "extra": {"thread_id": 2031, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b14a0eb01624709bf2cbb4b451f998a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about subskills related to collecting available data...\n\nIn Figure 1 on page 2, I can see the step-by-step reasoning process for answering a causal inference question. Step 4 is labeled \"Collect the available data\" and it has an associated subskill label.\n\nThe subskill associated with Step 4 \"Collect the available data\" is **Semantic Parsing**.\n\nThis is also confirmed in Section 3.2 where it mentions the six subskills:\n- \u2460 causal graph extraction\n- \u2461 correct query type interpretation\n- \u2462 symbolic formalization of the query\n- \u2463 semantic parsing to compile the available data\n- \u2464 estimand derivation\n- \u2465 arithmetic calculation\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 75267, "output_tokens": 185, "cost_micros": 228576, "latency_ms": 32921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the step-by-step process for answering a causal inference question, Step 4 is labeled as \"Collect the available data:\" and the corresponding subskill is clearly marked as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 31755, "output_tokens": 68, "cost_micros": 96285, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation for solving a causal inference question:\n\nStep 4 is labeled \"Collect the available data\" and the subskill required for this step is identified as \"Semantic Parsing\" (shown in the colored box in the figure).\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 38824, "output_tokens": 79, "cost_micros": 117657, "latency_ms": 5264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation, and Section 3.2 which discusses \"Generating the Explanations,\" I can identify the subskills mentioned.\n\nStep 4 in Figure 1 is labeled \"Collect the available data\" and the document describes this as requiring \"semantic parsing to compile the available data\" in Section 3.2.\n\nAnswer: semantic parsing", "citations": [], "input_tokens": 33636, "output_tokens": 96, "cost_micros": 102348, "latency_ms": 5125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can see that Step 4 is labeled \"Collect the available data\" and the subskill associated with it is \"Semantic Parsing.\"\n\nThis is also confirmed in Section A.8 (Explanation Template) where Step \u2463 states: \"Collect all the available data: The available data are: '$d$'.\"\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 39838, "output_tokens": 109, "cost_micros": 121149, "latency_ms": 5516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about what subskill is needed to collect the available data.Let me search for more specific terms related to data collection:Perfect! I found the answer. Let me read that section more carefully to get the complete context:Based on the document, I can now provide the answer. The document clearly shows that step 4 \"Collect the available data\" has the subskill listed as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32687, "error": null, "extra": {"thread_id": 2032, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_46f68e1513d341d0b0ee21cfa0dae2f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 137, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the rungs of the Ladder of Causation...\n\nFrom Section 2.1 \"The Ladder of Causation\" on page 3:\n\nThe document describes three distinct rungs:\n- Rung 1 (\"seeing\"): Statistical associations\n- Rung 2 (\"doing\"): Interventions\n- Rung 3 (\"imagining\"): Counterfactuals\n\nRung 2 is specifically described as dealing with interventions and the do-operator.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 75267, "output_tokens": 131, "cost_micros": 227766, "latency_ms": 31077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, Section 2.1 \"The Ladder of Causation\" describes the three rungs:\n\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nThe document states that Rung 2 \"enables us to formalize the concept of actively intervening in the world.\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 31755, "output_tokens": 111, "cost_micros": 96930, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the second rung of the ladder of causation...\n\nFrom Section 2.1 \"The Ladder of Causation\":\n\n\"Rung 2 (\"doing\"). This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"If I take an aspirin now, will my headache subside?\"). Interventions can be formalized using the do-operator [24] and Causal Bayesian Networks [67]...\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 38824, "output_tokens": 126, "cost_micros": 118362, "latency_ms": 6250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 which describes \"The Ladder of Causation\":\n\nThe three rungs are:\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nRung 2 is explicitly described as dealing with interventions, using the do-operator to formalize the concept of actively intervening in the world.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 33636, "output_tokens": 122, "cost_micros": 102738, "latency_ms": 5062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 \"The Ladder of Causation\":\n\n**Rung 2 (\"doing\").** This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"*If I take an aspirin now, will my headache subside?*\"). Interventions can be formalized using the *do-operator* [24] and *Causal Bayesian Networks* [67] to represent, for example, the distribution over $Y$ when intervening on $X$ to set its value to $x$ as $P(Y = y|do(X = x))$.\n\nAnswer: Intervention or \"doing\"", "citations": [], "input_tokens": 39838, "output_tokens": 159, "cost_micros": 121899, "latency_ms": 8094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about the second ladder of causation.Let me search for \"ladder of causation\" in the document:Let me search for \"second rung\" or information about the rungs:Good! I found a reference at line 553. Let me read that section:Perfect! I found the answer. The document clearly states that the three rungs of the Ladder of Causation are:\n- Rung 1: associational\n- Rung 2: interventional\n- Rung 3: counterfactual\n\nAnswer: interventional", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43718, "error": null, "extra": {"thread_id": 2033, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_7af68403347145c59b6ebb40237510ed", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 215, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.0, "method": "str_norm", "normalised_pred": "interventional", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the chain-type causal graph and count its directed edges.\n\nLooking through the document, I can find the causal graph structures in Figure 6 on page 19, which shows \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\"\n\nIn Figure 6, I can see a \"Chain\" graph structure. The Chain graph shows:\n- X \u2192 (middle node) \u2192 Y\n\nThis represents a chain with three nodes connected by two directed edges:\n1. One edge from X to the middle node\n2. One edge from the middle node to Y\n\nTherefore, a chain-type causal graph has 2 directed edges.\n\nAnswer: 2", "citations": [], "input_tokens": 75271, "output_tokens": 156, "cost_micros": 228153, "latency_ms": 30437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\", I can see the \"Chain\" graph structure.\n\nThe Chain graph shows: X \u2192 M \u2192 Y\n\nThis is a simple chain structure where:\n- X has an arrow to M (1 edge)\n- M has an arrow to Y (1 edge)\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 31759, "output_tokens": 133, "cost_micros": 97272, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, there is a table titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\" which shows:\n\nFor the \"Chain\" causal graph:\n- CGTE Alias: Chain\n- Treatment-Effect Pair: X \u2192 Y\n\nThe diagram shows a simple chain structure: X \u2192 Z \u2192 Y\n\nThis is a chain with 3 nodes (X, Z, Y) connected sequentially.\n\nCounting the directed edges:\n1. X \u2192 Z (one edge)\n2. Z \u2192 Y (one edge)\n\nTotal: 2 directed edges\n\nAnswer: 2", "citations": [], "input_tokens": 38828, "output_tokens": 165, "cost_micros": 118959, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 18, there is a \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\" The table shows:\n\n| Causal Graph | CGTE Alias | Treatment-Effect Pair |\n| ------------ | ----------- | --------------------- |\n| Chain        | Chain       | X Y                   |\n\nThe visual representation shows: X \u2192 M \u2192 Y\n\nThis indicates a chain structure where X points to M (a mediator), and M points to Y.\n\nTherefore, there are 2 directed edges in a chain-type causal graph: one from X to M, and one from M to Y.\n\nAnswer: 2", "citations": [], "input_tokens": 33640, "output_tokens": 174, "cost_micros": 103530, "latency_ms": 6796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of a chain-type causal graph.\n\nIn Section A.3 \"Collection of Causal Graphs,\" there is a table (Figure 6) that lists all causal graph types with their structures.\n\nFor the \"Chain\" graph, the table shows:\n- Causal Graph Alias: Chain\n- CGTE Alias: Chain  \n- Treatment-Effect Pair: X \u2192 Z \u2192 Y (X and Y are treatment-effect pair)\n\nThe notation X \u2192 Z \u2192 Y indicates:\n- One edge from X to Z\n- One edge from Z to Y\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 39842, "output_tokens": 164, "cost_micros": 121986, "latency_ms": 7343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the definition of a chain-type causal graph and count the directed edges.Let me search for \"chain-type\" or \"causal graph\" in the document:Let me search for \"causal graph\" to find the definition:Let me read the section around line 2387 where the causal graph definition appears:Now let me search for \"chain\" with \"graph\" or look for specific graph types. Let me check Figure 6 which shows all the causal graphs:Good! I can see \"Chain\" is listed as one of the causal graphs at line 2101-2104. Now let me look for the actual visualization or definition of the Chain graph. Let me check if there's a figure showing these graphs:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48766, "error": null, "extra": {"thread_id": 2034, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca8eb7f650264585a1c5b7287fc4aa99", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 247, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2387", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 on page 8, which shows \"Performance of all models on our CLADDER dataset v1.5\":\n\nThe \"Overall Acc.\" column shows:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe highest overall accuracy is achieved by CAUSALCOT with 70.40%.\n\nLooking at the document, CAUSALCOT is described as \"GPT-4 + CAUSALCOT\" in the context, which means it's GPT-4 enhanced with their causal chain-of-thought prompting strategy.\n\nThe best performing model is CAUSALCOT (which uses GPT-4 as the base model) with an accuracy of 70.40%.\n\nAnswer: 70", "citations": [], "input_tokens": 75259, "output_tokens": 336, "cost_micros": 230817, "latency_ms": 24141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in Table 2 (on page 8), I need to find which model achieves the highest overall accuracy on the CLADDER dataset v1.5.\n\nFrom Table 2:\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- **+ CAUSALCOT: 70.40%**\n\nThe model with the highest overall accuracy is **CAUSALCOT** (which is GPT-4 with the CausalCoT prompting strategy) at 70.40%.\n\nAnswer: 4", "citations": [], "input_tokens": 31747, "output_tokens": 263, "cost_micros": 99186, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in the document:\n\nFrom Table 2 (CLADDER v1.5 - the main/current version):\n- GPT-4 + CAUSALCOT achieves 70.40% accuracy, which is the highest performance among all models tested\n\nFrom Table 6 (CLADDER v1.0 - earlier version):\n- GPT-4 + CAUSALCOT achieves 66.64% accuracy, which is also the highest\n\nThe document clearly states on page 3: \"our CAUSALCOT achieves an accuracy of 70.40%, which substantially improves the performance of vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nAnd on page 8: \"And amongst all, our CAUSALCOT achieves the highest performance of 70.40%\"\n\nCAUSALCOT is described as a chain-of-thought prompting strategy used with GPT-4, so the best performing model is GPT-4 with CAUSALCOT.\n\nSince GPT-4 is mentioned to have approximately 1T (1 trillion) parameters, and CAUSALCOT is a prompting strategy applied to GPT-4:\n\nAnswer: 1000000000000", "citations": [], "input_tokens": 38816, "output_tokens": 288, "cost_micros": 120768, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1000000000000", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset according to the document.\n\nLooking at Table 2 (page 8), which shows \"Performance of all models on our CLADDER dataset v1.5\", I can see the overall accuracy for each model:\n\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- + CAUSALCOT: 70.40%\n\nThe model with the highest overall accuracy is \"GPT-4 + CAUSALCOT\" with 70.40%.\n\nThe text also states: \"our CAUSALCOT achieves the highest performance of 70.40%, which is substantially better than the vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nSince CAUSALCOT is based on GPT-4 (as stated in the document: \"We build our CAUSALCOT prompting strategy using GPT-4\"), the best performing model is GPT-4 when using the CAUSALCOT prompting strategy.\n\nThe overall accuracy is 70.40%, which rounds to 70%.\n\nAnswer: 70", "citations": [], "input_tokens": 33628, "output_tokens": 390, "cost_micros": 106734, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 (Performance of all models on CLADDER dataset v1.5), which shows the main results:\n\nThe models and their Overall Accuracy are:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe model with the highest overall accuracy is \"+ CAUSALCOT\" at 70.40%.\n\nCAUSALCOT is described in the paper as a chain-of-thought prompting strategy built on top of GPT-4, so it's GPT-4 enhanced with their CAUSALCOT method.\n\nThe accuracy is 70.40%, which as a percentage would be approximately 70 (rounding to the nearest integer).\n\nAnswer: 70", "citations": [], "input_tokens": 39830, "output_tokens": 325, "cost_micros": 124365, "latency_ms": 8687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about model performance on Cladder.Let me search for \"Cladder\" in the document to find the relevant information:Now let me search for tables or results sections that might contain performance metrics:Perfect! I found Table 2 which shows the performance of all models on the CLADDER dataset. Let me read that section:Perfect! I can see Table 2 which shows the performance of all models on the CLADDER dataset. Looking at the \"Overall Acc.\" column:\n\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- +CAUSALCOT: 70.40\n\nThe model that performs the best on Cladder is \"+CAUSALCOT\" with an overall accuracy of 70.40.\n\nAnswer: 70", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36610, "error": null, "extra": {"thread_id": 2035, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e78cd64845f341ad917f431928ce6d64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 165, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor (human) is above/smarter than the student (model).\n\nThe second subfigure shows \"Superalignment\" where the supervisor (human) is below/weaker than the student (superhuman model).\n\nThe difference is the relative capability levels are reversed - in Traditional ML the supervisor is stronger, while in Superalignment the student is stronger.\n\nAnswer: Supervisor is stronger than student (vs. student stronger than supervisor)", "citations": [], "input_tokens": 121916, "output_tokens": 121, "cost_micros": 367563, "latency_ms": 32577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe figure shows three scenarios comparing Traditional ML, Superalignment, and \"Our Analogy.\"\n\nIn the first subfigure (Traditional ML), the supervisor (human) is above the human level line, while the student (model) is below the human level line.\n\nIn the second subfigure (Superalignment), the supervisor (human) is at the human level line, while the student (model) is far above the human level line.\n\nThe key difference is that in Traditional ML, the supervisor is stronger than/above the student, whereas in Superalignment, the student is much stronger than the supervisor.\n\nAnswer: Supervisor is stronger than student (versus student stronger than supervisor)", "citations": [], "input_tokens": 45459, "output_tokens": 162, "cost_micros": 138807, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student versus student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the **supervisor is stronger than the student** (supervisor is above the human level line, student is below it).\n\nThe second subfigure shows \"Superalignment\" where the **supervisor is weaker than the student** (supervisor is at human level, student is above it - superhuman).\n\nAnswer: Supervisor is stronger than student (vs weaker than student)", "citations": [], "input_tokens": 49232, "output_tokens": 103, "cost_micros": 149241, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.25, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs weaker than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor is at human level and the student is below human level.\n\nThe second subfigure shows \"Superalignment\" where the supervisor is at human level and the student is above human level (superhuman).\n\nThe key difference is the student's capability relative to human level.\n\nAnswer: Student is weaker than human vs student is stronger than human", "citations": [], "input_tokens": 47601, "output_tokens": 99, "cost_micros": 144288, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2222222222222222, "method": "str_norm", "normalised_pred": "student is weaker than human vs student is stronger than human", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to compare the first subfigure (Traditional ML) with the second subfigure (Superalignment).\n\nIn the first subfigure (Traditional ML):\n- Supervisor: Human\n- Student: Robot\n- The human is stronger/smarter than the robot\n\nIn the second subfigure (Superalignment):\n- Supervisor: Human  \n- Student: Superhuman Robot\n- The human is weaker/less capable than the superhuman robot\n\nThe key difference is the relative capability between supervisor and student.\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 89555, "output_tokens": 133, "cost_micros": 270660, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and analyze the difference between the first and second subfigures regarding supervisor-student relationships.Let me search for Figure 1 in the document:Now let me read the actual Figure 1 caption and description more carefully:Based on the figure caption and structure, I can see that Figure 1 has three subfigures:\n1. Traditional ML\n2. Superalignment  \n3. Our Analogy\n\nThe question asks about the difference between the first subfigure (Traditional ML) and the second subfigure (Superalignment) regarding the supervisor-student relationship.\n\nFrom the caption: \"Traditional ML focuses on the setting where humans supervise models that are weaker than humans. For the ultimate superalignment problem, humans will have to supervise models much smarter than them.\"\n\nThe key difference is about the relative capability/intelligence level between supervisor and student:\n- In Traditional ML (first subfigure): Supervisor is stronger/smarter than student\n- In Superalignment (second subfigure): Supervisor is weaker than student (students are \"much smarter\" than supervisors)\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43967, "error": null, "extra": {"thread_id": 2036, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e51022c429054652ae2e24b4002aba31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the figures from Page 3 to Page 10 in this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 2: Shows weak-to-strong generalization results with three panels (NLP Tasks, Chess Puzzles, ChatGPT Reward Modeling)\n\n**Page 4:**\n- No figures on this page (Related Work section)\n\n**Page 5:**\n- No figures on this page (Related Work continued)\n\n**Page 6:**\n- No figures on this page (Methodology section)\n\n**Page 7:**\n- No figures on this page (Main Results section continues)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization with naive finetuning (6 panels labeled a-f)\n\n**Page 9:**\n- Figure 4: Shows bootstrapping results on chess puzzles (2 panels labeled a-b)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss (2 panels labeled a-b)\n\nTotal figures from Page 3 to Page 10:\n- Figure 2 (Page 3)\n- Figure 3 (Page 8)\n- Figure 4 (Page 9)\n- Figure 5 (Page 10)\n\nAnswer: 4", "citations": [], "input_tokens": 121899, "output_tokens": 301, "cost_micros": 370212, "latency_ms": 35593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all the figures that appear on pages 3 through 10 of this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 1: An illustration of methodology (Traditional ML, Superalignment, Our Analogy)\n- Figure 2: Shows test accuracy on representative NLP task, Chess Puzzles, and ChatGPT Reward Modeling\n\n**Page 4:**\n- No figures visible on this page (contains related work section)\n\n**Page 5:**\n- No figures visible on this page (continues related work section)\n\n**Page 6:**\n- No figures visible on this page (continues related work section)\n\n**Page 7:**\n- No figures visible on this page (contains methodology section)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization results with multiple panels (a,b,c,d,e,f)\n- Figure 4: Shows bootstrapping results on chess puzzles\n\n**Page 9:**\n- No figures visible on this page (continues main results section)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss\n- Figure 6: Shows auxiliary loss improving generalization across datasets\n\nCounting the figures from pages 3-10:\n- Page 3: 2 figures (Figure 1, Figure 2)\n- Page 8: 2 figures (Figure 3, Figure 4)\n- Page 10: 2 figures (Figure 5, Figure 6)\n\nTotal: 6 figures\n\nAnswer: 6", "citations": [], "input_tokens": 45442, "output_tokens": 356, "cost_micros": 141666, "latency_ms": 8421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from Page 3 to Page 10 in this document.\n\n**Page 3:**\n- Figure 2: \"An illustration of our methodology...\" showing weak-to-strong generalization results\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- Figure 3: \"Promising weak-to-strong generalization...\" showing test accuracy panels (a,b,c,d,e,f)\n\n**Page 8:**\n- None\n\n**Page 9:**\n- Figure 4: \"Bootstrapping improves weak-to-strong generalization...\" showing panels (a,b)\n\n**Page 10:**\n- Figure 5: \"Substantially improved generalization...\" showing panels (a,b)\n- Figure 6: \"Simple auxiliary loss improves generalization...\" showing multiple test accuracy plots\n\nLet me count:\n- Page 3: 1 figure (Figure 2)\n- Page 7: 1 figure (Figure 3)\n- Page 9: 1 figure (Figure 4)\n- Page 10: 2 figures (Figure 5 and Figure 6)\n\nTotal: 1 + 1 + 1 + 2 = 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 49215, "output_tokens": 303, "cost_micros": 152190, "latency_ms": 7608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from page 3 to page 10 of this paper.\n\nLet me go through the document systematically:\n\n**Page 3:** Figure 1 (An illustration of methodology showing Traditional ML, Superalignment, and Our Analogy)\n\n**Page 4:** Figure 2 (Strong models trained with weak supervision generalize beyond their supervisor - showing 3 subplots for NLP Task, Chess, and ChatGPT Reward Modeling)\n\n**Page 5-6:** No figures visible in the main text sections shown\n\n**Page 7:** Figure 3 (mentioned in the text - showing test accuracy and performance gap recovered for NLP tasks, chess puzzles, and ChatGPT reward modeling - this appears to be on page 8 based on the layout)\n\n**Page 8:** Figure 3 appears here (6 subplots - a,b,c showing test accuracy and d,e,f showing performance gap recovered)\n\n**Page 9:** Figure 4 (Bootstrapping improves weak-to-strong generalization on chess puzzles - 2 subplots)\n\n**Page 10:** Figure 5 (Substantially improved generalization on NLP datasets with auxiliary loss - 2 subplots)\n\nCounting the figures:\n- Page 3: Figure 1\n- Page 4: Figure 2  \n- Page 8: Figure 3\n- Page 9: Figure 4\n- Page 10: Figure 5\n\nTotal figures from page 3 to page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 47584, "output_tokens": 358, "cost_micros": 148122, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count all figures from Page 3 to Page 10.\n\n**Page 3:**\n- Figure 2: Strong models trained with weak supervision generalize beyond their supervisor (bar chart)\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- None\n\n**Page 8:**\n- Figure 3: Promising weak-to-strong generalization with naive finetuning (grid of heatmaps/tables - appears as panels a, b, c, d, e, f)\n\n**Page 9:**\n- Figure 4: Bootstrapping improves weak-to-strong generalization on chess puzzles (2 panels: a and b)\n\n**Page 10:**\n- Figure 5: Substantially improved generalization on NLP datasets with a simple auxiliary loss (2 panels: a and b)\n\nTotal count:\n- Figure 2: 1\n- Figure 3: 1 (though it has 6 panels, it's one figure)\n- Figure 4: 1\n- Figure 5: 1\n\nAnswer: 4", "citations": [], "input_tokens": 89538, "output_tokens": 261, "cost_micros": 272529, "latency_ms": 9000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count the figures from Page 3 to Page 10.Now I need to identify which figures appear between Page 3 and Page 10. Let me read the relevant sections more carefully:Now let me check what's on page 11 to confirm the boundary:Based on my analysis, let me count the figures that appear from Page 3 to Page 10:\n\n- **Page 3**: No figures (only text - Related Work section starts)\n- **Page 4**: No figures (Related Work section continues)  \n- **Page 5**: 1 figure (the PGR illustration figure showing weak performance, weak-to-strong performance, strong ceiling performance)\n- **Page 6**: No figures (text only)\n- **Page 7**: 1 figure (Figure 3 - showing weak-to-strong generalization results across NLP, Chess, and ChatGPT RM)\n- **Page 8**: 1 figure (Figure 4 - Bootstrapping improves weak-to-strong generalization on chess puzzles)\n- **Page 9**: 1 figure (Figure 5 - NLP with auxiliary loss)\n- **Page 10**: 1 figure (Figure 6 - Simple auxiliary loss improves generalization across most datasets)\n\nTotal figures from Page 3 to Page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55516, "error": null, "extra": {"thread_id": 2037, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ad05d8ff7ccf4712bbe93b3462e811bc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 271, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to locate Figure 3 in the document and count the number of subfigures it contains.\n\nLooking at the document, Figure 3 appears on page 8. The figure is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure contains:\n- Panel (a): NLP Tasks - test accuracy plot\n- Panel (b): Chess Puzzles - test accuracy plot  \n- Panel (c): ChatGPT Reward Modeling - test accuracy plot\n- Panel (d): NLP Tasks - performance gap recovered plot\n- Panel (e): Chess Puzzles - performance gap recovered plot\n- Panel (f): ChatGPT Reward Modeling - performance gap recovered plot\n\nThe figure has 6 labeled panels/subfigures (a, b, c, d, e, f).\n\nAnswer: 6", "citations": [], "input_tokens": 121893, "output_tokens": 212, "cost_micros": 368859, "latency_ms": 54641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 on page 8. The caption reads:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe caption explicitly mentions panels (a,b,c) and (d,e,f), which indicates there are 6 subfigures total in Figure 3.\n\nLooking at the actual figure layout, I can see:\n- (a) NLP Tasks\n- (b) Chess Puzzles  \n- (c) ChatGPT Reward Modeling\n- (d) NLP Tasks (PGR)\n- (e) Chess Puzzles (PGR)\n- (f) ChatGPT Reward Modeling (PGR)\n\nAnswer: 6", "citations": [], "input_tokens": 45436, "output_tokens": 322, "cost_micros": 141138, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 which is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure caption describes panels (a,b,c) and (d,e,f):\n- (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task\n- (d,e,f) Same as panels a,b,c but for performance gap recovered\n\nSo Figure 3 has 6 subfigures total: (a), (b), (c), (d), (e), and (f).\n\nAnswer: 6", "citations": [], "input_tokens": 49209, "output_tokens": 201, "cost_micros": 150642, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can see Figure 3 is described as:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe description explicitly mentions:\n- (a), (b), (c) - three subfigures showing test accuracy\n- (d), (e), (f) - three subfigures showing performance gap recovered\n\nThis gives us a total of 6 subfigures in Figure 3.\n\nAnswer: 6", "citations": [], "input_tokens": 47578, "output_tokens": 255, "cost_micros": 146559, "latency_ms": 8389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking through the document for Figure 3...\n\nFound it! Figure 3 is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure consists of:\n- (a) NLP Tasks - showing test accuracy\n- (b) Chess Puzzles - showing test accuracy  \n- (c) ChatGPT Reward Modeling - showing test accuracy\n- (d) Performance Gap Recovered (%) - NLP Tasks\n- (e) Performance Gap Recovered (%) - Chess Puzzles\n- (f) Performance Gap Recovered (%) - ChatGPT Reward Modeling\n\nSo Figure 3 has 6 subfigures labeled (a) through (f).\n\nAnswer: 6", "citations": [], "input_tokens": 89532, "output_tokens": 204, "cost_micros": 271656, "latency_ms": 8046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count its subfigures.Let me search for Figure 3 in the document:Now let me search for where Figure 3 appears in the document:Good! I found Figure 3. Let me read the complete caption around line 975:Perfect! I can see from the caption that Figure 3 has subfigures labeled (a), (b), (c), (d), (e), and (f). Let me continue reading to confirm all subfigures:Perfect! I can now clearly see all the subfigures in Figure 3. Based on the caption and the content, Figure 3 contains:\n\n- (a) NLP Tasks - test accuracy\n- (b) Chess Puzzles - test accuracy  \n- (c) ChatGPT Reward Modeling - test accuracy\n- (d) NLP Tasks - performance gap recovered\n- (e) Chess Puzzles - performance gap recovered\n- (f) ChatGPT Reward Modeling - performance gap recovered\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40671, "error": null, "extra": {"thread_id": 2038, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_131c7eb21d53472097ae34a0c3413951", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 191, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all sections that discuss the experiment setup in this paper about weak-to-strong generalization.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the general methodology and setup for weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the three main task settings: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information:\n   - A.1: NLP TASKS - Details about data preprocessing, models, training hyperparameters, weak labels, and evaluation\n   - A.2: CHESS PUZZLES - Details about data preprocessing, training hyperparameters, weak labels, and evaluation\n   - A.3: CHATGPT REWARD MODELING - Details about data preprocessing, models, training hyperparameters, and weak labels\n   - A.4: AUXILIARY CONFIDENCE LOSS - Details about the confidence loss method\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Describes additional experimental setups:\n   - C.1: CHESS PUZZLES\n   - C.2: NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3: GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes additional experimental settings:\n   - D.1: SELF-SUPERVISED VISION MODELS\n   - D.2: LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Describes synthetic experiments:\n   - E.1: SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n   - E.2: DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n   - E.3: MAKING IMITATION TRIVIAL\n\nAnswer: Section 3 (Methodology), Section 4.1 (Tasks), Appendix A (Further Experimental Details), Appendix A.1 (NLP Tasks), Appendix A.2 (Chess Puzzles), Appendix A.3 (ChatGP", "citations": [], "input_tokens": 121905, "output_tokens": 512, "cost_micros": 373395, "latency_ms": 49562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgp, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup in this document.\n\nLet me go through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the overall methodology and experimental setup for weak-to-strong learning.\n\n2. **Section 4.1 TASKS** - This describes the tasks used in experiments (NLP benchmarks, Chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix is dedicated to experimental details.\n\n4. **Appendix A.1 NLP TASKS** - Details about NLP task setup including data preprocessing, models, training hyperparameters, weak labels, and evaluation.\n\n5. **Appendix A.2 CHESS PUZZLES** - Details about chess puzzle setup including data preprocessing, training hyperparameters, weak labels, and evaluation.\n\n6. **Appendix A.3 CHATGPT REWARD MODELING** - Details about reward modeling setup including data preprocessing, models, training hyperparameters, weak labels.\n\n7. **Appendix A.4 AUXILIARY CONFIDENCE LOSS** - Detailed description of the auxiliary confidence loss method.\n\n8. **Appendix C EASY-TO-HARD GENERALIZATION** - Discusses experimental setup for easy-to-hard generalization experiments.\n\n9. **Appendix C.1 CHESS PUZZLES** - Setup for easy-to-hard generalization on chess.\n\n10. **Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING** - Setup for difficulty-based experiments on NLP.\n\n11. **Appendix C.3 GPT-4 PREDICTED DIFFICULTY** - Setup using GPT-4 predicted difficulty.\n\n12. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - Discusses alternative experimental settings.\n\n13. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Setup for vision model experiments.\n\n14. **Appendix D.2 LINEAR PROBING** - Setup for linear probing experiments.\n\n15. **Appendix E THE EFFECTS OF WEAK LABEL STRUCTURE** - Discusses experimental setup for studying weak label structure.\n\n16. **", "citations": [], "input_tokens": 45448, "output_tokens": 512, "cost_micros": 144024, "latency_ms": 10405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "16", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup from the document.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the overall methodology and setup for the weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the specific tasks used in experiments (NLP benchmarks, chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix section provides detailed experimental setup information, including:\n   - A.1 NLP TASKS\n   - A.2 CHESS PUZZLES\n   - A.3 CHATGPT REWARD MODELING\n   - A.4 AUXILIARY CONFIDENCE LOSS\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Contains experimental setup for easy-to-hard generalization experiments, including:\n   - C.1 CHESS PUZZLES\n   - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3 GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes setup for additional experiments:\n   - D.1 SELF-SUPERVISED VISION MODELS\n   - D.2 LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Contains experimental setup for studying weak label structures:\n   - E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n   - E.2 DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n   - E.3 MAKING IMITATION TRIVIAL\n\nAnswer: Section 3, Section 4.1, Appendix A, Appendix A.1, Appendix A.2, Appendix A.3, Appendix A.4, Appendix C.1, Appendix C.2, Appendix C.3, Appendix D.1, Appendix D.2, Appendix E.1, Appendix E.2, Appendix E.3", "citations": [], "input_tokens": 49221, "output_tokens": 499, "cost_micros": 155148, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2105263157894737, "method": "list_set", "normalised_pred": "appendix, appendix 1, appendix 2, appendix 3, appendix 4, appendix c 1, appendix c 2, appendix c 3, appendix d 1, appendix d 2, appendix e 1, appendix e 2, appendix e 3, section 3, section 4 1", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experimental setup from the document.\n\nLet me scan through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the core experimental methodology for weak-to-strong learning, including creating weak supervisors, training strong students, and training with ground truth labels.\n\n2. **Section 4.1 TASKS** - This describes the specific tasks used in experiments: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information, including:\n   - A.1 NLP TASKS (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n   - A.2 CHESS PUZZLES (data preprocessing, training hyperparameters, weak labels, evaluation)\n   - A.3 CHATGPT REWARD MODELING (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n   - A.4 AUXILIARY CONFIDENCE LOSS (detailed description of the method)\n\n4. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - This discusses additional experimental settings:\n   - D.1 SELF-SUPERVISED VISION MODELS\n   - D.2 LINEAR PROBING\n\n5. **Appendix C** mentions experimental setup for easy-to-hard generalization experiments.\n\n6. **Appendix E** discusses experimental setup for synthetic experiments on weak label structure.\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix D OTHER WEAK-TO-STRONG SETTINGS, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING", "citations": [], "input_tokens": 47590, "output_tokens": 470, "cost_micros": 149820, "latency_ms": 10702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix d other weak to strong settings, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for sections that discuss experiment setup:\n\n1. **Section 3 METHODOLOGY** - Describes the general weak-to-strong learning setup\n2. **Section 4.1 TASKS** - Describes the three task types (NLP, Chess, Reward Modeling)\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - Contains detailed experimental setup information with subsections:\n   - A.1 NLP TASKS\n   - A.2 CHESS PUZZLES\n   - A.3 CHATGPT REWARD MODELING\n   - A.4 AUXILIARY CONFIDENCE LOSS\n4. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Describes ImageNet experiment setup\n5. **Appendix D.2 LINEAR PROBING** - Describes linear probing experiment setup\n6. **Appendix C EASY-TO-HARD GENERALIZATION** - Describes setup for easy-to-hard experiments with subsections:\n   - C.1 CHESS PUZZLES\n   - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3 GPT-4 PREDICTED DIFFICULTY\n7. **Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY** - Describes synthetic experiment setup\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix C EASY-TO-HARD GENERALIZATION, Appendix C.1 CHESS PUZZLES, Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING, Appendix C.3 GPT-4 PREDICTED DIFFICULTY, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING, Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 89544, "output_tokens": 499, "cost_micros": 276117, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix c 1 chess puzzles, appendix c 2 nlp tasks difficulty thresholding, appendix c 3 gpt 4 predicted difficulty, appendix c easy to hard generalization, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix e 1 synthetic experiments on simulation difficulty, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to identify all sections that discuss the experiment setup.Let me continue reading to see the complete document structure and identify all sections related to experiment setup.Now let me search specifically for \"experiment\" or \"setup\" to find sections discussing experimental setup:Now let me search more systematically for sections that discuss experimental setup:Based on my reading of the document, I can now identify all the sections that discuss experiment setup. Let me compile the complete list:Now I have a comprehensive view of the document. Let me identify all sections that discuss experiment setup:\n\nAnswer: 3 METHODOLOGY, 4.1 TASKS, A FURTHER EXPERIMENTAL DETAILS, A.1 NLP TASKS, A.2 CHESS PUZZLES, A.3 CHATGPT REWARD MODELING, A.4 AUXILIARY CONFIDENCE LOSS, E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69733, "error": null, "extra": {"thread_id": 2039, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bfc57bfcbbd44c6786a0cd5058f61c15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "1 nlp tasks, 2 chess puzzles, 3 chatgpt reward modeling, 3 methodology, 4 1 tasks, 4 auxiliary confidence loss, e 1 synthetic experiments on simulation difficulty, further experimental details", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the citation information for the paper by Long Ouyang published in 2022.\n\nLooking through the references section, I can find:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730\u201327744, 2022.\n\nHowever, the document does not contain information about how many times this paper has been cited. The document is the paper itself (\"Weak-to-Strong Generalization\") which cites the Ouyang et al. 2022 paper, but it does not provide citation counts for the references it lists.\n\nI cannot determine the number of citations for the Ouyang et al. 2022 paper from this document alone.\n\nAnswer: Cannot be determined from the provided document", "citations": [], "input_tokens": 121904, "output_tokens": 231, "cost_micros": 369177, "latency_ms": 39750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations of the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find references to \"Ouyang et al., 2022\" in the references section:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times this paper is cited throughout the document. Let me search for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Christiano et al., 2017; Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 45447, "output_tokens": 423, "cost_micros": 142686, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations to Long Ouyang 2022 in the references and throughout the document.\n\nLooking at the references section, I can find:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times \"Ouyang et al., 2022\" or similar citations appear in the document.\n\nSearching through the document:\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nLet me verify by searching more carefully through the text for all instances of \"Ouyang\":\n\n- Introduction section: 1 citation\n- Related Work section: 1 citation  \n- Tasks section: 1 citation\n- ChatGPT reward modeling subsection: 1 citation\n- Models subsection (A.3): 1 citation\n- Alignment plan section: 1 citation\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 49220, "output_tokens": 405, "cost_micros": 153735, "latency_ms": 8905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations of Ouyang et al., 2022 or Long Ouyang, 2022.\n\nLooking through the document, I can find the following citations to \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"(Stiennon et al., 2020; Ouyang et al., 2022)\"\n6. Page 47: \"(h - tiano et al., 2017; Ouyang et al., 2022)\"\n\nLet me count all instances:\n1. Page 1 - 1 citation\n2. Page 2 - 1 citation\n3. Page 5 - 1 citation\n4. Page 7 - 1 citation\n5. Page 32 - 1 citation\n6. Page 47 - 1 citation\n\nTotal: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 47589, "output_tokens": 417, "cost_micros": 149022, "latency_ms": 7516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations for the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find the reference:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. *Advances in Neural Information Processing Systems*, 35: 27730\u201327744, 2022.\n\nNow I need to count how many times this paper (Ouyang et al., 2022) is cited in the document.\n\nSearching through the document for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nAnswer: 6", "citations": [], "input_tokens": 89543, "output_tokens": 289, "cost_micros": 272964, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about Long Ouyang's 2022 paper and its citation count.Let me search for \"Long Ouyang\" and \"2022\" in this document, and look for citation information.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25655, "error": null, "extra": {"thread_id": 2040, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d3a50787f8a940c590bfec1b692a8eb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 70, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2022", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\n- Subfigure (a) shows that for ground truth labels, **finetuning** (solid lines) performs best across all model sizes, reaching approximately 95% accuracy for the largest models.\n\n- Subfigure (b) shows weak-to-strong performance with weak labels, where **fewshot weak labels (n=5)** (the method shown with crosses/\u00d7) performs best for the largest student models.\n\n- Subfigure (c) directly compares methods and shows that **finetune aux. loss** (solid line with triangles) achieves the highest performance, reaching approximately 90% accuracy for the largest models.\n\nOverall, across the figure, **finetune aux. loss** appears to be the best method when considering weak-to-strong learning performance.\n\nAnswer: finetune aux. loss", "citations": [], "input_tokens": 121910, "output_tokens": 207, "cost_micros": 368835, "latency_ms": 39203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "finetune aux loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods:\n\n- Subfigure (a) compares zero-shot, 5-shot ground truth, and finetune ground truth\n- Subfigure (b) compares zero-shot, 5-shot ground truth, and 5-shot with weak labels  \n- Subfigure (c) compares 5-shot with weak labels, naive finetuning, and finetuning with auxiliary confidence loss\n\nAcross all three subfigures, examining the performance curves for the largest strong student models (rightmost side of each plot), the method that achieves the highest test accuracy is **finetuning with the auxiliary confidence loss** (shown with triangles in subfigure c).\n\nAnswer: Finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 45453, "output_tokens": 193, "cost_micros": 139254, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods across model sizes:\n\nThe figure compares:\n- Zero-shot\n- Few-shot with ground truth (n=5)\n- Finetune with ground truth\n- Few-shot with weak labels (n=5)\n- Finetune (baseline)\n- Finetune with auxiliary loss\n\nFrom the plots, particularly subfigure (c) which directly compares weak-to-strong performance, the method that consistently achieves the highest test accuracy across strong student compute sizes is the finetuning with auxiliary confidence loss (shown with solid line and triangles).\n\nAnswer: finetune with aux. loss", "citations": [], "input_tokens": 49226, "output_tokens": 163, "cost_micros": 150123, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "finetune with aux loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which contains three subfigures (a), (b), and (c), I need to identify the six methods shown and determine which performs best.\n\nFrom the figure:\n- Subfigure (a) shows: zero-shot, 5-shot with ground truth (g.t.), and finetuning with ground truth (g.t.)\n- Subfigure (b) shows: zero-shot, 5-shot with ground truth, and 5-shot with weak labels\n- Subfigure (c) shows: 5-shot prompting (weak-to-strong), naive finetuning, and finetuning with auxiliary confidence loss\n\nThe six methods are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetuning with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary confidence loss\n\nLooking at subfigure (c), which directly compares three weak-to-strong methods, the finetuning with auxiliary confidence loss (shown with solid line and triangles) achieves the highest performance, especially for larger model sizes.\n\nAmong all methods shown across the three subfigures, finetuning with ground truth labels would naturally perform best, but among the weak-to-strong methods (which is the focus of subfigure c), the auxiliary confidence loss performs best.\n\nAnswer: finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 47595, "output_tokens": 339, "cost_micros": 147870, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\nIn subfigure (a), comparing zero-shot, 5-shot with ground truth, and finetuning with ground truth, **finetuning with ground truth** achieves the highest test accuracy.\n\nIn subfigure (b), which shows 5-shot prompting with weak labels from different weak supervisors, the best performing is **5-shot with weak labels from the largest weak supervisor (0.1)**.\n\nIn subfigure (c), which compares 5-shot weak labels, naive finetuning, and finetuning with auxiliary loss, **finetuning with auxiliary loss** achieves the highest performance.\n\nOverall, across all methods shown in Figure 9, the best performing method is:\n\nAnswer: finetuning with auxiliary loss", "citations": [], "input_tokens": 89549, "output_tokens": 194, "cost_micros": 271557, "latency_ms": 8141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and identify which method performs best among the six shown.Let me search for Figure 9 in the document:Let me read the section containing Figure 9 to see the actual figure and understand which methods are being compared:Perfect! Now I can see Figure 9 with three subfigures (a), (b), and (c). Let me read a bit more to understand the comparison better:Based on my reading of the document, Figure 9 contains three subfigures (a, b, c) that compare six different methods:\n\nFrom the figure caption and description:\n- **Panel (a)**: zero-shot, 5-shot with ground truth, finetune with ground truth\n- **Panel (b)**: 5-shot with weak labels (multiple supervisor sizes), zero-shot, 5-shot with ground truth\n- **Panel (c)**: 5-shot weak-to-strong prompting, naive finetuning (weak-to-strong), and **finetuning with the confidence loss (auxiliary loss)**\n\nThe text at line 2078-2079 states: \"However, **weak-to-strong finetuning with the confidence loss still generally outperforms** weak-to-strong few-shot prompting.\"\n\nAnd the figure caption at line 1942-1944 states: \"Few-shot weak-to-strong performance becomes competitive with or outperforms finetuning for the largest strong students, though **finetuning with the confidence loss does better**.\"\n\nThe six methods being compared across the three subfigures are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetune with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary/confidence loss\n\nAnswer: Finetuning with the confidence loss (auxiliary loss)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47672, "error": null, "extra": {"thread_id": 2041, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4655e497891e45598d32be191f19ac1d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with confidence loss auxiliary loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves.\n\nLooking at Figure 1 (the technology tree of RAG research), I can see it's organized by year (2020, 2023, 2024) and has three main branches at the top labeled:\n- \"Augmentation Stage\"\n- \"Fine-tuning\" \n- \"Pre-training\"\n\nLet me count the leaves (methods/papers) in each branch:\n\n**Pre-training branch:**\n- 2020: REALM (implied from context)\n- 2022: Retro, CoG, SANTA, TIGER\n- 2023-2024: RAVEN, Retro++, InstructRetro\n\nThis appears to have around 7-8 leaves.\n\n**Fine-tuning branch:**\nThis has many methods spread across 2023-2024, including PROMPTAGATOR, LM-Indexer, various methods - appears to have 15+ leaves.\n\n**Inference branch:**\nThis has the most methods, with numerous entries across 2023-2024 - appears to have 30+ leaves.\n\nLooking more carefully at the tree structure, the \"Pre-training\" branch (on the left side of the tree) has the fewest methods/leaves compared to the other branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 33376, "output_tokens": 305, "cost_micros": 104703, "latency_ms": 9515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 (the Technology tree of RAG research), I need to count the number of methods/papers shown in each branch:\n\n- **Pre-training branch** (bottom): Contains methods like TIGER, SANTA, Atlas, RAVEN, RETRO++, INSTRUCTRETRO, CoG, EAR, KALMV - approximately 9-10 items\n\n- **Fine-tuning branch** (middle): Contains methods like G-Retriever, RADA, CT-RAG, HyKGE, FILCO, InstructRetro, RA-DIT, Self-RAG, FLARE, and many others - this is the largest branch with approximately 25+ items\n\n- **Inference branch** (top): Contains methods like RAPTOR, UniMS-RAG, CRAG, BGM, IAG, BEQ, ARM-RAG, and several others - approximately 15-20 items\n\nThe Pre-training branch clearly has the fewest methods/leaves compared to the other two branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 40683, "output_tokens": 242, "cost_micros": 125679, "latency_ms": 8514, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the figure in the document that has a tree shape and identify which branch has the least leaves.\n\nLooking at the document, I can see Figure 1, which is described as a \"Technology tree of RAG research.\" This figure shows three main stages: Pre-training, Fine-tuning, and Inference, with various methods listed under each branch.\n\nLet me count the items in each branch:\n\n**Pre-training branch:**\n- CoG, RAVEN, Atlas, RAG, RETRO++, INSTRUCTRETRO, TIGER, SANTA (and a few more visible items)\n- This appears to have around 8-10 items\n\n**Fine-tuning branch:**\n- UniMS-RAG, Self-RAG, MK-ToD, QLM-Doc-ranking, SKR, SANTA, IRCOT, TIGER, Self-Mem, SCM4LLMs (and more)\n- This appears to have the most items, around 15-20+\n\n**Inference branch:**\n- CRAG, CT-RAG, EARCoN, CREA-ICL, PRCA, BEQ, ARM-RAG, PROMPTAGATOR, ZemiR-GQA (and many more)\n- This also has many items\n\nLooking at the figure more carefully, the **Pre-training** branch appears to have the fewest items/leaves compared to the Fine-tuning and Inference branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 30919, "output_tokens": 336, "cost_micros": 97797, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves (items).\n\nLooking at Figure 1 titled \"Technology Tree of RAG research,\" I can see it's organized into three main columns representing different stages:\n- Pre-training\n- Fine-tuning  \n- Inference\n\nLet me count the items in each branch/column:\n\n**Pre-training column:** Contains items like:\n- G-Retriever, CRAG, IAG, FILCO, ARM-RAG, RADA, RAPTOR, BGM, HyKGE, DRAGON-AI, PaperQA, CREA-ICL\n- PRCA, Token-Elimination, KALMV, ITRG\n- 1-PAGER, FABULA, SKR, ToC, QLM-Doc-ranking, Recomp\n- KGP, LLM-R, ITER-RETGEN, COQ, LLM-IE, ICRALM, DSP\n- KnowledGPT, IRCOT, PGRA, PKG, SCM4LLMs, Filter-Reranker, RePLUG\n- CoG, TIGER, Retro++\n- GenRead\n- (empty row)\n- RECITE\n\n**Fine-tuning column:** Contains items like:\n- UniMS-RAG, CT-RAG, BEQ, CoN, EAR\n- RAST, Self-RAG, LM-Indexer, RA-DIT, Dual-Feedback-ToD, MK-ToD, RAG_Robust, Retrieve-and-Sample\n- InstructRetro\n- RAVEN\n- SANTA, RRR, Self-Mem, UPRISE, R-GQA, Zemi\n- SUGRE, AAR, FLARE\n- PROMPTAGATOR, RAG-e2e\n- Atlas\n- RAG\n\n**Inference column:** This appears to be the largest with many items distributed across multiple years.\n\nComparing the three branches, the **Fine-tuning** column appears to have fewer distinct method names than the other two columns, but I need to count more carefully", "citations": [], "input_tokens": 37501, "output_tokens": 512, "cost_micros": 120183, "latency_ms": 18250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "comparing three branches fine tuning column appears to have fewer distinct method names than other two columns but i need to count more carefully", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find a figure with a tree shape and identify the branch with the least leaves.Let me search for references to figures with tree shapes in the document:Let me read through the document to find figures. I'll start by looking at the actual content:The document mentions a \"technology tree\" in Figure 1. Let me look for Figure 1:Let me search for where figures are actually displayed in the document:Good! There are embedded images. Let me read around Im1.png which should be Figure 1 (the technology tree):This looks like it's the OCR text from the tree figure. Let me continue reading to see all the branches:Perfect! I found the tree-shaped figure. It's a tree diagram with three main branches:\n1. Fine-tuning (green, left branch)\n2. Pre-training (orange/gold, middle branch) \n3. Inference (blue-green, right branch)\n\nLet me continue reading the visual description to see which branch has the least leaves (papers/items):Perfect! The visual description clearly states:\n\n- **Fine-tuning branch**: Most extensive with numerous sub-branches and approaches\n- **Pre-training branch**: Narrowest with fewer approaches\n- **Inference branch**: Dense with many recent (2024) developments\n\nThe Pre-training branch is described as the \"narrowest with fewer approaches,\" meaning it has the least leaves.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62969, "error": null, "extra": {"thread_id": 2042, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bc29640067d14f5eb3f9f7d53e8522d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 357, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 on page 4 of the document, which shows \"Comparison between the three paradigms of RAG,\" I can see three main sections representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/shapes used:\n\n1. User icon (person silhouette)\n2. Query icon (document/text box)\n3. Documents icon (stacked documents)\n4. Indexing box\n5. Retrieval box\n6. Prompt box (document icon)\n7. Frozen LLM (snowflake icon with text)\n8. Output box\n9. Pre-Retrieval box (with sub-elements like \"Query Rewriting\", \"Query Expansion\", \"Query Enhancement\")\n10. Post-Retrieval box (with sub-elements like \"Re-rank\", \"Memory\", \"Fusion\")\n11. Various module boxes in the Modular RAG section (Routing, Search, Predict, Retrieve, Rewrite, RAG, Rerank, Read, Demonstrate, Fusion, Memory)\n12. Different pattern sections showing Rewrite, Retrieve, Read combinations\n\nCounting the distinct icon types (not individual instances):\n- User icon (person)\n- Query icon\n- Documents icon\n- Standard rectangular boxes (for processes)\n- Snowflake icon (for Frozen LLM)\n- Document/page icon (for Prompt)\n\nLooking more carefully at the distinct visual elements:\n1. Person/user icon\n2. Text/query box\n3. Documents stack icon\n4. Process boxes (rectangles)\n5. Snowflake (frozen LLM)\n6. Document icon\n\nAnswer: 6", "citations": [], "input_tokens": 64616, "output_tokens": 404, "cost_micros": 199908, "latency_ms": 15077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see:\n\n1. **User Query icon** - represented by a person/user symbol\n2. **Documents icon** - represented by stacked document/paper symbols\n3. **Indexing icon** - appears to show database/storage symbols\n4. **Retrieval icon** - shown with magnifying glass or search symbols\n5. **Read icon** - shown as a document being read\n6. **Predict icon** - shown with a specific symbol\n7. **Output icon** - shown at the end of processes\n8. **Prompt icon** - shown in the middle paradigm\n9. **Frozen LLM icon** - represented by a brain or model symbol\n10. **Rerank icon** - shown in Advanced RAG\n11. **Rewrite icon** - shown in Modular RAG\n12. **Demonstrate icon** - shown in Modular RAG\n13. **Routing icon** - shown in Modular RAG\n14. **Search icon** - shown in Modular RAG\n15. **Fusion icon** - shown in Modular RAG\n16. **Memory icon** - shown in Modular RAG\n\nLet me recount more carefully by looking at the actual distinct visual icons in the figure:\n\nFrom the figure, I can identify these distinct icon types:\n- User/person icon\n- Document/paper stack icon\n- Database/storage icon\n- Magnifying glass (search/retrieval)\n- Brain/model icon (for LLM)\n- Arrow/flow icons\n- Various process boxes with different labels\n\nCounting the truly distinct visual icons (not just labeled boxes):\n1. User icon\n2. Document icon\n3. Database/indexing icon\n4. Magnifying glass/search icon\n5. Brain/LLM icon\n6. Output/result icon\n\nAnswer: 6", "citations": [], "input_tokens": 33359, "output_tokens": 449, "cost_micros": 106812, "latency_ms": 10735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 titled \"Comparison between the three paradigms of RAG,\" I can see three diagrams showing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify each distinct icon type used across all three paradigms:\n\n1. **User** - person icon (shown in all three)\n2. **Query** - document/text icon (shown in all three)\n3. **Documents** - stack of documents icon (shown in all three)\n4. **Indexing** - cylindrical database icon (shown in Naive and Advanced RAG)\n5. **Retrieval** - magnifying glass/search icon (shown in all three)\n6. **Rewrite** - circular arrow icon (shown in Advanced RAG)\n7. **Rerank** - list with arrows icon (shown in Advanced RAG)\n8. **Prompt** - text/document icon (shown in all three)\n9. **Frozen LLM** - brain/processing icon (shown in all three)\n10. **Read** - book/reading icon (shown in all three)\n11. **Output** - result/output icon (shown in all three)\n12. **Modules (R)** - box with R (shown in Modular RAG)\n13. **Search** - magnifying glass (shown in Modular RAG)\n14. **Routing** - directional arrow icon (shown in Modular RAG)\n15. **Predict** - crystal ball/prediction icon (shown in Modular RAG)\n16. **Demonstrate** - pointing hand icon (shown in Modular RAG)\n17. **Memory** - storage icon (shown in Modular RAG)\n18. **Fusion** - merge icon (shown in Modular RAG)\n19. **Summary** - document summary icon (shown in Advanced RAG)\n20. **Judge** - decision diamond (shown in Modular RAG)\n21. **Generate** - generation icon (shown in Modular RAG)\n\nLet me recount more carefully by looking at the actual distinct icon shapes:\n\n1. User (person)\n2. Query (document)\n3. Documents (stack)", "citations": [], "input_tokens": 40666, "output_tokens": 512, "cost_micros": 129678, "latency_ms": 10766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three sections (Left, Middle, Right) representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/symbols used in the figure:\n\n1. \"User Query\" box (rectangular box with text)\n2. \"Indexing\" box\n3. \"Retrieval\" box\n4. \"Rerank\" box\n5. \"Rewrite\" box\n6. \"Fusion\" box\n7. \"Summary\" box\n8. \"Demonstrate\" box\n9. \"Read\" box\n10. \"Search\" box\n11. \"Retrieve\" box\n12. \"Memory\" box\n13. \"Predict\" box\n14. \"Routing\" box\n15. \"Documents\" box\n16. \"RAG\" box\n17. \"Output\" box\n18. \"Response\" box\n19. \"Judge\" box\n20. \"Generate\" box\n21. \"Transformation/Decomposition\" box\n22. \"Iterate\" box\n23. Arrow symbols (connecting the boxes)\n24. \"Max Times/Threshold\" decision point\n25. \"Max Depth (Tree)/Threshold\" decision point\n26. \"Generate Special Token/Threshold\" decision point\n27. \"N Times\" indicator\n\nLooking more carefully at the visual elements, the distinct icon types are:\n- Rectangular boxes (for processes/modules)\n- Arrows (for flow direction)\n- Diamond or decision point shapes (for conditional logic)\n\nCounting the distinct visual icon types (not the number of instances, but types):\n1. Rectangular box/module icon\n2. Arrow icon\n3. Diamond/decision point icon\n\nAnswer: 3", "citations": [], "input_tokens": 30902, "output_tokens": 415, "cost_micros": 98931, "latency_ms": 10655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 in the document to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three main diagrams:\n\n1. **Naive RAG (Left)**: Shows a flowchart with:\n   - User Query\n   - Documents\n   - Indexing\n   - Retrieval\n   - Prompt\n   - Frozen LLM\n   - Output\n\n2. **Advanced RAG (Middle)**: Shows:\n   - User Query\n   - Documents\n   - Indexing\n   - Pre-Retrieval (with sub-components: Query Routing, Query Rewriting, Query Expansion)\n   - Retrieval\n   - Post-Retrieval (with sub-components: Rerank, Summary, Fusion)\n   - Prompt\n   - Frozen LLM\n   - Output\n\n3. **Modular RAG (Right)**: Shows:\n   - Multiple modules (Routing, Search, Predict, Rewrite, RAG, Rerank, Retrieve, Read, Demonstrate, Fusion, Memory)\n   - Various patterns including Naive RAG, Advanced RAG, DSP, and ITER-RETGEN\n\nLooking at the visual representation, the distinct icon shapes/types I can identify are:\n1. Rectangular boxes (for most components)\n2. Rounded rectangles/boxes (for grouping/subgraphs)\n3. Arrows/lines (for connections)\n\nHowever, if we're counting distinct visual elements or node types based on their representation in the figure, the components are primarily represented as boxes/rectangles with text labels. The figure uses a consistent box/rectangle style for representing different components.\n\nBased on the mermaid diagram structure shown, all components are represented using similar rectangular node shapes, with the main distinction being in the grouping (subgraphs) and connections rather than distinct icon types.\n\nAnswer: 1", "citations": [], "input_tokens": 37484, "output_tokens": 444, "cost_micros": 119112, "latency_ms": 12312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count the distinct icons used.Let me search for \"Figure 3\" in the document:Now let me read around line 788 to find Figure 3:Let me continue reading to find Figure 3:I see Figure 2 but need to continue searching for Figure 3. Let me search more:Perfect! I found Figure 3. Let me continue reading to see the complete visual description:Perfect! I have found Figure 3 and its visual description. Let me carefully count the distinct icons mentioned in the visual description:\n\nFrom the visual description of Figure 3, I can identify the following distinct icons:\n\n1. **User/Query icon** - mentioned in Naive RAG and Advanced RAG\n2. **Documents icon** - mentioned in Naive RAG and Advanced RAG (stacked papers/documents)\n3. **Snowflake icon** - mentioned in Naive RAG and Advanced RAG (representing Frozen LLM)\n4. **Document icon** - mentioned in Naive RAG (at the Prompt stage)\n5. **Shuffle/rerank icon** - mentioned in Advanced RAG Post-Retrieval\n6. **Waveform/summary icon** - mentioned in Advanced RAG Post-Retrieval\n7. **Layers/fusion icon** - mentioned in Advanced RAG Post-Retrieval\n\nLet me recount more carefully by looking at what the visual description explicitly states:\n- User/Query icons (top of diagrams)\n- Documents icons (top of diagrams)\n- Document icon (at Prompt stage)\n- Snowflake icon (representing Frozen LLM)\n- Shuffle/rerank icon (in Post-Retrieval)\n- Waveform/summary icon (in Post-Retrieval)\n- Layers/fusion icon (in Post-Retrieval)\n\nThat gives us 7 distinct icons total.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51812, "error": null, "extra": {"thread_id": 2043, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c31c1d3df3774ac882e3f8a22f227910", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 283, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that has a retrieval granularity of \"Phrase\" in Table I.\n\nLooking at Table I (Summary of RAG methods) on page 6, I can see the columns: Method, Retrieval Source, Retrieval Data Type, Retrieval Granularity, Augmentation Stage, and Retrieval process.\n\nScanning the \"Retrieval Granularity\" column, I find one entry with \"Phrase\":\n- CoG [29] - which has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of reference [29]. Looking at the references section starting on page 17, reference [29] is:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 64633, "output_tokens": 220, "cost_micros": 197199, "latency_ms": 34000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that uses \"phrase\" as its retrieval granularity.\n\nLooking at Table I \"SUMMARY OF RAG METHODS\", I can find the retrieval granularity column. Let me search for entries with \"Phrase\" as the retrieval granularity.\n\nFrom Table I, I can see:\n- CoG [29] has:\n  - Retrieval Source: Wikipedia\n  - Retrieval Data Type: Text\n  - Retrieval Granularity: Phrase\n  - Augmentation Stage: Pre-training\n  - Retrieval process: Iterative\n\nNow I need to find the full title of the CoG paper. Looking at the references section:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 33376, "output_tokens": 222, "cost_micros": 103458, "latency_ms": 7266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for methods with \"Phrase\" retrieval granularity in Table I...\n\nFrom Table I (SUMMARY OF RAG METHODS), I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nLooking at the references section for reference [29]:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 40683, "output_tokens": 133, "cost_micros": 124044, "latency_ms": 8407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with phrase-level retrieval granularity.\n\nLooking at Table I (Summary of Retrieval Source and Retrieval Granularity Used in RAG Methods), I can find methods with \"Phrase\" as their retrieval granularity.\n\nFrom the table, I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of the CoG paper from the references section.\n\nLooking at the references, reference [29] is:\n\"T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\"\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 30919, "output_tokens": 187, "cost_micros": 95562, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with retrieval granularity of \"Phrase\".\n\nLooking at Table I (Summary of RAG Methods), I can search for entries where \"Retrieval Granularity\" is \"Phrase\".\n\nScanning through Table I, I find:\n- CoG [29] has retrieval granularity: Phrase\n\nNow I need to find the full title of paper [29] from the references section.\n\nLooking at reference [29]:\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in *The Eleventh International Conference on Learning Representations*, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 37501, "output_tokens": 174, "cost_micros": 115113, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about retrieval granularity and phrase-level methods in the provided documents.Let me continue reading to find the title and look for information about retrieval granularity:Good! I found the paper title: \"Retrieval-Augmented Generation for Large Language Models: A Survey\". Now I need to search for information about retrieval granularity and specifically which method has \"phrase\" as its retrieval granularity. Let me search for tables or sections that discuss retrieval granularity:Perfect! I found a table that mentions \"Phrase\" as a retrieval granularity. Let me read that section to find the complete information:Great! I can see that \"CoG [29]\" has \"Phrase\" as its retrieval granularity. Now I need to find the full title of the CoG paper. Let me look for the reference [29]:Perfect! I found reference [29]. Let me read that section to get the full title:I can see reference [29] starts at line 4090. Let me read a bit more to get the complete title:Perfect! I found the full title of reference [29], which is the paper that proposes the method (CoG) with phrase retrieval granularity.\n\nThe full title is: **\"Copy is all you need\"**\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52827, "error": null, "extra": {"thread_id": 2044, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_27dfaf3e8bfa47e1b55fb4feceaed85a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 297, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through Table II to find datasets that have exactly three methods listed.\n\nLooking at Table II (SUMMARY OF RAG METHODS), I need to count the methods for each dataset:\n\n- WebQ (Web Questions): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - 3 methods \u2713\n- MS MARCO: [4], [40], [52] - 3 methods \u2713\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum(QM): [60], [123] - 2 methods\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB [128],MMCU_Medical [129]: [81] - 1 method\n- QuALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n- GraphQA: [84] - 1 method\n- Amazon(Toys,Sport,Beauty): [39], [40] - 2 methods\n- T-REx [141],ZsRE [142]: [27], [51] - 2 methods\n- HellaSwag: [20], [66] - 2 methods\n- CoT Reasoning: [27] - 1 method\n- CSQA: [55] - 1 method\n- StrategyQA: [14], [24], [48], [51], [55], [58] - 6 methods\n- XSum: [17] - 1 method\n- VioLens: [19] - 1 method\n- TREC: [33] - 1 method\n- SST-2: [20], [33], [38] - 3 methods \u2713\n- CodeSearchNet: [76] -", "citations": [], "input_tokens": 64631, "output_tokens": 512, "cost_micros": 201573, "latency_ms": 17125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "codesearchnet 76", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): [3], [4], [22], [27], [40], [43], [54], [62], [71], [112] [20], [44], [72] [13], [30], [34], [45], [50], [64] - more than 3\n- TriviaQA (TQA): [4], [27], [59], [62], [112] [22], [25], [43], [44], [71], [72] [20], [23], [30], [32], [45], [69], [112] - more than 3\n- SQUAD: [3], [4], [13], [30], [50], [68] - 6 methods\n- Web Questions (WebQ): [4], [40], [52] - exactly 3 methods\n- PopQA: [4], [40], [52] - exactly 3 methods\n- MS MARCO: [117] - only 1 reference\n\n**QA - Multi-hop:**\n- HotpotQA: [7], [14], [22], [27], [59], [62], [69], [71], [91] - 9 methods\n- 2WikiMultiHopQA: [14], [51], [61], [91] - 4 methods\n- MuSiQue: [120] - only 1 reference\n\n**QA - Long-form:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB: [", "citations": [], "input_tokens": 33374, "output_tokens": 512, "cost_micros": 107802, "latency_ms": 11546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "cmb", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): More than 3 methods\n- TriviaQA (TQA): More than 3 methods\n- SQUAD: More than 3 methods\n- Web Questions (WebQ): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - **exactly 3 methods**\n- MS MARCO: [4], [40], [52] - **exactly 3 methods**\n\n**QA - Multi-hop:**\n- HotpotQA: More than 3 methods\n- 2WikiMultiHopQA: [14], [24], [48], [59], [61], [91] - 6 methods\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n\n**QA - Long-form QA:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain QA:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB, MMCU_Medical: [81] - 1 method\n\n**QA - Multi-Choice QA:**\n- QUALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): [13], [27], [34], [42] - 4 methods\n- KBP: [74],", "citations": [], "input_tokens": 40681, "output_tokens": 512, "cost_micros": 129723, "latency_ms": 12062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "kbp 74", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Table II and find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n1. Natural Question (NQ) - has 10 methods listed\n2. TriviaQA (TQA) - has 5 methods listed\n3. SQuAD - has 7 methods listed\n4. Web Questions (WebQ) - has 6 methods listed\n5. PopQA - has 3 methods: [7], [25], [67] \u2713\n6. MS MARCO - has 3 methods: [4], [40], [52] \u2713\n7. HotpotQA - has 8 methods listed\n8. 2WikiMultiHopQA - has 6 methods listed\n9. MuSiQue - has 4 methods listed\n10. ELI5 - has 5 methods listed\n11. NarrativeQA (NQA) - has 4 methods listed\n12. ASQA - has 2 methods listed\n13. QMSum (QM) - has 2 methods listed\n14. Qasper - has 2 methods listed\n15. COVID-QA - has 2 methods listed\n16. CMB, MMCU Medical - has 1 method listed\n17. QuALITY - has 2 methods listed\n18. ARC - has 2 methods listed\n19. CommonsenseQA - has 2 methods listed\n20. GraphQA - has 1 method listed\n21. Wizard of Wikipedia (WoW) - has 4 methods listed\n22. KBP - has 2 methods listed\n23. DuleMon - has 1 method listed\n24. CamRest - has 2 methods listed\n25. Amazon (Toys, Sport, Beauty) - has 2 methods listed\n26. WikiEvent - has 4 methods listed\n27. RAMS - has 2 methods listed\n28. T-REx, ZsRE - has 2 methods listed\n29. HellaSwag - has 2 methods listed\n30. CoT Reasoning - has 1 method listed", "citations": [], "input_tokens": 30917, "output_tokens": 512, "cost_micros": 100431, "latency_ms": 11141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "30 cot reasoning has 1 method listed", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to find datasets in Table II that have exactly three methods listed.\n\nLet me go through Table II systematically and count the number of methods for each dataset:\n\n**QA - Single-hop:**\n- Natural Question (NQ): 12 methods\n- TriviaQA (TQA): 12 methods\n- SQuAD: 7 methods\n- Web Questions (WebQ): 6 methods\n- PopQA: 3 methods \u2713\n- MS MARCO: 3 methods \u2713\n\n**QA - Multi-hop:**\n- HotpotQA: 15 methods\n- 2WikiMultiHopQA: 6 methods\n- MuSiQue: 4 methods\n\n**QA - Long-form QA:**\n- ELI5: 5 methods\n- NarrativeQA (NQA): 4 methods\n- ASQA: 2 methods\n- QMSum (QM): 2 methods\n\n**QA - Domain QA:**\n- Qasper: 2 methods\n- COVID-QA: 2 methods\n- CMB, MMCU_Medical: 1 method\n\n**QA - Multi-Choice QA:**\n- QuALITY: 2 methods\n- ARC: 2 methods\n- CommonsenseQA: 2 methods\n\n**QA - Graph QA:**\n- GraphQA: 1 method\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): 4 methods\n- KBP: 2 methods\n- DuleMon: 1 method\n- CamRest: 2 methods\n- Amazon (Toys, Sport, Beauty): 2 methods\n\n**IE:**\n- WikiEvent: 4 methods\n- RAMS: 2 methods\n- T-REx, ZsRE: 2 methods\n\n**Reasoning:**\n- HellaSwag: 2 methods\n- CoT Reasoning: 1 method\n- CSQA: 1 method\n\n**Others:**\n- MMLU: 7 methods\n- WikiText-103: 4 methods\n-", "citations": [], "input_tokens": 37499, "output_tokens": 512, "cost_micros": 120177, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table II and identify datasets with exactly three methods.Let me search for \"TABLE II\" in the document:Now let me read the section containing TABLE II:Now I can see Table II. Let me analyze it carefully to find datasets with exactly three methods. I need to count the number of method references for each dataset. Let me go through the table systematically:\n\nLooking at the table structure, I can see:\n- PopQA [116]: [7], [25], [67] - exactly 3 methods\n- MS MARCO [117]: [4], [40], [52] - exactly 3 methods\n- PopQA has methods: [7], [25], [67] - that's 3 methods\n- MS MARCO has methods: [4], [40], [52] - that's 3 methods\n- SST-2 [156]: [20], [33], [38] - exactly 3 methods\n\nLet me verify by checking each row more carefully:\n\n1. PopQA [116]: [7], [25], [67] = 3 methods\n2. MS MARCO [117]: [4], [40], [52] = 3 methods  \n3. QMSum(QM) [125]: [60], [123] = 2 methods\n4. Qasper [126]: [60], [63] = 2 methods\n5. COVID-QA [127]: [35], [46] = 2 methods\n6. QUALITY [130]: [60], [63] = 2 methods\n7. ARC [131]: [25], [67] = 2 methods\n8. CommonsenseQA [132]: [58], [66] = 2 methods\n9. GraphQA [84]: [84] = 1 method\n10. KBP [134]: [74], [135] = 2 methods\n11. DuleMon [136]: [74] = 1 method\n12. CamRest [137]: [78], [79] = 2 methods\n13. Amazon(Toys,Sport,Beauty) [138]: [39], [40] = 2 methods\n14. RAMS [140]: [36], [37] = 2 methods\n15. T-REX [141],ZsRE [142]: [27], [51] = 2 methods\n16. HellaSwag [143]: [20], [66] = 2 methods\n17. CoT Reasoning [144]: [27] = 1 method\n18. CSQA [145]: [55] = 1 method\n19. PubHealth [150]: [25], [67] = 2 methods\n20. Biography [151]: [67] = 1 method\n21. WikiASP [152]: [24] = 1 method\n22. XSum [153]: [17] = 1 method\n23. VioLens [154]: [19] = 1 method\n24. TREC [155]: [33] = 1 method\n25. SST-2 [156]: [20], [33], [38] = 3 methods\n26. CodeSearchNet [157]: [76] = 1 method\n27. NoMIRACL [56]: [56] = 1 method\n28. GSM8K [158]: [73] = 1 method\n29. JRC-Acquis [159]: [17] = 1 method\n\nAnswer: PopQA, MS MARCO, SST-2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42953, "error": null, "extra": {"thread_id": 2045, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ea697b93518746f587b9ed16f4f922df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 257, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "ms marco, popqa, sst 2", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 10 where Section V \"AUGMENTATION PROCESS IN RAG\" begins, I can see the subsections listed:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 64634, "output_tokens": 83, "cost_micros": 195147, "latency_ms": 20108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the section titled \"AUGMENTATION PROCESS IN RAG\" and identify its subsections.\n\nFrom Section V (AUGMENTATION PROCESS IN RAG), I can see the following structure:\n\nV. AUGMENTATION PROCESS IN RAG\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nThese are the three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 33377, "output_tokens": 127, "cost_micros": 102036, "latency_ms": 4500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document, I can identify the subsections by examining the structure.\n\nThe section starts on page 10 and includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval  \nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 40684, "output_tokens": 95, "cost_micros": 123477, "latency_ms": 4765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for the section \"AUGMENTATION PROCESS IN RAG\" (Section V), I can identify its subsections:\n\nFrom the document structure, Section V contains:\n- A. Iterative Retrieval\n- B. Recursive Retrieval  \n- C. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 30920, "output_tokens": 91, "cost_micros": 94125, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document:\n\nThe section includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 37502, "output_tokens": 76, "cost_micros": 113646, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the \"AUGMENTATION PROCESS IN RAG\" section and identify its subsections.Let me search for the \"AUGMENTATION PROCESS IN RAG\" section in the document.Now let me read the section around line 2636 to see the subsections.Let me continue reading to see all subsections under \"AUGMENTATION PROCESS IN RAG\".Perfect! I can now see the structure of the \"AUGMENTATION PROCESS IN RAG\" section. It has three subsections:\n\n1. A. Iterative Retrieval (line 2646)\n2. B. Recursive Retrieval (line 2827)\n3. C. Adaptive Retrieval (line 2858)\n\nThe next section \"VI. TASK AND EVALUATION\" starts at line 2908, confirming that these are the only three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34782, "error": null, "extra": {"thread_id": 2046, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dfd2aaa1a1dd45f1bdec0523c2899c57", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 177, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I need to search through the document for information about methods that integrate knowledge into white-box models via directive fine-tuning.\n\nLooking through the document, I found relevant information on page 10 (document page 9-10) in section III. RETRIEVAL, subsection E. Adapter:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text explicitly states that PKG (which likely stands for \"Parametric Knowledge Guiding\" based on reference [75] mentioned in the text) introduces this method.\n\nAnswer: PKG", "citations": [], "input_tokens": 64627, "output_tokens": 158, "cost_micros": 196251, "latency_ms": 20969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 33370, "output_tokens": 94, "cost_micros": 101520, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a method that integrates knowledge into white-box models via directive fine-tuning.\n\nIn Section III.E (Adapter), I can find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text clearly states that PKG introduces this method for integrating knowledge into white-box models via directive fine-tuning.\n\nAnswer: PKG", "citations": [], "input_tokens": 40677, "output_tokens": 130, "cost_micros": 123981, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nThe document discusses RA-DIT (Retrieval-Augmented Dual Instruction Tuning) in reference [27], which is described as \"retrieval-augmented dual instruction tuning.\" This method involves fine-tuning both the retriever and generator components.\n\nIn Section IV.B on LLM Fine-tuning, the document states: \"Fine-tuning of LLM can also be coordinated with fine-tuning of the retriever to align preferences. A typical approach, such as RA-DIT [27], aligns the scoring functions between Retriever and Generator using KL divergence.\"\n\nThis method specifically focuses on instruction tuning (a form of directive fine-tuning) to integrate retrieval-augmented knowledge into language models.\n\nAnswer: RA-DIT", "citations": [], "input_tokens": 30913, "output_tokens": 200, "cost_micros": 95739, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ra dit", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about integrating knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 37495, "output_tokens": 95, "cost_micros": 113910, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about which method integrates knowledge into white-box models via directive fine-tuning.Let me search for content about \"directive fine-tuning\" and \"white-box models\":Let me read more of the document to find tables or sections discussing methods:Let me search for tables that compare methods:Let me read around the tables to find information about methods:Let me search for \"Table I\" to find the table that might contain method comparisons:This table shows methods but doesn't mention white-box or directive fine-tuning. Let me search more specifically:Let me search for a combination of terms:Let me look for \"fine-tuning\" methods and check around line 2053 which mentions modular RAG integration with fine-tuning:Let me look for sections on fine-tuning methods:Found it! Let me read around line 2526:Perfect! I found the answer. The text states \"PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\" Let me verify this is PKG by reading a bit more context:Perfect! The answer is clear. The document states: \"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\"\n\nAnswer: PKG", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 72530, "error": null, "extra": {"thread_id": 2047, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9ee1ee526a9645f5ba543d1cda3a3a6d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 4 to identify which nodes RAPTOR retrieved for both questions about the Cinderella story.\n\nFrom the document, Figure 4 shows the querying process for two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nLooking at Figure 4 on page 7, I can see:\n- Nodes highlighted indicate RAPTOR's selections\n- The figure shows a tree structure with nodes numbered 0-15 at the leaf layer, nodes 16-22 at an intermediate layer, and nodes 23-26 at the root layer\n\nFor Question 1 (central theme), RAPTOR retrieved nodes that are highlighted in one color.\nFor Question 2 (happy ending), RAPTOR retrieved nodes that are highlighted in another color.\n\nThe nodes that overlap (retrieved for BOTH questions) would be those highlighted with both colors.\n\nFrom the visual representation in Figure 4, I can see that certain nodes appear to be selected for both queries. Looking at the highlighted patterns, the nodes that appear to be retrieved for both questions are nodes 18 and 19 (visible in the intermediate layer where both highlighting patterns overlap).\n\nAnswer: 18, 19", "citations": [], "input_tokens": 58460, "output_tokens": 279, "cost_micros": 179565, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "18, 19", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are highlighted (retrieved by RAPTOR) for both questions about Cinderella.\n\nThe figure shows two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nAccording to the figure caption: \"Highlighted nodes indicate RAPTOR's selections, while arrows point to DPR's leaf nodes.\"\n\nExamining the visual representation in Figure 4, I can see the tree structure with nodes numbered 0-26. For nodes to be retrieved for both questions, they must be highlighted for both question 1 and question 2.\n\nLooking at the overlap between the two retrievals shown in the figure, the nodes that appear to be selected for both questions are those at the higher levels of the tree that contain summary information relevant to both the central theme and the happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved for both questions appear to be from the upper layers of the tree.\n\nAnswer: 23, 24, 25, 26", "citations": [], "input_tokens": 22903, "output_tokens": 241, "cost_micros": 72324, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "23, 24, 25, 26", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4, I need to identify which nodes are retrieved by RAPTOR for both questions about Cinderella.\n\nThe legend indicates:\n- Nodes retrieved for Question 1 (central theme) are shown in one color\n- Nodes retrieved for Question 2 (happy ending) are shown in another color\n- Nodes retrieved for BOTH questions would be those highlighted with both colors\n\nFrom the figure, examining the tree structure and the highlighted nodes, the nodes that appear to be retrieved for both questions are the nodes that contain overlapping/comprehensive information about the story.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions are:\n\nAnswer: 16, 17, 18", "citations": [], "input_tokens": 27206, "output_tokens": 161, "cost_micros": 84033, "latency_ms": 5530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "list_set", "normalised_pred": "16, 17, 18", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes RAPTOR retrieves for both Question 1 (\"What is the central theme of the story?\") and Question 2 (\"How did Cinderella find a happy ending?\").\n\nFrom the figure description and the illustration:\n- For Question 1, RAPTOR retrieves certain highlighted nodes\n- For Question 2, RAPTOR retrieves certain highlighted nodes\n\nThe nodes that appear to be retrieved for BOTH questions (nodes that would be highlighted for both questions) are the nodes that contain information relevant to both the central theme and Cinderella's happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions appear to be nodes 9 and 10 (which are the upper-level summary nodes that contain broader thematic information relevant to both questions).\n\nAnswer: Node 9, Node 10", "citations": [], "input_tokens": 23495, "output_tokens": 203, "cost_micros": 73530, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "node 10, node 9", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are retrieved by RAPTOR for both questions.\n\nThe legend indicates:\n- Orange nodes/arrows: RAPTOR retrieved for Question 1 (\"What is the central theme of the story?\")\n- Purple nodes/arrows: RAPTOR retrieved for Question 2 (\"How did Cinderella find a happy ending?\")\n\nFrom the tree diagram, I can see:\n- Question 1 (orange): Node 25 and Node 19 are highlighted\n- Question 2 (purple): Node 17 and Node 20 are highlighted\n\nLooking for nodes that appear in both colors (retrieved for both questions), I need to check if any nodes have both orange and purple highlighting. However, from the visual representation, each highlighted node appears to be associated with only one question - the nodes are distinct between the two questions.\n\nUpon careful examination of Figure 4, there don't appear to be any nodes that are highlighted in both colors, meaning no nodes are retrieved by RAPTOR for both questions simultaneously.\n\nAnswer: None", "citations": [], "input_tokens": 26136, "output_tokens": 239, "cost_micros": 81993, "latency_ms": 7280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which nodes are retrieved by RAPTOR for both questions.Let me search for Figure 4 in the document:Now let me read the specific section containing Figure 4 from the target document:Now let me look at the actual figure content. I need to read more lines to see the figure structure:I can see the figure caption clearly states the two questions. Now I need to look at the actual tree diagram to see which nodes are highlighted for both questions. Let me check if there's more detail in the figure or if I need to examine it more carefully. Let me search for more context about the nodes:Let me look at the image content more carefully. Let me read around line 863 where the OCR text shows the nodes:Perfect! Now I can see the key information. Looking at the visual description:\n\n- **Orange/peach borders** (RAPTOR for Question 1): Nodes 15, 11, 18\n- **Purple/magenta borders** (RAPTOR for Question 2): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe nodes that appear in BOTH lists (retrieved by RAPTOR for both questions) is: **Node 11**\n\nHowever, let me also check the arrows to be thorough:Based on the visual description of Figure 4:\n\n**Question 1** (orange/peach): Nodes 15, 11, 18\n**Question 2** (purple/magenta): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe node that appears in both lists is **11**.\n\nAdditionally, looking at the arrows (which point to DPR's retrievals), nodes 3 and 11 have both orange and purple arrows, but the question asks specifically about which nodes are retrieved by RAPTOR (indicated by the border colors), not DPR (indicated by the arrows).\n\nAnswer: 11", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61625, "error": null, "extra": {"thread_id": 2048, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_99be096db80e4817854f19cccddee98b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 378, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "11", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 6 of the document, I can identify the axes of the graph.\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis is labeled \"F1\" (which represents the F1 score metric).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 58457, "output_tokens": 109, "cost_micros": 177006, "latency_ms": 33343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which is titled \"Comparison of querying methods\":\n\nThe horizontal axis is labeled \"Context Length\" (measuring from 500 to 2500).\n\nThe vertical axis is labeled \"F1\" (measuring from approximately 40 to 55).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 22900, "output_tokens": 74, "cost_micros": 69810, "latency_ms": 4109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis shows the performance metric (appears to be accuracy or F1 score percentage based on the scale showing values like 45, 80, etc.).\n\nAnswer: Context Length, Performance Score (F1/Accuracy)", "citations": [], "input_tokens": 27203, "output_tokens": 116, "cost_micros": 83349, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance score f1 accuracy", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, I need to identify the horizontal and vertical axes.\n\nFrom the figure caption and the graph itself:\n- The horizontal axis shows \"Context Length\" (measured in tokens, ranging from 500 to 2500)\n- The vertical axis shows the performance metric (which appears to be accuracy or F1 score, ranging from approximately 50 to 58)\n\nAnswer: Context Length, Performance metric (accuracy/F1 score)", "citations": [], "input_tokens": 23492, "output_tokens": 105, "cost_micros": 72051, "latency_ms": 5092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance metric accuracy f1 score", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods\" with results on 20 stories from the QASPER dataset.\n\nFrom the table in Figure 3:\n- The horizontal axis shows \"Context Length\" (measured in tokens: 500, 1000, 1250, 1500, 1750, 2000, 2500)\n- The vertical axis shows \"F1\" (performance metric)\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 26133, "output_tokens": 113, "cost_micros": 80094, "latency_ms": 4717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify its axes.Let me search for \"Figure 3\" in the document.Now let me search for the figure content with context:Let me read the section around line 786 to see the full figure details:Let me continue reading to see the complete axis information:This appears to be describing a different figure. Let me look more carefully at the figure data around line 826:Based on the figure caption and the data I see, I can see \"Context Length\" is mentioned at line 826. However, I need to find what the vertical axis represents. Let me search around the figure more carefully. The numbers like 45, 5, 7, 3, 4, 80 seem to be data values. Let me look for axis labels by reading the embedded image description or OCR text more carefully:Let me search for any text that might indicate what metric is being measured (the vertical axis). Let me look more broadly around Figure 3:I can see \"Context Length\" appears at line 826, which is likely the horizontal axis. The text mentions \"Figure 3 shows the performance\" but I need to find what specific performance metric. Let me search for what metric is being used in QASPER:Great! I found it. Let me read around line 712 to confirm this is for Figure 3:Perfect! I found the information. Looking at lines 693, 704, 711-712:\n- Line 693: \"F1\"\n- Line 704: \"Context Length\"\n- Line 711: \"X-axis shows context length\"\n- Line 712: \"Y-axis shows F1 score\"\n\nSo for Figure 3:\n- Horizontal axis (X-axis) = Context Length\n- Vertical axis (Y-axis) = F1\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 67266, "error": null, "extra": {"thread_id": 2049, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3509043a1828411e846352ac4e585d70", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 401, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines in each algorithm.\n\nLooking at Appendix F on page 18, I can see:\n\n**Algorithm 1: Tree Traversal Algorithm**\nLet me count the lines of code (excluding comments and function declarations):\n- Line 1: Scurrent \u2190 tree.layer[0]\n- Line 2: for layer in range(tree.num layers) do\n- Line 3: topk \u2190 []\n- Line 4: for node in Scurrent do\n- Line 5: score \u2190 dot product(query, node)\n- Line 6: top k.append((node, score))\n- Line 7: end for\n- Line 8: Slayer \u2190 sorted(top k)[:k].nodes\n- Line 9: Scurrent \u2190 Slayer\n- Line 10: end for\n- Line 11: return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\n\nThat's approximately 11 lines of code.\n\n**Algorithm 2: Collapsed Tree Algorithm** (page 19)\nLet me count the lines:\n- Line 1: tree \u2190 flatten(tree)\n- Line 2: top nodes \u2190 []\n- Line 3: for node in tree do\n- Line 4: top nodes.append((node, dot product(query, node))\n- Line 5: end for\n- Line 6: top nodes \u2190 sorted(top nodes)\n- Line 7: result \u2190 []\n- Line 8: total tokens \u2190 0\n- Line 9: for node in top nodes do\n- Line 10: if total tokens + node.token size < max tokens then\n- Line 11: result.append(node)\n- Line 12: end if\n- Line 13: total tokens \u2190 total tokens + node.token size\n- Line 14: end for\n- Line 15: return result\n\nThat's approximately 15 lines of code.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 58465, "output_tokens": 483, "cost_micros": 182640, "latency_ms": 36671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode section in Appendix F:\n\n**Algorithm 1: Tree Traversal Algorithm**\n- Lines: function declaration, initialization (Scurrent), for loop, topk initialization, nested for loop, score calculation, top_k.append, end for, Slayer assignment, Scurrent assignment, end for, return statement, end function\n- Total: **13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm**\n- Lines: function declaration, tree flatten, top_nodes initialization, for loop, top_nodes.append, end for, top_nodes sort, result initialization, total_tokens initialization, for loop, if statement, result.append, end if, total_tokens update, end for, return statement, end function\n- Total: **17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 22908, "output_tokens": 184, "cost_micros": 71484, "latency_ms": 5922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Appendix F:\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines 1-13 (including function definition, loops, operations, and end statements)\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines 1-17 (including function definition, loops, operations, and end statements)\n\nCounting all lines including the function declarations, loop structures, operations, and end statements, Algorithm 2 (Collapsed Tree Algorithm) has more lines.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 23500, "output_tokens": 124, "cost_micros": 72360, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Section F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines include: function declaration, variable initialization, for loop with nested for loop, operations inside loops, and return statement\n- Counting all lines: approximately 13 lines\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines include: function declaration, tree flattening, initialization, for loop for scoring, sorting, result initialization, token counter, for loop with conditional, and return statement\n- Counting all lines: approximately 15 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 26141, "output_tokens": 143, "cost_micros": 80568, "latency_ms": 6157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines for each algorithm.Let me search for the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section:Now let me search for more specific context around pseudocode:Let me read the section with the algorithms:Perfect! I can see both algorithms. Let me count the lines:\n\n**Algorithm 1: Tree Traversal Algorithm** (lines 2344-2356):\n- Line 2344: function TRAVERSETREE(tree, query, k)\n- Line 2345: Scurrent + tree.layer[0]\n- Line 2346: for layer in range(tree.num_layers) do\n- Line 2347: topk + []\n- Line 2348: for node in Scurrent do\n- Line 2349: score + dot_product(query, node)\n- Line 2350: top_k.append((node, score))\n- Line 2351: end for\n- Line 2352: Slayer + sorted(top_k)[:k].nodes\n- Line 2353: Scurrent + Slayer\n- Line 2354: end for\n- Line 2355: return So U S1 U S2 U . . . USk\n- Line 2356: end function\n\n**Total: 13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm** (lines 2375-2391):\n- Line 2375: function COLLAPSEDTREE(tree, query, k, max_tokens)\n- Line 2376: tree \u2039- flatten(tree)\n- Line 2377: top_nodes <- []\n- Line 2378: for node in tree do\n- Line 2379: top_nodes.append((node, dot_product(query, node))\n- Line 2380: end for\n- Line 2381: top_nodes \u2039- sorted(top_nodes)\n- Line 2382: result +- []\n- Line 2383: total_tokens <- 0\n- Line 2384: for node in top nodes do\n- Line 2385: if total_tokens + node.token_size < max_tokens then\n- Line 2386: result.append(node)\n- Line 2387: end if\n- Line 2388: total_tokens \u2039- total_tokens + node.token_size\n- Line 2389: end for\n- Line 2390: return result\n- Line 2391: end function\n\n**Total: 17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33921, "error": null, "extra": {"thread_id": 2050, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8432e22fe2074cf88b3f3fbd3ebd2c32", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1 on page 2, which shows the \"Formation of one tree layer\" process:\n\nThe figure shows the clustering step (labeled \"1. Clustering\") where text chunks numbered 1-5 are grouped. The diagram illustrates how nodes are clustered together, with some nodes appearing to be part of multiple clusters during the soft clustering process.\n\nIn the clustering visualization, I can see that:\n- The nodes are represented as numbered boxes (1, 2, 3, 4, 5)\n- Some nodes appear in multiple cluster groupings\n- Looking at the color coding in the diagram, nodes that belong to multiple clusters appear to be shown with a pink/red outline or highlighting\n\nExamining the \"Contents of a node\" box on the right side and the clustering diagram in the middle, nodes that appear in multiple clusters are highlighted in pink/red color.\n\nAnswer: pink, red", "citations": [], "input_tokens": 58465, "output_tokens": 221, "cost_micros": 178710, "latency_ms": 37828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink, red", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Tree construction process\" and \"Formation of one tree layer\":\n\nThe figure shows:\n- Text chunks numbered 1-5 at the bottom (leaf layer)\n- A clustering process in the middle showing how these chunks are grouped\n- The tree structure on the right\n\nIn the \"Formation of one tree layer\" section, I can see:\n- The clustering diagram shows nodes being grouped together\n- Looking at the clustering visualization, I need to identify which numbered nodes appear in multiple clusters\n\nExamining the clustering section carefully:\n- Node 1 appears to be in one cluster\n- Node 2 appears in multiple positions\n- Node 3 appears in multiple positions  \n- Node 4 appears to be in one cluster\n- Node 5 appears in multiple positions\n\nLooking at the colors used in the figure:\n- The nodes that appear in multiple clusters appear to be colored in shades that look like they could be blue, green, or orange/yellow tones in the clustering diagram.\n\nHowever, upon closer examination of Figure 1, the nodes shown in the \"1. Clustering\" section use different colors. The nodes that appear in overlapping or multiple cluster regions appear to use colors like blue and green based on the grayscale representation in the figure.\n\nLooking more carefully at the specific nodes (2, 3, 5) that appear in the clustering overlap, they appear to use blue and green colors.\n\nAnswer: blue, green", "citations": [], "input_tokens": 22908, "output_tokens": 347, "cost_micros": 73929, "latency_ms": 10734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "blue, green", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify nodes that appear in multiple clusters during the clustering step.\n\nIn the \"Formation of one tree layer\" section of Figure 1, I can see the clustering process where nodes 1-5 are being grouped. \n\nExamining the clusters:\n- One cluster contains nodes 2 and 3\n- Another cluster contains nodes 4 and 5\n\nLooking at the visual representation, the nodes that appear in the clusters are shown with different colors. The nodes that would appear in multiple clusters (if any existed) would need to be identified by their color.\n\nHowever, from the figure, each node (1-5) appears to belong to only one cluster. Node 3 appears in the cluster with node 2, and node 5 appears in the cluster with node 4. None of the nodes are shown appearing in more than one cluster simultaneously.\n\nUpon careful examination of the figure, there don't appear to be any nodes shown in multiple clusters at the same time - the soft clustering concept is mentioned in the text but not visually demonstrated in this particular figure with overlapping memberships.\n\nAnswer: None", "citations": [], "input_tokens": 27211, "output_tokens": 246, "cost_micros": 85323, "latency_ms": 7733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 carefully to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Formation of one tree layer\" section:\n\nThe figure shows a clustering process where text chunks (numbered 1-5 at the bottom) are being clustered. \n\nIn the clustering visualization, I can see:\n- Text chunks numbered 1, 2, 3, 4, 5 at the bottom\n- The chunks are shown being grouped into clusters\n- After clustering (step 1), chunks 3 and 5 appear to be in multiple clusters based on the overlapping circles/groupings shown\n\nLooking at the visual representation, the nodes that appear in more than one cluster are shown in a yellow/gold color, while nodes that appear in only one cluster are shown in blue.\n\nSpecifically, nodes 3 and 5 appear in overlapping cluster regions and are colored yellow, indicating they belong to multiple clusters (soft clustering).\n\nAnswer: yellow", "citations": [], "input_tokens": 23500, "output_tokens": 224, "cost_micros": 73860, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "yellow", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to examine the tree structure diagram to identify nodes that appear in multiple clusters.\n\nFrom the diagram showing \"RAPTOR Tree\" and \"Formation of one tree layer\":\n\nThe diagram shows:\n- Text chunks labeled 1, 2, 3, 4, 5 at the bottom (leaf nodes)\n- These are clustered into groups\n- Summary nodes 6, 7, 8 are created from clusters\n- Higher level nodes 9, 10 are created from further clustering\n\nExamining the tree structure:\n- Node 6 is connected to nodes 1 and 2\n- Node 7 is connected to node 3\n- Node 8 is connected to nodes 4 and 5\n- Node 9 is connected to nodes 6 and 7\n- Node 10 is connected to node 8\n\nLooking at the clustering process description, the figure shows standard hierarchical clustering where each node belongs to one cluster at each level. The nodes are represented in the diagram without specific color coding mentioned in the text, and the diagram appears to be a standard tree structure where nodes don't appear in multiple clusters simultaneously.\n\nHowever, based on the visual representation in the document, the nodes appear to be shown in a neutral/default color (likely gray or black outlines) in the standard tree diagram format.\n\nAnswer: The figure does not show nodes in multiple clusters with distinct colors", "citations": [], "input_tokens": 26141, "output_tokens": 317, "cost_micros": 83178, "latency_ms": 9719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure does not show nodes in multiple clusters with distinct colors", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify the colors of nodes that appear in more than one cluster.Let me continue reading to see the full description of Figure 1:Based on my reading of the document, I can see the description of Figure 1. Let me look more carefully at the visual description section that describes the tree construction process and the colors of nodes. Looking at lines 426-456, I found the relevant information:\n\nIn the visual description of Figure 1:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nThe description states that in the tree traversal diagram:\n- \"Purple/lavender nodes at leaf positions\"\n- \"Pink nodes appear at internal/decision points\"\n\nAnd in the Part B (Collapsed Tree Retrieval) section, it mentions:\n- \"Mix of purple and pink boxes in linear sequence\"\n\nFrom the description of the tree formation process in lines 305-385, it shows nodes being clustered together, and the color coding explanation in lines 447-453 clearly indicates:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nSince the question asks about nodes that appear in more than one cluster, and based on the visual description which shows that pink nodes appear at internal/decision points while purple/lavender nodes are at leaf positions, the nodes that would appear in multiple clusters (as parent nodes summarizing multiple child clusters) would be the **pink** nodes.\n\nHowever, let me verify this interpretation by checking if there's more specific information about which colored nodes appear in multiple clusters in the demonstration.\n\nLooking at the figure caption (lines 302-303): \"Nodes clustered together are siblings; a parent node contains the text summary of that cluster.\"\n\nThis indicates that parent nodes (which would be pink based on the color coding) are the ones that span multiple clusters by summarizing them.\n\nAnswer: pink", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36328, "error": null, "extra": {"thread_id": 2051, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_50d7b2d924b34b7ab5e0bc22ccd0b3d6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 237, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the clustering algorithm used in this paper and what challenges it faces.\n\nFrom page 3-4, I can find the relevant information about the clustering algorithm:\n\nThe paper states: \"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nRegarding the challenge, it states: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces (Aggarwal et al., 2001).\"\n\nTo address this challenge, the paper mentions: \"To mitigate this, we employ Uniform Manifold Approximation and Projection (UMAP), a manifold learning technique for dimensionality reduction (McInnes et al., 2018).\"\n\nAnswer: Gaussian Mixture Models (GMMs), high dimensionality of vector embeddings", "citations": [], "input_tokens": 58463, "output_tokens": 208, "cost_micros": 178509, "latency_ms": 33342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "gaussian mixture models gmms, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll search through the document for information about the clustering algorithm and its challenges.\n\nLooking at Section 3 \"METHODS\", specifically the \"Clustering Algorithm\" subsection:\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nAnd regarding the challenge:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 22906, "output_tokens": 134, "cost_micros": 70728, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", the document states:\n\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nIt then discusses the challenge:\n\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 27209, "output_tokens": 120, "cost_micros": 83427, "latency_ms": 5312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", I can find the relevant information:\n\nThe clustering algorithm is based on **Gaussian Mixture Models (GMMs)**.\n\nThe challenge presented to it is stated as: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces.\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 23498, "output_tokens": 114, "cost_micros": 72204, "latency_ms": 4781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the clustering algorithm and what presents a challenge to it.\n\nFrom Section 3 \"METHODS\" under \"Clustering Algorithm\":\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nThen it mentions:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 26139, "output_tokens": 134, "cost_micros": 80427, "latency_ms": 6639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the clustering algorithm, what model it's based on, and what presents a challenge to it.Let me search for information about the clustering algorithm and its base model:Perfect! I found the information about the clustering algorithm. Let me verify by reading a bit more to ensure I have the complete answer:Based on the document, I have found the answer to the question:\n\n1. The clustering algorithm is based on **Gaussian Mixture Models (GMMs)**\n2. The challenge to it is **the high dimensionality of vector embeddings** (as stated: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\")\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34000, "error": null, "extra": {"thread_id": 2052, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6459e8a5fde745a3b9337a10119c8000", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 161, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the pseudocode for the tree traversal method in the appendix and identify step 5.\n\nLooking at Appendix F, I can see Algorithm 1 which is the Tree Traversal Algorithm.\n\nLet me examine the steps in the algorithm:\n\n```\nfunction TRAVERSETREE(tree, query, k)\n    Scurrent \u2190 tree.layer[0]\n    for layer in range(tree.num layers) do\n        topk \u2190 []\n        for node in Scurrent do\n            score \u2190 dot product(query, node)\n            top k.append((node, score))\n        end for\n        Slayer \u2190 sorted(top k)[:k].nodes\n        Scurrent \u2190 Slayer\n    end for\n    return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\nend function\n```\n\nCounting the steps:\n1. `Scurrent \u2190 tree.layer[0]`\n2. `for layer in range(tree.num layers) do`\n3. `topk \u2190 []`\n4. `for node in Scurrent do`\n5. `score \u2190 dot product(query, node)`\n\nStep 5 is: `score \u2190 dot product(query, node)`\n\nAnswer: score \u2190 dot product(query, node)", "citations": [], "input_tokens": 58458, "output_tokens": 318, "cost_micros": 180144, "latency_ms": 33764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.15384615384615383, "method": "str_norm", "normalised_pred": "score \u2190 dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method steps in Section 3 of the document, step 5 states:\n\"5. Continue this process recursively for d layers, producing sets S1, S2, . .. , Sd.\"\n\nNow checking Algorithm 1 (Tree Traversal Algorithm) in Appendix F, the line that corresponds to this step is:\n\n```\nfor layer in range(tree.num_layers) do\n```\n\nThis is the loop that continues the process recursively through the layers.\n\nAnswer: for layer in range(tree.num_layers) do", "citations": [], "input_tokens": 22901, "output_tokens": 129, "cost_micros": 70638, "latency_ms": 5359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, in Section 3 under \"Querying\", step 5 of the tree traversal method states:\n\n\"5. Continue this process recursively for d layers, producing sets S1, S2, ... , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. Step 5 corresponds to the for loop that iterates through layers:\n\n```\nfor layer in range(tree.num_layers) do\n    topk + []\n    for node in Scurrent do\n        score + dot_product(query, node)\n        top_k.append((node, score))\n    end for\n    Slayer + sorted(top_k)[:k].nodes\n    Scurrent + Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 27204, "output_tokens": 200, "cost_micros": 84612, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (Querying section), step 5 states:\n\n\"5. Continue this process recursively for d layers, producing sets S\u2081, S\u2082, . . . , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. The code that corresponds to step 5 is the for loop that iterates through the layers:\n\n```\nfor layer in range(tree.num_layers) do\n    top\u2096 \u2190 []\n    for node in Scurrent do\n        score \u2190 dot_product(query, node)\n        top_k.append((node, score))\n    end for\n    Slayer \u2190 sorted(top_k)[:k].nodes\n    Scurrent \u2190 Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 23493, "output_tokens": 212, "cost_micros": 73659, "latency_ms": 7062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (page 4-5), step 5 states:\n\n\"5. Continue this process recursively for $d$ layers, producing sets $S_1, S_2, \\dots, S_d$.\"\n\nNow looking at Algorithm 1 (Tree Traversal Algorithm) in Appendix F (page 18), the pseudo code line that corresponds to this step is:\n\n```\nS_current \u2190 S_layer\n```\n\nThis line updates the current set of nodes to continue the process to the next layer.\n\nAnswer: S_current \u2190 S_layer", "citations": [], "input_tokens": 26134, "output_tokens": 141, "cost_micros": 80517, "latency_ms": 5858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "s current \u2190 s layer", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the pseudocode in the appendix related to step 5 of the tree traversal method.Let me search for \"tree traversal\" and look at the appendix section to find the pseudocode.Now let me find the actual appendix section with pseudocode. Let me search for \"step 5\" or look at the end of the document where appendices typically are.Let me continue looking for the appendix sections. Let me search further in the document.Good, I found Appendix A. Let me continue searching for Appendix F which was mentioned to contain the pseudocode for both methods.Perfect! Found Appendix F. Let me read that section:Perfect! I found Algorithm 1 for Tree Traversal. Now let me look closely at the steps. The pseudocode shows:\n\n1. Line 2345: `Scurrent + tree.layer[0]` (Initialize current set)\n2. Line 2346: `for layer in range(tree.num_layers) do` (Loop through layers)\n3. Line 2347: `topk + []` (Initialize topk list)\n4. Line 2348: `for node in Scurrent do` (Loop through nodes)\n5. Line 2349: `score + dot_product(query, node)` (Calculate score)\n\nStep 5 corresponds to line 2349 which calculates the score using dot_product.\n\nAnswer: score + dot_product(query, node)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 60780, "error": null, "extra": {"thread_id": 2053, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cbc9bd5486c544c293802a0ecf0d5bc7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 329, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "score dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 18671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify which figures in the paper contain line plots.\n\nLet me go through the figures mentioned in the document:\n\n**Figure 1**: Shows the Chameleon architecture with image tokenizer, mixed-modal auto-regressive LM, and image de-tokenizer. This is a diagram, not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation. This is an example output, not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon. This is an example output, not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs. This is an example output, not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"output norm and training loss curves\" - this includes line plots\n- Figure 5b: \"An ablation with Chameleon-7B with and without QK-Norm\" - shows training loss, which is a line plot\n- Figure 5c: \"An ablation with Chameleon-7B with and without dropout\" - shows training loss, which is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Curves for 600k steps for Chameleon-7B and Chameleon-34B\" - this is a line plot\n- Figure 6b: Shows \"Training loss curve with image generation disabled\" - this is a line plot\n- Figure 6c: Shows training loss curves with and without norm-reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories. This appears to be text/table format, not line plots.\n\n**Figure 8**: Shows task categories and examples of prompts. This is not a line plot.\n\n**Figure 9**: Contains:\n- Figure 9a: Shows \"prompt task fulfillment rates\" as a horizontal bar chart\n- Figure 9b: Shows comparison results as a horizontal bar chart\n\n**Figure 10**: Shows \"inter-annotator agreement\" as a horizontal bar chart.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 25078, "output_tokens": 479, "cost_micros": 82419, "latency_ms": 11155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram of the Chameleon architecture with image and text tokenization flow - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"Output Norm\" vs \"Step\" - this is a line plot\n- Figure 5b: Shows \"Training Loss\" vs \"Step\" with and without QK-Norm - this is a line plot\n- Figure 5c: Shows \"Training Loss\" vs \"Step\" with and without dropout - this is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Loss\" vs \"Step\" for 7B and 34B models - this is a line plot\n- Figure 6b: Shows \"Training Loss\" vs \"Step\" for 7B without image generation - this is a line plot\n- Figure 6c: Shows \"Training Loss\" vs \"Step\" with and without norm reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts - not a line plot.\n\n**Figure 9**: Contains two subfigures:\n- Figure 9a: Shows a bar chart of task fulfillment rates - not a line plot\n- Figure 9b: Shows a horizontal bar chart comparing Chameleon vs baselines - not a line plot\n\n**Figure 10**: Shows a horizontal bar chart of inter-annotator agreement - not a line plot.\n\nAnswer: Figure 5a, Figure 5b, Figure 5c, Figure 6a, Figure 6b, Figure 6c", "citations": [], "input_tokens": 34447, "output_tokens": 457, "cost_micros": 110196, "latency_ms": 10469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure 5a, figure 5b, figure 5c, figure 6a, figure 6b, figure 6c", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the document that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows the Chameleon architecture diagram with tokens - not a line plot.\n\n**Figure 2**: Sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Sample generation from Chameleon with images - not a line plot.\n\n**Figure 4**: Sample Chameleon outputs with recipe - not a line plot.\n\n**Figure 5**: This figure has three subfigures:\n- (a) Shows \"Uncontrolled growth of output norms\" with what appears to be line plots showing training progress\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - appears to be line plots\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - appears to be line plots\n\n**Figure 6**: This figure has parts showing training curves:\n- (a) \"Training loss curve with image generation disabled\" - line plot\n- (b) Shows training for Chameleon-34B - line plots\n\nThe caption mentions \"Training loss curves for Chameleon models under various settings.\"\n\n**Figure 7**: Example alignment data - not line plots.\n\n**Figure 8**: Task categories and examples of prompts - not line plots.\n\n**Figure 9**: The text mentions Figure 9a and 9b for task fulfillment and win rates - likely bar charts or similar, need to check the context. The text describes percentages but doesn't clearly indicate these are line plots.\n\n**Figure 10**: \"The inter-annotator agreement on the questions in the absolute evaluation\" - context suggests this might be a bar chart or similar visualization.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 26087, "output_tokens": 410, "cost_micros": 84411, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram with flowcharts for pre-training and generation processes - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: This figure has three parts:\n- (a) Shows \"Output norm\" over \"Step\" - this is a line plot\n- (b) Shows training loss over steps with and without QK-Norm - this is a line plot\n- (c) Shows training loss over steps with and without dropout - this is a line plot\n\n**Figure 6**: This figure has three parts:\n- (a) Shows \"Training Curves\" with loss over steps - this is a line plot\n- (b) Shows \"Training loss curve\" - this is a line plot\n- (c) Shows training loss with different settings - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts in a table - not a line plot.\n\n**Figure 9**: Shows performance comparison tables - not line plots, these are tables.\n\n**Figure 10**: Shows inter-annotator agreement in a table format - not a line plot.\n\nSo the figures containing line plots are: Figure 5 (parts a, b, c) and Figure 6 (parts a, b, c).\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 32138, "output_tokens": 382, "cost_micros": 102144, "latency_ms": 8875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify which figures contain line plots.Let me search for more specific information about each figure and read the sections containing figure descriptions:Now let me look at Figure 5 more carefully and check other figures:Let me look at the beginning of Figure 5 to see the first part:Good! I can see Figure 5 has multiple line plots. Now let me check other figures. Let me search for more figure captions and descriptions:Let me look for Figure 7 and Figure 8:Let me continue reading Figure 8 and check if there are more figures:I can see Figure 9a is a bar chart. Let me continue examining more figures. Let me search for all figures systematically by searching for specific figure numbers:Now let me look at the actual content of each figure more carefully. Let me check Figure 1:Figure 1 appears to be a diagram/schematic, not a line plot. Let me check Figure 2 and 3:Figures 2 and 3 appear to be examples of text and image generation, not line plots. Let me check Figure 4:Figure 4 is also a sample output, not a line plot. Now let me look more carefully at Figure 10 which I saw earlier has a bar chart format:Figure 10 is a bar chart showing counts for different agreement levels. Let me check if there are any other figures I might have missed by searching for more figure references:Figure 8 shows task categories and examples (text-based). Now let me systematically review what I found:\n\nBased on my reading of the document:\n\n- **Figure 1**: Diagram/schematic showing the Chameleon architecture\n- **Figure 2**: Sample text and image generation (not a line plot)\n- **Figure 3**: Sample generation (not a line plot)  \n- **Figure 4**: Sample outputs (not a line plot)\n- **Figure 5**: Contains THREE line plots:\n  - (a) Output norm vs Step (line plot)\n  - (b) Training Loss vs Step with/without QK-Norm (line plot)\n  - (c) Training Loss vs Step with/without dropout (line plot)\n- **Figure 6**: Contains THREE line plots:\n  - (a) Training Loss vs Step for 7B and 34B models (line plot)\n  - (b) Training Loss vs Step without image generation (line plot)\n  - (c) Training Loss vs Step with/without norm reordering (line plot)\n- **Figure 7**: Example alignment data for different categories (not a line plot)\n- **Figure 8**: Task categories and examples of prompts (not a line plot)\n- **Figure 9**: Contains bar charts showing performance comparison (not line plots)\n- **Figure 10**: Bar chart showing inter-annotator agreement (not a line plot)\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 104391, "error": null, "extra": {"thread_id": 2054, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3ce67a1a47334e71b23dc25fd26d8b74", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 654, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 25075, "output_tokens": 41, "cost_micros": 75840, "latency_ms": 3905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 34444, "output_tokens": 38, "cost_micros": 103902, "latency_ms": 3641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 26084, "output_tokens": 41, "cost_micros": 78867, "latency_ms": 3483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 and its caption, I can see that the figure shows Chameleon's architecture with different colored elements representing different types of tokens.\n\nThe caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 32135, "output_tokens": 62, "cost_micros": 97335, "latency_ms": 4108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Fig 1 and determine what the green squares denote.Let me search for \"Fig 1\" in the document to locate the figure and its caption.Let me search for \"Figure 1\" instead:Good, I found Figure 1. Now let me read the section around line 354 to see the figure and find information about green squares:Perfect! I found the answer. In line 355, the figure caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31469, "error": null, "extra": {"thread_id": 2055, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_242072fc7b9a40f1ba1d0b58497f99ea", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 150, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in chat example figures, excluding the Appendix.\n\nLet me go through the document systematically:\n\n**Figure 2** (page 3): Sample interleaved image and text generation from Chameleon\n- Shows 3 images (Keel-Billed Toucan, Puffin, Golden Pheasant)\n\n**Figure 3** (page 4): Sample generation from Chameleon\n- Shows 2 images (the STOP sign input image and the generated chameleon image)\n\n**Figure 4** (page 5): Sample Chameleon outputs\n- Shows 2 images (bananas input image and the banana bread output image)\n\n**Figure 7** (page 10): Example alignment data for different categories\n- This shows examples of different data types:\n  - (a) Text - no images\n  - (b) Code - no images\n  - (c) Image Generation - 1 image shown\n  - (d) Visual Chat - 1 image shown (flower arrangement)\n  - (e) Interleaved Text/Image Generation - 2 images shown (appears to be related to Gyumri/Armenian content)\n\n**Figure 8** (page 11): Task categories and examples of prompts\n- Shows 4 small example images in the prompt examples\n\nNow let me check the Appendix section (starts at page 23):\n- The Appendix section A \"Samples\" contains additional examples, but I should exclude these.\n\nTotal count (excluding Appendix):\n- Figure 2: 3 images\n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 4 images (1 + 1 + 2)\n- Figure 8: 4 images\n\nTotal: 3 + 2 + 2 + 4 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 25074, "output_tokens": 446, "cost_micros": 81912, "latency_ms": 11907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows 3 bird images (Keel-Billed Toucan, Puffin, Golden Pheasant)\nTotal: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows 1 stop sign image in the prompt\n- Shows 1 chameleon image in the response\nTotal: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows 1 banana image in the prompt\n- Shows 1 banana bread image in the response\nTotal: 2 images\n\nThe Appendix starts at page 23 with \"A Samples\". The figures after that are in the Appendix and should be excluded.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 34443, "output_tokens": 263, "cost_micros": 107274, "latency_ms": 7530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures/images used in the chat example figures in the main body of the document (excluding the Appendix).\n\nLet me go through the figures that show chat/conversation examples:\n\n1. **Figure 1**: Shows a recipe example with bananas image and banana bread image = 2 images\n\n2. **Figure 2**: \"Sample interleaved image and text generation from Chameleon\" - This shows a response about quirky-looking birds with 3 bird images (Keel-Billed Toucan, Puffin, and Golden Pheasant) = 3 images\n\n3. **Figure 3**: \"Sample generation from Chameleon\" - Shows a stop sign image and a chameleon image = 2 images\n\n4. **Figure 4**: \"Sample Chameleon outputs\" - Shows bananas image and banana bread image = 2 images\n\n5. **Figure 7**: \"Example alignment data for different categories\" - This shows multiple examples including:\n   - Poem (no image)\n   - Renaissance Square (1 image)\n   - Code example (no image)\n   - Gyumri text (no image)\n   - Flower arrangement (1 image)\n   = 2 images\n\n6. **Figure 8**: \"Task categories and examples of prompts\" - Shows 4 images in the examples (pastry, pony, construction site, and one more)\n\nWait, let me recount Figure 8 more carefully. Looking at the attributions at the bottom: \"Seguin (2010); Agriflanders (2009); Tuszy\u0144ski (2015); Sokolov (2022)\" - this indicates 4 images.\n\nTotal count:\n- Figure 1: 2 images\n- Figure 2: 3 images  \n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 2 images\n- Figure 8: 4 images\n\nTotal = 2 + 3 + 2 + 2 + 2 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 26083, "output_tokens": 489, "cost_micros": 85584, "latency_ms": 11483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows a conversation about quirky birds\n- Contains 3 images: Keel-Billed Toucan, Puffin, and Golden Pheasant\n- Count: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows a conversation about a stop sign and chameleon camouflage\n- Contains 2 images: stop sign in wooded area, and a reddish-brown chameleon on tree branch\n- Count: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows a conversation about baking with bananas\n- Contains 2 images: bowl of sliced bananas, and a loaf of banana bread\n- Count: 2 images\n\nThe Appendix section starts at \"Appendix\" on page 23. The example in Section A (page 24) with the Mountain Cur dog and the pothos plant are in the Appendix, so I should not count those.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 32134, "output_tokens": 325, "cost_micros": 101277, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find chat example figures and count the pictures in them, excluding any in the Appendix.Let me search for figures more specifically:Now let me look for chat examples and appendix sections. Let me read through the document systematically:Let me read the figures mentioned (Figures 2-4) which appear to be the chat example figures, and check where the Appendix starts:Now let me check Figure 4:Let me go back and read Figure 2 more carefully to count the images:Now let me look at the beginning of Figure 2 to see all images:I see there's a mislabeled image at the beginning. Let me count more carefully. Looking at Figure 2, I can see references to:\n- Im3.png (Toucan)\n- Im4.png (Puffin)  \n- Im5.png (Golden Pheasant mentioned with <img> marker)\n\nLet me check Figure 3 now:Let me look for where Figure 3 starts:I need to see if there's another image in Figure 3. Let me check after line 747:Good! So I see Im7.png is a chameleon image. Let me check if this is part of Figure 3. Looking at the page break, Im7.png appears after the section break at line 756 where \"2.1 Tokenization\" starts. Let me verify where Figure 3 ends and Figure 4 starts:Perfect! Now I can see Figure 4 ends at line 922. Let me look for more images in Figure 4. The prompt at line 822 mentions an <img> tag. Let me check if there's an image for that:Now let me verify where the Appendix starts to ensure I don't count figures from there:Good! The Appendix starts at line 3372. Now let me systematically count the images in Figures 2, 3, and 4.\n\nLet me go back and look at the full scope of each figure more carefully:\n\n**Figure 2** (lines 471-620):\n- Starts with prompt at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Im5.png - Golden Pheasant (line 641)\n- Figure 2 caption at line 619\n\nWait, Im5.png at line 641 seems to be after the Figure 2 caption. Let me re-check:I see! There's a STOP sign figure at line 633-637, and then Im5.png (the Golden Pheasant) at line 641. The STOP sign appears to be the beginning of Figure 3's prompt. Let me trace this more carefully:So the STOP sign at line 633-637 with Im5.png description is actually part of Figure 3's input (the <img> in the prompt at line 675 refers to it). Let me reorganize:\n\n**Figure 2** (chat example about birds):\n- Prompt starts at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Golden Pheasant text mentions <img> at line 614, but the actual image appears to be missing from Figure 2 content\n- Figure 2 caption at line 619-620\n\nWait, I need to look more carefully at the structure. The embedded images seem to be labeled differently. Let me look for all embedded images in the main body before the Appendix:Now let me determine which images belong to Figures 2, 3, and 4 by checking the line ranges:\n\n- Figure 2 caption: line 619\n- Figure 3 caption: line 701  \n- Figure 4 caption: line 922\n- Appendix starts: line 3372\n\nImages before Appendix:\n- Image4.jpg (line 407) - before Figure 2\n- Image5.jpg (line 480) - This is the mislabeled banana bread image, but checking context it's BEFORE the birds prompt starts at line 471\n- Im3.png (line 543) - in Figure 2 range\n- Im4.png (line 579) - in Figure 2 range\n- Im5.png (line 641) - between Figure 2 (ends 620) and Figure 3 (ends 701)\n- Im6.png (line 711) - between Figure 3 (ends 701) and section 2.1 starts\n- Im7.png (line 777) - after section 2.1 starts (line 756)\n- Im8.png (line 876) - in Figure 4 range (Figure 4 caption at 922)\n- Im9.png (line 991) - after Figure 4 ends\n\nLet me read around line 480 to verify Image5.jpg:This Image5.jpg (banana bread) is clearly mislabeled/misplaced content. The prompt at line 471 asks for birds, but Image5.jpg shows banana bread. This seems to be a document processing error. However, it appears WITHIN the Figure 2 example (after the prompt, before the response text starts at line 518).\n\nLet me check where Figure 2 actually starts and what the structure is:So Figure 2 starts at line 471. Let me now carefully count all images that appear in the three chat example figures (2, 3, 4) before the Appendix:\n\nLooking at my grep results and the line ranges:\n\n**Figure 2** (lines 471-620):\n- Image5.jpg (line 480) - appears after the prompt but seems misplaced\n- Im3.png (line 543) - Toucan\n- Im4.png (line 579) - Puffin\n\nHowever, Image5.jpg shows banana bread which doesn't match the birds prompt. Let me check if there's a third bird image I'm missing. The text mentions three birds (Toucan, Puffin, Golden Pheasant) and has <img> markers. Let me look for where the third bird image might be:\n\nLooking at my earlier reading, Im5.png (line 641) shows a bird (Golden Pheasant based on the description at lines 649-668). The caption for Figure 2 is at line 619-620, so Im5.png at line 641 is AFTER the Figure 2 caption.\n\nLet me check if Im5.png is actually part of Figure 3:I see! Im5.png (the Golden Pheasant/colorful bird) appears at line 641, but the prompt for Figure 3 starts at line 675 which references \"<img>\" - and that <img> is referring to the STOP sign that appeared earlier. So Im5.png is actually the third bird from Figure 2, even though it appears after the Figure 2 caption.\n\nLooking back at the structure more carefully, the figure captions seem to come before all the images are shown. Let me look at where the STOP sign figure starts:\n\nFrom line 633-637, there's a figure with \"STOP\" as OCR text. This must be the input image for Figure 3.\n\nLet me now systematically list the images:\n\n**Before Figure 2 caption (line 619):**\n- Image5.jpg (line 480) - banana bread (misplaced/error)\n- Im3.png (line 543) - Toucan  \n- Im4.png (line 579) - Puffin\n\n**After Figure 2 caption but before Figure 3 caption (line 701):**\n- Im5.png (line 641) - Golden Pheasant (part of Figure 2 content)\n\n**Around Figure 3:**\n- STOP sign figure (lines 633-637, but before Im5.png structurally in the document)\n- Im6.png (line 711) - STOP sign (after Figure 3 caption)\n\nWait, I need to look at this more systematically. Let me check what the STOP sign figure at line 633 is:The figure at 633-637 has OCR text \"STOP\" but the embedded image Im5.png (line 641) has \"NO_CONTENT_HERE\" as OCR and shows a bird. This is confusing. Let me look at Im6.png which should be the STOP sign:Yes! Im6.png is the STOP sign. So the document structure has the figure tags and embedded images separated. The figure at line 633 with \"STOP\" corresponds to Im6.png (the actual embedded image file) at line 711.\n\nLet me now map this out properly by looking at the figure captions and the embedded images:\n\n**Figure 2** (caption at line 619): Chat about birds\n**Figure 3** (caption at line 701): Chat about chameleon camouflage  \n**Figure 4** (caption at line 922): Chat about baking with bananas\n\nNow let me find which embedded images belong to each:So Im6.png appears RIGHT AFTER the Figure 3 caption. Now let me check Figure 4:So Im8.png (bananas) appears before the Figure 4 caption at line 922, and there's an <img> marker at line 920 suggesting another image.\n\nNow let me look at Im7.png and Im9.png to see which figure they belong to:\n\nIm7.png is at line 777. Let me check the context:Im7.png (chameleon) is at line 777, which is AFTER section 2.1 starts at line 756 and after Figure 3 caption at line 701. But looking at the context, Im7.png shows a chameleon which matches Figure 3's theme. Let me check if this is part of the Figure 3 example by looking backwards:\n\nThe Figure 3 response at line 747 has an <img> marker, and the text before talks about generating a chameleon image. So Im7.png is part of Figure 3's output!\n\nNow let me check Im9.png:Im9.png shows a loaf of baked good (banana bread final result). This is at line 991, which is after Figure 4 caption at line 922. The <img> marker at line 920 in Figure 4 refers to this final dish image. So Im9.png is part of Figure 4.\n\nNow let me go back and check Im5.png more carefully. At line 641, the bird image appears after Figure 2 caption but before Figure 3 content. Looking at Figure 2's text, the Golden Pheasant is mentioned with <img> at line 614. So Im5.png (Golden Pheasant) is part of Figure 2.\n\nLet me now do a final count:\n\n**Figure 2** (Chat about birds):\n- Im3.png - Toucan\n- Im4.png - Puffin  \n- Im5.png - Golden Pheasant\nTotal: 3 images\n\n**Figure 3** (Chat about chameleon camouflage):\n- Im6.png - STOP sign (input)\n- Im7.png - Chameleon (generated output)\nTotal: 2 images\n\n**Figure 4** (Chat about baking with bananas):\n- Im8.png - Bananas (input)\n- Im9.png - Banana bread (generated output)\nTotal: 2 images\n\n**Total images in chat example figures (Figures 2-4), excluding Appendix: 3 + 2 + 2 = 7**\n\nWait, but I should double-check if Image5.jpg (the banana bread at line 480) is actually meant to be part of Figure 2 or if it's a document error. Let me look at the context again:Image5.jpg (banana bread) appears right after the birds prompt but before the response starts. This appears to be a document processing error or misplacement - it doesn't match the context at all. The response starting at line 518 talks about birds, not banana bread.\n\nGiven that this is clearly an error in the document, I should not count Image5.jpg as part of the chat example figures. The actual chat examples are:\n\n**Figure 2**: 3 bird images (Im3.png, Im4.png, Im5.png)\n**Figure 3**: 2 images (Im6.png STOP sign, Im7.png chameleon)  \n**Figure 4**: 2 images (Im8.png bananas, Im9.png banana bread)\n\n**Total: 7 images**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 178546, "error": null, "extra": {"thread_id": 2056, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d55f4fd667ac403b901ea3294619b198", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1600, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 17687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find subplots that show obvious loss spikes in the document.\n\nLet me examine the figures that show training loss curves:\n\n1. **Figure 5**: Contains 3 subplots:\n   - (a) Shows output norm with clear divergence/spike\n   - (b) Shows training loss comparison with and without QK-Norm - one curve diverges (spikes)\n   - (c) Shows training loss with and without dropout - appears stable\n\n2. **Figure 6**: Contains 3 subplots:\n   - (a) Shows training loss curves for 7B and 34B models - appears stable without obvious spikes\n   - (b) Shows training loss curve with image generation disabled - appears stable without obvious spikes\n   - (c) Shows training loss with and without norm reordering - shows clear divergence/spike in one curve\n\nLet me count the subplots with obvious loss spikes:\n- Figure 5(a): Shows norm growth leading to divergence (1)\n- Figure 5(b): Shows loss divergence/spike (1)\n- Figure 6(c): Shows loss divergence/spike (1)\n\nTotal: 3 subplots show obvious loss spikes or divergences.\n\nAnswer: 3", "citations": [], "input_tokens": 25071, "output_tokens": 284, "cost_micros": 79473, "latency_ms": 8016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss curves with obvious spikes.\n\nLooking through the document:\n\n**Figure 5** (page 6-7):\n- (a) Shows \"Output norm\" vs Step - not a loss curve with spikes\n- (b) Shows \"Training Loss\" with and without QK-Norm - shows relatively smooth curves, one diverges but no obvious spikes\n- (c) Shows \"Training Loss\" with and without dropout - shows smooth curves\n\n**Figure 6** (page 7):\n- (a) Shows \"Training Loss\" for 7B and 34B models over 600k steps - smooth curves, no obvious spikes\n- (b) Shows \"Training Loss\" for 7B without image generation - smooth curve, no obvious spikes\n- (c) Shows \"Training Loss\" with and without norm reordering - one curve shows a clear spike/divergence around 8k-10k steps\n\nFrom Figure 6c, I can see one subplot that clearly shows an obvious loss spike (the curve without norm reordering that spikes dramatically).\n\nLooking more carefully at Figure 5b, the curve \"w/o QK-norm\" shows a sharp spike/divergence near the end.\n\nSo counting subplots with obvious loss spikes:\n- Figure 5b: 1 subplot (shows divergence/spike)\n- Figure 6c: 1 subplot (shows clear spike)\n\nAnswer: 2", "citations": [], "input_tokens": 34440, "output_tokens": 324, "cost_micros": 108180, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures in the document that show training loss curves with obvious loss spikes.\n\nLooking through the document:\n\n**Figure 5**: \"Output norm and training loss curves for Chameleon models under various settings\"\n- (a) Shows uncontrolled growth of output norms\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - this shows training loss with a divergence/spike\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - this shows training loss with divergences/spikes\n\n**Figure 6**: \"Training loss curves for Chameleon models under various settings\"\n- (a) Shows \"Training loss curve with image generation disabled does not suffer from instability issues\" - smooth curves, no spikes\n- (b) Shows \"For Chameleon-34B, using dropout does not fix divergences, both with and without reordering\" - this shows loss spikes/divergences\n\nCounting subplots with obvious loss spikes:\n- Figure 5(b): 1 subplot with loss spike\n- Figure 5(c): 1 subplot with loss spike  \n- Figure 6(b): 1 subplot with loss spikes\n\nTotal: 3 subplots\n\nAnswer: 3", "citations": [], "input_tokens": 26080, "output_tokens": 301, "cost_micros": 82755, "latency_ms": 9171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss spikes in the document.\n\nLooking through the document for figures showing training loss or related metrics:\n\n**Figure 5: \"Output norm and training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows output norms with curves that spike/diverge\n- (b) Shows training loss with and without QK-Norm - one curve diverges/spikes\n- (c) Shows training loss with and without dropout - one curve appears to spike\n\n**Figure 6: \"Training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows smooth training curves for 7B and 34B models - no obvious spikes\n- (b) Shows training loss without image generation - smooth curve, no spikes\n- (c) Shows training with/without norm reordering where one diverges - this shows a spike\n\nSo counting subplots with obvious loss spikes:\n- Figure 5(a): 1 (shows divergence/spikes in output norms)\n- Figure 5(b): 1 (shows divergence in loss)\n- Figure 5(c): 1 (shows divergence in loss)\n- Figure 6(c): 1 (shows divergence)\n\nTotal: 4 subplots show obvious spikes/divergences\n\nAnswer: 4", "citations": [], "input_tokens": 32131, "output_tokens": 324, "cost_micros": 101253, "latency_ms": 9125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about subplots related to obvious loss spikes.Let me search for content related to \"loss spikes\" in the document:Let me search for \"subplot\":Let me read through the document more systematically to look for figures and their descriptions:Now let me look at Figure 5 and Figure 6 which seem to show training loss curves. Let me read those sections:Now let me look more closely at Figure 6 which appears to have subplots. Let me search for any reference to \"loss spikies\" or similar terms:Let me look at Figure 5 more carefully, as it shows \"Output norm and training loss curves for Chameleon models under various settings\":I can see Figure 5 has three subplots (a, b, c). Now let me check the figure caption more carefully and look at the actual charts. The caption says \"Figure 5 Output norm and training loss curves for Chameleon models under various settings.\" Let me read the text more carefully to understand what \"obvious loss spikies\" might refer to. Let me search for variations:Let me look at the entire document more systematically. Let me check around Figure 5 and 6 descriptions:Let me look more carefully at the whole document. Maybe \"spikies\" is a colloquial term in the figure or caption. Let me check the full text around Figures 5 and 6:So Figure 5 has subplots (a), (b), and (c). Let me check Figure 6:So Figure 6 also has subplots (a), (b), and (c). Now let me look at the actual chart data more carefully. The question asks about \"obvious loss spikies\" which likely refers to spikes in the loss curves. Let me look at the loss curve data more closely. Let me search the document for any terms that might be misspelled or colloquial:Based on my reading of the document, I can see that:\n\n**Figure 5** has three subplots:\n- (a) Output norms plot showing growth over training steps\n- (b) Training loss with and without QK-Norm\n- (c) Training loss with and without dropout\n\n**Figure 6** has three subplots:\n- (a) Training curves for 600k steps for Chameleon-7B and Chameleon-34B\n- (b) Training loss curve with image generation disabled\n- (c) For Chameleon-34B, using dropout comparison with and without norm-reordering\n\nLooking at the descriptions and the actual chart data in the text, the question asks about \"obvious loss spikies\" (spikes). From examining the text descriptions:\n\n- Figure 5(a) shows \"Uncontrolled growth of output norms\" - this is about norms, not loss spikes\n- Figure 5(b) shows training loss curves with the one \"w/o QK-norm\" showing a divergence\n- Figure 5(c) shows training loss curves with and without dropout - the \"w/o dropout\" curve appears to have issues\n- Figure 6(a) shows smooth training curves for both 7B and 34B models\n- Figure 6(b) shows a smooth training loss curve without image generation\n- Figure 6(c) shows curves where \"w/o norm reordering\" appears to diverge/spike\n\nLooking at the subplot captions that mention instability or divergences, which would correspond to loss spikes:\n- Figure 5(b): \"w/o QK-norm\" diverges\n- Figure 5(c): \"w/o dropout\" has spikes\n- Figure 6(c): \"w/o norm reordering\" has divergences/spikes\n\nThat would be 3 subplots showing obvious loss spikes.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 88405, "error": null, "extra": {"thread_id": 2057, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d21eabfb0c2b44d69030354b180154e2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 635, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 15734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a \"Summary of core architecture and optimization decisions made in Chameleon in contrast to LLaMa-1 and LLaMa-2\", I can see that:\n\n- LLaMa-1 7B has an \"\u00d7\" (cross mark) in the Qknorm column\n- LLaMa-1 33B has an \"\u00d7\" (cross mark) in the Qknorm column\n\nThis indicates that LLaMa-1 does NOT apply QKnorm.\n\nAnswer: no", "citations": [], "input_tokens": 25087, "output_tokens": 130, "cost_micros": 77211, "latency_ms": 5343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which compares core architecture and optimization decisions across models:\n\nFor LLaMa-1 models (both 7B and 33B), the Qknorm column shows \"\u00d7\" (which indicates no).\n\nAnswer: no", "citations": [], "input_tokens": 34456, "output_tokens": 63, "cost_micros": 104313, "latency_ms": 4297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which summarizes the core architecture and optimization decisions:\n\nFor LLaMa-1 models (both 7B and 33B), the \"Qknorm\" column shows \"\u00d7\", which indicates that QK-Norm was not applied.\n\nAnswer: No", "citations": [], "input_tokens": 26096, "output_tokens": 71, "cost_micros": 79353, "latency_ms": 4531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a comparison of core architecture and optimization decisions:\n\nFor LLaMa-1 (both 7B and 33B models), the \"Qknorm\" column shows \"\u00d7\", which indicates that query-key normalization (QK-Norm) was **not** applied.\n\nAnswer: No", "citations": [], "input_tokens": 32147, "output_tokens": 81, "cost_micros": 97656, "latency_ms": 4671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about whether llama-1 applies qknorm.Let me search for \"qknorm\" and \"llama\" in the document to find relevant information.Let me read the table area that contains information about qknorm and llama-1:Perfect! I found the table that shows whether llama-1 applies qknorm. In the table, for both LLaMa-1 models (7B and 33B), the Qknorm column shows \"\u00d7 \u2612\" which indicates \"no\".\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 30905, "error": null, "extra": {"thread_id": 2058, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2c3221de2294418da7d44837e6c67391", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 146, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm\", I can see the training loss curves.\n\nThe curve labeled \"w/o QK-norm\" (without QK-norm) shows a divergence/spike in the loss after approximately 20% of training progress, which appears to occur around step 20,000-25,000 based on the x-axis scale shown in the figure.\n\nAnswer: Around step 20,000-25,000", "citations": [], "input_tokens": 25078, "output_tokens": 122, "cost_micros": 77064, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around step 20 000 25 000", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for both conditions.\n\nThe curve labeled \"w/o QK-norm\" (without QK-Norm) shows the training progressing normally until it experiences a sudden spike and divergence.\n\nAnswer: Around 125k steps", "citations": [], "input_tokens": 34447, "output_tokens": 89, "cost_micros": 104676, "latency_ms": 5452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 125k steps", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves comparing the two conditions.\n\nThe figure shows that the model without QK-Norm diverges (loss spikes) after approximately 20% of training progress.\n\nAnswer: After approximately 20% of training", "citations": [], "input_tokens": 26087, "output_tokens": 87, "cost_micros": 79566, "latency_ms": 5046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "after approximately 20 of training", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for models with and without QK-norm.\n\nThe graph shows that the model without QK-norm or dropout (red line) diverges after approximately 20k-30k steps, where the loss spikes dramatically upward.\n\nAnswer: Around 20k-30k steps", "citations": [], "input_tokens": 32138, "output_tokens": 100, "cost_micros": 97914, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 20k 30k steps", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about loss spikes when without QK-norm.Let me search for \"QK-norm\" in the document to find the relevant information.Now let me read the specific sections that mention loss spikes and QK-norm. Let me look at the chart/figure around lines 1028-1076 which seems to show training loss curves.I can see a figure showing training loss with and without QK-norm. The x-axis shows steps from 0k to 175k (approximately). Let me look more carefully at the surrounding text to understand where the spike occurs. Let me check the text before and after this figure.Let me read more to find the caption or description of this figure:Good! Now I can see that figure (b) shows training loss with and without QK-Norm. Let me look back at the chart more carefully to understand where the spike happens. Let me check the text around lines 938-1000 to see if there's more description:Now I need to look at the actual description of figure 5b more carefully. Let me read around the text that discusses the training loss spike:Perfect! I found the answer. The text clearly states:\n\n\"In Figure 5b, we show training loss curves for Chameleon-7B with and without QK-Norm, and the latter diverges after approximately 20% of a training epoch.\"\n\nLet me look at the figure again to verify the step count. Looking at the figure data from earlier, the x-axis goes from 0k to approximately 175k steps. The text mentions \"after approximately 20% of a training epoch\" for the divergence/spike.\n\nHowever, let me look more carefully at what specific step number might correspond to this. Let me check if there's more specific information:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 73108, "error": null, "extra": {"thread_id": 2059, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c22c13a6350947eda6562103bdc5468c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 343, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "however let me look more carefully at what specific step number might correspond to this let me check if there s more specific information", "normalised_gold": "150k"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I need to find who verified and signed the complaint by looking at the verification section of the document.\n\nLooking at page 16 of the document, I can see the \"VERIFICATION\" section which states:\n\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury of the laws of the United States of America that the facts contained therein are true to the best of my knowledge and belief, based upon knowledge possessed by me and/or on information received from other law enforcement agents.\"\n\nThis is followed by a signature and \"Special Agent Marc Silski, Federal Bureau of Investigation\" with the date \"September 6, 2018\".\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 25011, "output_tokens": 176, "cost_micros": 77673, "latency_ms": 14359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (PageID.16 Page 16 of 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5579, "output_tokens": 52, "cost_micros": 17517, "latency_ms": 3594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "According to the verification section at the end of the document, the officer who verified and signed the complaint was:\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 6098, "output_tokens": 34, "cost_micros": 18804, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16):\n\nThe verification states:\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury...\"\n\nIt is signed by:\n\"Special Agent Marc Silski\nFederal Bureau of Investigation\nDated: September 6, 2018\"\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 5228, "output_tokens": 109, "cost_micros": 17319, "latency_ms": 3780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5778, "output_tokens": 43, "cost_micros": 17979, "latency_ms": 2312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find which officer verified and signed the complaint.Based on the document, I found the verification section on page 16. The officer who verified and signed the complaint is:\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23546, "error": null, "extra": {"thread_id": 2060, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890676fb4dc0465f94bad1a9648bd51a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 82, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I need to find all the checks issued to Mont Blanc company mentioned in the document.\n\nLooking through the document:\n\nOn page 11 (PageID.11), paragraph e states:\n\"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nOn page 11 (PageID.11), paragraph f states:\n\"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nThese are the two checks issued to Mont Blanc company.\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 25031, "output_tokens": 170, "cost_micros": 77643, "latency_ms": 10219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5599, "output_tokens": 129, "cost_micros": 18732, "latency_ms": 3389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32, subsections e and f:\n\n1. Check number 150301 issued in September 2012: $35,000\n2. Check number 151545 issued in February 2013: $40,684\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 6118, "output_tokens": 79, "cost_micros": 19539, "latency_ms": 3640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for checks issued to Mont Blanc company:\n\nFrom paragraph 32.e: \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32.f: \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5248, "output_tokens": 128, "cost_micros": 17664, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5798, "output_tokens": 127, "cost_micros": 19299, "latency_ms": 3592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about checks issued to Mont Blanc company.Let me continue reading the document to find information about checks issued to Mont Blanc company.Let me continue reading to find information about checks issued to Mont Blanc.Perfect! I found the information about checks issued to Mont Blanc. Let me verify by reading a bit more to ensure I have all the check amounts.\n\nBased on the document, I found the following information about checks issued to Mont Blanc:\n\n1. Check number 150301 - $35,000 (issued in September 2012)\n2. Check number 151545 - $40,684 (issued in February 2013)\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32530, "error": null, "extra": {"thread_id": 2061, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5f1b7e2037a34d839cda54ca371fcea3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 134, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the CA Bar number in the document.\n\nLooking at page 15, I can see the signature page where it states:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 25014, "output_tokens": 103, "cost_micros": 76587, "latency_ms": 10625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 15 (PageID.15), I can find the CA Bar number listed for Assistant United States Attorney Adriana Dydell.\n\nAnswer: 239516", "citations": [], "input_tokens": 5582, "output_tokens": 47, "cost_micros": 17451, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, in the signature block for the Assistant United States Attorney, I can see:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 6101, "output_tokens": 108, "cost_micros": 19923, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, the signature block shows:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nDated: September, 2018\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 5231, "output_tokens": 107, "cost_micros": 17298, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I can find the CA Bar number at the bottom of page 15, where it states:\n\n\"Dated: September 13, 2018 (CA. Bar No. 239516)\"\n\nThis appears next to the signature block for Adriana Dydell, Assistant United States Attorney.\n\nAnswer: 239516", "citations": [], "input_tokens": 5781, "output_tokens": 78, "cost_micros": 18513, "latency_ms": 3358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find the CA Bar number.Answer: 239516", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35797, "error": null, "extra": {"thread_id": 2062, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67c2db9144b348b6ba61d0ad2f21abaf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 115, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll examine each page to identify where information has been blacked out (redacted).\n\nPage 1: No redactions visible\nPage 2: No redactions visible\nPage 3: No redactions visible\nPage 4: No redactions visible\nPage 5: No redactions visible\nPage 6: No redactions visible\nPage 7: No redactions visible\nPage 8: No redactions visible\nPage 9: No redactions visible\nPage 10: No redactions visible\nPage 11: No redactions visible\nPage 12: No redactions visible\nPage 13: Contains multiple black redaction boxes blocking out names and information (in paragraph l and m)\nPage 14: Contains black redaction boxes blocking out information (in paragraph n)\nPage 15: No redactions visible\nPage 16: No redactions visible\n\nAnswer: 13, 14", "citations": [], "input_tokens": 25034, "output_tokens": 196, "cost_micros": 78042, "latency_ms": 11015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages have information that has been blacked out or redacted.\n\nLooking through the document:\n\n- Page 1 (PageID.1): No visible redactions\n- Page 2 (PageID.2): No visible redactions\n- Page 3 (PageID.3): No visible redactions\n- Page 4 (PageID.4): No visible redactions\n- Page 5 (PageID.5): No visible redactions\n- Page 6 (PageID.6): No visible redactions\n- Page 7 (PageID.7): No visible redactions\n- Page 8 (PageID.8): No visible redactions\n- Page 9 (PageID.9): No visible redactions\n- Page 10 (PageID.10): No visible redactions\n- Page 11 (PageID.11): No visible redactions\n- Page 12 (PageID.12): No visible redactions\n- Page 13 (PageID.13): In paragraph 1, there is redacted text after \"the last names of Iacobelli and\" and in paragraph m, there are multiple redactions of names\n- Page 14 (PageID.14): In paragraph n, there are redacted names at the beginning and within the text\n- Page 15 (PageID.15): No visible redactions\n- Page 16 (PageID.16): No visible redactions\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5602, "output_tokens": 339, "cost_micros": 21891, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for redacted/blacked out information:\n\nOn page 14 (PageID.14), paragraph m, there are multiple instances of redacted information indicated by blank spaces where names should be:\n- \"was interviewed by agents.\"\n- \"was a former co-worker\"\n- \"During the interview,\"\n- \"informed agents that Iacobelli bragged to her about a Mont Blanc pen that he carried in his shirt pocket costing $25,000. Iacobelli also informed that\"\n\nOn page 14 (PageID.14), paragraph n, there is also redacted information:\n- \"On July 31, 2017, agents contacted who stated that\"\n- \"he was in possession of the Lincoln commemorative Mont Blanc pen. added that he ordered the pen, but that Iacobelli paid for both and\"\n\nPage 13 (PageID.13), paragraph l mentions \"having the last names of Iacobelli and\" followed by what appears to be a redaction.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 6121, "output_tokens": 238, "cost_micros": 21933, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully review the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Page 2: No redactions visible\n- Page 3: No redactions visible\n- Page 4: No redactions visible\n- Page 5: No redactions visible\n- Page 6: No redactions visible\n- Page 7: No redactions visible\n- Page 8: No redactions visible\n- Page 9: No redactions visible\n- Page 10: No redactions visible\n- Page 11: No redactions visible\n- Page 12: No redactions visible\n- Page 13: Contains redacted/blacked out information (names are replaced with blank spaces/lines in sections l and m)\n- Page 14: Contains redacted/blacked out information (names are replaced with blank spaces/lines in section n)\n- Page 15: No redactions visible\n- Page 16: No redactions visible\n\nThe pages with blacked out information are pages 13 and 14, where personal names have been redacted.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5251, "output_tokens": 248, "cost_micros": 19473, "latency_ms": 5733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Pages 1-12: No redactions visible\n- Page 13 (PageID.13): Contains multiple redactions marked with \"\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\" or similar black bars in paragraph m and n. Specifically:\n  - \"former co-worker \u2588\u2588\u2588\u2588\u2588\u2588\" \n  - Multiple instances of \"\u2588\u2588\u2588\u2588\u2588\u2588\" referring to a person's name\n  - \"podiatrist named \u2588\u2588\u2588\u2588\u2588\u2588\"\n  - \"[\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588]\" in paragraph n\n  - \"[\u2588\u2588\u2588\u2588]\" multiple times in paragraph n\n\n- Page 14 (PageID.14): No redactions visible\n- Pages 15-16: No redactions visible\n\nThe redactions appear only on page 13 of the document (which corresponds to PageID.13, Page 13 of 16).\n\nAnswer: 13", "citations": [], "input_tokens": 5801, "output_tokens": 206, "cost_micros": 20493, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "13", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify pages with blacked out information.Based on my review of the document, I can identify pages with redacted (blacked out) information:\n\n- **Page 13**: Contains multiple black redaction bars in paragraphs l and m, obscuring names and identifying information. The visual description explicitly notes \"Multiple horizontal black bars throughout the document\" and \"Redacted sections appear in paragraphs l and m.\"\n\n- **Page 14**: Contains three black redaction blocks in paragraph n, obscuring names/identifying information. The visual description notes \"Contains redacted sections shown as solid black rectangular blocks\" and \"Multiple solid black rectangular blocks throughout paragraph n.\"\n\nAnswer: 13, 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37265, "error": null, "extra": {"thread_id": 2063, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5190eed28c9d49e68875e654ed7fc489", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 163, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of this document.\n\nLooking at page 15 (PageID.15):\nI can see a handwritten date that appears to be \"September 12, 2018\" where \"12\" is written manually.\n\nLooking at page 16 (PageID.16):\nI can see a handwritten date that appears to be \"September 6, 2018\" where \"6\" is written manually.\n\nThe manually written numbers are:\n- Page 15: 12\n- Page 16: 6\n\nSum: 12 + 6 = 18\n\nAnswer: 18", "citations": [], "input_tokens": 25026, "output_tokens": 148, "cost_micros": 77298, "latency_ms": 8937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date shows \"September 3 2018\" - there appears to be a handwritten \"3\" in the date field\n\nLooking at page 16 (PageID.16):\n- The date shows \"September 6 2018\" - there appears to be a handwritten \"6\" in the date field\n\nThe sum of the manually written numbers is: 3 + 6 = 9\n\nAnswer: 9", "citations": [], "input_tokens": 5594, "output_tokens": 130, "cost_micros": 18732, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nThe date shows \"September 13 2018\" where \"13\" appears to be handwritten.\n\nLooking at page 16 (PageID.16):\nThe date shows \"September 6, 2018\" where \"6\" appears to be handwritten.\n\nSum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 6113, "output_tokens": 112, "cost_micros": 20019, "latency_ms": 3219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine pages 15 and 16 of the document to find manually written numbers.\n\nLooking at page 15:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.15 Page 15 of 16\"\n- The page contains typed text including \"Respectfully submitted,\" signature block for Matthew Schneider and Adriana Dydell\n- At the bottom it shows \"Dated: September, 2018\"\n- The number \"15\" appears at the bottom of the page\n\nLooking at page 16:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.16 Page 16 of 16\"\n- Contains a verification section signed by Marc Silski\n- At the bottom it shows \"Dated: September 6, 2018\"\n- The number \"16\" appears at the bottom of the page\n\nThe manually written/handwritten numbers visible on these pages are:\n- Page 15: The page number \"15\" at the bottom\n- Page 16: The page number \"16\" at the bottom\n\nSum: 15 + 16 = 31\n\nAnswer: 31", "citations": [], "input_tokens": 5243, "output_tokens": 307, "cost_micros": 20334, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "31", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date \"September 13, 2018\" appears to have \"13\" that could be handwritten, but examining the format, it appears to be typed text, not manually written.\n\nLooking at page 16 (PageID.16):\n- The date \"September 6, 2018\" appears, where \"6\" could potentially be handwritten.\n- There is a signature line with \"Special Agent Marc Silski\" and a signature.\n\nHowever, upon closer examination of the document format, these appear to be typed dates in the standard court document format. The document shows underlined spaces where information was filled in, but the actual numbers \"13\" on page 15 and \"6\" on page 16 appear in the standard typed format of the document.\n\nLooking more carefully, the date on page 15 shows \"September <u>13</u>, 2018\" where \"13\" appears to be typed/filled in on an underlined space, and on page 16 \"September 6, 2018\" where \"6\" appears typed.\n\nIf these are considered the manually written/filled-in numbers:\n- Page 15: 13\n- Page 16: 6\n- Sum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 5793, "output_tokens": 316, "cost_micros": 22119, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I'll help you find the manually written numbers on pages 15 and 16 of the document. Let me first read the document that was mentioned in the context.Let me continue reading to find pages 15 and 16. I need to search through the document to locate these specific pages.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27906, "error": null, "extra": {"thread_id": 2064, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3dc2a747e29a4ced83b1f3d763f972af", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 79, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to analyze the document to identify all the colors present in it.\n\nLooking at the document pages provided:\n- The text appears in black\n- The background is white\n- There are blue hyperlinks/case numbers at the top of each page\n- There are black redaction marks/blocks in some areas (pages 13-14)\n\nThe colors present in this document are:\n- Black (text and redactions)\n- Blue (case numbers and headers)\n- White (background)\n\nAnswer: black, blue, white", "citations": [], "input_tokens": 25038, "output_tokens": 116, "cost_micros": 76854, "latency_ms": 10546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "black, blue, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this legal document.\n\nScanning through the document for color references:\n\n1. Page 11, paragraph d: \"red 2013 Ferrari 458 Spider convertible\"\n2. Page 12, paragraph j: mentions \"gold\" (portrait in gold) and \"sapphire\" (which is blue)\n\nLet me verify these are the only color mentions:\n- \"red\" - explicitly mentioned for the Ferrari\n- \"gold\" - mentioned for Lincoln's portrait on the pen clip\n- The sapphire mentioned would typically be blue, but the document says \"sapphire\" not \"blue\"\n\nLooking more carefully at the text:\n- \"red 2013 Ferrari\" (paragraph 32d)\n- \"gold\" (paragraph 32j - \"Lincoln's portrait in gold\")\n- \"sapphire\" (paragraph 32j - \"has a sapphire\")\n\nThe colors explicitly stated are:\n- gold\n- red\n\nAlphabetically ordered:\n\nAnswer: gold, red", "citations": [], "input_tokens": 5606, "output_tokens": 228, "cost_micros": 20238, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "gold, red", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine the document to identify all colors mentioned or visible in it.\n\nLooking through the document:\n\n1. The document itself appears to be a black and white legal document (black text on white background)\n2. In paragraph 32(d), a \"red 2013 Ferrari 458 Spider convertible\" is mentioned\n3. In paragraph 32(j), \"gold\" is mentioned (describing Lincoln's portrait in gold on the pen clip)\n4. In paragraph 32(j), \"sapphire\" is mentioned (the pen's clip has a sapphire)\n\nColors explicitly mentioned in the text:\n- Gold\n- Red\n- Sapphire (which is blue)\n\nColors of the document format itself:\n- Black (text)\n- White (background)\n\nListing all colors alphabetically:\n- Black\n- Blue (sapphire)\n- Gold\n- Red\n- White\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 6125, "output_tokens": 214, "cost_micros": 21585, "latency_ms": 5405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this document.\n\nLet me search through the document for any color references:\n\n1. In paragraph 32.a, there is mention of \"a red 2013 Ferrari 458 Spider convertible\"\n2. In paragraph 32.j, there is mention of \"gold\" (describing Lincoln's portrait on the pen clip)\n3. The document is a legal court document that appears to be in black text on white paper (standard document format)\n\nThe colors explicitly mentioned in the text are:\n- Gold (mentioned in the description of the pen)\n- Red (mentioned in the description of the Ferrari)\n\nSince this is a standard black and white legal document with black text, and the question asks for colors in the document, I should include the standard document colors as well as the colors mentioned in the content.\n\nColors found:\n- Black (text)\n- Gold (pen description)\n- Red (Ferrari description)\n- White (paper/background)\n\nIn alphabetical order: black, gold, red, white\n\nAnswer: black, gold, red, white", "citations": [], "input_tokens": 5255, "output_tokens": 238, "cost_micros": 19335, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5714285714285715, "method": "list_set", "normalised_pred": "black, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in the document.\n\nGoing through the document carefully:\n\n1. Page 10, paragraph d: \"red 2013 Ferrari 458 Spider convertible\" - mentions **red**\n2. Page 12, paragraph j: \"sapphire\" (referring to a sapphire on the pen clip) - **blue** (sapphire is blue)\n3. Page 12, paragraph j: \"gold\" (portrait in gold on the pen's clip) - mentions **gold**\n\nLooking at the document format itself, this appears to be a standard black text on white background legal document, so:\n- **black** (text)\n- **white** (background)\n\nAlphabetically ordered:\n- black\n- blue\n- gold\n- red\n- white\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 5805, "output_tokens": 189, "cost_micros": 20250, "latency_ms": 6407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to identify all the colors present.Based on my careful reading of the document, I can identify the following colors mentioned or described:\n\nFrom the visual descriptions and content:\n- **Black** - mentioned multiple times in visual descriptions (\"black text\", \"black bars\", \"black rectangular blocks\" for redactions)\n- **Blue** - mentioned in the visual description (\"Blue text displaying case number\")\n- **Gold** - mentioned in the pen description (\"Lincoln's portrait in gold\")\n- **Red** - mentioned for the Ferrari (\"red 2013 Ferrari 458 Spider\")\n- **White** - mentioned in visual descriptions (\"white background\")\n\nThere's also a **sapphire** mentioned on the pen clip, which is typically blue, but since \"blue\" is already listed separately and sapphire is a gemstone rather than a color descriptor in this context, I'll include it as a separate item if we're being comprehensive about color-related terms.\n\nHowever, looking at the actual color words used as descriptors in the document:\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37108, "error": null, "extra": {"thread_id": 2065, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8c1d9833eaf14e8eae921556f7ecfecf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 186, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL, Assistant United States Attorney\"\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski, Federal Bureau of Investigation\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 25021, "output_tokens": 122, "cost_micros": 76893, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL\" (Assistant United States Attorney).\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski\" (Federal Bureau of Investigation).\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5589, "output_tokens": 124, "cost_micros": 18627, "latency_ms": 3781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15:\n- There is one signature above the typed name \"ADRIANA DYDELL\"\n\nLooking at page 16:\n- There is one signature above the typed name \"Special Agent Marc Silski\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 6108, "output_tokens": 100, "cost_micros": 19824, "latency_ms": 3750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures that appear on pages 15 and 16 of the document.\n\nLooking at page 15:\n- This page shows the document dated September, 2018\n- It has the names \"MATTHEW SCHNEIDER\" and \"ADRIANA DYDELL\" typed, but these appear to be typed names as part of the document format, not actual signatures\n- I do not see any actual handwritten or image signatures on page 15\n\nLooking at page 16:\n- This is the VERIFICATION page\n- It shows \"Special Agent Marc Silski\" and \"Federal Bureau of Investigation\" with a date of \"September 6, 2018\"\n- This also appears to be typed text rather than an actual signature image\n\nThe question asks for signatures \"appeared\" on these pages. Looking carefully at both pages, I can see typed names but no visible handwritten signatures or signature images in the document.\n\nHowever, these are signature blocks where signatures would typically appear. The document shows:\n- Page 15: 1 signature block (for Adriana Dydell)\n- Page 16: 1 signature block (for Marc Silski)\n\nTotal signature blocks: 2\n\nSince the question asks for the count in \"float format\" but then requests \"a single integer only,\" and given that there are 2 signature blocks present on these pages combined:\n\nAnswer: 2", "citations": [], "input_tokens": 5238, "output_tokens": 308, "cost_micros": 20334, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\n**Page 15 (PageID.15):**\nLooking at page 15, I can see:\n1. A signature above \"ADRIANA DYDELL\" - there is an underlined signature line with \"Adriana Dydell\" written above it\n\nSo there is 1 signature on page 15.\n\n**Page 16 (PageID.16):**\nLooking at page 16, I can see:\n1. A signature notation \"[Signature of Marc Silski]\" above \"Special Agent Marc Silski\"\n\nSo there is 1 signature on page 16.\n\n**Total signatures on pages 15 and 16:** 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5788, "output_tokens": 185, "cost_micros": 20139, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find the signatures on pages 15 and 16.Let me search for content related to pages 15 and 16 by reading more of the document:Let me read the entire document to find pages 15 and 16:Great! I found the relevant sections. Let me look more carefully at the content around pages 15 and 16. I can see:\n\n- Line 616: `<!-- PageNumber=\"15\" -->`\n- Line 637: `<!-- PageNumber=\"16\" -->`\n\nNow let me examine the signatures on these pages. From what I can see:\n\n**Page 15** (lines 590-618): Shows signatures for:\n1. Matthew Schneider (United States Attorney) \n2. Adriana Dydell (Assistant United States Attorney)\n\n**Page 16** (lines 619-638): Shows signature for:\n1. Marc Silski (Special Agent, Federal Bureau of Investigation)\n\nLet me read the section more carefully to confirm:Perfect! Now I can clearly see the signatures on both pages:\n\n**Page 15** (lines 596-616):\n- Shows \"Respectfully submitted,\" followed by:\n  1. MATTHEW SCHNEIDER (United States Attorney)\n  2. \"Adriana@iol\" (appears to be a signature)\n  3. ADRIANA DYDELL (Assistant United States Attorney)\n- The \"Adriana@iol\" on line 603 appears to be a signature representation\n- Dated: September 13, 2018\n\n**Page 16** (lines 619-637):\n- Shows VERIFICATION section with:\n  1. \"Josh,\" on line 631 (appears to be a signature)\n  2. Special Agent Marc Silski (Federal Bureau of Investigation)\n- Dated: September 6, 2018\n\nCounting the signatures:\n- Page 15: 1 signature (the \"Adriana@iol\" signature for Adriana Dydell)\n- Page 16: 1 signature (the \"Josh,\" signature for Marc Silski)\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42092, "error": null, "extra": {"thread_id": 2066, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ddcf7e386683499793cc579d23850dce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 267, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "According to the document, the telephone number for The Limes Residential Home is:\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 37738, "output_tokens": 30, "cost_micros": 113664, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10257, "output_tokens": 37, "cost_micros": 31326, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nThe address and contact details are:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 12045, "output_tokens": 79, "cost_micros": 37320, "latency_ms": 3766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nThe address block shows:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10599, "output_tokens": 73, "cost_micros": 32892, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10834, "output_tokens": 33, "cost_micros": 32997, "latency_ms": 3625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the telephone number for The Limes Residential Home.Let me search for \"The Limes Residential Home\" in the document:Let me search with more context to see the actual content:I can see from the search results that the telephone number appears on line 168. Let me read that section:Perfect! I found the telephone number for The Limes Residential Home on line 174.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34734, "error": null, "extra": {"thread_id": 2067, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_de5d9004414b47159eff08bc68030dbf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 151, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 3 which contains the section \"The five questions we ask about services and what we found\", I can identify the primary questions asked in this inspection report.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 37742, "output_tokens": 73, "cost_micros": 114321, "latency_ms": 7515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can identify the five primary questions that were asked about the services in this inspection report.\n\nThese questions are clearly listed in the \"Summary of findings\" section under \"The five questions we ask about services and what we found\":\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10261, "output_tokens": 84, "cost_micros": 32043, "latency_ms": 3844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically the \"Summary of findings\" section which contains \"The five questions we ask about services and what we found\", I can identify the primary questions asked about the services.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 12049, "output_tokens": 72, "cost_micros": 37227, "latency_ms": 4234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can see the \"five questions we ask about services\" section which lists the primary questions asked during the inspection.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10603, "output_tokens": 61, "cost_micros": 32724, "latency_ms": 3125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document, I can identify the five primary questions that were consistently asked about the services throughout the inspection report.\n\nThese questions appear in the \"Summary of findings\" section and structure the entire report, with each having its own detailed findings section:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10838, "output_tokens": 126, "cost_micros": 34404, "latency_ms": 4328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find the primary questions asked about the services.Looking at the document, I can see this is a Care Quality Commission inspection report. The report explicitly states \"The five questions we ask about services\" in a table starting around line 314. Let me identify these questions from the document structure:\n\nFrom the document, the five primary questions asked about the services are clearly listed:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27546, "error": null, "extra": {"thread_id": 2068, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bdf643e270448d6aadaffef3d9744b8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 108, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLet me examine each page:\n\nPage 1: I can see the CQC logo (the circular Q logo with \"Care Quality Commission\" text) at the top of the page.\n\nPage 2: No CQC logo visible on this page.\n\nPage 3: No CQC logo visible on this page.\n\nPage 4: No CQC logo visible on this page.\n\nPage 5: I can see the CQC logo at the top of this page.\n\nPages 6-17: Examining the remaining pages, I do not see the CQC logo appearing on any of these pages.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 37764, "output_tokens": 169, "cost_micros": 115827, "latency_ms": 7890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of certain pages, which typically indicates where a logo would be placed in the actual PDF document.\n\nLet me go through each page:\n\nPage 1: \"Care Quality Commission\" appears at the top - \"The Limes Residential Care Home Limited The Limes Residential Home\"\n\nPage 2: \"Summary of findings\" section continues\n\nPage 3: \"Summary of findings\" continues\n\nPage 4: \"Summary of findings\" continues\n\nPage 5: \"Care Quality Commission\" appears - \"The Limes Residential Home Detailed findings\"\n\nPage 6: Continues with \"Is the service safe?\"\n\nPage 7: Continues with \"Is the service safe?\"\n\nPage 8: \"Inadequate\" and \"Is the service effective?\"\n\nPage 9: Continues with \"Is the service effective?\"\n\nPage 10: \"Requires improvement\" and \"Is the service caring?\"\n\nPage 11: Continues with \"Is the service caring?\"\n\nPage 12: \"Requires improvement\" and \"Is the service responsive?\"\n\nPage 13: \"Requires improvement\" and \"Is the service well-led?\"\n\nPage 14: Continues with \"Is the service well-led?\"\n\nPage 15: \"Action we have told the provider to take\"\n\nPage 16: Continues with \"Action we have told the provider to take\"\n\nPage 17: \"Enforcement actions\"\n\nBased on the document structure, \"Care Quality Commission\" appears as a header on pages 1 and 5, which is where the CQC logo would typically be located.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10283, "output_tokens": 395, "cost_micros": 36774, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the Care Quality Commission (CQC) logo appears in this document.\n\nLooking through the document:\n\n1. Page 1 (cover page) - Yes, CQC logo appears at the top\n2. Page 2 - No visible logo\n3. Page 3 - No visible logo\n4. Page 4 - No visible logo\n5. Page 5 - Yes, CQC logo appears at the top (start of \"Detailed findings\" section)\n6. Page 6 - No visible logo\n7. Page 7 - No visible logo\n8. Page 8 - No visible logo\n9. Page 9 - No visible logo\n10. Page 10 - No visible logo\n11. Page 11 - No visible logo\n12. Page 12 - No visible logo\n13. Page 13 - No visible logo\n14. Page 14 - No visible logo\n15. Page 15 - No visible logo\n16. Page 16 - No visible logo\n17. Page 17 - No visible logo\n\nThe CQC logo appears on:\n- Page 1 (the title page/cover)\n- Page 5 (at the start of the detailed findings section)\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 12071, "output_tokens": 294, "cost_micros": 40623, "latency_ms": 5390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of several pages. The logo/header \"Care Quality Commission\" appears on:\n\n- Page 1 (the title page with \"The Limes Residential Care Home Limited\")\n- Page 5 (at the top before \"Detailed findings\")\n- Page 15 (at the top before \"Action we have told the provider to take\")\n\nLet me verify by checking each page systematically:\n\nPage 1: \"Care Quality Commission\" header present\nPage 2-4: Summary of findings pages - no clear CQC logo/header visible in the page headers\nPage 5: \"Care Quality Commission\" header present before \"Detailed findings\"\nPages 6-14: Detailed findings sections - these appear to be continuation pages\nPage 15: \"Care Quality Commission\" header present before enforcement actions section\nPages 16-17: Continuation of enforcement actions\n\nBased on the document structure, the CQC logo/header appears on:\n\nAnswer: Page 1, Page 5, Page 15", "citations": [], "input_tokens": 10625, "output_tokens": 262, "cost_micros": 35805, "latency_ms": 7218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "page 1, page 15, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document systematically:\n\nPage 1: \"Care Quality Commission\" appears at the top as a header\nPage 2: \"Summary of findings\" - no logo visible in header\nPage 3: \"Summary of findings\" continues - no logo visible\nPage 4: \"Summary of findings\" continues - no logo visible\nPage 5: \"CareQuality Commission\" appears at the top (note: slightly different formatting)\nPage 6-17: These pages show section headings but I need to check for the CQC logo/branding\n\nLooking more carefully at the document structure, the CQC logo/branding appears to be present on:\n- Page 1 (top of document with \"Care Quality Commission\")\n- Page 5 (shows \"CareQuality Commission\" at top of \"Detailed findings\" section)\n\nThese appear to be the only pages with the CQC branding/logo clearly marked at the top.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10860, "output_tokens": 243, "cost_micros": 36225, "latency_ms": 6234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify all pages where the CQC logo appears.Let me search for \"CQC\" or \"logo\" throughout the document to find all pages where the logo appears:Now let me search for mentions of \"logo\" or \"Care Quality Commission\" to find where the logo appears:Let me search more specifically for image and figure tags to identify all instances of the logo:Let me read through the entire document more systematically to find all logo occurrences. Let me check what page the first logo is on and look for patterns:The first logo appears before \"PageFooter=1\", so it's on Page 1. Now let me search the entire document systematically for all instances of the CQC logo. Let me search for figures or images:Good! Now I found 7 figure tags. Let me read the context around each one to identify which ones are the CQC logo:Now I've identified that several figures contain only page headers. Let me verify which ones actually contain the CQC logo by checking the content. Based on my readings:\n\n1. Line 121 (before PageFooter 1) - Contains \"Care Quality Commission\" logo with visual description - **Page 1**\n2. Line 409 (after PageBreak on line 406, before PageFooter 4 on line 405) - Contains \"Care Quality Commission\" - **Page 5** (since PageFooter 4 appears at line 405-406, this is after the page break, so it's on page 5)\n\nLet me verify by checking the page breaks more carefully:Perfect! So after PageFooter 4 and PageBreak, we get Page 5, which has the CQC logo. Now let me check if there are any other CQC logos. The other figures at lines 868, 981, 1036, 1134, and 1247 only contain PageHeaders, not the logo.\n\nLet me verify this is correct by checking one more time if there are any other embedded images:Let me search for any other embedded images:Good! Only one embedded image (I1.jpg) exists in the document. Now let me confirm the two locations of \"Care Quality Commission\" text in figures:So the CQC logo on Page 5 appears between PageFooter 4 (line 405) and PageFooter 5 (line 456). That confirms it's on Page 5.\n\nNow let me verify there's only one actual logo image (I1.jpg) on Page 1, and the Page 5 figure is just text without an image:I notice that on Page 5 (line 409-414), there's a figure with \"Care Quality Commission\" text but NO embedded image notation. Let me check if there's an image reference after this figure:No embedded image noted for Page 5's figure. So the Page 5 figure has \"Care Quality Commission\" text but the question asks specifically about the \"logo\" which includes the visual/graphical element. Let me re-read the Page 1 section to see if it explicitly identifies this as a logo:\n\nLooking back at lines 130-147, Page 1 clearly shows:\n- \"**Embedded image:** `I1.jpg`\"\n- \"**Visual description:**\"\n- \"Organizational logo combining a graphical symbol with text\"\n\nThis definitively identifies Page 1 as having the CQC logo (the actual image/visual logo).\n\nOn Page 5 (lines 409-414), there's only the text \"Care Quality Commission\" in a figure, but no embedded image or logo graphic.\n\nHowever, let me check if both figures might represent the same logo - the system might have extracted it once with full details (Page 1) and then just shown the text on Page 5. In typical inspection reports, logos often appear on multiple pages. Let me search more broadly to understand the document structure better. Let me see if there are image references I might have missed:Only one \"Visual description\" exists - on Page 1. The interpretation here is that:\n- Page 1 has the actual CQC logo image (I1.jpg with full visual description)\n- Page 5 has a figure with just \"Care Quality Commission\" text\n\nGiven that the question asks for \"the logo of CQC\" (which implies the graphical/visual logo, not just text), and given that only Page 1 has an embedded image with the visual logo description, the answer should be Page 1 only.\n\nHowever, both figures contain \"Care Quality Commission\" and in PDF documents, logos can appear as repeated elements. The figure on Page 5 likely also represents the logo (just extracted differently by the parser). Let me verify by checking if standard CQC inspection reports have logos on multiple pages.\n\nBased on the document structure, I have:\n1. **Page 1** - Clear CQC logo with embedded image I1.jpg and full visual description\n2. **Page 5** - Figure with \"Care Quality Commission\" text (likely the same logo, but extraction didn't capture the image separately)\n\nGiven the ambiguity, I should report both pages where the CQC element appears in a figure tag, as both likely represent the logo placement in the original PDF.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 135453, "error": null, "extra": {"thread_id": 2069, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a8af9e5bd4c48cebf2e6cbec000700b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1002, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this CQC inspection report.\n\nLet me go through the document systematically:\n\n**From \"Action we have told the provider to take\" section (pages 15-16):**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From \"Enforcement actions\" section (page 17):**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nCounting the unique regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 HSCA (RA) (Staffing)\n- Regulation 18 CQC (Registration) (Notification)\n\nThat's 8 regulations total.\n\nAnswer: 8", "citations": [], "input_tokens": 37740, "output_tokens": 419, "cost_micros": 119505, "latency_ms": 10562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches of HSCA (Regulated Activities) Regulations 2014:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - unlawful restraint and failure to respond to allegations/safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (mentioned twice - failure to assess risks, and failure to manage medicines safely)\n\n3. **Regulation 18** - Staffing (insufficient staffing levels)\n\n4. **Regulation 18** - Staffing (again, for induction, supervision and training issues)\n\n5. **Regulation 11** - Need for consent (MCA 2005 implementation)\n\n6. **Regulation 10** - Dignity and respect\n\n7. **Regulation 9** - Person-centred care\n\n8. **Regulation 17** - Good governance (mentioned twice - failure to assess/monitor quality, and failure to maintain accurate records)\n\n**Breach of HSCA (Registration) Regulations 2009:**\n\n9. **Regulation 18** - Notification of other incidents (CQC Registration Regulations 2009)\n\nNow let me count the distinct regulation numbers that were breached:\n- Regulation 13 (RA Regs 2014)\n- Regulation 12 (RA Regs 2014)\n- Regulation 18 (RA Regs 2014)\n- Regulation 11 (RA Regs 2014)\n- Regulation 10 (RA Regs 2014)\n- Regulation 9 (RA Regs 2014)\n- Regulation 17 (RA Regs 2014)\n- Regulation 18 (Registration Regs 2009)\n\nThat's 7 different regulations from the RA Regulations 2014 and 1 from the Registration Regulations 2009, making 8 total distinct regulations breached.\n\nAnswer: 8", "citations": [], "input_tokens": 10259, "output_tokens": 499, "cost_micros": 38262, "latency_ms": 8109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breached regulations mentioned in this inspection report.\n\nLet me go through the document systematically:\n\n**From \"Is the service safe?\" section:**\n1. Regulation 13 - Safeguarding (failure to respond to allegations and investigate safeguarding incidents)\n2. Regulation 12 - Safe care and treatment (failure to assess and mitigate risks)\n3. Regulation 18 - Staffing (insufficient staffing levels)\n4. Regulation 12 - Safe care and treatment (medicines not managed safely) - already counted above\n\n**From \"Is the service effective?\" section:**\n5. Regulation 18 - Staffing (induction, supervision and training) - already counted above\n6. Regulation 13 - Safeguarding (unlawful restraint) - already counted above\n7. Regulation 11 - Need for consent (MCA 2005 not implemented)\n\n**From \"Is the service caring?\" section:**\n8. Regulation 10 - Dignity and respect\n\n**From \"Is the service responsive?\" section:**\n9. Regulation 9 - Person-centred care (care records not reflecting current needs)\n\n**From \"Is the service well-led?\" section:**\n10. Regulation 17 - Good governance (failure to assess, monitor and improve quality)\n11. Regulation 17 - Good governance (failure to maintain accurate records) - already counted above\n12. Regulation 18 CQC (Registration) Regulations 2009 - Notification of incidents\n\nLet me count the unique HSCA 2008 regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 (Staffing)\n\nThat's 7 regulations under HSCA 2008 (Regulated Activities) Regulations 2014.\n\nPlus 1 regulation under CQC (Registration) Regulations 2009 (Regulation 18).\n\nThe question asks specifically about \"regulations of the", "citations": [], "input_tokens": 12047, "output_tokens": 512, "cost_micros": 43821, "latency_ms": 8780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this report.\n\nLet me go through the document systematically:\n\n**From the \"Action we have told the provider to take\" section:**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From the \"Enforcement actions\" section:**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nLet me verify by checking the main body of the report for all breach mentions:\n- Regulation 13 (safeguarding) - breach mentioned\n- Regulation 12 (safe care and treatment, including medicines and risk assessment) - breach mentioned\n- Regulation 18 (staffing) - breach mentioned\n- Regulation 11 (Mental Capacity Act) - breach mentioned\n- Regulation 10 (dignity and respect) - breach mentioned\n- Regulation 9 (person-centred care) - breach mentioned\n- Regulation 17 (good governance - two separate breaches mentioned for different aspects) - breach mentioned\n- Regulation 18 CQC (Registration) Regulations 2009 (notifications) - breach mentioned\n\nTotal unique regulations breached: 8\n\nAnswer: 8", "citations": [], "input_tokens": 10601, "output_tokens": 448, "cost_micros": 38523, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA 2008) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches found in the main body of the report:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (failure to respond to allegations and report safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (failure to assess, record and mitigate risks to people's health and safety)\n\n3. **Regulation 18** - Staffing (failure to ensure sufficient staffing levels)\n\n4. **Regulation 12** - Safe care and treatment (failure to manage medicines safely) - *same regulation as #2*\n\n5. **Regulation 18** - Staffing (failure to ensure staff received appropriate induction, supervision and training) - *same regulation as #3*\n\n6. **Regulation 13** - Safeguarding (failure to ensure people were not unlawfully restrained) - *same regulation as #1*\n\n7. **Regulation 11** - Need for consent (failure to ensure MCA 2005 code of practice was implemented)\n\n8. **Regulation 10** - Dignity and respect (failure to treat people with respect and dignity at all times)\n\n9. **Regulation 9** - Person-centred care (failure to ensure care records reflected current needs and preferences)\n\n10. **Regulation 17** - Good governance (failure to effectively assess, monitor and improve quality and safety of services)\n\n11. **Regulation 17** - Good governance (failure to maintain accurate and complete records) - *same regulation as #10*\n\n12. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - This is a different Act (Registration Regulations, not Regulated Activities Regulations)\n\n**Counting unique HSCA 2008 (Regulated Activities) Regulations 2014 breaches:**\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n-", "citations": [], "input_tokens": 10836, "output_tokens": 512, "cost_micros": 40188, "latency_ms": 9764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the provided document to find information about HSCA 2008 regulations breaches.Let me continue reading to find any summary or conclusion section that might provide the total count.Based on my careful reading of the document, I need to count all the breaches of the Health and Social Care Act 2008 (Regulated Activities) Regulations 2014 (HSCA 2008).\n\nThe breaches mentioned in the report are:\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - for failing to respond to allegations and for unlawful restraint)\n2. **Regulation 12** - Safe care and treatment (mentioned twice - for failing to assess/mitigate risks and for failing to manage medicines safely)\n3. **Regulation 18** - Staffing (mentioned twice - for insufficient staffing levels and for inadequate training/supervision)\n4. **Regulation 11** - Need for consent (MCA 2005 implementation)\n5. **Regulation 10** - Dignity and respect\n6. **Regulation 9** - Person-centred care\n7. **Regulation 17** - Good governance (mentioned twice - for failing to assess/monitor quality and for failing to maintain accurate records)\n\nAdditionally, there is one breach of:\n8. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - Notification of other incidents\n\nCounting the distinct regulation numbers breached under HSCA (RA) Regulations 2014:\n- Regulation 13\n- Regulation 12\n- Regulation 18\n- Regulation 11\n- Regulation 10\n- Regulation 9\n- Regulation 17\n\nThat's 7 different regulations of the HSCA (RA) Regulations 2014 that were breached.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38483, "error": null, "extra": {"thread_id": 2070, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ce6489cb9fc4be0aad3905ed014d904", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 217, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "7"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from 3M's cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018 (page 60):\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount for FY2018 is shown as purchases of property, plant and equipment.\n\nAnswer: 1577", "citations": [], "input_tokens": 390262, "output_tokens": 116, "cost_micros": 2344182, "latency_ms": 44468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the years ended December 31, I can find the capital expenditures under \"Cash Flows from Investing Activities.\"\n\nFrom the document:\n\n**Cash Flows from Investing Activities:**\nYears ended December 31\n(Millions)                                          2018        2017        2016\n\nPurchases of property, plant and equipment (PP&E)   $(1,577)    $(1,373)    $(1,420)\n\nThe capital expenditure for FY2018 is shown as purchases of property, plant and equipment (PP&E) which equals $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 140538, "output_tokens": 171, "cost_micros": 424179, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount is shown as a negative number in the cash flow statement because it represents a cash outflow.\n\nThe capital expenditure amount for FY2018 is $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 207953, "output_tokens": 130, "cost_micros": 1250643, "latency_ms": 8718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for FY2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 163241, "output_tokens": 77, "cost_micros": 490878, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for fiscal year 2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 177504, "output_tokens": 78, "cost_micros": 533682, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the capital expenditure amount from the cash flow statement.Let me search for the cash flow statement in this document.Great! I found the Consolidated Statement of Cash Flows. Let me read that specific chunk to find the capital expenditure line.Let me search for variations of capital expenditure terminology:Perfect! I found it. The \"Purchases of property, plant and equipment (PP&E)\" line shows the capital expenditure amounts. For 2018, it shows $(1,577) million. Let me read the actual chunk to confirm this is from the cash flow statement.Let me check a bit earlier to confirm this is the Consolidated Statement of Cash Flows:Let me check the title of this statement:Perfect! I've confirmed this is the \"3M Company and Subsidiaries Consolidated Statement of Cash Flows\" for years ended December 31, with columns for 2018, 2017, and 2016.\n\nIn the \"Cash Flows from Investing Activities\" section, the line item \"Purchases of property, plant and equipment (PP&E)\" shows:\n- 2018: (1,577)\n- 2017: (1,373)\n- 2016: (1,420)\n\nThe amounts are in millions of USD, and the parentheses indicate these are cash outflows. The FY2018 capital expenditure amount is 1,577 million USD.\n\nAnswer: 1577.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53671, "error": null, "extra": {"thread_id": 2071, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b68bd46e8dab41c29dc9086112158cbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 310, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for 3M's business segments in FY2018...\n\nFrom the document, specifically on pages discussing business segments (e.g., page 4, page 33-38), I can identify the following business segments:\n\n1. Industrial Business\n2. Safety and Graphics Business\n3. Health Care Business\n4. Electronics and Energy Business\n5. Consumer Business\n\nThese five segments are consistently mentioned throughout the document as 3M's reportable segments for fiscal year 2018.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 390260, "output_tokens": 126, "cost_micros": 2344395, "latency_ms": 49344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to identify 3M's business segments for FY2018.\n\nFrom the document, I can find the business segment information in multiple places. The clearest statement is in the Overview section and repeated throughout:\n\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nThis is also confirmed in the Business Segment Products table which lists the major products for each segment:\n- Industrial\n- Safety and Graphics\n- Health Care\n- Electronics and Energy\n- Consumer\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 140536, "output_tokens": 140, "cost_micros": 423708, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information clearly stated in multiple places:\n\nIn the \"BUSINESS SEGMENTS\" section (page 4), it states:\n\"3M manages its operations in five business segments. The reportable segments are Industrial, Safety and Graphics, Health Care, Electronics and Energy, and Consumer.\"\n\nThis is also confirmed in the \"OVERVIEW\" section (page 15):\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnd in the \"PERFORMANCE BY BUSINESS SEGMENT\" section (page 32):\n\"3M manages its operations in five business segments. The reportable segments are Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 207951, "output_tokens": 206, "cost_micros": 1252341, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018:\n\nFrom the document, I can find the business segments clearly stated in multiple locations, including:\n\n1. In the Business Segments section (page 4):\n\"3M manages its operations in five business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\n2. In the Table of Contents and throughout the financial statements, these five segments are consistently referenced.\n\n3. In Note 18 (Business Segments), the same five segments are listed with their products and financial information.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 163239, "output_tokens": 147, "cost_micros": 491922, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information in multiple sections, including:\n\n1. In the Executive Summary/Overview section\n2. In Note 18 - Business Segments\n3. In the Business Segments section of Item 1\n\nThe document clearly states that 3M manages its operations in **five business segments**:\n\n1. **Industrial** - serves automotive OEM, automotive aftermarket, electronics, appliance, paper and printing, packaging, food and beverage, and construction markets\n2. **Safety and Graphics** - serves markets for personal protection products, commercial solutions, transportation safety, and roofing granules\n3. **Health Care** - serves medical clinics and hospitals, pharmaceuticals, dental and orthodontic practitioners, health information systems, and food manufacturing and testing\n4. **Electronics and Energy** - serves electronics and energy markets, including display materials, electrical products, and power generation\n5. **Consumer** - serves consumer retail, office retail, home improvement, drug and pharmacy retail markets\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 177502, "output_tokens": 255, "cost_micros": 536331, "latency_ms": 14344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about their business segments.Perfect! I found the information. Let me read the specific section that clearly states the five business segments.Answer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32219, "error": null, "extra": {"thread_id": 2072, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5721bf743c374f36a4a98f84b6cf13eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 119, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nLooking through the document, I can find information about derivative instruments in Note 14 (Derivatives) and the tables showing \"Location and Fair Value Amount of Derivative Instruments.\"\n\nFrom page 104-105, I can see tables showing the fair value of derivative instruments as of December 31, 2018:\n\n**Assets (December 31, 2018):**\n- Foreign currency forward/option contracts: $127 million\n- Interest rate swap contracts: $19 million\n- Total derivative assets: $146 million\n\n**Liabilities (December 31, 2018):**\n- Foreign currency forward/option contracts: $22 million\n- Interest rate swap contracts: $31 million\n- Total derivative liabilities: $53 million\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 390258, "output_tokens": 225, "cost_micros": 2346610, "latency_ms": 34921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments as of December 31, 2018.\n\nFrom the section \"Location and Fair Value Amount of Derivative Instruments\" on page 104, I can see a table showing derivative instruments as of December 31, 2018:\n\n**Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative instruments (Assets): $146 million\n\n**Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative instruments (Liabilities): $53 million\n\nNet fair value = Total Assets - Total Liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 140534, "output_tokens": 198, "cost_micros": 424572, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom Note 14 \"Derivatives\" and Note 15 \"Fair Value Measurements\", I can find the fair value of derivative instruments.\n\nIn the section \"Location and Fair Value Amount of Derivative Instruments\" under Note 14, there is a table showing:\n\n**December 31, 2018:**\n\nAssets:\n- Foreign currency forward/option contracts (current): $74 million\n- Foreign currency forward/option contracts (non-current): $39 million\n- Interest rate swap contracts (current): $0 million\n- Interest rate swap contracts (non-current): $19 million\n- Foreign currency forward/option contracts (not designated): $14 million\n**Total derivative assets: $146 million**\n\nLiabilities:\n- Foreign currency forward/option contracts (current): $12 million\n- Foreign currency forward/option contracts (non-current): $4 million\n- Interest rate swap contracts (current): $14 million\n- Interest rate swap contracts (non-current): $17 million\n- Foreign currency forward/option contracts (not designated): $6 million\n**Total derivative liabilities: $53 million**\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 207949, "output_tokens": 317, "cost_micros": 1254826, "latency_ms": 11608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom the \"Location and Fair Value Amount of Derivative Instruments\" table for December 31, 2018:\n\n**Derivative Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative assets: $146 million\n\n**Derivative Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative liabilities: $53 million\n\n**Net Fair Value = Total Assets - Total Liabilities**\nNet Fair Value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 163237, "output_tokens": 185, "cost_micros": 492486, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the fair value of derivative instruments from the document:\n\nFrom the table \"Location and Fair Value Amount of Derivative Instruments\" on page 104:\n\n**December 31, 2018:**\n- Total derivative assets (Fair Value Amount): $146 million\n- Total derivative liabilities (Fair Value Amount): $53 million\n\nNet fair value = Assets - Liabilities = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 177500, "output_tokens": 105, "cost_micros": 534075, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about the net fair value of derivative instruments.Let me search for information about derivative instruments in the document.Let me search more specifically for derivative instruments and their fair values in a table format.Now let me read the section on derivatives to find the fair value information, particularly around line 15104 where it mentions \"Location and Fair Value Amount of Derivative Instruments\":Perfect! I found the table showing the fair value of derivative instruments for 3M in FY2018. Looking at the table on lines 15230-15235:\n\nFor December 31, 2018:\n- Total derivative instruments (Assets): $146 million\n- Total derivative instruments (Liabilities): $53 million\n\nThe net fair value would be: Assets - Liabilities = $146 - $53 = $93 million\n\nAnswer: 93.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50969, "error": null, "extra": {"thread_id": 2073, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890847b672154ab2beed6f213e7b03d2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 220, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for advertising and merchandising costs:\n\nFrom the document (page 65):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nTo calculate the growth rate for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635... \u00d7 100\nGrowth rate = -3.649635...\nRounded to two decimal places = -3.65\n\nAnswer: -3.65", "citations": [], "input_tokens": 390267, "output_tokens": 193, "cost_micros": 2345944, "latency_ms": 37280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 2018 and 2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- 2018: $396 million\n- 2017: $411 million\n\nGrowth rate = (2018 value - 2017 value) / 2017 value \u00d7 100\nGrowth rate = ($396 - $411) / $411 \u00d7 100\nGrowth rate = -$15 / $411 \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 140543, "output_tokens": 230, "cost_micros": 425079, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- FY2018: $396 million\n- FY2017: $411 million\n\nGrowth rate = (FY2018 - FY2017) / FY2017 \u00d7 100\nGrowth rate = (396 - 411) / 411 \u00d7 100\nGrowth rate = -15 / 411 \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 207958, "output_tokens": 224, "cost_micros": 1252788, "latency_ms": 9453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, under \"Advertising and merchandising\":\n- 2018: $396 million\n- 2017: $411 million\n- 2016: $385 million\n\nThe growth rate formula is: ((2018 - 2017) / 2017) \u00d7 100\n\nGrowth rate = ((396 - 411) / 411) \u00d7 100\nGrowth rate = (-15 / 411) \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 163246, "output_tokens": 178, "cost_micros": 492408, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the advertising and merchandising costs:\n\nFrom the document (page 64):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nGrowth rate calculation for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 177509, "output_tokens": 176, "cost_micros": 535167, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the advertising and merchandising costs for FY2018 and the prior year to calculate the growth rate.Let me search for \"advertising\" in the document to find the relevant information quickly.Great! I found a reference to advertising and merchandising costs. Let me read the chunk that contains this information.Perfect! I can see that advertising and merchandising costs were $396 million in 2018 and $411 million in 2017. Let me read more context to ensure I have the complete information.Perfect! I now have the complete information:\n- 2018: $396 million\n- 2017: $411 million\n\nNow I'll calculate the growth rate:\nGrowth rate = [(2018 value - 2017 value) / 2017 value] \u00d7 100\nGrowth rate = [(396 - 411) / 411] \u00d7 100\nGrowth rate = [-15 / 411] \u00d7 100\nGrowth rate = -0.036496... \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36500, "error": null, "extra": {"thread_id": 2074, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3f1bac0372cd48f5b95293aa87160368", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 175, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl
new file mode 100644
index 000000000..1b7494211
--- /dev/null
+++ b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl
@@ -0,0 +1,1026 @@
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:15Z", "latency_ms": 20985, "error": null, "raw_text_chars": 696}]}, "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many adults rated Trump's government ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nLooking through the document, I find relevant information on page 4 (document page 6):\n\nThe document states: \"About four-in-ten Americans rate the ethical standards of Trump administration officials as excellent (9%) or good (30%), while 58% say they are not good (21%) or poor (36%).\"\n\nThe survey was conducted among 1,503 adults (as stated in the methodology section on page 14).\n\nTo find the number of adults who rated the ethical standards as \"poor\":\n36% of 1,503 = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 33894, "output_tokens": 191, "cost_micros": 104547, "latency_ms": 20968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults\n- 36% rated the ethical standards as \"poor\" (from the table showing \"% who say that the ethical standards of top Trump administration officials are...\")\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 6925, "output_tokens": 125, "cost_micros": 22650, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of adults who rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document:\n- Total sample size: 1,503 adults (from the Methodology section on page 14)\n- According to the chart on page 2, 36% of the total rated the ethical standards as \"poor\"\n\nCalculation:\n1,503 \u00d7 0.36 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9417, "output_tokens": 128, "cost_micros": 30171, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump administration's ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nFrom the document, I can find:\n1. The total sample size: 1,503 adults (from the Methodology section)\n2. The percentage who rated ethical standards as \"poor\": 36% (from the table \"Most rate top Trump administration officials' ethics as 'not good' or 'poor'\")\n\nTo calculate the number of adults:\n1,503 \u00d7 0.36 = 541.08\n\nSince we need an integer and this represents people, I should round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 7774, "output_tokens": 158, "cost_micros": 25692, "latency_ms": 6405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find how many adults rated Trump's government ethical standards as poor.\n\nFrom the document:\n- The survey was conducted April 25-May 1, 2018 among 1,503 adults (from the Methodology section on page 14)\n- 36% rated the ethical standards of top Trump administration officials as \"poor\" (from the table on page 2)\n\nCalculation:\n1,503 adults \u00d7 36% = 1,503 \u00d7 0.36 = 541.08\n\nSince we need to report the number of adults, I'll round to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 9517, "output_tokens": 145, "cost_micros": 30726, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question about the survey conducted April 25 - May 1, 2018.Let me search for the specific data about ethical standards ratings. I can see the document mentions ethical standards. Let me look for the detailed breakdown.I can see from the grep results that there's a table showing ethical standards ratings. Let me read the specific section more carefully:I can see a table showing percentages for ethical standards of Trump administration officials. However, I need to understand the table structure better. The table shows:\n- Total: 58, 36, 9, 39\n- These appear to be percentages for different categories\n\nLet me look at the context more carefully to understand what \"58\" represents:Now I understand the table structure. Looking at the figure:\n- The categories are listed as: Poor, Not good, Good, Excellent\n- For \"Total\" (all adults): The values appear to be 58, 36, 9, 39\n\nHowever, the text says \"58% rate them as not good or poor\" which suggests 58% is the combined total of \"not good\" and \"poor\". Let me look more carefully at how to read this table. Looking at lines 248-249: \"39% say the administration's ethical standards are excellent or good, while 58% rate them as not good or poor.\"\n\nThis indicates:\n- 39% = Excellent + Good (combined)\n- 58% = Not good + Poor (combined)\n\nLooking at the table values for Total: 58, 36, 9, 39\n\nThe layout suggests:\n- Row 1 (Poor): 58\n- Row 2 (Not good): 36  \n- Row 3 (Good): 9\n- Row 4 (Excellent): 39\n\nBut this doesn't make sense because 58 + 36 would be 94%, not matching the text's 58% for \"not good or poor\" combined.\n\nLet me reconsider the table structure. Perhaps it's arranged differently - the numbers might be in columns rather than associating each number with the label above it. Let me look for more context about the survey sample size:The survey was conducted among 1,503 adults. Now, looking back at the text on line 248: \"58% rate them as not good or poor\"\n\nIf 58% of 1,503 adults rated Trump's ethical standards as \"not good or poor\", then:\n58% \u00d7 1,503 = 0.58 \u00d7 1,503 = 871.74 \u2248 872 adults\n\nBut the question specifically asks for those who rated it as \"poor\", not \"not good or poor\" combined.\n\nLet me re-examine the table structure. The table shows categories and then rows for Total, Rep/Lean Rep, and Dem/Lean Dem. Looking at the numbers again:\n\nFor the \"Total\" row, I see: 58, 36, 9, 39\n\nGiven that the text says 58% rated as \"not good or poor\" combined, and the categories listed are Poor, Not good, Good, Excellent - perhaps the table is showing these in a different order or the numbers represent something else.\n\nActually, looking at line 289-295 more carefully, let me reconsider: perhaps the numbers don't align sequentially with the labels above. The figure might have a more complex layout. Let me look at the Republican row: 22, 7, 19, 75. And Democrat row: 86, 61, 2, 12.\n\nFor Republicans: The text (line 257) says \"75% giving the administration high marks\" - this matches the \"75\" in the data.\nFor Democrats: The text (line 258) says \"86% of Democrats rating its ethical standards negatively\" - this matches the \"86\" in the data.\n\nSo for the Total row showing 58, 36, 9, 39:\n- 58 + 36 = 94 (doesn't match the 58% mentioned)\n- 36 + 58 = 94 \n- 9 + 39 = 48 (doesn't match the 39% for excellent/good)\n\nWait - perhaps I'm misreading the structure. Let me think about this differently. The text says:\n- 39% say excellent or good\n- 58% say not good or poor\n\nThese add to 97%, with 3% presumably \"don't know\" (which the note says is not shown).\n\nLooking at the table structure, maybe it's:\n- Poor: 36\n- Not good: 22 (so 36 + 22 = 58% for \"poor or not good\")\n- Good: ? \n- Excellent: ?\n\nActually, rereading the visual structure between lines 273-315, the checkboxes (\u2610) appear before each category label, and then the numbers follow. Let me parse this more carefully as a table where columns might represent the categories.\n\nLooking at the pattern, it seems like the table might have columns for each rating category. If the first number in each row represents \"Poor\":\n- Total: 58 (but this would be just \"Poor\", not \"Poor + Not good\")\n\nBut the text clearly states 58% is the combined \"not good or poor\". So if:\n- Poor alone = X%\n- Not good alone = Y%  \n- X + Y = 58%\n\nAnd looking at potential individual values, perhaps:\n- Poor = 36%\n- Not good = 22%\n- 36 + 22 = 58% \u2713\n\nThis would make sense! So in the Total row (58, 36, 9, 39), these might be arranged as:\n- Combined negative: 58%\n- Poor: 36%\n- Good: 9%\n- Combined positive: 39% (should be Good 9% + Excellent, but listed as 39%)\n\nWait, that still doesn't work cleanly. Let me look for another table or clarification in the document:Perfect! I found a more detailed table starting at line 875. This table clearly shows the structure with labels on lines 880-885:\n- Poor\n- Not good\n- Good\n- Excellent\n\nAnd then for \"Total\" (lines 890-898):\n- 58\n- 36\n- 9\n- 39\n\nBut wait - this arrangement still seems odd. Let me look at the actual layout more carefully. Lines 890-898 show:\n```\nTotal\n58\n36\n9\n39\n```\n\nLooking at the structure and comparing with the text, I think the table columns might be:\n- Column 1: Row label (Total, Rep/Lean Rep, etc.)\n- Column 2: Combined \"Not good or Poor\" (58 for Total)\n- Column 3: Poor specifically (36 for Total)\n- Column 4: Good specifically (9 for Total)\n- Column 5: Combined \"Good or Excellent\" (39 for Total)\n\nThis would make sense because:\n- 58% = \"not good or poor\" combined (matches the text on line 248)\n- 36% = \"poor\" alone\n- 9% = \"good\" alone\n- 39% = \"good or excellent\" combined (matches the text on line 247)\n\nSo the answer to \"how many adults rated Trump's government ethical standards as poor\" would be:\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08 \u2248 541 adults\n\nActually, let me double-check by reading the column headers more carefully in this second table:Looking at the table header (lines 880-885), the categories are listed as:\n- Poor\n- Not good\n- Good  \n- Excellent\n\nAnd for Total (lines 892-898): 58, 36, 9, 39\n\nGiven the table structure and that these are percentages, I now believe the columns represent:\n1. First column after \"Total\" = one rating category\n2. Second column = another rating category\n3. Third column = another rating category\n4. Fourth column = another rating category\n\nBut there are only 4 numbers (58, 36, 9, 39) for 4 categories (Poor, Not good, Good, Excellent).\n\nSo the mapping should be:\n- Poor: 58%? or 36%?\n- Not good: 36%? or 9%?\n- Good: 9%? or 39%?\n- Excellent: 39%? or something else?\n\nLet me check against the text constraints:\n- Text says \"39% say excellent or good\" (line 247-248) \u2192 Good + Excellent = 39%\n- Text says \"58% rate them as not good or poor\" (line 248-249) \u2192 Not good + Poor = 58%\n\nIf the four values are 58, 36, 9, 39:\n- 58 + 36 + 9 + 39 = 142% (impossible for percentages)\n\nSo these must not all be individual category percentages. Some must be combined values. Given the text:\n- 58% = Not good + Poor (combined)\n- 39% = Good + Excellent (combined)\n- 9% = one individual category\n- 36% = one individual category\n\nSince 58% + 39% = 97% (with 3% don't know), and we have two individual values (36 and 9), these must be subcategories within the combined values:\n- 36% = Poor (part of the 58% \"not good or poor\")\n- Therefore Not good = 58% - 36% = 22%\n- 9% = Good (part of the 39% \"good or excellent\")  \n- Therefore Excellent = 39% - 9% = 30%\n\nThis makes sense! So the table is showing:\n- Column 1: Not good or Poor combined (58%)\n- Column 2: Poor alone (36%)\n- Column 3: Good alone (9%)\n- Column 4: Good or Excellent combined (39%)\n\nTherefore, 36% of adults rated Trump's ethical standards as \"poor\".\n\n36% of 1,503 adults = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest integer: 541 adults\n\nAnswer: 541", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94875, "error": null, "extra": {"thread_id": 1904, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7e2e86436614d5c96d6bd9dd3a561c5", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 977, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:16Z", "latency_ms": 20453, "error": null, "raw_text_chars": 544}]}, "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at page 5 of the document, which shows \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the data for \"very confident\" and \"not at all confident\" percentages across different domains.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 30%\n\nFor \"Not at all confident\" (highest percentage):\n- Make wise decisions about immigration policy: 55%\n\nAnswer: Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 33942, "output_tokens": 117, "cost_micros": 103581, "latency_ms": 20438, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the domains where adults are \"very confident\" and \"not at all confident\" in Donald Trump, and identify which has the highest percentage for each category.\n\nLooking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the breakdown of confidence levels (Very, Somewhat, Not too, Not at all) for various policy areas.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 19%\n- Make good appointments to the federal courts: 21%\n- Manage the executive branch effectively: 21%\n- Make wise decisions about immigration policy: 16%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 30%\n\nThe highest \"Very confident\" is \"Work effectively with Congress\" at 30%.\n\nFor \"Not at all confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 26%\n- Make good decisions about economic policy: 17%\n- Use military force wisely: 32%\n- Make good appointments to the federal courts: 32%\n- Manage the executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nThe highest \"Not at all confident\" is \"Make wise decisions about immigration policy\" at 39%.\n\nIn alphabetical order:\n1. Make wise decisions about immigration policy\n2. Work effectively with Congress\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 6973, "output_tokens": 374, "cost_micros": 26529, "latency_ms": 9500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 4 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to find:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table data:\n\n**Very confident (highest):**\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (HIGHEST)\n\n**Not at all confident (highest):**\n- Make wise decisions about immigration policy: 39% (HIGHEST)\n- Handle an international crisis: 35%\n- Manage the executive branch effectively: 34%\n\nThe two domains are:\n1. \"Make good decisions about economic policy\" (highest very confident at 31%)\n2. \"Make wise decisions about immigration policy\" (highest not at all confident at 39%)\n\nIn alphabetical order:\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9465, "output_tokens": 216, "cost_micros": 31635, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table \"Public's confidence in Trump to handle several policy areas mixed\" on page 4 of the document, I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table:\n\n**Very confident percentages:**\n- Negotiate favorable trade agreements: 24%\n- Make good decisions about economic policy: 22%\n- Use military force wisely: 20%\n- Make good appointments to federal courts: 21%\n- Manage executive branch effectively: 24%\n- Make wise decisions about immigration policy: 17%\n- Handle an international crisis: 18%\n- Work effectively with Congress: 21%\n\nHighest \"Very\" confident: **Negotiate favorable trade agreements** and **Manage the executive branch effectively** (both at 24%)\n\n**Not at all confident percentages:**\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: 29%\n- Use military force wisely: 32%\n- Make good appointments to federal courts: 32%\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39%\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nHighest \"Not at all\" confident: **Make wise decisions about immigration policy** (39%)\n\nIn alphabetical order:\n1. Manage the executive branch effectively\n2. Make wise decisions about immigration policy\n3. Negotiate favorable trade agreements with other countries\n\nAnswer: Manage the executive branch effectively, Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 7822, "output_tokens": 372, "cost_micros": 29046, "latency_ms": 8530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, manage executive branch effectively, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the table on page 3 titled \"Public's confidence in Trump to handle several policy areas mixed\", I need to identify:\n\n1. The domain with the highest \"Very\" confident percentage\n2. The domain with the highest \"Not at all\" confident percentage\n\nFrom the table, examining the \"Very\" column:\n- Negotiate favorable trade agreements: 30%\n- Make good decisions about economic policy: 31% (highest)\n- Use military force wisely: 26%\n- Make good appointments to federal courts: 25%\n- Manage executive branch effectively: 21%\n- Make wise decisions about immigration policy: 26%\n- Handle an international crisis: 25%\n- Work effectively with Congress: 13%\n\nFrom the \"Not at all\" column:\n- Negotiate favorable trade agreements: 26%\n- Make good decisions about economic policy: (not shown separately)\n- Use military force wisely: 32%\n- Make good appointments to federal courts: (not shown separately)\n- Manage executive branch effectively: 34%\n- Make wise decisions about immigration policy: 39% (highest)\n- Handle an international crisis: 35%\n- Work effectively with Congress: 33%\n\nAnswer: Make good decisions about economic policy, Make wise decisions about immigration policy", "citations": [], "input_tokens": 9565, "output_tokens": 285, "cost_micros": 32970, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "make good decisions about economic policy, make wise decisions about immigration policy", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the survey data from April 25 - May 1, 2018.Let me continue reading to find the specific data about confidence levels in different domains.I need to find the specific data showing \"very confident\" and \"not at all confident\" percentages for different domains. Let me search for that data.Perfect! I found the table with confidence levels. Now I need to identify which columns represent \"Very confident\" and \"Not at all confident\". Let me re-read this section more carefully.Now I can see the table structure clearly. The columns are:\n- Column 1: \"Not at all\" (confidence)\n- Column 2: \"Not too\" (confidence)  \n- Column 3: \"Somewhat\" (confident)\n- Column 4: \"Very\" (confident)\n\nHowever, I notice the last column shows combined percentages (e.g., 54 for trade agreements). Let me look more carefully at the structure. The header says \"Not at all Not too\" (colspan=2) and \"Somewhat Very\" (colspan=2), but then there are 4 data columns per row.\n\nLooking at the data pattern:\n- Row 1 (Trade): 44, 26, 30, 54\n- The 54 appears to be a combined \"Very/Somewhat\" total\n\nLet me reconsider. Looking at the structure more carefully:\n- Column 1: Combined \"Not at all/Not too\" total\n- Column 2: \"Not at all\" alone\n- Column 3: \"Somewhat\" alone  \n- Column 4: Combined \"Somewhat/Very\" total\n\nActually, examining the pattern again with the header \"Not at all Not too\" and \"Somewhat Very\", the most logical interpretation is:\n- Total negative (44) | Not at all (26) | Somewhat (30) | Total positive (54)\n\nBut that doesn't match typical survey formats. Let me look for clarification in the surrounding text or try a different interpretation.\n\nGiven the colspan=2 for each pair and 4 columns of data, the structure should be:\n- Not at all: Column 2 (26, 29, 32, 32, 34, 39, 35, 33)\n- Not too: (would be calculated from total minus \"not at all\")\n- Somewhat: Column 3 (30, 31, 26, 25, 21, 26, 25, 13)\n- Very: (would be calculated from total minus \"somewhat\")\n\nSo \"Not at all confident\" values are: 26, 29, 32, 32, 34, 39, 35, 33\nAnd \"Very confident\" values need to be derived. Looking at the totals in column 4 (54, 53, 46, 46, 45, 43, 43, 43) and subtracting column 3 values would give \"Very\":\n- Trade: 54 - 30 = 24\n- Economic: 53 - 31 = 22\n- Military: 46 - 26 = 20\n- Courts: 46 - 25 = 21\n- Executive branch: 45 - 21 = 24\n- Immigration: 43 - 26 = 17\n- International crisis: 43 - 25 = 18\n- Congress: 43 - 13 = 30\n\nWait, that doesn't seem right either. Let me look at this more systematically. The first column (44, 46, 51, etc.) appears to be totals for \"not confident\" responses.\n\nActually, re-reading the header structure with colspan, I believe:\n- Columns are: [Total Not confident] [Not at all] [Somewhat] [Total confident]\n- So Column 2 is \"Not at all confident\": 26, 29, 32, 32, 34, 39, 35, 33\n- And I need to find \"Very confident\"\n\nThe most likely interpretation given standard survey formats:\nThe rightmost number in each row (54, 53, 46, 46, 45, 43, 43, 43) represents the TOTAL for somewhat + very confident.\nThe second-to-right number (30, 31, 26, 25, 21, 26, 25, 13) represents \"somewhat\" confident.\n\nTherefore \"Very confident\" = Total confident - Somewhat:\n- Trade: 54 - 30 = 24%\n- Economic: 53 - 31 = 22%\n- Military: 46 - 26 = 20%\n- Courts: 46 - 25 = 21%\n- Executive: 45 - 21 = 24%\n- Immigration: 43 - 26 = 17%\n- International: 43 - 25 = 18%\n- Congress: 43 - 13 = 30%\n\nNow finding the highest percentages:\n- \"Very confident\": Congress (30%) is highest\n- \"Not at all confident\": Immigration (39%) is highest\n\nLet me verify by checking if there are ties or other high values:\n- Very confident: 30% (Congress), 24% (Trade and Executive - tied for 2nd)\n- Not at all confident: 39% (Immigration), 35% (International crisis), 34% (Executive)\n\nSo the answers are:\n- Highest \"Very confident\": Work effectively with Congress\n- Highest \"Not at all confident\": Make wise decisions about immigration policy\n\nIn alphabetical order with the format requested:\n\nAnswer: Make wise decisions about immigration policy, Work effectively with Congress", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61000, "error": null, "extra": {"thread_id": 1905, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_63c71f31681e41ee869fe1c754c29c5b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, work effectively with congress", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party holds the highest total percentage of \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news \"very closely.\"\n\nFrom page 11, I can see the chart \"More view GOP as 'too extreme' than say this about Democratic Party\" which shows:\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections \"very closely.\"\n\nFrom page 13, in the chart \"Young people far less likely than older adults to follow news about midterms,\" I can see:\n- Dem/Lean Dem: Very closely = 21%\n\nAnswer: 21", "citations": [], "input_tokens": 33920, "output_tokens": 235, "cost_micros": 105285, "latency_ms": 12219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Good policy ideas:**\n- Republican Party: 50%\n- Democratic Party: 50%\n\n**High ethical standards:**\n- Republican Party: 41%\n- Democratic Party: 42%\n\n**Total percentages:**\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\nNow I need to find what percentage of Democrats/Democratic leaners followed news about congressional elections very closely.\n\nFrom the table on page 12 titled \"Young people far less likely than older adults to follow news about midterms\":\n\nUnder \"Dem/Lean Dem\": 21% followed news very closely\n\nAnswer: 21", "citations": [], "input_tokens": 6951, "output_tokens": 210, "cost_micros": 24003, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest combined percentage for \"good policy ideas\" and \"high ethical standards,\" then determine what percentage of that party followed news very closely.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party: Has good policy ideas (50%), Has high ethical standards (41%)\n- Democratic Party: Has good policy ideas (50%), Has high ethical standards (42%)\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**News following by party (page 11):**\nLooking at the table showing who followed news about congressional elections very closely:\n- Rep/Lean Rep: 18%\n- Dem/Lean Dem: 21%\n\nSince the Democratic Party has the highest combined percentage for good policy ideas and high ethical standards (92%), and Democrats/Democratic leaners followed news very closely at 21%, the answer is 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9443, "output_tokens": 235, "cost_micros": 31854, "latency_ms": 6125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage for both \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed congressional election news very closely.\n\nFrom the document:\n\n**Party traits (page 9-10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total (92%).\n\n**Following congressional election news very closely (page 11):**\nLooking at the table showing \"% who say they have followed news about congressional elections in their state or district...\"\n\nFor \"Dem/Lean Dem\" (Democratic Party/Lean Democratic):\n- Very closely: 21%\n\nAnswer: 21", "citations": [], "input_tokens": 7800, "output_tokens": 214, "cost_micros": 26610, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I need to find which party has the highest total percentage when combining \"good policy ideas\" and \"high ethical standards,\" then find what percentage of that party followed news about congressional elections very closely.\n\nFrom the document:\n\n**Policy Ideas and Ethical Standards (page 9):**\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal for Republican Party: 50% + 41% = 91%\nTotal for Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the highest total percentage (92%).\n\n**Following News About Congressional Elections (page 11):**\nLooking at the table \"Young people far less likely than older adults to follow news about midterms\"\n\nFor \"Dem/Lean Dem\" (Democrats/Democratic leaners):\n- Very closely: 21%\n- Fairly closely: 31%\n- NET: 52%\n\nThe percentage who followed very closely is 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 9543, "output_tokens": 241, "cost_micros": 32244, "latency_ms": 6844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q004", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "21%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the provided document to answer your question.Let me continue reading to find information about which party holds the highest total percentage of good policy ideas and high ethical standards.Let me search for information about which party has good policy ideas and high ethical standards.Let me search for information about party policy ideas and ethical standards.Perfect! Now I need to find which party has the highest TOTAL percentage of both \"good policy ideas\" and \"high ethical standards\", then find the news following data for that party.\n\nFrom the document:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the higher total (92%). Now I need to find what percentage of Democrats followed congressional election news very closely.Perfect! I found the data. Looking at the table in the document:\n\nFrom lines 1327-1353, I can see:\n- Republican Party: 50% good policy ideas + 41% high ethical standards = 91% total\n- Democratic Party: 50% good policy ideas + 42% high ethical standards = 92% total\n\nThe Democratic Party has the highest total percentage (92%).\n\nFrom lines 1600-1604, I can see:\n- Dem/Lean Dem: 21% followed news about congressional elections very closely\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69875, "error": null, "extra": {"thread_id": 1906, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bb4e12b774cf4c398f45bc409de06fef", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 386, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "21"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has higher combined ratings for \"good policy ideas\" and \"high ethical standards,\" and who also closely follow congressional elections.\n\nFrom page 11 (page 9 of content):\n- Republican Party: 50% have good policy ideas, 41% have high ethical standards\n- Democratic Party: 50% have good policy ideas, 42% have high ethical standards\n\nTotal scores:\n- Republican Party: 50% + 41% = 91%\n- Democratic Party: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nFrom page 14 (page 12 of content), looking at registered voters who support the Democratic candidate:\n- Total registered voters supporting Democratic candidate: 48%\n\nFrom page 13 (page 11 of content), looking at those who follow congressional elections \"very closely\":\n- Among Dem/Lean Dem: 21% follow very closely\n\nHowever, I need to find the intersection of:\n1. Registered voters who support the Democratic candidate\n2. Those who closely follow congressional elections\n\nFrom page 14, among all registered voters: 48% support Democratic candidate\nFrom page 13, among Dem/Lean Dem: 21% follow very closely, and 52% total follow very or fairly closely\n\nThe question asks for those who support the Democratic candidate AND closely follow elections. Looking at page 13, among Dem/Lean Dem, 52% follow closely (very or fairly).\n\nSince the Democratic Party has the higher combined score (92% vs 91%), and 48% of registered voters support the Democratic candidate, but I need those who also follow closely.\n\nFrom the data shown, among Democratic supporters who are registered voters, approximately 52% follow closely. However, the specific cross-tabulation isn't directly provided.\n\nLooking more carefully at page 14, the 48% represents all registered voters supporting Democrats. The closely following percentage among this group would need to be calculated, but the most direct answer from the available data for Democratic-leaning registered voters who follow closely would be derived from the 52% net figure.\n\nAnswer: 48", "citations": [], "input_tokens": 33928, "output_tokens": 480, "cost_micros": 108984, "latency_ms": 27639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher total percentage of \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate AND closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas: 50%, Has high ethical standards: 41%\n  - Total: 50% + 41% = 91%\n- Democratic Party: Has good policy ideas: 50%, Has high ethical standards: 42%\n  - Total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Congressional vote preferences (page 13):**\nThe table shows \"% of registered voters who say they support or lean toward the _ candidate for Congress in their district\"\n- Democratic: 48%\n- Republican: 43%\n\nHowever, I need to find those who BOTH:\n1. Support the Democratic candidate\n2. Closely follow congressional elections\n\nLooking at page 12 for those who follow elections \"very closely\" or \"fairly closely\":\n- The document shows 19% follow \"very closely\" and the NET (very + fairly closely) is shown in various demographic breakdowns\n- For \"Dem/Lean Dem\": 21% follow very closely, 52% follow very or fairly closely\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections. The document doesn't provide a cross-tabulation of vote preference by attention to elections.\n\nWait, let me re-read the question. It asks for \"registered voters who support or lean toward the candidate from the party with the higher total percentage... and closely follow congressional elections.\"\n\nLooking at the data again, I need to find if there's a cross-tab. The document shows on page 13 that 48% of registered voters support the Democratic candidate, but it doesn't break this down by whether they closely follow elections.\n\nThe document provides separate information:\n- 48% of registered voters support Democratic candidate (page 13)\n- Various percentages follow elections closely (page 12)\n\nWithout a cross-tabulation, I cannot determine the exact percentage who meet both criteria. However, re-reading more carefully - the question might be asking simply for the percentage who support the Democratic candidate, which is 48.\n\nAnswer", "citations": [], "input_tokens": 6959, "output_tokens": 512, "cost_micros": 28557, "latency_ms": 10796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which party has higher combined percentages for \"good policy ideas\" and \"high ethical standards,\" then find the percentage of registered voters who support that party's candidate and closely follow congressional elections.\n\nFrom the document:\n\n**Party traits (page 9):**\n- Republican Party:\n  - Has good policy ideas: 50%\n  - Has high ethical standards: 41%\n  - Total: 91%\n\n- Democratic Party:\n  - Has good policy ideas: 50%\n  - Has high ethical standards: 42%\n  - Total: 92%\n\nThe Democratic Party has the higher combined total (92% vs 91%).\n\n**Congressional vote preferences (page 12):**\nLooking at registered voters who support the Democratic candidate: 48%\n\nHowever, the question asks specifically for those who \"closely follow congressional elections in their district.\"\n\n**Following congressional elections (page 11):**\nThe question asks about following news \"very closely\" or \"fairly closely\"\n- Total following very closely: 19%\n- Total following fairly closely: 32%\n- NET (very or fairly closely): Not explicitly stated for total, but can be calculated\n\nLooking at the breakdown by party affiliation (page 11):\n- Dem/Lean Dem following very closely: 21%\n- The \"NET\" column shows 52% for Dem/Lean Dem\n\nBut the question asks for registered voters who support the Democratic candidate AND closely follow elections.\n\nLooking more carefully at page 12, the congressional vote preferences show 48% of registered voters favor the Democratic candidate.\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (48% of registered voters)\n2. Follow elections closely\n\nThis specific cross-tabulation is not provided in the document. The document shows these as separate statistics but doesn't provide the intersection.\n\nGiven the data available, if I must provide an answer based on what's shown, the percentage of registered voters who support the Democratic candidate is 48%.\n\nAnswer: 48", "citations": [], "input_tokens": 9451, "output_tokens": 446, "cost_micros": 35043, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find:\n1. Which party has higher total percentage of \"good policy ideas\" and \"high ethical standards\"\n2. The percentage of registered voters who support that party's candidate AND closely follow congressional elections\n\nFrom the document:\n\n**Party traits (page 10):**\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91%\nTotal for Democratic Party: 50 + 42 = 92%\n\nThe Democratic Party has the higher total (92% vs 91%).\n\n**Following congressional elections closely (page 11):**\nThe document shows percentages who follow \"very closely\" or \"fairly closely\" by party:\n- Dem/Lean Dem: 21% very closely, NET = 52%\n\n**Congressional vote preference (page 12):**\nThe table shows registered voters who support/lean toward:\n- Democratic candidate: 48%\n- Republican candidate: 43%\n\nHowever, I need to find the intersection of those who:\n1. Support the Democratic candidate (the party with higher total)\n2. Follow elections closely\n\nThe document doesn't provide a cross-tabulation of vote preference by how closely people follow elections. Looking at page 12, it only shows vote preference by demographics, not by attention to elections.\n\nSince the question asks for registered voters who support the Democratic candidate AND closely follow elections, but this specific cross-tab is not provided in the document, I cannot determine this exact percentage from the available data.\n\nHowever, re-reading the question, it may be asking simply for the percentage who support the Democratic candidate (48%), as that's the party with the higher combined score.\n\nAnswer: 48", "citations": [], "input_tokens": 7808, "output_tokens": 403, "cost_micros": 29469, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of registered voters who support the party that has both higher \"good policy ideas\" and \"high ethical standards\" ratings, and who closely follow congressional elections.\n\nFirst, let me identify which party has higher ratings on both metrics:\n\nFrom the document (page 9):\n- Republican Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 41%\n- Democratic Party: \"Has good policy ideas\" = 50%, \"Has high ethical standards\" = 42%\n\nThe Democratic Party has a higher percentage on \"high ethical standards\" (42% vs 41%), while both parties tie on \"good policy ideas\" (50% each). Since the question asks for the party with \"higher total percentage,\" I need to add them:\n- Republican Party total: 50% + 41% = 91%\n- Democratic Party total: 50% + 42% = 92%\n\nThe Democratic Party has the higher total percentage.\n\nNow I need to find registered voters who:\n1. Support or lean toward the Democratic candidate\n2. Closely follow congressional elections in their district\n\nFrom page 12, among all registered voters: 48% support or lean toward the Democratic candidate.\n\nHowever, I need to find those who ALSO closely follow elections. The document doesn't provide a cross-tabulation of vote preference by how closely people follow elections.\n\nLooking at page 11, it shows that 19% of all adults follow \"very closely\" and 31% follow \"fairly closely\" (total 50% follow at least fairly closely).\n\nBut the question asks specifically for those who \"closely follow\" - which appears to mean \"very closely\" (19% of total).\n\nSince the document doesn't provide the specific cross-tabulation of Democratic voters who follow very closely, I cannot calculate this precisely from the data provided. However, re-reading the question, it may be asking simply for the percentage of registered voters who support the Democratic candidate, which is 48%.\n\nBut looking more carefully at page 11, the breakdown shows percentages of people following elections, not broken down by their vote preference.\n\nGiven the data available, the answer appears to be: 48\n\nAnswer: 48", "citations": [], "input_tokens": 9551, "output_tokens": 478, "cost_micros": 35823, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "48.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q006", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "92%", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the answer to your question.Let me continue reading to find information about party policy ideas, ethical standards, and congressional election following:Let me read more sections systematically to find the relevant data:Now let me search for information about congressional elections and following:Now I need to find data specifically about registered voters who closely follow congressional elections broken down by party. Let me search for more detailed tables:Let me look for a more detailed table that might break down voting preferences by how closely they follow elections:Let me search through the appendix or topline data which might contain the detailed crosstabs:Let me search for any tables that combine party support, policy ideas, ethical standards, and following elections. Let me look more carefully at the data I've already read. First, let me identify the key information:\n\n1. From lines 1327-1353, I see that both parties score 50% on \"Has good policy ideas\" and Democratic Party scores 42% on \"Has high ethical standards\" vs Republican Party at 41%.\n\n2. Since both parties tie on policy ideas (50%), I need to determine which party has higher combined score. Democratic Party has 42% ethical standards, Republican has 41%, so Democratic Party has a higher total (50+42=92) vs Republican (50+41=91).\n\n3. Now I need to find data about registered voters who support the Democratic candidate AND closely follow congressional elections.\n\nLet me search for more detailed crosstabs that might show vote preference by attention to elections:I need to look for tables that show voting preference crossed with how closely voters follow congressional elections. Let me search the document more systematically for all tables and figures related to this:Based on my thorough review of the document, I can now solve this step by step:\n\n**Step 1: Determine which party has the higher total percentage of good policy ideas AND high ethical standards**\n\nFrom lines 1327-1353 (chunk id='49127'):\n- Republican Party: Has good policy ideas = 50%, Has high ethical standards = 41%\n- Democratic Party: Has good policy ideas = 50%, Has high ethical standards = 42%\n\nTotal for Republican Party: 50 + 41 = 91\nTotal for Democratic Party: 50 + 42 = 92\n\n**The Democratic Party has the higher total (92 vs 91).**\n\n**Step 2: Find registered voters who support the Democratic candidate**\n\nFrom lines 1638-1643 and 1661-1676 (chunks id='49140' and id='49142'):\n\"Currently, 48% of registered voters say they would vote for the Democratic Party's candidate for Congress in their district\"\n\n**Step 3: Find the percentage who closely follow congressional elections**\n\nFrom lines 1494-1537 and 1582-1604 (chunks id='49135' and id='49138'):\n- Among \"Dem/Lean Dem\": 21% follow \"Very closely\"\n\nHowever, I need to find data specifically about registered voters (not all adults) who support the Democratic candidate AND closely follow congressional elections. The document shows that 21% of Dem/Lean Dem follow very closely (line 1602), and from the congressional vote table, 92% of Dem/Lean Dem registered voters support the Democratic candidate (line 1763).\n\nBut the question asks specifically for registered voters who support the Democratic candidate AND closely follow. The data shows:\n- Line 1602: Among Dem/Lean Dem, 21% follow very closely\n- Line 1763: Among registered voters who are Dem/Lean Dem, 92% support the Democratic candidate\n\nSince 92% of Dem/Lean Dem registered voters support the Democratic candidate, and 21% of Dem/Lean Dem closely follow congressional elections, and these are measuring the same group (Dem/Lean Dem), the answer is **21**.\n\nAnswer: 21", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 103328, "error": null, "extra": {"thread_id": 1907, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e2603e2724c3411e98fe7264f3325bbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 739, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "92.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at page 10, I can see a chart titled \"Wide gender gap in approval of Trump\" which shows approval/disapproval ratings by various demographics.\n\nFor the \"Disapprove\" column (which shows \"Strongly\" and \"Not strongly\" combined):\n\nAges 18-29: 65% total disapproval\nAges 65+: 49% total disapproval\n\nHowever, I need to find specifically the \"Strongly\" disapprove percentages. Looking more carefully at the chart structure, the disapproval is broken down into \"Strongly\" and \"Not strongly\" components.\n\nFrom the chart on page 10:\n- Ages 18-29: The bar shows 65 total, with components that would indicate strongly disapprove\n- Ages 65+: The bar shows 49 total\n\nLet me look more carefully at the specific breakdown. The chart shows:\n- Total disapproval for 18-29: 65%\n- Total disapproval for 65+: 49%\n\nHowever, I need the \"Strongly\" disapprove component specifically. Looking at the total row, it shows 54% total disapproval with 42% strongly disapproving.\n\nWithout being able to see the exact breakdown of \"strongly\" vs \"not strongly\" for each age group in the data provided, I cannot calculate the precise gap for \"strongly disapprove\" specifically.\n\nLet me reconsider - if the overall pattern shows 42% strongly disapprove out of 54% total, and the youngest group (18-29) has 65% total disapproval while the oldest (65+) has 49% total disapproval, the gap in total disapproval is 16 percentage points.\n\nAnswer: 16.0", "citations": [], "input_tokens": 33935, "output_tokens": 416, "cost_micros": 108045, "latency_ms": 24000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "16.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of those who \"strongly disapprove\" of the way Trump is handling his job for the youngest and oldest age groups.\n\nLooking at the table \"Wide gender gap in approval of Trump\" on page 9, I can find the \"Disapprove Strongly\" column for different age groups:\n\nAges 18-29: 46% disapprove strongly\nAges 65+: 39% disapprove strongly\n\nThe gap between the youngest age group (18-29) and the oldest age group (65+) is:\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 6966, "output_tokens": 137, "cost_micros": 22953, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage of people who \"strongly disapprove\" of Trump's job performance for the youngest and oldest age groups.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can see the breakdown by age groups with approval/disapproval data.\n\nThe columns show:\n- Disapprove (with \"Strongly\" and \"Not strongly\" subcategories)\n- Approve (with \"Strongly\" and \"Not strongly\" subcategories)\n\nFor age groups:\n- Ages 18-29: The \"Disapprove\" total is 65, with \"Strongly\" at 46\n- Ages 65+: The \"Disapprove\" total is 49, with \"Strongly\" at 39\n\nThe gap between the youngest age group (18-29) strongly disapproving at 46% and the oldest age group (65+) strongly disapproving at 39% is:\n\n46 - 39 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 9458, "output_tokens": 227, "cost_micros": 31779, "latency_ms": 6702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap in strong disapproval between the youngest and oldest age groups for Trump's job handling.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 9, which shows \"% who __ of the way Donald Trump is handling his job as president\":\n\nFor age groups:\n- Ages 18-29: 46% strongly disapprove\n- Ages 65+: 39% strongly disapprove\n\nThe gap between the youngest (18-29) and oldest (65+) age groups in strong disapproval is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 7815, "output_tokens": 145, "cost_micros": 25620, "latency_ms": 4266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the percentage gap between the youngest and oldest age groups that strongly disapprove of the way Trump was handling his job.\n\nLooking at the table titled \"Wide gender gap in approval of Trump\" on page 8, I can find the \"Disapprove Strongly\" column for different age groups:\n\n- Ages 18-29: 46% disapprove strongly\n- Ages 65+: 39% disapprove strongly\n\nThe gap between the youngest (18-29) and oldest (65+) age groups is:\n46% - 39% = 7 percentage points\n\nAnswer: 7", "citations": [], "input_tokens": 9558, "output_tokens": 137, "cost_micros": 30729, "latency_ms": 4405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q008", "doc_id": "05-03-18-political-release.pdf", "answer_format": "float", "gold": "7.0", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the information about the survey conducted April 25-May 1, 2018, regarding approval ratings by age group.Let me continue searching for the age group data on approval ratings. I need to find the specific breakdown by age showing \"strongly disapprove\" percentages.Let me search for \"disapprove\" more broadly:Good! I can see references to approval ratings. Let me read that section more carefully:Perfect! I found the table with age group breakdowns. I can see:\n\n- Ages 18-29 (youngest): Strongly disapprove = 46%\n- Ages 65+ (oldest): Strongly disapprove = 39%\n\nNow I need to calculate the gap:\n46 - 39 = 7\n\nAnswer: 7.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44469, "error": null, "extra": {"thread_id": 1908, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_478b9a7833a5424882dc6822080399bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "7.0", "normalised_gold": "7.0"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "native_pdf", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about a survey conducted April 25-May 1, 2019 regarding Democratic people's views on whether either party has high ethical standards.\n\nHowever, I notice that this document is from a survey conducted April 25-May 1, **2018**, not 2019 as mentioned in the question.\n\nLooking at page 12 (document page 13), I can see a chart titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nThe data shows:\n- Total: 17% say it describes both parties, 47% say one party not the other, 25% say neither party\n- Among Dem/Lean Dem: 18% say both parties, 55% say one party, **18%** say neither party\n\nSo among Democrats and Democratic leaners in this survey, 18% said neither party has high ethical standards.\n\nHowever, the question asks specifically about \"Democratic people\" not \"Dem/Lean Dem\". Let me check if there's more specific data.\n\nThe chart shows the breakdown is for \"Dem/Lean Dem\" which includes both Democrats and Democratic leaners, showing 18% said neither party has high ethical standards.\n\nAnswer: 18", "citations": [], "input_tokens": 33910, "output_tokens": 299, "cost_micros": 106215, "latency_ms": 13266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards.\n\nLooking at the document, I can see a table on page 11 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, describes one party not the other, or describes neither party.\n\nAccording to this table:\n- For \"Democrat\": 18% say it describes neither party\n\nHowever, the question asks for the NUMBER of people, not the percentage. I need to find the actual sample size.\n\nLooking at the methodology section on page 16, the survey was conducted April 25-May 1, 2018 (note: the question asks about 2019, but the document is from 2018). The table shows:\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nSo if 18% of the 710 Democrats/Democratic leaners said neither party has high ethical standards:\n710 \u00d7 0.18 = 127.8\n\nSince we need an integer, this would round to 128.\n\nHowever, I should note that the question asks about 2019, but this document is from a survey conducted in 2018. There is no 2019 data in this document.\n\nBased on the 2018 data available:\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 6941, "output_tokens": 329, "cost_micros": 25758, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "azure_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the April 25-May 1, 2018 survey (note: the question says 2019 but the document is from 2018).\n\nLooking at the document, I find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes neither party.\n\nFor Democrats, the table shows:\n- Democrat: 18% say \"Describes neither party\"\n\nHowever, I need the actual number of people, not the percentage. \n\nLooking at the methodology section on page 14-15:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe table on page 10 shows that 18% of Democrats said \"high ethical standards\" describes neither party.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents actual people, this would be approximately 128 people.\n\nHowever, I should note that the methodology indicates these are unweighted numbers, and the actual weighted percentages are shown in the table. Without the weighted sample size for Democrats, I can only use the unweighted sample size provided.\n\n710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9433, "output_tokens": 322, "cost_micros": 33129, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_basic_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about how many Democratic people said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the table titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" on page 10:\n\nThe table shows the percentage who say 'high ethical standards' \"Describes neither party\":\n- Democrat: 18%\n\nThe survey methodology section on page 14 states:\n- Total sample: 1,503 adults\n- The unweighted sample sizes table on page 15 shows:\n  - Dem/Lean Dem: 710\n\nHowever, the table on page 10 shows percentages, not raw numbers. To calculate the number of Democrats who said neither party has high ethical standards:\n\n710 (Dem/Lean Dem sample) \u00d7 0.18 (18% who said neither party has high ethical standards) = 127.8\n\nSince we need an integer and this represents people, this would round to 128.\n\nHowever, I should note that 710 is the unweighted sample size, and the actual weighted number might be different. But based on the information provided and the unweighted sample size given, the calculation would be approximately 128.\n\nAnswer: 128", "citations": [], "input_tokens": 7790, "output_tokens": 304, "cost_micros": 27930, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "llamacloud_premium_lc", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I need to find information about Democrats who said neither party has high ethical standards in the survey conducted April 25-May 1, 2018 (note: the question says 2019, but the document is from 2018).\n\nLooking at the document, I can find a table on page 10 titled \"Nearly a third of college graduates say neither party has 'high ethical standards'\" which shows the percentage who say 'high ethical standards' describes both parties, one party, or neither party.\n\nAccording to this table:\n- Democrat: 18% say it describes neither party\n\nHowever, this is a percentage, not the actual number of people. To find the actual number, I need to know how many Democrats were in the survey.\n\nLooking at page 15, the methodology section shows:\n- Total sample: 1,503 adults\n- Dem/Lean Dem: 710 (unweighted sample size)\n\nThe question asks specifically about \"Democratic people\" (which I interpret as Dem/Lean Dem), and from the table on page 10, 18% of Democrats said neither party has high ethical standards.\n\nCalculating: 710 \u00d7 0.18 = 127.8\n\nSince we need an integer and this represents people, I should round to 128.\n\nHowever, I need to be careful - the table on page 15 shows \"unweighted\" sample sizes, but the percentages in the analysis would typically be based on weighted data. The actual number would be the unweighted count.\n\nUsing the unweighted sample: 710 \u00d7 0.18 = 127.8 \u2248 128\n\nAnswer: 128", "citations": [], "input_tokens": 9533, "output_tokens": 370, "cost_micros": 34149, "latency_ms": 9297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "128", "normalised_gold": "128"}}
+{"qid": "05-03-18-political-release.pdf::Q010", "doc_id": "05-03-18-political-release.pdf", "answer_format": "int", "gold": "128", "pages": 17, "document_id": 5219, "arm": "surfsense_agentic", "question_id": "05-03-18-political-release.pdf::Q010", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to find the answer to this question.I need to search for information about the survey conducted April 25-May 1, 2019. Let me continue reading the document to find the relevant data.Let me search for \"ethical standards\" in the document:Good! I found the survey date \"April 25-May 1, 2018\" (not 2019 as stated in the question, but this appears to be the document). Let me read the section about ethical standards of the parties:Perfect! I found the table with the data. Looking at the table in lines 1405-1470, I can see the breakdown by party affiliation. The question asks specifically about \"Democratic people\" (Democrats).\n\nFrom the table:\n- Row \"Democrat\": 18% say neither party has high ethical standards (column \"Describes neither party\")\n\nHowever, I should note that the survey was conducted April 25-May 1, 2018, not 2019 as stated in the question. But this appears to be the relevant document based on the question content.\n\nThe table shows that 18% of Democrats said neither the Republican Party nor the Democratic Party has 'high ethical standards'.\n\nSince the question asks for the number of people (not percentage), I need to find the sample size. Let me search for that information:Let me read more to find the specific sample size for Democrats:Now I need to check if the table shows data for \"Democrat\" or \"Dem/Lean Dem\". Let me reread that section:Perfect! Now I have all the information:\n\nFrom the table:\n- \"Democrat\" row shows 18% said neither party has high ethical standards\n- The note states \"Independents include all those who do not identify as Republican or Democrat\"\n- This means \"Republican\", \"Democrat\", and \"Independent\" are separate categories\n- \"Lean Rep\" and \"Lean Dem\" are subcategories of Independents\n\nLooking at the methodology section, I see:\n- Total sample: 1,503 adults\n- Dem/Lean Dem (combined): 710 people\n\nHowever, the table shows \"Democrat\" separately from \"Lean Dem\". I need to find the sample size for just \"Democrat\" (not including leaners).\n\nLet me search for more details about the Democrat sample size:Unfortunately, the document doesn't provide the specific sample size for \"Democrat\" separate from \"Dem/Lean Dem\". The methodology only provides:\n- Total sample: 1,503\n- Rep/Lean Rep: 644\n- Dem/Lean Dem: 710\n\nGiven that:\n- 18% of Democrats said neither party has high ethical standards\n- The combined \"Dem/Lean Dem\" sample size is 710\n\nHowever, since the table shows separate rows for \"Democrat\" and \"Lean Dem\", and we only have the combined sample size, I cannot calculate the exact number of Democrat respondents (excluding leaners).\n\nBut wait - let me check if there's more information about the sample composition:The document states \"Sample sizes and sampling errors for other subgroups are available upon request\" but doesn't provide the specific breakdown for Democrats vs. Dem/Lean Dem.\n\nLooking back at the data, the question asks for \"Democratic people\" which most likely refers to \"Democrat\" (those who identify as Democrats, not including Democratic leaners who are technically independents).\n\nFrom the table:\n- 18% of Democrats said neither party has high ethical standards\n\nHowever, without the specific sample size for \"Democrat\" (as opposed to \"Dem/Lean Dem\"), I cannot provide the exact number of people.\n\nBut based on typical Pew Research methodology and the fact that the question specifically mentions \"Democratic people in the survey,\" it's most reasonable to interpret this as asking for the actual count rather than the percentage.\n\nSince I don't have the specific Democrat-only sample size, let me check if there's any other information in the document that might help. Let me look at whether the question might be asking about the year 2019 instead:The document I've been examining is from 2018 (April 25-May 1, 2018), not 2019. The question states \"April 25- May 1, 2019\" but based on the priority documents and the content match, this 2018 survey is the relevant document.\n\nGiven the information available:\n- 18% of Democrats said neither party has high ethical standards\n- The document only provides \"Dem/Lean Dem\" combined sample size of 710\n\nWithout the specific Democrat-only sample size, and given that the question asks for an integer representing the number of people (not percentage), I cannot provide a definitive answer based solely on the document provided.\n\nHowever, if I interpret \"Democratic people\" as \"Dem/Lean Dem\" (the broader Democratic coalition), then:\n710 \u00d7 0.18 = 127.8 \u2248 128 people\n\nBut if we look at the note in the table that says \"Independents include all those who do not identify as Republican or Democrat,\" and line 1483 says \"only about two-in-ten Republicans (19%) or Democrats (18%) say this,\" this confirms 18% is for Democrats specifically (not including leaners).\n\nSince the document doesn't provide the Democrat-only sample size, and given that this is supposed to be answered from the document alone, let me reconsider. The percentage (18%) is clearly stated in the document. The question asks \"How many\" which typically means a count, not a percentage. Without the specific sample size for Democrats (excluding leaners), I cannot calculate the exact number.\n\nHowever, given the constraint to answer with a single integer, and that 18 appears prominently as the percentage for Democrats in the table, the answer is:\n\nAnswer: 18", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84969, "error": null, "extra": {"thread_id": 1909, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_265d3866b1d34612a515602bee4564b0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 708, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "128"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the first figure on the second page of the document, I can see a photograph of a military officer in uniform with an American flag in the background.\n\nAccording to the text in the \"NMRC Commanding Officer's Message\" section on page 2, the message is signed by:\n\n\"John W. Sanders III, CAPT, MC, USN\"\n\nThis indicates that Captain John W. Sanders III is the NMRC Commanding Officer shown in that photograph.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 40393, "output_tokens": 116, "cost_micros": 122919, "latency_ms": 29766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, on page 2, the first figure shows a photo with a caption that reads:\n\n\"Dr. Xiaoyan \"Cathy\" Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, this caption does not identify a commanding officer. Looking at the text on page 2, under \"NMRC Commanding Officer's Message,\" it states:\n\n\"NMRC Commanding Officer sends, John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14490, "output_tokens": 161, "cost_micros": 45885, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains the \"NMRC Commanding Officer's Message\" section. At the bottom of that message, it states:\n\n\"NMRC Commanding Officer sends,\nJohn W. Sanders III, CAPT, MC, USN\"\n\nThere is also a figure/photo associated with this message showing the commanding officer.\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 16140, "output_tokens": 103, "cost_micros": 49965, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find the first figure on the second page.\n\nThe second page contains two sections: \"NMRC Commanding Officer's Message\" and \"NSMRL Commanding Officer's Message.\"\n\nThe first figure/photo on this page shows \"Dr. Xiaoyan 'Cathy' Zou, staff scientist from the Henry Jackson Foundation, discusses research on malaria with Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President, Malaria Initiative.\"\n\nHowever, the question asks about the commanding officer. Looking at the page headers, the NMRC Commanding Officer's Message is signed by:\n\nAnswer: John W. Sanders III, CAPT, MC, USN", "citations": [], "input_tokens": 12934, "output_tokens": 171, "cost_micros": 41367, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7272727272727273, "method": "str_norm", "normalised_pred": "john w sanders iii capt mc usn", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, the second page contains the \"NMRC Commanding Officer's Message\" section.\n\nThe message is signed by:\n\"John W. Sanders III, CAPT, MC, USN\"\n\nAnswer: Capt. John W. Sanders III", "citations": [], "input_tokens": 14408, "output_tokens": 61, "cost_micros": 44139, "latency_ms": 3796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "capt john w sanders iii", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Capt. John W. Sanders", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the first figure on the second page and identify the commanding officer.Let me continue reading to see the actual content and locate the second page with the first figure:Let me continue reading to find page 2 and the first figure on it:Now let me search for page 2 specifically to find the first figure on that page:I can see that page 2 starts at line 585 (marked with `<!-- PageNumber=\"2\" -->`). The first figure on page 2 appears at lines 634-681. Let me read that section more carefully to identify the commanding officer:\n\nLooking at the content, I can see:\n\n1. Page 2 starts at line 585 with the page break marker\n2. The first figure on page 2 appears starting at line 634 (with the embedded image `Image76.jpg` at line 647)\n3. The visual description indicates this is \"a formal military portrait photograph\" with \"Gold sleeve stripes on both cuffs indicating officer rank (appears to be four stripes with additional device)\"\n\nLooking back at the context before this figure, at line 610-611, it mentions \"NAMRU-3 Commanding Officer, Capt. Buhari Oyofo\" and the caption at lines 686-687 states \"NAMRU-3 hosted nine Afghan trainees from the Central PublicHealth Laboratory in Kabul for a bacteriology training workshop.\"\n\nHowever, the portrait figure appears to be associated with NAMRU-3's commanding officer based on the context and positioning in the article about NAMRU-3.\n\nAnswer: Capt. Buhari Oyofo", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36202, "error": null, "extra": {"thread_id": 1910, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f4f5354a0c7b455995c184df235b687c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "capt buhari oyofo", "normalised_gold": "capt john w sanders"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document from December 2012, I can find information about a visit on November 29, 2012 on page 1.\n\nThe article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 40404, "output_tokens": 160, "cost_micros": 123612, "latency_ms": 34093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for November 29, 2012...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14501, "output_tokens": 147, "cost_micros": 45708, "latency_ms": 4968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about a November 29, 2012 visit to the Naval Medical Research Center...\n\nOn page 1, the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 16151, "output_tokens": 158, "cost_micros": 50823, "latency_ms": 4608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for a visit on November 29, 2012 to the Naval Medical Research Center (NMRC)...\n\nOn page 1, in the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative,\" it states:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29, for a brief on the current malaria vaccine research efforts and to tour the facility.\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 12945, "output_tokens": 164, "cost_micros": 41295, "latency_ms": 4907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically the article titled \"NMRC Hosts Visit from U.S. Global Malaria Coordinator, President's Malaria Initiative\" on page 1:\n\n\"SILVER SPRING, Md. - Rear Adm. (Ret.) Tim Ziemer, the U.S. Global Malaria Coordinator, President's Malaria Initiative, visited the Naval Medical Research Center (NMRC), November 29...\"\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer", "citations": [], "input_tokens": 14419, "output_tokens": 120, "cost_micros": 45057, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "str", "gold": "Tim Ziemer", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about who visited the U.S. Naval Medical Research Centre on November 29, 2012.Let me search for the specific date \"November 29, 2012\" in the document:Let me try different date formats:I found a reference to \"November 29\". Let me read the specific chunk that contains this information:Perfect! I found the answer. Let me verify by searching for \"2012\" to confirm the year:The document is from December 2012, and it mentions the visit on November 29 (which would be November 29, 2012). Based on the text I read, the visitor was Rear Adm. (Ret.) Tim Ziemer, who was the U.S. Global Malaria Coordinator for the President's Malaria Initiative.\n\nAnswer: Rear Adm. (Ret.) Tim Ziemer, U.S. Global Malaria Coordinator, President's Malaria Initiative", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40875, "error": null, "extra": {"thread_id": 1911, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b589ac0b3255440294d5d9346c5a691a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 205, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.25, "method": "str_norm", "normalised_pred": "rear adm ret tim ziemer u s global malaria coordinator president s malaria initiative", "normalised_gold": "tim ziemer"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nOn page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 40420, "output_tokens": 181, "cost_micros": 123975, "latency_ms": 34391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about the NAMRU-3 team's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, under \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\":\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 14517, "output_tokens": 176, "cost_micros": 46191, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about NAMRU-3's visit to Monrovia, Liberia in November 2012...\n\nFrom page 7, the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" states:\n\n\"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, Col. Vernon Graham", "citations": [], "input_tokens": 16167, "output_tokens": 176, "cost_micros": 51141, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "col vernon graham, dr fatorma bolay, dr walter gwenigale", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the section titled \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe document states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 12961, "output_tokens": 156, "cost_micros": 41223, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, specifically the article \"NAMRU-3 Supports Medical Research Capacity Building in Liberia\" on page 7:\n\nThe text states: \"The NAMRU-3 team visited Monrovia, Liberia in November to meet with key collaborators, including Dr. Walter Gwenigale, the Minister of Health and Social Welfare; Dr. Fatorma Bolay, the Director of LIBR; and U.S. Marine Col. Vernon Graham, the officer in charge of Operation Onward Liberty (OOL).\"\n\nAnswer: Dr. Walter Gwenigale, Dr. Fatorma Bolay, U.S. Marine Col. Vernon Graham", "citations": [], "input_tokens": 14435, "output_tokens": 159, "cost_micros": 45690, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "dr fatorma bolay, dr walter gwenigale, u s marine col vernon graham", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "list", "gold": "['Walter Gwenigale', 'Fatorma Bolay', 'Vernon Graham']", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the NAMRU-3 team visit to Monrovia, Liberia in November 2012.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23750, "error": null, "extra": {"thread_id": 1912, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_83c19c542b6a41ceac941d5981d28799", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 42, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find information about namru 3 team visit to monrovia, liberia in november 2012", "normalised_gold": "fatorma bolay, vernon graham, walter gwenigale"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 41750, "error": null, "raw_text_chars": 1479}]}, "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I need to count the pictures in the document that contain only one person.\n\nLet me go through the document page by page:\n\nPage 1:\n- Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\nPage 2:\n- Photo of Capt. John W. Sanders - 1 person\n- Photo of Capt. Steven M. Wechsler - 1 person\n\nPage 3:\n- Photo of Afghan trainees in laboratory - multiple people\n\nPage 4:\n- Group photo of Pacific Partnership 2012 Internal Medicine Department staff - multiple people\n- Photo of Cmdr. Charmagne Beckett on flight deck - 1 person\n\nPage 5:\n- Photo of Petty Officer 1st Class Dennis Gonzales - 1 person\n\nPage 6:\n- Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n\nPage 7:\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Chris Martinez, Capt. Buhari Oyofo, Col. Vernon Graham, and Lt. Joseph Diclaro - 5 people\n\nPage 8:\n- Group photo of Kazakh scientists and NMRC staff - multiple people\n\nPage 9:\n- Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n- Photo of U.S. Marines and Sailors in aircraft - multiple people\n\nPage 10:\n- Group photo of Joint Combat Casualty Research Team - multiple people\n\nPage 12:\n- Photo of NMRC 2012 Dining Out Event - multiple people\n- Photo with Rear Adm. Bruce Doll, Capt. John Sanders and others - multiple people\n\nPage 13:\n- Photo with Ellen Flannery, Emmanuel Adu-Gyamfi, Eil", "citations": [], "input_tokens": 40387, "output_tokens": 512, "cost_micros": 128841, "latency_ms": 41735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine each image in the document to count those containing only one person.\n\nGoing through the document:\n\n1. Page 1: Image of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Image of Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4: Group photo of Internal Medicine Department staff - multiple people\n4. Page 5: Image of Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 5: Image of Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n6. Page 7: Group photo with Col. Vernon Graham - 5 people\n7. Page 7: Group photo with Dr. Walter T. Gwenigale - 5 people\n8. Page 8: Group photo of Kazakh scientists and staff - 9 people\n9. Page 9: Image of Lt. j.g. Michael Rucker treating a child - 2 people\n10. Page 9: Image of U.S. Marines and Sailors - multiple people\n11. Page 10: Group photo of Joint Combat Casualty Research Team - 9 people\n12. Page 11: Graphic illustration (not a photograph of people)\n13. Page 12: Multiple images from Dining Out event - multiple people in each\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14: Image of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n16. Page 15: Images of officers teaching - appears to show teachers with students (multiple people)\n\nCounting images with only one person:\n- Cmdr. Charmagne Beckett on flight deck\n- Petty Officer 1st Class Dennis Gonzales\n\nAnswer: 2", "citations": [], "input_tokens": 14484, "output_tokens": 453, "cost_micros": 50247, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture/photo in the document systematically to count those containing only one person.\n\n1. Page 1: Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n2. Page 2: Photo of Capt. John W. Sanders III - 1 person \u2713\n3. Page 2: Photo of Capt. Steven M. Wechsler - 1 person \u2713\n4. Page 3: Photo of Afghan trainees in laboratory - multiple people\n5. Page 4: Photo of Cmdr. Charmagne Beckett - 1 person \u2713\n6. Page 4: Group photo of Internal Medicine Department staff - multiple people\n7. Page 5: Photo of Petty Officer 1st Class Dennis Gonzales - 1 person \u2713\n8. Page 6: Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n9. Page 7: Photo with Col. Vernon Graham and team - multiple people\n10. Page 7: Photo with Dr. Gwenigale and team - multiple people\n11. Page 7: Photo of Pfc. Nadoris Nador (mentioned in text but appears to be with another person based on context)\n12. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n13. Page 9: Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n14. Page 9: Photo of U.S. Marines and Sailors - multiple people\n15. Page 10: Group photo of Joint Combat Casualty Research Team - multiple people\n16. Page 12: Group photos from Dining Out event - multiple people in each\n17. Page 13: Photo with Eileen Franke Villasante and others - multiple people\n18. Page 14: Photo of Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n19. Page 14: Photo of scouts learning flag etiquette - multiple people\n20. Page 15: Two photos of officers at", "citations": [], "input_tokens": 16134, "output_tokens": 512, "cost_micros": 56082, "latency_ms": 10422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each picture in the document systematically to count how many contain only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. Ziemer - 2 people\n2. Page 4: Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 5: Petty Officer 1st Class Dennis Gonzales getting swabbed - 2 people\n4. Page 6: Lance Cpl. Kip Boker standing while Cpl. Jacob Echeverri collects sample - 2 people\n5. Page 8: Group photo of Kazakh scientists and NMRC staff - multiple people\n6. Page 9: Lt. j.g. Michael Rucker treating a girl - 2 people\n7. Page 10: JC2RT Team Members group photo - multiple people\n8. Page 11: Graphic illustration (not a photo of people)\n9. Page 12: Group photo at dining out - multiple people\n10. Page 14: Cmdr. Daniel Szumlas with Rear Adm. Ziemer - 2 people\n11. Page 15: Two photos - Cmdr. Charmagne Beckett talking with students (multiple people) and Lt. Cmdr. Janine Danko talking with students (multiple people)\n\nCounting the pictures with only one person:\n- Page 4: Cmdr. Charmagne Beckett on flight deck\n\nAnswer: 1", "citations": [], "input_tokens": 12928, "output_tokens": 360, "cost_micros": 44184, "latency_ms": 8217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll go through each image in the document and count those containing only one person.\n\n1. Page 1: Dr. Xiaoyan \"Cathy\" Zou discussing research - 2 people visible\n2. Page 4 (top): Cmdr. Charmagne Beckett on flight deck - 1 person\n3. Page 4 (bottom): Pacific Partnership 2012 Internal Medicine Department staff - multiple people (10)\n4. Page 5: Petty Officer 1st Class Dennis Gonzales - 1 person\n5. Page 6: Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n6. Page 7 (top): Group photo with Dr. Gwenigale - 5 people\n7. Page 7 (bottom): Group photo with Col. Graham - 5 people\n8. Page 8: Group photo of Kazakh scientists - 9 people\n9. Page 9 (top): U.S. Marines and Sailors in aircraft - multiple people\n10. Page 9 (bottom): Lt. j.g. Michael Rucker treating girl - 2 people\n11. Page 10: Joint Combat Casualty Research Team - 9 people\n12. Page 12 (top): Dining Out Event photo - multiple people at tables\n13. Page 12 (bottom): Group photo at Dining Out - 6 people\n14. Page 13: Group photo at Notre Dame - 4 people\n15. Page 14 (top): Cmdr. Szumlas with Rear Adm. Ziemer - 3 people\n16. Page 14 (middle): Dr. Andrea Keane-Myers at podium - 1 person\n17. Page 14 (bottom): NAMRU-3 staff with Cub Scouts - multiple people\n18. Page 15 (left): Cmdr. Charmagne Beckett with student - 2 people\n19. Page 15 (right): Lt. Cmdr. Janine Danko with students - 3 people\n20. Page 15 (bottom): Lt. Danett Bishop in classroom - 1 ", "citations": [], "input_tokens": 14402, "output_tokens": 512, "cost_micros": 50886, "latency_ms": 10640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I'll examine the document to count pictures containing only one person.Let me continue reading to see all images in the document.Let me read the rest of the document more systematically by checking remaining chunks.Now let me read the visual descriptions for each image systematically to count how many contain only one person. Let me read through the remaining images I haven't seen yet.Now let me read the final images:Now let me count all the images with only one person based on the visual descriptions I've read:\n\nBased on my review of all the images in the document, here are the ones that contain only ONE person:\n\n1. **Image75.jpg** - Formal military portrait photograph of a naval officer (one person)\n2. **Image76.jpg** - Formal military portrait photograph (one person in uniform)\n3. **Image83.jpg** - Person on naval vessel deck wearing Navy uniform (one person)\n4. **Image86.jpg** - Close-up photograph of a young male service member in desert camouflage (one person)\n5. **Image125.jpg** - Woman speaking at a podium at Johns Hopkins (one person)\n6. **Image133.jpg** - Person in military uniform standing and presenting with equipment (one person)\n\nImages with MULTIPLE people:\n- Image9.jpg - Banner header (no people, just background with multiple individuals in soft focus)\n- Image55.png - QR code (no people)\n- Image69.jpg - Laboratory setting with TWO individuals\n- Image79.jpg - Laboratory scene with approximately 6 people\n- Image82.jpg - Group of 10 individuals\n- Image89.jpg - Emblem/insignia (no people)\n- Image90.jpg - Three individuals visible\n- Image93.jpg - Five individuals\n- Image94.jpg - Five individuals\n- Image97.jpg - Group of approximately 10 individuals\n- Image100.jpg - Healthcare provider and pediatric patient (TWO people)\n- Image101.jpg - Military transport aircraft interior with many personnel\n- Image103.jpg - Nine military personnel in front of helicopter\n- Image109.jpg - Diagram (no people)\n- Image112.jpg - Banquet hall with many attendees\n- Image113.jpg - Five individuals\n- Image116.jpg - Four individuals\n- Image127.jpg - Group of 10-12 people\n- Image128.jpg - Three men in conversation\n- Image131.jpg - Two women in foreground\n- Image132.jpg - Three individuals\n- Image139.jpg - Emblem/logo (no people)\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57125, "error": null, "extra": {"thread_id": 1913, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8b24b6e5b403450996ab78f93d47888d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 381, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "native_pdf", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the images in the document to count the number of people in each.\n\nLet me go through each page:\n\nPage 1: Contains one photo showing two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Tim Ziemer)\n\nPage 2: Contains two photos - one of Capt. Sanders (1 person) and one of Capt. Wechsler (1 person)\n\nPage 3: Contains one photo showing multiple people in a laboratory setting - I can count approximately 9 people\n\nPage 4: Contains one photo showing a group of medical staff - I can count approximately 10 people in the Internal Medicine Department staff photo\n\nPage 5: Contains one photo showing one person (Petty Officer 1st Class Dennis Gonzales)\n\nPage 6: Contains two photos - one showing multiple people swabbing (2 people visible), and one showing a group photo with approximately 9 people\n\nPage 7: Contains two photos - both showing groups of approximately 4-5 people each\n\nPage 8: Contains one photo showing approximately 8 people (Kazakh scientists and NMRC staff)\n\nPage 9: Contains two photos - one showing many Marines/Sailors on aircraft (approximately 50+ people), and one showing 2 people\n\nPage 10: Contains one photo showing approximately 9 people (JC2RT team members)\n\nPage 11: Contains one image/diagram (not a photo of people)\n\nPage 12: Contains two photos showing dining out events with multiple people at tables\n\nPage 13: Contains one photo showing 4 people\n\nPage 14: Contains two photos showing 2-3 people in each\n\nPage 15: Contains two photos showing 2-3 people in each\n\nPage 16: Contains no photos of people\n\nThe page with the most people is page 9, which contains the photo of U.S. Marines and Sailors on the aircraft with approximately 50+ people visible.\n\nAnswer: 9", "citations": [], "input_tokens": 40400, "output_tokens": 450, "cost_micros": 127950, "latency_ms": 40312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine each page of the document to count the number of people visible in pictures.\n\nPage 1: Picture shows one person (Dr. Xiaoyan \"Cathy\" Zou) with another person (Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: Picture shows people in protective equipment - difficult to count exactly but appears to be a few people\n\nPage 4: Multiple pictures - one shows Cmdr. Charmagne Beckett (1 person), another group photo shows approximately 10 people (the Internal Medicine Department staff)\n\nPage 5: Two pictures - one shows a person treating a child (2 people), another shows Marines/Sailors (multiple people but distant)\n\nPage 6: Two pictures - one shows a person standing while another collects a sample (2 people), another shows people at a food event (several people visible)\n\nPage 7: Two pictures - one shows 5 people posing together, another shows 5 people in a meeting\n\nPage 8: Picture shows 9 people (Kazakh scientists and staff)\n\nPage 9: Two pictures - one shows medical treatment (2-3 people), another shows troops (multiple but distant)\n\nPage 10: Picture shows approximately 9 people in the JC2RT team photo\n\nPage 11: One diagram/illustration, not a photograph of real people\n\nPage 12: Two pictures - one shows a dining event with many people, another shows 6 people\n\nPage 13: Picture shows 4 people at Notre Dame\n\nPage 14: Two pictures - one shows 2 people in lab, another shows scouts with adults (approximately 6-8 people)\n\nPage 15: Two pictures at Elizabeth Seton High School showing interactions with students - multiple people in each\n\nPage 16: No pictures with people\n\nThe page with the most people appears to be page 12, which shows the NMRC Dining Out event with many attendees visible in the first picture.\n\nAnswer: 12", "citations": [], "input_tokens": 14497, "output_tokens": 449, "cost_micros": 50226, "latency_ms": 12717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "azure_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people visible in each one to determine which page has the picture with the most people.\n\nLet me go through each page systematically:\n\n**Page 1:** Picture shows two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\n**Page 2:** Two headshot photos of commanding officers (single person each)\n\n**Page 3:** One picture showing multiple people in what appears to be a laboratory setting with people in protective gear\n\n**Page 4:** One picture showing Cmdr. Charmagne Beckett (single person), and one group photo with approximately 10 people (Internal Medicine Department staff physicians)\n\n**Page 5:** Two pictures - one showing a person getting a swab sample (2 people), another showing two people (one standing, one collecting sample)\n\n**Page 6:** Two pictures with small groups (3-5 people each)\n\n**Page 7:** Two pictures with small groups (3-5 people each)\n\n**Page 8:** One group photo showing approximately 9 people (Kazakh scientists and NMRC staff)\n\n**Page 9:** Two individual pictures (single subjects each)\n\n**Page 10:** No pictures with people visible in detail\n\n**Page 11:** One diagram/illustration (not a photograph of actual people)\n\n**Page 12:** Two pictures - one appears to be a group photo at a dining event, and another showing 6 people (smaller group photo with officers and spouses)\n\n**Page 13:** One picture showing 4 people\n\n**Page 14:** One picture showing people in what appears to be a Scout activity\n\n**Page 15:** Two pictures showing classroom/teaching scenes with multiple students\n\n**Page 16:** One picture showing a single person\n\nThe largest group photo appears to be on **page 4**, which shows the Internal Medicine Department staff physicians with approximately 10 people clearly visible in the photograph.\n\nAnswer: 4", "citations": [], "input_tokens": 16147, "output_tokens": 443, "cost_micros": 55086, "latency_ms": 11452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_basic_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each one.\n\nLet me go through each page:\n\nPage 1: Picture of two people (Dr. Xiaoyan \"Cathy\" Zou and Rear Adm. Ziemer)\n\nPage 2: No pictures with people\n\nPage 3: No pictures with people\n\nPage 4: Picture of one person (Cmdr. Charmagne Beckett on flight deck)\n\nPage 5: Picture of one person (Petty Officer 1st Class Dennis Gonzales getting swabbed)\n\nPage 6: Picture of two people (Lance Cpl. Kip Boker and Cpl. Jacob Echeverri)\n\nPage 7: No pictures with people\n\nPage 8: Picture with 9 people (Kazakh scientists group photo - Back row has 7 people: Gulnar Omasheva, Alexey Andryuchshenko, Sarah Pisarcik, Heidi St. John, Dr. Kenneth Yeh, Dr. Allen Richards, and Talgat Nurmakhanov. Bottom row has 3 people: Dr. Ekaterine Adeishvili, Lyazzat Musralina, and Dr. Ju Jiang. Total: 10 people)\n\nPage 9: Picture of one person (Lt. j.g. Michael Rucker treating a patient)\n\nPage 10: Picture with 9 people (JC2RT Team Members - Back row: 5 people, Front row: 4 people)\n\nPage 11: Graphic illustration (no people photo)\n\nPage 12: Picture with 6 people (Rear Adm. Bruce Doll, NMRC Commanding Officer Capt. John Sanders, Dr. Leighann Sanders, NMRC Executive Officer Capt. Elizabeth Montcalm-Smith, and Dr. Chris Smith - actually appears to be 5 people based on the caption listing)\n\nPage 13: No pictures with people\n\nPage 14: Picture with 2 people (Cmdr. Daniel Szumlas and Rear Adm. Ziemer with one other person visible = 3 people total in the image)\n\nPage ", "citations": [], "input_tokens": 12941, "output_tokens": 512, "cost_micros": 46503, "latency_ms": 12328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "llamacloud_premium_lc", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine all the pictures in the document and count the number of people in each to determine which page has the picture with the most people.\n\nLet me go through the document page by page:\n\n**Page 1:** Picture of Dr. Xiaoyan \"Cathy\" Zou discussing research with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\n**Page 4:** Two pictures:\n- Cmdr. Charmagne Beckett on flight deck - 1 person\n- Pacific Partnership 2012 Internal Medicine Department staff - 10 people\n\n**Page 5:** No pictures with people clearly visible\n\n**Page 6:** Two pictures:\n- Petty Officer 1st Class Dennis Gonzales - 1 person\n- Lance Cpl. Kip Boker with Cpl. Jacob Echeverri - 2 people\n\n**Page 7:** Two pictures:\n- Group with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Group with military personnel - 5 people\n\n**Page 8:** Group photo with Kazakh scientists - 9 people (Top row: 7 people, Bottom row: 3 people, total 10 people visible)\n\n**Page 9:** Two pictures:\n- U.S. Marines and Sailors in aircraft cargo hold - This appears to have many people seated in rows, likely 30+ people\n- Lt. j.g. Michael Rucker with young girl - 2 people\n\n**Page 10:** Group photo of Joint Combat Casualty Research Team - 9 people\n\n**Page 12:** Picture at Dining Out event - 6 people visible\n\n**Page 13:** Picture with 4 people outdoors\n\n**Page 14:** Two pictures:\n- Cmdr. Daniel Szumlas with visitors - 3 people\n- NAMRU-3 staff teaching Cub Scouts flag folding - approximately 8-10 people\n\n**Page 15:** Two pictures with 2-3 people each\n\nThe picture on page 9 showing \"U", "citations": [], "input_tokens": 14415, "output_tokens": 512, "cost_micros": 50925, "latency_ms": 12344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "answer_format": "int", "gold": "9", "pages": 16, "document_id": 5220, "arm": "surfsense_agentic", "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find all pictures and count the number of people in each.Let me continue reading the document to find all images and their descriptions:Now let me read each image section to count the people in each. Let me check the images systematically:Let me search for page numbers and images with people descriptions:Now let me systematically check each page for images with people. Let me read the sections around each page number:Good! This image on page 3 has \"approximately 6 people\". Let me continue checking other pages:Excellent! This image has \"10 individuals\". Let me continue checking:This image has 1 person. Let me check page 5:This image has 1 person. Let me check more pages:This is an emblem with 0 people. Let me continue:This image has \"Three individuals visible in foreground and midground\" plus \"Additional blurred figures visible in far background\". Let me read more:This image has \"Five individuals\". Let me continue checking more pages:This has 5 people. Let me check page 8:Good! This image has \"approximately 10 individuals\". Let me check page 9:This image has 2 people (healthcare provider and patient). Let me check page 9 more:This aircraft image has many people, but let me get a more specific count. Let me continue checking other pages:Excellent! This image has \"nine military personnel\". Let me check page 11:This dining hall image has many people seated. Let me count more carefully by reading the description again. It mentions \"Audience members\" and \"Three individuals in naval/formal uniforms standing at front\" plus \"Head table visible in background with seated guests\". This is a large gathering but not specified exactly.\n\nLet me check more pages systematically:This has 5 people. Let me check page 13:This has 1 person at the podium. Let me check page 14:Page 14 has an image with \"approximately 10-12 people\" (Image127.jpg) and another with 3 people (Image128.jpg). Let me continue:This has \"Two women standing in foreground center\" plus \"Multiple people in burgundy/maroon uniforms scattered throughout background\". Let me check page 15:This image has \"Three individuals posing together\" plus \"Additional person visible in background on right side\" = 4 people total. Let me check page 16:Page 16 has an image with 1 person (Image133.jpg) and an emblem (Image139.jpg) with 0 people.\n\nNow let me summarize what I found. Let me verify the counts more carefully by re-reading specific key images:\n\nBased on my review:\n- Page 1: Header image with \"Multiple individuals visible in soft focus\"\n- Page 2: Individual portraits (1 person each)\n- Page 3: Laboratory scene with \"approximately 6 people\" (Image79.jpg) AND group photo with \"10 individuals\" (Image82.jpg)\n- Page 4: 1 person\n- Page 5: 1 person\n- Page 6: 5 people (two images with 3 and 5 people)\n- Page 7: 5 people\n- Page 8: \"approximately 10 individuals\" (Image97.jpg)\n- Page 9: Aircraft with many people + medical scene with 2 people\n- Page 10: Helicopter group with \"nine military personnel\" (Image103.jpg)\n- Page 11: Diagram (0 people) and banquet hall with many people (Image112.jpg - but not exactly counted)\n- Page 12: 5 people\n- Page 13: 4 people and 1 person\n- Page 14: \"approximately 10-12 people\" and 3 people\n- Page 15: Multiple people but not clearly counted\n- Page 16: 1 person and emblem\n\nThe images with the most people appear to be:\n- Page 3: Image82.jpg with \"10 individuals\"\n- Page 8: Image97.jpg with \"approximately 10 individuals\"  \n- Page 10: Image103.jpg with \"nine military personnel\"\n- Page 14: Image127.jpg with \"approximately 10-12 people\"\n- Page 11: Image112.jpg (banquet hall with many people)\n\nLet me check the banquet hall image more carefully:\n\nThe banquet hall description mentions \"Audience members\" seated at tables and \"Three individuals in naval/formal uniforms standing at front\" and \"Head table visible in background with seated guests\" but doesn't give an exact count. Given it's a formal dining out event, there are likely many more than 12 people, but without an exact count stated, I should go with the clearly stated counts.\n\nThe highest clearly stated count is **\"approximately 10-12 people\"** on **page 14**.\n\nAnswer: 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 167000, "error": null, "extra": {"thread_id": 1914, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a7ec370e12904bd3b5569447e3077906", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1160, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "9"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically page 2, I can find information about where Gestalt psychology was conceived.\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 24360, "output_tokens": 56, "cost_micros": 73920, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I can find information about Gestalt psychology in the section titled \"Gestalt Principles of Visual Perception.\"\n\nThe text states: \"Gestalt psychology was conceived in the Berlin School of Experimental Psychology\"\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 5194, "output_tokens": 62, "cost_micros": 16512, "latency_ms": 3421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 6446, "output_tokens": 32, "cost_micros": 19818, "latency_ms": 2437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 4354, "output_tokens": 32, "cost_micros": 13542, "latency_ms": 3719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "According to the document, Gestalt psychology was conceived in the Berlin School of Experimental Psychology.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 9744, "output_tokens": 32, "cost_micros": 29712, "latency_ms": 3890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Berlin School of Experimental Psychology", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the relevant document to find information about where Gestalt psychology was conceived.Let me search for \"Gestalt\" to find the relevant section:Perfect! I found the answer. The document clearly states where Gestalt psychology was conceived.\n\nAnswer: Berlin School of Experimental Psychology", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28250, "error": null, "extra": {"thread_id": 1915, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a7b4cbe305b48eca21d964a816bfb01", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 102, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "berlin school of experimental psychology", "normalised_gold": "berlin school of experimental psychology"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at page 14, I can see a chart titled \"Time spent on weekends\" comparing 2005 and 2010. The chart shows two pie charts/clock-like circular diagrams.\n\nFor the 2010 chart (the larger circle on the right), I can see a segment labeled \"with family and friends\" which shows 21%.\n\nAnswer: 21", "citations": [], "input_tokens": 24371, "output_tokens": 109, "cost_micros": 74748, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a chart titled \"Time spent on weekends\" that compares 2005 and 2010 data.\n\nIn this chart, for 2010, I can see:\n- Shopping 10%\n- Fitness 6%\n- With family and friends 35%\n- Eating out 17%\n- Hobbies 4%\n- Net surfing 10%\n- Travelling 6%\n- Reading 4%\n- Watching films 22%\n\nWait, I see \"With family and friends\" appears to be listed as 35% for 2010.\n\nHowever, looking more carefully at the layout, the percentages seem to be:\n- With family and friends 21% (this appears to be for 2005)\n- With family and friends 35% (this appears to be for 2010)\n\nThe chart shows that in 2010, 35% of time on weekends was spent with family and friends.\n\nAnswer: 35", "citations": [], "input_tokens": 5205, "output_tokens": 251, "cost_micros": 19380, "latency_ms": 5953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the chart on page 14 titled \"Time spent on weekends\", I can see two pie charts comparing 2005 and 2010.\n\nIn the 2010 chart (on the right), the segment labeled \"With family and friends\" shows 35%.\n\nAnswer: 35", "citations": [], "input_tokens": 6457, "output_tokens": 67, "cost_micros": 20376, "latency_ms": 3467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "35.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking through the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nIn this table, the row \"With family and friends\" shows:\n- 2005: 21%\n- 2010: 17%\n\nAnswer: 17", "citations": [], "input_tokens": 4365, "output_tokens": 97, "cost_micros": 14550, "latency_ms": 3280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the chart on page 14 that shows time spent with family and friends in 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" which shows data for 2005 and 2010.\n\nThe table shows:\n| Activity                | 2005 | 2010 |\n| ----------------------- | ---- | ---- |\n| With family and friends | 35   | 21   |\n\nAccording to this table, time spent with family and friends in 2010 was 21.\n\nAnswer: 21", "citations": [], "input_tokens": 9755, "output_tokens": 132, "cost_micros": 31245, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "21.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "float", "gold": "21%", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the chart on page 14 and extract the information about time spent with family and friends in 2010.Let me search for page 14 content in the document:Let me search for content related to \"family and friends\" and \"2010\":Great! I found the relevant information. Let me read that specific chunk to get the exact details:Perfect! I can see from the OCR text that the chart shows time spent on weekends in 2005 and 2010. Looking at the data:\n\nFor 2010:\n- \"With family and friends 21%\" appears on line 2410\n- \"With family and friends 17%\" appears on line 2412-2413\n\nWait, let me read this more carefully. The structure shows:\n- Line 2403: \"2005\"\n- Line 2404: \"2010\"\n- Line 2410: \"With family and friends 21%\"\n- Line 2412-2413: \"With family and friends 17%\"\n\nIt appears that 21% is associated with 2005, and 17% is associated with 2010 based on the layout. Let me verify by reading more context:Looking at the OCR text structure, it shows two columns (2005 and 2010). The pattern suggests:\n- Shopping: 10% (2005) and 10% (2010)\n- Fitness: 5% (2005) and 6% (2010)\n- With family and friends: 21% (2005) and 17% (2010)\n\nSo for 2010, the time spent with family and friends is 17%.\n\nLet me verify this is from page 14 by looking for page markers:Let me search for \"14\" near this chart content:Let me look further back in the document to find page numbering:Let me look for page markers earlier in the document structure:Let me check the document structure at the beginning to understand how pages are marked:I can see there are PageBreak markers. Let me count the PageBreaks to find page 14. Let me search more systematically:I can see the PageBreaks. The one at line 2340 appears to be just before the \"Time spent on weekends\" chart. Let me count the PageBreaks:\n\n1. Line 135\n2. Line 153\n3. Line 360\n4. Line 564\n5. Line 830\n6. Line 1381\n7. Line 1630\n8. Line 1676\n9. Line 1882\n10. Line 1951\n11. Line 2012\n12. Line 2143\n13. Line 2340\n14. Line 2470\n\nThe PageBreak at line 2340 is the 13th PageBreak. If we count pages starting from page 1, then after the 13th PageBreak we would be on page 14. The chart \"Time spent on weekends\" starts at line 2344, which is right after the 13th PageBreak at line 2340, so this chart is indeed on page 14.\n\nBased on the OCR text I found earlier, the chart shows:\n- 2005 column and 2010 column\n- \"With family and friends 21%\" (for 2005)\n- \"With family and friends 17%\" (for 2010)\n\nAnswer: 17", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79187, "error": null, "extra": {"thread_id": 1916, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6038a83cdf13408480954ef7afd6b6b7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 590, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "17.0", "normalised_gold": "21.0"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the chart that tracks the West Nile Virus and look for the legend showing what the red color represents for distance from the Mississippi River.\n\nLooking at page 10, I can see a chart titled \"Tracking the West Nile Virus\" which shows a 3D map of what appears to be the United States with different colored regions.\n\nThe legend on the right side shows \"Approximate Distance from the Mississippi River (miles)\" with the following color-coded ranges:\n- Red: 0 - 375\n- Orange: 375 - 750\n- Yellow: 750 - 1125\n- Light yellow/cream: 1125 - 1500\n\nThe red color represents the closest distance range to the Mississippi River.\n\nAnswer: 0 - 375 miles", "citations": [], "input_tokens": 24377, "output_tokens": 178, "cost_micros": 75801, "latency_ms": 9609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart tracking the West Nile Virus and identify what the red color represents.\n\nI can see the \"Tracking the West Nile Virus\" chart which shows:\n\n**Approximate Distance from the Mississippi River (miles)**\n\nThe legend shows four distance ranges with different colors:\n- 0-375\n- 375 - 750\n- 750 -1125\n- 1125 - 1500\n\nBased on the typical color coding in such charts where red usually represents the most extreme or furthest category, the red color would represent the range **1125 - 1500** miles.\n\nNote: The question mentions \"in Europe\" but the chart shows distances from the Mississippi River, which is in North America, not Europe. The chart appears to be tracking West Nile Virus in relation to the Mississippi River region.\n\nAnswer: 1125 - 1500 miles", "citations": [], "input_tokens": 5211, "output_tokens": 207, "cost_micros": 18738, "latency_ms": 6687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "1125 1500 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I need to look at the \"Tracking the West Nile Virus\" chart in the document.\n\nLooking at the chart, I can see a legend that shows \"Approximate Distance from the Mississippi River (miles)\" with different ranges:\n\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nThe legend shows different shading/colors corresponding to these ranges. Based on the visual representation in the legend, the red color corresponds to the range 0-375 miles.\n\nHowever, I should note that the question asks about tracking West Nile Virus \"in Europe,\" but the chart actually shows tracking relative to the Mississippi River, which is in the United States, not Europe.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 6463, "output_tokens": 170, "cost_micros": 21939, "latency_ms": 6297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the \"Tracking the West Nile Virus\" chart in the document, I can see a legend that shows height-based ranges for approximate distance from the Mississippi River in miles.\n\nThe legend shows:\n- 0-375\n- 375-750\n- 750-1125\n- 1125-1500\n\nHowever, the document does not specify which color (including red) corresponds to which distance range in this chart. Additionally, the chart tracks West Nile Virus in relation to the Mississippi River, which is in the United States, not Europe as stated in the question.\n\nAnswer: The color coding for distance ranges is not specified in the document, and the chart is about the U.S., not Europe.", "citations": [], "input_tokens": 4371, "output_tokens": 160, "cost_micros": 15513, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color coding for distance ranges is not specified in document and chart is about u s not europe", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the chart about West Nile Virus and check what the red color represents.\n\nOn the page titled \"Tracking the West Nile Virus,\" I can see a 3D map of the United States (not Europe) with a legend showing:\n\n\"Approximate Distance from the Mississippi River (miles)\n[red square] 0 - 375\"\n\nHowever, I should note that this chart tracks West Nile Virus in the United States, not Europe. There is no chart about West Nile Virus in Europe in this document.\n\nAnswer: The chart tracks the United States, not Europe; red represents 0-375 miles from the Mississippi River.", "citations": [], "input_tokens": 9761, "output_tokens": 157, "cost_micros": 31638, "latency_ms": 4453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.35294117647058826, "method": "str_norm", "normalised_pred": "chart tracks united states not europe red represents 0 375 miles from mississippi river", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "0-375 miles", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the chart about West Nile Virus in Europe and identify what the red color represents for distance from the Mississippi River.Let me search for content related to West Nile Virus and charts showing distance from the Mississippi River.I found a reference to a red zone around the Mississippi River. Let me read that section more carefully:Let me look at the beginning of this section to find the title and legend information:Perfect! I found the chart. Now let me check which color corresponds to which distance range. The legend shows:\n\n- 0-375 miles\n- 375-750 miles\n- 750-1125 miles\n- 1125-1500 miles\n\nAnd the description mentions that the red zone is the highest concentration around the Mississippi River area. Let me verify by checking if there's more specific information about the color coding:Based on the document, I can see that:\n- The chart shows \"Tracking the West Nile Virus\" (not in Europe as the question states, but in the US)\n- The legend shows distance ranges from the Mississippi River: 0-375, 375-750, 750-1125, 1125-1500 miles\n- The red zone represents the highest concentration, which is in the Central US region, particularly around the Mississippi River area\n- The color gradient goes: Red \u2192 Orange \u2192 Yellow \u2192 Pale Yellow (indicating decreasing distance/intensity)\n\nSince red is the highest concentration around the Mississippi River area, red represents the closest distance range.\n\nAnswer: 0-375 miles", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54702, "error": null, "extra": {"thread_id": 1917, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_78ac91a640ab4a709acb37a25bd11e6b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "0 375 miles", "normalised_gold": "0 375 miles"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at page 11 of the document, the Law of Good Gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nThe document further explains that this law means individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 24362, "output_tokens": 109, "cost_micros": 74721, "latency_ms": 10577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for the definition of the law of good Gestalt...\n\nThe document states: \"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. This law implies that as individuals perceive the world, they eliminate complexity and unfamiliarity so they can observe a reality in its most simplistic form.\"\n\nAnswer: Elements are grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 5196, "output_tokens": 104, "cost_micros": 17148, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7878787878787878, "method": "str_norm", "normalised_pred": "elements are grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good Gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The document also explains that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 6448, "output_tokens": 102, "cost_micros": 20874, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "According to the document, the law of good gestalt is defined as:\n\nElements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly. The law implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form. It focuses on the idea of conciseness and implies a global regularity that is mentally prioritized over spatial relations.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 4356, "output_tokens": 111, "cost_micros": 14733, "latency_ms": 3484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document's section on \"Gestalt Principles of Visual Perception\" and specifically the \"Law of good Gestalt\":\n\nThe document defines it as: **Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.**\n\nThe document further explains this implies that individuals eliminate complexity and unfamiliarity to observe reality in its most simplistic form, and that this law focuses on the idea of conciseness.\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 9746, "output_tokens": 125, "cost_micros": 31113, "latency_ms": 4062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the definition of the law of good gestalt.Let me search for \"gestalt\" in this document to find the relevant section.Let me read the document content to find mentions of \"gestalt\":Now let me read the specific section around line 1965 where \"good gestalt\" is mentioned:Perfect! I found the definition. Let me read a bit more context to ensure I have the complete definition:Based on the document, the law of good gestalt is defined as:\n\n\"Elements of objects tend to be perceptually grouped together if they form a pattern that is regular, simple, and orderly.\"\n\nAnswer: Elements grouped together if they form a pattern that is regular, simple, and orderly", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36484, "error": null, "extra": {"thread_id": 1918, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d9af7588088a4b3184b7d88eec528363", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 192, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8125000000000001, "method": "str_norm", "normalised_pred": "elements grouped together if they form pattern that is regular simple and orderly", "normalised_gold": "elements of objects tend to be perceptually grouped together if they form pattern that is regular simple and orderly"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the chart on page 14 that shows \"Time spent on weekends\" comparing 2005 and 2010.\n\nLooking at the two pie charts:\n\n2005 (smaller chart):\n- With family and friends: 35%\n- Watching films: 20%\n- Reading: 10%\n- Travelling: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n- Eating out: 10%\n- Fitness: 5%\n- Shopping: 10%\n\n2010 (larger chart):\n- With family and friends: 21%\n- Watching films: 22%\n- Eating out: 17%\n- Shopping: 10%\n- Fitness: 6%\n- Hobbies: 4%\n- Net surfing: 4%\n- Travelling: 6%\n\nCalculating the changes:\n- With family and friends: 21% - 35% = -14% (decrease)\n- Watching films: 22% - 20% = +2% (increase)\n- Reading: appears to be gone or very small\n- Eating out: 17% - 10% = +7% (increase)\n- Shopping: 10% - 10% = 0%\n- Fitness: 6% - 5% = +1% (increase)\n- Hobbies: 4% - 2% = +2% (increase)\n- Net surfing: 4% - 3% = +1% (increase)\n- Travelling: 6% - 5% = +1% (increase)\n\nThe largest increase is \"Eating out\" with a 7 percentage point increase.\n\nAnswer: Eating out", "citations": [], "input_tokens": 24374, "output_tokens": 401, "cost_micros": 79137, "latency_ms": 13093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the table showing time spent on weekends comparing 2005 and 2010.\n\nLooking at the document, I can see a table titled \"Time spent on weekends\" with data for 2005 and 2010:\n\n2005 \u2192 2010:\n- Shopping: 10% \u2192 10% (0% change)\n- Fitness: 5% \u2192 6% (1% increase)\n- With family and friends: 21% \u2192 35% (14% increase)\n- Eating out: 10% \u2192 17% (7% increase)\n- Hobbies: 2% \u2192 4% (2% increase)\n- Net surfing: 3% \u2192 6% (3% increase)\n- Travelling: 5% \u2192 10% (5% increase)\n- Reading: 10% \u2192 4% (6% decrease)\n- Watching films: 20% \u2192 22% (2% increase)\n\nThe largest increase is \"With family and friends\" which went from 21% to 35%, an increase of 14 percentage points.\n\nAnswer: With family and friends", "citations": [], "input_tokens": 5208, "output_tokens": 260, "cost_micros": 19524, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" chart comparing 2005 and 2010:\n\n2005:\n- With family and friends: 21%\n- Watching films: 20%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 10%\n- Travelling: 5%\n- Fitness: 5%\n- Net surfing: 3%\n- Hobbies: 2%\n\n2010:\n- With family and friends: 35%\n- Watching films: 22%\n- Reading: 10%\n- Shopping: 10%\n- Eating out: 6%\n- Travelling: 4%\n- Fitness: 6%\n- Net surfing: 4%\n- Hobbies: 4%\n\nCalculating increases:\n- With family and friends: 35% - 21% = +14%\n- Watching films: 22% - 20% = +2%\n- Hobbies: 4% - 2% = +2%\n- Fitness: 6% - 5% = +1%\n- Net surfing: 4% - 3% = +1%\n\nAnswer: With family and friends", "citations": [], "input_tokens": 6460, "output_tokens": 284, "cost_micros": 23640, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "with family and friends", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the \"Time spent on weekends\" table comparing 2005 and 2010:\n\n- Shopping: 10% to 10% (no change)\n- Fitness: 5% to 6% (+1%)\n- With family and friends: 21% to 17% (-4%)\n- Eating out: 10% to [value not shown]\n- Hobbies: 2% to 4% (+2%)\n- Net surfing: 3% to 4% (+1%)\n- Travelling: 5% to [value not shown]\n- Reading: 10% to [value not shown]\n- Watching films: 20% to [value not shown]\n\nFrom the visible data, Hobbies shows the largest increase at 2 percentage points (from 2% to 4%).\n\nAnswer: Hobbies", "citations": [], "input_tokens": 4368, "output_tokens": 197, "cost_micros": 16059, "latency_ms": 5109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "hobbies", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the table \"Time spent on weekends\" comparing 2005 and 2010:\n\n- With family and friends: 35 \u2192 21 (decrease of 14)\n- Watching films: 20 \u2192 22 (increase of 2)\n- Reading: 10 \u2192 10 (no change)\n- Travelling: 5 \u2192 6 (increase of 1)\n- Net surfing: 3 \u2192 4 (increase of 1)\n- Hobbies: 2 \u2192 4 (increase of 2)\n- Eating out: 10 \u2192 17 (increase of 7)\n- Fitness: 5 \u2192 6 (increase of 1)\n- Shopping: 10 \u2192 10 (no change)\n\nThe category with the most increase is \"Eating out\" with an increase of 7.\n\nAnswer: Eating out", "citations": [], "input_tokens": 9758, "output_tokens": 213, "cost_micros": 32469, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "eating out", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Eating out", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about time spent on weekends from 2005 to 2010.Let me continue reading the document to find information about time spent on weekends:This doesn't seem to be the right content. Let me search through more of the document to find information about time spent on weekends:Great! I found a reference to \"Time spent on weekends\" at line 2446. Let me read that section:Perfect! I found the data. Let me analyze the percentages for each category from 2005 to 2010:\n\nFrom the OCR text (lines 2402-2424):\n\n**2005 \u2192 2010 comparisons:**\n- Shopping: 10% \u2192 10% (increase: 0%)\n- Fitness: 5% \u2192 6% (increase: 1%)\n- With family and friends: 21% \u2192 17% (decrease: -4%)\n- Eating out: 10% \u2192 10% (increase: 0%)\n- Hobbies: 2% \u2192 4% (increase: 2%)\n- Net surfing: 3% \u2192 6% (increase: 3%)\n- Travelling: 5% \u2192 10% (increase: 5%)\n- Reading: 10% \u2192 4% (decrease: -6%)\n- Watching films: 20% \u2192 35% (increase: 15%)\n\nThe category with the most increase from 2005 to 2010 is **Watching films** with an increase of 15 percentage points (from 20% to 35%).\n\nAnswer: Watching films", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42125, "error": null, "extra": {"thread_id": 1919, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5fc07b74eefd45dcbf475865f8a3bf94", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "watching films", "normalised_gold": "eating out"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at page 13, I can see an infographic titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO (Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization).\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A world map with numbers indicating participants by region:\n  - 43 (North America)\n  - 5 (Central/South America)\n  - 130 (Europe - shown as the largest red circle)\n  - 80 (Asia/Middle East region)\n  - 77 (Africa/Asia region)\n  - 30 (Africa)\n  - 25 (Asia-Pacific region)\n\nBased on the map visualization, Europe has the largest circle with 130 participants, which is the highest number shown.\n\nAnswer: Europe", "citations": [], "input_tokens": 24369, "output_tokens": 216, "cost_micros": 76347, "latency_ms": 10344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants for the advanced science course in CTBTO by continent.\n\nLooking at the document, I can see a section titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO PREPARATORY COMMISSION.\n\nThe infographic shows:\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (NGO, Station Operator, Ministry of FA, etc.)\n- Regional breakdown showing numbers: 98, 55, 43, 80, 66, 20, 77, 28, 30, 80, 8, 1\n\nI can see there's a map visualization with numbers distributed across different regions. Looking at the geographic distribution:\n- The number 98 appears to be positioned over Asia\n- 80 appears twice in different locations\n- Other numbers are distributed across continents\n\nBased on the positioning on the world map in the infographic, the largest number (98) appears to be associated with the Asian region.\n\nAnswer: Asia", "citations": [], "input_tokens": 5203, "output_tokens": 229, "cost_micros": 19044, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about registered participants by continent for the CTBTO Advanced Science Course.\n\nLooking at the document, I can see a visualization titled \"ADVANCED SCIENCE COURSE at a glance\" from the CTBTO Preparatory Commission. It shows:\n\n- 425 registered participants from 105 countries\n- A breakdown by institutional affiliation (University, Research Institute, National Data Centre, etc.)\n\nHowever, I do not see any breakdown of participants by continent in this visualization or anywhere else in the document. The document shows the total number of participants (425) and their institutional affiliations, but does not provide geographical/continental distribution data.\n\nAnswer: Information not provided in the document", "citations": [], "input_tokens": 6455, "output_tokens": 152, "cost_micros": 21645, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "information not provided in document", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the CTBTO Advanced Science Course data on page 12, I can see a visualization showing participants from 43 countries with numbers:\n\n- 77\n- 130\n- 80\n- 55\n- 66\n\nThe chart shows \"registered institutional affiliation: 425\" total participants and \"participants from 43 countries.\"\n\nThe number 130 appears to be the largest among the values shown in the visualization, which represents participants by region/continent. Based on the positioning and context of the chart layout, this would represent Asia.\n\nAnswer: Asia", "citations": [], "input_tokens": 4363, "output_tokens": 125, "cost_micros": 14964, "latency_ms": 4905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "asia", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the \"ADVANCED SCIENCE COURSE at a glance\" section in the document, I can find the \"Participants by Region\" table which shows:\n\n- North America: 43\n- Central America: 20\n- South America: 8\n- Europe: 130\n- Africa (West/Central): 77\n- Africa (South): 30\n- Middle East/Asia: 80\n- South East Asia/Oceania: 28\n\nEurope has the highest number with 130 registered participants.\n\nAnswer: Europe", "citations": [], "input_tokens": 9753, "output_tokens": 120, "cost_micros": 31059, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "europe", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "str", "gold": "Europe", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about CTBTO registered participants for advanced science courses by continent.Let me search for information about CTBTO and advanced science course participants by continent.Let me read the entire document to find charts or tables about participant registration by continent.Let me continue reading to find information about CTBTO:Let me search for \"participant\" to find relevant information:Let me look at later sections of the document:Let me search more specifically:Let me continue reading the document to find the CTBTO-related content:Great! I found the CTBTO content. Let me read that specific section more carefully:I can see the CTBTO Advanced Science Course infographic, but I need to see if there's a visualization showing participant numbers by continent. Let me look for the visual description of this image:That's a different image. Let me search for more info about the CTBTO figure. Let me look for text that mentions continents or regions in the numbers shown:The numbers I see in the CTBTO section are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These numbers appear to be on a chart/map. Let me check if there's a visual description for the CTBTO image. Let me search around that area:Let me look for an embedded image description that might be for the CTBTO figure. The figure ends at line 2239. Let me check if there's a visual description after that:Let me look at what comes before the CTBTO figure to find if there's an embedded image marker:I notice Image18.jpg is about Indonesia entrepreneurship, not CTBTO. Let me search for Image17:That's also not it. The CTBTO figure must be between Image17 and Image18. Let me look for any embedded image between lines 2000-2150:Now I see the CTBTO figure starts at line 2146. Based on what I've seen, the numbers shown are: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98. These appear to be data points on what's likely a world map. Let me look for any visual description or search for these specific numbers in context. Let me search for \"world\" or \"map\":Looking at the numbers from the CTBTO figure more carefully:\n- 43\n- 130 (appears as \"A0 130\" which might be \"Africa 130\")\n- 80\n- 66\n- 20\n- 77\n- 28\n- 30\n- 8\n- 80\n- 98\n\nThe \"A0 130\" at line 2216 could potentially be \"Africa 130\". Let me check if there are continent names mixed in with these numbers. Let me search the exact context:Based on the data I can see from the CTBTO Advanced Science Course infographic, I have:\n- 425 registered participants from 105 countries\n- Numbers: 43, 130, 80, 66, 20, 77, 28, 30, 8, 80, 98\n\nThese numbers likely represent participants by geographic region/continent on a world map visualization. The largest number is 130, which likely corresponds to a continent. Without a clear visual description identifying which number belongs to which continent, I need to make an inference. Given that 130 is the highest value and appears with \"A0\" (possibly Africa or Asia), and considering typical patterns in international scientific courses, the highest participation often comes from either Europe or Asia.\n\nHowever, I cannot definitively determine from the OCR text alone which continent each number represents. Let me see if I can find more context by reading around these numbers more carefully:The OCR shows \"A0 130\" on line 2216. This could be \"Africa 130\" or \"Asia 130\" where the character wasn't recognized properly. Given that 130 is the highest number and that this appears to be showing registered participants by continent on a world map, I need to determine which continent this refers to.\n\nLooking at the pattern, if this is a world map with continental data, and 130 is preceded by what looks like \"A0\" (possibly \"Africa\" or \"Asia\" with poor OCR), I cannot make a definitive determination from the text alone.\n\nHowever, based on the structure and the fact that the highest number is 130, and without additional visual context to confirm which specific continent each number represents, I cannot provide a certain answer from this document.\n\nAnswer: Cannot be determined from the document text alone - the highest value is 130 but the continent labels are not clearly identifiable in the OCR text", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 144672, "error": null, "extra": {"thread_id": 1920, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b3899f4c01ab4b9084195ac54d3f04aa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1007, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document text alone highest value is 130 but continent labels are not clearly identifiable in ocr text", "normalised_gold": "europe"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "native_pdf", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that discusses the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking at page 8, I can see a slide titled \"Gestalt Principles of Visual Perception\" that includes information about Closure. The text states: \"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nBelow this text, there is an illustration showing incomplete shapes. Looking at the image, I can see:\n- An incomplete circle (dotted/dashed lines forming most of a circle)\n- An incomplete rectangle or square (dotted/dashed lines)\n- Another incomplete rectangular shape (dotted/dashed lines)\n\nThe shapes used to illustrate closure are geometric shapes with gaps or missing parts that our minds fill in to perceive complete shapes.\n\nAnswer: circle, rectangle, square", "citations": [], "input_tokens": 24379, "output_tokens": 216, "cost_micros": 76377, "latency_ms": 13483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "0e94b4197b10096b1f4c699701570fbf.pdf"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "circle, rectangle, square", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the section about \"Closure\" in the Gestalt Principles of Visual Perception and identify what shapes are shown.\n\nLooking at the document, I can see the section on \"Gestalt Principles of Visual Perception\" that explains Closure:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nLooking at the visual examples provided in the Closure section of the slide, I can see incomplete shapes that our minds complete. The shapes shown appear to be:\n- A circle (incomplete/broken circle)\n- A square or rectangle (incomplete/broken square)\n- A triangle (incomplete/broken triangle)\n\nAnswer: circle, square, triangle", "citations": [], "input_tokens": 5213, "output_tokens": 183, "cost_micros": 18384, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "circle, square, triangle", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "azure_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide about the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses Closure. The text states:\n\n\"Closure. We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nThis section appears on a page that also discusses \"Continuity.\" The document indicates there is a figure on this page, but the actual visual content of the figure is not provided in the text transcription.\n\nHowever, based on the context and typical Gestalt closure demonstrations, and looking at the structure of the document, I cannot definitively identify the specific shapes used in the illustration from the provided text alone, as the figure content is not described in detail in the transcription.\n\nSince I cannot see the actual shapes in the figure that illustrates closure, I cannot provide an accurate answer based solely on what is visible in this document transcription.\n\nAnswer: Unable to determine from provided document", "citations": [], "input_tokens": 6465, "output_tokens": 251, "cost_micros": 23160, "latency_ms": 7265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided document", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_basic_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the slide that illustrates the \"Closure\" aspect of Gestalt Principles of Visual Perception.\n\nLooking through the document, I can see a section titled \"Gestalt Principles of Visual Perception\" that discusses \"Closure\". The text states:\n\n\"Closure: We tend to perceive objects such as shapes, letters, pictures, etc., as being whole when they are not complete. Specifically, when parts of a whole picture are missing, our perception fills in the visual gap.\"\n\nHowever, I need to identify what shapes are actually shown in the visual illustration for this principle. Looking at the page layout, I can see there are visual examples accompanying the Gestalt principles, but the specific shapes used to illustrate closure are not clearly described in the extracted text.\n\nFrom the document structure, I can see that the Closure section appears on a page, but the specific shapes or visual elements used to demonstrate closure are not explicitly listed in the text content that was extracted.\n\nBased on typical Gestalt closure examples and what might be visible in such presentations, common shapes used include circles, triangles, or other geometric shapes that are incomplete but our minds perceive as complete. However, I cannot definitively identify the specific shapes from the text content provided.\n\nAnswer: I cannot determine the specific shapes from the provided document text", "citations": [], "input_tokens": 4373, "output_tokens": 284, "cost_micros": 17379, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i cannot determine specific shapes from provided document text", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "llamacloud_premium_lc", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for information about the Closure aspect of Gestalt Principle of Visual Perception...\n\nOn the page titled \"Gestalt Principles of Visual Perception\" that discusses Closure and Continuity, I can see the following description:\n\n\"The image illustrates the principle of **Closure** with a dashed outline of a circle and a dashed outline of a square, which the mind perceives as complete shapes despite the gaps.\"\n\nAnswer: circle, square", "citations": [], "input_tokens": 9763, "output_tokens": 105, "cost_micros": 30864, "latency_ms": 4812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "circle, square", "normalised_gold": "circle, rectangle"}}
+{"qid": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "doc_id": "0e94b4197b10096b1f4c699701570fbf.pdf", "answer_format": "list", "gold": "['Circle', 'Rectangle']", "pages": 15, "document_id": 5221, "arm": "surfsense_agentic", "question_id": "0e94b4197b10096b1f4c699701570fbf.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the Gestalt Principle of Visual Perception and the closure aspect.Let me search for content about Gestalt Principle and closure:Now let me read the specific section around line 1631-1640 where closure is mentioned:Let me continue reading to see if there are visual examples for the closure principle:This doesn't seem to be the right section. Let me search more systematically for visual examples of the closure principle. Let me look at earlier sections:Perfect! I can see that Image13.jpg appears right before the Gestalt Principles section and shows \"dashed circle outline\" and \"dashed rectangle outline\". This image is likely illustrating the closure principle. Let me verify by reading a bit more context before the Gestalt section:Based on my reading of the document, Image13.jpg appears immediately before the Gestalt Principles of Visual Perception section discussing closure. The visual description clearly states that this image shows:\n- Left shape: dashed circle outline\n- Right shape: dashed rectangle outline\n\nThese dashed outlines (incomplete shapes) are used to illustrate the closure principle, where we perceive complete shapes even when they are not fully drawn.\n\nAnswer: circle, rectangle", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46890, "error": null, "extra": {"thread_id": 1921, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_76cfee4a20ba492dba921a5a3cdf2231", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 294, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "circle, rectangle", "normalised_gold": "circle, rectangle"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about voters' perceptions of mudslinging in the 2016 election compared to past elections.\n\nLooking through the document, I find a chart on page 7 titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more mudslinging than in past elections from 1992 to 2016.\n\nThe chart shows:\n- 1992: 68%\n- 1996: 49%\n- 2000: 46%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 90784, "output_tokens": 179, "cost_micros": 275037, "latency_ms": 41734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the chart titled \"More see 'mud-slinging,' less focus on issues\" on page 33 of the document, I can see the data points for \"More mudslinging than usual\" across different election years:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe document also explicitly states on page 6: \"Fully 92% of all voters - including 87% of Clinton supporters and 60% of Trump backers - say they were surprised by Trump's victory\" and \"Nearly three-quarters (73%) 0f all voters say there was more 'mudslinging' or negative campaigning compared with previous contests...\"\n\nHowever, looking at page 33 more carefully, it states: \"About nine-in-ten (92%) say there was more mudslinging or negative campaigning compared with previous contests, up from 68% who said that in 2012, up 38 points from 2008 (54% more negative) and 20 points higher than the previous high of 72% in 2004.\"\n\nAnswer: 92%, 2016", "citations": [], "input_tokens": 19137, "output_tokens": 292, "cost_micros": 61791, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "2016, 92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on \"mudslinging\" or negative campaigning:\n\nOn page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing the percentage of voters who say there was more mudslinging than in past elections:\n\n- 1992: 46%\n- 1996: 49%\n- 2000: 54%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24416, "output_tokens": 147, "cost_micros": 75453, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking at the document, I can find a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" on page 6.\n\nThe chart shows percentages for different election years:\n- 1996: 46%\n- 2000: 49%\n- 2004: 72%\n- 2008: 54%\n- 2012: 68%\n- 2016: 92%\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 21637, "output_tokens": 142, "cost_micros": 67041, "latency_ms": 4875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the highest percentage of voters who said there was more mudslinging than in past elections.\n\nLooking through the document for information about mudslinging:\n\nOn page 5, there's a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a table showing:\n\n% of voters who say there was ___ than in past elections\n\n| Year | More mudslinging | Less mudslinging |\n| ---- | ---------------- | ---------------- |\n| 1992 | 68               | 16               |\n| 1996 | 49               | 36               |\n| 2000 | 46               | 34               |\n| 2004 | 72               | 14               |\n| 2008 | 54               | 27               |\n| 2012 | 68               | 19               |\n| 2016 | 92               | 4                |\n\nThe text also states: \"Fully 92% say there was more \"mudslinging\" or negative campaigning than in past elections \u2013 which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92%", "citations": [], "input_tokens": 24377, "output_tokens": 286, "cost_micros": 77421, "latency_ms": 7157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "92", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about voters' perceptions of mudslinging compared to past elections.Let me search for content related to mudslinging:Let me read through the document to find the specific data about mudslinging. Let me start by reading the beginning sections:Let me continue searching for information about mudslinging. Let me read more of the document:Great! I found the relevant information. Let me continue reading this section to get the complete data:Perfect! I found the data about mudslinging. From the document, I can see the chart showing \"% of voters who say there was more mudslinging than in past elections\" with the following values across different election years:\n\n- 1992: 36%\n- 1996: 34%\n- 2000: 27%\n- 2004: 72%\n- 2008: 68%\n- 2012: 68%\n- 2016: 92%\n\nThe text also confirms: \"Fully 92% say there was more 'mudslinging' or negative campaigning than in past elections - which is 20 percentage points higher than the previous high (72% after the 2004 election).\"\n\nThe highest point is 92% in 2016.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39625, "error": null, "extra": {"thread_id": 1922, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_252b1c025dac41a5b4c7b4fbfefc6f87", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 214, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about whether men or women are more likely to think a female president will be elected in their lifetime.\n\nLooking through the document, I find on page 10 (document page 8) a section titled \"Will the country elect a female president in your lifetime?\"\n\nThe data shows:\n- All voters: No 17%, Yes 79%\n- Men: No 16%, Yes 81%\n- Women: No 18%, Yes 78%\n- Trump voters: No 17%, Yes 78%\n- Clinton voters: No 17%, Yes 81%\n\nComparing men (81% Yes) to women (78% Yes), more men think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 90776, "output_tokens": 163, "cost_micros": 274773, "latency_ms": 43483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on expectations of a female president by gender...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\"\n\nThe table shows:\n- Men who say \"Yes\": 81%\n- Women who say \"Yes\": 78%\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 19129, "output_tokens": 80, "cost_micros": 58587, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the table on page 8 titled \"Will the country elect a female president in your lifetime?\", I can find the breakdown by gender:\n\n- Men: 81% say yes\n- Women: 78% say yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 24408, "output_tokens": 64, "cost_micros": 74184, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for data on whether men or women think a female president will be elected in their lifetime...\n\nOn page 9, there is a table titled \"Will the country elect a female president in your lifetime?\" showing:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nAnswer: More men (81% vs 78%)", "citations": [], "input_tokens": 21629, "output_tokens": 80, "cost_micros": 66087, "latency_ms": 4219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "more men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to find the data about whether men or women think a female president will be elected in their lifetime.\n\nOn page 8, there is a table titled \"Will the country elect a female president in your lifetime?\" that shows:\n\n- Men: 81% say Yes\n- Women: 78% say Yes\n\nSince 81% > 78%, more men than women think a female president will be elected in their lifetime.\n\nAnswer: Men", "citations": [], "input_tokens": 24369, "output_tokens": 102, "cost_micros": 74637, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "men", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "men", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find information about opinions on whether a female president will be elected in a lifetime.Let me search for content related to female president and lifetime:Let me read the specific section around line 1095 to get the complete data:The text states \"There are no significant differences in these opinions among men and women,\" meaning men and women have similar percentages. Let me search for any table or chart that might have the exact percentages broken down by gender:I can see a table starting. Let me read more to see if it has the gender breakdown:Perfect! I found the table with the gender breakdown. Looking at the \"Yes\" column (indicating those who think a female president will be elected in their lifetime):\n\n- Men: 81%\n- Women: 78%\n\nAnswer: Men (81% vs 78%)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46859, "error": null, "extra": {"thread_id": 1923, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_47a779db7256446daa01071f4e8824a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 246, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "men 81 vs 78", "normalised_gold": "men"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll carefully examine the document to count all line plots.\n\nGoing through the document page by page:\n\n**Page 3:** Contains a line plot showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages from 1988-2016.\n\n**Page 5:** Contains a line plot showing \"Record share of voters say there was more campaign 'mudslinging'\" with two lines (More mudslinging and Less mudslinging) from 1992-2016.\n\n**Page 9:** Contains a line plot showing \"Half say they are happy Trump was elected president\" with percentages from 1996-2016.\n\n**Page 21:** Contains a line plot showing \"Will relations between Republicans and Democrats improve?\" with three lines from 1992-2016.\n\n**Page 24:** Contains two separate line plots:\n- One for Republican/Rep-leaning voters (More moderate vs More conservative) from 2008-2016\n- One for Democratic/Dem-leaning voters (More moderate vs More liberal) from 2008-2016\n\n**Page 27:** Contains two separate line plots showing grades for:\n- The Republican Party (1988-2016)\n- The Democratic Party (1988-2016)\n\n**Page 29:** Contains two separate line plots showing grades for:\n- The press (1988-2016)\n- The pollsters (1988-2016)\n\n**Page 31:** Contains a line plot showing \"For the first time in eight elections, most are dissatisfied with vote choices\" from 1988-2016.\n\n**Page 32:** Contains a line plot showing \"More see 'mud-slinging,' less focus on issues\" with two sets of lines from 1992-2016.\n\n**Page 33:** Contains two separate line plots showing \"Learning about the candidates and issues\" from 1988-2016.\n\n**Page 36:** Contains a line plot showing \"Most voters are confident own vote, national votes counted accurately\" from 2004-2016.\n\n**Page 37:** Contains a line plot showing \"Similar shares of Trump, Clinton voters very confident in national vote count\" from 2004-2016.\n\nTotal count:\nPage 3: 1\nPage 5: 1\nPage 9: 1\nPage ", "citations": [], "input_tokens": 90760, "output_tokens": 512, "cost_micros": 279960, "latency_ms": 41093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the number of line plots in this document.\n\nLet me go through the document systematically to identify line plots:\n\n1. Page 1: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with two lines (Winning candidate and Losing candidate) from 1988 to 2016.\n\n2. Page 6: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with two lines (More mudslinging and Less mudslinging) from 1992 to 2016.\n\n3. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows a line plot with two lines (Very/Fairly satisfied and Not very/Not at all satisfied) from 1988 to 2016.\n\n4. Page 32: Below the first plot on page 31, there's another line plot showing \"% of voters who say they were very/fairly satisfied with choice of presidential candidates\" with data points from 1988 to 2016 for winning and losing candidates.\n\n5. Page 33: \"More see 'mud-slinging,' less focus on issues\" - This contains TWO separate line plots:\n   - First plot: Shows \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992 to 2016\n   - Second plot: Shows discussion of issues \"Less than usual\" vs \"More than usual\" from 1992 to 2016\n\n6. Page 34: \"Learning about the candidates and issues\" - This contains TWO separate line plots:\n   - First plot: \"Learned enough\" vs \"Did not learn enough\" from 1988 to 2016\n   - Second plot: Presidential debates helpfulness from 1988 to 2016\n\nLet me recount to be sure:\n- Page 1: 1 line plot\n- Page 6: 1 line plot  \n- Page 31: 2 line plots (one for all voters, one comparing winning/losing candidate voters)\n- Page 33: 2 line plots (mudslinging and issues discussion)\n- Page 34: 2 line plots (learning about candidates and debates)\n\nTotal:", "citations": [], "input_tokens": 19113, "output_tokens": 512, "cost_micros": 65019, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document to count all line plots (graphs that show data points connected by lines over time or categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This appears to be a line plot showing grades from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This is a line plot showing data from 1992-2016.\n\n3. Page 9: \"Half say they are happy Trump was elected president\" - This is a line plot showing data from 1996-2016.\n\n4. Page 24: Two separate line plots:\n   - \"Most Republicans continue to say GOP should be more conservative\" (showing data from 2008-2016)\n   - \"As the share of Democrats who want their party to be more liberal grows\" (showing data from 2008-2016)\n\n5. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This is a line plot showing data from 1988-2016.\n\n6. Page 32: Two separate line plots:\n   - One showing \"More mudslinging than usual\" vs \"Less mudslinging than usual\" from 1992-2016\n   - One showing discussion of issues \"Less than usual\" vs \"More than usual\" from 1992-2016\n\n7. Page 33: Two separate line plots:\n   - One showing \"Learned enough\" vs \"Did not learn enough\" from 1988-2016\n   - One showing helpfulness of debates from 1988-2016\n\n8. Page 34: \"More say press had 'too much' influence on outcome of the election\" - This is a line plot showing data from 1992-2016.\n\nTotal count: 1 + 1 + 1 + 2 + 1 + 2 + 2 + 1 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 24392, "output_tokens": 466, "cost_micros": 80166, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades...\" - This appears to be a bar chart showing percentages, not a line plot.\n\n2. Page 4: \"Large share of Trump voters are confident...\" - Bar chart\n   \"How Clinton voters feel about 'giving Trump a chance'\" - Bar chart\n\n3. Page 5: \"% of voters saying Democratic leaders should...\" - Bar charts comparing 2016 and 2008\n\n4. Page 6: \"% of voters saying Donald Trump ___ appoint Democrats...\" - Table format\n   \"% of voters who said Barack Obama ___ appoint Republicans...\" - Table format\n\n5. Page 7: \"Election report card: Barely passing grades\" - Table format\n   \"Record share of voters say there was more campaign 'mudslinging'\" - This appears to be a LINE PLOT showing trend over years 1996-2016\n\n6. Page 8: \"Top reactions to Trump's victory\" - Bar chart\n\n7. Page 9: \"Trump's victory in a word\" - Word cloud/frequency table\n\n8. Page 10: \"Will the country elect a female president in your lifetime?\" - Bar chart\n\n9. Throughout the rest of the document, I see mostly bar charts, tables, and no additional line plots.\n\nLet me recheck page 7 more carefully. The chart showing \"More mudslinging\" and \"Less mudslinging\" from 1992-2016 appears to be displayed as a line plot with two lines tracking these measures over time.\n\nAfter careful review, I can identify 1 line plot in this document on page 7.\n\nAnswer: 1", "citations": [], "input_tokens": 21613, "output_tokens": 393, "cost_micros": 70734, "latency_ms": 11437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (charts that show data points connected by lines over time or across categories).\n\nLet me go through the document systematically:\n\n1. Page 3: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" - This shows a line plot with percentages over years from 1988-2016.\n\n2. Page 5: \"Record share of voters say there was more campaign 'mudslinging'\" - This shows a line plot with \"More mudslinging\" and \"Less mudslinging\" lines from 1992-2016.\n\n3. Page 9: \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" (repeated from earlier) - This is a table showing A or B grades, labeled as \"Note: Percent of 'A' or 'B' grades\" - This appears to be the same as #1.\n\n4. Page 31: \"For the first time in eight elections, most are dissatisfied with vote choices\" - This shows two line plots tracking satisfaction levels from 1988-2016.\n\n5. Page 32: \"More see 'mud-slinging,' less focus on issues\" - This shows two separate line plots, one for mudslinging and one for discussion of issues from 1992-2016.\n\nLet me recount more carefully by looking at actual visual line plots:\n\n- Page 3: One line plot showing grades over time\n- Page 5: One line plot showing mudslinging trends\n- Page 31: Two line plots (satisfaction trends)\n- Page 32: Two line plots (mudslinging and issues discussion)\n\nTotal: 1 + 1 + 2 + 2 = 6 line plots\n\nAnswer: 6", "citations": [], "input_tokens": 24353, "output_tokens": 397, "cost_micros": 79014, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "8", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the report.Let me continue reading the document to see all the content and identify line plots:Let me continue reading to see if there are more charts/figures:Let me search for all figures/charts in the document to count line plots specifically:Now I need to look at the actual figures to determine which ones are line plots. Let me examine the key figure sections more carefully. Based on what I've already read, I saw one chart around line 798-853 that appears to be about \"mudslinging\" over time. Let me check the visual descriptions and content more carefully:This appears to be a line plot showing data over time (years 92, 96, 00, 04, 08, 12, 16). Let me check another figure:This appears to be another line plot showing data over time from 1996 to 2016. Let me check the first figure to see if it's a line plot:This first figure shows data for winning and losing candidates across years from 1988 to 2016. This appears to be a line plot with two lines (winning candidate and losing candidate). Let me check if there are any other potential line plots by examining more figures. Let me look at the figure around line 2240-2290:This appears to be a bar chart, not a line plot. Let me continue scanning through the document to identify all line plots. Let me check around line 2333:This appears to be a bar chart comparing 2016 and 2008 data, not a line plot. Based on my review of the document so far, I've identified:\n\n1. **Line plot 1** (lines 264-360): Shows grades for winning and losing candidates over time from 1988 to 2016 (two lines)\n2. **Line plot 2** (lines 798-848): Shows \"mudslinging\" over time with two lines (More mudslinging and Less mudslinging) from 1992 to 2016\n3. **Line plot 3** (lines 1209-1241): Shows happiness with election results from 1996 to 2016\n\nThese all show data trends over multiple time points, which is characteristic of line plots. Most other figures in the document appear to be bar charts showing comparisons between groups. Let me verify by checking if there are any other time-series charts I might have missed. Let me do a final check of the document to ensure I haven't missed any line plots.\n\nBased on my thorough review of the document, I found three distinct line plots that show data trends over time:\n\n1. A line plot showing grades for winning and losing candidates from 1988-2016\n2. A line plot showing perceptions of campaign mudslinging from 1992-2016  \n3. A line plot showing voter happiness with election results from 1996-2016\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 65172, "error": null, "extra": {"thread_id": 1924, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dcd15be6dac34256a7ae1a01a0998a5a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 476, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "8"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades information...\n\nOn page 3, there is a chart showing \"Voters give Trump worse grades than they have for any winning candidate in recent decades\" with percentages of \"A\" or \"B\" grades on an A, B, C, D, F scale.\n\nThe chart shows:\n- Trump (2016): 30% gave A or B grades\n- Clinton (2016): 43% gave A or B grades\n\nThe document also states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nAdditionally, on page 26, there is a table showing average grades:\n- Trump: C- (average grade)\n- Clinton: C (average grade)\n\nSince C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 90768, "output_tokens": 246, "cost_micros": 275994, "latency_ms": 41342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the campaign grades data in the document:\n\nFor **Trump**:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor **Clinton**:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nTo find the median, I need to determine which grade represents the 50th percentile:\n\nFor Trump: 30% (A/B) + 19% (C) = 49%, then adding the next 15% (D) brings us to 64%. The 50th percentile falls in the D range.\n\nFor Clinton: 43% (A/B) + 20% (C) = 63%. The 50th percentile falls in the C range.\n\nThe document also explicitly states Trump receives a \"C-\" average grade while Clinton receives a \"C\" average grade, which is consistent with this analysis.\n\nAnswer: Clinton", "citations": [], "input_tokens": 19121, "output_tokens": 227, "cost_micros": 60768, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for campaign grades:\n\nOn page 26, there is a table titled \"Election report card: Barely passing grades\" showing:\n- Trump: 30% gave A or B, Average grade: C-\n- Clinton: 43% gave A or B, Average grade: C\n\nThe document states on page 5: \"For the first time in Pew Research Center post-election surveys, voters give the losing candidate higher grades than the winner. About four-in-ten (43%) give Clinton an A or B, which is comparable to the share giving Mitt Romney top letter grades in 2012 (44%) and 13 percentage points higher than Trump's (30%).\"\n\nClinton received an average grade of C, while Trump received an average grade of C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24400, "output_tokens": 176, "cost_micros": 75840, "latency_ms": 6358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Let me find the campaign grades for Trump and Clinton from the document.\n\nFrom the section \"Voters give Trump worse grades than they have for any winning candidate in recent decades\":\n\nFor Trump:\n- 30% gave A or B\n- 19% gave C\n- 15% gave D\n- 35% gave F\n\nFor Clinton:\n- 43% gave A or B\n- 20% gave C\n- 16% gave D\n- 21% gave F\n\nThe document also shows average grades:\n- Trump: C-\n- Clinton: C\n\nTo determine the median, I need to consider the distribution. With:\n- Trump: 30% (A/B), 19% (C), 15% (D), 35% (F)\n- Clinton: 43% (A/B), 20% (C), 16% (D), 21% (F)\n\nFor Trump, the cumulative percentages are: 30% at B or above, 49% at C or above, 64% at D or above. The median falls around the C grade.\n\nFor Clinton, the cumulative percentages are: 43% at B or above, 63% at C or above, 79% at D or above. The median falls around the C grade.\n\nHowever, since Clinton has a higher percentage (43%) receiving A or B compared to Trump (30%), and a lower percentage receiving F (21% vs 35%), Clinton's distribution is shifted toward higher grades. The average grades confirm this: Clinton received C while Trump received C-.\n\nAnswer: Clinton", "citations": [], "input_tokens": 21621, "output_tokens": 349, "cost_micros": 70098, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the campaign grades for Trump and Clinton.\n\nOn page 26-27, there is a table showing \"Voters grade the parties, press and pollsters quite negatively\" with the percentage of voters who give each a grade of A, B, C, D, or F:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n- Average grade: C-\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n- Average grade: C\n\nThe document explicitly states that Trump receives an average grade of C- and Clinton receives an average grade of C. Since C is higher than C-, Clinton has a higher median/average campaign grade.\n\nAnswer: Clinton", "citations": [], "input_tokens": 24361, "output_tokens": 195, "cost_micros": 76008, "latency_ms": 5969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "str", "gold": "Clinton", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about campaign grades for Trump and Clinton.Let me search for \"campaign grade\" in the document to find relevant information.Let me search for \"grade\" to find relevant sections.Now let me read the specific sections that mention grades for Trump and Clinton. Let me look at the chunks around line 260 and 2800-2900.Good! Now let me look for more specific information about median grades. Let me check the section around line 2800-2900 where grades are discussed in detail.Perfect! I found the information. Let me look at the table that shows the grade distribution to calculate the median. From lines 2820-2838, I can see:\n\n**Trump:**\n- A or B: 30%\n- C: 19%\n- D: 15%\n- F: 35%\n\n**Clinton:**\n- A or B: 43%\n- C: 20%\n- D: 16%\n- F: 21%\n\nThe document also explicitly states:\n- Trump receives a C- grade on average (line 2801-2802)\n- Clinton's average grade is a C (line 2897)\n\nTo find the median, I need to look at where the 50th percentile falls:\n\n**Trump:**\n- 0-30%: A or B\n- 31-49%: C\n- 50-64%: D (the 50th percentile falls here)\n- 65-100%: F\n\n**Clinton:**\n- 0-43%: A or B\n- 44-63%: C (the 50th percentile falls here)\n- 64-79%: D\n- 80-100%: F\n\nSo Trump's median grade is D, and Clinton's median grade is C.\n\nAnswer: Clinton", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45171, "error": null, "extra": {"thread_id": 1925, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5b7e95c94e72489983c39189c2251166", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 287, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "clinton", "normalised_gold": "clinton"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to search through the document for charts related to mudslinging.\n\nLooking through the document, I can find references to mudslinging in the following locations:\n\n1. On page 5 (document page 7), there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows the percentage of voters who say there was more or less mudslinging than in past elections from 1992 to 2016.\n\n2. On page 32 (document page 34), there is another chart with the same title \"More see 'mud-slinging,' less focus on issues\" which shows similar data about mudslinging perceptions.\n\nLet me verify these are distinct charts:\n- The first chart on page 5 shows data points: 68, 49, 34, 72, 54, 68, 92 for \"More mudslinging\" \n- The second chart on page 32 shows the same data\n\nThese appear to be the same chart repeated in different sections of the document.\n\nTherefore, there is 1 unique chart related to mudslinging (though it appears twice in the document).\n\nAnswer: 1", "citations": [], "input_tokens": 90760, "output_tokens": 261, "cost_micros": 276195, "latency_ms": 37187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages from 1992-2016 of voters who say there was more or less mudslinging than in past elections.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows the same data about mudslinging over time (1992-2016) plus a separate section about discussion of issues.\n\nLet me verify if these are two separate charts or if one is a repeat:\n- Page 6 shows the mudslinging chart in the context of the executive summary\n- Page 33 shows the same mudslinging data but combined with another chart about discussion of issues in a single figure\n\nThese appear to be presenting the same mudslinging data, but in different contexts. The page 33 version combines it with the issues discussion data.\n\nCounting distinct charts that contain mudslinging data:\n1. The chart on page 6 (standalone mudslinging chart)\n2. The chart on page 33 (mudslinging combined with issues discussion)\n\nSince the question asks about charts \"related to mudslinging,\" both of these count as they both contain mudslinging data.\n\nAnswer: 2", "citations": [], "input_tokens": 19113, "output_tokens": 323, "cost_micros": 62184, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/figures in the document that are related to mudslinging.\n\nLet me search through the document for references to \"mudslinging\":\n\n1. On page 5, there is a section titled \"Record share of voters say there was more campaign 'mudslinging'\" with a chart showing \"% of voters who say there was ___ than in past elections\" with \"More mudslinging\" and \"Less mudslinging\" data from years 92, 96, 00, 04, 08, 12, 16.\n\n2. On page 32, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" showing \"% of voters who say there was ... More mudslinging than usual\" and \"Less mudslinging than usual\" from years 92, 96, 00, 04, 08, 12, 16.\n\nThese appear to be the same chart repeated in different sections of the document (one in the executive summary and one in the detailed findings).\n\nLet me verify if there are any other mudslinging-related charts... No, I only find these two instances, which appear to be the same chart shown in different parts of the document.\n\nSince the question asks how many charts are related to mudslinging, and there are 2 instances of mudslinging charts in the document (even though they show the same data), the answer would be 2.\n\nHowever, if we're counting unique charts, it would be 1 chart that appears twice.\n\nLooking at the context of the question, it seems to be asking for the count of chart occurrences in the document, which would be 2.\n\nAnswer: 2", "citations": [], "input_tokens": 24392, "output_tokens": 372, "cost_micros": 78756, "latency_ms": 10358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts in the document that are related to \"mudslinging.\"\n\nLet me search through the document for references to mudslinging:\n\n1. On page 6, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" which shows percentages of voters who say there was more or less mudslinging than in past elections from 1996-2016.\n\n2. On page 33, there is another chart titled \"More see 'mud-slinging,' less focus on issues\" which shows percentages of voters who say there was more mudslinging than usual and less mudslinging than usual from 1992-2016.\n\nLet me verify there are no other charts about mudslinging by carefully reviewing the document again.\n\nAfter reviewing the document thoroughly, I can confirm there are 2 charts specifically related to mudslinging:\n- One on page 6 showing the record share saying there was more mudslinging\n- One on page 33 showing mud-slinging trends and comparison with issue discussion\n\nAnswer: 2", "citations": [], "input_tokens": 21613, "output_tokens": 237, "cost_micros": 68394, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all charts/tables in the document that are related to \"mudslinging\" or negative campaigning.\n\nLet me search through the document:\n\n1. On page 5, there is a chart titled \"Record share of voters say there was more campaign 'mudslinging'\" - This shows data about mudslinging from 1992-2016.\n\n2. On page 32, there is a chart titled \"More see 'mud-slinging,' less focus on issues\" - This shows data about mudslinging and discussion of issues from 1992-2016.\n\nThese appear to be the only two charts specifically related to mudslinging in the document. The first chart shows the percentage of voters who say there was more mudslinging than in past elections over different election years. The second chart also shows mudslinging data alongside data about discussion of issues.\n\nAnswer: 2", "citations": [], "input_tokens": 24353, "output_tokens": 194, "cost_micros": 75969, "latency_ms": 8342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "int", "gold": "3", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find charts related to mudslinging.Let me read the full document to search for content related to mudslinging and identify charts:Let me continue reading to see if there are more charts related to mudslinging:Based on my thorough reading of the document, I found the following charts related to mudslinging:\n\n1. **Lines 788-853 (chunk 49482)**: A chart titled \"Record share of voters say there was more campaign 'mudslinging'\" showing percentages of voters who said there was more or less mudslinging than in past elections, with data from years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\n2. **Lines 3933-4048 (chunk 49595)**: A chart in the section \"More see 'mud-slinging,' less focus on issues\" showing percentages of voters who say there was \"More mudslinging than usual\" and \"Less mudslinging than usual\" across election years 1992, 1996, 2000, 2004, 2008, 2012, and 2016.\n\nThese appear to be two representations of the same data - one earlier in the document and one later in a more detailed section about campaign evaluations.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62437, "error": null, "extra": {"thread_id": 1926, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bd535bded05431a9423ba96684e000e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 344, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "3"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically looking for percentages who feel \"uneasy\" and \"excited.\"\n\nLooking through the document, I can find on page 6 (document page 8) a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows voter reactions.\n\nThe chart shows:\n- 53% say Trump's election makes them feel \"uneasy\"\n- 51% say it makes them feel \"hopeful\"\n- 41% say \"sad\"\n- 41% say \"scared\"\n- 36% say \"proud\"\n- 31% say \"angry\"\n\nI don't see \"excited\" listed as one of the emotional reactions measured. The document shows \"hopeful,\" \"proud,\" \"uneasy,\" \"sad,\" \"scared,\" and \"angry\" but not \"excited.\"\n\nFor \"uneasy,\" the answer is 53%.\n\nSince \"excited\" is not measured in this survey, I cannot provide a percentage for it.\n\nAnswer: 53% uneasy, excited not measured", "citations": [], "input_tokens": 90783, "output_tokens": 245, "cost_micros": 276024, "latency_ms": 34905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document for voters' reactions to Trump's victory:\n\nFrom page 7, there is a table titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n- Uneasy: 53%\n\nFrom page 8, there is a word cloud table showing one-word reactions. Under \"Trump voters,\" I can see:\n- Excited: 16 (this is the actual number of respondents who mentioned this word, not a percentage)\n\nHowever, the question asks for percentages. Looking at page 11, there is a more detailed table showing emotional reactions:\n\"% of voters who say the election of Donald Trump makes them feel ...\"\n- Uneasy: 53%\n\nThe document does not provide \"excited\" as a percentage in the main emotional reactions table on pages 7 and 11. The word \"excited\" only appears in the word cloud with an actual count of 16 mentions from Trump voters, but this is not presented as a percentage of all voters.\n\nBased on the data provided in percentage form:\n- Uneasy: 53%\n- Excited: Not provided as a percentage of all voters\n\nAnswer: 53%, Not provided", "citations": [], "input_tokens": 19136, "output_tokens": 269, "cost_micros": 61443, "latency_ms": 9281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not provided", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, on page 6 (and repeated on page 10), there is a section titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo \"uneasy\" is 53%.\n\nHowever, I don't see \"excited\" listed as one of the reaction options in this chart.\n\nLooking at page 7, there is a word cloud section showing individual words mentioned by Trump and Clinton voters. For Trump voters, \"Excited\" appears with 16 mentions, but this is not a percentage - these are actual counts of how many people mentioned that specific word.\n\nThe main emotional reactions chart on page 6 does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nSince \"excited\" is not measured as a percentage in the same way as \"uneasy,\" I can only provide the percentage for \"uneasy\" which is 53%.\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24415, "output_tokens": 331, "cost_micros": 78210, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find a section titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel \u2026\n\n| Feeling | % of Voters |\n| ------- | ----------- |\n| Uneasy  | 53          |\n| Hopeful | 51          |\n| Proud   | 36          |\n| Scared  | 41          |\n| Sad     | 41          |\n| Angry   | 31          |\n\nI can see that 53% feel \"uneasy.\"\n\nFor \"excited,\" I need to look at the word cloud section titled \"Trump's victory in a word\" which shows responses from Trump voters and Clinton voters. Under Trump voters, I can see:\n- 16 Excited\n\nHowever, this appears to be a count (16 respondents), not a percentage. The note states \"Figures show actual number of respondents who offered each response; these numbers are not percentages.\"\n\nLooking back at the main emotional reactions table on the previous page, \"excited\" is not listed as one of the specific emotions measured in the survey question about how Trump's election makes them feel.\n\nBased on the available data in the document:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the emotional reactions survey (only appears as a word count of 16 in the open-ended response)\n\nAnswer: 53%, not measured as percentage", "citations": [], "input_tokens": 21636, "output_tokens": 364, "cost_micros": 70368, "latency_ms": 11468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "53, not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentages of voters who felt \"uneasy\" and \"excited\" about Trump's victory.\n\nLooking at the document, I can find information about emotional reactions to Trump's election on page 10:\n\n\"Voters express a mix of emotional reactions to the election of Donald Trump. On the positive side, 51% say that Trump's election makes them feel hopeful; somewhat fewer say it makes them feel proud (36%).\n\nOn the negative side, 53% say Trump's election makes them feel uneasy. About four-in-ten say his election makes them feel sad (41%) or scared (41%), and 31% say the election of Trump makes them feel angry.\"\n\nSo 53% felt \"uneasy.\"\n\nLooking at the table on page 6 titled \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" and the word cloud on page 7, I can see the following reactions listed:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nIn the word cloud on page 7 showing \"Trump's victory in a word,\" I can see \"Excited\" listed among Trump voters with 16 mentions, but this is showing the actual number of respondents, not percentages.\n\nThe main table showing percentage reactions does not include \"excited\" as one of the measured emotions. The document only provides percentages for: hopeful, proud, uneasy, sad, scared, and angry.\n\nBased on the available data:\n- Uneasy: 53%\n- Excited: Not measured as a percentage in the survey\n\nAnswer: 53% uneasy, excited not measured as percentage", "citations": [], "input_tokens": 24376, "output_tokens": 401, "cost_micros": 79143, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "53 uneasy, excited not measured as percentage", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "list", "gold": "[53, 1.4]", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the specific percentages for \"uneasy\" and \"excited\" reactions to Trump's victory.Let me read the specific section with the data about voter reactions to see the percentages clearly.Based on the document, I can see the percentages clearly in the figure at lines 890-916:\n\n- **Uneasy: 53%**\n- **Excited: 16** (this appears to be a count in the open-ended word table, not a percentage)\n\nLet me verify if there's a percentage for \"excited\" elsewhere in the document.The document shows a figure with percentages of voter reactions to Trump's victory. The measured reactions with percentages are:\n- Hopeful: 51%\n- Proud: 36%\n- **Uneasy: 53%**\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\n\"Excited\" appears in a different table showing word counts from open-ended responses (16 mentions from Trump voters), not as a percentage measurement of all voters.\n\nBased on the document, the percentage for \"uneasy\" is **53%**. There is no percentage given for \"excited\" as a reaction category - it only appears as a word count (16) in the open-ended responses table.\n\nAnswer: 53%, 16", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44733, "error": null, "extra": {"thread_id": 1927, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5850dcc4518c4b98b48b2eae8c928f95", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 326, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "16, 53", "normalised_gold": "1 4, 53"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "native_pdf", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find information about voters' reactions to Trump's victory, specifically the percentage who felt \"angry.\"\n\nLooking through the document, I find a section on page 10 titled \"Emotional reactions to Trump's election\" with a chart showing \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\"\n\nThe chart shows percentages of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nAnswer: 31", "citations": [], "input_tokens": 90769, "output_tokens": 150, "cost_micros": 274557, "latency_ms": 34717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "11-21-16-Updated-Post-Election-Release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's victory.\n\nLooking through the document for information about emotional reactions to Trump's election:\n\nOn page 7, there is a section titled \"Election reactions: Nearly all Trump supporters feel 'hopeful'\" which includes a chart showing \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\"\n\nThe chart shows the percentage of voters who say the election of Donald Trump makes them feel various emotions:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states: \"About half of voters (53%) say his election makes them feel 'uneasy,' while nearly as many (51%) say it makes them feel 'hopeful'. Smaller shares say his election triumph makes them feel 'scared', 'sad' (41% each), 'proud' (36%) or 'angry' (31%).\"\n\nAnswer: 31", "citations": [], "input_tokens": 19122, "output_tokens": 243, "cost_micros": 61011, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "azure_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document for information about voters' reactions to Trump's victory.\n\nOn page 6 (and repeated on page 10), there is a chart titled \"Top reactions to Trump's victory - 'uneasy,' 'hopeful'\" which shows the percentage of voters who say the election of Donald Trump makes them feel various emotions.\n\nAccording to this chart:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nThe document states that 31% of voters say Trump's election makes them feel \"angry.\"\n\nAnswer: 31", "citations": [], "input_tokens": 24401, "output_tokens": 157, "cost_micros": 75558, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_basic_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the percentage of voters who feel \"angry\" about Trump's election.\n\nLooking through the document for information about voters' emotional reactions to Trump's victory...\n\nOn page 12 (in the section \"Voters express a mix of emotional reactions to the election of Donald Trump\"), I find a table showing:\n\n\"% of voters who say the election of Donald Trump makes them feel \u2026\"\n\nThe feelings listed include:\n- Hopeful: 51%\n- Proud: 36%\n- Uneasy: 53%\n- Sad: 41%\n- Scared: 41%\n- Angry: 31%\n\nSo 31% of voters say the election of Trump makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 21622, "output_tokens": 166, "cost_micros": 67356, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "llamacloud_premium_lc", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for emotional reactions to Trump's victory...\n\nOn page 6, I find the section \"Top reactions to Trump's victory \u2013 'uneasy,' 'hopeful'\" which shows:\n\n% of voters who say the election of Donald Trump makes them feel ...\n\n| Reaction | %  |\n| -------- | -- |\n| Hopeful  | 51 |\n| Proud    | 36 |\n| Uneasy   | 53 |\n| Sad      | 41 |\n| Scared   | 41 |\n| Angry    | 31 |\n\nThe table clearly shows that 31% of voters say Trump's election makes them feel angry.\n\nAnswer: 31", "citations": [], "input_tokens": 24362, "output_tokens": 166, "cost_micros": 75576, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "doc_id": "11-21-16-Updated-Post-Election-Release.pdf", "answer_format": "float", "gold": "31%", "pages": 45, "document_id": 5222, "arm": "surfsense_agentic", "question_id": "11-21-16-Updated-Post-Election-Release.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about voter reactions to Trump's victory.Let me search for sections that discuss reactions or emotions about Trump's victory.Great! I found a reference to \"angry\" in the context of reactions. Let me read the specific chunk that contains this information.Perfect! I found the information. According to the document, 31% of voters say Trump's election makes them feel \"angry\".\n\nAnswer: 31", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32359, "error": null, "extra": {"thread_id": 1928, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4e0d0df3d4f749b8a5376414cce75543", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 148, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "31.0", "normalised_gold": "31.0"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find data from 2015 about how many people believed the government was doing very/fairly well in reducing the threat of terrorism.\n\nLooking at page 2, I can see a chart titled \"For first time since 9/11, government's job rating on terrorism turns negative\" which shows the percentage who say the government is doing \"Very/Fairly well\" in reducing the threat of terrorism.\n\nFrom the chart on page 2, at the end point in 2015 (specifically December 2015), I can see:\n- Total: 46%\n\nLooking at page 10-11, there's a detailed breakdown by party. The chart on page 10 shows \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" with partisan breakdowns.\n\nFrom the chart on page 10, for 2015 (the rightmost data point):\n- Republican: 27%\n- Democrat: 64%\n- Independent: 44%\n\nSo for the question asking about adults (total), Republicans, and Democrats in 2015:\n- Adults (Total): 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 58172, "output_tokens": 269, "cost_micros": 178551, "latency_ms": 22812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentage of adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section \"Views of how the government is handling the terrorist threat\" on page 10-11.\n\nThe document states:\n- Total (all adults): 46% say the government is doing very/fairly well\n- Democrats: 64% say the government is doing very/fairly well  \n- Republicans: 27% say the government is doing very/fairly well\n\nThis data is from the December 2015 survey (conducted Dec. 8-13, 2015).\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 13406, "output_tokens": 163, "cost_micros": 42663, "latency_ms": 5468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for the specific data on government performance in reducing the threat of terrorism in 2015:\n\nFrom the chart on page 10 titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" showing percentages saying government is doing very/fairly well in reducing the threat of terrorism:\n\nFor December 2015:\n- Total/All adults: 46%\n- Republicans: 27%\n- Democrats: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 19266, "output_tokens": 113, "cost_micros": 59493, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the percentages of surveyed adults, Republicans, and Democrats who believed the government was doing very/fairly well in reducing the threat of terrorism in 2015.\n\nLooking at the document, I can find this information in the section titled \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" on page 10.\n\nThe chart shows \"% saying govt is doing very/fairly well in reducing the threat of terrorism\" for December 2015:\n- Total (all adults): 46%\n- Republican: 27%\n- Democrat: 64%\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 14501, "output_tokens": 140, "cost_micros": 45603, "latency_ms": 5233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document for data on how well the government is doing in reducing the threat of terrorism in 2015:\n\nFrom the table on page 10 titled \"How well is govt doing reducing threat of terrorism?\" and the section \"Views of how the government is handling the terrorist threat\" on pages 10-11:\n\nIn December 2015:\n- Total: 46% said very/fairly well\n- Republican: 27% said very/fairly well  \n- Democrat: 64% said very/fairly well\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 18633, "output_tokens": 125, "cost_micros": 57774, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "list", "gold": "['46', '27', '64']", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the specific data about beliefs regarding government performance in reducing terrorism threats in 2015.Let me search for data about government performance in reducing terrorism threats:I can see a figure showing data about \"How well is govt doing reducing threat of terrorism?\" with percentages for \"Very/Fairly well\" across years. I can see 46 for 2015. Now let me look for the breakdown by party affiliation (Republicans and Democrats):Let me search more specifically for partisan breakdown of government performance on terrorism:I found a reference to partisan data. Let me read that section:The figure I found shows partisan data over time. Let me read more context around this section to understand the 2015 data points:Perfect! I found the data. The text clearly states:\n\n- Overall in 2015: 46% say government is doing very or fairly well\n- Democrats in 2015: 64% say government is doing at least fairly well  \n- Republicans in 2015: 27% say government is doing very or fairly well\n\nThis is also confirmed by the figure showing the data points for year '15' with Democrat at 64, Republican at 27.\n\nAnswer: 46, 27, 64", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42844, "error": null, "extra": {"thread_id": 1929, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cb2b43cade5c41b98919c13d86621c55", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 281, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "27, 46, 64", "normalised_gold": "27, 46, 64"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nLooking at page 15, I can see a chart titled \"ISIS rises higher atop public's threat list\" showing various threats with their percentages in December 2015 and August 2014, plus the change.\n\nThe threats that increased (positive change) from August 2014 to December 2015 are:\n- ISIS: +16 points (67% to 83%)\n- Iran's nuclear program: +3 points (59% to 62%)\n- North Korea's nuclear program: +2 points (57% to 59%)\n- China's emergence as a world power: +1 point (48% to 49%)\n- Global climate change: +1 point (48% to 49%)\n\nNow I need to find the R-D difference for each of these threats. Looking at page 15, there's another chart showing \"Partisan differences in views of top threats to U.S.\" with R-D diff column:\n\n- Iran's nuclear program: R+27\n- ISIS: R+14\n- China's emergence as a world power: R+18\n- North Korea's nuclear program: R+13\n- Global climate change: D+51 (this is actually a Democratic advantage, so -51 for R-D)\n\nAmong the threats that increased, Iran's nuclear program has the biggest R-D difference at R+27.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 58152, "output_tokens": 342, "cost_micros": 179586, "latency_ms": 35328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find which threats had bigger percentages in December 2015 compared to August 2014, and then determine which has the largest Republican-Democrat difference.\n\nLooking at the table \"ISIS rises higher atop public's threat list\" on page 14:\n\nThreats that increased from August 2014 to December 2015:\n1. ISIS: 67% \u2192 83% (+16)\n2. Iran's nuclear program: 59% \u2192 62% (+3)\n3. North Korea's nuclear program: 57% \u2192 59% (+2)\n4. China's emergence as a world power: 48% \u2192 49% (+1)\n5. Global climate change: 48% \u2192 49% (+1)\n\nNow I need to find the R-D differences for these threats from the table \"Partisan differences in views of top threats to U.S.\" on page 15:\n\n1. ISIS: R-D difference = +14 (93% Republican - 79% Democrat)\n2. Iran's nuclear program: R-D difference = +27 (79% Republican - 52% Democrat)\n3. North Korea's nuclear program: R-D difference = +13 (70% Republican - 57% Democrat)\n4. China's emergence as a world power: R-D difference = +18 (62% Republican - 44% Democrat)\n5. Global climate change: R-D difference = -51 (22% Republican - 73% Democrat = -51, but this is negative)\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 percentage points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 13386, "output_tokens": 380, "cost_micros": 45858, "latency_ms": 8984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find threats that increased from August 2014 to December 2015, and then determine which has the biggest Republican-Democrat difference.\n\nFrom page 14, the table \"ISIS rises higher atop public's threat list\" shows changes from August 2014 to December 2015:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n\nFrom page 15, the table \"Partisan differences in views of top threats to U.S.\" shows R-D differences in December 2015:\n- ISIS: 93% (R) - 79% (D) = +14 R-D difference\n- Iran's nuclear program: 79% (R) - 52% (D) = +27 R-D difference\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13 R-D difference\n- China's emergence as a world power: 62% (R) - 44% (D) = +18 R-D difference\n\nAmong threats that increased from August 2014 to December 2015, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 19246, "output_tokens": 314, "cost_micros": 62448, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the threats that increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" (page 14), the threats that increased are:\n- ISIS: +16 points\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence: +1 point\n- Global climate change: +1 point\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" (page 15), the R-D differences are:\n- ISIS: 93% (R) - 79% (D) = +14\n- Iran's nuclear program: 79% (R) - 52% (D) = +27\n- North Korea's nuclear program: 70% (R) - 57% (D) = +13\n- China's emergence: 62% (R) - 44% (D) = +18\n\nAmong the threats that increased from August 2014, Iran's nuclear program has the biggest R-D difference at 27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 14481, "output_tokens": 271, "cost_micros": 47508, "latency_ms": 7327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I need to find threats where concern increased from August 2014 to December 2015, then identify which has the biggest Republican-Democrat difference.\n\nFrom the table \"ISIS rises higher atop public's threat list\" on page 14, I can see the changes from August 2014 to December 2015:\n\n- ISIS: +16 points (83% in Dec 2015 vs 67% in Aug 2014)\n- Iran's nuclear program: +3 points\n- North Korea's nuclear program: +2 points\n- China's emergence as a world power: +1 point\n- Global climate change: +1 point\n- Israeli-Palestinian conflict: -5 points (decreased)\n- Growing authoritarianism in Russia: -11 points (decreased)\n\nSo the threats that increased are: ISIS, Iran's nuclear program, North Korea's nuclear program, China's emergence, and global climate change.\n\nFrom the table \"Partisan differences in views of top threats to U.S.\" on page 15, the R-D differences in December 2015 are:\n\n- ISIS: +14 (93% Rep - 79% Dem)\n- Iran's nuclear program: +27 (79% Rep - 52% Dem)\n- North Korea's nuclear program: +13 (70% Rep - 57% Dem)\n- China's emergence as a world power: +18 (62% Rep - 44% Dem)\n- Global climate change: -51 (22% Rep - 73% Dem) - Note: This is negative, meaning Democrats see it as more of a threat\n\nAmong those that increased from 2014 to 2015, Iran's nuclear program has the biggest R-D difference at +27 points.\n\nAnswer: Iran's nuclear program", "citations": [], "input_tokens": 18613, "output_tokens": 402, "cost_micros": 61869, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "iran s nuclear program", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "Iran's nuclear program", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that appears most relevant based on the priority list: the ISIS and terrorism release from December 2015.Let me continue reading to find the data about threats and R-D differences:I can see the document mentions ISIS increased from 67% in August 2014 to 83% as a major threat. Let me continue reading to find data about other threats and their R-D differences:Let me search for data about threats to well-being and R-D differences. I need to find a table or chart comparing different threats:Good! I found references to \"R-D diff\" in the document. Let me read the section around line 1364:This table shows the \"most important problem\" but I need to find the table about \"threats to well-being\" comparing 2015 to August 2014. Let me continue searching:Perfect! I found the section about threats. Let me read the full table around line 1974-2076:Great! Now I need to see the partisan breakdown table with R-D differences. Let me continue reading:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1930, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd9ddaac9b56498ab35c8337203470ec", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 315, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document that appears most relevant based on priority list isis and terrorism release from december 2015 let me continue reading to find data about threats and r d differences i can see document mentions isis increased from 67 in august 2014 to 83 as major threat let me continue reading to find data about other threats and their r d differences let me search for data about threats to well being and r d differences i need to find table or chart comparing different threats good i found references to r d diff in document let me read section around line 1364 this table shows most important problem but i need to find table about threats to well being comparing 2015 to august 2014 let me continue searching perfect i found section about threats let me read full table around line 1974 2076 great now i need to see partisan breakdown table with r d differences let me continue reading", "normalised_gold": "iran s nuclear program"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll go through the document systematically to count all bar charts.\n\nLooking through each page:\n\nPage 1: Title page - no charts\n\nPage 2: \n- One line chart showing \"Views of Government's Handling of Terrorism\"\n- No bar charts\n\nPage 3:\n- One line chart showing partisan gaps\n- One pie chart showing scrutiny of Muslims\n- No bar charts\n\nPage 4:\n- One table showing party preferences on various issues\n- No bar charts\n\nPage 5:\n- One line chart showing views on Islam and violence over time\n- One line chart showing partisan divide\n- One table with demographic data\n- No bar charts\n\nPage 6:\n- Multiple horizontal bar charts showing \"Should Muslims be subject to greater scrutiny\" broken down by different groups\n- Count: approximately 3-4 bar charts on this page\n\nPage 7:\n- Multiple horizontal bar charts continuing the scrutiny question by demographics\n- Count: approximately 3-4 bar charts on this page\n\nPage 8:\n- One table showing \"Most important problem facing nation\"\n- No bar charts\n\nPage 9:\n- One table showing partisan differences\n- No bar charts\n\nPage 10:\n- One line chart showing government ratings over time by party\n- No bar charts\n\nPage 11:\n- One table showing demographic views\n- No bar charts\n\nPage 12:\n- One line chart showing concerns about anti-terrorism policies\n- No bar charts\n\nPage 13:\n- One line chart showing partisan views on anti-terrorism policies\n- One table\n- No bar charts\n\nPage 14:\n- Multiple horizontal bar charts showing ISIS as threat\n- Count: approximately 2 bar charts\n\nPage 15:\n- Multiple horizontal bar charts showing partisan differences in threats\n- Count: approximately 2 bar charts\n\nPage 16:\n- Line charts showing views of U.S. campaign\n- No bar charts\n\nPage 17:\n- Horizontal bar charts showing current assessments\n- Count: approximately 2 bar charts\n\nPage 18:\n- Multiple horizontal bar charts about concerns and ground troops\n- Count: approximately 3-4 bar charts\n\nPage 19:\n- Line chart and horizontal bar charts\n- Count: approximately 2 bar charts\n\nPage 20:\n- Line charts showing views on military force\n-", "citations": [], "input_tokens": 58124, "output_tokens": 512, "cost_micros": 182052, "latency_ms": 17578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with bar-like elements showing percentages, but it's formatted as a comparative table.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table.\n\n8. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n9. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n10. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n11. Page 9: \"More Republicans than Democrats cite immigration, terrorism as top problems\" - This is a table.\n\n12. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n13. Page 11: \"Evaluations of the government's job reducing the threat of terrorism\" - This is a table.\n\n14. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n15. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n16. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n17. Page 14: \"ISIS rises higher atop", "citations": [], "input_tokens": 13358, "output_tokens": 512, "cost_micros": 47754, "latency_ms": 11733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart, not a bar chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This is a pie chart, not a bar chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This appears to be a table with horizontal bars showing differences.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - Line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - Line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This shows horizontal bar comparisons.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - Horizontal bars.\n\n9. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - Line chart.\n\n10. Page 12: \"More concern with country's protection than civil liberties\" - Line chart.\n\n11. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - Line chart.\n\n12. Page 14: \"ISIS rises higher atop public's threat list\" - Horizontal bars.\n\n13. Page 15: \"Partisan differences in views of top threats to U.S.\" - Horizontal bars.\n\n14. Page 16: \"Most continue to support U.S. military campaign against ISIS\" - Line chart.\n\n15. Page 17: \"Few say current military effort in Iraq and Syria is going well\" - Horizontal bars.\n\n16. Page 17: \"More long-term optimism in ISIS fight\" - Vertical bars.\n\n17. Page 18: \"Bigger concern: Will U.S. go too far or not far enough in Iraq and Syria?\" - Table format with categories.\n\n18. Page ", "citations": [], "input_tokens": 19218, "output_tokens": 512, "cost_micros": 65334, "latency_ms": 13172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll carefully go through the document to count all bar charts.\n\nLet me examine each chart in the document:\n\n1. Page 2: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line graph, not a bar chart.\n\n2. Page 3: \"Most say more scrutiny of Muslims not warranted\" - This is a bar chart showing percentages for Republicans, Independents, and Democrats.\n\n3. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a bar chart comparing Democratic Party vs Republican Party on various issues.\n\n4. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line graph.\n\n5. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a bar chart showing Republican, Independent, and Democrat responses.\n\n6. Page 5: \"Demographic divides in views of Islam and violence\" - This is a table, not a bar chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a bar chart showing responses by different political groups.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - This is a bar chart showing responses by different demographic groups.\n\n9. Page 8: \"Most important problem facing nation\" - This is a table, not a bar chart.\n\n10. Page 9: \"Most important problem facing nation\" (partisan breakdown) - This is a table, not a bar chart.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a bar chart showing percentages by party affiliation.\n\n12. Page 11: \"Older, less educated give more negative ratings\" - This is a bar chart showing responses by age and education.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line graph.\n\n14. Page 13: Bar chart showing partisan views on anti-terrorism policies - This is a line graph.\n\n15. Page 13: \"Young adults more concerned about civil liberties\" - This is a bar chart showing responses by age group.\n\n16. Page 14: \"", "citations": [], "input_tokens": 14453, "output_tokens": 512, "cost_micros": 51039, "latency_ms": 11922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "16", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the bar charts in this document.\n\nLet me go through the document systematically:\n\n1. Page 1: \"For first time since 9/11, government's job rating on terrorism turns negative\" - This is a line chart, not a bar chart.\n\n2. Page 2: \"Growing partisan gap in views of whether Islam encourages violence\" - This is a line chart.\n\n3. Page 2: \"Most say more scrutiny of Muslims not warranted\" - This appears to be a simple text display with percentages, not a chart.\n\n4. Page 3: \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - This is a table, not a bar chart.\n\n5. Page 4: \"Does the Islamic religion encourage violence more than other religions?\" - This is a line chart.\n\n6. Page 4: \"Wide partisan divide over whether Islam is more likely to encourage violence\" - This is a line chart.\n\n7. Page 6: \"Should Muslims be subject to greater scrutiny because of their religion?\" - This is a table.\n\n8. Page 7: \"Young people, minorities, less likely to say Muslims should receive greater scrutiny because of their faith\" - This is a table.\n\n9. Page 8: \"Growing share cite terrorism as the most important problem facing the U.S.\" - This is a table.\n\n10. Page 9: \"Most important problem facing nation\" - This is a table.\n\n11. Page 10: \"Across-the-board drop in ratings of govt efforts to reduce terrorist threat\" - This is a line chart.\n\n12. Page 11: \"How well is govt doing reducing threat of terrorism?\" - This is a table.\n\n13. Page 12: \"More concern with country's protection than civil liberties\" - This is a line chart.\n\n14. Page 13: \"Most Republicans say govt anti-terror policies have not gone 'far enough'\" - This is a line chart.\n\n15. Page 13: \"Young adults more concerned about civil liberties than older people\" - This is a table.\n\n16. Page 14: \"ISIS rises higher atop public's threat list\" - This is a table.\n\n17. Page 15: \"Partisan differences in views of top threats to U.S.\" - This is a table.", "citations": [], "input_tokens": 18585, "output_tokens": 512, "cost_micros": 63435, "latency_ms": 12656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "int", "gold": "1", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the bar charts in the report.Let me continue reading to count all the bar charts in the document.Let me search for \"figure\" tags and examine the visual content descriptions more carefully:I found 31 figures in the document. Now I need to examine each one to determine which are bar charts. Let me read through the document systematically to identify the type of each chart. Based on what I've already read, I can see several line charts and some other visualizations. Let me look for more specific patterns that indicate bar charts.\n\nLooking at the content I've already read, I can identify the following charts:\n\n1. Lines 188-253: Line chart showing government's job rating on terrorism over time\n2. Lines 306-354: Line chart showing partisan gap in views of whether Islam encourages violence  \n3. Lines 372-413: Pie chart showing views on Muslim scrutiny\n4. Lines 436-505: Horizontal bar chart comparing parties on various issues (terrorist threat, gun control, economy, immigration, climate change, environment)\n5. Lines 563-617: Line chart about Islam and violence over time\n6. Lines 618-657: Line chart showing partisan differences over time\n7. Lines 937-1006: Horizontal bar chart showing Muslim scrutiny views by demographic groups\n8. Lines 1055-1193: Horizontal bar chart showing scrutiny views by various demographics\n9. Lines 1508-1563: Line chart showing government ratings on terrorism by party\n10. Lines 1738-1802: Line chart about civil liberties concerns\n11. Lines 1850-1905: Line chart showing partisan views on anti-terror policies\n12. Lines 2004-2072: Vertical bar chart comparing threat levels (ISIS, Iran, North Korea, etc.)\n13. Lines 2117-2210: Horizontal bar chart showing partisan differences in threat views\n14. Lines 2239-2304: Line chart about military campaign approval\n15. Lines 2353-2425: Two separate charts about military campaign assessment\n\nLet me carefully review the entire document to count specifically the bar charts (not line charts or pie charts).\n\nBased on my thorough reading of the document, I can identify the following **bar charts**:\n\n1. **Page 4** (lines 436-505): Horizontal bar chart - \"GOP favored on terrorism, gun control; Democrats lead on climate change\" - comparing Democratic Party vs Republican Party on 6 issues\n2. **Page 6** (lines 937-1006): Horizontal bar chart - \"Should Muslims be subject to greater scrutiny because of their religion?\" - showing responses by party/ideology\n3. **Page 7** (lines 1055-1193): Horizontal bar chart - \"Young people, minorities, less likely to say Muslims should receive greater scrutiny\" - showing responses by demographics\n4. **Page 14** (lines 2004-2072): Mixed chart showing ISIS and other threats with numerical comparisons\n5. **Page 15** (lines 2117-2210): Horizontal bar chart - \"Partisan differences in views of top threats to U.S.\"\n\nAfter careful examination, the document contains **5 bar charts**.\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46078, "error": null, "extra": {"thread_id": 1931, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6dc6e43bf0ee4e80af05e0ae2c870d11", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 404, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "native_pdf", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically page 17 (document page 18), I can find information about public attitudes regarding the ISIS fight.\n\nThe chart titled \"More long-term optimism in ISIS fight\" shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria.\n\nThe text confirms: \"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nAnswer: Two-thirds believe the U.S. and allies will succeed", "citations": [], "input_tokens": 58160, "output_tokens": 159, "cost_micros": 176865, "latency_ms": 22375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "12-15-15-ISIS-and-terrorism-release-final.pdf"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Let me search the document for information about people's attitudes regarding the ultimate success of the U.S. and its allies in the fight against ISIS.\n\nI found the relevant section on page 17, which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also notes this represents an increase: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Long-term optimism that U.S. and allies will succeed", "citations": [], "input_tokens": 13394, "output_tokens": 182, "cost_micros": 42912, "latency_ms": 6734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "long term optimism that u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "azure_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, on page 17, there is a section titled \"More long-term optimism in ISIS fight\" which shows:\n\n\"Two-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail. The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Will definitely or probably succeed", "citations": [], "input_tokens": 19254, "output_tokens": 118, "cost_micros": 59532, "latency_ms": 4703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.888888888888889, "method": "str_norm", "normalised_pred": "will definitely or probably succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_basic_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically the section titled \"More long-term optimism in ISIS fight\" on page 16, I can find the answer.\n\nThe document shows that in December 2015, 66% of people said the U.S. and its allies will \"Definitely/Probably succeed\" in their military campaign against Islamic militants in Iraq and Syria, compared to only 27% who said they will \"Definitely/Probably fail.\"\n\nThe text states: \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: They will succeed/be successful", "citations": [], "input_tokens": 14489, "output_tokens": 141, "cost_micros": 45582, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4444444444444445, "method": "str_norm", "normalised_pred": "they will succeed be successful", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "llamacloud_premium_lc", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document for information about public attitudes on the ultimate success of the U.S. and its allies against ISIS in 2015...\n\nFrom page 17, I can see the section \"Current assessments of U.S. military campaign, prospects for success\" which states:\n\n\"Views are more positive when it comes to whether the U.S. and its allies will succeed or fail in their military campaign against ISIS.\n\nTwo-thirds (66%) now say they think the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria, while just 27% say they will definitely or probably fail.\"\n\nThe document also shows that \"The share who say the U.S. and its allies will succeed is up 11 points from July (from 55%).\"\n\nAnswer: Two-thirds believe U.S. and allies will succeed", "citations": [], "input_tokens": 18621, "output_tokens": 191, "cost_micros": 58728, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.30769230769230765, "method": "str_norm", "normalised_pred": "two thirds believe u s and allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "doc_id": "12-15-15-ISIS-and-terrorism-release-final.pdf", "answer_format": "str", "gold": "will definitely/probably succeed", "pages": 28, "document_id": 5223, "arm": "surfsense_agentic", "question_id": "12-15-15-ISIS-and-terrorism-release-final.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about public attitudes toward the U.S. and its allies' efforts against ISIS in 2015.Let me continue reading to find the specific information about public attitudes:Let me search for information about attitudes toward the fight against ISIS and whether the U.S. and allies will succeed:Perfect! I found the information. Let me read a bit more to confirm the complete data:Based on the document, I found the answer. The document states that although few say the current military effort in Iraq and Syria is going well (only 35% in December 2015 say it's going very/fairly well), when it comes to long-term outlook, two-thirds (66%) believe the U.S. and its allies will either definitely or probably succeed in their campaign against Islamic militants in Iraq and Syria.\n\nAnswer: Two-thirds (66%) believe the U.S. and its allies will succeed", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43858, "error": null, "extra": {"thread_id": 1932, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a011d8590eae4d86a7883730ba9de008", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 232, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.26666666666666666, "method": "str_norm", "normalised_pred": "two thirds 66 believe u s and its allies will succeed", "normalised_gold": "will definitely probably succeed"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1 on page 26:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nand\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 59872, "output_tokens": 134, "cost_micros": 181626, "latency_ms": 31250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 14:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 32, the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 19062, "output_tokens": 151, "cost_micros": 59451, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 18:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe output dimension of the linear_class layer is `num_classes + 1`.\n\nFrom line 31, we can see that the model is instantiated with:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22767, "output_tokens": 155, "cost_micros": 70626, "latency_ms": 4282, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the output dimension of the linear_class layer in the DETR PyTorch inference code.\n\nLooking at the code in Listing 1:\n\n```python\ndef __init__(self, num_classes, hidden_dim, nheads,\n             num_encoder_layers, num_decoder_layers):\n    super().__init__()\n    # ... other layers ...\n    self.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nThe linear_class layer is defined as `nn.Linear(hidden_dim, num_classes + 1)`, which means it takes `hidden_dim` as input and outputs `num_classes + 1` dimensions.\n\nThen looking at line 33:\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe model is instantiated with `num_classes=91`.\n\nTherefore, the output dimension of the linear_class layer is:\nnum_classes + 1 = 91 + 1 = 92\n\nAnswer: 92", "citations": [], "input_tokens": 19551, "output_tokens": 270, "cost_micros": 62703, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the PyTorch inference code in Listing 1, specifically line 15:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 33:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nThe `linear_class` layer has an output dimension of `num_classes + 1`, where `num_classes=91`.\n\nTherefore, the output dimension is 91 + 1 = 92.\n\nAnswer: 92", "citations": [], "input_tokens": 22903, "output_tokens": 155, "cost_micros": 71034, "latency_ms": 4562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q000", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "92", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the DETR document to find the output dimension of the linear_class layer in the PyTorch inference code.Let me search for \"linear_class\" in the document to find the PyTorch inference code:Let me search for PyTorch code or inference code in the document:Now let me read the section with the PyTorch inference code around line 2864:Perfect! I found the PyTorch inference code. Looking at line 2898, I can see:\n\n```python\nself.linear_class = nn.Linear(hidden_dim, num_classes + 1)\n```\n\nAnd from line 2915, the model is instantiated with:\n\n```python\ndetr = DETR(num_classes=91, hidden_dim=256, nheads=8, num_encoder_layers=6, num_decoder_layers=6)\n```\n\nSo the output dimension of the linear_class layer is `num_classes + 1 = 91 + 1 = 92`.\n\nAnswer: 92", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 39343, "error": null, "extra": {"thread_id": 1933, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e081bc008e9a4c03bbbaf39b68e10bd7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 162, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "92", "normalised_gold": "92"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about missed instances when the number of visible instances is 60.\n\nLooking at Figure 12 on page 25, I can see a graph titled \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nThe graph shows three colored lines representing different categories:\n- Dog (blue line)\n- Person (orange line)\n- Apple (green line)\n\nThe x-axis shows \"Number of visible instances\" and the y-axis shows \"% of missed instances.\"\n\nAt the point where the number of visible instances = 60, I need to compare the values for each category:\n\nLooking at the graph at x=60:\n- The dog (blue) line appears to be at approximately 20-25%\n- The person (orange) line appears to be at approximately 15-20%\n- The apple (green) line appears to be at approximately 10-15%\n\nAt 60 visible instances, the dog category shows the highest percentage of missed instances.\n\nAnswer: dog", "citations": [], "input_tokens": 59873, "output_tokens": 231, "cost_micros": 183084, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, I can see a graph plotting \"% of missed instances\" (y-axis) against \"Number of visible instances\" (x-axis) for three categories: dog, person, and apple.\n\nAt 60 visible instances, I need to identify which line/category has the highest percentage of missed instances:\n\n- The dog line (appears to be one color)\n- The person line (appears to be another color)  \n- The apple line (appears to be a third color)\n\nFrom the graph, at the 60 visible instances mark, the \"apple\" category appears to have the highest percentage of missed instances, showing approximately 20-25% missed instances, while \"dog\" and \"person\" show lower percentages (around 10-15%).\n\nAnswer: apple", "citations": [], "input_tokens": 19063, "output_tokens": 203, "cost_micros": 60234, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows \"Analysis of the number of instances of various classes missed by DETR depending on how many are present in the image.\"\n\nLooking at the graph in Figure 12, I can see three lines representing different categories: dog, person, and apple.\n\nAt 60 visible instances (x-axis), I need to compare the percentage of missed instances (y-axis) for each category:\n\n- Dog (appears to be around 20-25%)\n- Person (appears to be around 15-20%)\n- Apple (appears to be the highest, around 30-35%)\n\nThe apple category clearly shows the highest percentage of missed instances at 60 visible instances.\n\nAnswer: apple", "citations": [], "input_tokens": 22768, "output_tokens": 160, "cost_micros": 70704, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "apple", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 12, which shows the analysis of missed instances depending on the number of visible instances.\n\nLooking at Figure 12 on page 25, it shows a graph with \"Number of visible instances\" on the x-axis (from 0 to 100) and \"% of missed instances\" on the y-axis (from 0 to 70). The graph shows three lines for different categories: dog, apple, and one other category.\n\nAt the point where the number of visible instances = 60, I need to identify which category line is highest (meaning the largest proportion of missed instances).\n\nFrom the graph, at 60 visible instances:\n- The \"dog\" line (appears to be one line)\n- The \"apple\" line (appears to be another line)\n- There appears to be a third line\n\nLooking at the vertical position at x=60, the \"dog\" line appears to be the highest among the three categories shown, indicating the largest proportion of missed instances at that point.\n\nAnswer: dog", "citations": [], "input_tokens": 19552, "output_tokens": 226, "cost_micros": 62046, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 12 (the table on page 25), I need to find the number of missed instances for each category when there are 60 visible instances.\n\nFrom the table:\n- dog: 5 missed instances\n- person: 22 missed instances  \n- apple: 15 missed instances\n\nOut of 60 visible instances:\n- dog: 5/60 = 8.3%\n- person: 22/60 = 36.7%\n- apple: 15/60 = 25%\n\nThe person category has the largest proportion of missed instances.\n\nAnswer: person", "citations": [], "input_tokens": 22904, "output_tokens": 139, "cost_micros": 70797, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "person", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q001", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Person", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about visible instances and missed instances by category.Let me search for content about \"missed instances\" and look for figures or charts.Let me read the section with Figure 12 which discusses missed instances:Perfect! I found Figure 12 which shows the chart. Let me read the data more carefully. The chart shows:\n- Y-axis: % of missed instances (0 to 70)\n- X-axis: Number of visible instances (20 to 100)\n- Three categories plotted: dog, person, and apple\n\nAt 60 visible instances, I need to identify which category has the largest proportion of missed instances. From the text data shown:\n- Lines 2795-2796: dog\n- Lines 2797-2798: person  \n- Lines 2801-2802: apple\n\nThe chart shows these three categories, and I need to determine which has the highest value at x=60. Based on the chart layout and the typical pattern where lines are listed in the legend in order of their values, and given that \"dog\" is listed first in the legend (line 2795), followed by \"person\" (line 2797), and then \"apple\" (line 2801), this typically indicates their relative positions on the chart.\n\nLet me look for any additional data that might give exact values:Based on the chart data from Figure 12, I can see:\n- Y-axis values listed: 70, 60, 50, 40, 30, 20, 10, 0\n- X-axis values: 20, 40, 60, 80, 100\n- Three categories in the legend: dog, person, apple\n\nThe legend positioning typically indicates the order of lines on the chart. In this case, at x=60 (60 visible instances), \"dog\" appears to be listed first in the legend (lines 2795-2796), suggesting it has the highest proportion of missed instances at that point, followed by \"person\" and then \"apple\".\n\nGiven the layout where:\n- \"dog\" is listed at the top of the legend\n- \"person\" is in the middle\n- \"apple\" is at the bottom\n\nAnd the typical convention where legend items are ordered by their y-values at key points or overall prominence, \"dog\" would have the largest proportion of missed instances when the number of visible instances equals 60.\n\nAnswer: dog", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47125, "error": null, "extra": {"thread_id": 1934, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39684984e24b48b0a29495ffed5edb23", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 304, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "dog", "normalised_gold": "person"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 on page 13, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP50, \u0394, APS, APM, APL\n\nThe rows show different combinations:\n- Row 1: X (class), X (\u21131), blank (no GIoU): AP=35.8, AP50=57.3\n- Row 2: X (class), blank (no \u21131), X (GIoU): AP=39.9, AP50=61.6\n- Row 3: X (class), X (\u21131), X (GIoU): AP=40.6, AP50=61.6\n\nThe question asks for DETR with L1 loss and WITHOUT GIoU loss, which corresponds to the first row where class=X, \u21131=X, and GIoU is blank.\n\nAnswer: 57.3", "citations": [], "input_tokens": 59883, "output_tokens": 274, "cost_micros": 183759, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three different configurations:\n\n1. First row: class \u2713, L1 \u2717, GIoU \u2713 \u2192 AP: 35.8, AP50: 57.3\n2. Second row: class \u2713, L1 \u2713, GIoU \u2717 \u2192 AP: 39.9, AP50: 61.6\n3. Third row (baseline): class \u2713, L1 \u2713, GIoU \u2713 \u2192 AP: 40.6, AP50: 61.6\n\nThe question asks for the configuration with L1 loss and without GIoU loss, which corresponds to the second row.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19073, "output_tokens": 223, "cost_micros": 60564, "latency_ms": 5608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value for DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see three rows with different loss combinations:\n\n1. Row 1: class \u2612, l1 \u2612, GIoU \u2610 (class and L1, but no GIoU)\n   - AP: 35.8\n   - AP50: 57.3\n\n2. Row 2: class \u2612, l1 \u2610, GIoU \u2612 (class and GIoU, but no L1)\n   - AP: 39.9\n   - AP50: 61.6\n\n3. Row 3 (baseline): class \u2612, l1 \u2612, GIoU \u2612 (all three losses)\n   - AP: 40.6\n   - AP50: 61.6\n\nThe question asks for DETR with L1 loss and without GIoU loss, which corresponds to the first row in Table 4.\n\nAnswer: 57.3", "citations": [], "input_tokens": 22778, "output_tokens": 278, "cost_micros": 72504, "latency_ms": 7140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\", I can see the ablation study for different loss combinations:\n\nThe table shows three rows:\n1. class \u2713, L1 \u2717, GIoU \u2713: AP = 35.8, AP50 = 57.3\n2. class \u2713, L1 \u2713, GIoU \u2717: AP = 39.9, AP50 = 61.6\n3. class \u2713, L1 \u2713, GIoU \u2713: AP = 40.6, AP50 = 61.6 (baseline)\n\nThe second row shows the model with L1 loss but without GIoU loss (class \u2713, L1 \u2713, GIoU \u2717).\n\nThe AP50 value for this configuration is 61.6.\n\nAnswer: 61.6", "citations": [], "input_tokens": 19562, "output_tokens": 256, "cost_micros": 62526, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "61.6", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the AP50 value of DETR with L1 loss but without GIoU loss on the COCO validation set.\n\nLooking at Table 4 in the document, which shows \"Effect of loss components on AP\":\n\nThe table has columns for: class, \u21131, GIoU, AP, \u0394, AP\u2085\u2080, \u0394, AP\u209b, AP\u2098, AP\u2097\n\nThe rows show different combinations:\n1. Row with \u2713 for class and \u21131, but no GIoU: AP = 35.8, AP\u2085\u2080 = 57.3\n2. Row with \u2713 for class and GIoU, but no \u21131: AP = 39.9, AP\u2085\u2080 = 61.6\n3. Row with \u2713 for all three (class, \u21131, GIoU): AP = 40.6, AP\u2085\u2080 = 61.6\n\nThe first row corresponds to the model with L1 loss but without GIoU loss, which shows AP\u2085\u2080 = 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 22914, "output_tokens": 285, "cost_micros": 73017, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q002", "doc_id": "2005.12872v3.pdf", "answer_format": "float", "gold": "57.3", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for the AP50 value of DETR with L1 loss and without GIoU loss in the provided document.Let me read the table that shows the effect of loss components:Perfect! I found Table 4 which shows the effect of loss components on AP. Looking at the table:\n\n- The first row shows: class=\u2612, l1=\u2612, GIoU=(empty), which means L1 loss is used but GIoU loss is not used\n- For this configuration, the AP50 value is 57.3\n\nAnswer: 57.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32109, "error": null, "extra": {"thread_id": 1935, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_984a5f35790043fbba33381ecf31e0b6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "57.3", "normalised_gold": "57.3"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "native_pdf", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 on page 22, which shows the \"Architecture of DETR's transformer,\" I can see the Multi-Head Self-Attention layers are shown in the diagram.\n\nThe Multi-Head Self-Attention boxes in the diagram are colored pink/salmon.\n\nAnswer: Pink (or salmon)", "citations": [], "input_tokens": 59883, "output_tokens": 71, "cost_micros": 180714, "latency_ms": 31750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "pink or salmon", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 \"Architecture of DETR's transformer\" in the document, I need to identify the color of the Multi-Head Self-Attention layer.\n\nIn the diagram, the Multi-Head Self-Attention layer appears in both the Encoder and Decoder sections. These layers are shown in a light orange or peach color.\n\nAnswer: Orange (or peach/light orange)", "citations": [], "input_tokens": 19073, "output_tokens": 89, "cost_micros": 58554, "latency_ms": 3953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange or peach light orange", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I can see the architecture diagram of DETR's transformer.\n\nThe Multi-Head Self-Attention layer boxes in the diagram are colored blue.\n\nAnswer: Blue", "citations": [], "input_tokens": 22778, "output_tokens": 48, "cost_micros": 69054, "latency_ms": 4203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, I need to identify the color of the Multi-Head Self-Attention layer in the architecture diagram.\n\nIn the figure, the Multi-Head Self-Attention layer appears in the Encoder section of the architecture.\n\nAnswer: Orange", "citations": [], "input_tokens": 19562, "output_tokens": 62, "cost_micros": 59616, "latency_ms": 3406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 10 in the document, which shows the architecture of DETR's transformer:\n\nThe Multi-Head Self-Attention layers are shown in the diagram boxes in the Encoder and Decoder sections.\n\nAnswer: The diagram does not specify colors for the layers in Fig 10", "citations": [], "input_tokens": 22914, "output_tokens": 65, "cost_micros": 69717, "latency_ms": 3734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "diagram does not specify colors for layers in fig 10", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q004", "doc_id": "2005.12872v3.pdf", "answer_format": "str", "gold": "Red", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 10 and identify the color of the Multi-Head Self-Attention layer in DETR's transformer architecture.Let me search for \"Fig 10\" or \"Figure 10\" in the document:Let me search for references to transformer architecture figures:Great! I found that Figure 10 shows the \"Architecture of DETR's transformer\" at line 2409. Let me read that section:I can see the text content of Figure 10, but I need to look for color information. The document mentions it's embedded as an image. Let me search for more detailed description of the figure or any color information:The visual description I found was for a different image (zebras). Let me go back and look more carefully at the Figure 10 architecture diagram. Let me search around that area more carefully:I see the text extracted from Figure 10, but the visual description that follows (zebras) is clearly for a different image (Im7.png). Let me search for visual descriptions that might be associated with the transformer architecture diagram. Let me look before Figure 10:Let me search for where there might be an embedded image description for Figure 10. Let me check if there's an image description before line 2400:Let me search for image descriptions or embedded images related to Figure 10. Let me check what comes right before the figure starts:Let me look more carefully. The embedded image Im7.png comes AFTER Figure 10. Let me check if there's an embedded image BEFORE Figure 10 that might contain the actual architecture diagram:The document doesn't seem to have a visual description embedded for Figure 10's architecture diagram itself within the accessible text. However, looking at the structure, it appears Figure 10 is presented as a figure with text labels but the actual image with colors might not have been described in the OCR/visual description section.\n\nLet me search more broadly for any mentions of colors in the architecture or in figures:Let me search for other color terms that might be in Figure 10:These color mentions don't seem to be about Figure 10. Let me try to find if there's an image file reference specifically for the transformer architecture. Let me look for \"Im\" images around Figure 10:Image Im8.png is not Figure 10 either. Let me look between Figure 10 text and Im7.png more carefully. Actually, let me check if there's an embedded image right after the </figure> tag for Figure 10. Let me look at the exact area around the figure closing:So Figure 10 ends at line 2485, and then Im7.png starts at line 2489, which is clearly about zebras, not the transformer architecture. The Figure 10 text content doesn't include a visual description with colors. Let me search for if there's any embedded image metadata that might describe colors for transformer diagrams. Let me try searching for \"green\" or \"red\" along with terms near Figure 10 context:Let me try a different approach. Perhaps the PDF was processed and the image containing Figure 10 wasn't given a separate visual description. Let me look for if there's any image file reference or visual annotation BEFORE Figure 10 that might be the diagram itself:It seems the document processing didn't include a detailed visual description with colors for Figure 10's architecture diagram. This could mean the image was processed but only the text labels were extracted, not the visual styling information like colors of the boxes.\n\nLet me try searching the entire document for any reference to colors of \"Self-Attention\" layers or transformer layers:Based on my thorough search through the document, I cannot find any visual description that specifies the color of the Multi-Head Self-Attention layer in Figure 10. The document contains the text content and labels from Figure 10, but the visual styling information (colors of boxes/components) was not captured in the OCR or visual description process.\n\nWithout access to the actual image file or a visual description that includes color information, I cannot determine from this document what color the Multi-Head Self-Attention layer is depicted as in Figure 10.\n\nAnswer: Color information not available in the provided document text.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 122281, "error": null, "extra": {"thread_id": 1936, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_be29632a506941cfa603de6bebd1c2eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 844, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "color information not available in provided document text", "normalised_gold": "red"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "original_error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 36889, "error": null, "raw_text_chars": 1214}]}, "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure, specifically the number of multi-head self-attention layers.\n\nLet me search through the document for information about DETR's architecture.\n\nFrom page 2, I can see that DETR has a transformer encoder-decoder architecture.\n\nOn page 10 (Section 4.1), I find: \"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nLooking at the detailed architecture description on page 22 (Section A.3) and Figure 10, I can see that:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing multi-head self-attention AND multi-head attention (cross-attention)\n\nFrom page 9-10, the baseline model uses \"6 encoder, 6 decoder layers\"\n\nEach encoder layer has one multi-head self-attention layer.\nEach decoder layer has one multi-head self-attention layer and one multi-head cross-attention layer.\n\nSo the total number of multi-head self-attention layers (not counting cross-attention) is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 59866, "output_tokens": 318, "cost_micros": 184368, "latency_ms": 36875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the number of multi-head self-attention layers in DETR's default structure.\n\nLooking at the document, I need to identify the default configuration of DETR.\n\nFrom Section 4 (Experiments), specifically the \"Technical details\" subsection:\n\"we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nHowever, I need to understand the architecture better. Looking at Section 3.2 and Figure 10:\n\n1. The **encoder** has 6 layers, and each encoder layer contains one multi-head self-attention module (as stated: \"Each encoder layer has a standard architecture and consists of a multi-head self-attention module and a feed forward network (FFN)\")\n\n2. The **decoder** has 6 layers. Looking at the description and Figure 10, each decoder layer contains:\n   - Multi-head self-attention\n   - Multi-head attention (encoder-decoder attention)\n\nSo each decoder layer has one multi-head self-attention layer.\n\nFrom the default configuration mentioned in the experiments:\n- 6 encoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n- 6 decoder layers \u00d7 1 multi-head self-attention per layer = 6 multi-head self-attention layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19056, "output_tokens": 334, "cost_micros": 62178, "latency_ms": 8546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "azure_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about DETR's default structure regarding multi-head self-attention layers.\n\nFrom Section 4.1 \"Comparison with Faster R-CNN\":\n\"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nFrom Section 4.2 \"Ablations\":\n\"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\nFrom the architecture description in Section 3.2:\n- The transformer encoder has multiple layers, each containing a multi-head self-attention module\n- The transformer decoder has multiple layers, each containing multi-head self-attention and encoder-decoder attention mechanisms\n\nSo the default DETR structure has:\n- 6 encoder layers (each with 1 multi-head self-attention layer) = 6 layers\n- 6 decoder layers (each with 1 multi-head self-attention layer) = 6 layers\n\nTotal multi-head self-attention layers = 6 + 6 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22761, "output_tokens": 271, "cost_micros": 72348, "latency_ms": 7187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_basic_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and the number of multi-head self-attention layers.\n\nLooking through the document:\n\n1. In Section 4 \"Experiments\" under \"Technical details\", it states:\n\"We use scale augmentation... we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\n2. In Section A.3 \"Detailed architecture\", the document describes the transformer architecture with both encoder and decoder layers.\n\n3. From the architecture description and Figure 10:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing:\n  - Multi-head self-attention\n  - Multi-head attention (encoder-decoder attention)\n\n4. From the technical details in Section 4:\n- 6 encoder layers (each with multi-head self-attention)\n- 6 decoder layers (each with multi-head self-attention)\n\nSo the total number of multi-head self-attention layers would be:\n- 6 encoder layers \u00d7 1 self-attention layer each = 6\n- 6 decoder layers \u00d7 1 self-attention layer each = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 19545, "output_tokens": 287, "cost_micros": 62940, "latency_ms": 7234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "llamacloud_premium_lc", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure and count the multi-head self-attention layers.\n\nFrom the document, I can find the architecture details:\n\n1. In Section 4.2 (Ablations), it states: \"For the study we choose ResNet-50-based DETR model with 6 encoder, 6 decoder layers and width 256.\"\n\n2. Looking at Figure 10 and Section A.3 (Detailed architecture), the structure shows:\n   - Encoder has N layers, each with Multi-Head Self-Attention\n   - Decoder has M layers, each with Multi-Head Self-Attention AND Multi-Head Attention (cross-attention)\n\n3. From the baseline model description: \"we choose a model with 6 transformer and 6 decoder layers\"\n\n4. In the encoder: Each of the 6 encoder layers contains 1 multi-head self-attention layer = 6 layers\n\n5. In the decoder: Each of the 6 decoder layers contains 1 multi-head self-attention layer = 6 layers\n   (Note: The decoder also has multi-head cross-attention, but that's different from self-attention)\n\nTotal multi-head self-attention layers = 6 (encoder) + 6 (decoder) = 12\n\nAnswer: 12", "citations": [], "input_tokens": 22897, "output_tokens": 303, "cost_micros": 73236, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "arm": "surfsense_agentic", "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll search for information about DETR's structure in the provided document.Now I found the key information. Let me search for more details about the architecture to confirm the total number of self-attention layers.Let me check the decoder structure to understand how many self-attention layers are in the decoder:Now let me look at the supplementary material section that describes the architecture in detail:Based on the document, I can see that:\n\n1. The default DETR model has **6 encoder layers** (line 1054, 1073: \"6 transformer and 6 decoder layers\" and \"6 encoder, 6 decoder layers\")\n\n2. Each encoder layer has a **multi-head self-attention module** (line 624)\n\n3. The default DETR model has **6 decoder layers** (line 1054, 1073)\n\n4. Each decoder layer has **multi-head self-attention** (line 2403-2404: \"multiple multi-head self-attention and decoder-encoder attention\")\n\nSo the total number of multi-head self-attention layers in DETR's default structure is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61719, "error": null, "extra": {"thread_id": 1937, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_12c4837a12434eeca194f2ee2772f40e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 320, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this InVue 2021 Product Catalog.\n\nLooking through the document systematically:\n\n1. **Series 2865** (page 9) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - Multi-position security system with sensors\n\n2. **OnePOD** (page 10) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - POD display solution\n\n3. **Series 960** (page 12) - For iPad Pro, iPad Air, iPad, iPad mini 4\n   - POD with exposed cable\n\n4. **iPad + Keyboard Solutions** (pages 17-19):\n   - Standard Solution for iPad Pro + Keyboards (page 18)\n   - High Security Solution for iPad Pro + Keyboards (page 19)\n\n5. **iPad Digital Display** (pages 15-16) - Bracketed pedestal solution\n\nSo counting the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. iPad + Keyboard Standard Solution\n5. iPad + Keyboard High Security Solution\n6. iPad Digital Display\n\nAnswer: 6", "citations": [], "input_tokens": 96851, "output_tokens": 271, "cost_micros": 294618, "latency_ms": 54405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this Apple catalog document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** (pages 3-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 6-9) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (pages 10-12) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 13) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 14) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 15-16) - A bracketed pedestal solution for iPads as digital displays\n\n7. **iPad + Keyboard Solutions** (pages 17-19):\n   - iPad Pro + Keyboards Standard Solution (page 18)\n   - iPad Pro + Keyboards High Security Solution (page 19)\n\nLet me count the distinct iPad solutions:\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nAnswer: 8", "citations": [], "input_tokens": 11786, "output_tokens": 335, "cost_micros": 40383, "latency_ms": 6532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in this document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays\n\n7. **iPad Pro + Keyboards Standard Solution** (page 11) - For iPad Pro with Magic Keyboard and Smart Keyboard\n\n8. **iPad Pro + Keyboards High Security Solution** (page 12) - For iPad Pro with keyboards\n\nSo there are **8** different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 18388, "output_tokens": 271, "cost_micros": 59229, "latency_ms": 6733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions mentioned in the document.\n\nLet me go through the document systematically:\n\n1. **Series 2865** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without Keyboard) on page 38\n\n2. **OnePOD** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 40\n\n3. **Series 960** - mentioned for iPad, iPad mini, iPad Air and iPad Pro (without keyboard) on page 41\n\n4. **iPad Digital Display** - mentioned as a \"Bracketed pedestal for utilizing iPads as digital displays on Apple Fixtures\" on pages 11-12\n\n5. **iPad Pro + Keyboards Standard Solution** - mentioned on page 11 for \"iPad Pro, Magic Keyboard, Smart Keyboard and Apple Pencil\"\n\n6. **iPad Pro + Keyboards High Security Solution** - mentioned on page 12\n\nSo I can identify 6 different iPad solutions in the document:\n- Series 2865 (for standard iPads)\n- OnePOD (for standard iPads)\n- Series 960 (for standard iPads)\n- iPad Digital Display\n- iPad Pro + Keyboards Standard Solution\n- iPad Pro + Keyboards High Security Solution\n\nAnswer: 6", "citations": [], "input_tokens": 12620, "output_tokens": 289, "cost_micros": 42195, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I need to count the different iPad solutions shown in the document.\n\nLooking through the document systematically:\n\n1. **Series 2865** (pages 1-5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n2. **OnePOD** (pages 3-4) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n3. **Series 960** (page 5) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n4. **One60 Tethered** (page 6) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n5. **One90QR** (page 7) - Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n\n6. **iPad Digital Display** (pages 8-9) - For utilizing iPads as digital displays on Apple Fixtures\n\n7. **iPad + Keyboard Solutions - Standard Solution** (page 11) - For iPad Pro with Magic Keyboard, Smart Keyboard and Apple Pencil\n\n8. **iPad + Keyboard Solutions - High Security Solution** (page 12) - For iPad Pro with Magic and Smart Folio keyboards\n\nSo there are 8 different iPad solutions shown in the document.\n\nAnswer: 8", "citations": [], "input_tokens": 16954, "output_tokens": 288, "cost_micros": 55182, "latency_ms": 7030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q000", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for iPad solutions in the provided document.Let me search for \"solution\" to find where iPad solutions are mentioned:Now let me read the table of contents section more carefully to see the iPad solutions listed:I can see there are two main sections: \"iPhone & iPad Solutions\" and \"iPad + Keyboard Solutions\". Let me now read through these sections to count the individual iPad solutions. Let me start with the \"iPhone & iPad Solutions\" section:Let me read the document more systematically. Let me check the pages 1-10 which should cover the \"iPhone & iPad Solutions\" section:Let me search for specific product series names to count the solutions:Let me look more systematically at the document. Let me read pages that should list individual iPad solutions:I can see Series 2865, OnePOD, and Series 960 mentioned. Let me continue reading to find all the different solutions and then look at the iPad + Keyboard Solutions section:Let me search for more product names. Let me look for what comes after Series 960:Good, I found \"One60 Tethered\". Let me continue:Good, I found \"One90QR\". Let me continue searching through the document:Good, I found \"iPad Digital Display\". Now let me check if there are more iPhone & iPad solutions and then look at the iPad + Keyboard Solutions section. Let me look at page 10 where iPad + Keyboard Solutions should start:Good! I found \"iPad Pro + Keyboards Standard Solution\". Let me continue to find the next one:Good! I found \"iPad Pro + Keyboards High Security Solution\". Now let me continue to see if there are more iPad solutions. Let me check what comes on page 12 and beyond, which should start MacBook Solutions (page 13 according to the table of contents):Perfect! Now I can see that page 13 starts MacBook Solutions, which means the iPad + Keyboard Solutions section ends at page 12. Let me now compile all the iPad solutions I found. Let me verify by checking if there are any other iPad product names I might have missed. Let me look at pages 10-11 more carefully:Now let me look more carefully at page 9 to see if there are more solutions listed on that introductory page:Good. Now let me compile the complete list. I need to be systematic. Let me re-examine the section headers carefully. Let me look at what page 9 shows at the bottom to clarify:\n\nActually, looking back at my findings, page 9 seems to be a transition page showing the \"iPad + Keyboard Solutions\" section header with compatibility information. Pages 10-11 then have two specific solutions.\n\nLet me now count all the different iPad solutions I found in the document:\n\n**From \"iPhone & iPad Solutions\" section (pages 1-8):**\n1. Series 2865\n2. OnePOD\n3. Series 960\n4. One60 Tethered\n5. One90QR\n6. iPad Digital Display\n\n**From \"iPad + Keyboard Solutions\" section (pages 10-11):**\n7. iPad Pro + Keyboards Standard Solution\n8. iPad Pro + Keyboards High Security Solution\n\nThat gives me a total of **8** different iPad solutions.\n\nBut wait, let me verify this more carefully. OnePOD was mentioned on page 2 in the product comparisons - let me verify if it's a distinct solution:Yes, OnePOD is a distinct solution. Let me also verify the others are all distinct iPad solutions and not just iPhones. Let me check the compatibility sections more carefully. Looking back at my readings, all of these products list iPad compatibility:\n\n- Series 2865: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- OnePOD: There's a specific OnePOD section for iPad ordering\n- Series 960: Compatible with iPads\n- One60 Tethered: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- One90QR: Compatible with iPad Pro, iPad Air, iPad, iPad mini 4\n- iPad Digital Display: Specifically for iPads on Apple Fixtures\n- iPad Pro + Keyboards Standard Solution: For iPad Pro with keyboards\n- iPad Pro + Keyboards High Security Solution: For iPad Pro with keyboards\n\nAll 8 are indeed iPad solutions. \n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 126703, "error": null, "extra": {"thread_id": 1938, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_18872fac42c746e097bc8836213e95a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 870, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features are listed but not shown in the illustration.\n\nLooking at page 15 (document page 22), I can see the \"MacBook High Security Solution\" section.\n\nThe \"FEATURES AND BENEFITS\" lists 6 numbered features:\n\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow looking at the illustrations on the same page, I can see:\n- Image showing features 3, 6, 1, 6, and 2 labeled\n- Image showing feature 5 labeled\n\nLet me verify which features are NOT shown in the illustrations:\n- Feature 1: Shown (labeled in bottom left image)\n- Feature 2: Shown (labeled in bottom left image)\n- Feature 3: Shown (labeled in bottom left image)\n- Feature 4: NOT shown in any illustration\n- Feature 5: Shown (labeled in top right image)\n- Feature 6: Shown (labeled in bottom left image)\n\nAnswer: 1", "citations": [], "input_tokens": 96859, "output_tokens": 327, "cost_micros": 295482, "latency_ms": 47640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which feature numbers are mentioned in the text but not shown in the illustration.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section.\n\nThe FEATURES AND BENEFITS list shows:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to look at the illustration to see which numbers are labeled in the image.\n\nFrom the illustration, I can see the following numbers are clearly marked:\n- 1 (pointing to the rear anchor area)\n- 2 (pointing to the cable/tether area)\n- 3 (pointing to bracket/arm area)\n- 5 (pointing to the metal tabs)\n- 6 (appears twice, pointing to the molded feet)\n\nThe numbers NOT shown in the illustration are:\n- 4 (Ability to power/alarm MacBook with USB-C Port)\n\nAnswer: 4", "citations": [], "input_tokens": 11794, "output_tokens": 307, "cost_micros": 39987, "latency_ms": 8657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed in text with what's shown in the illustration.\n\nLooking at pages 14-17, I find the MacBook High Security Solution on page 15.\n\nThe text lists these FEATURES AND BENEFITS:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration to see which features are labeled. The illustration shows numbered callouts (1, 2, 3, 6) pointing to different parts of the MacBook security solution.\n\nFrom the visible callouts in the illustration:\n- Feature 1 is shown\n- Feature 2 is shown\n- Feature 3 is shown\n- Feature 6 is shown\n\nFeatures NOT shown in the illustration:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nTherefore, 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 18396, "output_tokens": 323, "cost_micros": 60033, "latency_ms": 9530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and compare the features listed with what's shown in the illustration.\n\nLooking at page 15, I can see the \"HIGH SECURITY EXCEPTION PROGRAM - MACBOOK\" section titled \"MacBook High Security Solution\".\n\nThe FEATURES AND BENEFITS section lists 6 numbered features:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nNow I need to examine the illustration on that page to see which features are NOT shown visually. Looking at the image description \"delete alarm\" and \"return\" text visible, this appears to be showing a MacBook with the security solution.\n\nFrom the numbered features:\n- Feature 4 (USB-C Port) - this is an internal capability, not visually shown in an illustration\n- Feature 5 (Metal tabs with rubber pads preventing closing) - this would be visible if shown\n- Feature 6 (Stable, molded feet) - these would be underneath and likely not visible in a standard product photo\n\nWithout being able to see the exact details of the illustration clearly, but based on typical product photography, features that are functional/internal capabilities (like feature 4 - power/alarm ability) or underneath components (like feature 6 - molded feet) are typically not shown in product illustrations.\n\nBased on the context and typical product documentation, features 4 and 6 would most likely not be presented in the illustration, giving us 2 features.\n\nAnswer: 2", "citations": [], "input_tokens": 12628, "output_tokens": 418, "cost_micros": 44154, "latency_ms": 12765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the MacBook High Security Solution section and identify which features listed are NOT shown in the illustrations.\n\nLooking at page 15, I can see the \"MacBook High Security Solution\" section with:\n\n**FEATURES AND BENEFITS** (numbered 1-6):\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe page shows 4 images with numbered callouts:\n- Image 1 (Top Left): Shows callout 5\n- Image 2 (Top Right): Shows callout 5 (close-up)\n- Image 3 (Bottom Left): Shows callouts 1, 2, 3, and 6\n- Image 4 (Bottom Right): Shows callout 2 (close-up)\n\nFeatures shown in illustrations:\n- Feature 1: YES (shown in Image 3)\n- Feature 2: YES (shown in Images 3 and 4)\n- Feature 3: YES (shown in Image 3)\n- Feature 4: NO (not shown in any image)\n- Feature 5: YES (shown in Images 1 and 2)\n- Feature 6: YES (shown in Image 3)\n\nOnly Feature 4 (Ability to power/alarm MacBook with USB-C Port) is NOT presented in the illustrations.\n\nAnswer: 4", "citations": [], "input_tokens": 16962, "output_tokens": 396, "cost_micros": 56826, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q002", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "int", "gold": "4", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about MacBook high security solution and identify which features are not presented in the illustration.Let me search for \"security\" in the document to find the relevant section:Let me search for \"MacBook\" and \"high security\" together to find the specific section:Now let me read the specific section about \"MacBook High Security Solution\":Perfect! I found the MacBook High Security Solution section. Now let me read it more carefully to see the full list of features and the illustration. Let me go back to line 3132:I can see the features listed are numbered 1-6:\n1. Rigid anchor design on rear of MacBook\n2. Steel cable co-molded for added strength, secured to table with OneKEY Padlock\n3. Bracket bar arms and custom form factor to capture and prevent lateral movement\n4. Ability to power/alarm MacBook with USB-C Port\n5. Metal tabs with rubber pads prevents closing of MacBook for added security and accidental damage during installation\n6. Stable, molded feet prevent rocking and protect fixture table\n\nThe illustration shows numbers 1, 2, 3, and 6 marked on the MacBook keyboard image in the figure. The OCR text shows these numbers visible in the illustration. However, I notice the visual description after the figure is describing a smartwatch, which seems incorrect. Let me check if there's a better description of the MacBook illustration by reading a bit more context around this section.\n\nLooking at the figure content (lines 3159-3225), I can see the illustration has:\n- Number 1 appears twice (lines 3185, 3189, 3223)\n- Number 2 appears twice (lines 3137, 3179, 3191, 3217)\n- Number 3 appears twice (lines 3173, 3215)\n- Number 6 appears three times (lines 3213, 3221)\n\nFrom the text, the 6 features are listed, and from the illustration markers I can identify which numbers are shown:\n- 1 is shown (rigid anchor design)\n- 2 is shown (steel cable)\n- 3 is shown (bracket bar arms)\n- 6 is shown (stable molded feet)\n\nThe features that are NOT shown in the illustration are:\n- Feature 4 (Ability to power/alarm MacBook with USB-C Port)\n- Feature 5 (Metal tabs with rubber pads prevents closing of MacBook)\n\nSo 2 features are not presented in the illustration.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46969, "error": null, "extra": {"thread_id": 1939, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_abe777eda57d4187b59395c83dff7b8b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 346, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "native_pdf", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about \"One40\"...\n\nI cannot find any mention of \"One40\" in this Apple Solutions 2021 Product Catalog from InVue. The document discusses various security solutions for Apple products including:\n\n- Series 2865\n- OnePOD (One55, One60, One65)\n- Series 960\n- W2000\n- WS2\n- HSW100\n- One60 Wearable\n- OnePOD Wearable\n\nBut there is no product called \"One40\" mentioned in this catalog.\n\nAnswer: Cannot determine - product not found in document", "citations": [], "input_tokens": 96873, "output_tokens": 141, "cost_micros": 292734, "latency_ms": 38656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2021-Apple-Catalog.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine product not found in document", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and whether it can only be used for Apple Watch.\n\nOn page 21, in the \"OnePOD Wearable / Apple Watch Solutions\" section, under \"FEATURES AND BENEFITS,\" it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is mentioned in the context of Apple Watch solutions. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nSearching through the rest of the document, One40 is only mentioned in this one location in relation to Apple Watch compatibility. There is no indication that One40 is used for any other products in this catalog.\n\nHowever, the question asks if it can \"only\" be used for Apple Watch. The document doesn't explicitly state that One40 cannot be used for other purposes - it simply doesn't mention One40 being used for anything else in this particular catalog.\n\nGiven that this is specifically an Apple catalog and One40 is only mentioned in the Apple Watch section without any other applications shown, and based solely on what is presented in this document:\n\nAnswer: yes", "citations": [], "input_tokens": 11808, "output_tokens": 265, "cost_micros": 39399, "latency_ms": 8577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "azure_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about One40 and what products it can be used with.\n\nOn page 21, under \"OnePOD Wearable\" section for Apple Watch Solutions, it states:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is compatible with the OnePOD Wearable system for Apple Watch. However, this doesn't definitively state that One40 can ONLY be used for Apple Watch.\n\nLooking through the rest of the document, I can see that One40 is only mentioned in the context of Apple Watch solutions. There is no mention of One40 being used for iPhones, iPads, or other Apple products in their respective sections.\n\nAnswer: No", "citations": [], "input_tokens": 18410, "output_tokens": 179, "cost_micros": 57915, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_basic_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I need to search the document for information about \"One40\" and what products it can be used with.\n\nLooking through the document, I can find mentions of \"One40\" on page 21 in the OnePOD Wearable section:\n\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis appears in the context of Apple Watch solutions, but it only states compatibility with these stands. It doesn't state that One40 can ONLY be used for Apple Watch or that it's exclusive to Apple Watch.\n\nThe document doesn't provide enough information to definitively say whether One40 can only be used for Apple Watch or if it has other uses beyond what's mentioned in this catalog.\n\nHowever, since the question asks for a direct yes/no answer based on what's in the document, and the document only shows One40 mentioned in the Apple Watch context without explicitly stating it's exclusive to Apple Watch, I cannot confirm the statement is true.\n\nAnswer: No", "citations": [], "input_tokens": 12642, "output_tokens": 221, "cost_micros": 41241, "latency_ms": 6593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "llamacloud_premium_lc", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "Let me search through the document for information about One40 and its compatibility.\n\nLooking at the Apple Watch Solutions section, I can see mentions of:\n- W2000\n- WS2\n- OnePOD Wearable (compatible with One55, One60, One65 and One40)\n- One60 Wearable (High Security)\n- HSW100\n\nOn page 21, under \"OnePOD Wearable\" features and benefits, it states:\n\"Compatible with One55, One60, One65 and One40 standard and split-level stands\"\n\nThis indicates that One40 is a stand that is compatible with the OnePOD Wearable system for Apple Watch. However, the document does not explicitly state that One40 can ONLY be used for Apple Watch. The document doesn't provide comprehensive information about all possible uses of the One40 stand.\n\nBased on the limited information provided in the document, I cannot definitively confirm that One40 is exclusively for Apple Watch only.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 16976, "output_tokens": 239, "cost_micros": 54513, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "yes"}}
+{"qid": "2021-Apple-Catalog.pdf::Q003", "doc_id": "2021-Apple-Catalog.pdf", "answer_format": "str", "gold": "Yes", "pages": 55, "document_id": 7578, "arm": "surfsense_agentic", "question_id": "2021-Apple-Catalog.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the Apple Catalog document to find information about One40 and its compatibility.Let me search for \"One40\" in the document.Now let me read the section around line 4032 to get more context about One40.This section is about Apple Watch products. Let me search for more information about One40 to see if it's used with other products.Let me search for other mentions of One40 in the document to see if it's used with other products besides Apple Watch.Let me search for \"One55\" or \"One60\" to understand what these stands are used for, which will help determine if One40 is only for Apple Watch.Good! I can see One55 is used for iPhone and iPad. Now let me check around line 1666 to see what products use these stands.Let me look for what products are associated with One40 specifically. Let me search for \"One40\" more broadly in the document.Let me search the entire document more carefully. I'll look for sections that mention what One40 is used for.I notice the table only shows One55, One65, and One60, but not One40. Let me look at sections that specifically discuss iPhone and iPad product ordering to see if One40 is mentioned there.I can see iPhone ordering uses One55, One60, and One65, but not One40. Let me check iPad ordering and then look specifically for what products use One40.iPad also uses One55, not One40. Now let me search for a broader section around the Apple Watch mention to understand the context better. Let me look for tables or product ordering sections related to Apple Watch and One40.Let me search for Apple Watch product ordering section specifically.Now let me read the Apple Watch product ordering section around line 6290.I can see the Apple Watch product ordering sections, but they don't mention One40 in the stand options. They mention DBD910-W, DBD600-W, DBD650-W, AC271-W, and ZW2000 for Apple Watch. Let me go back to where One40 was mentioned (line 4032) and read more context to understand what section that is part of.Perfect! Now I have clear context. The section starting at line 3973 is titled \"ONEPOD WEARABLE\" and is under \"/ Apple Watch Solutions\" (line 3974). The section at line 4021-4060 describes the OnePOD Wearable product which:\n\n1. Has \"Harness bands secure Apple Watch to sensor\" (line 4028)\n2. Is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\" (line 4032)\n3. Has \"OEM charging compatibility for Apple Watch models\" (line 4035)\n4. Shows compatibility with various Apple Watch Series (lines 4043-4059)\n\nThis clearly indicates that One40 is mentioned specifically in the Apple Watch section. However, the statement says One40 is compatible with the OnePOD Wearable sensor/product along with One55, One60, and One65 stands. Let me verify if One40 stand is used for other products as well by searching more thoroughly.One40 is mentioned only once in the entire document, and it's in the Apple Watch section. However, I notice that it says the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands\". This means the Apple Watch sensor can work with multiple stand types including One40, but it doesn't necessarily mean One40 can ONLY be used for Apple Watch.\n\nLet me check if there's a definition or description of what One40 is elsewhere, or if it appears in any product tables.Let me look at what the table at line 1666 shows - it lists One55, One65, and One60 but notably does NOT include One40. This suggests One40 might be a different or less common stand type. Let me see if I can find any section that specifically describes One40 as a product.The table comparison shows One55, One65, and One60, but NOT One40. This table appears on page 3 under \"/ iPhone & iPad Solutions\" and \"ONEPOD\u2122\" headers (lines 1615-1616).\n\nBased on my search:\n1. One40 is mentioned only ONCE in the entire document (line 4032)\n2. It's mentioned in the \"Apple Watch Solutions\" section under \"OnePOD Wearable\" product description\n3. The comparative table of stands (lines 1658-1730) for iPhone/iPad products shows One55, One65, and One60, but NOT One40\n4. iPhone ordering sections show One55, One60, One65 but not One40\n5. iPad ordering sections show One55 but not One40\n6. Apple Watch ordering sections don't explicitly list One40 in their product codes\n\nThe document states that the OnePOD Wearable (for Apple Watch) is \"Compatible with One55, One60, One65 and One40 standard and split-level stands.\" This indicates One40 CAN be used with Apple Watch, but since One55, One60, and One65 are used for multiple products (iPhone, iPad) and are also compatible with Apple Watch, it's possible One40 could also be used for other products.\n\nHowever, the fact that:\n- One40 appears nowhere else in the document\n- It's not in the comparison table\n- It's not in iPhone/iPad ordering sections\n- It only appears in the Apple Watch compatibility statement\n\nThis strongly suggests One40 is specifically or primarily designed for Apple Watch, though I cannot definitively say it can ONLY be used for Apple Watch based solely on this document.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 125969, "error": null, "extra": {"thread_id": 1940, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_274957bdc18c45a58945780a57a038df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 928, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "yes"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the paper to identify the function names with blue background in the program execution.\n\nLooking at Figure 1 (Overview of PROGRAMFC model) on page 2, I can see the \"Functions\" column which shows the program execution functions. The functions shown with blue background are:\n\n1. Fact Checker (appears twice - for S1 and S3)\n2. QA Model (for S2)\n3. Logical Reasoner (for S4)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 65107, "output_tokens": 141, "cost_micros": 197436, "latency_ms": 20703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names with blue background in the \"Functions\" column of the Program Execution section.\n\nFrom the figure, I can see the following functions are called in the reasoning program:\n- S1: Verify (Fact Checker)\n- S2: Question (QA Model)\n- S3: Verify (Fact Checker)\n- S4: Predict (Logical Reasoner)\n\nThe functions with blue background are:\n- Fact Checker\n- QA Model\n- Logical Reasoner\n\nHowever, the question asks for \"function names\" which appear to be the ACTION names in the program format. Looking at the actual function calls in the program:\n- Verify\n- Question\n- Predict\n\nIn ascending alphabetical order:\n\nAnswer: Predict, Question, Verify", "citations": [], "input_tokens": 26912, "output_tokens": 192, "cost_micros": 83616, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I can see the \"Functions\" column which shows the sub-task functions that are called during program execution.\n\nThe functions shown with blue backgrounds in the figure are:\n- Fact Checker (called in steps S1 and S3 for the Verify function)\n- QA Model (called in step S2 for the Question function)\n- Logical Reasoner (called in step S4 for the Predict function)\n\nArranging these in ascending alphabetical order:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 29934, "output_tokens": 135, "cost_micros": 91827, "latency_ms": 5031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names that appear with blue background in the \"Functions\" column during program execution.\n\nFrom the figure, I can see the following functions listed in the execution steps:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, examining the actual function names called in the \"Reasoning Program\" column:\n- FACT_1 uses a fact verification function\n- ANSWER_1 uses Question (QA)\n- FACT_2 uses a fact verification function\n- PREDICTED_LABEL uses Predict (logical reasoning)\n\nLooking at Section 3.4 which describes the sub-task functions, the three function names are:\n1. QUESTION\n2. VERIFY\n3. PREDICT\n\nThese correspond to the blue-backgrounded function types shown in Figure 1.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 22321, "output_tokens": 230, "cost_micros": 70413, "latency_ms": 7172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the function names shown in the \"Functions\" column that correspond to the program execution steps.\n\nFrom the figure, the functions shown are:\n- Fact Checker (for S1 and S3)\n- QA Model (for S2)\n- Logical Reasoner (for S4)\n\nHowever, looking more carefully at the actual function names used in the reasoning program section and throughout the paper, the standardized function names are:\n- VERIFY (for fact checking)\n- QUESTION (for question answering)\n- PREDICT (for logical reasoning)\n\nThese are the three sub-task functions described in Section 3.4.\n\nIn ascending alphabetical order:\n\nAnswer: PREDICT, QUESTION, VERIFY", "citations": [], "input_tokens": 29236, "output_tokens": 174, "cost_micros": 90318, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "predict, question, verify", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q000", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['Fact Checker']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q000", "answer_letter": null, "raw_text": "I can see Figure 1 in the document. Let me search for information about the program execution functions with blue backgrounds. I need to look more carefully at the figure description and any additional details about the model architecture.Let me look at the actual figure more carefully. I need to examine the visual description and the figure content in detail to identify functions with blue backgrounds.I can see the figure content, but I need to look for more detailed visual information about the figure. Let me search for image descriptions or additional figure details.Let me look for any embedded images or visual descriptions that might contain details about the colors in Figure 1.Let me search for more embedded images near Figure 1 to see if there's a better visual description.Let me look for the actual embedded image description near Figure 1 more carefully. Let me search around line 408 where Im5.png is mentioned.Let me search for more specific information about the visual elements of Figure 1. Let me look for descriptions that might mention colors or boxes in the figure.Let me look for image descriptions near the figure that might contain more details about the visual layout. Let me read more around the figure area to find additional embedded images or descriptions.Based on Figure 1 in the document, I can see the \"Functions\" column which shows the program execution functions. Looking at the structure, I can see:\n- S1: Verify \u2192 Fact Checker\n- S2: Question \u2192 QA Model  \n- S3: Verify \u2192 Fact Checker\n- S4: Predict \u2192 Logical Reasoner\n\nHowever, I need to find which ones have blue backgrounds. Let me search more carefully for visual information about the figure or look for any other embedded images that might be Figure 1 itself.From Figure 1, I can see the functions column showing:\n- S1: Fact Checker\n- S2: QA Model\n- S3: Fact Checker\n- S4: Logical Reasoner\n\nHowever, the text-based XML doesn't explicitly specify which ones have blue backgrounds. Let me search the document for any other references to colors or visual styling in Figure 1, or look for the actual Figure 1 image description more thoroughly. Let me search for \"Fact Checker\", \"QA Model\", \"Logical Reasoner\" to understand the context better.Based on my analysis of Figure 1 from the document, I can see the \"Functions\" column contains:\n- Fact Checker (appears for S1 and S3 - Verify operations)\n- QA Model (appears for S2 - Question operation)\n- Logical Reasoner (appears for S4 - Predict operation)\n\nGiven the typical visualization patterns in academic papers where functions are color-coded, and looking at the structure where the three main function types are displayed, the functions with blue background would likely be the distinct function types shown in the \"Functions\" column. Without explicit color information in the XML, but based on the structure and common visualization practices, the three function types are: \"Fact Checker\", \"Logical Reasoner\", and \"QA Model\".\n\nIn alphabetical order, these would be:\n\nAnswer: Fact Checker, Logical Reasoner, QA Model", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 80155, "error": null, "extra": {"thread_id": 1941, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6bd06724d924284a60194ba98ad4765", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 579, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "fact checker, logical reasoner, qa model", "normalised_gold": "fact checker"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to find how many prompt examples are shown in the paper's main body (not appendices).\n\nLet me search through the document for prompt examples:\n\n1. **Figure 1** (page 2): Shows a complete example of the PROGRAMFC model with a reasoning program for the claim about James Cameron and Interstellar. This includes the full program structure.\n\n2. **Figure 2** (page 5): Shows \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the <input_claim>.\" This shows one example of a reasoning program for the James Cameron/Interstellar claim.\n\n3. **Figure 3** (page 5): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book).\n\n4. **Section 3.4** (page 4): Shows prompt templates for the QUESTION, VERIFY, and PREDICT functions in text form.\n\nLooking at Figure 2 specifically, it shows one complete example of a prompt with the reasoning program for the James Cameron claim.\n\nIn the main body (before Appendices which start at page 14), the primary prompt example shown is in Figure 2, which displays one reasoning program example. The text also mentions using \"K = 20\" in-context examples (page 4), but these full 20 examples are not shown in the main body - they are referenced to be in Appendix D.\n\nThe main body shows **1** complete prompt example explicitly in Figure 2.\n\nAnswer: 1", "citations": [], "input_tokens": 65062, "output_tokens": 356, "cost_micros": 200526, "latency_ms": 28827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLet me go through the paper systematically:\n\n1. **Figure 2** (page 6985): Shows one prompt template for Codex used to generate reasoning programs. This includes:\n   - Task instruction\n   - One example showing a claim about James Cameron and Interstellar\n   - Mentions \"more in-context examples here\"\n   - Input claim placeholder\n   This counts as **1 prompt example**.\n\n2. **Figure 3** (page 6985): Shows the implementation of the question-answering sub-task function for three different settings (Gold Evidence, Open-book, Closed-book). These show prompt templates for FLAN-T5 with question-answering format. This counts as **1 prompt example** (showing variations for different settings).\n\n3. **Section 3.4** (page 6984): Shows the VERIFY module prompt:\n   ```\n   EVIDENCE\n   Q: Is it true that CLAIM ? True or False? The answer is:\n   ```\n   This counts as **1 prompt example**.\n\n4. The paper mentions prompts for baselines in Appendix A, but those are not in the main body.\n\n5. The detailed prompts in Appendix D and E are explicitly in appendices, not the main body.\n\nLet me recount more carefully:\n- Figure 2: 1 prompt template\n- Figure 3: 3 variations of the same prompt structure (but essentially 1 prompt design)\n- Section 3.4 VERIFY: 1 prompt\n\nLooking at the question more carefully - it asks for \"prompt examples\" shown in the main body. The main body ends before the appendices.\n\nIn the main body:\n1. Figure 2 shows one complete prompt example\n2. Figure 3 shows prompt variations (3 settings)\n3. Section 3.4 shows the VERIFY prompt\n\nIf counting each distinct prompt shown: Figure 2 (1), Figure 3 (3 variations but let me check if they're separate examples), Section 3.4 (1).\n\nActually, looking at Figure 3 more carefully, it shows one prompt design with three different implementations for three settings. The question", "citations": [], "input_tokens": 26867, "output_tokens": 512, "cost_micros": 88281, "latency_ms": 15515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in the appendices).\n\nLooking through the paper:\n\n1. **Figure 2** (page 6985) shows a prompt template for generating reasoning programs with Codex. This includes:\n   - A task instruction\n   - One complete example showing the claim about James Cameron and the director of Interstellar\n   - A note saying \"(...more in-context examples here...)\"\n   - An input claim placeholder\n\nThis appears to be showing **1 complete example** in the prompt template.\n\n2. **Figure 1** (page 6982) shows an overview diagram with the same example claim about James Cameron, but this is showing the system architecture rather than being a separate prompt example.\n\n3. **Section 3.4** describes the prompt formats for different sub-task functions (QUESTION, VERIFY, PREDICT), but these are described in text format rather than being full examples.\n\n4. **Section 4.4** mentions prompts for closed-book fact-checking and states \"The detailed prompting templates are given in Appendix E\" - so the full examples are in the appendix, not the main body.\n\nIn the main body, I can only count **1** complete prompt example that is actually shown (the one in Figure 2 about James Cameron and Interstellar).\n\nAnswer: 1", "citations": [], "input_tokens": 29889, "output_tokens": 308, "cost_micros": 94287, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the document systematically:\n\n1. **Figure 1**: This shows an overview of the model with what appears to be a simplified example of the reasoning process for the claim about James Cameron and Christopher Nolan. This shows one example of how the program works but not a full prompt template.\n\n2. **Figure 2**: This is explicitly labeled as \"The Codex prompt template used to generate reasoning programs\" and shows:\n   - A task instruction\n   - One complete in-context example (the James Cameron/Interstellar claim)\n   - A note that there are \"more in-context examples here\"\n   - A prompt template for the input claim\n\n   So this shows **1 complete prompt example** in the template.\n\n3. **Figure 3**: This shows the implementation of the question-answering sub-task function with different prompt formats for different settings (Gold Evidence, Open-book, Closed-book). These are partial prompts for sub-tasks, showing **3 prompt format variations**.\n\n4. In the text around Section 3.4, there are descriptions of prompts:\n   - For QUESTION (closed-book): \"Q: QUESTION ? The answer is:\" - **1 prompt**\n   - For QUESTION (other settings): \"EVIDENCE Q: QUESTION ? The answer is:\" - **1 prompt**\n   - For VERIFY: \"EVIDENCE Q: Is it true that CLAIM ? True or False? The answer is:\" - **1 prompt**\n\nHowever, I need to be careful about what counts as \"shown\" vs just described in text.\n\nLooking more carefully at what is explicitly displayed in figures/boxes in the main body:\n- Figure 1: Shows the example execution but not a full prompt\n- Figure 2: Shows 1 complete prompt example\n- Figure 3: Shows 3 prompt format examples (though these are sub-task prompts)\n\nThe question asks for \"prompt examples\" in the main body. The most prominent and complete prompt example shown is in Figure 2, which displays **1** complete example.\n\nAnswer: 1", "citations": [], "input_tokens": 22276, "output_tokens": 485, "cost_micros": 74103, "latency_ms": 14203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the number of prompt examples shown in the main body of the paper (not in appendices).\n\nLooking through the main body of the paper:\n\n1. **Figure 2** (page 6984): Shows the Codex prompt template used to generate reasoning programs. This shows one prompt example with the claim about James Cameron and Interstellar.\n\n2. **Figure 3** (page 6985): Shows implementation of question-answering sub-task function with different prompt formats for three settings (Gold Evidence, Open-book, Closed-book). This shows prompt examples for the QA function.\n\n3. **Section 3.4** discusses the VERIFY and PREDICT functions with their prompt formats shown inline in the text.\n\nLet me count more carefully:\n\nIn **Figure 2**: This shows 1 complete prompt template example.\n\nIn **Section 3.4** (page 6984):\n- QUESTION function shows prompts for closed-book and other settings (2 prompt formats)\n- VERIFY function shows 1 prompt format\n\nSo in the main body, I can identify:\n- Figure 2: 1 prompt example\n- Section 3.4 for QUESTION: Shows prompt formats but these are templates\n- Section 3.4 for VERIFY: Shows 1 prompt template\n\nActually, looking at Figure 2 more carefully, it shows one example claim with its program within a larger prompt template that includes \"(... more in-context examples here ...)\".\n\nThe question asks for \"prompt examples\" - I should count distinct prompt templates or examples shown.\n\nIn the main body:\n1. Figure 2 - shows the program generation prompt template (1)\n2. Figure 3 - shows QA implementation prompts (multiple formats shown)\n3. Section 3.4 text - shows VERIFY prompt format (1)\n\nBut Figure 2 actually shows a full prompt with one complete example visible.\n\nLooking at this more carefully, Figure 2 shows **one** concrete example in the prompt template.\n\nAnswer: 1", "citations": [], "input_tokens": 29191, "output_tokens": 456, "cost_micros": 94413, "latency_ms": 14032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q002", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find how many prompt examples are shown in the paper's main body.Let me continue reading to see the document structure and find prompt examples:Let me search for prompt examples in the document:Now let me look at Figure 2 and Figure 3 which show the prompt templates:Let me search for \"Figure\" to understand how many figures/examples are in the main body:Let me check where the appendix starts to understand what's in the main body vs appendix:Let me look for where the paper sections end and appendix begins:Now let me search for where the appendix starts:Good! So \"## A Implementation Details about the Baselines\" starts the appendix. Let me find the line number:So the appendix starts at line 2301. Now let me search for all figures and tables to count prompt examples in the main body (before line 2301):I can see Figure 2 shows a prompt template with one example shown (the James Cameron / Interstellar example), and it says \"( ... more in-context examples here .. . )\" indicating there are more but not shown. Let me look at Figure 6 which shows an error case:Let me look before this to see the actual example in Figure 6:Good! Figure 6 shows one example. Now let me check if there are any other figures or examples shown in the main body. Let me look for examples in sections discussing the prompts:Now let me look at the Limitations section which might have another example:Good! I found another example in the Limitations section - the \"Aristotle couldn't have used a laptop\" example. Let me now carefully count all prompt/program examples shown in the main body (before line 2301 where Appendix starts):\n\n1. **Figure 1** (line 335-402) - Shows the \"James Cameron and Interstellar director\" example with the full reasoning program\n2. **Figure 2** (line 747-770) - Shows the same \"James Cameron and Interstellar director\" example as part of the prompt template \n3. **Figure 6** (line 1441-1464) - Shows the \"Emery ghost town\" error case example\n4. **Limitations section** (line 1699-1706) - Shows the \"Aristotle couldn't have used a laptop\" example\n\nHowever, I need to be careful - Figure 1 and Figure 2 show the SAME example (James Cameron/Interstellar). So let me count unique examples:\n\n1. James Cameron/Interstellar example (shown in both Figure 1 and Figure 2)\n2. Emery ghost town example (Figure 6)\n3. Aristotle laptop example (Limitations section)\n\nThat's **3 unique prompt/program examples** shown in the main body of the paper.\n\nWait, let me re-read the question: \"How many prompts examples are shown in the paper's main body?\" - this could mean distinct prompt templates or distinct claim examples with programs. Let me verify by looking at what constitutes an example.\n\nLooking back at the figures:\n- Figure 2 explicitly shows a prompt template with ONE example shown (the James Cameron one) plus \"( ... more in-context examples here .. . )\"\n- Figure 6 shows ONE error case example\n- Limitations shows ONE example\n\nSo there are **3 prompt examples** shown in the main body.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 87217, "error": null, "extra": {"thread_id": 1942, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fcb323f039bb45c4bc9d5aa08b03f871", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 646, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at page 6985 (which is page 5 of the document), I can see Figure 2 which shows a prompt template for Codex. The figure contains in-context examples demonstrating the program generation format.\n\nIn the figure, I can see:\n1. One complete example starting with \"# The claim is that Both James Cameron and the director of the film Interstellar were born in Canada.\" with a full program definition.\n2. After that, there is text \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples.\n3. Then \"# The claim is that <input_claim>\" followed by \"def program():\" which is the prompt for the input.\n\nSo there is 1 complete in-context example explicitly shown in full in this figure, with an indication that more examples would be included in the actual prompt.\n\nAnswer: 1", "citations": [], "input_tokens": 65078, "output_tokens": 222, "cost_micros": 198564, "latency_ms": 13453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at Figure 2 on page 6985, which shows \"The Codex prompt template used to generate reasoning programs\", I can see:\n\n1. One complete example about James Cameron and the director of Interstellar (showing the full program with fact_1, Answer_1, fact_2, and label)\n\n2. After that, there's a notation \"( ... more in-context examples here . . . )\" indicating additional examples are included but not shown\n\n3. Then there's the prompt for the input claim\n\nSo the figure explicitly shows **1** complete in-context example in full detail, though it indicates more examples are present in the actual implementation (they're just not displayed in the figure).\n\nAnswer: 1", "citations": [], "input_tokens": 26883, "output_tokens": 187, "cost_micros": 83454, "latency_ms": 7171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at the figure at the top of page 6985 to count the complete in-context examples shown.\n\nLooking at page 6985, I can see Figure 2, which shows \"The Codex prompt template used to generate reasoning programs.\"\n\nThe figure contains:\n1. A task instruction at the top\n2. One complete example showing:\n   - A claim: \"Both James Cameron and the director of the film Interstellar were born in Canada.\"\n   - A program with:\n     - fact_1 = Verify(\"James Cameron was born in Canada.\")\n     - Answer_1 = Question(\"Who is the director of the film Interstellar?\")\n     - fact_2 = Verify(\"{Answer_1} was born in Canada.\")\n     - label = Predict(fact_1 and fact_2)\n3. A note saying \"( ... more in-context examples here .. . )\"\n4. A prompt for the input claim\n\nSo there is **1** complete in-context example fully shown in this figure.\n\nAnswer: 1", "citations": [], "input_tokens": 29905, "output_tokens": 242, "cost_micros": 93345, "latency_ms": 7921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 6985 and count the complete in-context examples shown.\n\nLooking at the document, page 6985 corresponds to the section showing Figure 2, which displays the Codex prompt template used to generate reasoning programs.\n\nIn Figure 2, I can see:\n1. A task instruction at the top explaining the three functions that can be called\n2. One complete example showing:\n   - The claim about \"Both James Cameron and the director of the film Interstellar were born in Canada\"\n   - The complete program with multiple steps (fact_1, Answer_1, fact_2, label)\n3. A note saying \"(\u00b7 \u00b7 \u00b7 more in-context examples here \u00b7 \u00b7 \u00b7)\" indicating additional examples are present but not shown\n4. The input prompt template at the bottom\n\nThe figure shows 1 complete in-context example explicitly displayed in full, with an indication that more examples exist but are not shown in the figure.\n\nAnswer: 1", "citations": [], "input_tokens": 22292, "output_tokens": 222, "cost_micros": 70206, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the figure on page 6985 (Figure 2: The Codex prompt template), I can see the prompt template structure.\n\nThe template shows:\n1. A task instruction at the top\n2. One complete example showing a claim about James Cameron and the Interstellar director, with its full program implementation\n3. A note \"(... more in-context examples here ...)\" indicating additional examples would go here\n4. The input claim placeholder at the bottom\n\nSo there is **1** complete in-context example explicitly shown in the figure, though the text indicates more examples would be included in the actual prompt (the paper mentions using K=20 examples total).\n\nAnswer: 1", "citations": [], "input_tokens": 29207, "output_tokens": 151, "cost_micros": 89886, "latency_ms": 7562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q003", "doc_id": "2023.acl-long.386.pdf", "answer_format": "int", "gold": "1", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 6985 and count the complete in-context examples shown.Let me search for page 6985 in the document:Let me read through the document to find the page number references:I can see the document is from ACL conference pages 6981-7004. Let me search for figures and read the content more systematically:Now let me look at Figure 2 which seems to be the prompt template. Let me read around line 769 where Figure 2 is mentioned:I can see Figure 2 template ends around line 770. Now I need to find what page this corresponds to. Let me check if there are any page markers. Let me look for the beginning of the document to understand the structure:Good! I can see page numbers now. Line 327 shows \"<!-- PageNumber=\"6981\" -->\". Now let me search for page 6985:Great! Page 6985 starts at line 902. Let me read the content around that line to find the figure at the top of the page:I can see that at the top of page 6985 (starting at line 902), there's a table (Table 1), not a figure. Let me continue reading to see if there's a figure further down on this page:I see Figure 4 is on page 6986 (line 1108). Let me look back more carefully. The question asks about the figure at the TOP of page 6985. Let me check if there's a figure that spans from page 6984 to 6985, or if I need to look more carefully at what's immediately after the page break at line 902:So at the top of page 6985 (line 902-906), there's a Table 1, not a figure. Let me check what's on page 6984 to see if there's a figure that might be spanning or near the top of 6985:Let me read around line 744 to see what's on page 6984:I can see that Figure 2 is on page 6984 (lines 747-769), and it shows the Codex prompt template. Looking at the content of Figure 2, I can see:\n\n- Line 747-750: Task instruction\n- Lines 752-762: One complete in-context example showing the claim about James Cameron and the director of Interstellar\n- Line 764: \"( ... more in-context examples here .. . )\" - indicating there are more examples\n- Lines 766-767: The prompt for the input claim\n\nThe figure caption at line 769 says \"The Codex prompt template used to generate reasoning programs, consisting of a task instruction, in-context examples, and a prompt for the <input_claim>.\"\n\nHowever, I see only ONE complete example shown explicitly (lines 752-762), and then it says \"more in-context examples here\" without showing them.\n\nWait, let me re-read the question. It asks about \"the figure at the top of page 6985\". Let me check if Figure 2 actually spans to page 6985 or if there's another figure. Looking at the structure:\n- Page 6984 starts at line 744\n- Figure 2 content is at lines 747-769\n- Page 6985 starts at line 902\n\nSo Figure 2 is entirely on page 6984, not 6985. At the top of page 6985 (line 902-906) there's Table 1, not a figure. Let me search more carefully for any figures that might be between these pages or check if I'm missing something:Wait, I think I need to reconsider. Let me look at the actual visual structure. Lines 747-769 show Figure 2 which is on page 6984. Let me check if the complete Figure 2 example I see is showing all the in-context examples or if there's more detail I should look at. Let me read the full Figure 2 content more carefully:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 91078, "error": null, "extra": {"thread_id": 1943, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_479f734c9e744aafac61b60b60655bab", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 680, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Reasoning program evaluation for incorrectly-predicted examples from each hop length in HOVER\":\n\nFor semantic errors:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 65070, "output_tokens": 105, "cost_micros": 196785, "latency_ms": 20235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs across HOVER 2-hop, 3-hop, and 4-hop claims:\n\nFor semantic errors, the three types and their totals are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 26875, "output_tokens": 120, "cost_micros": 82425, "latency_ms": 4672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, which shows the error analysis for reasoning programs on the HOVER dataset broken down by hop length (2-hop, 3-hop, 4-hop):\n\nThe three types of semantic errors are:\n- Token: 8% + 20% + 18% = 46%\n- Structure: 19% + 13% + 57% = 89%\n- Subtask: 2% + 5% + 2% = 9%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29897, "output_tokens": 116, "cost_micros": 91431, "latency_ms": 5218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows the error analysis for reasoning programs on the HOVER dataset, I need to examine the semantic error breakdown across the three hop types (2-hop, 3-hop, and 4-hop).\n\nThe semantic errors are broken down into three categories:\n- Token: 8% (2-hop) + 20% (3-hop) + 18% (4-hop) = 46% total\n- Structure: 19% (2-hop) + 13% (3-hop) + 57% (4-hop) = 89% total\n- Subtask: 2% (2-hop) + 5% (3-hop) + 2% (4-hop) = 9% total\n\nAnswer: Subtask", "citations": [], "input_tokens": 22284, "output_tokens": 180, "cost_micros": 69552, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2 which shows the error analysis for the HOVER dataset across 2-hop, 3-hop, and 4-hop claims:\n\n**Subtask errors:**\n- 2-hop: 2%\n- 3-hop: 5%\n- 4-hop: 2%\n- Total: 9%\n\n**Token errors:**\n- 2-hop: 8%\n- 3-hop: 20%\n- 4-hop: 18%\n- Total: 46%\n\n**Structure errors:**\n- 2-hop: 19%\n- 3-hop: 13%\n- 4-hop: 57%\n- Total: 89%\n\nAnswer: Subtask", "citations": [], "input_tokens": 29199, "output_tokens": 171, "cost_micros": 90162, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q005", "doc_id": "2023.acl-long.386.pdf", "answer_format": "str", "gold": "Subtask", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about semantic errors in the HOVER dataset.Let me search for information about semantic errors and HOVER dataset in the document:Let me search for tables or figures that show error analysis:Now let me read the section with Table 2 that shows the error analysis:Perfect! I found Table 2 which shows the error analysis for the HOVER dataset. Let me examine the semantic error types and their percentages:\n\nFrom the table, the semantic errors are broken down into three types:\n- Token: 8% (2-hop), 20% (3-hop), 18% (4-hop) = Total: 46%\n- Structure: 19% (2-hop), 13% (3-hop), 57% (4-hop) = Total: 89%\n- Subtask: 2% (2-hop), 5% (3-hop), 2% (4-hop) = Total: 9%\n\nThe semantic error type with the lowest total percentages across all HOVER dataset hop lengths is \"Subtask\" with 2% + 5% + 2% = 9%.\n\nAnswer: Subtask", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41280, "error": null, "extra": {"thread_id": 1944, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_964d1076448248edb0a0c213d4b3aab0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "subtask", "normalised_gold": "subtask"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 on page 7, I can see the retrieval recall@10 comparison:\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nFEVEROUS-S has the highest ProgramFC retrieval recall@10 at 85.65.\n\nNow I need to find the InstructGPT Self-Ask performance on FEVEROUS-S in the closed-book setting.\n\nLooking at Table 3 on page 8, which shows closed-book setting results:\n- InstructGPT - Self-Ask on FEVEROUS: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 65104, "output_tokens": 223, "cost_micros": 198657, "latency_ms": 22157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. What is the InstructGPT Self-Ask performance on that dataset in the closed-book setting\n\nFirst, let me look at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ProgramFC = 77.13\n- HOVER (3-hop): ProgramFC = 73.18\n- HOVER (4-hop): ProgramFC = 85.65\n- FEVEROUS-S: ProgramFC = 76.25\n\nThe highest ProgramFC retrieval recall@10 is 85.65 for HOVER (4-hop).\n\nNow, I need to find the InstructGPT Self-Ask performance on HOVER 4-hop in the closed-book setting from Table 3:\n\nLooking at Table 3 (Closed-book setting):\n- InstructGPT - Self-Ask on HOVER 4-hop: 52.45\n\nHowever, I notice the column headers are \"2-hop\", \"HOVER 3-hop\", \"4-hop\", and \"FEVEROUS\". The 4-hop column shows 52.45 for Self-Ask.\n\nAnswer: 52.5", "citations": [], "input_tokens": 26909, "output_tokens": 292, "cost_micros": 85107, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find two pieces of information:\n1. Which dataset has the highest ProgramFC retrieval recall@10\n2. The Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 for retrieval recall@10:\n- HOVER (2-hop): ~73.18\n- HOVER (3-hop): ~77.13\n- HOVER (4-hop): ~85.65\n- FEVEROUS-S: ~76.25\n\nThe highest ProgramFC retrieval recall@10 is for HOVER (4-hop) at approximately 85.65.\n\nNow looking at Table 3 (Closed-book setting) for InstructGPT with Self-Ask on HOVER (4-hop):\nThe Self-Ask row shows: 52.45 for HOVER (4-hop)\n\nAnswer: 52.5", "citations": [], "input_tokens": 29931, "output_tokens": 198, "cost_micros": 92763, "latency_ms": 9953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "52.5", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall at 10\n2. The InstructGPT model with Self-Ask performance on that dataset in closed-book setting\n\nLooking at Figure 5 (mentioned in the text on page 7), which discusses retrieval performance, I need to find the retrieval recall@10 values. However, the actual figure is not fully detailed in the text.\n\nFrom Table 3 on page 7, I can see closed-book fact-checking results. The table shows performance across HOVER (2-hop, 3-hop, 4-hop) and FEVEROUS datasets.\n\nLooking at Table 3:\n- The models listed include: InstructGPT (with variants Direct, ZS-CoT, CoT, Self-Ask), Codex, FLAN-T5, and ProgramFC\n- Self-Ask row shows: 51.54, 51.47, 52.45, 56.82\n\nThe text mentions that Figure 5 compares retrieval performance, and discusses that \"iterative retrieval guided by the reasoning program yields better results.\" However, without seeing the actual Figure 5 data clearly showing which dataset has the highest recall@10 for ProgramFC, I need to infer from the context.\n\nBased on the text discussing HOVER and FEVEROUS-S datasets, and looking at Table 3's structure with columns for \"2-hop\", \"3-hop\", \"4-hop\", and what appears to be FEVEROUS, the Self-Ask scores are: 51.54, 51.47, 52.45, 56.82.\n\nThe highest value for Self-Ask appears to be 56.82, which corresponds to the FEVEROUS column.\n\nAnswer: 56.8", "citations": [], "input_tokens": 22318, "output_tokens": 404, "cost_micros": 73014, "latency_ms": 14375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I need to find:\n1. The dataset with the highest ProgramFC retrieval recall@10\n2. The InstructGPT Self-Ask performance on that dataset in closed-book setting\n\nFrom Figure 5 (Retrieval recall@10):\n- HOVER (2-hop): 77.13\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 49.93\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is FEVEROUS-S at 85.65.\n\nFrom Table 3 (Closed-book setting macro-F1 scores), looking at InstructGPT with Self-Ask on FEVEROUS:\n- Self-Ask: 56.82\n\nAnswer: 56.8", "citations": [], "input_tokens": 29233, "output_tokens": 186, "cost_micros": 90489, "latency_ms": 6452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q007", "doc_id": "2023.acl-long.386.pdf", "answer_format": "float", "gold": "56.8", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q007", "answer_letter": null, "raw_text": "I'll help you find this information from the document. Let me start by reading the relevant document that was identified as most relevant.Let me continue reading to find information about ProgramFC retrieval recall and InstructGPT performance with Self-Ask.Let me search for \"recall\" to find tables with retrieval metrics:Good! I found information about retrieval recall@10. Let me read the section with Figure 5 and the surrounding content:Perfect! I can see from Figure 5 that the ProgramFC retrieval recall@10 values for different datasets are:\n- HOVER (2-hop): 73.18\n- HOVER (3-hop): 59.17\n- HOVER (4-hop): 51.33\n- FEVEROUS-S: 85.65\n\nThe highest ProgramFC retrieval recall@10 is **85.65** for **FEVEROUS-S**.\n\nNow I need to find the performance of InstructGPT with Self-Ask in the closed-book setting on FEVEROUS-S. Let me search for tables with these results:Let me read Table 1 which shows performance on FEVEROUS-S:This table shows results but doesn't specifically mention InstructGPT with Self-Ask in a closed-book setting. Let me search for \"closed-book\" or \"closed book\":Let me read the section about closed-book setting evaluation:Perfect! I found Table 3 which shows the closed-book setting results. Looking at the table:\n\nFor **FEVEROUS** (which corresponds to FEVEROUS-S in this context):\n- InstructGPT with Self-Ask: **56.82**\n\nLet me verify this is indeed FEVEROUS-S by checking if they use the same notation:The table says \"FEVEROUS\" but earlier in the document they established that they use FEVEROUS-S (the sentence-only subset). Let me verify by checking the dataset description section again:Yes, they use FEVEROUS-S throughout the paper. In Table 3, \"FEVEROUS\" refers to FEVEROUS-S. \n\nNow let me confirm the answer:\n1. From Figure 5, the highest ProgramFC retrieval recall@10 is **85.65** for FEVEROUS-S\n2. From Table 3, InstructGPT with Self-Ask in the closed-book setting on FEVEROUS achieves **56.82**\n\nBut let me double-check this value is actually 56.82 by reading that specific cell again:Perfect! Confirmed. The row for \"Self-Ask\" shows:\n- HOVER 2-hop: 51.54\n- HOVER 3-hop: 51.47\n- HOVER 4-hop: 52.45\n- FEVEROUS: 56.82\n\nThe answer is **56.82**, which in 1 decimal format is **56.8**.\n\nWait, let me reconsider. The question asks for \"1 decimal\" which means I should provide it with 1 decimal place. 56.82 rounded to 1 decimal place would be 56.8.\n\nHowever, looking at the exact value in the table, it shows 56.82. Since they're asking for float format with 1 decimal, I should present it as 56.8.\n\nAnswer: 56.8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84281, "error": null, "extra": {"thread_id": 1945, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f2cf8d23b121431e8f0ffe2a73635d4e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 567, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "56.8", "normalised_gold": "56.8"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "native_pdf", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I need to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to FLAN-T5-small.\n\nLooking at Figure 4 on page 7, which shows \"F1 score for fact-checking with gold evidence using FLAN-T5 (blue line) and PROGRAMFC (green line) for language models of increasing sizes.\"\n\nThe figure shows three graphs for:\n1. HOVER (2-hop)\n2. HOVER (3-hop)\n3. HOVER (4-hop)\n\nFor FLAN-T5-small (80M), I can read the values:\n- HOVER (2-hop): FLAN-T5 is around 47.75, ProgramFC is around 64.35\n- HOVER (3-hop): FLAN-T5 is around 49.29, ProgramFC is around 62.23\n- HOVER (4-hop): FLAN-T5 is around 48.59, ProgramFC is around 62.46\n\nThe differences (ProgramFC - FLAN-T5) are:\n- HOVER (2-hop): 64.35 - 47.75 = 16.6\n- HOVER (4-hop): 62.46 - 48.59 = 13.9\n- HOVER (3-hop): 62.23 - 49.29 = 12.9\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 65112, "output_tokens": 350, "cost_micros": 200586, "latency_ms": 12921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.acl-long.386.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4, which shows F1 scores for fact-checking with gold evidence using FLAN-T5 and PROGRAMFC for different model sizes including FLAN-T5-small (80M):\n\nFor HOVER 2-hop:\n- FLAN-T5-small: approximately 47.75\n- ProgramFC with FLAN-T5-small: approximately 64.35\n- Difference: 64.35 - 47.75 = 16.6\n\nFor HOVER 3-hop:\n- FLAN-T5-small: approximately 48.59\n- ProgramFC with FLAN-T5-small: approximately 61.36\n- Difference: 61.36 - 48.59 = 12.8\n\nFor HOVER 4-hop:\n- FLAN-T5-small: approximately 49.29\n- ProgramFC with FLAN-T5-small: approximately 63.05\n- Difference: 63.05 - 49.29 = 13.8\n\nSorting in descending order: 16.6, 13.8, 12.8\n\nAnswer: 16.6, 13.8, 12.8", "citations": [], "input_tokens": 26917, "output_tokens": 288, "cost_micros": 85071, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 13 8, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "azure_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to find the F1 score differences between ProgramFC and FLAN-T5 using FLAN-T5-small (80M) for gold evidence setting across the three HOVER datasets.\n\nFrom Figure 4:\n- HOVER (2-hop): ProgramFC \u2248 62.23, FLAN-T5 \u2248 47.75, difference = 14.5\n- HOVER (3-hop): ProgramFC \u2248 61.36, FLAN-T5 \u2248 48.59, difference = 12.8\n- HOVER (4-hop): ProgramFC \u2248 63.39, FLAN-T5 \u2248 49.29, difference = 14.1\n\nSorting in descending order:\n\nAnswer: 14.5, 14.1, 12.8", "citations": [], "input_tokens": 29939, "output_tokens": 219, "cost_micros": 93102, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 8, 14 1, 14 5", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_basic_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence...\n\nIn Figure 4 (Section 4.2), the document discusses comparing performance across different model sizes, including FLAN-T5-small. The text mentions:\n\n\"Our results show that the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nHowever, Figure 4 is referenced but the actual figure with specific F1 scores is not fully visible in the provided text. The document shows results for different hop counts (2-hop, 3-hop, 4-hop) on HOVER dataset in the gold evidence setting.\n\nFrom Table 1, I can see overall results but these are for FLAN-T5-XXL, not FLAN-T5-small.\n\nThe text in Section 4.2 discusses Figure 4 which would contain the specific comparisons with FLAN-T5-small, but the exact numerical values for the F1 score differences are not explicitly stated in the text portions provided.\n\nWithout access to the actual Figure 4 with the specific numerical values, I cannot provide the exact F1 scores requested.\n\nAnswer: Unable to determine from provided text", "citations": [], "input_tokens": 22326, "output_tokens": 321, "cost_micros": 71793, "latency_ms": 9483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "unable to determine from provided text", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "llamacloud_premium_lc", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "Looking through the document for F1 scores comparing ProgramFC to FLAN-T5-small with gold evidence.\n\nFrom Figure 4, I can extract the following F1 scores for ProgramFC (green line) with FLAN-T5-small (80M):\n- HOVER 2-hop: 64.35\n- HOVER 3-hop: 62.23\n- HOVER 4-hop: 62.46\n\nAnd for FLAN-T5-small (80M):\n- HOVER 2-hop: 47.75\n- HOVER 3-hop: 49.29\n- HOVER 4-hop: 48.59\n\nThe differences (ProgramFC - FLAN-T5-small):\n- HOVER 2-hop: 64.35 - 47.75 = 16.6\n- HOVER 4-hop: 62.46 - 48.59 = 13.9\n- HOVER 3-hop: 62.23 - 49.29 = 12.9\n\nSorting in descending order with one decimal place:\n\nAnswer: 16.6, 13.9, 12.9", "citations": [], "input_tokens": 29241, "output_tokens": 279, "cost_micros": 91908, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "12 9, 13 9, 16 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.acl-long.386.pdf::Q008", "doc_id": "2023.acl-long.386.pdf", "answer_format": "list", "gold": "['64.3', '62.4', '62.2']", "pages": 24, "document_id": 7579, "arm": "surfsense_agentic", "question_id": "2023.acl-long.386.pdf::Q008", "answer_letter": null, "raw_text": "I'll read the document to find the F1 scores for ProgramFC on different datasets with gold evidence in relation to Flan-T5-small.Let me search for tables containing F1 scores for ProgramFC with gold evidence and Flan-T5-small:Let me read the section with Figure 4 and look for tables with F1 scores:Let me search for tables with gold evidence:Let me read Table 1 which contains F1 scores with gold evidence:Now I have the table data. I can see:\n- FLAN-T5 Gold scores: 73.69 (HOVER 2-hop), 65.66 (HOVER 3-hop), 58.08 (HOVER 4-hop), 90.81 (FEVEROUS-S)\n- ProgramFC (N=5) Gold scores: 75.65 (HOVER 2-hop), 68.48 (HOVER 3-hop), 66.75 (HOVER 4-hop), 92.69 (FEVEROUS-S)\n\nHowever, I need to check if these are specifically for Flan-T5-small. Let me look at Figure 4 data more carefully, as it mentions different sizes of Flan-T5:Looking at the data structure in Figure 4, I need to identify which values correspond to Flan-T5-small (80M) for ProgramFC. The chart shows alternating values. Let me read more of this section:Based on Figure 4 data, I can see there are three charts (HOVER 2-hop, 3-hop, 4-hop) with values at different model sizes (80M, 250M, 780M, 3B, 11B). \n\nLooking at the structure:\n- The first chart (HOVER 2-hop) has values from lines 1130-1148\n- The second chart (HOVER 3-hop) has values from lines 1152-1188  \n- The third chart (HOVER 4-hop) has values from lines 1192-1210\n\nFor 80M (FLAN-T5-small), looking at the ProgramFC values (green line):\n- HOVER 2-hop: 75.65 (line 1132)\n- HOVER 3-hop: 68.48 (line 1156)\n- HOVER 4-hop: 58.08 (line 1198)\n\nWait, let me verify this more carefully by looking at the pattern. The chart shows alternating FLAN-T5 and ProgramFC values. Let me trace through more carefully:\n\nFor HOVER 2-hop at 80M:\n- Lines around 1130-1135 show values near 76-77\n- This corresponds to 80M (first position)\n\nFor HOVER 3-hop at 80M:\n- Starting around line 1152-1158\n- Values around 67-69\n\nFor HOVER 4-hop at 80M:\n- Starting around line 1192-1198\n- Values around 56-58\n\nLooking at the caption again: \"FLAN-T5 (blue line) and PROGRAMFC (green line)\"\n\nFrom the table structure and comparing with Table 1 values (FLAN-T5 has 73.69 for 2-hop, ProgramFC N=5 has 75.65), I can deduce:\n\nFor 80M (FLAN-T5-small) with gold evidence, ProgramFC scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48  \n- HOVER 4-hop: 58.08\n\nIn descending order: 75.65, 68.48, 58.08\n\nHowever, I should check if there are more datasets. Let me verify if FEVEROUS-S is included in this figure:Figure 4 only shows HOVER 2-hop, 3-hop, and 4-hop. Let me check if there's a figure or table specifically showing FEVEROUS-S scores broken down by model size. But based on Table 1, the FEVEROUS-S score for ProgramFC (N=5) with gold evidence is 92.69, and this appears to be using FLAN-T5-XXL based on the context.\n\nLet me search for more information about which model size was used for the main results:Now I need to carefully parse Figure 4 data. The figure shows three charts for HOVER 2-hop, 3-hop, and 4-hop. Looking at the data structure, I see values followed by model sizes (80M, 250M, 780M, 3B, 11B) repeated three times.\n\nLet me trace through the data more systematically. From line 1130 onwards, reading in groups:\n\nHOVER 2-hop chart appears to show pairs of values for each model size (FLAN-T5 then ProgramFC based on the caption mentioning blue and green lines):\n- At 80M: 76.11, 75.65\n- Then 77.62\n\nLet me look for a clearer pattern by examining all the values more carefully:Good! I found key information at lines 1385-1388: \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers can achieve comparable performance to the 137x larger FLAN-T5-XXL (11B) model with end-to-end reasoning for 4-hop claims.\"\n\nThis confirms that Figure 4 shows results using different-sized FLAN-T5 models. Now let me parse the Figure 4 data correctly. Based on the structure and comparing with Table 1:\n\nFrom Table 1, I know:\n- FLAN-T5 gold evidence scores: 73.69 (2-hop), 65.66 (3-hop), 58.08 (4-hop)\n- ProgramFC (N=5) gold evidence scores: 75.65 (2-hop), 68.48 (3-hop), 66.75 (4-hop)\n\nThese match values in Figure 4 data. Now looking at the chart data structure more carefully, the values appear to be organized by model size. Let me trace the 80M (FLAN-T5-small) values for ProgramFC:\n\nLooking at lines 1130-1246, the pattern seems to show data points for both lines (FLAN-T5 and ProgramFC) across multiple model sizes for three different charts.\n\nFrom the visible data and comparing with Table 1 values at line 1148 (73.69) which is FLAN-T5 for HOVER 2-hop, and line 1132 (75.65) which matches ProgramFC (N=5) for HOVER 2-hop:\n\nFor FLAN-T5-small (80M) ProgramFC with gold evidence:\n- HOVER 2-hop: 72.56 (line 1140)\n- HOVER 3-hop: 68.55 (line 1154)  \n- HOVER 4-hop: 63.39 (line 1194)\n\nWait, let me reconsider. Looking at the x-axis labels (80M, 250M, 780M, 3B, 11B) appearing three times (once per chart), and the fact that there are two lines per chart (FLAN-T5 and ProgramFC), I need to identify which values correspond to which.\n\nLet me look for clearer structure or additional tables:Figure 4 only shows HOVER datasets (2-hop, 3-hop, 4-hop), not FEVEROUS-S. Let me carefully re-parse the figure data. \n\nLooking at the chart data structure: there are 3 separate line charts, each showing F1 scores for 5 different model sizes. Each chart has 2 lines (FLAN-T5 and ProgramFC).\n\nThe data flows:\n- Lines 1130-1148: HOVER 2-hop chart data\n- Lines 1152-1188: HOVER 3-hop chart data  \n- Lines 1192-1210: HOVER 4-hop chart data\n\nFor each chart, there should be approximately 10 data points (2 lines \u00d7 5 model sizes).\n\nLet me map this out for HOVER 2-hop (starting line 1130):\nLooking at the pattern and knowing from Table 1 that ProgramFC (N=5) gets 75.65 for 2-hop (line 1132), this should be for the largest model (11B based on Table 1 context).\n\nActually, let me reconsider the entire approach. The question asks for F1 scores for \"ProgramFC on different datasets with gold evidence in relation to Flan-T5-small\". \n\nThis might mean: the F1 scores when ProgramFC uses Flan-T5-small as its sub-task solver, across different datasets.\n\nFrom Figure 4 caption and the data structure, for 80M (FLAN-T5-small) as the sub-task solver in ProgramFC:\n\nLooking at line 1140 (72.56), 1154 (68.55), 1194 (63.39) - these appear to be the first ProgramFC values in each chart.\n\nBut I need to verify this interpretation. Let me check if there are explicit values stated in the text:From the text at lines 1368-1388, it discusses Figure 4 and mentions that \"the program-guided model using FLAN-T5-small (80M parameters) as sub-task solvers\" achieves certain performance. This confirms Figure 4 shows ProgramFC results when using different sizes of FLAN-T5 as sub-task solvers.\n\nNow I need to carefully extract the FLAN-T5-small (80M) ProgramFC values from Figure 4 data. Let me re-examine the data structure.\n\nBased on the chart showing 5 model sizes (80M, 250M, 780M, 3B, 11B) and 2 lines (FLAN-T5 and ProgramFC), there should be interleaved or grouped values.\n\nLooking at lines 1128-1248, I see numeric values followed by model size labels. The pattern suggests the chart data is organized with Y-axis values first, then X-axis labels.\n\nFor a line chart with 5 points on each of 2 lines, I'd expect 10 Y values per chart. Let me count the values before the first \"80M\" marker at line 1218:\n\nFrom 1130-1216, there appear to be values organized in groups. \n\nLet me try a different approach - look at the actual layout. The values from 1130-1148 before seeing the next section, and knowing Table 1 shows ProgramFC (N=5) has 75.65 for HOVER 2-hop with presumably the larger model:\n\nFirst chart (HOVER 2-hop) - 5 model sizes, 2 lines = 10 points\nLet me identify based on comparing with known Table 1 values. Line 1148 shows 73.69, which exactly matches FLAN-T5 HOVER 2-hop gold from Table 1. This suggests this is for the larger model (XXL/11B).\n\nLooking backward from there and forward, the organization seems to show alternating or separate line data. Since line charts typically show both lines across all X values, let me map:\n\nValues around lines 1130-1148 for HOVER 2-hop\nValues around lines 1152-1188 for HOVER 3-hop\nValues around lines 1192-1210 for HOVER 4-hop\n\nFor 80M (first model size), I need the first pair of values (FLAN-T5 and ProgramFC) for each chart.\n\nGiven the complexity and my uncertainty about the exact data structure, let me look for any other tables or explicit statements:Looking at this continuous stream helps! Let me parse it:\n\n\"80 76.11 75.65 77.62 80 80 72.56 77.07 70 71.69 73.69 70 67.88 68.55 68.48 69.56 70 68.37 68.56 66.75 68.18 64.35 68.24 62.23 66.89 62.46 60 60 63.05 65.07 65.66 60 61.36 63.39 56.58 58.08 50 50 50 47.75 49.29 48.59 40 40 40 80M 250M 780M 3...\"\n\nThe \"80\" values appear to be Y-axis scale markers. Let me focus on the actual data values and model sizes.\n\nParsing more carefully:\n- After the initial scale markers, values like 76.11, 75.65, 77.62 appear\n- Then 72.56, 77.07\n- Then 71.69, 73.69\n- Etc.\n\nKnowing from Table 1 that:\n- FLAN-T5 HOVER 2-hop gold = 73.69 \n- ProgramFC (N=5) HOVER 2-hop gold = 75.65\n\nI can see 73.69 and 75.65 in the data stream. These are likely for the XXL model since Table 1 results use the larger model.\n\nIf the data is organized as alternating FLAN-T5/ProgramFC for each model size from 80M to 11B, then:\n- First pair (80M): 76.11 (FLAN-T5), 75.65 (ProgramFC)? But 75.65 matches Table 1 ProgramFC which should be for the larger model...\n\nThis is confusing. Let me try another interpretation: perhaps all FLAN-T5 values come first, then all ProgramFC values for each chart.\n\nOr perhaps the values are arranged as: HOVER 2-hop FLAN-T5 (all 5 sizes), HOVER 2-hop ProgramFC (all 5 sizes), then HOVER 3-hop data, etc.\n\nWithout seeing the actual visual chart, this is challenging. Let me look for any other references or tables that might clarify:From the stream, 68.48 from Table 1 (ProgramFC N=5 HOVER 3-hop gold) appears right after 68.55.\n\nLet me hypothesize: if the chart shows both lines across all model sizes, and the data is organized left-to-right, then for each X position (model size) moving left to right, we have interleaved FLAN-T5 and ProgramFC Y values.\n\nFor HOVER 2-hop at 80M (first model size):\n- Looking at the early values: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69...\n\nOne pattern could be: pairs of (FLAN-T5, ProgramFC) for 80M, then for 250M, etc.\n- 80M: (76.11, 75.65)? But this doesn't align with Table 1 where 75.65 is the high-model result...\n\nOr: all 5 FLAN-T5 points, then all 5 ProgramFC points.\n\nLet me try: \nHOVER 2-hop FLAN-T5: 76.11, 77.62, 72.56, 71.69, 73.69 (for 80M, 250M, 780M, 3B, 11B)\nHOVER 2-hop ProgramFC: 75.65, 77.07, ... (for 80M, 250M, 780M, 3B, 11B)\n\nBut wait, 73.69 matches Table 1 FLAN-T5 which should be for 11B based on Table 1 being the best model. So if 73.69 is the 5th FLAN-T5 value (11B), then:\nHOVER 2-hop FLAN-T5: 76.11 (80M), 77.62 (250M), 72.56 (780M), 71.69 (3B), 73.69 (11B)\n\nThat doesn't make sense as performance should generally increase with model size, but we see 76.11 \u2192 77.62 \u2192 72.56 which decreases.\n\nLet me reconsider: perhaps the visual layout in the PDF is organized differently than the XML text extraction shows. The values might not be in simple left-to-right order.\n\nGiven the time constraints and complexity, let me make my best educated guess based on patterns:\n\nLooking at Table 1 reference values and Figure 4 caption mentioning these are results across different model sizes, and considering that:\n1. Figure 4 shows HOVER 2-hop, 3-hop, 4-hop only (not FEVEROUS-S)\n2. The question asks for ProgramFC scores with gold evidence for Flan-T5-small across different datasets\n\nThe likely answer based on extracting what appear to be the first ProgramFC values in each section that would correspond to the smallest model (80M):\n- HOVER 2-hop: 72.56\n- HOVER 3-hop: 68.55\n- HOVER 4-hop: 63.39\n\nIn descending order: 72.56 \u2192 68.55 \u2192 63.39\n\nBut let me verify one more time by looking at the complete pattern:Now I see the pattern more clearly! The model size labels (80M, 250M, 780M, 3B, 11B) appear THREE times (lines 1218-1246), once for each of the three charts (HOVER 2-hop, 3-hop, 4-hop).\n\nSo the data structure is:\n- Lines 1130-1148: HOVER 2-hop chart Y-axis values\n- Lines 1152-1188: HOVER 3-hop chart Y-axis values\n- Lines 1192-1210: HOVER 4-hop chart Y-axis values\n- Lines 1218-1246: X-axis labels (model sizes) for all three charts\n\nFor each chart with 2 lines and 5 X-values, I'd expect about 10 Y-values.\n\nHOVER 2-hop (lines 1130-1148): 76.11, 75.65, 77.62, (80, 80 are axis markers), 72.56, 77.07, (70), 71.69, 73.69, (70)\n\nRemoving axis markers: 76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69\n\nThat's 7 values. For 2 lines with 5 points each = 10 values, so I might be missing some or there's overlap.\n\nLooking at known reference: 73.69 (Table 1 FLAN-T5 2-hop) and 75.65 (Table 1 ProgramFC 2-hop) both appear in this section.\n\nIf arranged as: FLAN-T5 first 5 values, then ProgramFC 5 values:\nFLAN-T5: 76.11 (80M), 75.65 (250M), 77.62 (780M), 72.56 (3B), 77.07 (11B)?  \nBut 73.69 should be FLAN-T5 and it's not in this sequence...\n\nAlternative: interleaved pairs:\n- (76.11, 75.65) = (FLAN-T5, ProgramFC) at 80M\n- (77.62, 72.56) = (FLAN-T5, ProgramFC) at 250M  \n- (77.07, 71.69) = (FLAN-T5, ProgramFC) at 780M\n- (73.69, ???) = (FLAN-T5, ProgramFC) at some size\n\nBut this only gives 4 pairs...\n\nLet me try yet another approach. Reading the values without the axis scale markers (70, 80):\n76.11, 75.65, 77.62, 72.56, 77.07, 71.69, 73.69 for HOVER 2-hop\n\nIf this represents 2 lines of 5 points each minus 3 values (perhaps some overlap at endpoints or data points), let me see if the pattern makes sense as:\n\nLine 1 (FLAN-T5): 71.69, 73.69, 72.56, 77.07, 76.11 (in size order 80M to 11B)? Reverse order?\nLine 2 (ProgramFC): 75.65, 77.62, ..., ..., ... \n\nThis is getting too speculative. Let me use logical reasoning instead:\n\n1. Table 1 shows ProgramFC (N=5) achieves 75.65, 68.48, 66.75 for HOVER 2/3/4-hop with gold evidence\n2. These are the best results, so likely using the largest model (11B/XXL)\n3. Figure 4 shows how performance varies with model size for both FLAN-T5 and ProgramFC\n4. The text states that FLAN-T5-small (80M) ProgramFC can achieve comparable performance to the large model for 4-hop claims\n\nLooking at 4-hop values (lines 1192-1210): 61.36, 63.39, 56.58, 58.08, (50s), 47.75, 49.29, 48.59, (40s)\n\n58.08 appears here and also matches Table 1 FLAN-T5 4-hop gold.\n66.75 (ProgramFC 4-hop from Table 1) does NOT appear in this 4-hop section, which is strange...\n\nUnless Table 1 uses N=5 program aggregation while Figure 4 uses N=1? Let me check:\n\nLooking back at Table 1, there's both \"ProgramFC (N=1)\" and \"ProgramFC (N=5)\". \n\nProgramFC (N=1) gold scores from Table 1:\n- HOVER 2-hop: 74.10\n- HOVER 3-hop: 66.13\n- HOVER 4-hop: 65.69\n\nProgramFC (N=5) gold scores:\n- HOVER 2-hop: 75.65\n- HOVER 3-hop: 68.48\n- HOVER 4-hop: 66.75\n\nLet me check if 74.10, 66.13, or 65.69 appear in Figure 4 data... No, they don't appear in the extracted values.\n\nGiven this complexity and the fact that the visual chart layout isn't perfectly captured in the XML extraction, let me make my best inference:\n\nFor FLAN-T5-small (80M) as sub-task solvers in ProgramFC with gold evidence:\n- The first ProgramFC value in each chart section should correspond to 80M\n- Based on positioning and eliminating known FLAN-T5 values from Table 1:\n\nHOVER 2-hop: 72.56\nHOVER 3-hop: 68.55\nHOVER 4-hop: 63.39\n\nIn descending order: 72.6, 68.6, 63.4 (to one decimal place)\n\nAnswer: 72.6, 68.6, 63.4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 174562, "error": null, "extra": {"thread_id": 1946, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b81fd9ccbe6f4839823c9313fb619eb2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2087, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "63 4, 68 6, 72 6", "normalised_gold": "62 2, 62 4, 64 3"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 on page 5, I can see the symbolic solvers used for different datasets:\n- Pyke: ProntoQA, ProofWriter\n- Prover9: FOLIO\n- python-constraint: LogicalDeduction\n- Z3: AR-LSAT\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section on page 6:\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAlso, Table 5 in Appendix B on page 14 confirms:\n- FOLIO has a Test Size of 204\n\nAnswer: 204", "citations": [], "input_tokens": 53261, "output_tokens": 253, "cost_micros": 163578, "latency_ms": 13937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProntoQA and ProofWriter use Pyke\n- FOLIO uses Prover9\n- LogicalDeduction uses python-constraint\n- AR-LSAT uses Z3\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 22914, "output_tokens": 223, "cost_micros": 72087, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nFrom Table 1 in the document, I can see that Prover9 is used as the solver for the FOLIO dataset, which is categorized under \"First-Order Logic\" reasoning.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at Table 5 \"Statistics of the logical reasoning datasets\" in Appendix B:\n\nFOLIO:\n- Reasoning: FOL\n- Test Size: 204\n- #Opts: 3\n\nThe document also confirms in the main text (Section 4, Datasets): \"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the questions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 26784, "output_tokens": 232, "cost_micros": 83832, "latency_ms": 8031, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I need to find which dataset uses Prover9 as the symbolic solver and how many examples are used for evaluation.\n\nLooking at Table 1 in the document, I can see the symbolic solvers used for each dataset:\n- ProofWriter uses \"ProntoQA, Pyke\"\n- FOLIO uses \"Prover9\"\n- LogicalDeduction uses \"phn-constraint\"\n- AR-LSAT uses \"Z3\"\n\nSo FOLIO is the dataset that uses Prover9.\n\nNow I need to find how many examples from FOLIO are used for evaluation. Looking at the \"Datasets\" section under \"4 Experiments\":\n\n\"FOLIO (Han et al., 2022) is a challenging expert-written dataset for logical reasoning. The problems are mostly aligned with real-world knowledge and use highly natural wordings, and the solutions require complex first-order logic reasoning to solve. We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 20095, "output_tokens": 232, "cost_micros": 63765, "latency_ms": 6891, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find which dataset uses Prover9 as the symbolic solver and how many examples are in its test set.\n\nFrom Table 1 (page 3810), I can see that FOLIO uses Prover9 as its symbolic solver under the \"FOL\" (First-Order Logic) problem type.\n\nFrom Table 5 in Appendix B (page 3819), which shows dataset statistics, I can see:\n- FOLIO has a Test Size of 204\n\nThis is also confirmed in the text on page 3811 where it states: \"We use the entire FOLIO test set for evaluation, consisting of 204 examples.\"\n\nAnswer: 204", "citations": [], "input_tokens": 25913, "output_tokens": 159, "cost_micros": 80124, "latency_ms": 5094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q000", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "204", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about a dataset that uses Prover9 as the symbolic solver and determine how many examples are used for evaluation.Let me search for \"Prover9\" in the document to find the relevant information:Now let me search for sections that discuss datasets and evaluation:Let me read the section about datasets more carefully. Let me look for \"ProofWriter\" which seems to be mentioned and might use Prover9:Let me search more specifically for information about which dataset uses Prover9:I see \"First-order Logic Prover\" mentioned. Let me search for more specific information about datasets and their properties:Now let me look at the appendix section that might have more details about data statistics and which solver is used for which dataset:Let me search for tables or sections that describe which solver is used for each dataset:Great! I found a reference to Table 1 which summarizes the symbolic solvers used for each dataset. Let me read that table:Perfect! I found Table 1 which shows that **Prover9** is used as the symbolic solver for the **FOLIO** dataset. Now I need to find how many examples from FOLIO are used for evaluation. Let me go back to the section where FOLIO is described:Perfect! I found the answer. According to the document:\n\n1. From Table 1 (lines 846-849): **Prover9** is the symbolic solver used for the **FOLIO** dataset.\n2. From the dataset description (lines 1024-1026): \"We use the entire FOLIO test set for evaluation, consisting of **204 examples**.\"\n\nAnswer: 204", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64250, "error": null, "extra": {"thread_id": 1947, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e89f611945d040b28ca2dcfe99b6b2c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 410, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "204", "normalised_gold": "204"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5 (Page 9):**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\" Netflix shows.\n- The predicted answer is shown as: B\n- No indication of whether this is correct or incorrect is given in the figure.\n\n**Figure 6 (Page 19):**\nThis shows 4 examples with predicted answers:\n\n1. **Example 1** (Wild turkeys): \n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction changed from C to A\n\n2. **Example 2** (Legend of Zelda):\n   - Predicted answer: A\n   - No indication of correctness shown\n\n3. **Example 3** (Golf tournament):\n   - Predicted answer: E \u2192 B\n   - The arrow indicates the prediction changed from E to B\n\n4. **Example 4** (Restaurant/Subway):\n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction changed from C to A\n\nLooking at the figure caption: \"Examples of generated symbolic representations and predicted answers. The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThe red and green markings indicate errors in the symbolic formulation, not whether the final answer was correct. The figure shows the symbolic representations had errors that were corrected, but doesn't explicitly state which final answers were correct.\n\nHowever, based on the examples shown, I cannot definitively determine from the information provided how many predictions were actually correct, as the figure focuses on showing the symbolic formulation errors rather than answer correctness.\n\nSince I cannot determine this with certainty from the document, I need to reconsider. The document shows 5 total examples (1 in Figure 5, 4 in Figure 6) but doesn't explicitly mark which final predictions were correct.\n\nAnswer: 0", "citations": [], "input_tokens": 53263, "output_tokens": 443, "cost_micros": 166434, "latency_ms": 15125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "answer_line", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThere is 1 example shown with:\n- Problem about \"Stranger Things\" and \"Black Mirror\"\n- Predicted answer: B\n- No indication that this is incorrect\n\n**Figure 6:**\nThere are 4 examples shown:\n\n**Example 1:** Wild turkeys problem\n- Predicted answer: \u20ac > A (this appears to be garbled text)\n- The arrow pointing to \"A\" suggests the correct answer should be A\n- The red marking indicates this is incorrect\n\n**Example 2:** Legend of Zelda problem\n- Predicted answer: A\n- No red markings indicating error\n- This appears correct\n\n**Example 3:** Golf tournament problem\n- Predicted answer: + + B (garbled)\n- The arrow pointing to \"B\" and red marking indicates this is incorrect\n\n**Example 4:** Restaurant/Subway problem\n- Predicted answer: ( + A (garbled)\n- The arrow pointing to \"A\" and red marking indicates this is incorrect\n\nFrom the caption of Figure 6: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis indicates that Figure 6 shows ERROR cases where Logic-LM made mistakes.\n\nFigure 5 shows a successful case (no red markings).\n\nSo:\n- Figure 5: 1 correct\n- Figure 6: 0 correct (all 4 are error examples)\n\nTotal correct: 1\n\nAnswer: 1", "citations": [], "input_tokens": 22916, "output_tokens": 365, "cost_micros": 74223, "latency_ms": 9516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to check each example in Figure 5 and Figure 6 to see if Logic-LM predicted the correct answer.\n\n**Figure 5:**\n- Problem about wild turkeys and Tom\n- Question: \"Is the following statement true, false, or uncertain? 'Black Mirror' is popular.\"\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: B\n- Looking at the logic: The problem states \"Karen does not download 'Black Mirror'\" and \"If and only if Karen binge-watches a Netflix show, she will download it.\" This means Karen doesn't binge-watch Black Mirror. Since \"If a Netflix show is popular, Karen will binge-watch it,\" this means Black Mirror is NOT popular.\n- So B (False) is correct. \u2713\n\n**Figure 6:**\n\n**Example 1:**\n- About Tom the wild turkey\n- Question: Is Tom an Ocellated wild turkey?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A (marked with ++)\n- Logic: Tom is a wild turkey but NOT Eastern, Osceola, Gould's, Merriam's, or Rio Grande. Since there are only 6 types and Tom is eliminated from 5, he must be Ocellated.\n- A (True) is correct. \u2713\n\n**Example 2:**\n- About Legend of Zelda\n- Question: Is it in the Top 10 list?\n- Options: (A) True, (B) False, (C) Uncertain\n- Predicted answer: A\n- Logic: Legend of Zelda sold more than 1 million copies \u2192 selected into Top 10 list.\n- A (True) is correct. \u2713\n\n**Example 3:**\n- Golf tournament with 5 golfers\n- Question: Which finished third?\n- Options: (A) Rob, (B) Eve, (C) Eli, (D) Amy, (E) Dan\n- Predicted answer: B (marked with =>)\n- But there's an error in the constraints shown: \"Dan > Eve > Dan < Eve\" which is contradictory\n- The problem states: Dan finished 2nd, Dan", "citations": [], "input_tokens": 26786, "output_tokens": 512, "cost_micros": 88038, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 5 and Figure 6 to count how many examples Logic-LM predicted correctly.\n\n**Figure 5:**\nThis shows one example about \"Stranger Things\" and \"Black Mirror\". The predicted answer is B, and there's no indication this is wrong, so I'll assume it's correct: **1 correct**\n\n**Figure 6 (labeled as \"Example Generations of LOGIC-LM\"):**\nThis contains 4 examples:\n\n1. **Example 1** (Wild turkey problem): \n   - Predicted answer: C \u2192 A\n   - The arrow notation indicates the prediction was C (Uncertain) but should have been A (True)\n   - **Incorrect**\n\n2. **Example 2** (Legend of Zelda problem):\n   - Predicted answer: A\n   - No arrow or correction shown, indicating this is correct\n   - **Correct**\n\n3. **Example 3** (Golf tournament problem):\n   - Predicted answer: E \u2192 B\n   - The arrow indicates the prediction was E (Dan finished third) but should have been B (Eve finished third)\n   - **Incorrect**\n\n4. **Example 4** (Restaurant/Subway problem):\n   - Predicted answer: C \u2192 A\n   - The arrow indicates the prediction was C (Uncertain) but should have been A (True)\n   - **Incorrect**\n\n**Total count:**\n- Figure 5: 1 correct\n- Figure 6: 1 correct (Example 2 only)\n\n**Total: 2 correct predictions**\n\nAnswer: 2", "citations": [], "input_tokens": 20097, "output_tokens": 361, "cost_micros": 65706, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many examples in Figure 5 and Figure 6 have Logic-LM predicting the correct answer.\n\n**Figure 5:**\n- Example 1: Predicted answer is B (shown as correct based on the context)\n- Count: 1 correct\n\n**Figure 6:**\n- Example 1: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\n- Example 2: Predicted answer is A (shown as correct, no corrections indicated)\n- Count: 1 correct\n\n- Example 3: Predicted answer changed from E to B. The arrows indicate E was wrong and B is the correction, meaning the final prediction is B (correct).\n- Count: 1 correct\n\n- Example 4: Predicted answer changed from C to A. The arrows indicate C was wrong and A is the correction, meaning the final prediction is A (correct).\n- Count: 1 correct\n\nTotal examples with correct predictions: 1 (Figure 5) + 4 (Figure 6) = 5\n\nAnswer: 5", "citations": [], "input_tokens": 25915, "output_tokens": 263, "cost_micros": 81690, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q002", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "2", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q002", "answer_letter": "A", "raw_text": "I'll read the document to find Figures 5 and 6 and count how many examples Logic-LM predicted correctly.Let me search for Figure 5 and Figure 6 in the document:Let me search for the actual content around these figures:Now let me look for Figure 6 in the appendix:I need to find the actual content of Figure 6 with the examples. Let me search more carefully:Now let me look back at Figure 5 to get the complete example:Perfect! Now I have both figures. Let me analyze them:\n\n**Figure 5** (lines 1612-1645): Contains 1 example about \"Black Mirror\" and \"Stranger Things\"\n- Predicted answer: B\n- I need to check if this is correct by looking at the problem logic\n\n**Figure 6** (lines 2993-3146): Contains 4 examples:\n1. Example 1 (lines 2994-3018): Wild turkey problem - Predicted answer: A (with ++ A notation, but marked with red X's indicating errors)\n2. Example 2 (lines 3021-3044): Legend of Zelda problem - Predicted answer: A\n3. Example 3 (lines 3047-3109): Golf tournament problem - Predicted answer: B (with => B notation, and red X's indicating errors)\n4. Example 4 (lines 3115-3146): Subway restaurant problem - Predicted answer: A (with <> A notation, and red X's with checkmarks indicating errors)\n\nAccording to the caption at line 3148-3149: \"The incorrect segment(s) and their correspondence in the problem are marked in red, and the correct revisions are marked in green.\"\n\nThis means Figure 6 shows ERROR CASES where Logic-LM made mistakes. The red X's (\u2612) indicate incorrect segments.\n\nFor Figure 5, I need to verify if the answer is correct. Looking at the logic:\n- \"Black Mirror\" is a Netflix show\n- Karen does not download \"Black Mirror\"\n- If and only if Karen binge-watches a show, she will download it\n- Therefore, Karen does not binge-watch \"Black Mirror\"\n- If a Netflix show is popular, Karen will binge-watch it\n- Since Karen doesn't binge-watch \"Black Mirror\", it is NOT popular\n- Answer B (False) appears to be CORRECT\n\nSo the count is:\n- Figure 5: 1 example, 1 correct (Example about Black Mirror)\n- Figure 6: 4 examples, 1 correct (Example 2 - Legend of Zelda), 3 incorrect (Examples 1, 3, and 4 have red X marks)\n\nTotal correct: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54812, "error": null, "extra": {"thread_id": 1948, "search_space_id": 55, "answer_letter_strategy": "answer_line", "user_message_id": "msg_4ece07b065344ab588935735051340b9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 440, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\" starting on page 14:\n\nThe appendix states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThe sections are:\n- C.1 PrOntoQA Prompts (3 types: Standard, CoT, Logic-LM)\n- C.2 ProofWriter Prompts (3 types: Standard, CoT, Logic-LM)\n- C.3 FOLIO Prompts (3 types: Standard, CoT, Logic-LM)\n- C.4 LogicalDeduction Prompts (3 types: Standard, CoT, Logic-LM)\n- C.5 AR-LSAT Prompts (3 types: Standard, CoT, Logic-LM)\n\nEach subsection (C.1 through C.5) contains 3 prompt types (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM).\n\nSo there are 5 datasets \u00d7 3 prompt types = 15 prompt examples total.\n\nAnswer: 15", "citations": [], "input_tokens": 53247, "output_tokens": 276, "cost_micros": 163881, "latency_ms": 13389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\", I can see it covers prompts for different datasets:\n\n1. **C.1 PrOntoQA Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n2. **C.2 ProofWriter Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n3. **C.3 FOLIO Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n4. **C.4 LogicalDeduction Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\n5. **C.5 AR-LSAT Prompts** - Contains 3 prompt types:\n   - Standard In-Context Learning (1 example)\n   - Chain-of-Thought Prompting (1 example)\n   - Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 22900, "output_tokens": 398, "cost_micros": 74670, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples given in Appendix C.\n\nLooking at Appendix C \"Prompt Examples\":\n\n**C.1 PrOntoQA Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 LogicalDeduction Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count: 5 datasets \u00d7 3 prompting methods = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 26770, "output_tokens": 323, "cost_micros": 85155, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in the Appendix section of the document.\n\nLooking at the Appendix sections:\n\n**Section C: Prompt Examples**\n\nThe document states: \"For brevity, we show only a single demonstration for each setting in the following sections.\"\n\nThen it provides:\n\n**C.1 PrOntoQA Prompts:**\n- Standard In-Context Learning (1 example)\n- Logic-LM (1 example)\n\n**C.2 ProofWriter Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.3 FOLIO Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.4 Logical Deduction Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\n**C.5 AR-LSAT Prompts:**\n- Standard In-Context Learning (1 example)\n- Chain-of-Thought Prompting (1 example)\n- Logic-LM (1 example)\n\nTotal count:\n- C.1: 2 examples\n- C.2: 3 examples\n- C.3: 3 examples\n- C.4: 3 examples\n- C.5: 3 examples\n\nTotal: 2 + 3 + 3 + 3 + 3 = 14 examples\n\nAnswer: 14", "citations": [], "input_tokens": 20081, "output_tokens": 407, "cost_micros": 66348, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I need to count the number of prompt examples provided in Appendix C.\n\nLooking at Appendix C (Prompt Examples), I can see it's organized by dataset:\n\n**C.1 PrOntoQA Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.2 ProofWriter Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.3 FOLIO Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.4 LogicalDeduction Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\n**C.5 AR-LSAT Prompts:**\n1. Standard In-Context Learning - 1 example\n2. Chain-of-Thought Prompting - 1 example\n3. Logic-LM - 1 example\n\nTotal count: 5 datasets \u00d7 3 prompt types = 15 prompt examples\n\nAnswer: 15", "citations": [], "input_tokens": 25899, "output_tokens": 346, "cost_micros": 82887, "latency_ms": 6280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q004", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "15", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about prompt examples in the Appendix.Let me search for the Appendix section in the document to find information about prompt examples.Now let me read the section with prompt examples to count them. Let me find where this content is located.Let me search for the Appendix C section with prompt examples more directly:Let me continue to search for all the sections to count the prompt examples. Based on what I've seen so far, I need to count the subsections in Appendix C.Perfect! I can see there are 5 subsections in Appendix C:\n- C.1 PrOntoQA Prompts\n- C.2 ProofWriter Prompts  \n- C.3 FOLIO Prompts\n- C.4 LogicalDeduction Prompts\n- C.5 AR-LSAT Prompts\n\nEach subsection shows prompts for 3 different methods (Standard In-Context Learning, Chain-of-Thought Prompting, and Logic-LM), and according to the introduction text, each shows \"only a single demonstration for each setting.\"\n\nSo the count is: 5 datasets \u00d7 3 methods = 15 prompt examples in Appendix C.\n\nAnswer: 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55203, "error": null, "extra": {"thread_id": 1949, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_28321722a0164737934f82e2d1d378ae", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 318, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "15"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the performance comparison between Logic-LM and the two baseline models (Standard and CoT) when using GPT-4 as the base language model.\n\nLooking at Table 2, which shows the accuracy results for different models across five datasets, I can see the GPT-4 (gpt-4) columns:\n\n1. **PrOntoQA**: \n   - Standard: 77.40\n   - CoT: 98.79\n   - Logic-LM: 83.20\n   - Logic-LM does NOT outperform CoT (98.79 > 83.20), but does outperform Standard\n\n2. **ProofWriter**:\n   - Standard: 52.67\n   - CoT: 68.11\n   - Logic-LM: 79.66\n   - Logic-LM outperforms both baselines \u2713\n\n3. **FOLIO**:\n   - Standard: 69.11\n   - CoT: 70.58\n   - Logic-LM: 78.92\n   - Logic-LM outperforms both baselines \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 71.33\n   - CoT: 75.25\n   - Logic-LM: 87.63\n   - Logic-LM outperforms both baselines \u2713\n\n5. **AR-LSAT**:\n   - Standard: 33.33\n   - CoT: 35.06\n   - Logic-LM: 43.04\n   - Logic-LM outperforms both baselines \u2713\n\nLogic-LM outperforms BOTH baseline models in 4 out of 5 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 53272, "output_tokens": 424, "cost_micros": 166176, "latency_ms": 23500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 2, I need to compare Logic-LM's performance against both Standard and CoT baselines for GPT-4 across all 5 datasets.\n\nFor GPT-4:\n\n1. **PrOntoQA**: \n   - Logic-LM: 83.20\n   - Standard: 77.40 \u2713\n   - CoT: 98.79 \u2717\n   - Logic-LM outperforms Standard only\n\n2. **ProofWriter**:\n   - Logic-LM: 79.66\n   - Standard: 52.67 \u2713\n   - CoT: 68.11 \u2713\n   - Logic-LM outperforms both\n\n3. **FOLIO**:\n   - Logic-LM: 78.92\n   - Standard: 69.11 \u2713\n   - CoT: 70.58 \u2713\n   - Logic-LM outperforms both\n\n4. **LogicalDeduction**:\n   - Logic-LM: 87.63\n   - Standard: 71.33 \u2713\n   - CoT: 75.25 \u2713\n   - Logic-LM outperforms both\n\n5. **AR-LSAT**:\n   - Logic-LM: 43.04\n   - Standard: 33.33 \u2713\n   - CoT: 35.06 \u2713\n   - Logic-LM outperforms both\n\nLogic-LM outperforms both baseline models in 4 datasets (ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT).\n\nAnswer: 4", "citations": [], "input_tokens": 22925, "output_tokens": 391, "cost_micros": 74640, "latency_ms": 7687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Table 2 to compare Logic-LM's performance against the two baselines (Standard and CoT) when using GPT-4.\n\nFrom Table 2, the GPT-4 results are:\n\n**PrOntoQA:**\n- Standard: 77.40\n- CoT: 98.79\n- Logic-LM: 83.20\n- Logic-LM does NOT outperform both (CoT is better)\n\n**ProofWriter:**\n- Standard: 52.67\n- CoT: 68.11\n- Logic-LM: 79.66\n- Logic-LM outperforms both \u2713\n\n**FOLIO:**\n- Standard: 69.11\n- CoT: 70.58\n- Logic-LM: 78.92\n- Logic-LM outperforms both \u2713\n\n**LogicalDeduction:**\n- Standard: 71.33\n- CoT: 75.25\n- Logic-LM: 87.63\n- Logic-LM outperforms both \u2713\n\n**AR-LSAT:**\n- Standard: 33.33\n- CoT: 35.06\n- Logic-LM: 43.04\n- Logic-LM outperforms both \u2713\n\nLogic-LM outperforms both baseline models on 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 26795, "output_tokens": 332, "cost_micros": 85365, "latency_ms": 7342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2 in the document, I need to compare Logic-LM's performance against Standard LLMs and Chain-of-Thought (CoT) for GPT-4 across all datasets.\n\nFrom Table 2, the GPT-4 results show:\n\n1. **PrOntoQA**: \n   - Standard: 99.4%\n   - CoT: 84.9%\n   - Logic-LM: 100.0%\n   - Logic-LM outperforms both \u2713\n\n2. **ProofWriter**:\n   - Standard: 87.3%\n   - CoT: 73.6%\n   - Logic-LM: 99.0%\n   - Logic-LM outperforms both \u2713\n\n3. **FOLIO**:\n   - Standard: 66.7%\n   - CoT: 61.8%\n   - Logic-LM: 79.9%\n   - Logic-LM outperforms both \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 11.3%\n   - CoT: 57.7%\n   - Logic-LM: 60.0%\n   - Logic-LM outperforms both \u2713\n\n5. **AR-LSAT**:\n   - Standard: 32.6%\n   - CoT: 60.0%\n   - Logic-LM: 39.8%\n   - Logic-LM does NOT outperform CoT (39.8% < 60.0%) \u2717\n\nLogic-LM outperforms both baseline models in 4 out of 5 datasets when using GPT-4.\n\nAnswer: 4", "citations": [], "input_tokens": 20106, "output_tokens": 440, "cost_micros": 66918, "latency_ms": 8265, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I need to find how many datasets Logic-LM (without self-refinement) outperforms both baseline models (Standard and CoT) when using GPT-4.\n\nLooking at Table 2, for GPT-4 (gpt-4):\n\n1. **PrOntoQA**: \n   - Standard: 77.40\n   - CoT: 98.79\n   - Logic-LM: 83.20\n   - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**:\n   - Standard: 52.67\n   - CoT: 68.11\n   - Logic-LM: 79.66\n   - Logic-LM outperforms both (79.66 > 68.11 > 52.67) \u2713\n\n3. **FOLIO**:\n   - Standard: 69.11\n   - CoT: 70.58\n   - Logic-LM: 78.92\n   - Logic-LM outperforms both (78.92 > 70.58 > 69.11) \u2713\n\n4. **LogicalDeduction**:\n   - Standard: 71.33\n   - CoT: 75.25\n   - Logic-LM: 87.63\n   - Logic-LM outperforms both (87.63 > 75.25 > 71.33) \u2713\n\n5. **AR-LSAT**:\n   - Standard: 33.33\n   - CoT: 35.06\n   - Logic-LM: 43.04\n   - Logic-LM outperforms both (43.04 > 35.06 > 33.33) \u2713\n\nLogic-LM outperforms both baselines on 4 datasets: ProofWriter, FOLIO, LogicalDeduction, and AR-LSAT.\n\nAnswer: 4", "citations": [], "input_tokens": 25924, "output_tokens": 456, "cost_micros": 84612, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q005", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "int", "gold": "4", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about Logic-LM's performance compared to baseline models when using GPT-4.Let me continue reading to find information about GPT-4 performance comparisons.Let me search for tables or sections that compare GPT-4 performance across datasets.Now let me read the table with GPT-4 results to count the datasets where Logic-LM outperforms baselines.Let me look at the table header to understand the structure better.Perfect! Now I have the complete table. Let me analyze the GPT-4 results:\n\nFor GPT-4 (the last 3 columns):\n- Standard, CoT, Logic-LM\n\nThe two baseline models are:\n1. Standard prompting\n2. Chain-of-thought (CoT) prompting\n\nI need to count how many datasets where Logic-LM (without self-refinement) outperforms BOTH baselines when using GPT-4.\n\nLet me check each dataset:\n\n1. **PrOntoQA**: Logic-LM = 83.20, Standard = 77.40, CoT = 98.79\n   - Logic-LM does NOT outperform CoT (83.20 < 98.79)\n\n2. **ProofWriter**: Logic-LM = 79.66, Standard = 52.67, CoT = 68.11\n   - Logic-LM > Standard (79.66 > 52.67) \u2713\n   - Logic-LM > CoT (79.66 > 68.11) \u2713\n   - **Outperforms both** \u2713\n\n3. **FOLIO**: Logic-LM = 78.92, Standard = 69.11, CoT = 70.58\n   - Logic-LM > Standard (78.92 > 69.11) \u2713\n   - Logic-LM > CoT (78.92 > 70.58) \u2713\n   - **Outperforms both** \u2713\n\n4. **LogicalDeduction**: Logic-LM = 87.63, Standard = 71.33, CoT = 75.25\n   - Logic-LM > Standard (87.63 > 71.33) \u2713\n   - Logic-LM > CoT (87.63 > 75.25) \u2713\n   - **Outperforms both** \u2713\n\n5. **AR-LSAT**: Logic-LM = 43.04, Standard = 33.33, CoT = 35.06\n   - Logic-LM > Standard (43.04 > 33.33) \u2713\n   - Logic-LM > CoT (43.04 > 35.06) \u2713\n   - **Outperforms both** \u2713\n\nLogic-LM (without self-refinement) outperforms both baseline models on 4 datasets when using GPT-4 as the base language model.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44437, "error": null, "extra": {"thread_id": 1950, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cfca2042672b455980d6e47d98e5f851", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 311, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nFrom Table 1 on page 5, I can see that for \"Deductive Reasoning\" problems (which corresponds to the LP - Logic Programming formulation), the solver used is **Pyke**.\n\nThis is further confirmed in Section 3.2 on page 5, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 53262, "output_tokens": 128, "cost_micros": 161706, "latency_ms": 10532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver implements the logic programming module.\n\nIn Table 1 on page 3810, the paper summarizes the symbolic solvers used for different problem types. For \"Deductive Reasoning\" which uses \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 22915, "output_tokens": 137, "cost_micros": 70800, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the symbolic solver used for logic programming.\n\nIn Table 1 on page 3810, the paper shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems that use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 26785, "output_tokens": 150, "cost_micros": 82605, "latency_ms": 6202, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about the symbolic solver used for logic programming...\n\nIn Table 1 on page 3805, the document shows a summary of symbolic formulations and symbolic solvers used for different datasets. For \"Deductive Reasoning\" problems (which correspond to the logic programming formulation mentioned in Figure 1), the solver listed is \"ProntoQA, Pyke\".\n\nAdditionally, in Section 3.2 and footnotes, the document references Pyke as the logic programming engine. In Section D (page 3823), it explicitly states: \"For PrOntoQA and ProofWriter, the Pyke logic programming engine returns the inferred value...\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 20096, "output_tokens": 158, "cost_micros": 62658, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find which symbolic solver is used for the logic programming module.\n\nFrom Table 1 on page 3810, I can see that for \"Deductive Reasoning\" problems which use \"LP\" (Logic Programming) formulation, the solver listed is \"Pyke\".\n\nThis is confirmed in Section 3.2 \"Symbolic Reasoner\" on page 3810, which states:\n\n\"**LP System.** For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nAnswer: Pyke", "citations": [], "input_tokens": 25914, "output_tokens": 143, "cost_micros": 79887, "latency_ms": 5453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q006", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "Pyke", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about the symbolic solver used for the logic programming module in Figure 1.Let me search for information about Figure 1 and the symbolic solver:Let me read the document content to find references to Figure 1 and the solver:Good! I can see Figure 1 is mentioned. Now let me search for specific mentions of the solver implementation:I can see references to solvers. Let me look at line 711 where it mentions \"logic programming engine\" and around line 823 where Table 1 is mentioned:Perfect! I found Table 1. Let me continue reading to see all the solvers listed:Perfect! I found the information. Looking at Table 1 and the description in section 3.2, I can see that:\n\n- For **Deductive Reasoning** (Logic Programming / LP), the solver used is **Pyke**\n- For **First-Order Logic** (FOL), the solver used is **Prover9**\n- For **Constraint Satisfaction** (CSP), the solver used is **python-constraint**\n- For **Analytical Reasoning** (SAT), the solver used is **Z3**\n\nNow, let me verify which one corresponds to the \"logic programming module\" mentioned in Figure 1. Based on the document:\n\n1. Figure 1 shows the overall LOGIC-LM framework with a \"Symbolic Reasoner\" component\n2. Section 3.2 describes the symbolic reasoner and mentions that for deductive reasoning using the Logic Programming (LP) language, they use **Pyke**\n3. Section 3.2.1 specifically states: \"For deductive reasoning, we incorporate the Pyke expert system (Frederiksen, 2008), which makes inferences based on the logic programming language.\"\n\nThe logic programming module shown in Figure 1 is implemented using **Pyke**.\n\nAnswer: Pyke", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58280, "error": null, "extra": {"thread_id": 1951, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_708b2982536441eabb41dbb732343ffa", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pyke", "normalised_gold": "pyke"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "native_pdf", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the datasets table (Table 5 on page 14), I can see the following datasets with their number of options:\n\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options\n- AR-LSAT: 5 options\n\nAR-LSAT has 5 options for all questions. Looking at the references and dataset descriptions, AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions\" and the test set has \"231 multiple-choice questions.\"\n\nFrom the paper text: \"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 53272, "output_tokens": 258, "cost_micros": 163686, "latency_ms": 21859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2023.findings-emnlp.248.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 1 and the dataset descriptions in Section 4:\n\n- **PrOntoQA (2023)**: 2 options (True/False)\n- **ProofWriter (2021)**: 3 options\n- **FOLIO (2022)**: 3 options\n- **LogicalDeduction (2022)**: Variable options (3, 5, or 7 according to Table 5)\n- **AR-LSAT (2022)**: 5 options\n\nFrom the paper text (Section 4):\n\"AR-LSAT (Zhong et al., 2022) is a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016. We use the test set which has 231 multiple-choice questions.\"\n\nAnd from Table 5, AR-LSAT has \"#Opts: 5\".\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 22925, "output_tokens": 253, "cost_micros": 72570, "latency_ms": 7828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "azure_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking through the document for datasets proposed in 2022 with 5-option multiple-choice questions:\n\nFrom Table 5 (Dataset Statistics):\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (varies)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT (Zhong et al., 2022) is described as \"a dataset that collects all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\" with \"231 multiple-choice questions.\"\n- Table 5 confirms AR-LSAT has 5 options (#Opts column)\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 26795, "output_tokens": 198, "cost_micros": 83355, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_basic_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find datasets that:\n1. Were proposed in 2022\n2. Have all problems as multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the number of options (#Opts) for each dataset:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3, 5, 7 options (variable)\n- AR-LSAT: 5 options\n\nFrom the Datasets section (Section 4), I can find when each dataset was created:\n- PrOntoQA: 2023 (Saparov and He, 2023)\n- ProofWriter: 2021 (Tafjord et al., 2021)\n- FOLIO: 2022 (Han et al., 2022)\n- LogicalDeduction: 2022 (from BigBench - Srivastava et al., 2022)\n- AR-LSAT: 2022 (Zhong et al., 2022)\n\nAmong the 2022 datasets:\n- FOLIO has 3 options\n- LogicalDeduction has variable options (3, 5, or 7)\n- AR-LSAT has 5 options\n\nOnly AR-LSAT consistently has 5 options for all questions.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 20106, "output_tokens": 343, "cost_micros": 65463, "latency_ms": 7766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "llamacloud_premium_lc", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to find a dataset that:\n1. Was proposed in 2022\n2. Has all multiple-choice questions with 5 options\n\nFrom Table 5 (Dataset Statistics), I can see the \"#Opts\" column which shows the number of options:\n- PrOntoQA: 2 options\n- ProofWriter: 3 options\n- FOLIO: 3 options\n- LogicalDeduction: 3,5,7 options (varying)\n- AR-LSAT: 5 options\n\nFrom the dataset descriptions in Section 4:\n- AR-LSAT is described as collecting \"all analytical logic reasoning questions from the Law School Admission Test from 1991 to 2016\"\n- The citation is \"Zhong et al. 2022\"\n\nLooking at the references, AR-LSAT is cited as:\n\"Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, and Nan Duan. 2022. Analytical reasoning of text.\"\n\nAR-LSAT is the only dataset with consistently 5 options and was published in 2022.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 25924, "output_tokens": 307, "cost_micros": 82377, "latency_ms": 8733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2023.findings-emnlp.248.pdf::Q007", "doc_id": "2023.findings-emnlp.248.pdf", "answer_format": "str", "gold": "ProofWriter", "pages": 19, "document_id": 7580, "arm": "surfsense_agentic", "question_id": "2023.findings-emnlp.248.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about datasets used in the paper, focusing on those proposed in 2022 with multiple-choice questions having 5 options.Let me search for information about the datasets used in this paper, particularly focusing on those from 2022 and their characteristics.I can see references to datasets from 2022. Let me search for information about AR-LSAT and other datasets, particularly focusing on their characteristics regarding multiple-choice questions with 5 options.Let me search for more specific information about the datasets, particularly looking for details about multiple-choice options.Let me read more about each dataset to find their characteristics. Let me look at the section describing LogicalDeduction.Let me search for information about LogicalDeduction specifically and check the appendix for detailed data statistics.Let me search for \"Appendix B\" which was mentioned as containing detailed data statistics.Perfect! I found the information I need in Table 5. Let me verify the details about AR-LSAT:\n\nFrom the table:\n- AR-LSAT has 5 options (#Opts: 5)\n- AR-LSAT was proposed by Zhong et al., 2022\n\nLet me verify that AR-LSAT is specifically about logical reasoning and all problems are multiple-choice.Based on the document:\n\n1. AR-LSAT was proposed by Zhong et al., 2022\n2. It collects all **analytical logic reasoning questions** from the Law School Admission Test\n3. The test set has 231 **multiple-choice questions**\n4. From Table 5 (Appendix B), AR-LSAT has **5 options** (#Opts: 5)\n\nThis matches all the criteria in the question: proposed in 2022, logical reasoning problems, all multiple-choice questions with 5 options.\n\nAnswer: AR-LSAT", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69453, "error": null, "extra": {"thread_id": 1952, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4c068961fe9145d382e57492c359de62", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 452, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ar lsat", "normalised_gold": "proofwriter"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about application periods for different groups of applicants in this undergraduate prospectus.\n\nLooking at page 25 (document page 47), I can see a table titled \"APPLICATION PERIOD AND FEE\" that shows different application groups and their respective application periods.\n\nFrom this table, I can see the following end dates for different application groups:\n\n1. Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End: 21 Feb 2024\n3. NUS High School Diploma: End: 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n5. Part-Time B.Eng: End: 15 Jan 2024\n6. Part-Time B.Tech in Computing: End: 15 Jan 2024\n\nComparing these dates, the latest end date is 19 Mar 2024, which applies to two groups:\n- Singapore-Cambridge GCE 'A' Level applicants\n- International Baccalaureate (IB) Diploma applicants\n\nAnswer: Singapore-Cambridge GCE 'A' Level applicants, International Baccalaureate (IB) Diploma applicants", "citations": [], "input_tokens": 60375, "output_tokens": 307, "cost_micros": 185730, "latency_ms": 53796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma applicants, singapore cambridge gce level applicants", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the APPLICATION PERIOD AND FEE table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n\n1. **Singapore-Cambridge GCE 'A' Level**: End: 19 Mar 2024\n2. **Diploma Awarded by a Polytechnic or equivalent**: End: 20 Jan 2024\n3. **NUS High School Diploma**: End: 19 Mar 2024\n4. **International Baccalaureate (IB) Diploma**: End: 19 Mar 2024\n5. **Part-Time B.Eng**: End date not fully specified (appears cut off)\n6. **Part-Time B.Tech in Computing**: End: 21 Feb 2024\n\nFrom the complete dates shown, the latest end date is **19 Mar 2024**, which applies to three groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, NUS High School Diploma, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 15947, "output_tokens": 244, "cost_micros": 51501, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, nus high school diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on pages 46-47:\n\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng and Part-Time B.Tech: End 15 Jan 2024\n\nThe latest end date is 19 March 2024, which applies to two groups.\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 19386, "output_tokens": 173, "cost_micros": 60753, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application periods table on the document, I need to identify which application groups have the latest end date.\n\nFrom the \"APPLICATION PERIOD AND FEE\" table:\n\n1. Singapore-Cambridge GCE 'A' Level: End date is **19 Mar 2024**\n2. Diploma Awarded by a Polytechnic or equivalent institution in Singapore: End date is 21 Feb 2024\n3. NUS High School Diploma: End date is 20 Jan 2024\n4. International Baccalaureate (IB) Diploma: End date is **19 Mar 2024**\n\nThe latest end date is 19 Mar 2024, which is shared by two groups:\n- Singapore-Cambridge GCE 'A' Level\n- International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 14659, "output_tokens": 220, "cost_micros": 47277, "latency_ms": 4921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the application period table on pages 46-47, I need to identify which application groups have the latest end date.\n\nThe application periods listed are:\n- Singapore-Cambridge GCE 'A' Level: End 19 Mar 2024\n- Diploma from Polytechnic: End 21 Feb 2024\n- NUS High School Diploma: End 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End 19 Mar 2024\n- Part-Time B.Eng: End 15 Jan 2024\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 18593, "output_tokens": 183, "cost_micros": 58524, "latency_ms": 5078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q000", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "[\"Singapore-Cambridge GCE 'A' Level\", 'International Baccalaureate (IB) Diploma']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about application periods for different groups of applicants.Let me search more specifically for dates and application-related content:Let me look at pages 46-47 which should contain the table with deadlines:Let me search for typical date formats and applicant categories:Great! I found the table. Let me read the specific section with the application periods:Perfect! I found the application period table. Now I can see the end dates for each group. Let me compare them:\n\n- Singapore-Cambridge GCE 'A' Level: End: 19 Mar 2024\n- Diploma Awarded by a Polytechnic: End: 21 Feb 2024\n- NUS High School Diploma: End: 20 Jan 2024\n- International Baccalaureate (IB) Diploma: End: 19 Mar 2024\n- Part-Time B.Eng: End: 15 Jan 2024\n- Part-Time B.Tech in Computing: 15 Jan 2024 (this appears to be both start and end date based on the context)\n\nThe latest end date is **19 Mar 2024**, which applies to two groups:\n1. Singapore-Cambridge GCE 'A' Level\n2. International Baccalaureate (IB) Diploma\n\nAnswer: Singapore-Cambridge GCE 'A' Level, International Baccalaureate (IB) Diploma", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44218, "error": null, "extra": {"thread_id": 1953, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4551739abb3d476cb8bf4c0348ee5447", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 278, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "international baccalaureate ib diploma, singapore cambridge gce level", "normalised_gold": "international baccalaureate ib diploma, singapore cambridge gce level"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at page 3 of the document, which shows \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\", I need to examine each numbered reason and its corresponding image to find which one does not include any person.\n\nGoing through the reasons with their images:\n\n1. Interdisciplinary Learning - shows a group of students\n2. Space to Innovate - shows people in a collaborative setting\n3. Industry-ready - shows a person at a desk\n4. Immersion in Research - shows a person in a lab setting (Rolls-Royce)\n5. Internship Opportunities - shows the \"AMD\" text/logo area with people\n6. Global Exposure - shows a person in a winter setting\n7. Be Among the Top Ranked - shows students walking\n8. Prestigious Scholarships and Comprehensive Financial Aid - shows students\n9. Most Beautiful Campus - shows an aerial view of the campus with greenery and pathways\n10. Vibrant Campus Life - shows students/people\n\nReason #9 \"Most Beautiful Campus\" shows an aerial photograph of the campus featuring buildings, green spaces, and pathways, but does not show any people in the image.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 60381, "output_tokens": 271, "cost_micros": 185208, "latency_ms": 35592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to identify which reason's corresponding figure/photo does not include any person.\n\nGoing through each reason with its associated image:\n\n1. Interdisciplinary Learning - has people\n2. Space to Innovate - has people  \n3. Industry-ready - has a photo credited to Rolls-Royce showing industrial equipment/machinery without visible people\n4. Immersion in Research - has people\n5. Prestigious Scholarships - not clearly shown with people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - appears to be text/rankings\n8. Most Beautiful Campus - shows campus architecture/scenery\n9. Internship Opportunities - has people\n10. Vibrant Campus Life - has people\n\nThe reason labeled \"3 Industry-ready\" with the photo credit to Rolls-Royce shows industrial/laboratory equipment without any visible people in the image.\n\nAnswer: Industry-ready", "citations": [], "input_tokens": 15953, "output_tokens": 236, "cost_micros": 51399, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "industry ready", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section, I need to examine each reason and its corresponding figure to identify which one does not include any person.\n\nGoing through the reasons:\n1. Interdisciplinary Learning - has figures/images with people\n2. Space to Innovate - has people in the image\n3. Industry-ready - has people\n4. Immersion in Research - has people (photo credit: Rolls-Royce)\n5. AMD Internship Opportunities - has people\n6. Global Exposure - has people\n7. Be Among the Top Ranked - this appears to be primarily text/rankings focused\n8. Prestigious Scholarships and Comprehensive Financial Aid - appears to be icons/symbols\n9. Most Beautiful Campus - this is described as about the campus itself\n10. Vibrant Campus Life - has people\n\nLooking at reason #9 \"Most Beautiful Campus\" - the description states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world.\" This reason is about the campus architecture and grounds themselves, and the corresponding figure would logically show the campus landscape/buildings without people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 19392, "output_tokens": 273, "cost_micros": 62271, "latency_ms": 8437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section and examine the figures/images associated with each reason to identify which one does not include any person.\n\nFrom the document, the top 10 reasons are listed on pages 2-3:\n\n1. Be Among the Top Ranked\n2. Space to Innovate\n3. Industry-ready\n4. Prestigious Scholarships and Comprehensive Financial Aid\n5. Most Beautiful Campus\n6. Vibrant Campus Life\n7. Internship Opportunities\n8. Immersion in Research\n9. (appears to have a photo credit: Rolls-Royce)\n10. Vibrant Campus Life (repeated)\n\nThe document mentions \"Photo credit: Rolls-Royce\" which suggests there's an image for reason #9 \"Immersion in Research.\"\n\nLooking at the layout, reason #5 is \"Most Beautiful Campus\" - this reason would typically show campus buildings and architecture rather than people, as it's about the physical beauty of the campus itself.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 14665, "output_tokens": 246, "cost_micros": 47685, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the \"TOP 10 REASONS WHY NTU SMART CAMPUS IS FOR YOU\" section on pages 2-3, I need to examine the images associated with each reason.\n\nThe 10 reasons are:\n1. Interdisciplinary Learning\n2. Space to Innovate\n3. Industry-ready\n4. Immersion in Research\n5. Internship Opportunities\n6. Global Exposure\n7. Be Among the Top Ranked\n8. Prestigious Scholarships and Comprehensive Financial Aid\n9. Most Beautiful Campus\n10. Vibrant Campus Life\n\nLooking at the corresponding figures/images described:\n- Reason #4 \"Immersion in Research\" includes a photo credit to Rolls-Royce, and the image appears to show industrial/research equipment rather than people.\n- Reason #9 \"Most Beautiful Campus\" states \"The University's main campus is frequently listed among the Top 15 most beautiful university campuses in the world\" and the cover page shows \"a large, modern building with a curved facade featuring prominent vertical wooden louvers or fins\" with \"green landscaping visible at the base\" - this is an architectural shot without people visible.\n\nThe most likely answer based on the description is reason #9, as it explicitly describes a campus building without mentioning any people in the scene.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 18599, "output_tokens": 300, "cost_micros": 60297, "latency_ms": 9328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q001", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Most Beautiful Campus", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the top 10 reasons why NTU smart campus is for you and identify which reason has a corresponding figure without any person.Let me search for \"top 10 reasons\" or \"smart campus\" in the document:Let me read the section around line 448-488 where the \"TOP 10\" appears:Good! I can see the beginning of the top 10 reasons. Let me continue reading to see all 10 reasons and their corresponding figures:Let me continue reading to see all the images associated with each reason:Now let me go back and carefully read the section with the 10 reasons to identify each reason and its associated image description. Let me re-read more carefully:Based on my reading of the document, I can now identify the 10 reasons and their corresponding figures. Let me analyze the images:\n\n1. **Interdisciplinary Learning** (Reason 1) - Image shows a female researcher/scientist in a laboratory (person present)\n\n2. **Space to Innovate** (Reason 2) - Image shows a person positioned on left side operating equipment in a laboratory (person present)\n\n3. **Industry-ready** (Reason 3) - Image shows six young adults standing in a horizontal line (people present)\n\n4. **Immersion in Research** (Reason 4) - Image shows a single person standing next to AMD logo (person present)\n\n5. **Internship Opportunities** (Reason 5) - The image associated with reasons 5-10 appears to be Im5.jpg\n\n6. **Global Exposure** (Reason 6) - Image (Im5.jpg) shows a single person standing on snowy terrace/viewing area with ch\u00e2teau backdrop (person present)\n\n7. **Be Among the Top Ranked** (Reason 7) - This appears to share imagery with the ranking pages\n\n8. **Prestigious Scholarships and Comprehensive Financial Aid** (Reason 8) - Part of the same figure group\n\n9. **Most Beautiful Campus** (Reason 9) - The next distinct image is Im7.jpg which shows an aerial view of a modern curved/spiral building with NO PEOPLE mentioned in the description - only architecture and landscape features\n\n10. **Vibrant Campus Life** (Reason 10) - Part of the combined figure\n\nThe reason that does not include any person in the corresponding figure is **Reason 9: Most Beautiful Campus** - the image shows only an aerial view of the building architecture and landscape with no people.\n\nAnswer: Most Beautiful Campus", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48717, "error": null, "extra": {"thread_id": 1954, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_939ae241eba1446aa240613de9b11e4b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 370, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "most beautiful campus", "normalised_gold": "most beautiful campus"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for images of students playing badminton and identify their major.\n\nLooking through the pages, I can see several student testimonials with photos and their majors listed.\n\nOn page 22 (document page 41), there are two images at the bottom of the page showing students in athletic settings. The text mentions:\n\n\"\u2014 JADE LIM SI MIN\nPhilosophy, Class of 2023\n\nNTU has been a springboard for my growth as a student leader. Through the Student Leadership Development Programme, Varsity Sports Association and FISU World Forum, NTU has equipped me with the leadership skills necessary to succeed both in my sport and beyond.\"\n\nAnd:\n\n\"\u2014 GRACE CHUA HUI ZHEN\nMedicine, Class of 2023\n\nNTU has allowed me to pursue my two passions: academics and athletics. From letting me participate in competitions to offering generous subsidies to giving encouragement, I really feel grateful for the school's the tremendous support.\"\n\nThe image on the right shows what appears to be a badminton player in action. Based on the layout and the caption, this appears to be Grace Chua Hui Zhen from Medicine.\n\nAnswer: Medicine", "citations": [], "input_tokens": 60367, "output_tokens": 265, "cost_micros": 185076, "latency_ms": 45125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images or mentions of students playing badminton.\n\nLooking through the document, I can find several student testimonials with their names, majors, and years listed. Let me check for any mention of badminton or sports activities.\n\nOn page 41, there is a section about \"SPORTS @ NTU\" with several images. I can see mentions of:\n- JADE LIM SI MIN, Philosophy, Class of 2023 - with text about varsity sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - with text about academics and athletics\n\nHowever, I need to look more carefully at the actual images in the document. The document mentions \"Griffin at Lee Wee Nam Library\" as an image caption on page 42.\n\nLooking more carefully at the sports section and student profiles, I can see references to students involved in sports, but I need to identify specifically which student's picture shows them playing badminton.\n\nUpon careful review of the document, I cannot definitively identify from the text alone which specific student's picture shows them playing badminton, as the document doesn't explicitly describe the content of each photograph beyond the captions with names and majors.\n\nHowever, based on the context in the sports section where student testimonials are shown, and the typical layout of such brochures, if there is a badminton-related image, it would likely be associated with one of the students featured in the sports section.\n\nAnswer: I cannot determine this from the text provided in the document.", "citations": [], "input_tokens": 15939, "output_tokens": 339, "cost_micros": 52902, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i cannot determine this from text provided in document", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for images of someone playing badminton and identify which major that student belongs to.\n\nLooking through the document, I can see several student photos with their names and majors mentioned:\n\n1. Nick Erwin Chia Zhiyang - Renaissance Engineering Programme, Year 4\n2. Gladys Phua Min - Communication Studies, Year 2\n3. Elijah Adam Bin Abdul Haady Aldrin - Chemistry & Biological Chemistry, Year 1\n4. Joey Tay - NTU-NIE TSP, Year 2\n5. Stella Chua Zhi Yun - Mechanical Engineering, Year 3\n6. Seth Low - Communication Studies, Year 4\n7. Balaji Srinivasan - Biological Sciences and Psychology, Year 4\n8. Muhamed Shamir Jaffar Hussein - MBBS, Year 4\n9. Wang Shi Lin Nicole - Business, Year 3\n10. Ruth Koh - Sport Science & Management, Year 3\n\nOn page 41, there is a section about sports and I can see images related to sports activities. Looking at the bottom of page 41, there is a photo with Grace Chua Hui Zhen from Medicine, Class of 2023, and the image appears to show her playing badminton (based on the context of sports activities and the layout).\n\nAnswer: Medicine", "citations": [], "input_tokens": 19378, "output_tokens": 320, "cost_micros": 62934, "latency_ms": 10280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for any images or references to badminton playing.\n\nLooking through the document, I can see several student profiles with quotes and their majors listed, but I need to find one specifically associated with a badminton image.\n\nOn page 41, I can see there are sports-related images and student profiles. The document shows:\n\n- JADE LIM SI MIN, Philosophy, Class of 2023 - associated with FISU WORLD FORUM and sports\n- GRACE CHUA HUI ZHEN, Medicine, Class of 2023 - mentioned in relation to academics and athletics\n\nHowever, I cannot definitively identify which specific image shows badminton or which student's picture is specifically playing badminton from the text content provided. The document mentions \"SPORTS @ NTU\" section and various athletic activities, but the actual image descriptions are not clear enough in the text to determine which major the badminton player belongs to.\n\nBased on the limited visual descriptions available in the text, I cannot provide a definitive answer about which student major is shown playing badminton.\n\nAnswer: Cannot be determined from the provided document text", "citations": [], "input_tokens": 14651, "output_tokens": 250, "cost_micros": 47703, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided document text", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the badminton image...\n\nOn page 41, there is a caption under an image that states:\n\n> GRACE CHUA HUI ZHEN\n> Medicine, Class of 2023\n\nThe caption appears below text mentioning \"a student playing badminton in a competition.\"\n\nAnswer: Medicine", "citations": [], "input_tokens": 18585, "output_tokens": 74, "cost_micros": 56865, "latency_ms": 5875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "medicine", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q002", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "str", "gold": "Medicine", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about a student playing badminton in the brochure document.Let me search for \"badminton\" in the document:Now let me read the context around the badminton image to find the student's major:Let me look earlier in the document to see the context. The badminton image appears on page about \"Communication-Focused\". Let me search for more context around this section:Let me look at what comes before this section to identify which school/major this is part of:I can see this is about CoHASS (College of Humanities, Arts, and Social Sciences). Let me search for where specific majors or student testimonials are mentioned near the badminton image:I see Seth Low is from Communication Studies. Let me check if the badminton image is associated with him or if there's another student profile. Let me look at the pages more carefully:Let me look at the earlier mention at line 2200 to see if there's another student:That's not the badminton image. Let me look more systematically. The badminton image appears around line 3343. Let me check what's before and after to see which student profile or section it's associated with:The badminton image appears on page 20 (based on the page numbers), and the text after it (page 21) shows Seth Low from Communication Studies. But let me look more carefully at the layout. Let me check the section just before the badminton image to see which school section it belongs to:I see on page 19 there's a quote from Seth Low, Communication Studies. Now let me look at page 20 where the badminton image is:Let me look at the structure better. The Im1.jpg is on page 19 (Seth Low's image), then page 20 starts with CoHASS section. The badminton image Im0.jpg should be on page 20. Let me check the page markers more carefully:So page 19 ends at line 3127, then the next page marker is page 21 at line 3387. So the badminton image (around line 3343) is on page 20 which doesn't have an explicit marker. Let me look at the entire section more carefully to see which school this belongs to:Now I see WKWSCI (Wee Kim Wee School of Communication and Information) section starts around line 3548, but the badminton image is earlier around line 3343. Let me trace back to see which section the badminton image actually belongs to. Let me read backwards from line 3320:Perfect! Now I can see the structure. The badminton image (Im0.jpg) appears under the \"Communication-Focused\" section which is part of the CoHASS (College of Humanities, Arts and Social Sciences) overview on page 20. This section appears before the individual school pages. Then on page 21, we see Seth Low from Communication Studies.\n\nSo the badminton image is associated with Communication Studies, which is evident from:\n1. It's on the CoHASS overview page (page 20)\n2. The \"Communication-Focused\" heading right before the badminton image\n3. Seth Low, Communication Studies student, appears on the next page (21)\n4. WKWSCI (Wee Kim Wee School of Communication and Information) is listed as one of the schools under CoHASS\n\nAnswer: Communication Studies", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 83500, "error": null, "extra": {"thread_id": 1955, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2258a5b949624b49af1a1b98a8b2e791", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 658, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "communication studies", "normalised_gold": "medicine"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document to identify which colleges/schools/institutes feature Year 3 students on their cover pages or promotional sections.\n\nLooking through the document:\n\n1. Page 10 (College of Engineering section) - Features **Stella Chua Zhi Yun, Mechanical Engineering, Year 3**\n\n2. Page 12 (College of Humanities, Arts and Social Sciences) - Features **Seth Low, Communication Studies, Year 4** (not Year 3)\n\n3. Page 14 (College of Science) - Features **Balaji Srinivasan, Biological Sciences and Psychology, Year 4** (not Year 3)\n\n4. Page 16 (Lee Kong Chian School of Medicine) - Features **Muhamed Shamir Jaffar Hussein, MBBS, Year 4** (not Year 3)\n\n5. Page 17 (Nanyang Business School) - Features **Wang Shi Lin Nicole, Business, Year 3**\n\n6. Page 18 (National Institute of Education) - Features **Ruth Koh, Sport Science & Management, Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 60377, "output_tokens": 272, "cost_micros": 185211, "latency_ms": 42937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to look through the document to identify which colleges/schools/institutes feature Year 3 students on their cover/featured pages.\n\nLet me examine each section:\n\n1. **College of Engineering** (page 16-17): Features STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21): Features SETH LOW, Communication Studies, Year 4 (not Year 3)\n\n3. **College of Science** (page 24-25): Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not Year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28-29): Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not Year 3)\n\n5. **Nanyang Business School** (page 30-31): Features WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33): Features RUTH KOH, Sport Science & Management, Year 3\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 15949, "output_tokens": 282, "cost_micros": 52077, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine the document to identify which colleges/schools/institutes feature year 3 students on their cover pages.\n\nLooking through the document:\n\n1. **College of Engineering** (page 16) - Features STELLA CHUA ZHI YUN, Mechanical Engineering, **Year 3**\n\n2. **College of Humanities, Arts and Social Sciences** (page 20) - Features SETH LOW, Communication Studies, Year 4 (not year 3)\n\n3. **College of Science** (page 24) - Features BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4 (not year 3)\n\n4. **Lee Kong Chian School of Medicine** (page 28) - Features MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4 (not year 3)\n\n5. **Nanyang Business School** (page 30) - Features WANG SHI LIN NICOLE, Business, **Year 3**\n\n6. **National Institute of Education** (page 32) - Features RUTH KOH, Sport Science & Management, **Year 3**\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 19388, "output_tokens": 278, "cost_micros": 62334, "latency_ms": 6905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through the document for references to Year 3 students who appear as student models on cover pages or in promotional materials.\n\nLooking through the document, I can find the following Year 3 students quoted/featured:\n\n1. Page 16 - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3 - This is from the **College of Engineering** section\n\n2. Page 30 - WANG SHI LIN NICOLE, Business, Year 3 - This is from the **Nanyang Business School** section (she appears twice on this page)\n\n3. Page 33 - RUTH KOH, Sport Science & Management, Year 3 - This is from the **National Institute of Education** section (she appears twice on this page)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 14661, "output_tokens": 193, "cost_micros": 46878, "latency_ms": 6233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify which colleges/schools/institutes feature Year 3 students on their cover/promotional pages in this prospectus.\n\nLet me search through the document for mentions of students with their year level and their associated colleges/schools:\n\n1. **College of Engineering** (page 16-17):\n   - STELLA CHUA ZHI YUN, Mechanical Engineering, Year 3\n\n2. **College of Humanities, Arts and Social Sciences** (page 20-21):\n   - SETH LOW, Communication Studies, Year 4\n\n3. **College of Science** (page 24-25):\n   - BALAJI SRINIVASAN, Biological Sciences and Psychology, Year 4\n\n4. **Lee Kong Chian School of Medicine** (page 28-29):\n   - MUHAMED SHAMIR JAFFAR HUSSEIN, MBBS, Year 4\n\n5. **Nanyang Business School** (page 30-31):\n   - WANG SHI LIN NICOLE, Business, Year 3\n\n6. **National Institute of Education** (page 32-33):\n   - RUTH KOH, Sport Science & Management, Year 3\n\nFrom this analysis, the colleges/schools/institutes that use Year 3 students as their cover page student models are:\n- College of Engineering (Stella Chua)\n- Nanyang Business School (Wang Shi Lin Nicole)\n- National Institute of Education (Ruth Koh)\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 18595, "output_tokens": 352, "cost_micros": 61065, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q004", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "list", "gold": "['College of Engineering', 'Nanyang Business School', 'National Institute of Education']", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find which colleges/schools/institutes from NTU use year 3 students as cover page student models.Let me search for information about cover page student models and year 3 students.Let me search for \"Year 3\" with capital letters and look at the beginning of the document which might contain information about cover pages or student models.Now let me search for information about cover pages and models. Let me read the beginning of the document more carefully:Let me search more specifically for information about models or photos. Let me look for text that mentions students being models or featured on covers:Let me look at the end of the document, which often contains credits or acknowledgments about models or photos:Let me search for the total lines in this document and read from the end:Let me read closer to the end of the document where credits or acknowledgments are typically found:Let me continue reading towards the very end where photo credits or acknowledgments might be:Let me search near the very end of the document:The document ended without showing photo credits. Let me search more specifically for mentions of colleges and Year 3 together. Let me look at those specific line numbers from the grep results:Good! I found one: Stella Chua Zhi Yun is from Mechanical Engineering, Year 3. Now let me check the other instances:Good! Wang Shi Lin Nicole is from Business (Nanyang Business School), Year 3. Now let me check the other instances:Good! Ruth Koh is from Sport Science & Management (National Institute of Education), Year 3. Now let me check the next instance:This is another mention of Ruth Koh. Now let me look more carefully at the context. I need to understand which of these students are featured on \"cover pages\" of their respective schools/colleges. Let me search for any section that lists or describes cover page models:None of these matches refer to \"cover page\" in the context I'm looking for. Let me look at the actual pages where these students appear to see if they are featured as cover page models. Let me check around the Mechanical Engineering section:Now let me look before this section to see if there's a cover page for College of Engineering:Perfect! I can see that Stella Chua Zhi Yun (Mechanical Engineering, Year 3) is featured on the cover page for the College of Engineering section. Now let me check the other sections:Great! I can see Wang Shi Lin Nicole (Business, Year 3) is featured on the cover page for Nanyang Business School. Now let me check the NIE section:Perfect! I can see Ruth Koh (Sport Science & Management, Year 3) is featured on the cover page for the National Institute of Education (NIE) section.\n\nNow let me check if there are any other colleges/schools that I might have missed. Let me look at the table of contents and see what major sections exist:Let me check if there's a cover page model for College of Humanities, Arts and Social Sciences (CoHASS):I can see Seth Low (Communication Studies, Year 4) is mentioned but not Year 3. Let me check the College of Science:I can see Balaji Srinivasan (Biological Sciences and Psychology, Year 4) is mentioned for College of Science, but not Year 3. Let me check if there's a cover page before the College of Science section:So for College of Science, the cover page model is Balaji Srinivasan (Year 4), not Year 3. Let me also check Lee Kong Chian School of Medicine:Let me check the cover page before Lee Kong Chian School of Medicine:So for Lee Kong Chian School of Medicine, the cover page model is Muhamed Shamir Jaffar Hussein (MBBS, Year 4), not Year 3.\n\nNow let me systematically check all the major schools/colleges. Based on my review:\n\n**Year 3 students as cover page models:**\n1. **College of Engineering** - Stella Chua Zhi Yun (Mechanical Engineering, Year 3)\n2. **Nanyang Business School** - Wang Shi Lin Nicole (Business, Year 3)\n3. **National Institute of Education** - Ruth Koh (Sport Science & Management, Year 3)\n\nLet me verify there are no other colleges I missed by checking the table of contents more carefully:Based on the table of contents, the main colleges/schools are:\n1. College of Engineering (has Year 3 student - Stella Chua)\n2. Colleges of Humanities, Arts and Social Sciences (has Year 4 student - Seth Low)\n3. Colleges of Science (has Year 4 student - Balaji Srinivasan)\n4. Lee Kong Chian School of Medicine (has Year 4 student - Muhamed Shamir)\n5. Nanyang Business School (has Year 3 student - Wang Shi Lin Nicole)\n6. National Institute of Education (has Year 3 student - Ruth Koh)\n\nSo the colleges/schools/institutes that use Year 3 students as cover page models are:\n1. College of Engineering\n2. Nanyang Business School\n3. National Institute of Education\n\nAnswer: College of Engineering, Nanyang Business School, National Institute of Education", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 154578, "error": null, "extra": {"thread_id": 1956, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_071293dfad4941b99ec3b52bc671c7da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1092, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "college of engineering, nanyang business school, national institute of education", "normalised_gold": "college of engineering, nanyang business school, national institute of education"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "native_pdf", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the College of Humanities, Arts, and Social Sciences (CoHASS) and count how many schools are listed but not individually introduced in detail.\n\nFrom page 12, I can see the College of Humanities, Arts and Social Sciences section lists \"OUR SCHOOLS\":\n- School of Art, Design and Media\n- School of Humanities\n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 7 entities listed under \"OUR SCHOOLS\".\n\nLooking at pages 13-14, I can see detailed introductions for:\n1. School of Art, Design and Media (ADM) - page 13\n2. School of Humanities (SoH) - page 13\n3. School of Social Sciences (SSS@NTU) - page 13\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - page 13\n\nThese 4 schools have detailed \"ABOUT\" sections with descriptions of their programs and advantages on pages 22-23.\n\nThe entities that are listed but NOT individually introduced in detail are:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nThat's 3 entities.\n\nAnswer: 3", "citations": [], "input_tokens": 60364, "output_tokens": 324, "cost_micros": 185952, "latency_ms": 38015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2024.ug.eprospectus.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools in the College of Humanities, Arts, and Social Sciences (CoHASS) and determine which ones are NOT introduced in detail.\n\nFrom page 20-21, the CoHASS section lists \"OUR SCHOOLS\":\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n\nIt also mentions:\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow, let me check which schools get detailed individual introductions on pages 22-23:\n\n1. **School of Art, Design and Media (ADM)** - YES, has detailed section on page 22\n2. **School of Humanities (SoH)** - YES, has detailed section on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, has detailed section on page 23\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, has detailed section on page 23\n\nThe three centers/institutes mentioned (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences) are listed under \"OUR SCHOOLS\" but do NOT have individual detailed introductions.\n\nTherefore, 3 entities listed under CoHASS schools do not have individual detailed introductions.\n\nAnswer: 3", "citations": [], "input_tokens": 15936, "output_tokens": 340, "cost_micros": 52908, "latency_ms": 8296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "azure_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools that belong to the College of Humanities, Arts and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFrom page 20, the document lists the schools under CoHASS:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow I need to check which schools have individual detailed introductions on pages 22-23 (the \"LEARN MORE ABOUT OUR SCHOOLS\" section):\n\nPage 22:\n- School of Art, Design and Media (ADM) - YES, has detailed introduction\n- School of Humanities (SoH) - YES, has detailed introduction\n\nPage 23:\n- School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n- Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nSchools that are NOT individually introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 19375, "output_tokens": 302, "cost_micros": 62655, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_basic_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify the schools within the College of Humanities, Arts, and Social Sciences (CoHASS) and then determine which ones are NOT individually introduced in detail.\n\nFirst, let me find the list of schools under CoHASS from page 20:\n\n**OUR SCHOOLS:**\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nThat's 7 schools total listed under CoHASS.\n\nNow let me check which schools have detailed individual introductions in the document (pages 21-23):\n\n1. **School of Art, Design and Media (ADM)** - YES, detailed introduction on page 22\n2. **School of Humanities (SoH)** - YES, detailed introduction on page 22\n3. **School of Social Sciences (SSS@NTU)** - YES, detailed introduction on page 22\n4. **Wee Kim Wee School of Communication and Information (WKWSCI)** - YES, detailed introduction on page 23\n\nThe following schools are listed but do NOT have individual detailed introductions:\n5. Nanyang Centre for Public Administration - NO detailed introduction\n6. NTU Centre for Contemporary Art Singapore - NO detailed introduction\n7. Centre for Liberal Arts and Social Sciences - NO detailed introduction\n\nTherefore, 3 schools are not individually introduced in detail.\n\nAnswer: 3", "citations": [], "input_tokens": 14648, "output_tokens": 358, "cost_micros": 49314, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "llamacloud_premium_lc", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify which schools from the College of Humanities, Arts, and Social Sciences (CoHASS) are listed, and then determine which ones are NOT introduced in detail.\n\nFrom the document, the CoHASS schools listed on page 20 are:\n1. School of Art, Design and Media\n2. School of Humanities\n3. School of Social Sciences\n4. Wee Kim Wee School of Communication and Information\n5. Nanyang Centre for Public Administration\n6. NTU Centre for Contemporary Art Singapore\n7. Centre for Liberal Arts and Social Sciences\n\nNow, looking at the detailed introductions on pages 22-23 under \"LEARN MORE ABOUT OUR SCHOOLS\":\n1. School of Art, Design and Media (ADM) - YES, has detailed introduction\n2. School of Social Sciences (SSS@NTU) - YES, has detailed introduction\n3. School of Humanities (SoH) - YES, has detailed introduction\n4. Wee Kim Wee School of Communication and Information (WKWSCI) - YES, has detailed introduction\n\nThe schools that are NOT introduced in detail:\n1. Nanyang Centre for Public Administration\n2. NTU Centre for Contemporary Art Singapore\n3. Centre for Liberal Arts and Social Sciences\n\nAnswer: 3", "citations": [], "input_tokens": 18582, "output_tokens": 292, "cost_micros": 60126, "latency_ms": 7359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2024.ug.eprospectus.pdf::Q005", "doc_id": "2024.ug.eprospectus.pdf", "answer_format": "int", "gold": "3", "pages": 27, "document_id": 7581, "arm": "surfsense_agentic", "question_id": "2024.ug.eprospectus.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the College of Humanities, Arts, and Social Sciences and count which schools are not individually introduced in detail.Let me search for \"College of Humanities, Arts, and Social Sciences\" in the document:Let me try searching for variations:Let me read the section about the College of Humanities:Let me search for more information about the college structure. Let me look for \"COLLEGE OF HUMANITIES\" in caps:Found it! Let me read that section:Good! I can see the College of Humanities, Arts and Social Sciences has 4 schools listed:\n1. School of Art, Design and Media (page 22)\n2. School of Humanities (page 23)\n3. School of Social Sciences (page 23)\n4. Wee Kim Wee School of Communication and Information (page 24)\n\nNow let me check which ones are introduced in detail. Let me read pages 22-24:Let me search for where each school is detailed. Let me look at the actual page content around lines 3200-3300 where I saw \"School of Humanities\" mentioned:Good! I can see the CoHASS section lists their schools. Now let me find where individual schools are detailed. Let me search for detailed sections of each school:Let me read the ADM section:Great! Now let me check for Wee Kim Wee School:Perfect! Now let me also check if there are any other schools in CoHASS that I might have missed. Let me go back to the list to verify:I see that the College of Humanities, Arts and Social Sciences has the following units listed:\n- School of Art, Design and Media\n- School of Humanities  \n- School of Social Sciences\n- Wee Kim Wee School of Communication and Information\n- Nanyang Centre for Public Administration\n- NTU Centre for Contemporary Art Singapore\n- Centre for Liberal Arts and Social Sciences\n\nNow let me check if the last three (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) have detailed introductions. Let me search:So the three centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, and Centre for Liberal Arts and Social Sciences) are only listed in the OUR SCHOOLS section but don't have detailed individual introductions like the four main schools do.\n\nNow let me verify the total count by checking what the document considers as \"schools\" in CoHASS. The question asks specifically about \"schools\" - let me see if these centres are considered schools:\n\nLooking at the structure:\n- In the table of contents (lines 398-413), only 4 schools are listed under \"COLLEGES OF HUMANITIES, ARTS AND SOCIAL SCIENCES\":\n  1. School of Art, Design and Media (page 22)\n  2. School of Humanities (page 23)\n  3. School of Social Sciences (page 23)\n  4. Wee Kim Wee School of Communication and Information (page 24)\n\nAll 4 of these have detailed individual introductions as I found above.\n\nBut in the \"OUR SCHOOLS\" section (lines 3216-3236), it lists 7 entities:\n- 4 Schools (the ones mentioned above)\n- 3 Centres (Nanyang Centre for Public Administration, NTU Centre for Contemporary Art Singapore, Centre for Liberal Arts and Social Sciences)\n\nThe question asks about \"schools\" specifically. The centres are NOT called \"schools\" - they are called \"centres\". So the question is asking only about the 4 schools.\n\nSince all 4 schools (School of Art, Design and Media, School of Humanities, School of Social Sciences, and Wee Kim Wee School of Communication and Information) ARE individually introduced in detail, the answer is:\n\nAnswer: 0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97500, "error": null, "extra": {"thread_id": 1957, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_84cc170418784e7aaee4655dd9c795d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 722, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to analyze Figure 1 and find other figures that convey similar messages.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" across four datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10). The key message is that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries, especially in the low budget regime.\n\nNow let me look through the document for figures with similar messages:\n\n**Figure 6** (page 16): Shows \"Label diversity yields more performant and robust active querying strategies\" on PathMNIST, OrganAMNIST, and BloodMNIST. This compares strategies with and without label diversity, not the cold start problem specifically.\n\n**Figure 9** (page 20): Shows \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, focuses on label diversity rather than cold start.\n\n**Figure 4** (page 7): Shows \"Quantitative comparison of map-based querying strategies\" comparing easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast with random selection as baseline. This shows performance at low budgets and demonstrates the cold start problem.\n\n**Figure 5** (page 8): Shows \"On the importance of selecting a superior initial query\" with performance across active learning cycles starting from initial queries. This demonstrates how different initial query strategies perform, related to the cold start problem.\n\n**Figure 13** (page 24): Shows performance of active learning strategies with different initial query strategies on BloodMNIST, demonstrating the importance of initial query selection.\n\n**Figure 14** (page 24): Same as Figure 13 but for PathMNIST.\n\nFigures that convey the same core message as Figure 1 (that active learning fails at initial selection/cold start):\n- Figure 4: Shows the cold start problem through comparison with random\n- Figure 5: Shows importance of initial query selection\n- Figure 13: Shows initial query performance across cycles\n- Figure 14:", "citations": [], "input_tokens": 58482, "output_tokens": 512, "cost_micros": 183126, "latency_ms": 44218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" where most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. The figure has 4 subplots (a-d) for different datasets showing AUC performance vs. number of images.\n\nLet me search for other figures with similar messages:\n\n**Figure 6**: \"Label diversity yields more performant and robust active querying strategies\" - Shows 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) with 6 subplots each comparing strategies with/without diversity. This is about diversity improvement, not the cold start problem itself.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10 and CIFAR-10-LT. Also about diversity improvement rather than demonstrating the cold start problem.\n\n**Figure 4**: \"Quantitative comparison of map-based querying strategies\" - Shows random selection as a baseline and compares different strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across 4 datasets. This demonstrates that random selection is competitive and that hard-to-contrast performs best, which is related to the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST\" - This is about subsequent cycles after initial query.\n\n**Figure 14**: Similar to Figure 13 but for PathMNIST - Also about subsequent cycles.\n\n**Figure 5**: \"On the importance of selecting a superior initial query\" on OrganAMNIST - Shows active learning cycles but focuses on the importance of initial query selection.\n\nThe figures that most clearly convey the same core message as Figure 1 (that existing active querying strategies fail to outperform random selection at the start) are:\n\n- **Figure 4**: Directly compares strategies at low budgets and shows random selection as competitive baseline\n- Possibly Figure 6 and 9, but these focus more on the solution (diversity) rather than the", "citations": [], "input_tokens": 21463, "output_tokens": 512, "cost_micros": 72069, "latency_ms": 12625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1.\n\nFigure 1's main message is about the \"Cold start problem in vision active learning\" - showing that most existing active querying strategies (BALD, Consistency, Coreset, Margin, VAAL, Entropy) are outperformed by random selection in selecting initial queries at low budgets. It displays AUC performance curves across different numbers of images for multiple datasets.\n\nLet me go through the other figures to find similar messages:\n\n**Figure 2**: Shows label diversity of querying criteria - different message (about class distribution bias)\n\n**Figure 3**: Shows Dataset Maps and easy/hard-to-learn vs easy/hard-to-contrast data - different message (about data selection strategies)\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies, comparing hard-to-contrast with other strategies against random selection - **similar message** (comparing active learning strategies with random selection)\n\n**Figure 5**: Shows importance of selecting superior initial query on OrganAMNIST - **similar message** (about initial query performance across active learning cycles)\n\n**Figure 6**: Extended from Table 1, shows label diversity yields more performant strategies - different message (about adding label diversity)\n\n**Figure 7**: Continued from Figure 2, about label diversity - different message\n\n**Figure 8**: Visualization of K-means clustering - different message\n\n**Figure 9**: Similar to Figure 6, about diversity and performance - different message (about label diversity effect)\n\n**Figure 10**: Similar to Figure 2, about label diversity - different message\n\n**Figure 11**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 12**: Similar to Figure 3, Dataset Maps - different message\n\n**Figure 13**: Performance across active learning cycles on BloodMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\n**Figure 14**: Performance across active learning cycles on PathMNIST - **similar message** (comparing initial query strategies across active learning cycles)\n\nFigures that convey similar messages to Figure 1 (comparing active learning strategies, showing cold start problem):\n- Figure 4\n- Figure 5\n- Figure 13\n- Figure 14\n\nAnswer: 4", "citations": [], "input_tokens": 26468, "output_tokens": 506, "cost_micros": 86994, "latency_ms": 11765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey similar messages to Figure 1.\n\nFigure 1 shows the \"Cold start problem in vision active learning\" - it demonstrates that most existing active querying strategies (like BALD, Consistency, etc.) are outperformed by random selection when selecting initial queries, particularly in low budget regimes.\n\nLet me go through the document to find figures with similar messages:\n\n**Figure 4**: Shows comparison of different querying strategies including easy-to-learn, hard-to-learn, easy-to-contrast, and hard-to-contrast across different datasets. This shows performance comparisons but focuses on the proposed method rather than demonstrating the cold start problem itself.\n\n**Figure 6**: Explicitly states it is \"Extended from Table 1\" and shows \"Label diversity yields more performant and robust active querying strategies.\" It compares active querying strategies with and without label diversity on PathMNIST, OrganAMNIST, and BloodMNIST. This is about the solution (label diversity) rather than demonstrating the cold start problem.\n\n**Figure 9**: States \"Diversity yields more performant and robust active querying strategies\" on CIFAR-10 and CIFAR-10-LT. Similar to Figure 6, this focuses on the solution rather than demonstrating the cold start problem.\n\n**Figure 13**: Shows \"Performance of each active learning querying strategies with different initial query strategies on BloodMNIST.\" This compares different strategies across active learning cycles but focuses on the full active learning procedure rather than specifically demonstrating the cold start problem.\n\n**Figure 14**: Similar to Figure 13, shows \"Performance of each active learning querying strategies with different initial query strategies on PathMNIST.\"\n\nLooking back at the document more carefully, none of the other figures directly replicate Figure 1's specific message about the cold start problem where active learning strategies fail to outperform random selection at the first choice. The other figures either:\n- Show solutions to the problem (Figures 6, 9)\n- Show active learning trajectories (Figures 13, 14)\n- Show other aspects like dataset maps or visualizations (Figures 3, 7, 8, 10, 11, 12)\n\nAnswer: 0", "citations": [], "input_tokens": 23168, "output_tokens": 504, "cost_micros": 77064, "latency_ms": 11578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify figures that convey a similar message to Figure 1, which shows the \"Cold start problem in vision active learning\" - demonstrating that most existing active querying strategies are outperformed by random selection when selecting initial queries.\n\nLet me examine each figure:\n\n**Figure 1**: Shows cold start problem - active learning strategies perform worse than random selection at initial queries across multiple datasets.\n\n**Figure 2**: Shows label diversity of querying criteria - demonstrates class distribution bias in active querying strategies.\n\n**Figure 3**: Shows active querying based on Dataset Maps - compares easy/hard-to-learn vs easy/hard-to-contrast data.\n\n**Figure 4**: Shows quantitative comparison of map-based querying strategies - compares different strategies including hard-to-contrast vs random selection.\n\n**Figure 5**: Shows importance of selecting superior initial query - correlation analysis showing hard-to-contrast outperforms others across active learning cycles.\n\n**Figure 6**: Extended from Table 1 - shows label diversity yields more performant strategies, comparing with/without label diversity across budgets.\n\n**Figure 7**: Continued from Figure 2 - shows label diversity for OrganAMNIST and BloodMNIST.\n\n**Figure 8**: Visualization of K-means clustering and active selection using UMAP.\n\n**Figure 9**: Similar to Figure 6 but for CIFAR-10-LT - shows diversity yields more performant strategies.\n\n**Figure 10**: Similar to Figure 2 but for CIFAR-10 and CIFAR-10-LT - shows label diversity.\n\n**Figure 11**: Similar to Figure 3 - Active querying based on Dataset Maps for PathMNIST and OrganAMNIST.\n\n**Figure 12**: Similar to Figure 3 - Active querying based on Dataset Maps for CIFAR-10-LT.\n\n**Figure 13**: Performance of active learning strategies with different initial queries on BloodMNIST - shows correlation across cycles.\n\n**Figure 14**: Performance of active learning strategies with different initial queries on PathMNIST - shows correlation across cycles.\n\nFigures conveying a similar message to Figure 1 (showing the cold start problem where active learning strategies underperform random selection at the start):\n- **Figure ", "citations": [], "input_tokens": 37261, "output_tokens": 512, "cost_micros": 119463, "latency_ms": 12672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q000", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "2", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to understand the first figure and then identify how many other figures convey similar messages.Let me continue reading to find all figures in the document and understand what the first figure conveys.Now let me read more sections to understand all the figures and their messages better.Let me continue reading to see more figures.Let me continue reading to identify all figures.Let me search for more figures to get a complete count.Now let me understand what message Figure 1 conveys and identify similar figures. Based on my reading, let me analyze:\n\n**Figure 1** (lines 490-584): Shows four subplots (a-d) of line graphs plotting AUC vs Number of images, demonstrating the \"cold start problem\" where active querying strategies are outperformed by random selection in selecting initial queries. The message is: **existing active learning strategies perform worse than random selection at the beginning (cold start problem)**.\n\nNow let me identify which other figures convey similar messages:\n\n1. **Figure 4** (lines 1346-1455): Shows four subplots comparing map-based querying strategies with random selection as baseline, showing that random selection is \"a highly competitive baseline in cold start because it outperforms six popular active querying strategies as shown in Figure 1.\" This conveys the **same message about cold start problem**.\n\n2. **Figure 6** (lines 2545-2805): Shows plots about label diversity yielding more performant strategies - this is about **solutions**, not about demonstrating the cold start problem itself.\n\n3. **Figure 7** (lines 2840-2920): Shows bar charts about label diversity - this is about **class distribution bias**, related but different message.\n\n4. **Figure 9** (lines 3454-3640): Similar to Figure 6, about diversity yielding better strategies - this is about **solutions**.\n\n5. **Figure 10** (lines 3689-3800): Bar charts about label diversity - about **class distribution bias**.\n\nLet me verify by reading more carefully about what each figure demonstrates:Based on my thorough analysis of the document, Figure 1's main message is: **Active learning strategies are outperformed by random selection at the cold start (initial query selection) due to the cold start problem.**\n\nFigures that convey this same or very similar message:\n- **Figure 4**: Explicitly shows random selection as a competitive baseline that outperforms popular active querying strategies in the cold start problem, directly referencing Figure 1.\n\nAll other figures (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14) convey different messages:\n- Figures 2, 7, 10: Show class distribution bias\n- Figures 3, 11, 12: Show dataset maps for selecting data\n- Figure 5, 13, 14: Show importance of superior initial query across learning cycles\n- Figures 6, 9: Show that diversity improves querying strategies\n- Figure 8: Shows visualization of clustering\n\nTherefore, **1 figure** (Figure 4) conveys a similar message as Figure 1.\n\nAnswer: 1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78500, "error": null, "extra": {"thread_id": 1958, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cace0f3b35134ef58541b082bc26d8e7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 533, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "2"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1, which shows the \"Cold start problem in vision active learning,\" I can see performance comparisons on different datasets including CIFAR-10 (subplot d). The figure shows that most existing active querying strategies (BALD, Consistency, Margin, VAAL, Coreset, Entropy) are outperformed by random selection in selecting initial queries.\n\nLooking at Table 1, which compares different querying strategies on CIFAR-10-LT at 10% and 20% budgets:\n- At 10% budget: Random achieves 91.6\u00b11.1\n- At 20% budget: Random achieves 93.1\u00b10.6\n\nComparing with other strategies at these budgets:\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%, strategies that outperform random (91.6): VAAL (92.6), Entropy (92.0), Margin (91.7), BALD (92.8) = 4 strategies\n\nAt 20%, strategies that outperform random (93.1): Consistency (93.4), VAAL (93.7), Margin (93.2), Coreset (93.6) = 4 strategies\n\nHowever, the question asks generally how many strategies outperform random selection. Looking at the paper's proposed \"Ours\" strategy (hard-to-contrast), the results show it significantly outperforms random", "citations": [], "input_tokens": 58490, "output_tokens": 512, "cost_micros": 183150, "latency_ms": 41641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about strategies that outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10, I can see performance curves for different strategies. The document mentions CIFAR-10-LT specifically in several places.\n\nFrom Figure 4(d), I can see a comparison of different strategies on CIFAR-10-LT including:\n- Random (baseline, shown as dotted line)\n- Easy-to-learn\n- Hard-to-learn\n- Easy-to-contrast\n- Hard-to-contrast\n\nLooking at the quantitative results in Figure 4(d) for CIFAR-10-LT at different budget levels (20.0% and 30.0% with 2481 and 3721 images), I can observe which strategies perform above the random baseline.\n\nFrom the text in section 3.2: \"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nLooking at Figure 4(d) more carefully, I can see that at the budget levels shown:\n1. Hard-to-contrast (performs best, clearly above random)\n2. Easy-to-learn (appears to perform above random)\n3. Hard-to-learn (appears close to or slightly above random)\n4. Easy-to-contrast (appears below random)\n\nFrom the visual inspection of Figure 4(d), it appears that 2-3 strategies outperform random selection, with hard-to-contrast being the clear winner, and easy-to-learn also performing above the baseline.\n\nBased on the figure, I count: Hard-to-contrast and Easy-to-learn as clearly outperforming random selection.\n\nAnswer: 2", "citations": [], "input_tokens": 21471, "output_tokens": 478, "cost_micros": 71583, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows the cold start problem on CIFAR-10, I can see performance curves for different strategies compared to random selection. However, the question asks specifically about CIFAR-10-LT.\n\nLet me check the text more carefully. In Section 3.2, it states:\n\n\"Similarly on CIFAR-10-LT, hard-to-contrast significantly outperforms random selection by 21.2% (87.35%\u00b10.0% vs. 66.12%\u00b10.9%) and 24.1% (90.59%\u00b10.1% vs. 66.53%\u00b10.5%) by querying 20% and 30% of entire dataset respectively.\"\n\nThis indicates that the \"hard-to-contrast\" strategy (which is the authors' proposed method) outperforms random selection.\n\nLooking at Figure 1(d) which shows CIFAR-10 (not CIFAR-10-LT, but related), most of the active learning strategies shown (BALD, Consistency, Coreset, Margin, VAAL, Entropy) appear to perform worse than or similar to random selection at the initial stages (the \"cold start\" problem that the paper discusses).\n\nThe main point of the paper is that existing active learning strategies fail to outperform random selection at the cold start (initial query selection), which is why they propose their hard-to-contrast method.\n\nLooking at Table 1 and Table 7, which compare different strategies on CIFAR-10-LT with different budget levels, at low budgets (10%, 20%), the proposed method with diversity enforcement shows the best performance. However, most existing strategies WITHOUT diversity enforcement perform poorly or fail to select all classes (marked as \"-\").\n\nBased on the paper's main finding that existing active querying strategies are \"outperformed by random selection\" at the cold start (as stated in Figure 1 caption and throughout the introduction), and that only their proposed \"hard-to-contrast\" method significantly outperforms random selection on CIFAR-10-LT, the answer appears to be:\n\nAnswer: 1", "citations": [], "input_tokens": 26476, "output_tokens": 501, "cost_micros": 86943, "latency_ms": 12703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many active learning strategies outperform random selection on CIFAR-10-LT in the cold start problem (initial query selection).\n\nLooking at Figure 1 in the document, which illustrates the \"Cold start problem in vision active learning,\" I can see panel (d) shows results for CIFAR-10-LT.\n\nThe document states in the caption of Figure 1: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries... However, some classes are not selected by active querying strategies due to selection bias, so their results are not presented in the low budget regime.\"\n\nThe document consistently emphasizes that existing active learning strategies fail to outperform random selection at the cold start (initial query stage). The main contribution of the paper is proposing a new strategy (hard-to-contrast) that does outperform random selection.\n\nLooking at Table 1, for CIFAR-10-LT at 10% and 20% budgets:\n- Random: 91.6\u00b11.1 (10%), 93.1\u00b10.6 (20%)\n- Consistency: 91.4\u00b11.1 (10%), 93.4\u00b10.2 (20%)\n- VAAL: 92.6\u00b10.2 (10%), 93.7\u00b10.4 (20%)\n- Margin: 91.7\u00b10.9 (10%), 93.2\u00b10.2 (20%)\n- Entropy: 92.0\u00b11.2 (10%), 91.9\u00b11.3 (20%)\n- Coreset: 91.5\u00b10.4 (10%), 93.6\u00b10.2 (20%)\n- BALD: 92.8\u00b10.1 (10%), 90.8\u00b12.4 (20%)\n\nAt 10%: VAAL (92.6), Entropy (92.0), BALD (92.8), and Margin (91.7) appear close or slightly better than Random (91.6)\nAt 20%: VAAL (93.7), Consistency (93.4), Coreset (93.", "citations": [], "input_tokens": 23176, "output_tokens": 512, "cost_micros": 77208, "latency_ms": 12390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "20", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many strategies outperform random selection on CIFAR-10-LT.\n\nLooking at Figure 1(d) which shows \"Cold start problem in vision active learning\" for CIFAR-10-LT:\n\nThe figure shows performance curves for different strategies:\n- Random (baseline)\n- BALD (Kirsch et al., 2019)\n- Consistency (Gao et al., 2020)\n- Coreset (Sener et al., 2017)\n- Margin (Balcan et al., 2007)\n- VAAL (Sinha et al., 2019)\n- Entropy (Wang et al., 2014)\n\nThe text states: \"Most existing active querying strategies (e.g. BALD, Consistency, etc.) are outperformed by random selection in selecting initial queries, since random selection is i.i.d. to the entire dataset.\"\n\nFrom Figure 1(d), I can see that at the initial query stage (low budget regime), the random selection baseline appears to outperform most other strategies. The figure caption states \"Active learning fails to select data as effectively as random selection at the first choice.\"\n\nHowever, looking more carefully at the results, I need to determine which strategies actually outperform random at the cold start (initial query selection).\n\nAccording to the paper's main findings and Figure 4, the proposed \"hard-to-contrast\" strategy significantly outperforms random selection on CIFAR-10-LT. The paper states this is their contribution - addressing the cold start problem where existing strategies fail to beat random.\n\nFrom the various results shown, only the authors' proposed strategy (hard-to-contrast) consistently outperforms random selection at the initial query stage on CIFAR-10-LT.\n\nAnswer: 1", "citations": [], "input_tokens": 37269, "output_tokens": 407, "cost_micros": 117912, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q001", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "4", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about strategies that outperform the random selection baseline on CIFAR-10-LT.Let me search for content related to CIFAR-10-LT and performance comparisons:Let me read through the document more systematically to find tables or figures with results:Let me search for tables that compare strategies on CIFAR-10-LT:Perfect! I found Table 1. Let me continue reading to see the complete table for CIFAR-10-LT results:Now I can see Table 1 clearly. Let me analyze the CIFAR-10-LT columns (10% and 20% budget). The table shows performance with and without enforcing label diversity. Looking at the CIFAR-10-LT columns:\n\nFor the 10% budget (column 9), I need to compare each strategy's score with Random baseline (91.6\u00b11.1):\n- Random (baseline): 91.6\u00b11.1\n- Consistency with diversity (\u2713): 91.4\u00b11.1 - DOES NOT outperform\n- Consistency without diversity (\u2717): 67.1\u00b117.1 - DOES NOT outperform\n- VAAL with diversity (\u2713): 92.6\u00b10.2 - OUTPERFORMS\n- VAAL without diversity (\u2717): - (missing)\n- Margin with diversity (\u2713): 91.7\u00b10.9 - OUTPERFORMS (slightly)\n- Margin without diversity (\u2717): 81.9\u00b10.8 - DOES NOT outperform\n- Entropy with diversity (\u2713): 92.0\u00b11.2 - OUTPERFORMS (slightly)\n- Entropy without diversity (\u2717): 65.6\u00b115.6 - DOES NOT outperform\n- Coreset with diversity (\u2713): 91.5\u00b10.4 - DOES NOT outperform\n- Coreset without diversity (\u2717): 65.9\u00b115.9 - DOES NOT outperform\n- BALD with diversity (\u2713): 92.8\u00b10.1 - OUTPERFORMS\n- BALD without diversity (\u2717): 64.9\u00b114.9 - DOES NOT outperform\n\nLooking at 10% budget, strategies that outperform random (91.6):\n1. VAAL with diversity: 92.6\n2. Margin with diversity: 91.7\n3. Entropy with diversity: 92.0\n4. BALD with diversity: 92.8\n\nThat's 4 strategies at 10% budget.\n\nFor the 20% budget (column 10), comparing with Random baseline (93.1\u00b10.6):\n- Random: 93.1\u00b10.6\n- Consistency with diversity: 93.4\u00b10.2 - OUTPERFORMS\n- Consistency without diversity: 88.6\u00b10.3 - DOES NOT outperform\n- VAAL with diversity: 93.7\u00b10.4 - OUTPERFORMS\n- VAAL without diversity: - (missing)\n- Margin with diversity: 93.2\u00b10.2 - OUTPERFORMS (slightly)\n- Margin without diversity: 86.3\u00b10.3 - DOES NOT outperform\n- Entropy with diversity: 91.9\u00b11.3 - DOES NOT outperform\n- Entropy without diversity: 86.4\u00b10.2 - DOES NOT outperform\n- Coreset with diversity: 93.6\u00b10.2 - OUTPERFORMS\n- Coreset without diversity: 86.9\u00b10.1 - DOES NOT outperform\n- BALD with diversity: 90.8\u00b12.4 - DOES NOT outperform\n- BALD without diversity: 84.7\u00b10.6 - DOES NOT outperform\n\nAt 20% budget, strategies that outperform random (93.1):\n1. Consistency with diversity: 93.4\n2. VAAL with diversity: 93.7\n3. Margin with diversity: 93.2\n4. Coreset with diversity: 93.6\n\nThat's 4 strategies at 20% budget.\n\nBut the question asks about \"CIFAR-10-LT\" in general. Let me count unique strategies (considering both budgets):\n\nStrategies that outperform at 10%: VAAL, Margin, Entropy, BALD (all with diversity)\nStrategies that outperform at 20%: Consistency, VAAL, Margin, Coreset (all with diversity)\n\nCombined unique strategies: VAAL, Margin, Entropy, BALD, Consistency, Coreset = 6 strategies\n\nBut I should check if the question means at ANY budget level or at BOTH budget levels. Looking at Figure 1d in the document might also help clarify. Let me check that figure.I notice Figure 1(d) is labeled \"CIFAR-10\" not \"CIFAR-10-LT\". But the question specifically asks about CIFAR-10-LT. Let me look more carefully at Table 1. The table clearly has a column header \"CIFAR-10-LT\" with two sub-columns (10% and 20%).\n\nBased on Table 1, I need to count how many strategies (considering different variations - with/without diversity) outperform the random baseline on CIFAR-10-LT. Let me be more systematic:\n\nAt 10% budget, Random = 91.6\u00b11.1:\n1. Consistency (\u2713): 91.4 - NO\n2. Consistency (\u2717): 67.1 - NO  \n3. VAAL (\u2713): 92.6 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 91.7 - YES\n6. Margin (\u2717): 81.9 - NO\n7. Entropy (\u2713): 92.0 - YES\n8. Entropy (\u2717): 65.6 - NO\n9. Coreset (\u2713): 91.5 - NO\n10. Coreset (\u2717): 65.9 - NO\n11. BALD (\u2713): 92.8 - YES\n12. BALD (\u2717): 64.9 - NO\n\nAt 10%: 4 strategies outperform random\n\nAt 20% budget, Random = 93.1\u00b10.6:\n1. Consistency (\u2713): 93.4 - YES\n2. Consistency (\u2717): 88.6 - NO\n3. VAAL (\u2713): 93.7 - YES\n4. VAAL (\u2717): - (missing)\n5. Margin (\u2713): 93.2 - YES\n6. Margin (\u2717): 86.3 - NO\n7. Entropy (\u2713): 91.9 - NO\n8. Entropy (\u2717): 86.4 - NO\n9. Coreset (\u2713): 93.6 - YES\n10. Coreset (\u2717): 86.9 - NO\n11. BALD (\u2713): 90.8 - NO\n12. BALD (\u2717): 84.7 - NO\n\nAt 20%: 4 strategies outperform random\n\nThe question asks \"How many strategies outperform the random selection baseline on CIFAR-10-LT?\" \n\nLooking at both budget levels (10% and 20%), and counting each unique strategy configuration:\n- At 10%: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713) = 4 \n- At 20%: Consistency(\u2713), VAAL(\u2713), Margin(\u2713), Coreset(\u2713) = 4\n\nIf we count unique strategy configurations across both budgets: VAAL(\u2713), Margin(\u2713), Entropy(\u2713), BALD(\u2713), Consistency(\u2713), Coreset(\u2713) = 6 total\n\nBut the most straightforward interpretation is: at a given budget level. Since the table shows two budget levels and the question doesn't specify which, I should look at what makes most sense. Given that 4 strategies outperform at each budget level separately, and 6 unique strategy variations outperform across at least one budget level, the answer is likely **4** (at each specific budget) or **6** (total unique strategies that ever outperform).\n\nHowever, re-reading the question more carefully - it asks how many strategies outperform, not how many strategy-variants. If we count base strategies (ignoring the diversity modifier):\n- VAAL outperforms (with diversity at both budgets)\n- Margin outperforms (with diversity at both budgets)  \n- Entropy outperforms (with diversity at 10%)\n- BALD outperforms (with diversity at 10%)\n- Consistency outperforms (with diversity at 20%)\n- Coreset outperforms (with diversity at 20%)\n\nThat would be 6 unique base strategies.\n\nBut looking at the data more carefully at the initial query (lowest budget), which is what the paper focuses on for \"cold start\", the answer is most likely **4** for the 10% budget.\n\nActually, let me reconsider - in Table 1, the \"Unif.\" column indicates whether uniform/diversity enforcement is applied. Each strategy has 2 rows (with and without diversity). So we have 7 base strategies tested: Random, Consistency, VAAL, Margin, Entropy, Coreset, BALD.\n\nIf I count how many of these 7 base strategies (in any configuration) outperform random at either budget:\n- Consistency: NO at 10%, YES at 20% (with diversity)\n- VAAL: YES at 10%, YES at 20% (with diversity)\n- Margin: YES at 10%, YES at 20% (with diversity)\n- Entropy: YES at 10%, NO at 20%\n- Coreset: NO at 10%, YES at 20% (with diversity)\n- BALD: YES at 10%, NO at 20%\n\nCount = 6 base strategies outperform random in at least one configuration at at least one budget level.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 81906, "error": null, "extra": {"thread_id": 1959, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c7833996f4234f1db0fa9af29b8ebd6c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 805, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "4"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7, which show label diversity comparisons across different datasets (PathMNIST, OrganAMNIST, BloodMNIST), I can see the class distributions for different querying strategies.\n\nFrom these figures, VAAL consistently shows the poorest label diversity across datasets:\n- In PathMNIST (Figure 2), VAAL heavily selects from the \"muscle\" class while largely ignoring \"mucus\" and \"mucosa\" classes\n- In OrganAMNIST (Figure 7), VAAL prefers \"femur-right\" and \"platelet\" classes while largely ignoring lung, liver and monocyte classes\n- In BloodMNIST (Figure 7), VAAL shows similarly poor label diversity with heavy bias toward specific classes\n\nThis is further supported by Table 1, where VAAL frequently has missing results (marked as \"-\") because it fails to sample at least one data point for each class.\n\nAnswer: VAAL", "citations": [], "input_tokens": 58491, "output_tokens": 229, "cost_micros": 178908, "latency_ms": 35750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 2 and Figure 7 in the document, which show label diversity comparisons across different datasets, I can observe the class distribution patterns for various active querying strategies.\n\nFrom the figures, VAAL consistently shows highly imbalanced distributions with strong bias towards specific classes:\n- In PathMNIST (Figure 2): VAAL heavily favors muscle class while largely ignoring mucus and mucosa\n- In OrganAMNIST (Figure 7a): VAAL prefers femur-right while ignoring lung and liver\n- In BloodMNIST (Figure 7b): VAAL favors platelet while ignoring other classes\n\nThe document also states: \"For example, VAAL prefers selecting data in the muscle class, but largely ignores data in the mucus and mucosa classes.\"\n\nTable 2 further confirms this, showing VAAL has very poor class coverage (0.11-0.30) compared to other methods at low budgets.\n\nAnswer: VAAL", "citations": [], "input_tokens": 21472, "output_tokens": 230, "cost_micros": 67866, "latency_ms": 25187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity figures and tables in the document:\n\nIn Figure 2 (PathMNIST), VAAL shows extremely poor label diversity with an entropy of only 2.800, heavily biased toward the muscle class while largely ignoring mucus and mucosa classes.\n\nIn Figure 7 (OrganAMNIST and BloodMNIST), VAAL again shows the poorest label diversity, preferring specific classes like femur-right and platelet while ignoring others.\n\nIn Table 2 (class coverage), VAAL consistently has the lowest class coverage across datasets:\n- PathMNIST: 0.11 (lowest)\n- OrganAMNIST: 0.18 (lowest)\n- BloodMNIST: 0.13 (lowest)\n- CIFAR-10-LT: 0.30 (tied for lowest)\n\nThe document also notes that VAAL results are often missing (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 26477, "output_tokens": 247, "cost_micros": 83136, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 2 in the document, which shows \"Class coverage of selected data\" across different datasets (PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10-LT), I can see the class coverage values for different querying strategies.\n\nVAAL consistently shows the lowest class coverage values across the datasets:\n- PathMNIST: 0.11 at 0.00015%, 0.11 at 0.00030%, 0.18 at 0.001%, 0.18 at 0.002%\n- OrganAMNIST: 0.13 at 0.001%, 0.13 at 0.002%\n- BloodMNIST: values not explicitly shown but indicated as poor\n- CIFAR-10-LT: 0.30 at 0.2%, 0.30 at 0.3%\n\nAdditionally, Table 1 shows that VAAL has many missing results (marked as \"-\") because it \"fails to sample at least one data point for each class.\"\n\nAnswer: VAAL", "citations": [], "input_tokens": 23177, "output_tokens": 263, "cost_micros": 73476, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the label diversity analysis in the document, I need to examine Figure 2, Table 2, Figure 7, and Figure 10 which show class distributions and coverage across different querying strategies.\n\nFrom Figure 2 (PathMNIST), VAAL shows extremely poor label diversity - it selects 25% from muscle class but only 1% and 2% from mucosa and mucus classes respectively, with an entropy of 2.800 (lowest among all strategies).\n\nFrom Table 2 (class coverage):\n- VAAL consistently shows the poorest class coverage across datasets\n- PathMNIST (0.00015%): VAAL covers only 0.11 (11% of classes)\n- OrganAMNIST (0.001%): VAAL covers only 0.18 (18% of classes)  \n- BloodMNIST (0.001%): VAAL covers only 0.13 (13% of classes)\n- CIFAR-10-LT (0.2%): VAAL covers only 0.30 (30% of classes)\n\nFigure 7 confirms VAAL selects 100% from femur-right and platelet while selecting 0% from most other classes.\n\nFigure 10 shows VAAL fails to select any samples from 9 out of 10 classes in CIFAR-10-LT.\n\nAnswer: VAAL", "citations": [], "input_tokens": 37270, "output_tokens": 330, "cost_micros": 116760, "latency_ms": 9437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "vaal", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q003", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "VAAL", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find information about querying criteria and label diversity across datasets.Let me continue reading the document to find information about querying criteria and label diversity:Let me search for information about label diversity across different querying criteria in the document:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 28125, "error": null, "extra": {"thread_id": 1960, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9f3482197c3247b0aae1aed4062d9a6a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 96, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read priority document to find information about querying criteria and label diversity across datasets let me continue reading document to find information about querying criteria and label diversity let me search for information about label diversity across different querying criteria in document", "normalised_gold": "vaal"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each page systematically.\n\nPage 1 (Figure 1): Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. This appears to be 4 figures with multiple lines each.\n\nPage 2 (Figure 1 continued): Same figure from page 1.\n\nPage 4 (Figure 2): Shows a bar chart comparing class distributions, not line plots.\n\nPage 5 (Figure 3): Shows three panels with scatter plots (Dataset Maps), not line plots.\n\nPage 7 (Figure 4): Shows 4 subplots (a-d), each containing line plots with error bars comparing different strategies. This is 4 figures with multiple lines each.\n\nPage 8 (Figure 5): Shows 10 subplots arranged in 2 rows, each containing multiple line plots. This is 10 figures with multiple lines each.\n\nPage 16 (Figure 6): Shows 18 subplots (6 strategies \u00d7 3 datasets), each containing line plots. This is 18 figures with multiple lines each.\n\nPage 17 (Figure 7): Shows bar charts, not line plots.\n\nPage 18 (Figure 8): Shows UMAP visualizations (scatter plots), not line plots.\n\nPage 20 (Figure 9): Shows 12 subplots (6 strategies \u00d7 2 datasets), each containing line plots. This is 12 figures with multiple lines each.\n\nPage 21 (Figure 10): Shows bar charts, not line plots.\n\nPage 22 (Figure 11): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 23 (Figure 12): Shows scatter plots (Dataset Maps), not line plots.\n\nPage 24 (Figure 13 and 14): Each figure shows 10 subplots with line plots. This is 20 figures with multiple lines each.\n\nCounting the figures that contain line plots:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal: 4", "citations": [], "input_tokens": 58479, "output_tokens": 512, "cost_micros": 183117, "latency_ms": 36375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure systematically.\n\n**Figure 1** (page 2): Shows 4 subplots (a-d), each containing multiple line plots comparing different methods. Each subplot has multiple lines.\n\n**Figure 2** (page 4): This is a bar chart showing class distributions, not line plots.\n\n**Figure 3** (page 5): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 4** (page 8): Shows 4 subplots (a-d), each containing multiple line plots (4 different methods plotted).\n\n**Figure 5** (page 9): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 6** (page 16): Shows 3 rows \u00d7 6 columns = 18 subplots, each containing line plots (with and without diversity).\n\n**Figure 7** (page 17): These are bar charts showing class distributions, not line plots.\n\n**Figure 8** (page 18): These are scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9** (page 20): Shows 2 rows \u00d7 6 columns = 12 subplots, each containing line plots.\n\n**Figure 10** (page 21): These are bar charts showing class distributions, not line plots.\n\n**Figure 11** (page 22): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 12** (page 23): These are scatter plots (Dataset Maps), not line plots.\n\n**Figure 13** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\n**Figure 14** (page 24): Shows 2 rows \u00d7 5 columns = 10 subplots, each containing multiple line plots.\n\nNow let me count the total number of individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 4 panels  \n- Figure 5: 10 panels\n- Figure 6: 18 panels\n- Figure 9: 12 panels\n- Figure 13: 10 panels", "citations": [], "input_tokens": 21460, "output_tokens": 512, "cost_micros": 72060, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document page by page:\n\n**Figure 1 (page 2)**: Contains 4 line plots (a, b, c, d) showing cold start problem comparisons.\n\n**Figure 2 (page 3)**: This is a bar chart showing label diversity, not line plots.\n\n**Figure 3 (page 5)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 4 (page 7)**: Contains 4 line plots (a, b, c, d) comparing map-based querying strategies.\n\n**Figure 5 (page 8)**: Contains 10 line plots (2 rows \u00d7 5 columns) showing training from scratch and fine-tuning results.\n\n**Figure 6 (page 16)**: Contains 18 line plots (3 rows \u00d7 6 columns) showing extended results from Table 1.\n\n**Figure 7 (page 17)**: Contains bar charts, not line plots.\n\n**Figure 8 (page 18)**: Contains scatter plots (UMAP visualizations), not line plots.\n\n**Figure 9 (page 20)**: Contains 12 line plots (2 rows \u00d7 6 columns) showing results on CIFAR-10 and CIFAR-10-LT.\n\n**Figure 10 (page 21)**: Contains bar charts, not line plots.\n\n**Figure 11 (page 22)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 12 (page 23)**: Contains scatter plots (Dataset Maps), not line plots.\n\n**Figure 13 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for BloodMNIST results.\n\n**Figure 14 (page 24)**: Contains 10 line plots (2 rows \u00d7 5 columns) for PathMNIST results.\n\nTotal count:\n- Figure 1: 4\n- Figure 4: 4\n- Figure 5: 10\n- Figure 6: 18\n- Figure 9: 12\n- Figure 13: 10\n- Figure 14: 10\n\nTotal =", "citations": [], "input_tokens": 26465, "output_tokens": 512, "cost_micros": 87075, "latency_ms": 8219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper systematically.\n\nLet me go through the document:\n\n**Figure 1**: Shows 4 subplots (a-d), each containing multiple line plots comparing different active learning strategies. Each subplot has multiple lines representing different strategies.\n\n**Figure 4**: Shows 4 subplots for different datasets (PathMNIST, OrganAMNIST, BloodMNIST, CIFAR-10-LT), each with multiple line plots.\n\n**Figure 6**: Shows 6 subplots (one for each strategy: BALD, Consistency, Coreset, Margin, VAAL, Entropy) across 3 datasets (PathMNIST, OrganAMNIST, BloodMNIST) = 18 subplots total, each containing 2 line plots (with and without diversity).\n\n**Figure 9**: Shows 6 subplots (one for each strategy) for 2 datasets (CIFAR-10 and CIFAR-10-LT) = 12 subplots total, each containing 2 line plots.\n\n**Figure 13**: Shows performance plots for BloodMNIST with two parts (a) and (b). Each part shows 6 subplots (Random, Entropy, Margin, BALD, Coreset, and appears to show multiple strategies). Each subplot contains multiple line plots showing different initial query strategies.\n\n**Figure 14**: Shows performance plots for PathMNIST with two parts (a) and (b). Similar structure to Figure 13, with 6 subplots in each part, each containing multiple line plots.\n\nLet me count more carefully by looking at each figure:\n\n- Figure 1: 4 subplots \u00d7 ~7 lines each = 28 line plots\n- Figure 4: 4 subplots \u00d7 4 lines each = 16 line plots\n- Figure 6: 18 subplots \u00d7 2 lines each = 36 line plots\n- Figure 9: 12 subplots \u00d7 2 lines each = 24 line plots\n- Figure 13: 2 parts \u00d7 6 subplots \u00d7 ~4 lines = ", "citations": [], "input_tokens": 23165, "output_tokens": 512, "cost_micros": 77175, "latency_ms": 10296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "13", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to count all the line plots in this paper by going through each figure.\n\n**Figure 1:** Shows 4 subplots (a-d) for PathMNIST, OrganAMNIST, BloodMNIST, and CIFAR-10. Each subplot contains multiple line plots comparing different strategies. This appears to be 4 plots with multiple lines each.\n\n**Figure 2:** This is a table showing label diversity, not line plots.\n\n**Figure 3:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 4:** Shows 4 sets of comparisons (a-d) with 2 budget levels each. Each comparison shows line plots. This gives us 8 line plot panels.\n\n**Figure 5:** Shows 2 subplots (a-b) for \"Training from scratch\" and \"Fine-tuning from self-supervised pre-training\". Each contains multiple line plots.\n\n**Figure 6:** Shows a 3\u00d76 grid (3 datasets \u00d7 6 methods) = 18 line plot panels.\n\n**Figure 7:** This is a table, not line plots.\n\n**Figure 8:** Shows UMAP visualizations (scatter plots), not line plots.\n\n**Figure 9:** Shows 2 rows \u00d7 6 columns = 12 line plot panels.\n\n**Figure 10:** This is a table, not line plots.\n\n**Figure 11:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 12:** Shows dataset maps with scatter plots, not line plots.\n\n**Figure 13:** Shows 2 subplots (a-b) with multiple line plots each.\n\n**Figure 14:** Shows 2 subplots (a-b) with multiple line plots each.\n\nNow let me count more carefully by counting individual line plot panels:\n- Figure 1: 4 panels\n- Figure 4: 8 panels (4 datasets \u00d7 2 budgets)\n- Figure 5: 2 panels\n- Figure 6: 18 panels (3 datasets \u00d7 6 methods)\n- Figure 9: 12 panels (2 datasets \u00d7 6 methods)\n- Figure 13: 2 panels\n- Figure 14: 2 panels", "citations": [], "input_tokens": 37258, "output_tokens": 512, "cost_micros": 119454, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q004", "doc_id": "2210.02442v1.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to count the line plots in the paper.Let me continue reading to see the full document and search for figures/plots:Now let me search for mentions of plots, charts, or examine the figures more carefully:I can see Figure 1 has 4 subfigures (1a, 1b, 1c, 1d) and they appear to be line plots. Let me continue examining other figures:Figure 2 appears to be a bar chart/heatmap showing class distributions. Let me check Figure 3:Figure 3 appears to be scatter", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 46530, "error": null, "extra": {"thread_id": 1961, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5cc38c9bf2aa4c2ba5f55758d03cf5bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 228, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "3"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the optimizers used in this research by searching through the document.\n\nLooking at the implementation configurations section (Appendix A), specifically:\n\n1. In Table 3a (MedMNIST pre-training), the optimizer is listed as \"SGD\"\n2. In Table 3b (CIFAR-10-LT pre-training), the optimizer is listed as \"SGD\"\n3. In Table 6 (Benchmark settings), the optimizer is listed as \"SGD\"\n\nThroughout all the experimental settings described in the document, SGD (Stochastic Gradient Descent) is consistently used as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 58486, "output_tokens": 152, "cost_micros": 177738, "latency_ms": 32796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the optimizers used in the research.\n\nIn Table 3 (page 14), which shows \"Contrastive learning settings on MedMNIST and CIFAR-10-LT,\" I can see:\n\nFor both MedMNIST pre-training (Table 3a) and CIFAR-10-LT pre-training (Table 3b):\n- optimizer: SGD\n\nIn Table 6 (page 15), which shows \"Benchmark settings,\" the optimizer listed is also:\n- optimizer: SGD\n\nThroughout the document, only SGD (Stochastic Gradient Descent) is mentioned as the optimizer.\n\nAnswer: SGD", "citations": [], "input_tokens": 21467, "output_tokens": 164, "cost_micros": 66861, "latency_ms": 6155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (pages 13-14), which shows contrastive learning settings:\n- For MedMNIST pre-training: optimizer = SGD\n- For CIFAR-10-LT pre-training: optimizer = SGD\n\nIn Table 6 (page 15), which shows benchmark settings:\n- For training MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 26472, "output_tokens": 120, "cost_micros": 81216, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (page 12), the contrastive learning settings show:\n- optimizer: SGD\n- optimizer momentum: 0.9\n\nIn Table 6 (page 13), the benchmark settings show:\n- optimizer: SGD\n\nAnswer: SGD", "citations": [], "input_tokens": 23172, "output_tokens": 72, "cost_micros": 70596, "latency_ms": 3764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for optimizer information:\n\nIn Table 3 (Contrastive learning settings), the optimizer is listed as **SGD** for both MedMNIST and CIFAR-10-LT pre-training.\n\nIn Table 6 (Benchmark settings), the optimizer is also listed as **SGD** for training MedMNIST, CIFAR-10, and CIFAR-10-LT.\n\nAnswer: SGD", "citations": [], "input_tokens": 37265, "output_tokens": 106, "cost_micros": 113385, "latency_ms": 6156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "sgd", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q005", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "SGD", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the optimizers used in the research.Let me search for more specific terms related to optimizers:Good! I found SGD. Let me read the sections that mention optimizer settings to get a complete picture:Let me check the benchmark settings table as well:Perfect! I found the optimizer information. Based on the document, I can see that SGD (Stochastic Gradient Descent) is consistently used as the optimizer across different experimental settings:\n\n1. Table 3(a) - MedMNIST pre-training: optimizer = SGD\n2. Table 3(b) - CIFAR-10-LT pre-training: optimizer = SGD  \n3. Table 6 - Benchmark settings for MedMNIST, CIFAR-10, and CIFAR-10-LT: optimizer = SGD\n\nAnswer: SGD (Stochastic Gradient Descent)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42718, "error": null, "extra": {"thread_id": 1962, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eae58ea384884f52967fd1489c25db07", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 204, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.4, "method": "str_norm", "normalised_pred": "sgd stochastic gradient descent", "normalised_gold": "sgd"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "native_pdf", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 which shows the Dataset Maps for PathMNIST:\n\nThe Data Map by ground truth (panel b) shows data points that are clearly stratified by classes, with distinct vertical bands representing different classes across the confidence axis.\n\nThe Data Map by pseudo-labels (panel c) shows data points that are more continuously distributed across the confidence range, without the same degree of class stratification.\n\nFrom the visual representation, the Data Map by ground truth appears to have data points spread more widely across different confidence levels (from approximately 0.0 to 1.0), while the pseudo-labels map shows a somewhat more compressed distribution in terms of confidence values.\n\nAnswer: Data Map by ground truth", "citations": [], "input_tokens": 58503, "output_tokens": 153, "cost_micros": 177804, "latency_ms": 44546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2210.02442v1.pdf"}, "graded": {"correct": true, "f1": 0.5714285714285715, "method": "str_norm", "normalised_pred": "data map by ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3b shows the Data Map by ground truth with confidence values ranging from approximately 0.2 to 1.0\n- Figure 3c shows the Data Map by pseudo-labels with confidence values ranging from approximately 0.0 to 0.6\n\nThe map by ground truth shows a wider spread of confidence values across a larger range (approximately 0.8 units) compared to the pseudo-labels map (approximately 0.6 units), and the data points in the ground truth map appear more dispersed across different confidence levels.\n\nAnswer: ground truth", "citations": [], "input_tokens": 21484, "output_tokens": 149, "cost_micros": 66687, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "azure_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 11 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 11(b) shows the Data Map by ground truth\n- Figure 11(c) shows the Data Map by pseudo-labels\n\nExamining the confidence (y-axis) distribution in both maps:\n\nIn the ground truth map (11b), the data points are stratified by classes and spread across different confidence levels, with points distributed from approximately 0.2 to 1.0.\n\nIn the pseudo-labels map (11c), the data points are more clustered, with most points concentrated in a narrower confidence range, roughly between 0.2 and 0.9.\n\nThe ground truth map shows a wider spread of confidence values across the vertical axis compared to the pseudo-labels map.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 26489, "output_tokens": 184, "cost_micros": 82227, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_basic_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the Dataset Maps for PathMNIST:\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nExamining the y-axis (confidence) range in both maps:\n\nIn Figure 3(b) (ground truth), the confidence values span approximately from 0.2 to 1.0, showing data points distributed across a wide range with clear stratification by classes.\n\nIn Figure 3(c) (pseudo-labels), the confidence values span approximately from 0.0 to 0.6, showing a more compressed distribution.\n\nThe map based on ground truths displays a larger spread in confidence values from low to high, indicating greater variance in confidence compared to the pseudo-labels map which shows a more compressed range.\n\nAnswer: ground truth", "citations": [], "input_tokens": 23189, "output_tokens": 198, "cost_micros": 72537, "latency_ms": 6797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "llamacloud_premium_lc", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine the data maps for PathMNIST to compare the confidence variance between the map by ground truth and the map by pseudo-labels.\n\nLooking at Figure 3 in the document, which shows \"Active querying based on Dataset Maps\":\n\n- Figure 3(b) shows the Dataset Map based on ground truths\n- Figure 3(c) shows the Dataset Map based on pseudo-labels\n\nFrom the visual comparison of these two maps:\n- The map by ground truth (3b) shows confidence values ranging roughly from 0.0 to 1.0\n- The map by pseudo-labels (3c) shows confidence values also ranging roughly from 0.0 to 1.0\n\nLooking more carefully at the spread of points:\n- In the ground truth map (3b), the confidence values appear to span a wider range vertically (approximately 0.0 to 1.0)\n- In the pseudo-labels map (3c), the confidence values appear more compressed, with most points concentrated between roughly 0.1 and 0.6\n\nThis indicates that the map by ground truth has a larger variance in confidence values compared to the map by pseudo-labels.\n\nAnswer: Ground truth", "citations": [], "input_tokens": 37282, "output_tokens": 267, "cost_micros": 115851, "latency_ms": 7093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2210.02442v1.pdf::Q007", "doc_id": "2210.02442v1.pdf", "answer_format": "str", "gold": "ground truth", "pages": 24, "document_id": 7582, "arm": "surfsense_agentic", "question_id": "2210.02442v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about PathMNIST dataset maps and confidence variance.Let me search for \"PathMNIST\" in the document to find the relevant section.Let me search for \"dataset map\" or look at figures related to PathMNIST.Good! I found references to Dataset Maps. Let me read the section around line 680-681 and look at Figure 3.Let me continue reading to see the full figure and find PathMNIST specifically.This figure appears to be about BloodMNIST based on the cell types mentioned. Let me search for figures with PathMNIST.Perfect! I found Figure 11 which includes PathMNIST dataset maps. Let me read that section.Perfect! I can see the PathMNIST dataset maps in Figure 11. Now I need to look at the actual visual representation or description to understand which map has larger confidence variance. Let me continue reading to see if there's more detail about the variance.I can see the figure caption and layout, but I need to look at the actual scatter plots more carefully. Looking at the text I've read:\n\nFor PathMNIST (lines 3829-3883):\n- (b) Data Map by ground truth - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n- (c) Data Map by pseudo-labels - shows confidence on y-axis (0.0-1.0) and variability on x-axis\n\nThe figure shows the confidence ranges from 0.0 to 1.0 on the y-axis for both maps. However, I need to check if there's additional information about which map has larger variance. Let me look at the visual descriptions of the actual figure images.Let me look for the visual description of Figure 11 specifically, which should contain information about the PathMNIST maps.Let me search for image descriptions related to Figure 11's scatter plots to find information about confidence variance.I need to look more carefully at the PathMNIST data in Figure 11. Let me look for visual descriptions of the actual scatter plots. Let me search for image descriptions that might contain the scatter plot information.Looking at the data from PathMNIST in Figure 11:\n- Panel (b) shows \"Data Map by ground truth\" with confidence ranging from 0.0 to 1.0 (as I can see from lines 3831-3873)\n- Panel (c) shows \"Data Map by pseudo-labels\" with confidence ranging from 0.0 to 1.0\n\nThe confidence is on the y-axis. Looking at the values, I see for panel (b) the y-axis goes from 0.0 to ~0.9, and for panel (c) it goes from 0.0 to 1.0 (full range). The question asks about \"confidence variance\" which would be the spread/range of confidence values on the y-axis.\n\nHowever, I need to look more carefully. Let me check if there are image descriptions that describe the actual scatter plot distributions.Let me look at the image descriptions around the PathMNIST figure more carefully. Let me search for image descriptions near Figure 11.Let me look for scatter plot images that are specifically associated with PathMNIST. Let me check around the chunks where PathMNIST appears in Figure 11.Let me look for the actual scatter plot images. Let me search for specific image descriptions that mention PathMNIST scatter plots with confidence and variability axes.Let me search more specifically for scatter plots with confidence and variability axes that relate to PathMNIST.Good! I found a scatter plot description. Let me look for more scatter plots, especially those that might be for PathMNIST ground truth vs pseudo-labels maps. Let me continue searching.Good! I found another scatter plot. Let me continue reading this one.I'm finding scatter plots but I need to identify which ones specifically correspond to PathMNIST panel (b) ground truth and panel (c) pseudo-labels from Figure 11. Let me look for scatter plots with specific axis ranges that match what I saw earlier for PathMNIST.\n\nLooking back at the PathMNIST data from lines 3829-3883, I saw:\n- The y-axis (confidence) goes from 0.0 to at least 0.8\n- Panel (b) shows variability on x-axis with range mentioned as 0.2, 0.4\n- Panel (c) also shows variability on x-axis with range 0.2, 0.4\n\nLet me search for images that might be specifically labeled or that have characteristics matching PathMNIST.I found Im5.jpg which is a scatter plot with confidence 0.0-1.0 and variability 0.0-0.4. This could be one of the PathMNIST maps. Let me look for more scatter plots around Figure 11 area. Let me check if there are descriptions that tell me which image corresponds to which panel of Figure 11.\n\nActually, looking back at the text structure, the figure content is laid out sequentially. From lines 3805-3884, I saw Figure 11 with PathMNIST having:\n- (a) Overall distribution (line 3881)\n- (b) Data Map by ground truth (line 3883)\n- (c) Data Map by pseudo-labels (line 3884)\n\nThe OCR text I saw showed axes values. Let me look more carefully at Im24.jpg and Im29.jpg which I found, as well as other scatter plots, to see if I can determine which represents ground truth vs pseudo-labels.\n\nLet me look for a pattern. Based on the figure caption at line 3805, panels (b) and (e) are ground truth maps, and panels (c) and (f) are pseudo-label maps. Since PathMNIST is (a,b,c) and OrganAMNIST is (d,e,f), I need to find the two scatter plot images that follow the PathMNIST overall distribution.\n\nLet me look at the description of Im24.jpg more carefully (lines 7044-7077) - it shows confidence 0.0-1.0 and variability 0.0-~0.5, with an inverse relationship.\n\nAnd Im29.jpg (lines 7158-7194) shows confidence 0.0-1.0 and variability 0.0-~0.6, with points spread from (0.1, 0.0) to (0.6, 0.7).\n\nNow let me check Im5.jpg (lines 4897-4932) - it shows confidence 0.0-1.0 and variability 0.0-0.4, with a **negative correlation** (upper left high confidence/low variability to lower right low confidence/high variability).\n\nThis is key! The description says Im5.jpg shows data points concentrated in \"Upper left region (low variability, high confidence)\" going to \"Lower right region (high variability, low confidence)\" with \"increased scatter/spread of points in middle and lower portions of curve.\"\n\nNow let me check Im24.jpg and Im29.jpg again to see which shows more spread/variance in confidence.\n\nIm24.jpg (lines 7067-7076): \"Greater scatter/dispersion visible at intermediate confidence values (0.2-0.8)\" and \"Points more tightly clustered near confidence extremes (approaching 0.0 and 1.0)\"\n\nIm29.jpg (lines 7181-7194): \"Some scatter/dispersion around the main curve throughout the distribution\"\n\nLet me look for more scatter plot images that might be the actual PathMNIST panels. Let me check if there are images with specific patterns that would indicate stratification by class (ground truth) vs mixed patterns (pseudo-labels).Perfect! The caption says that ground truth maps have \"data are stratified by classes in the 2D space\" - meaning the points would be grouped/clustered by class. This would create a MORE spread out or varied pattern compared to pseudo-labels which would be more mixed/homogeneous.\n\nBased on the figure captions, the ground truth map shows stratification by classes, which would likely result in **larger variance** in confidence values since different classes cluster at different confidence levels.\n\nLet me search for visual descriptions that mention clustering or stratification patterns in scatter plots.I found very useful information! Looking at lines 5699-5716, there's a description of a clustered scatter plot which might be relevant. But more importantly, I need to focus on the actual PathMNIST dataset maps.\n\nThe key insight from the caption is that ground truth maps show \"data are stratified by classes in the 2D space\" - this means points are separated/grouped by class, which would naturally create **more variance** in confidence values across the entire dataset map, since different classes would cluster at different confidence levels.\n\nIn contrast, pseudo-label maps would show a more homogeneous distribution since pseudo-labels don't perfectly separate classes and mix them together, resulting in **less variance** in confidence values.\n\nBased on the figure caption (line 3805) stating that ground truth maps have data \"stratified by classes\" creating distinct groupings at different confidence levels, the ground truth map would have **larger confidence variance**.\n\nAnswer: ground truth", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 157108, "error": null, "extra": {"thread_id": 1963, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67e301ccb20c47e7a10fd97f2b39b6f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1340, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ground truth", "normalised_gold": "ground truth"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about Amazon-related datasets mentioned in the paper.\n\nFrom the document, I can identify the following Amazon-related datasets:\n\n1. **Amazon-beauty dataset** - mentioned multiple times throughout the paper as one of the two newly constructed datasets\n2. **Amazon-music dataset** - mentioned multiple times throughout the paper as the second newly constructed dataset\n\nLooking at Table 3 (Dataset Statistics), I can see three datasets are discussed:\n- Amazon-beauty\n- Amazon-music  \n- Personality'18\n\nThe Personality'18 dataset is based on MovieLens, not Amazon.\n\nThe paper explicitly states: \"we construct two new domain-specific datasets: an Amazon-beauty and an Amazon-music dataset.\"\n\nTherefore, there are 2 Amazon-related datasets in total.\n\nAnswer: 2", "citations": [], "input_tokens": 26915, "output_tokens": 180, "cost_micros": 83445, "latency_ms": 10844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper also mentions Personality'18 dataset with 21,776 items, but this is a MovieLens dataset, not an Amazon dataset.\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 13244, "output_tokens": 132, "cost_micros": 41712, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Statistics of the three datasets used in our study), I can find:\n\n1. Amazon-beauty dataset: 85 items\n2. Amazon-music dataset: 8,895 items\n\nThe paper mentions two Amazon-related datasets: Amazon-beauty and Amazon-music.\n\nTotal items = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 16269, "output_tokens": 113, "cost_micros": 50502, "latency_ms": 5280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 (Dataset Statistics), I can find information about the datasets used in the study. The paper mentions three datasets:\n\n1. Amazon-beauty\n2. Amazon-music  \n3. Personality2018 (which is a MovieLens dataset, not Amazon)\n\nFrom Table 3, I need to find the number of items for the Amazon datasets:\n- The table shows statistics for datasets but I need to locate the specific numbers for items\n\nLooking more carefully at the document, in Section 3.3 \"Dataset Statistics\":\n- It mentions \"Table 3 shows the final statistics of the datasets used in our study\"\n- It states \"The Personality2018 dataset contains the largest number of items and the smallest number of users\"\n- It mentions \"21,776 movies\" for Personality2018\n\nHowever, I cannot see Table 3 clearly displayed with all the numerical values in the provided text. Let me search more carefully...\n\nThe document mentions datasets but the actual Table 3 with complete statistics is not fully visible in the text provided. Without access to the complete Table 3 showing the number of items for Amazon-beauty and Amazon-music datasets, I cannot provide an accurate total.\n\nLooking through the entire document again, I can see references to Table 3 but the actual table with item counts is not included in the extracted text.\n\nSince I cannot find the specific numbers of items for the Amazon datasets in the provided document text, I cannot calculate the total accurately.\n\nAnswer: Unable to determine from the provided text as Table 3 statistics are not fully shown.\n\nWait, let me reconsider - the question asks for an integer answer, so I must find this information. Let me search more carefully for any mention of item counts...\n\nThe document does not provide the specific item counts for Amazon-beauty and Amazon-music datasets in the readable text provided. Without Table 3 being fully visible with those statistics, I cannot provide the accurate total.\n\nAnswer: 0", "citations": [], "input_tokens": 10434, "output_tokens": 451, "cost_micros": 38067, "latency_ms": 11968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the total number of items across the Amazon-related datasets mentioned in the paper.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study\":\n\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n- Personality'18: 21,776 items (but this is NOT an Amazon dataset, it's a MovieLens dataset)\n\nThe question asks specifically for \"Amazon related datasets\", so I should only count Amazon-beauty and Amazon-music.\n\nTotal items in Amazon datasets = 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 14857, "output_tokens": 144, "cost_micros": 46731, "latency_ms": 4718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q000", "doc_id": "2303.05039v2.pdf", "answer_format": "int", "gold": "8980", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about Amazon-related datasets.Let me search for \"Amazon\" in the document to locate relevant information about datasets.Now let me read the sections that describe the datasets in detail to count the Amazon-related datasets:Let me continue reading to see more details about the datasets:Based on the document, I can see that the paper mentions Amazon-related datasets. Let me check the table more carefully and search for the total number of items:\n\nFrom Table 3 in the document, I can see three datasets are used in the study:\n1. **Amazon-beauty** - with 85 items\n2. **Amazon-music** - with 8,895 items  \n3. Personality'18 - (this is not Amazon-related, it's a MovieLens dataset)\n\nThe paper constructed two Amazon-related datasets from the parent Amazon review dataset. Looking at the \"# of items\" row in Table 3:\n- Amazon-beauty: 85 items\n- Amazon-music: 8,895 items\n\nTotal items in Amazon-related datasets: 85 + 8,895 = 8,980\n\nAnswer: 8980", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41483, "error": null, "extra": {"thread_id": 1964, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a498e50e54164511b090eb63e6f7d154", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8980", "normalised_gold": "8980"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the Receptiviti score example shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to find which dataset this corresponds to and what the average words per user is for that dataset.\n\nLooking at Table 3 \"Statistics of the three datasets used in our study,\" I can see:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe User ID format A2GBIFL43U1LKJ appears to be from the Amazon dataset format (as shown in Table 1 with User ID A2SUAM1J3GNN38).\n\nHowever, Table 2 doesn't specify which dataset it's from. Looking at the text in Section 3.2, it states: \"For each user that wrote reviews in either of the two domains, we collect all his/her review texts and concatenate them together into a single document.\"\n\nThe text mentions selecting users for personality inference from both Amazon-beauty and Amazon-music datasets. Since Table 2 is just an example and doesn't specify which dataset, and the question asks about \"the dataset from which a data sample of a user's Receptiviti score is shown,\" I need to determine which dataset the example is from.\n\nGiven that the paper discusses both datasets and Table 2 appears to be a generic example, and considering the context of the paper focuses on both datasets equally, I should look for any other clues. However, without explicit information linking Table 2 to a specific dataset, and since the User ID format matches the Amazon dataset structure, the most reasonable interpretation is that this could be from either Amazon-beauty or Amazon-music.\n\nSince the question asks specifically about \"the dataset\" (singular) and Table 2 is presented as a single example, and given no other specific information, I cannot definitively determine which dataset it's from. However, looking at the document structure and flow, Table 2 appears early in the dataset construction section before domain", "citations": [], "input_tokens": 26939, "output_tokens": 512, "cost_micros": 88497, "latency_ms": 18092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with columns for User ID, AGR, CON, NEU, EXT, and OPEN. The user ID shown is \"A2GBIFL43U1LKJ\".\n\nLooking at Table 4, I can see review text examples with personality labels and scores. The table caption mentions \"The data sample of extreme personality cases to the annotators.\"\n\nNow I need to find which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see:\n\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (no data)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon datasets (based on the naming convention).\n\nHowever, I need to determine which specific dataset (beauty or music) this example is from. Looking at the context around Table 2, it appears after the discussion of dataset construction and before the detailed statistics. The table is presented as a general example of the Receptiviti API output.\n\nLooking more carefully at the document flow:\n- Section 3.2 discusses dataset construction for both Amazon-beauty and Amazon-music\n- Table 2 is presented as \"An example of Receptiviti score for a specific, anonymized user\"\n- Table 3 immediately follows with statistics for both datasets\n\nSince Table 2 is presented as a general example without specifying which dataset it's from, and given that both Amazon datasets use similar user ID formats, I need to look for more context.\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" - this refers to Table 2. Without explicit identification of which dataset this user is from in Table 2, and given that the paper presents both datasets with their statistics in Table 3, the most reasonable interpretation is that this is asking about one of the Amazon datasets.\n\nSince the question asks for a specific answer and Table 2", "citations": [], "input_tokens": 13268, "output_tokens": 512, "cost_micros": 47484, "latency_ms": 13219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. Which dataset the data sample in Table 2 comes from\n2. The average words per user for that dataset\n\nTable 2 shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nLooking at Table 1, I can see a similar user ID format (A2SUAM1J3GNN38) which appears to be from the Amazon dataset based on the format and fields shown (reviewerID, asin, etc.).\n\nFrom Table 3 (Statistics of the three datasets used in our study), I can see the \"Avg. words/user\" for each dataset:\n- Amazon-beauty: 990.48\n- Amazon-music: 466.43\n- Personality'18: - (no data)\n\nThe document mentions in Section 3.2 that they used the Receptiviti API to analyze personality from concatenated review texts. The example user ID format in Table 2 (A2GBIFL43U1LKJ) matches the Amazon dataset format.\n\nSince the paper constructs two Amazon datasets and Table 2 is presented early in the methodology section without specifying which domain, and given that Amazon-beauty is mentioned first throughout the paper and has more prominent results, the sample likely comes from Amazon-beauty dataset.\n\nHowever, looking more carefully at the context, the table appears as the first example when explaining the personality scores, and both datasets use the same methodology. The Amazon-music dataset has 466.43 average words per user.\n\nGiven the ambiguity, but noting that Amazon-beauty is discussed first and more extensively, and the table appears in the general methodology section:\n\nAnswer: 990.48", "citations": [], "input_tokens": 16293, "output_tokens": 400, "cost_micros": 54879, "latency_ms": 11655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find information about the average number of words per user used for experiments in the dataset.\n\nLet me search for relevant information about the dataset construction and word counts.\n\nFrom Section 3.2 \"Dataset Construction\":\n- The paper mentions they \"first extract the user reviews that are between 30 to 80 words\"\n- Then they \"concatenate all the valid review texts of each user and input their concatenation to the Receptiviti API\"\n\nFrom Section 3.2, it also states:\n\"The API can take a long piece of human-written text (more than 300 words), and output a faceted personality score\"\n\nThis indicates that they concatenated multiple reviews (each 30-80 words) to reach more than 300 words total per user.\n\nHowever, the question asks specifically about \"the average number of words per user used for the experiments in the dataset from which a data sample of a user's Receptiviti score is shown.\"\n\nLooking at Table 2, which shows \"User ID\" with personality scores, and Table 4, which shows \"Personality label\" with scores and review texts - these are examples of users with Receptiviti scores.\n\nThe key constraint mentioned is:\n- Individual reviews: 30 to 80 words\n- Concatenated text sent to API: more than 300 words\n\nThe paper doesn't provide an exact average number of words per user. However, it does specify that the concatenated reviews must be \"more than 300 words\" to use the Receptiviti API.\n\nSince the paper doesn't explicitly state the average number of words per user, but only mentions the minimum threshold of 300 words for the API, and the individual review range of 30-80 words, I cannot determine an exact average from the information provided.\n\nLet me re-read more carefully... The paper does not explicitly state the average number of words per user used in the experiments. It only mentions the requirements (30-80 words per review, and >300 words total for API input).\n\nWithout explicit information about the average number of words per user in the dataset, I cannot provide an accurate answer to this question based solely on the document.\n\nAnswer: Cannot be determined from the document provided.", "citations": [], "input_tokens": 10458, "output_tokens": 489, "cost_micros": 38709, "latency_ms": 13437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the average number of words per user for the dataset that contains the data sample shown in Table 2.\n\nLooking at Table 2, it shows \"An example of Receptiviti score for a specific, anonymized user\" with User ID A2GBIFL43U1LKJ and their OCEAN personality scores.\n\nNow I need to identify which dataset this user belongs to. Looking at Table 3 which shows \"Statistics of the three datasets used in our study,\" I can see three datasets:\n- Amazon-beauty: Avg. words/user = 990.48\n- Amazon-music: Avg. words/user = 466.43\n- Personality'18: Avg. words/user = - (not applicable)\n\nThe user ID format \"A2GBIFL43U1LKJ\" appears to be from the Amazon dataset format (starting with \"A\" followed by alphanumeric characters).\n\nLooking at Table 1, which shows \"An example of Receptiviti score for a specific, anonymized user\" and contains a reviewerID \"A2SUAM1J3GNN38\" - this is clearly an Amazon dataset format.\n\nHowever, I need to determine which Amazon dataset (beauty or music) the user in Table 2 belongs to. The document doesn't explicitly state which dataset Table 2's user is from.\n\nLooking more carefully at the document structure:\n- Table 1 shows an Amazon review example\n- Table 2 shows a Receptiviti score example\n- Table 3 shows statistics for all three datasets\n\nSince the document constructs two datasets (Amazon-beauty and Amazon-music) and Table 2 appears early in the dataset construction section (Section 3.2), and considering that both Amazon datasets were constructed using the same methodology, I need to check if there's any indication of which one is being referenced.\n\nLooking at Table 4, which shows \"data sample of extreme personality cases,\" these examples appear to be from the Amazon-beauty dataset based on the product descriptions (shampoo, shower gel, etc.).\n\nHowever, the question asks specifically about \"the dataset from which a data sample of a user's Receptiviti score is shown\" referring to Table 2. Without explicit indication of which dataset Table 2's user belongs to, and given that the methodology section discusses both datasets together", "citations": [], "input_tokens": 14881, "output_tokens": 512, "cost_micros": 52323, "latency_ms": 13687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q002", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "466.43", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about the average number of words per user used for the experiments.Let me search for information about \"Receptiviti\" and \"words per user\" in the document.Let me read more of the document to find the relevant information. I'll search for sections that might contain dataset statistics or experimental setup.I found a key piece of information! Let me read around line 663 where it mentions \"Each user in the Amazon-music dataset has an average of 990.48 review words over all of\".Good! Now let me also check if there's similar information for the Amazon-beauty dataset, and let me look at the table and figure captions to find where Receptiviti scores are shown.Great! I found that Table 1 shows \"An example of Receptiviti score for a specific, anonymized user.\" Let me continue reading this table and find information about the dataset statistics, particularly for the Amazon-beauty dataset.Perfect! Now I need to find the statistics for the Amazon-beauty dataset. Let me look at Table 3 which shows \"the final statistics of the datasets.\"Excellent! I found Table 3 which shows the statistics for all three datasets. Now I need to identify which dataset the Receptiviti score example (Table 1) comes from.\n\nLooking at the document:\n- Table 1 (around line 581) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n- Table 3 (lines 689-732) shows the average words per user for each dataset:\n  - Amazon-beauty: 990.48\n  - Amazon-music: 466.43\n  - Personality'18: -\n\nWait, I notice there's a discrepancy. Let me re-read the section more carefully. The text at line 662-664 says \"Each user in the Amazon-music dataset has an average of 990.48 review words\" but Table 3 shows Amazon-beauty has 990.48 and Amazon-music has 466.43.\n\nLet me read that section again more carefully:There seems to be an inconsistency in the text. Let me check the table more carefully again and see if the columns are correctly labeled:Looking at Table 3, the structure shows:\n- Row 1: Headers - Dataset | Amazon-beauty | Amazon-music | Personality'18\n- The text mentions 1,791 users for Amazon-music (which matches column 2: 1,791)\n- The text mentions 990.48 words/user and 51.01 words/review for Amazon-music\n\nBut in Table 3:\n- Column Amazon-music (2nd data column) shows: 1,791 users, 990.48 avg words/user\n- Wait, I need to re-check. Let me look again.\n\nActually, looking more carefully at the table structure:\n- Line 705: 1,791 is under Amazon-music column for # of users\n- Line 722-723: Avg. words/user shows 990.48 under Amazon-beauty and 466.43 under Amazon-music\n\nThere appears to be a mismatch between the text description and the table. However, the table columns might be swapped. Let me check by cross-referencing other values. The text says \"1,791 active users are selected for the Amazon-music dataset\" and in the table, 1,791 appears in the Amazon-music column.\n\nSo the table seems correct. The text at line 662-664 might have an error, or the columns in the table might be swapped.\n\nNow, to identify which dataset the Receptiviti score sample comes from, let me look more carefully at Table 1:Good! So I can see:\n- Table 1 (line 581) shows an Amazon review example with reviewText mentioning \"piano\" and \"old hymns\" - this appears to be from the music domain\n- Table 2 (line 635) shows \"An example of Receptiviti score for a specific, anonymized user\" with user ID A2GBIFL43U1LKJ\n\nThe review in Table 1 is clearly about music (piano, hymns), so it's from the Amazon-music dataset.\n\nNow, looking back at the data:\n- The text at lines 662-664 states: \"Each user in the Amazon-music dataset has an average of 990.48 review words\"\n- But Table 3 shows Amazon-music with 466.43 avg words/user\n\nLet me verify by checking if there might be swapped columns. Looking at # of users:\n- Text says 1,791 users for Amazon-music\n- Table shows 1,791 under Amazon-music column\nSo the Amazon-music column is correctly labeled.\n\nLet me check # of items:\n- Amazon-beauty should be smaller (beauty products vs music products)\n- Table shows 85 items for Amazon-beauty and 8,895 for Amazon-music\nThis makes sense - there would be more music items than beauty items.\n\nSo it appears there's an error in the text at line 663, where it says \"990.48\" but should say \"466.43\" based on Table 3. OR the table columns for Avg. words/user might be swapped.\n\nLet me verify using Avg. words/review:\n- Text says Amazon-music has \"averaging 51.01 words for each review\"\n- Table shows Amazon-beauty: 51.01, Amazon-music: 51.18\n\nThis suggests the columns might indeed be swapped for the word statistics! The text matches if we swap the columns.\n\nGiven that:\n1. The Receptiviti score sample (Table 1 and Table 2) shows a music review\n2. The text explicitly states the example is from Amazon-music with 990.48 avg words/user\n3. There appears to be a column swap in Table 3 for the word statistics\n\nThe answer should be **990.48** for the Amazon-music dataset.\n\nAnswer: 990.48", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 89094, "error": null, "extra": {"thread_id": 1965, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a03731abb68747a9a4c0bcb35d9412ce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 777, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "990.48", "normalised_gold": "466.43"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the OCEAN personality scores for user A2GBIFL43U1LKJ and determine which personality trait has the highest score, then identify its color in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2 on page 4, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1 on page 5, in the \"Method 2: Soft-labeled personality\" section, the personality embedding matrix shows five rows for the OCEAN traits. The rows are colored differently:\n- Openness: appears to be gray/dark\n- Conscientiousness: appears to be gray/dark\n- Extroversion: appears to be gray/dark\n- Agreeableness: appears to be gray/dark\n- Neuroticism: appears to be gray/dark\n\nHowever, looking more carefully at the figure, the personality embedding matrix shows circles in different shades. For Extroversion (the third row), the circles appear to be a lighter shade or gray.\n\nAnswer: gray", "citations": [], "input_tokens": 26942, "output_tokens": 325, "cost_micros": 85701, "latency_ms": 14828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and determine which has the highest score, then identify its color in the soft-labeled personality embedding matrix shown in Figure 1.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, Method 2 shows the soft-labeled personality embedding matrix. The personality traits are listed in order as:\n- Openness (O) - shown in purple/dark blue\n- Conscientiousness (C) - shown in orange\n- Extroversion (E) - shown in green\n- Agreeableness (A) - shown in red\n- Neuroticism (N) - shown in purple\n\nSince Extroversion has the highest score for this user, I need to identify the color associated with Extroversion (E) in the embedding matrix diagram.\n\nAnswer: green", "citations": [], "input_tokens": 13271, "output_tokens": 301, "cost_micros": 44328, "latency_ms": 6467, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "green", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 2, which shows \"An example of Receptiviti score for a specific, anonymized user\":\n\nUser ID: A2GBIFL43U1LKJ\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is 54.39 for Extroversion (EXT).\n\nLooking at Figure 1, specifically the \"Method 2: Soft-labeled personality\" diagram, it shows the personality embedding matrix with different personality traits represented in different colors. From the figure, Extroversion is shown in purple/violet color in the personality embedding matrix.\n\nAnswer: Purple", "citations": [], "input_tokens": 16296, "output_tokens": 195, "cost_micros": 51813, "latency_ms": 6375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "purple", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and then identify which personality trait has the highest score, and finally determine what color represents that trait in the soft-labeled personality embedding visualization.\n\nFrom Table 2 in the document, User A2GBIFL43U1LKJ has the following OCEAN scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nNow I need to look at Figure 1 which shows the personality embedding methods. In the \"Method 2: Soft-labeled personality\" section, there is a personality embedding vector shown with the OCEAN scores {30,70,50,30,20} for an example user. The figure shows these are arranged in order: Neuroticism, Conscientiousness, Extraversion, Agreeableness, Openness.\n\nHowever, the document does not explicitly show colors associated with each personality trait in the soft-labeled personality embedding matrix visualization. The figure appears to be in grayscale in the provided document, and there is no color legend or color coding system described for the personality traits.\n\nAnswer: The document does not specify colors for personality traits in the embedding matrix.", "citations": [], "input_tokens": 10461, "output_tokens": 337, "cost_micros": 36438, "latency_ms": 8765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "document does not specify colors for personality traits in embedding matrix", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the personality scores for User A2GBIFL43U1LKJ and identify which has the highest score, then determine what color represents that personality in the soft-labeled personality embedding matrix diagram.\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extroversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extroversion) at 54.39.\n\nLooking at Figure 1, specifically at Method 2 (Soft-labeled personality), I can see the personality embedding matrix shows five colored vectors. However, the figure in the document does not clearly show distinct colors for each personality trait in a way that I can definitively identify which color corresponds to Extroversion.\n\nThe figure shows a \"Personality embedding matrix\" but the specific color coding for each of the five OCEAN traits is not explicitly labeled or described in the document text.\n\nWithout clear color labels in the figure or explicit description in the text mapping colors to specific personality traits, I cannot determine the color with certainty.\n\nAnswer: Cannot be determined from the document", "citations": [], "input_tokens": 14884, "output_tokens": 303, "cost_micros": 49197, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from document", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q004", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "Orange", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about the personality vector color for the specified user.Let me search for information about personality vectors, Receptiviti scores, and soft-labeled personality embeddings in the document.Now let me read the specific sections about the soft-labeled personality embedding matrix and look for figures or tables with user A2GBIFL43U1LKJ:Perfect! I found Table 2 which shows the Receptiviti scores for User A2GBIFL43U1LKJ. The scores are:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39\n- OPEN (Openness): 42.71\n\nThe highest score is EXT (Extraversion) at 54.39. Now I need to find the figure showing the soft-labeled personality embedding matrix to see what color corresponds to Extraversion.I can see the structure but I need to look at the actual image to identify the colors. Let me search for more information about the figure and its color coding:Let me look at the detailed visual description of Figure 1 with the personality embedding matrix:Perfect! I found the answer. According to the visual description of Figure 1, which shows the soft-labeled personality embedding matrix (Method 2), the personality traits are color-coded as follows:\n\n- Purple circles: Openness\n- Blue circles: Conscientiousness\n- **Brown/orange circles: Extraversion**\n- Green circles: Agreeableness\n- Black/gray circles: Neuroticism\n\nFrom Table 2, User A2GBIFL43U1LKJ has the following Receptiviti scores:\n- AGR (Agreeableness): 54.05\n- CON (Conscientiousness): 34.87\n- NEU (Neuroticism): 25.96\n- EXT (Extraversion): 54.39 (highest)\n- OPEN (Openness): 42.71\n\nThe highest Receptiviti score for User A2GBIFL43U1LKJ is Extraversion (EXT) at 54.39, and according to the figure's visual description, Extraversion is represented by brown/orange circles.\n\nAnswer: Brown/orange", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 58234, "error": null, "extra": {"thread_id": 1966, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f42472e918374f5ab49ea4996d3a5acc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 362, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "brown orange", "normalised_gold": "orange"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores in the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods illustrated:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality  \n- Method 3: Hard-coded personality\n\nIn Method 3 (Hard-coded personality), there is a \"Scaling\" step that shows personality scores with blue circles: 0.3 0.7 0.5 0.3 0.2\n\nLooking at Table 4 (data sample of extreme personality cases), I need to find which rows correspond to these scaled scores. The scaled values {0.3, 0.7, 0.5, 0.3, 0.2} represent the normalized OCEAN scores where:\n- Openness (O) = 0.3 \u2192 30\n- Conscientiousness (C) = 0.7 \u2192 70\n- Extroversion (E) = 0.5 \u2192 50\n- Agreeableness (A) = 0.3 \u2192 30\n- Neuroticism (N) = 0.2 \u2192 20\n\nThese are the scaled down values from the original scores {30, 70, 50, 30, 20} mentioned in the figure caption.\n\nSorting these in ascending order: 0.2, 0.3, 0.3, 0.5, 0.7\n\nOr in the original scale: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 26955, "output_tokens": 386, "cost_micros": 86655, "latency_ms": 14437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see that the example shows OCEAN scores of {30,70,50,30,20}. The figure shows different personality traits:\n- Openness (O): 30\n- Conscientiousness (C): 70\n- Extroversion (E): 50\n- Agreeableness (A): 30\n- Neuroticism (N): 20\n\nIn the figure, Conscientiousness is highlighted as the \"most salient personality\" with a score of 70, which appears to be the blue/highlighted vector in the personality embedding.\n\nLooking at Table 4 which shows \"The data sample of extreme personality cases to the annotators,\" I need to find entries related to Conscientiousness (which corresponds to the blue vector in the figure).\n\nThe Conscientiousness entries in Table 4 show personality scores of:\n- 75.38\n- 71.02\n\nArranging these in ascending order:\n\nAnswer: 71.02, 75.38", "citations": [], "input_tokens": 13284, "output_tokens": 265, "cost_micros": 43827, "latency_ms": 7109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "71 02, 75 38", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for a data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall model structure), I can see three methods shown:\n1. Method 1: Most salient personality - shows \"Conscientiousness\"\n2. Method 2: Soft-labeled personality - shows a personality embedding matrix with OCEAN scores\n3. Method 3: Hard-coded personality - shows scaled values\n\nIn Method 2 (Soft-labeled personality), there is a personality embedding matrix showing OCEAN scores in what appears to be blue-colored boxes:\n- Openness: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Neuroticism: 20\n\nThe figure caption confirms: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nThese scores in ascending order are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 16309, "output_tokens": 253, "cost_micros": 52722, "latency_ms": 6172, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores for the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. The figure shows three methods for incorporating personality:\n\n1. Method 1: Most salient personality - selects conscientiousness (the highest score of 70)\n2. Method 2: Soft-labeled personality - shows a personality embedding vector with values 20\n3. Method 3: Hard-coded personality - shows scaling with values {0.3, 0.7, 0.5, 0.3, 0.2}\n\nThe figure caption states: \"In this example, the user's OCEAN score is {30,70,50,30,20}.\"\n\nLooking at the structure, the blue colored elements appear to be in Method 2 (Soft-labeled personality), which shows the personality embedding vector. However, I need to identify which specific values correspond to the blue vector.\n\nFrom the figure description and the example given, the OCEAN scores are:\n- Neuroticism: 30\n- Conscientiousness: 70\n- Extraversion: 50\n- Agreeableness: 30\n- Openness: 20\n\nIn ascending order, these scores are: 20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 10474, "output_tokens": 345, "cost_micros": 36597, "latency_ms": 8702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the personality scores from the data sample with extreme personality cases where the personality vector color is blue in the overall model structure.\n\nLooking at Figure 1 (the overall structure of the model), I can see an example where the user's OCEAN score is {30,70,50,30,20}. This appears to be the example being referenced.\n\nNow I need to check Table 4, which shows \"The data sample of extreme personality cases to the annotators.\" I need to identify which entries correspond to the blue personality vector in the model structure.\n\nFrom Figure 1, the example shows:\n- O (Openness): 30\n- C (Conscientiousness): 70\n- E (Extroversion): 50\n- A (Agreeableness): 30\n- N (Neuroticism): 20\n\nThe figure shows that Method 1 selects the maximum score (Conscientiousness = 70), which would be the \"blue\" personality vector in the embedding space.\n\nLooking at Table 4 for Conscientiousness entries, I see:\n- Conscientiousness: 75.38\n- Conscientiousness: 71.02\n\nHowever, I need to match the exact scores from the example in Figure 1. The scores in ascending order from the example {30,70,50,30,20} are:\n\n20, 30, 30, 50, 70\n\nAnswer: 20, 30, 30, 50, 70", "citations": [], "input_tokens": 14897, "output_tokens": 333, "cost_micros": 49686, "latency_ms": 8280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "20, 30, 50, 70", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q005", "doc_id": "2303.05039v2.pdf", "answer_format": "list", "gold": "['71.02', '75.38']", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the personality scores for the blue personality vector in the extreme personality cases.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62187, "error": null, "extra": {"thread_id": 1967, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3a604ba7af854be1897689cf0dae5312", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "i ll read document to find personality scores for blue personality vector in extreme personality cases", "normalised_gold": "71 02, 75 38"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 on page 6, which shows the distribution of personality traits in Amazon-beauty and Amazon-music datasets, I can see five personality traits displayed: OPEN, CON, EXT, AGR, and NEU.\n\nThe personality trait furthest to the left in the layout is OPEN (Openness).\n\nNow I need to find the highest hit rate (HR) for OPEN across all the datasets and metrics shown in the tables.\n\nLooking at Table 6 on page 8, which shows HR and NDCG results grouped by 5 personality traits:\n- For OPEN in Amazon-beauty: HR = 0.833 (+) and 0.750 (-)\n- For OPEN in Amazon-music: HR = 0.330 (+) and 0.313 (-)\n- For OPEN in Personality2018: HR = 0.535 (+) and 0.547 (-)\n\nThe highest hit rate for OPEN is 0.833 in the Amazon-beauty dataset.\n\nAnswer: 0.833", "citations": [], "input_tokens": 26925, "output_tokens": 268, "cost_micros": 84795, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.833", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate in the results.\n\nLooking at Figure 2 (Distribution of personality traits in Amazon-beauty and Amazon-music datasets), I need to identify which personality trait has its distribution furthest to the left. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU.\n\nFrom the figure description and visual analysis:\n- The median lines (red lines) show the central tendency of each distribution\n- According to the text in Section 5.2: \"agreeable people have the highest median score, and neurotic people have the lowest median score\"\n- This means NEU (Neuroticism) is furthest to the left (lowest scores)\n\nNow I need to find the highest hit rate for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows results grouped by personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty HR: 0.933 (with +) and 0.833 (with -)\n- Amazon-music HR: 0.397 (with +) and 0.254 (with -)\n- Personality2018 HR: 0.489 (with +) and 0.390 (with -)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 13254, "output_tokens": 324, "cost_micros": 44622, "latency_ms": 7905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify the personality trait located furthest to the left in Figure 2, then find its highest hit rate in the results tables.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait appears furthest to the left in the distribution plots. The figure shows distributions for OPEN, CON, EXT, AGR, and NEU for both Amazon-beauty and Amazon-music datasets.\n\nFrom the figure, examining the distributions from left to right, NEU (Neuroticism) appears to have its distribution positioned furthest to the left, with the lowest median scores (indicated by the red line) compared to the other personality traits.\n\nNow I need to find the highest hit rate (HR) value for NEU (Neuroticism) in the experimental results.\n\nLooking at Table 6, which shows HR and NDCG results grouped by 5 personality traits:\n\nFor NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (+) and 0.397 (-)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate value for NEU across all datasets is 0.933 (from Amazon-beauty with the + model).\n\nAnswer: 0.933", "citations": [], "input_tokens": 16279, "output_tokens": 313, "cost_micros": 53532, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that appears furthest to the left in Figure 2 (the distribution of personality traits), and then find its highest hit rate from the experimental results.\n\nLooking at Figure 2, I need to identify which personality trait appears furthest to the left. The figure shows distributions for five personality traits: OPEN (Openness), CON (Conscientiousness), EXT (Extroversion), AGR (Agreeableness), and NEU (Neuroticism).\n\nAccording to the text describing Figure 2: \"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nThis indicates that Neuroticism (NEU) has the lowest median score, meaning it would be positioned furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism (NEU) from Table 6, which shows the breakdown by personality traits.\n\nLooking at Table 6 in the document, I need to find the Hit Rate (HR) values for Neuroticism across the three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nHowever, Table 6 is not fully visible in the provided text. But I can see from the discussion in Section 5.4 that it mentions performance changes for different personality traits.\n\nLooking back at Table 5, which shows overall results, I need to check if there's personality-specific data. The table shows H@3, H@5, and H@10 values across different models and datasets.\n\nFrom the text in Section 5.4: \"In the Amazon-music dataset, openness (+27%), agreeableness (+10%), extroversion (+5%) improve while neuroticism (\u201318%) and conscientiousness (\u201312%) decreases.\"\n\nThis suggests neuroticism showed decreases in the Amazon-music dataset. However, I need to find the actual highest hit rate value for neuroticism from the tables.\n\nSince Table 6 details are not fully provided in the visible text, and Table 5 shows overall results rather than personality-specific breakdowns, I need to infer from the context. The highest hit rates in Table 5 are around 0.948-0.977 for H@10 in Amazon-beauty dataset.", "citations": [], "input_tokens": 10444, "output_tokens": 512, "cost_micros": 39012, "latency_ms": 11484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "6.0", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the personality trait that is furthest to the left in the distribution figure, then find its highest hit rate.\n\nLooking at Figure 2 (Distribution of personality traits), I need to identify which personality trait has the lowest median score (furthest left).\n\nFrom the text in Section 5.2:\n\"We observe a similar trend in both domains: agreeable people have the highest median score, and neurotic people have the lowest median score.\"\n\nSo Neuroticism (NEU) is the personality trait furthest to the left in the distribution.\n\nNow I need to find the highest hit rate for Neuroticism in Table 6, which shows results grouped by personality traits.\n\nLooking at Table 6 for NEU (Neuroticism):\n- Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n- Amazon-music: HR = 0.397 (both + and -)\n- Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for Neuroticism across all datasets is 0.933 (Amazon-beauty with personality information).\n\nAnswer: 0.933", "citations": [], "input_tokens": 14867, "output_tokens": 267, "cost_micros": 48606, "latency_ms": 8485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q006", "doc_id": "2303.05039v2.pdf", "answer_format": "float", "gold": "0.833", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the distribution of personality traits figure and then identify the corresponding hit rate data.Let me search for figures related to personality traits distribution:Let me read around line 1011 where it mentions \"distribution of users' personalities\":Let me search for Figure 2 which shows the personality distribution:Now let me read Figure 2 in detail:Let me continue reading to see all the personality trait figures:Now let me search for Table 6 which shows the hit rates for different personality traits:Now I need to find which personality trait is \"furthest to the left\" in the distribution figure. Looking back at Figure 2, I need to examine the visual descriptions more carefully. Let me search for more details about each personality trait's distribution:The document states that \"neurotic people have the lowest median score\" (line 1509-1510). This means that NEU (Neuroticism) is the personality trait furthest to the left in the distribution. Now I need to find the highest hit rate for NEU from Table 6.\n\nLooking at Table 6 data I found earlier:\n- NEU (Neuroticism) row shows:\n  - Amazon-beauty: HR = 0.933 (+) and 0.833 (-)\n  - Amazon-music: HR = 0.397 (+) and 0.397 (-)\n  - Personality2018: HR = 0.489 (+) and 0.511 (-)\n\nThe highest hit rate for NEU across all datasets is 0.933.\n\nAnswer: 0.933", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 70937, "error": null, "extra": {"thread_id": 1968, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1c93a76cd24f45808d3740ab6074704b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.933", "normalised_gold": "0.833"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "native_pdf", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n\n1. Identify the model structure figure - This is Figure 1 on page 5\n2. Find the method at the bottom of this figure - This is \"NCF + Hard-coded personality\"\n3. Look at Table 5 (page 8) which shows results across the three datasets (Amazon-beauty, Amazon-music, and Personality2018)\n4. Find the highest metric value for NCF+Hard-Coded across all metrics\n\nFrom Table 5, for NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value is 0.977 for H@10 in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 26935, "output_tokens": 314, "cost_micros": 85515, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.05039v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at the document, I need to:\n1. Identify the method at the bottom of the model structure figure (Figure 1)\n2. Find its performance across the three datasets in Table 5\n3. Determine which metric has the highest value\n\nFrom Figure 1, the three methods shown are:\n- Method 1: Most salient personality\n- Method 2: Soft-labeled personality  \n- Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nLooking at Table 5, the row for \"NCF+Hard-Coded\" shows performance across three datasets (Amazon-beauty, Amazon-music, Personality2018) with metrics H@3, H@5, H@10, N@3, N@5, N@10.\n\nThe values for NCF+Hard-Coded are:\n- Amazon-beauty: 0.948, 0.961, 0.977, 0.849, 0.826, 0.848\n- Amazon-music: 0.175, 0.232, 0.345, 0.147, 0.160, 0.189\n- Personality2018: 0.503, 0.622, 0.758, 0.398, 0.447, 0.498\n\nThe highest value across all three datasets for this method is 0.977, which corresponds to H@10 (Hit Rate at 10) in the Amazon-beauty dataset.\n\nAnswer: H@10", "citations": [], "input_tokens": 13264, "output_tokens": 347, "cost_micros": 44997, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "azure_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF + Hard-coded Personality model.\n\nFrom Table 5, examining the results for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018) and all metrics (H@3, H@5, H@10, N@3, N@5, N@10):\n\nAmazon-beauty: N@3 = 0.849 (highest value)\nAmazon-music: N@10 = 0.189 (highest value)\nPersonality2018: N@5 = 0.447 (highest value)\n\nThe metric that appears most frequently as the highest is NDCG-related metrics, with N@3 having the highest single value (0.849).\n\nAnswer: N@3", "citations": [], "input_tokens": 16289, "output_tokens": 205, "cost_micros": 51942, "latency_ms": 6843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_basic_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 1, the method at the bottom of the model structure is \"Method 3: Hard-coded personality\" which corresponds to the NCF+Hard-Coded model.\n\nFrom Table 5, I need to find the highest metric value for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality 2018):\n\nAmazon-beauty: N@3 = 0.849 (highest)\nAmazon-music: N@10 = 0.189\nPersonality 2018: H@10 = 0.758\n\nThe highest value is 0.849 for N@3 (NDCG@3) in the Amazon-beauty dataset.\n\nAnswer: NDCG@3", "citations": [], "input_tokens": 10454, "output_tokens": 171, "cost_micros": 33927, "latency_ms": 6750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ndcg 3", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "llamacloud_premium_lc", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to identify the method at the bottom of the model structure figure and find its highest evaluation metric across the three datasets.\n\nLooking at Figure 1, the three methods shown are:\n1. Method 1: Most salient personality\n2. Method 2: Soft-labeled personality\n3. Method 3: Hard-coded personality\n\nMethod 3 (Hard-coded personality) appears at the bottom of the figure.\n\nNow I need to look at Table 5 to find the highest metric for NCF+Hard-Coded across all three datasets (Amazon-beauty, Amazon-music, and Personality2018).\n\nFor NCF+Hard-Coded:\n- Amazon-beauty: H@3=0.948, H@5=0.961, H@10=0.977, N@3=0.849, N@5=0.826, N@10=0.848\n- Amazon-music: H@3=0.175, H@5=0.232, H@10=0.345, N@3=0.147, N@5=0.160, N@10=0.189\n- Personality2018: H@3=0.503, H@5=0.622, H@10=0.758, N@3=0.398, N@5=0.447, N@10=0.498\n\nThe highest value across all metrics and datasets for NCF+Hard-Coded is 0.977 for H@10 in Amazon-beauty.\n\nAnswer: H@10", "citations": [], "input_tokens": 14877, "output_tokens": 346, "cost_micros": 49821, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "h 10", "normalised_gold": "h 10"}}
+{"qid": "2303.05039v2.pdf::Q007", "doc_id": "2303.05039v2.pdf", "answer_format": "str", "gold": "H@10", "pages": 9, "document_id": 7583, "arm": "surfsense_agentic", "question_id": "2303.05039v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find a model structure figure in this document first, then identify the method at the bottom of that figure, and finally check which evaluation metric has the highest number across three datasets for that method.Let me search for figures in the document more systematically:Great! I found Figure 1 showing the model structure. Now I can see that the three methods are:\n1. Method 1: Most salient personality - at the top\n2. Method 2: Soft-labeled personality - in the middle\n3. Method 3: Hard-coded personality - at the bottom\n\nThe method at the **bottom** is \"Hard-coded personality\" (Method 3). Now I need to find evaluation results for this method across the three datasets to determine which metric has the highest number.Now let me search for the experimental results tables:Let me continue searching for the results table:Perfect! Now I have found Table 5 which shows the evaluation results for all methods including \"NCF+Hard-Coded\" (the method at the bottom of the model structure figure) across the three datasets:\n\nFor **NCF+Hard-Coded** method across the three datasets:\n\n**Amazon-beauty:**\n- H@3: 0.948\n- H@5: 0.961  \n- H@10: 0.977\n- N@3: **0.849** \u2190 Highest\n- N@5: 0.826\n- N@10: 0.848\n\n**Amazon-music:**\n- H@3: 0.175\n- H@5: 0.232\n- H@10: 0.345\n- N@3: 0.147\n- N@5: 0.160\n- N@10: **0.189** \u2190 Highest\n\n**Personality2018:**\n- H@3: 0.503\n- H@5: 0.622\n- H@10: **0.758** \u2190 Highest\n- N@3: 0.398\n- N@5: 0.447\n- N@10: 0.498\n\nLooking at the highest value for each dataset:\n- Amazon-beauty: N@3 = 0.849\n- Amazon-music: N@10 = 0.189\n- Personality2018: H@10 = 0.758\n\nThe evaluation metric with the highest number across all three datasets for the Hard-coded method is **N@3** (with value 0.849 in Amazon-beauty).\n\nAnswer: N@3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 93687, "error": null, "extra": {"thread_id": 1969, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f41f50a7be1a46d59602bb6f59d5d5fd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 515, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "n 3", "normalised_gold": "h 10"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3 on page 9, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV 50-shot:\n- Previous SoTA (line 4): 68.5(1.6)\n- The filter-then-rerank methods without ensemble are shown in the rows labeled \"+ Rerank (L)\"\n\nLooking at the three reranker options for 50-shot TACREV:\n- Vicuna-13B + Rerank (L): 70.8(1.6)\n- InstructGPT + Rerank (L): 72.3(1.4)\n- GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which refers to the \"+ Rerank (L)\" rows without the \"+ Ensemble (S)\" component.\n\nSince the question uses singular \"methods\" but there are multiple values, I should identify which one is the main result. Looking at the context, InstructGPT appears to be the primary model discussed in the main experiments (as mentioned in Section 5.3: \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nAnswer: 72.3", "citations": [], "input_tokens": 82081, "output_tokens": 358, "cost_micros": 251613, "latency_ms": 38407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) task, I need to find the 50-shot column.\n\nLooking at the rows for filter-then-rerank without ensemble:\n- Line 6: Vicuna-13B + Rerank (L): 70.8(1.6)\n- Line 7: InstructGPT + Rerank (L): 72.3(1.4)\n- Line 9: GPT-4 + Rerank (L): 72.3(0.3)\n\nThe question asks about \"filter-then-rerank methods\" in general (plural), but I need to identify which specific method is being asked about. \n\nLooking at the context, when comparing with baseline methods, the paper typically refers to InstructGPT as the main reranker in their comparisons (as mentioned in section 5.3 where they state \"with InstructGPT, reranking provides an average F1 gain of 2.4%\").\n\nFor 50-shot TACREV with InstructGPT + Rerank (L) without ensemble:\n\nAnswer: 72.3", "citations": [], "input_tokens": 35348, "output_tokens": 333, "cost_micros": 111039, "latency_ms": 9656, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV dataset, I can see columns for 20-shot, 50-shot, and 100-shot settings.\n\nLooking at the 50-shot column for TACREV:\n\nThe rows showing filter-then-rerank methods without ensemble (marked as \"+ Rerank (L)\") are:\n- With Vicuna-13B as reranker: 70.8(1.6)\n- With InstructGPT as reranker: 72.3(1.4)\n- With GPT-4 as reranker: 72.3(0.3)\n\nThe question asks for \"filter-then-rerank methods (w.o. ensemble)\" which means without the \"+ Ensemble (S)\" prefix. The previous SoTA method (line 4) shows 68.5(1.6) for comparison.\n\nSince there are three different LLM rerankers tested, and the question asks for \"filter-then-rerank methods\" in general, I should identify which specific result is being asked for. Looking at the context, when InstructGPT is used as the reranker (which appears to be the main model discussed in the results section based on Table 4 and other analyses), the performance is 72.3.\n\nAnswer: 72.3", "citations": [], "input_tokens": 46313, "output_tokens": 370, "cost_micros": 144489, "latency_ms": 11125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nLooking at Table 3, which shows \"Overall results of LLM-based ICL methods, SLM-based supervised methods, and our proposed filter-then-rerank (SLM+LLM) methods.\"\n\nFor TACREV (RE) at 50-shot setting:\n\nLooking at the rows with filter-then-rerank methods without ensemble:\n- Line 4: \"Previous SoTA\" (this is the baseline SLM)\n- Line 7: \"+ Rerank (L)\" with InstructGPT - this adds reranking to the previous SoTA without ensemble\n\nFrom Table 3, TACREV 50-shot column:\n- Previous SoTA: 68.5(1.6)\n- SLM + Rerank (L) with InstructGPT: 70.8(1.6)\n\nThe filter-then-rerank method without ensemble using InstructGPT shows 70.8.\n\nAnswer: 70.8", "citations": [], "input_tokens": 33414, "output_tokens": 254, "cost_micros": 104052, "latency_ms": 9844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "70.8", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, I need to find the performance of filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nIn Table 3, under the TACREV (RE) 50-shot column:\n- Previous SoTA (SLM baseline): 72.6(1.5)\n- + Rerank (L) with Vicuna-13B: 73.8(1.7)\n- + Rerank (L) with InstructGPT: 75.4(1.5)\n- + Rerank (L) with GPT-4: 74.5(1.5)\n\nThe question asks for filter-then-rerank methods without ensemble. The \"+ Rerank (L)\" rows show the filter-then-rerank results without ensemble (the ensemble results are shown in the \"+ Ensemble (S) + Rerank (L)\" rows).\n\nSince there are three different rerankers tested, and the question asks for \"filter-then-rerank methods\" in general without specifying which LLM reranker, I should provide the InstructGPT result as it appears to be the primary comparison model used throughout the paper (it's the middle option and shows the best performance).\n\nAnswer: 75.4", "citations": [], "input_tokens": 45083, "output_tokens": 294, "cost_micros": 139659, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.4", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q000", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "72.3%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about filter-then-rerank methods performance on the 50-shot TACREV dataset in the provided document.Let me search for tables that contain both TACREV and 50-shot data:Perfect! I found Table 3 which contains the performance data. Let me look at the specific row for filter-then-rerank methods without ensemble on the 50-shot TACREV dataset.\n\nFrom the table, I can see:\n- Column headers show: FewNERD (NER) with 5-shot, 10-shot, 20-shot, then TACREV (RE) with 20-shot, 50-shot, 100-shot\n- The filter-then-rerank method without ensemble is the \"+ Rerank (L)\" row (Line 7 as mentioned in the text)\n\nLooking at the data:\n- Under \"SLM\" section, the \"+ Rerank (L)\" row (line 2050-2060) shows the Vicuna-13B results\n- For TACREV 50-shot (5th data column), the value is: 70.8(1.6)\n\nLet me also check the InstructGPT rerank results:\n- \"+ Rerank (L)\" with InstructGPT (line 2080-2090) shows for TACREV 50-shot: 72.3(1.4)\n\nAnd GPT-4 rerank results:\n- \"+ Rerank (L)\" with GPT-4 (line 2111-2121) shows for TACREV 50-shot: 72.3(0.3)\n\nThe question asks for filter-then-rerank methods (w.o. ensemble), which would be the \"+ Rerank (L)\" rows. Looking at line 2084-2085, I can see the InstructGPT version shows 72.3 for 50-shot TACREV.\n\nHowever, I need to verify which specific implementation is being referred to. Let me check the context around line 1903 which mentions \"Lines 4 vs. 7\" for the comparison.\n\nBased on the table structure and the text mentioning \"Lines 4 vs. 7\" where Line 7 is the rerank method, and looking at the TACREV 50-shot column, the filter-then-rerank method (w.o. ensemble) with InstructGPT shows: 72.3\n\nAnswer: 72.3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50031, "error": null, "extra": {"thread_id": 1970, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cec9e66ec5254f8aa0ad0e5be252d80b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 338, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "72.3", "normalised_gold": "72.3"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left) in the document, which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset,\" the left panel shows \"ChatGPT's performance (F1 Score) across six instruction variants.\"\n\nThe red box in the left panel of Figure 3 highlights the instruction formats labeled I0 through I5 on the x-axis.\n\nBased on the context provided in Appendix E.2, these instruction variants range from simple to complex, with I0 being empty (no instruction) and I1 through I5 being increasingly detailed instructions for the NER task.\n\nAnswer: ChatGPT's performance (F1 Score) across six instruction variants", "citations": [], "input_tokens": 82074, "output_tokens": 166, "cost_micros": 248712, "latency_ms": 34046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "chatgpt s performance f1 score across six instruction variants", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants\" and displays \"ChatGPT's performance (F1 Score) across six instruction variants\" labeled I0 through I5.\n\nThe document provides the full text of these six instruction variants in Section E.2 \"Finding Better Instruction.\"\n\nThe instructions are:\n\n**I0:** [empty]\n\n**I1:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2:** Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1]: [Definition_1], [Type_2]: [Definition_2], ..., [Type_N]: [Definition_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I3:** Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. The possible entity types are listed as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] <SEP> identified_entity: [Entity_1]), ([Type_2] <SEP> identified_entity: [Entity_2])'. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I4:** Assume you are an entity-instance annotator. Your objective is to perform a series of intricate steps for Named Entity Recognition. Firstly, you have to identify a particular word or phrase in the sentence that corresponds to an entity. Following this, classify the entity into one of the potential entity types. The potential entity types are provided as below: [Type_1], [Type_2], ..., [Type_N]. Please note that your annotation results must follow such format: 'Answer: ([Type_1] <SEP> identified_entity: [Entity_", "citations": [], "input_tokens": 35341, "output_tokens": 512, "cost_micros": 113703, "latency_ms": 13984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.1702127659574468, "method": "str_norm", "normalised_pred": "type 1 sep identified entity entity", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"ChatGPT's performance (F1 Score) across six instruction variants,\" I need to find the instructions labeled I0 through I5.\n\nFrom Section E.2 \"Finding Better Instruction\" in the document, the six instruction variants are listed. The red box in Figure 3 (left) shows the x-axis labels I0, I1, I2, I3, I4, I5.\n\nThese correspond to:\n\n**I0**: [empty]\n\n**I1**: Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ... , [Type_N]. If you do not find any entity in this sentence, just output 'Answer: No entities found.'\n\n**I2**: Same as I1 but with definitions added for each type\n\n**I3**: Assume you are an entity-instance annotator. Given a sentence, you need to (1) identify the word or phrase about the entity in the sentence, and (2) classify its entity type. [includes format specification]\n\n**I4**: Similar to I3 but with more elaborate language about \"intricate steps\"\n\n**I5**: Combines detailed task description with entity type definitions\n\nAnswer: Six instruction variants from empty (I0) to increasingly complex formats with definitions and detailed task descriptions (I1-I5)", "citations": [], "input_tokens": 46306, "output_tokens": 319, "cost_micros": 143703, "latency_ms": 11812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.06779661016949154, "method": "str_norm", "normalised_pred": "six instruction variants from empty i0 to increasingly complex formats with definitions and detailed task descriptions i1 i5", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), I can see it's showing \"Relationship between confidence scores and performance with/without LLM reranking.\"\n\nThe red box in the left figure appears to be highlighting the legend/experimental settings, which shows:\n\n**Answer: RoBERTa-large as filter and InstructGPT as reranker**", "citations": [], "input_tokens": 33407, "output_tokens": 81, "cost_micros": 101436, "latency_ms": 4281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.04081632653061225, "method": "str_norm", "normalised_pred": "roberta large as filter and instructgpt as reranker", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 (left), which shows \"LLMs' performance w.r.t prompt variants on 20-shot FewNERD dataset\" with six instruction variants (I0-I5), I need to find the instruction text from the document.\n\nFrom Section E.2 \"Finding Better Instruction\", the six instruction variants for the NER task are listed. These are the instructions being tested in the red box of Figure 3 (left).\n\nAnswer: The six instruction variants I0 through I5 listed in Section E.2, ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "citations": [], "input_tokens": 45076, "output_tokens": 140, "cost_micros": 137328, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.17910447761194032, "method": "str_norm", "normalised_pred": "six instruction variants i0 through i5 listed in section e 2 ranging from empty instruction to complex instructions with entity type definitions and annotation format requirements", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q002", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "Identify the entities expressed by each sentence, and locate each entity to words in the sentence. The possible entity types are: [Type_1], [Type_2], ..., [Type_N]. If you do not find any entity in this sentence, just output \u2018Answer: No entities found.\u2019", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the instructions corresponding to the settings shown in the red box.Let me search for Figure 3 in the document:Now let me read the section around Figure 3 to see the instruction variants and identify which ones are shown in the red box:I can see that Figure 3 Left shows ChatGPT's performance across six instruction variants labeled I0 through I5. I need to find the details about these instruction variants in Appendix E.2. Let me search for the appendix:Let me search for instruction variants more broadly:Let me search for the appendix sections with \"E.2\" or look for where instruction formats I0-I5 are defined:Let me look at the entire document more systematically. Let me search for sections starting with \"E.\" or \"Appendix\":Great! Now let me read section E.2 which should contain the instruction variants:Perfect! I found the six instruction variants (I0-I5). Now I need to look at Figure 3 more carefully to see which instructions are in the red box. Let me read around the figure again and look for any visual description of the red box:The image X14.png seems to be after Figure 3 in the document. Let me look for other embedded images that might be part of Figure 3:Let me look before Figure 3 to see if there's an image associated with the figure itself:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77483, "error": null, "extra": {"thread_id": 1971, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_538fbe85cd9b43f5a8b0d97c996a7e15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.08301886792452831, "method": "str_norm", "normalised_pred": "i ll read document to find figure 3 and identify instructions corresponding to settings shown in red box let me search for figure 3 in document now let me read section around figure 3 to see instruction variants and identify which ones are shown in red box i can see that figure 3 left shows chatgpt s performance across six instruction variants labeled i0 through i5 i need to find details about these instruction variants in appendix e 2 let me search for appendix let me search for instruction variants more broadly let me search for appendix sections with e 2 or look for where instruction formats i0 i5 are defined let me look at entire document more systematically let me search for sections starting with e or appendix great now let me read section e 2 which should contain instruction variants perfect i found six instruction variants i0 i5 now i need to look at figure 3 more carefully to see which instructions are in red box let me read around figure again and look for any visual description of red box image x14 png seems to be after figure 3 in document let me look for other embedded images that might be part of figure 3 let me look before figure 3 to see if there s image associated with figure itself", "normalised_gold": "identify entities expressed by each sentence and locate each entity to words in sentence possible entity types are type 1 type 2 type n if you do not find any entity in this sentence just output \u2018answer no entities found \u2019"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find how many datasets are used for experiments in this paper.\n\nLooking at the document, in Section 3.1 \"Task, Dataset and Evaluation\", it states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe document then lists:\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD (3 datasets)\n- Relation Extraction (RE): TACRED, TACREV (2 datasets)\n- Event Detection (ED): ACE05, MAVEN, ERE (3 datasets)\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS (3 datasets)\n\nHowever, I need to count unique datasets. ACE05 and ERE appear in both ED and EAE tasks.\n\nCounting unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nThis gives us 9 unique datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 82066, "output_tokens": 265, "cost_micros": 250173, "latency_ms": 34217, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the datasets used:\n\nFrom the Abstract and Introduction:\n- \"Through extensive experiments on nine datasets across four IE tasks\"\n\nFrom Section 3.1 (Task, Dataset and Evaluation):\n- \"We run experiments on nine widely-used datasets across four IE tasks.\"\n- The nine datasets are listed as:\n  1. CONLL03 (NER)\n  2. OntoNotes (NER)\n  3. FewNERD (NER)\n  4. TACRED (RE)\n  5. TACREV (RE)\n  6. ACE05 (ED)\n  7. MAVEN (ED)\n  8. ERE (ED)\n  9. ACE05, ERE, RAMS (EAE)\n\nWait, let me recount more carefully from the text:\n- NER: CONLL03, OntoNotes, FewNERD = 3 datasets\n- RE: TACRED, TACREV = 2 datasets\n- ED: ACE05, MAVEN, ERE = 3 datasets\n- EAE: ACE05, ERE, RAMS = 3 datasets\n\nHowever, ACE05 and ERE appear in multiple tasks. The document clearly states \"nine datasets\" multiple times.\n\nLooking at Table 6 which shows \"Statistics of nine datasets used\", I can count the distinct dataset names:\n1. CONLL\n2. OntoNotes\n3. FewNERD\n4. TACREV\n5. TACRED\n6. ACE05 (appears for both ED and EAE)\n7. MAVEN\n8. ERE (appears for both ED and EAE)\n9. RAMS\n\nThis confirms 9 distinct datasets total.\n\nAnswer: 9", "citations": [], "input_tokens": 35333, "output_tokens": 414, "cost_micros": 112209, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to identify all the datasets used across the four IE tasks.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n\n1. **Named Entity Recognition (NER)**: CONLL03, OntoNotes, FewNERD\n2. **Relation Extraction (RE)**: TACRED, TACREV\n3. **Event Detection (ED)**: ACE05, MAVEN, ERE\n4. **Event Argument Extraction (EAE)**: ACE05, RAMS, ERE\n\nCounting unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE, but counted once)\n- MAVEN\n- ERE (used in both ED and EAE, but counted once)\n- RAMS\n\nTotal unique datasets: 9\n\nThis is confirmed by the abstract which states \"extensive experiments on nine datasets across four IE tasks\" and multiple mentions throughout the paper of \"nine datasets.\"\n\nAnswer: 9", "citations": [], "input_tokens": 46298, "output_tokens": 251, "cost_micros": 142659, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the total number of datasets used in the experiments described in this paper.\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\", the paper states:\n\n\"We run experiments on nine widely-used datasets across four IE tasks.\"\n\nThe four tasks and their associated datasets are:\n\n1. **Named Entity Recognition (NER)**: \n   - CONLL03\n   - OntoNotes\n   - FewNERD\n\n2. **Relation Extraction (RE)**:\n   - TACRED\n   - TACREV\n\n3. **Event Detection (ED)**:\n   - ACE05\n   - MAVEN\n   - ERE\n\n4. **Event Argument Extraction (EAE)**:\n   - ACE05\n   - ERE\n   - RAMS\n\nCounting the unique datasets:\n- CONLL03\n- OntoNotes\n- FewNERD\n- TACRED\n- TACREV\n- ACE05 (used in both ED and EAE)\n- MAVEN\n- ERE (used in both ED and EAE)\n- RAMS\n\nThe paper explicitly states \"nine widely-used datasets\" and when counting unique dataset names, we have 9 distinct datasets (ACE05 and ERE are each used for two different tasks but are still single datasets).\n\nAnswer: 9", "citations": [], "input_tokens": 33399, "output_tokens": 309, "cost_micros": 104832, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for the datasets used in experiments:\n\nFrom Section 3.1 \"Task, Dataset and Evaluation\":\n- Named Entity Recognition (NER): CONLL03, OntoNotes, FewNERD\n- Relation Extraction (RE): TACRED, TACREV\n- Event Detection (ED): ACE05, MAVEN, ERE\n- Event Argument Extraction (EAE): ACE05, ERE, RAMS\n\nCounting the unique datasets:\n1. CONLL03\n2. OntoNotes\n3. FewNERD\n4. TACRED\n5. TACREV\n6. ACE05\n7. MAVEN\n8. ERE\n9. RAMS\n\nNote: ACE05 and ERE are used for both Event Detection and Event Argument Extraction tasks, but they are counted once as unique datasets.\n\nAnswer: 9", "citations": [], "input_tokens": 45068, "output_tokens": 207, "cost_micros": 138309, "latency_ms": 6390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q003", "doc_id": "2303.08559v2.pdf", "answer_format": "int", "gold": "9", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the datasets used in the experiments.Let me search for a table or figure that lists all the datasets clearly.Perfect! I found Table 6 which clearly lists all the datasets. Let me count them:\n\nFrom the document, the paper uses experiments on **nine datasets** across four IE tasks:\n\n1. **Named Entity Recognition (NER)**: \n   - CONLL03\n   - OntoNotes\n   - FewNERD\n\n2. **Relation Extraction (RE)**:\n   - TACRED\n   - TACREV\n\n3. **Event Detection (ED)**:\n   - ACE05\n   - MAVEN\n   - ERE\n\n4. **Event Argument Extraction (EAE)**:\n   - ACE05 (same dataset used for both ED and EAE)\n   - RAMS\n   - ERE (same dataset used for both ED and EAE)\n\nCounting unique datasets: CONLL03, OntoNotes, FewNERD, TACRED, TACREV, ACE05, MAVEN, ERE, RAMS = **9 datasets**\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43891, "error": null, "extra": {"thread_id": 1972, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_efab7508547a4ca893cf8d3cb1478856", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 235, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "9"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for information about the filter-then-rerank method and prompt templates...\n\nIn Table 21 (page 29), which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 82080, "output_tokens": 100, "cost_micros": 247740, "latency_ms": 31592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template used for the 'Contact.Meet' label in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 35347, "output_tokens": 142, "cost_micros": 108171, "latency_ms": 4609, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for the 'Contact.Meet' event label in the filter-then-rerank paradigm.\n\nIn Table 21 (Templates for ACE05 dataset), which shows the templates used in the filter-then-rerank method, I can find:\n\nContact.Meet: \"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 46312, "output_tokens": 143, "cost_micros": 141081, "latency_ms": 6046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the prompt template for 'Contact.Meet' used in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find the template for Contact.Meet:\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 33413, "output_tokens": 106, "cost_micros": 101829, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find the template for 'Contact.Meet' in the filter-then-rerank method.\n\nIn Table 21, which shows \"Templates for ACE05 dataset, where {evt} is the placeholder for event type,\" I can find:\n\nContact.Meet | The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 45082, "output_tokens": 135, "cost_micros": 137271, "latency_ms": 4452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q004", "doc_id": "2303.08559v2.pdf", "answer_format": "str", "gold": "The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find information about the prompt template for label 'Contact.Meet' in the filter-then-rerank method.Let me search for information about \"Contact.Meet\" and \"filter-then-rerank\" in this document.Let me search for content with more context:Now I need to find the section that shows the prompt templates. Let me read around line 5446 where Contact.Meet was mentioned, and also look for tables or appendices with prompt templates:Perfect! I found the prompt template for Contact.Meet in Table 21 for ACE05 dataset. According to the document, the template for 'Contact.Meet' in the filter-then-rerank method is:\n\n\"The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.\"\n\nAnswer: The word {evt} triggers a MEET event: two or more entities come together at a single location and interact with one another face-to-face.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45328, "error": null, "extra": {"thread_id": 1973, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5466a03f139642c1a7d3c68dd6b01473", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face", "normalised_gold": "word evt triggers meet event two or more entities come together at single location and interact with one another face to face"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "native_pdf", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the green module, then look for the 100-shot TACREV setting to calculate the absolute percentage drop.\n\nLooking through the document, I find Figure 6 on page 8, which shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" In this figure, I can see colored modules - orange for \"Easy Sample\" and pink for \"Hard Sample\". The diagram shows a filter (Small LM) and a reranker (Large LM).\n\nHowever, I need to look at Table 5 on page 9, which shows \"Ablation study on three datasets\" and examines the effectiveness of modules in the adaptive filter-then-rerank system. The table shows results for TACREV (100-shot).\n\nLooking at the table columns, I can see:\n- CoT (Chain of Thought)\n- Demo\n- LF (Label Filtering) \n- AD (Adaptive)\n\nThe green box in Figure 6 appears to correspond to the demonstration/demo component based on the visual representation.\n\nFrom Table 5, for TACREV (100-shot):\n- With all modules (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without Demo but with others (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe absolute drop is: 75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 82081, "output_tokens": 344, "cost_micros": 251403, "latency_ms": 36125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2303.08559v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance impact of dropping the green module (demonstration) shown in Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can see the ablation results for different components on three datasets including TACREV (100-shot).\n\nThe table shows:\n- Full system (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9(2.4)\n- Without Demo (\u2717 Demo \u2717 CoT \u2713 LF \u2713 AD): 74.9(2.2)\n\nHowever, I need to isolate just the Demo effect. Looking more carefully:\n- With CoT and Demo (\u2713 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.9\n- Without Demo but with CoT (\u2713 CoT \u2717 Demo \u2713 LF \u2713 AD): This line doesn't exist\n\nBut I can see:\n- Without CoT but with Demo (\u2717 CoT \u2713 Demo \u2713 LF \u2713 AD): 75.4\n- Without both CoT and Demo (\u2717 CoT \u2717 Demo \u2713 LF \u2713 AD): 74.9\n\nLet me look at the correct comparison for just dropping Demo:\nFrom the table, comparing rows where only Demo changes:\n- Row 1 (\u2713 CoT \u2713 Demo): 75.9\n- Row 2 (\u2717 CoT \u2713 Demo): 75.4\n- Row 3 (\u2717 CoT \u2717 Demo): 74.9\n\nThe drop from having Demo to not having Demo (when CoT is also removed) is:\n75.4 - 74.9 = 0.5\n\nHowever, to properly isolate the Demo effect with CoT present, I need to estimate. The pattern suggests:\n- Full system: 75.9\n- Without Demo (keeping other factors): approximately 75.4 - 75.9 range\n\nActually, looking at row 2 vs row 3: the difference is 75.4 -", "citations": [], "input_tokens": 35348, "output_tokens": 512, "cost_micros": 113724, "latency_ms": 11858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "2.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "azure_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the performance drop when removing the green module (demonstration) from Figure 6 on the 100-shot TACREV setting.\n\nLooking at Table 5 (Ablation study), I can find the relevant information:\n- The table shows ablation results on three datasets including TACREV (100-shot)\n- The rows show different configurations with checkmarks (\u2713) and crosses (\u2717)\n\nThe green module in Figure 6 represents the \"Demonstration\" component.\n\nFrom Table 5, for TACREV (100-shot):\n- With CoT \u2713, Demo \u2713, LF \u2713, AD \u2713: 75.9(2.4)\n- With CoT \u2717, Demo \u2717, LF \u2713, AD \u2713 (no demo): 74.9(2.2)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 46313, "output_tokens": 231, "cost_micros": 142404, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_basic_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to find Figure 6 and identify the \"green module,\" then look at the 100-shot TACREV setting to determine the performance drop.\n\nLooking at Figure 6 in the document, it shows \"The overall architecture of our adaptive filter-then-rerank paradigm.\" The green box is labeled as \"demos\" (demonstration examples).\n\nNow I need to find the ablation study that shows what happens when demos are removed. Looking at Table 5 (Ablation study), I can see different configurations where modules are removed:\n\nThe table shows results for FewNERD (20-shot), TACREV (100-shot), and ACE05 (20-shot).\n\nFor TACREV (100-shot):\n- Full system (\u2713 \u2713 \u2713 \u2713): 75.9(2.4)\n- Without CoT (\u2717 \u2713 \u2713 \u2713): 75.4(2.4)\n- Without Demo (\u2717 \u2717 \u2713 \u2713): 74.9(2.2)\n\nThe \"Demo\" module corresponds to the green box in Figure 6. When we remove it (going from \u2717 \u2713 \u2713 \u2713 to \u2717 \u2717 \u2713 \u2713), the performance drops from 75.4 to 74.9.\n\nThe absolute drop is: 75.4 - 74.9 = 0.5\n\nAnswer: 0.5", "citations": [], "input_tokens": 33414, "output_tokens": 351, "cost_micros": 105507, "latency_ms": 9827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.5", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "llamacloud_premium_lc", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the \"green module\" in Figure 6 and its effect on the 100-shot TACREV setting.\n\nFrom Figure 6, the green box represents the **Demonstration** component in the reranker process.\n\nFrom Table 5 (Ablation Study), I can see the results when removing the Demo component:\n- The row \"[ ] [ ] [x] [x]\" indicates: no CoT, no Demo, with LF, with AD\n- For TACREV (100-shot): 74.9(2.2)\n- The full system \"[x] [x] [x] [x]\" achieves: 75.9(2.4)\n\nThe absolute percentage drop when removing Demo is:\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 45083, "output_tokens": 198, "cost_micros": 138219, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2303.08559v2.pdf::Q006", "doc_id": "2303.08559v2.pdf", "answer_format": "float", "gold": "0.5%", "pages": 30, "document_id": 7584, "arm": "surfsense_agentic", "question_id": "2303.08559v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and the information about dropping the green module in the 100-shot TACREV setting.Let me search for Figure 6 in the document:Now let me search for more context around Figure 6:Good! I found Figure 6. It mentions a green box representing \"demos\". Let me read the full content around Figure 6 and look for ablation studies or tables that show what happens when dropping the green module (demos):Perfect! Figure 6 shows that the green box represents \"Demonstration\" (demos). Now I need to find the ablation study or table that shows the performance drop when removing demos. Let me search for ablation or table related to this:Let me search for ablation study or a table showing what happens when demos are removed:Perfect! I found the ablation study in Table 5. Let me read that section:Let me search more specifically for Table 5:Great! I found Table 5. Let me continue reading to see all rows:Perfect! Now let me look for the explanation of what each column means. Let me read the section before Table 5:Let me find the section that explains the ablation study (section 5.5):Let me continue reading the ablation study explanation:I see the explanation was cut off. Let me search for where it continues:Let me look for text after Table 3 that continues the ablation study explanation:Perfect! Now I have the full explanation. Looking at Table 5, I need to identify which row corresponds to dropping the \"Demo\" (green module). Let me re-read Table 5 more carefully:Now let me understand the table structure. The columns are:\n- CoT (Chain of Thought)\n- Demo (Demonstration - the green module)\n- LF (Label Filtering)\n- AD (Adaptive)\n\nThe rows show different configurations with checkmarks (\u2713) meaning present and X (\u2717) meaning removed.\n\nLooking at the table:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 TACREV (100-shot) = 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 TACREV (100-shot) = 74.9\n- Row 4: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2612 AD \u2192 TACREV (100-shot) = 73.8\n- Row 5: \u2717 CoT, \u2717 Demo, \u2717 LF, \u2717 AD \u2192 TACREV (100-shot) = 59.9\n\nWait, I need to understand the notation better. Let me look at the explanation again. From the text at lines 2189-2194:\n\n\"(2) Demo: We remove all examples, rendering the reranking a zero-shot problem.\"\n\nSo when Demo is removed (\u2717), all examples are removed. Looking at the table structure, I need to find the row where Demo changes from \u2713 to \u2717 while keeping other factors constant.\n\nThe best comparison to isolate the effect of Demo would be:\n- Row 1 (full model with all components): CoT \u2713, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.9\n- Row 2 (without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n\nTo find the effect of dropping Demo specifically, I should compare:\n- Row 2 (with Demo, without CoT): CoT \u2717, Demo \u2713, LF \u2713, AD \u2713 \u2192 75.4\n- Row 3 (without Demo and CoT): CoT \u2717, Demo \u2717, (different LF/AD settings) \u2192 74.9\n\nBut wait, the LF and AD settings change. Let me reconsider. Looking more carefully at the symbols, I see \"\u2612\" which might be different from \"\u2713\" and \"\u2717\".\n\nLet me look for a clearer comparison. The green module in Figure 6 is \"Demo\". To find the effect of dropping it:\n\nFrom the full model (row 1): 75.9\nTo without Demo - I need to find which row removes only Demo while keeping everything else.\n\nActually, looking at row 2 vs row 3:\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, \u2612 LF, \u2612 AD \u2192 74.9\n\nBut LF and AD also change, so this isn't a clean comparison.\n\nLet me think about this differently. According to the explanation text (lines 2197-2199):\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\"\n\nAnd (line 2199-2200):\n\"(2) Demos without explanations also contribute to performance improvement.\"\n\nSo the comparison should be:\n- With Demo + CoT (row 1): 75.9\n- Without CoT but with Demo (row 2): 75.4 \n- Without both CoT and Demo (row 3 onwards)\n\nThe cleanest way to see the effect of dropping Demo is to compare a configuration with Demo vs without Demo. Looking at row 2 (has Demo but no CoT) = 75.4, and the next logical step would be row 3 (no Demo, no CoT) = 74.9.\n\nBut let me also check if there's a better comparison. Actually, looking at the structure, row 3 onwards all have \"\u2717 \u2612\" for both CoT and Demo columns, meaning both are removed.\n\nSo the drop from having Demo (row 2: 75.4) to not having Demo (row 3: 74.9) = 75.4 - 74.9 = 0.5\n\nBut wait, in row 3, the LF and AD settings also changed (\u2612 instead of \u2713).\n\nLet me reconsider what \u2612 means. It might mean the opposite of the feature (e.g., if \u2713 means \"with\", then \u2612 might mean \"without\" for LF and AD columns).\n\nActually, based on the explanation:\n- LF (label filtering): \u2713 means using top-N labels, \u2717 means retaining all labels\n- AD (adaptive): \u2713 means only hard samples, \u2717 means all samples\n\nSo \u2612 might indicate the opposite setting.\n\nGiven the complexity, let me focus on finding where the Demo is specifically dropped. The clearest comparison would be between:\n- Row 1 (everything included): 75.9\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.9 - 74.9 = 1.0\n\nBut this includes the effect of removing CoT as well. To isolate Demo:\n- Row 2 (no CoT, yes Demo): 75.4\n- Row 3 (no CoT, no Demo): 74.9\n\nDifference: 75.4 - 74.9 = 0.5\n\nHowever, LF and AD settings also changed between row 2 and 3.\n\nLet me look at this more systematically. Actually, I think I'm overcomplicating this. The question asks specifically about dropping \"the green module\" which is Demo. Looking at the full system (row 1) vs without demo, I need to find the appropriate ablation.\n\nActually, re-reading the table more carefully with the column headers having 2 cells labeled \"CoT Demo\", I think these might be a single column header spanning 2 rows. Let me re-examine:\n\nLooking at lines 2298-2303:\n```\n<th colspan=\"2\">CoT Demo</th>\n```\n\nThis suggests \"CoT Demo\" is a single header spanning 2 columns. So the structure might be:\n- Column 1: CoT\n- Column 2: Demo\n- Column 3: LF\n- Column 4: AD\n\nWith this understanding:\n- Row 1: \u2713 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.9\n- Row 2: \u2717 CoT, \u2713 Demo, \u2713 LF, \u2713 AD \u2192 75.4\n- Row 3: \u2717 CoT, \u2717 Demo, ? LF, ? AD \u2192 74.9\n\nSo dropping Demo (from row 2 to row 3) causes a drop from 75.4 to 74.9 = 0.5\n\nBut the LF and AD settings are unclear in row 3 with the \u2612 symbol.\n\nGiven the question asks specifically for dropping the green module (Demo), and looking at the cleanest comparison while keeping CoT removed:\n- With Demo (row 2): 75.4\n- Without Demo (row 3): 74.9\n- Drop: 75.4 - 74.9 = 0.5\n\nBut if we compare from the full model:\n- Full model (row 1): 75.9\n- Without Demo but with CoT - this row doesn't exist in the table\n\nActually, maybe I should look at row 5 which has everything removed. Let me reconsider all rows:\n1. All components: 75.9\n2. No CoT: 75.4\n3. No CoT, No Demo: 74.9\n4. No CoT, No Demo, No LF: 73.8\n5. Nothing (all removed): 59.9\n\nThe progression suggests cumulative removal. To isolate Demo's effect:\nRow 2 (75.4) - Row 3 (74.9) = 0.5\n\nWait, but I should verify this interpretation by looking at the explanation text again. From lines 2196-2200:\n\n\"(1) Demos with explanations consistently enhance the reranking ability of LLMs across all datasets.\n(2) Demos without explanations also contribute to performance improvement.\"\n\nThis suggests:\n- Row 1 has Demo WITH CoT (explanations)\n- Row 2 has Demo WITHOUT CoT (no explanations) \n- Row 3 onwards has NO Demo\n\nSo the effect of dropping Demo entirely (going from row 2 to row 3): 75.4 - 74.9 = 0.5\n\nBut wait, there might be more to consider. Let me check if there's another row that might represent dropping just Demo while keeping CoT. Looking at the table again... no, there isn't such a row.\n\nGiven the structure and the question asking about dropping the green module (Demo), the most reasonable answer based on the ablation study is:\n\nFrom row 1 (full model with all components) = 75.9\nTo the configuration without Demo... I need to find which row that is.\n\nIf I assume row 3 still has other components but not Demo:\nRow 1: 75.9\nRow 3: 74.9\nDifference: 1.0\n\nBut this seems to also remove CoT. Let me think about what makes sense given Figure 6. The green module is specifically the \"Demonstration\" box shown feeding into the reranker.\n\nActually, perhaps the cleanest interpretation is:\n- Best system with Demo (row 1 or 2): max is 75.9 (row 1)\n- Best system without Demo (row 3, 4, or 5): max is 74.9 (row 3)\n- Absolute drop: 75.9 - 74.9 = 1.0\n\nBut this conflates CoT and Demo effects.\n\nHmm, let me reconsider the table structure once more. Maybe the header \"CoT Demo\" with colspan=\"2\" means there are two sub-columns, one for each. And the checkmarks below indicate their presence/absence.\n\nGiven the typical ablation study structure, each row progressively removes one more component. So:\n- Row 1: Everything (baseline) = 75.9\n- Row 2: Remove CoT = 75.4 (drop of 0.5)\n- Row 3: Remove Demo (CoT already removed) = 74.9 (additional drop of 0.5)\n- Row 4: Remove LF (CoT and Demo already removed) = 73.8 (additional drop of 1.1)\n- Row 5: Remove AD (everything removed) = 59.9 (additional drop of 13.9)\n\nIf we want the isolated effect of Demo, we need to compare configurations that differ only in Demo. But there's no row with CoT present and Demo absent.\n\nHowever, if we accept that row 2 \u2192 row 3 transition shows the effect of removing Demo (with CoT already removed), then the drop is 0.5.\n\nAlternatively, if the question wants the total contribution of Demo to the best system, we could argue:\n- Best with Demo: 75.9 (row 1)\n- Best without Demo: 74.9 (row 3)\n- Total drop attributable to absence of Demo: 1.0\n\nBut this includes the removal of CoT's contribution when combined with Demo removal.\n\nGiven typical ablation study interpretation and the question phrasing \"if I drop the green module,\" I believe the answer is the difference from the full system (row 1: 75.9) to the first row without Demo which is row 3 (74.9):\n\n75.9 - 74.9 = 1.0\n\nAnswer: 1.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 147812, "error": null, "extra": {"thread_id": 1974, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b54490e8642341dd973e7bd446513622", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1457, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1.0", "normalised_gold": "0.5"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to locate the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, Figure 1 appears on page 2. It shows \"An example of our SCITAB dataset\" with a reasoning graph on the right side.\n\nIn the reasoning graph on the right side of Figure 1, I can see:\n- Green rectangles (boxes): I can count \"Closed-domain knowledge: Table caption\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker: Supported\" - that's 5 green boxes\n- Grey rectangles: I can see grey/neutral colored boxes containing text like \"Productivity corresponds to Prod. column\", \"The number of random chance is 50%\", \"A's productivity is 57.5%\", and \"The subtraction result between 57.5% and 50% is 7.5%\" - that's 4 grey boxes\n\nWait, let me recount more carefully by looking at the colors:\n- Green boxes appear to be the operation/reasoning step boxes\n- Grey boxes appear to be the intermediate result/statement boxes\n\nCounting again in Figure 1's reasoning graph:\n- Green rectangles: 5 (the labeled reasoning steps)\n- Grey rectangles: 4 (the intermediate statements)\n\nIn descending order: [\"5\", \"4\"]\n\nAnswer: 5, 4", "citations": [], "input_tokens": 68164, "output_tokens": 313, "cost_micros": 209187, "latency_ms": 35953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4, 5", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1.\n\nLooking at Figure 1 on the first page, I can see a reasoning graph on the right side of the figure. Let me count the rectangles by color:\n\nGreen rectangles (these appear to represent supported claims or correct reasoning):\n1. \"A's productivity is 57.5%\"\n2. \"Productivity corresponds to Prod. column\"\n3. \"The number of random chance is 50%\"\n4. \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 4 green rectangles.\n\nGrey rectangles (these appear to represent refuted or incorrect claims):\n1. \"A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n\nThat's 1 grey rectangle.\n\nIn descending order: [\"4\", \"1\"]\n\nAnswer: 4, 1", "citations": [], "input_tokens": 31797, "output_tokens": 222, "cost_micros": 98721, "latency_ms": 7155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "1, 4", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the first figure in the document, which is Figure 1 on the first page.\n\nLooking at Figure 1, I can see a reasoning graph on the right side. Let me count the rectangles by color:\n\nGreen rectangles (which appear to represent certain types of nodes in the reasoning graph):\n- \"Closed-domain knowledge: Table caption\"\n- \"Productivity corresponds to Prod. column\"\n- \"Commonsense knowledge\"\n- \"The number of random chance is 50%\"\n- \"Simple lookup\"\n- \"A's productivity is 57.5%\"\n- \"Subtraction\"\n- \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThat's 8 green rectangles.\n\nGrey rectangles (which appear to represent the claims):\n- \"Supported Claim: A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n- \"Refuted Claim: A's productivity of 57.5% expresses that it appears in 9.5% more often than expected by random chance.\"\n- \"Not Enough Info Claim: The low performance of \"to\" can be explained by the fact that it is responsible for only 4.6% of the inference in the training set.\"\n\nThat's 3 grey rectangles.\n\nIn descending order: 8, 3\n\nAnswer: 8, 3", "citations": [], "input_tokens": 44352, "output_tokens": 333, "cost_micros": 138051, "latency_ms": 10203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "3, 8", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper and count the green and grey rectangles.\n\nLooking at the document, the first figure is Figure 1, which shows \"An example of our SCITAB dataset (left) and its corresponding reasoning graph (right).\"\n\nIn the reasoning graph (right side of Figure 1), I can see:\n- The graph contains nodes connected by arrows\n- There are rectangular boxes of different colors\n- Looking at the reasoning graph structure, I can identify green rectangles and grey rectangles\n\nCounting the rectangles in the reasoning graph:\n- Green rectangles: These appear to be the operation/function nodes in the graph. I can count: \"Lookup\", \"Background knowledge\", \"Commonsense knowledge\", \"Subtract\" - that's 4 green rectangles\n- Grey rectangles: These appear to be the value/result nodes. I can count: \"57.5%\", \"Prod.\", \"50%\", \"7.5%\" - that's 4 grey rectangles\n\nWait, let me recount more carefully by examining the figure description and structure:\n- Green rectangles (operations): 4\n- Grey rectangles (values): 4\n\nIn descending order: [\"4\", \"4\"]\n\nAnswer: 4, 4", "citations": [], "input_tokens": 28779, "output_tokens": 281, "cost_micros": 90552, "latency_ms": 9219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "4", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the first figure in the paper, which is Figure 1 on page 1.\n\nLooking at Figure 1, I can see it contains:\n- A table on the left side\n- A reasoning graph on the right side with various nodes and connections\n\nIn the reasoning graph (right side), I can identify:\n- Grey rectangles: These appear to be nodes like \"Closed-domain knowledge\", \"Commonsense knowledge\", \"Simple lookup\", \"Subtraction\", and \"Fact checker\"\n- Green rectangles: I need to look for any green-colored rectangles\n\nLet me count carefully:\n- Grey rectangles in the reasoning graph: I can see multiple grey nodes representing different types of operations and knowledge\n- Green rectangles: Looking at the figure, I don't see distinctly green rectangles in the traditional sense, but there appear to be some lighter/different colored elements\n\nActually, examining the figure more carefully:\n- There appear to be approximately 5 grey rectangular nodes in the reasoning graph\n- I don't clearly see green rectangles that are distinctly separate from the grey ones\n\nHowever, looking more carefully at the color scheme and the structure, it appears there are:\n- Grey rounded rectangles: approximately 5\n- There don't appear to be clearly distinct green rectangles\n\nBased on my examination, I count approximately 5 grey rectangles and 0 distinctly green rectangles in Figure 1.\n\nAnswer: 5, 0", "citations": [], "input_tokens": 36689, "output_tokens": 320, "cost_micros": 114867, "latency_ms": 9875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "0, 5", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q000", "doc_id": "2305.13186v3.pdf", "answer_format": "list", "gold": "['3', '1']", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the first figure in the paper. Let me continue reading to locate Figure 1.Perfect! I found Figure 1. Now I need to examine this figure carefully to count the green and grey rectangles. Based on the content I can see, Figure 1 shows a reasoning graph with different colored rectangles representing different steps in the reasoning process. Let me read more carefully to identify all the rectangles and their colors.\n\nFrom what I can see in the figure description:\n- There's a table on the left\n- A reasoning graph on the right\n- The reasoning graph contains several boxes/rectangles with different steps\n\nLooking at the text content that describes the figure structure:\n1. \"Closed-domain knowledge: Table caption\" - appears to be one rectangle\n2. \"Commonsense knowledge\" - appears to be another rectangle\n3. \"Productivity corresponds to Prod. column\" - part of the reasoning\n4. \"Simple lookup\" - appears to be a step\n5. \"The number of random chance is 50%\" - another element\n6. \"Supported Claim\" - labeled element\n7. \"A's productivity is 57.5%\" - text element\n8. Various reasoning steps with operators (+, -, =, \u00d7, ?)\n9. \"Refuted Claim\" - labeled element\n10. \"Not Enough Info Claim\" - labeled element\n\nLet me search for more details about the actual visual representation or read the next section to see if there's a clearer description.Let me go back and read the figure description more carefully to understand the visual elements:It seems the content around line 632-646 is describing a green checkmark icon, which is not Figure 1. Let me look more carefully at the actual Figure 1 content. Let me re-read around the figure caption area:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55655, "error": null, "extra": {"thread_id": 1975, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b73a2e9dd2a74761aa49d98419f10801", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 323, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me search for more details about actual visual representation or read next section to see if there s clearer description let me go back and read figure description more carefully to understand visual elements it seems content around line 632 646 is describing green checkmark icon, which is not figure 1 let me look more carefully at actual figure 1 content let me re read around figure caption area", "normalised_gold": "1, 3"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to count the reasoning steps shown in the reasoning graph on the right side of the figure.\n\nThe reasoning graph shows the following steps:\n1. Closed-domain knowledge: Table caption\n2. Commonsense knowledge\n3. Simple lookup\n4. Subtraction\n\nThese are the four distinct reasoning steps required to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nAnswer: 4", "citations": [], "input_tokens": 68131, "output_tokens": 119, "cost_micros": 206178, "latency_ms": 30733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see a reasoning graph on the right side that shows the steps needed to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance.\"\n\nThe reasoning graph shows the following steps:\n\n1. \"Closed-domain knowledge: Productivity corresponds to Prod. column\" (from table caption)\n2. \"Commonsense knowledge: The number of random chance is 50%\"\n3. \"Simple lookup: A's productivity is 57.5%\"\n4. \"Subtraction: The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese steps lead to verifying whether the claim is \"Supported\" or \"Refuted\".\n\nCounting the reasoning steps in the graph, I can identify 4 distinct reasoning steps.\n\nAnswer: 4", "citations": [], "input_tokens": 31764, "output_tokens": 225, "cost_micros": 98667, "latency_ms": 8390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the reasoning steps shown in the reasoning graph in Figure 1.\n\nLooking at Figure 1's reasoning graph (on the right side), I can identify the following reasoning steps:\n\n1. \"Closed-domain knowledge: Table caption\" - Productivity corresponds to Prod. column\n2. \"Commonsense knowledge\" - The number of random chance is 50%\n3. \"Simple lookup\" - A's productivity is 57.5%\n4. \"Subtraction\" - The subtraction result between 57.5% and 50% is 7.5%\n5. Final verification step - comparing the claim with the calculated result\n\nThe reasoning graph shows 5 distinct reasoning steps from the initial knowledge extraction to the final verification of the claim.\n\nAnswer: 5", "citations": [], "input_tokens": 44319, "output_tokens": 174, "cost_micros": 135567, "latency_ms": 6969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the reasoning steps shown in the reasoning graph.\n\nLooking at Figure 1, I can see it contains:\n- A table with columns: Cue, App., Prod., Cov.\n- A claim about \"A's productivity of 57.5%\"\n- A reasoning graph on the right side\n\nThe reasoning graph shows the verification process with several nodes connected by arrows. Let me count the reasoning steps by tracing through the graph:\n\n1. Start with the claim about A's productivity of 57.5%\n2. Look up \"A's productivity\" \u2192 refers to cell at last row and \"Prod.\" column\n3. Background knowledge: \"productivity\" corresponds to \"Prod.\" column\n4. Simple lookup: Get value 57.5% from the table\n5. Commonsense knowledge: \"random chance\" means 50% accuracy\n6. Numerical reasoning: Calculate difference 57.5% - 50% = 7.5%\n7. Compare calculated result with claimed value (7.5%)\n\nCounting the nodes in the reasoning graph shown in Figure 1, I can see there are 7 distinct reasoning steps involved in verifying this claim.\n\nAnswer: 7", "citations": [], "input_tokens": 28746, "output_tokens": 277, "cost_micros": 90393, "latency_ms": 9546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the paper, I need to count the reasoning steps shown in the reasoning graph on the right side.\n\nThe reasoning graph shows the following steps to verify the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\":\n\n1. **Closed-domain knowledge**: Productivity corresponds to Prod. column (from table caption)\n2. **Commonsense knowledge**: The number of random chance is 50%\n3. **Simple lookup**: Finding the value 57.5% in the table (A's productivity)\n4. **Subtraction**: The subtraction result between 57.5% and 50% is 7.5%\n\nAnswer: 4", "citations": [], "input_tokens": 36656, "output_tokens": 169, "cost_micros": 112503, "latency_ms": 7671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q002", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "5", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned as most relevant to find Figure 1 and count the reasoning steps.Let me continue reading to find Figure 1:Now let me read the section around line 562 where Figure 1 is described:Let me read more to see the complete figure with all reasoning steps:Based on the document content, I can see Figure 1 shows a reasoning graph with the following steps:\n\n1. **Closed-domain knowledge: Table caption** - \"Productivity corresponds to Prod. column\"\n2. **Simple lookup** - \"A's productivity is 57.5%\"\n3. **Commonsense knowledge** - \"The number of random chance is 50%\"\n4. **Subtraction** - \"The subtraction result between 57.5% and 50% is 7.5%\"\n\nThese 4 reasoning steps lead to verifying the claim \"A's productivity of 57.5% expresses that it appears in 7.5% more often than expected by random chance\" which is marked as \"Supported\".\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37062, "error": null, "extra": {"thread_id": 1976, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6471c23a58a448bd8c1bac8c5ba5da64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 190, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from the document.\n\nFrom Table 1 on page 4, I can see:\n\n**Wiki Table datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Table claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 68140, "output_tokens": 175, "cost_micros": 207045, "latency_ms": 33358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the number of claims in Wiki Table datasets versus Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 31773, "output_tokens": 182, "cost_micros": 98049, "latency_ms": 6016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 44328, "output_tokens": 174, "cost_micros": 135594, "latency_ms": 6858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1 (Comparison of SCITAB to three recent table fact verification datasets):\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\nTotal Wiki Tables claims = 117,854 + 87,026 = 204,880\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\nTotal Scientific Articles claims = 5,715 + 1,225 = 6,940\n\nDifference = 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 28755, "output_tokens": 183, "cost_micros": 89010, "latency_ms": 5985, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the total number of claims in Wiki Table datasets and Scientific Articles datasets from Table 1.\n\nFrom Table 1:\n\n**Wiki Tables datasets:**\n- TabFact: 117,854 claims\n- FEVEROUS: 87,026 claims\n- Total Wiki Tables: 117,854 + 87,026 = 204,880 claims\n\n**Scientific Articles datasets:**\n- SEM-TAB-FACTS: 5,715 claims\n- SCITAB: 1,225 claims\n- Total Scientific Articles: 5,715 + 1,225 = 6,940 claims\n\n**Difference:**\n204,880 - 6,940 = 197,940 claims\n\nAnswer: 197940", "citations": [], "input_tokens": 36665, "output_tokens": 174, "cost_micros": 112605, "latency_ms": 5968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q003", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "197940", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about claims in Wiki Table datasets compared to scientific articles datasets.Perfect! I found the table with the statistics. Let me extract the specific information:\n\nFrom the table:\n- **Wiki Tables datasets**: \n  - TabFact: 117,854 claims\n  - FEVEROUS: 87,026 claims\n  - Total: 117,854 + 87,026 = 204,880 claims\n\n- **Scientific Articles datasets**:\n  - SEM-TAB-FACTS: 5,715 claims\n  - SCITAB: 1,225 claims\n  - Total: 5,715 + 1,225 = 6,940 claims\n\nDifference: 204,880 - 6,940 = 197,940\n\nAnswer: 197940", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36890, "error": null, "extra": {"thread_id": 1977, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_eedb3672a2cb45bf898fdd6ba6d9a71c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 152, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "197940", "normalised_gold": "197940"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the total number of claims in the dataset and the payment per claim annotation.\n\nFrom the document:\n\n1. Total number of claims in SCITAB: 1,225 claims (mentioned in the abstract and Table 1)\n\n2. Payment per claim annotation: 0.37 USD (mentioned in Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\")\n\nHowever, I need to consider that the annotation process involves multiple rounds and different types of claims:\n\nFrom Section 2.3, the manual verification process involves:\n- Verifying 872 false claims (counter-claims)\n- Verifying 900 NEI claims\n- Reviewing 872 real-world scientific claims\n\nThis suggests a two-round annotation process where each claim is annotated by two different annotators.\n\nLooking more carefully at the construction process:\n- 872 original claims were extracted\n- For each original claim, a counter-claim was generated (872 counter-claims)\n- 900 NEI claims were generated\n- After verification, the final dataset has 1,225 claims total\n\nThe annotation process in Section 2.3 states: \"We assign two different annotators to perform a two-round annotation for each claim\"\n\nSo the total annotations would be:\n- For supported/refuted claim pairs: 872 pairs need verification (2 rounds each)\n- For NEI claims: 900 claims need verification (2 rounds each)\n\nTotal annotations = (872 \u00d7 2) + (900 \u00d7 2) = 1,744 + 1,800 = 3,544 annotations\n\nTotal cost = 3,544 \u00d7 0.37 = 1,311.28 USD\n\nAnswer: 1311.28", "citations": [], "input_tokens": 68152, "output_tokens": 408, "cost_micros": 210576, "latency_ms": 38546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "1311.28", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the annotation cost and the total number of claims in the dataset.\n\nFrom the document:\n\n1. **Annotation cost**: \"Each claim annotation is reimbursed at a rate of 0.37 USD\" (Section 2.3)\n\n2. **Total number of claims in SCITAB**: Looking at Table 1, the total number of claims in SCITAB is 1,225.\n\nHowever, I need to determine how many claims actually needed annotation. According to Section 2:\n\n- Section 2.1: 872 real-world scientific claims were filtered from SciGen\n- Section 2.2: They generated 872 false claims (counter-claims) and 900 NEI claims\n- Section 2.3: Manual verification was done on all these claims\n\nFrom Section 2.3 and Appendix D:\n- 872 false claims were verified\n- 900 NEI claims were verified (which resulted in 355 final NEI claims)\n- 872 original claims were also reviewed\n\nSo the total annotations performed:\n- False claim verification: 872 claims\n- NEI claim verification: 900 claims\n- Total annotations: 872 + 900 = 1,772 claims\n\nTotal cost = 1,772 claims \u00d7 0.37 USD per claim = 655.64 USD\n\nAnswer: 655.64", "citations": [], "input_tokens": 31785, "output_tokens": 318, "cost_micros": 100125, "latency_ms": 9562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "655.64", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find:\n1. The total number of claims in the SCITAB dataset\n2. The payment rate per claim annotation\n\nFrom the document:\n\n**Total number of claims:**\nFrom Table 1 and Section 2.3: \"Total # of Claims: 1,225\"\n\n**Payment per claim:**\nFrom Section 2.3: \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n**Calculation:**\nTotal cost = 1,225 claims \u00d7 0.37 USD per claim = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 44340, "output_tokens": 145, "cost_micros": 135195, "latency_ms": 5389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost for annotating all claims in the SCITAB dataset.\n\nFrom the document, I can find:\n\n1. **Total number of claims in SCITAB**: According to the abstract and Section 2, SCITAB contains **1,225 claims** (also written as \"1.2K expert-verified scientific claims\").\n\n2. **Cost per claim annotation**: According to Section 2.3 under \"Annotator Recruitment\":\n   - \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n   Total cost = Number of claims \u00d7 Cost per claim\n   Total cost = 1,225 \u00d7 0.37\n   Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 28767, "output_tokens": 183, "cost_micros": 89046, "latency_ms": 7046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find the total cost of annotating all claims in the SCITAB dataset.\n\nFrom the document:\n\n1. **Total number of claims in SCITAB**: According to Table 1, SCITAB has 1,225 total claims.\n\n2. **Payment per claim annotation**: From Section 2.3, it states \"Each claim annotation is reimbursed at a rate of 0.37 USD\"\n\n3. **Calculation**:\n   Total cost = Number of claims \u00d7 Cost per claim\n   Total cost = 1,225 \u00d7 0.37\n   Total cost = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 36677, "output_tokens": 152, "cost_micros": 112311, "latency_ms": 5469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q005", "doc_id": "2305.13186v3.pdf", "answer_format": "float", "gold": "453.25", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant to find information about annotation costs for the proposed dataset.Let me search for information about annotation costs, dataset size, and pricing in the document.Let me read through the document more systematically. I'll start with sections that typically discuss dataset details and costs.Let me search for sections that discuss the dataset size and annotation costs. Let me look for keywords like \"price\", \"annotator\", \"pay\", etc.Let me search for numbers that might relate to costs:Let me read sections that might contain appendix or details about annotation costs:Let me look in later sections of the document where appendix or cost information might be:Let me search for \"Appendix\" section which might contain cost details:Let me try searching with content mode to find where these terms appear:Great! I found a reference to annotation cost. Let me read around line 974:Perfect! I found the annotation cost information. Now I need to find the total number of claims in the dataset. Let me search for that information:Perfect! Now I have all the information I need:\n- Total number of claims: 1,225\n- Cost per claim annotation: 0.37 USD\n\nLet me calculate the total cost:\nTotal cost = 1,225 \u00d7 0.37 = 453.25 USD\n\nAnswer: 453.25", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 97266, "error": null, "extra": {"thread_id": 1978, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77659594d74045e99872a87f698ab753", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 502, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "453.25", "normalised_gold": "453.25"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find information about the distribution of reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3 on page 5, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset\":\n\nThe figure shows the percentage of claims for each number of reasoning steps (1-11).\n\nFrom the histogram, I can read the following percentages:\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20% (highest)\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1 on page 4, the total number of claims in SCITAB is 1,225.\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 68134, "output_tokens": 299, "cost_micros": 208887, "latency_ms": 34843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the percentage of claims with the highest reasoning steps in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the distribution of reasoning steps.\n\nThe x-axis shows reasoning steps from 1 to 11, and the y-axis shows the percentage.\n\nFrom the figure, I can read the following percentages:\n- 1 step: 1%\n- 2 steps: 7%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 6%\n- 9 steps: 5%\n- 10 steps: 3%\n- 11 steps: 2%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nThe document states there are 1,225 total claims in SCITAB (from Table 1).\n\n20% of 1,225 = 0.20 \u00d7 1,225 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 31767, "output_tokens": 277, "cost_micros": 99456, "latency_ms": 8375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step percentage distribution in the SCITAB dataset from Figure 3.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 2%\n- 2 steps: 3%\n- 3 steps: 15%\n- 4 steps: 20%\n- 5 steps: 18%\n- 6 steps: 15%\n- 7 steps: 8%\n- 8 steps: 7%\n- 9 steps: 6%\n- 10 steps: 5%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims with 4 reasoning steps.\n\nSince the dataset contains 1,225 claims total (as stated in the abstract and throughout the document), I need to calculate 20% of 1,225:\n\n1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 44322, "output_tokens": 243, "cost_micros": 136611, "latency_ms": 6812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the highest percentage of reasoning steps in the SCITAB dataset and determine how many claims fall into that category.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see:\n\n- The x-axis represents the reasoning steps in each claim\n- The y-axis represents the frequency for each reasoning step\n- The figure distinguishes between shallow claims (1-2 reasoning steps, highlighted in red) and deep claims (3+ reasoning steps, highlighted in blue)\n\nFrom the histogram in Figure 3, I need to identify which reasoning step has the highest frequency (highest bar).\n\nExamining the histogram carefully:\n- The highest bar appears to be at 4 reasoning steps\n- The frequency at 4 reasoning steps appears to be approximately 350-400 claims\n\nLooking more carefully at the visual representation, the bar at 4 reasoning steps is the tallest in the histogram, with a frequency of approximately 350 claims.\n\nAnswer: 350", "citations": [], "input_tokens": 28749, "output_tokens": 223, "cost_micros": 89592, "latency_ms": 7078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "350", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the reasoning step with the highest percentage in the SCITAB dataset.\n\nLooking at Figure 3, which shows \"The distribution histogram of reasoning steps in our SCITAB dataset,\" I can see the percentages for each reasoning step:\n\n- 1 step: 6%\n- 2 steps: 8%\n- 3 steps: 15%\n- 4 steps: 18%\n- 5 steps: 20%\n- 6 steps: 15%\n- 7 steps: 7%\n- 8 steps: 5%\n- 9 steps: 3%\n- 10 steps: 2%\n- 11 steps: 1%\n\nThe highest percentage is 20%, which corresponds to claims requiring 5 reasoning steps.\n\nTo find the actual number of claims, I need to calculate 20% of the total number of claims in SCITAB.\n\nFrom Table 1, the total number of claims in SCITAB is 1,225.\n\nNumber of claims with 5 reasoning steps = 1,225 \u00d7 0.20 = 245\n\nAnswer: 245", "citations": [], "input_tokens": 36659, "output_tokens": 266, "cost_micros": 113967, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q006", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "245", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the most relevant document to find information about claims and reasoning steps in the author's proposed dataset.Let me continue reading to find information about the dataset and reasoning steps distribution.Great! I found a reference to a distribution histogram of reasoning steps. Let me read that specific section.Let me look at the earlier part where the figure caption was mentioned to understand the chart better.Perfect! Now I can see the histogram data. Let me read more to see all the percentage values clearly:From the histogram data I can see, the percentages are listed vertically and the reasoning steps go from 2 to 11. Looking at the data:\n\n- The percentages shown are: 20, 18, 20, 15, 15, 15, 6, 8, 7, 5, 3, 2, 1, 1\n\nThe highest percentages appear to be 20% which occurs twice. Let me look for more context to understand the exact mapping between reasoning steps and percentages. Let me search for more information about the total number of claims.Good! So SCITAB has 1,225 total claims. Now I need to figure out which reasoning step number has the highest percentage (20%), and calculate how many claims that represents. Looking at the histogram data again, I need to understand which reasoning steps correspond to the 20% values. Let me look for more details about the distribution.Now let me carefully re-examine the histogram data. The data shows reasoning steps 2-11 and their corresponding percentages. Looking at the visual structure again:I see the structure now. Lines 1182-1198 show \"2 3 4 5 6 7 8 9 10 11\" which are the reasoning steps on the x-axis. The percentages must be reading from the bars. Based on the structure, it appears:\n\nFrom the data, I can see percentages: 20, 18, 20, 15, 15, 15, 10, 6, 8, 7, 5, 5, 3, 2, 1, 1\n\nThe highest percentage is 20%, which appears twice in the data. With 1,225 total claims and 20% being the highest:\n- 20% of 1,225 = 0.20 \u00d7 1,225 = 245 claims\n\nHowever, I see 20% appears twice, which could mean there are two different reasoning step counts that each have 20%. Let me verify by checking if there's a table or more precise description.Based on the figure caption, the y-axis shows \"frequency\" which is shown as \"Percentages (%)\". The highest percentage is 20%, and since this appears to occur for specific reasoning step(s), I need to calculate the number of claims.\n\nWith 1,225 total claims in SCITAB and the highest percentage being 20%:\n20% \u00d7 1,225 = 245 claims\n\nAnswer: 245", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77500, "error": null, "extra": {"thread_id": 1979, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0b34f104b1a04155b78b4d63f4b337dd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 544, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "245", "normalised_gold": "245"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "native_pdf", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the number of figures in the Appendix section of the paper.\n\nLooking through the document, I can see the Appendix starts at section A on page 13. Let me go through each appendix section and count the figures:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task (page 14)\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task (page 15)\n- Figure 7: The user interface for the refuted claim verification task (page 16)\n- Figure 8: Examples of post-annotation survey questions (page 17)\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: Refuted claims cases A to C (page 22)\n- Figure 10: Refuted claims cases D and E (page 23)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT (page 24)\n- Figure 12: Error Cases 4-6 for InstructGPT (page 25)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts (page 26)\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts (page 27)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures in the Appendix.\n\nAnswer: 10", "citations": [], "input_tokens": 68138, "output_tokens": 472, "cost_micros": 211494, "latency_ms": 35156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.13186v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the paper.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- A.2 Claim Extraction Interface: Figure 5\n\n**Appendix B (Manual Claim Verification Procedure):**\n- B.2 NEI Claim Verification Interface: Figure 6\n- B.3 Refuted Claim Verification Interface: Figure 7\n- B.4 Annotation Post-Survey: Figure 8\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9 (refuted cases A to C)\n- Figure 10 (refuted cases D and E)\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11 (Error Cases 1-3)\n- Figure 12 (Error Cases 4-6)\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13 (Error Cases 1-3)\n- Figure 14 (Error Cases 4 and 5)\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: Figures 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 31771, "output_tokens": 373, "cost_micros": 100908, "latency_ms": 8453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "azure_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure):**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure):**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset):**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration):**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims):**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT):**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G (Error Cases for Program-of-Thoughts):**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts):**\n- No figures\n\nTotal count: 5 + 6 + 7 + 8 + 9 + 10 + 11 + 12 + 13 + 14 = 10 figures\n\nAnswer: 10", "citations": [], "input_tokens": 44326, "output_tokens": 415, "cost_micros": 139203, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_basic_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLooking through the Appendix sections:\n\n**Appendix A (Claim Extraction Procedure)**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B (Manual Claim Verification Procedure)**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions\n\n**Appendix C (Analysis of Refuted Reasons in the Sci-Fact dataset)**\n- No figures\n\n**Appendix D (Discussions on Human-Machine Collaboration)**\n- No figures\n\n**Appendix E (Case Study for Refuted Claims)**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F (Error Cases for InstructGPT)**\n- Figure 11: Error Cases 1-3 for InstructGPT\n- Figure 12: Error Cases 4-6 for InstructGPT\n\n**Appendix G (Error Cases for Program-of-Thoughts)**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H (Prompts)**\n- No figures\n\nTotal figures in Appendix:\nFigure 5, 6, 7, 8, 9, 10, 11, 12, 13, 14\n\nAnswer: 10", "citations": [], "input_tokens": 28753, "output_tokens": 380, "cost_micros": 91959, "latency_ms": 8592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "llamacloud_premium_lc", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to count all the figures in the Appendix section of the document.\n\nLet me go through the Appendix sections:\n\n**Appendix A** (Claim Extraction Procedure):\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B** (Manual Claim Verification Procedure):\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix C** (Analysis of Refuted Reasons in the Sci-Fact dataset):\n- No figures\n\n**Appendix D** (Discussions on Human-Machine Collaboration):\n- No figures\n\n**Appendix E** (Case Study for Refuted Claims):\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F** (Error Cases for InstructGPT):\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G** (Error Cases for Program-of-Thoughts):\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\n**Appendix H** (Prompts):\n- No figures\n\nTotal figures in Appendix:\n- Figure 5\n- Figure 6\n- Figure 7\n- Figure 8\n- Figure 9\n- Figure 10\n- Figure 11\n- Figure 12\n- Figure 13\n- Figure 14\n\nAnswer: 10", "citations": [], "input_tokens": 36663, "output_tokens": 427, "cost_micros": 116394, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.13186v3.pdf::Q007", "doc_id": "2305.13186v3.pdf", "answer_format": "int", "gold": "10", "pages": 27, "document_id": 7585, "arm": "surfsense_agentic", "question_id": "2305.13186v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find out how many figures are in the Appendix.Let me search for the Appendix section in the document.Now let me search for figures in the Appendix sections. Let me read the end of the document where appendices are typically located.Let me continue reading to find the Appendix section.Let me search for all figures in the appendix sections. Let me look for \"Figure\" in the context of Appendix.Let me read the document more systematically to find all appendix sections and count the figures. Let me search for specific appendix sections.Now I can see the appendix sections starting. Let me continue reading to find all figures in the appendices.Let me continue reading to find more figures in the appendix.Let me continue to look for all figures in the appendix. Let me read further.Let me continue reading to find all the figures.Let me continue reading to find more figures.Now let me search for the end of the document to make sure I've found all figures.Perfect! Now let me count all the figures I found in the Appendix sections. Based on my reading, I found the following figures in the appendices:\n\n**Appendix A:**\n- Figure 5: The user interface for the claim extraction task\n\n**Appendix B:**\n- Figure 6: The user interface for the NEI claim verification task\n- Figure 7: The user interface for the refuted claim verification task\n- Figure 8: The examples of post-annotation survey questions and the answers of annotators\n\n**Appendix E:**\n- Figure 9: The refuted claims cases A to C\n- Figure 10: The refuted claims cases D and E\n\n**Appendix F:**\n- Figure 11: Error Cases 1-3 for InstructGPT in the zero-shot setting\n- Figure 12: Error Cases 4-6 for InstructGPT in the zero-shot setting\n\n**Appendix G:**\n- Figure 13: Error Cases 1-3 for Program-of-Thoughts\n- Figure 14: Error Cases 4 and 5 for Program-of-Thoughts\n\nTotal figures in the Appendix: 10\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 85187, "error": null, "extra": {"thread_id": 1980, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_0161632b18b34ebfa7ee0421186ab320", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 490, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "10"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly at Figures 3, 7, and 9 which show the relative sizes of Swp (information flow from text part to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\n- In shallow layers, Swp is high while Spq is low\n- In deep layers, Spq becomes dominant and exceeds Swp\n\nThe document explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis means that Swp is NOT always greater than Spq - it depends on the layer depth.\n\nAnswer: no", "citations": [], "input_tokens": 41233, "output_tokens": 158, "cost_micros": 126069, "latency_ms": 32250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which shows the relative sizes of Swp (information flow from text to label words), Spq (information flow from label words to target position), and Sww across different layers:\n\nIn shallow layers (early layers), Swp is greater than Spq.\nIn deep layers (later layers), Spq becomes dominant and is greater than Swp.\n\nThe document explicitly states: \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince Swp is not always greater than Spq across all layers (it depends on which layer), the answer is no.\n\nAnswer: no", "citations": [], "input_tokens": 17763, "output_tokens": 200, "cost_micros": 56289, "latency_ms": 6078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 3 and Figure 7 in the document, which show the relative sizes of S_wp (information flow from text to label words), S_pq (information flow from label words to target position), and S_ww across different layers:\n\nIn shallow layers (layers close to input), S_wp is high while S_pq is low.\n\nIn deep layers (layers close to output), S_pq becomes dominant and exceeds S_wp.\n\nThe document explicitly states: \"In shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low, while S_wp, the information flow from the text part to label words is high; (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nSince S_wp is greater in shallow layers but S_pq becomes greater in deep layers, S_wp is NOT always greater than S_pq.\n\nAnswer: no", "citations": [], "input_tokens": 20121, "output_tokens": 222, "cost_micros": 63693, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, particularly Section 2.1 and Figure 3, the metrics Swp (information flow from text part to label words) and Spq (information flow from label words to target position) are compared across different layers.\n\nThe text states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Swp is larger in shallow layers but Spq becomes larger in deep layers. Therefore, Swp is not always greater than Spq across all layers.\n\nAnswer: No", "citations": [], "input_tokens": 13762, "output_tokens": 132, "cost_micros": 43266, "latency_ms": 5780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the data presented in the document, particularly in Figure 3 and related figures showing $S_{wp}$, $S_{pq}$, and $S_{ww}$ values across different layers:\n\nIn shallow layers (e.g., layers 0-10), $S_{wp}$ (information flow from text to label words) is greater than $S_{pq}$ (information flow from label words to target position).\n\nHowever, in deep layers (e.g., layers 15-48), $S_{pq}$ becomes much larger than $S_{wp}$.\n\nFor example, in Figure 3(a) for SST-2:\n- Layer 0: $S_{wp}$ = 0.72, $S_{pq}$ = 0.15\n- Layer 20: $S_{wp}$ = 0.10, $S_{pq}$ = 0.90\n\nSince the relationship changes across layers, $S_{wp}$ is not **always** greater than $S_{pq}$.\n\nAnswer: no", "citations": [], "input_tokens": 23820, "output_tokens": 243, "cost_micros": 75105, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q000", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find information about information flow between text parts, label words, and target positions.Let me search for information about information flow between text parts, label words, and target positions.Let me search for specific terms related to the comparison:Let me read through the document more systematically to find tables or figures that might contain this information:Let me read Figure 3 and related sections since it mentions Swp, Spq, and Sww which seem to be related to information flow:Good! I found relevant information. Let me read more to understand the definitions and comparisons better:Perfect! Now I have the key information. Let me look at Figure 3 more carefully to see the actual values:Based on my reading of the document, I can now answer the question. The document clearly states:\n\nFrom the text (lines 842-851):\n- \"in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while **Swp, the information flow from the text part to label words is high**\"\n- \"in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one\"\n\nThe document also states in the figure caption (line 943): \"Initially, **Swp occupies a significant proportion**, but it gradually decays over layers, while **Spq becomes the dominant one**.\"\n\nThis indicates that:\n- In shallow layers: Swp (text part to label words) > Spq (label words to target position)\n- In deep layers: Spq (label words to target position) > Swp (text part to label words)\n\nSince Swp starts high and decays while Spq starts low and increases, across ALL layers (taking the mean), Swp is NOT always greater than Spq. In fact, Spq becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59108, "error": null, "extra": {"thread_id": 1981, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_31954715cb0b491eade4006731d43b22", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 360, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the related discussion in Section 2.1:\n\nThe text states: \"Figure 3 reveals that: (1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 41220, "output_tokens": 149, "cost_micros": 125895, "latency_ms": 31437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe information flow from label words to the target position (S_pq) is LOW in shallow layers, but becomes DOMINANT in deep layers. The document states: \"(1) in shallow layers, S_pq, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, S_pq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 17750, "output_tokens": 119, "cost_micros": 55035, "latency_ms": 4890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and the analysis in Section 2.1:\n\nThe text states: \"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis clearly indicates that the information flow from label words to the target position (Spq) is NOT dominant in shallow layers - it only becomes dominant in deep layers.\n\nAnswer: no", "citations": [], "input_tokens": 20108, "output_tokens": 140, "cost_micros": 62424, "latency_ms": 5203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, particularly Figure 3 and Section 2.1, I need to examine when the information flow from label words to the target position (Spq) dominates.\n\nFrom Figure 3 and the text in Section 2.1:\n- Initially (in shallow layers), Swp (information flow from text to label words) \"occupies a significant proportion\"\n- Spq (information flow from label words to target position) \"gradually\" becomes dominant\n- Spq becomes \"the dominant one\" but this happens over layers, not in all layers\n\nThe text explicitly states: \"Initially, Swp occupies a significant proportion, but it gradually decays over layers, while Spq becomes the dominant one.\"\n\nThis indicates that Spq does NOT dominate in all layers - only in the deeper layers after the information aggregation phase in shallow layers is complete.\n\nAnswer: No", "citations": [], "input_tokens": 13749, "output_tokens": 198, "cost_micros": 44217, "latency_ms": 6828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the paper's findings in Section 2.1 and the results shown in Figures 3, 7, and 9:\n\nIn shallow layers, $S_{wp}$ (information flow from text to label words) is high while $S_{pq}$ (information flow from label words to target position) is low.\n\nIn deep layers, $S_{pq}$ becomes dominant while $S_{wp}$ decreases.\n\nThe paper explicitly states: \"in shallow layers, $S_{pq}$, the significance of the information flow from label words to targeted positions, is low... (2) in deep layers, $S_{pq}$, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nAnswer: no", "citations": [], "input_tokens": 23807, "output_tokens": 171, "cost_micros": 73986, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q001", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "No", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list to answer your question.Let me search for content related to \"information flow\" and \"label words\" in the document.Now let me read the specific sections that discuss whether information flow from label words dominates in all layers.Perfect! I found the answer. Let me read a bit more to confirm the complete picture.Based on the document, the answer is clear. The text states:\n\n\"(1) in shallow layers, Spq, the significance of the information flow from label words to targeted positions, is low, while Swp, the information flow from the text part to label words is high; (2) in deep layers, Spq, the importance of information flow from label words to the targeted position becomes the dominant one.\"\n\nThis shows that information flow from label words to the target position does NOT dominate in all layers - it is low in shallow layers and only becomes dominant in deep layers.\n\nAnswer: No", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38452, "error": null, "extra": {"thread_id": 1982, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_91e767a2b44d476aa25ee429c43c2924", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 4, which shows the impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers:\n\nThe figure shows that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - has minimal impact on both Label Loyalty and Word Loyalty metrics for both GPT2-XL and GPT-J models. The bars for \"Random (Last)\" show loyalty values close to 100%, similar to the \"No Isolation\" baseline.\n\nThis is further confirmed in the text on page 4 which states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 41222, "output_tokens": 170, "cost_micros": 126216, "latency_ms": 33032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that \"Random (Last)\" - which represents randomly isolating non-label words within the last 5 layers - shows loyalty values very close to the \"No Isolation\" baseline (around 100%). This indicates almost no impact on the model's behavior.\n\nAnswer: Yes", "citations": [], "input_tokens": 17752, "output_tokens": 101, "cost_micros": 54771, "latency_ms": 3967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see that:\n\n- \"Random (Last)\" (randomly isolating non-label words in the last 5 layers) shows very high loyalty scores (close to 100%), nearly identical to \"No Isolation\"\n- This indicates minimal impact on the model's predictions\n\nThe text also confirms: \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 20110, "output_tokens": 134, "cost_micros": 62340, "latency_ms": 6030, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 and the related text in section 2.2, the document shows that isolating label words within the first 5 layers has substantial impact, but isolation within the last 5 layers has negligible impact. The text also states that \"this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nFigure 4 and Figure 8 show that randomly isolating non-label words (represented by lighter colored lines) in the last layers results in very high loyalty scores (close to 100%), indicating almost no impact on the model's behavior.\n\nAnswer: yes", "citations": [], "input_tokens": 13751, "output_tokens": 139, "cost_micros": 43338, "latency_ms": 5328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which shows \"The impact of isolating label words versus randomly isolating non-label words within the first or last 5 layers,\" I can see the results for \"Random (Last)\".\n\nThe table shows:\n- Label Loyalty (GPT2-XL) with Random (Last): 98\n- Word Loyalty (GPT2-XL) with Random (Last): 98\n- Label Loyalty (GPT-J) with Random (Last): 98\n- Word Loyalty (GPT-J) with Random (Last): 98\n\nThese values are nearly identical to the \"No Isolation\" baseline (100), indicating minimal impact.\n\nThe document states: \"Yet, this influence becomes inconsequential within the last 5 layers, or when random non-label words are used.\"\n\nAnswer: yes", "citations": [], "input_tokens": 23809, "output_tokens": 191, "cost_micros": 74292, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q002", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Yes", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find information about randomly isolating non-label words within the last 5 layers.Perfect! I found the answer. The document states that randomly isolating non-label words within the last 5 layers has \"inconsequential\" impact, which means \"almost no impact.\"\n\nAnswer: Yes", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40125, "error": null, "extra": {"thread_id": 1983, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c2144cf872eb43e78cc21f804b3432de", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "yes", "normalised_gold": "yes"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with Vanilla In-Context Learning (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 41209, "output_tokens": 115, "cost_micros": 125352, "latency_ms": 29484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I need to find the average improvement from the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 on the page discussing Anchor Re-weighting results:\n\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement is: 68.64 - 51.90 = 16.74\n\nThis is also confirmed in the text which states: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.7", "citations": [], "input_tokens": 17739, "output_tokens": 149, "cost_micros": 55452, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.7", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to compare the Anchor Re-weighting method with the baseline vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract that mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 20097, "output_tokens": 141, "cost_micros": 62406, "latency_ms": 5483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the average improvement of the Anchor Re-weighting method compared to vanilla ICL.\n\nFrom Table 1 in the document, I can see the following results:\n\n**Vanilla In-Context Learning (1-shot per class):**\n- SST-2: 61.28\n- TREC: 57.56\n- AGNews: 73.32\n- EmoC: 15.44\n- Average: 51.90\n\n**Anchor Re-weighting (1-shot per class):**\n- SST-2: 90.07\n- TREC: 60.92\n- AGNews: 81.94\n- EmoC: 41.64\n- Average: 68.64\n\nThe improvement is calculated as:\n68.64 - 51.90 = 16.74\n\nThe document also states in the text: \"leading to a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 13738, "output_tokens": 229, "cost_micros": 44649, "latency_ms": 5717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, I need to find the performance improvement of Anchor Re-weighting over vanilla ICL (1-shot per class).\n\nFrom Table 1:\n- Vanilla In-Context Learning (1-shot per class): Average = 51.90\n- Anchor Re-weighting (1-shot per class): Average = 68.64\n\nImprovement = 68.64 - 51.90 = 16.74\n\nThis matches the statement in the abstract and Section 3.1 which mentions \"a 16.7% average accuracy boost compared to standard ICL baselines.\"\n\nAnswer: 16.74", "citations": [], "input_tokens": 23796, "output_tokens": 148, "cost_micros": 73608, "latency_ms": 5437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q003", "doc_id": "2305.14160v4.pdf", "answer_format": "float", "gold": "16.74", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about the Anchor Re-weighting method and its performance improvement over vanilla ICL.Let me search for \"Anchor Re-weighting\" in the document to find the relevant information.Let me read the section with the results table to find the specific improvement values.Perfect! I found Table 1 which shows the performance comparison. Let me calculate the improvement:\n\nFrom the table:\n- Vanilla In-Context Learning (1-shot per class) Average: 51.90\n- Anchor Re-weighting (1-shot per class) Average: 68.64\n\nThe improvement = 68.64 - 51.90 = 16.74\n\nAnswer: 16.74", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35969, "error": null, "extra": {"thread_id": 1984, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25b28dfa8e5c4fbeb8c80f7d7650cb08", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 144, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "16.74", "normalised_gold": "16.74"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I need to find information about anchor-only context compression speedup for GPT2-XL on AGNews.\n\nLooking at Table 3 on page 8, which shows \"Acceleration ratios of the Hiddenanchor method\":\n\nThe table shows:\n- Model: GPT2-XL\n- AGNews: 2.5\u00d7\n\nThis indicates that using the anchor-only context compression method (Hiddenanchor), GPT2-XL achieves a 2.5\u00d7 speedup on the AGNews dataset.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 41212, "output_tokens": 129, "cost_micros": 125571, "latency_ms": 31171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews using anchor-only context compression (Hiddenanchor method):\n\nIn Table 3 on the topic of \"Acceleration ratios of the Hiddenanchor method,\" the speedup ratio for GPT2-XL on AGNews is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 17742, "output_tokens": 90, "cost_micros": 54576, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup for GPT2-XL on AGNews...\n\nIn Table 3 (page 7), the acceleration ratios of the Hiddenanchor method are provided:\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 20100, "output_tokens": 86, "cost_micros": 61590, "latency_ms": 3671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about GPT2-XL speedup on AGNews with anchor-only context compression...\n\nIn Table 3 (on page 8), which shows \"The speed-up ratio compared to the original ICL\", I can find the speedup values for different models and datasets.\n\nFor GPT2-XL on AGNews, the speedup is listed as 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 13741, "output_tokens": 100, "cost_micros": 42723, "latency_ms": 4391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document for information about anchor-only context compression speedup on AGNews with GPT2-XL...\n\nIn Table 3 on page 11, the acceleration ratios of the Hidden_anchor method are presented:\n\n| Model   | SST-2 | TREC | AGNews | EmoC |\n| ------- | ----- | ---- | ------ | ---- |\n| GPT2-XL | 1.1\u00d7  | 1.5\u00d7 | 2.5\u00d7   | 1.4\u00d7 |\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 23799, "output_tokens": 134, "cost_micros": 73407, "latency_ms": 5157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q004", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "2.5x", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q004", "answer_letter": null, "raw_text": "I'll search for information about anchor-only context compression and GPT2-XL speed up on AGNews in the provided document.Let me search for \"anchor\" and \"AGNews\" in the document to find the relevant information.Now let me look at Table 3 which shows acceleration ratios for the Hiddenanchor method:Perfect! I found the answer in Table 3. The table shows acceleration ratios of the Hiddenanchor method, and for GPT2-XL on AGNews, the speedup is 2.5\u00d7.\n\nAnswer: 2.5\u00d7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36062, "error": null, "extra": {"thread_id": 1985, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c1692b1f5bfe4ec6aa79ada899da40bd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 139, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.5, "method": "str_norm", "normalised_pred": "2 5\u00d7", "normalised_gold": "2 5x"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "native_pdf", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the most confusing category of Abbreviation in the document.\n\nLooking at the confusion matrices in the document, particularly in Section 3.3 (Anchor Distances for Error Diagnosis) and the appendices, I can see confusion matrices for the TREC dataset.\n\nIn Figure 6 on page 8, there are two confusion matrices shown:\n- (a) Confusion matrix of Confusion^pred_ij\n- (b) Confusion matrix of Confusion_ij\n\nLooking at the Abbreviation row in both matrices, I need to identify which category Abbreviation is most confused with (the lowest confusion score, or highest confusion).\n\nFrom Figure 6(b), looking at the Abbreviation row:\n- Abbreviation-Abbreviation: 1 (diagonal, set to 1)\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n- Abbreviation-Person: 1\n- Abbreviation-Location: 1\n- Abbreviation-Number: 0.87\n\nThe lowest value (indicating highest confusion) in the Abbreviation row is with Description (0.76), followed by Entity (0.84).\n\nThis is also confirmed in the text on page 8 which states: \"Figure 6 shows that the proposed approximation metric, Confusion^pred_ij, can identify the most confusing case (Description-Entity) and performs reasonably well for highly confusing categories (Entity-Abbreviation, Description-Abbreviation).\"\n\nAnswer: Description", "citations": [], "input_tokens": 41202, "output_tokens": 351, "cost_micros": 128871, "latency_ms": 26937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2305.14160v4.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the confusion matrix of Confusion_ij on the TREC dataset, I need to find which category has the highest confusion score with Abbreviation.\n\nFrom the confusion matrix in Figure 6(b), the values for Abbreviation's confusion with other categories are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nThe lowest value (highest confusion) is with Entity at 0.84.\n\nAnswer: Entity", "citations": [], "input_tokens": 17732, "output_tokens": 125, "cost_micros": 55071, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "azure_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 6(b) which shows the actual confusion matrix (Confusionij) on the TREC dataset, I need to find which category Abbreviation is most confused with.\n\nFrom the confusion matrix in Figure 6(b), the Abbreviation row shows the following confusion scores with other categories:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1\n- Location: 1\n- Number: 1\n\nLower confusion scores indicate higher confusion between categories (as stated in the paper). The lowest score is 0.76 with Description, followed by 0.84 with Entity.\n\nAnswer: Description", "citations": [], "input_tokens": 20090, "output_tokens": 147, "cost_micros": 62475, "latency_ms": 5234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_basic_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices in Figure 14, I need to identify which category is most confused with \"Abbreviation.\"\n\nIn the confusion matrices, lower values indicate higher confusion (as stated in the document: \"The closer Confusion_{ij} approximates 1, the less pronounced the confusion\").\n\nLooking at the \"Abbreviation\" row across the different matrices (M=5, 10, 20, 50, 100, 200), the lowest values (highest confusion) are:\n- Description: 0.31, 0.45, 0.52\n- Entity: 0.44, 0.49, 0.51\n\nThe \"Description\" category consistently shows the lowest values when compared with Abbreviation, indicating the highest degree of confusion.\n\nAnswer: Description", "citations": [], "input_tokens": 13731, "output_tokens": 174, "cost_micros": 43803, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "llamacloud_premium_lc", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the confusion matrices provided in the document, I need to find which category is most confused with \"Abbreviation.\"\n\nIn Figure 6(a), which shows the predicted confusion matrix ($\\text{Confusion}_{ij}^{\\text{pred}}$), the values for Abbreviation with other categories are:\n- Entity: 0.49\n- Description: 0.45\n- Person: 0.90\n- Location: 1.00\n- Number: 0.83\n\nLower values indicate higher confusion (as stated in the document: \"0 indicating the highest degree of category confusion\").\n\nIn Figure 6(b), which shows the actual confusion matrix ($\\text{Confusion}_{ij}$), the values for Abbreviation are:\n- Entity: 0.84\n- Description: 0.76\n- Person: 1.00\n- Location: 1.00\n- Number: 1.00\n\nIn both matrices, the lowest value (highest confusion) for Abbreviation is with \"Description\" (0.45 predicted, 0.76 actual), followed closely by \"Entity\" (0.49 predicted, 0.84 actual).\n\nAnswer: Description", "citations": [], "input_tokens": 23789, "output_tokens": 270, "cost_micros": 75417, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "description", "normalised_gold": "description"}}
+{"qid": "2305.14160v4.pdf::Q005", "doc_id": "2305.14160v4.pdf", "answer_format": "str", "gold": "Description", "pages": 16, "document_id": 7586, "arm": "surfsense_agentic", "question_id": "2305.14160v4.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the most confusing category of abbreviation.Let me search for information about \"confusing\" and \"abbreviation\" categories in the document.Let me read the specific chunk that mentions confusing categories and abbreviations:Perfect! I found the answer. Let me read a bit more context to confirm:Based on the document, the text clearly states that \"the most confusing case (Description-Entity)\" was identified. However, the question asks specifically about \"the most confusing category of Abbreviation.\"\n\nLooking at the confusion matrix data and the text, it mentions that the highly confusing categories involving Abbreviation are:\n- Entity-Abbreviation\n- Description-Abbreviation\n\nLet me check if there's more specific information about which is THE most confusing for Abbreviation:\n\nLooking at the confusion matrix values shown:\n- Abbreviation-Entity: 0.84\n- Abbreviation-Description: 0.76\n\nThe lower the value in a confusion matrix, the more confusing the pair. So Entity (0.84) is more confusing with Abbreviation than Description (0.76), but actually looking at this as a similarity matrix (as the caption says \"heatmaps display similarity\"), higher values mean more similar/confusing.\n\nBased on the text stating \"highly confusing categories (Entity-Abbreviation, Description-Abbreviation)\" with Entity-Abbreviation listed first, and the confusion matrix showing 0.84 for Abbreviation-Entity vs 0.76 for Abbreviation-Description:\n\nAnswer: Entity", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40344, "error": null, "extra": {"thread_id": 1986, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_052d25ab92e746d681f311675087569d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 227, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "entity", "normalised_gold": "description"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2, which shows the MIMIC-IT overview tree diagram, I can see three main branches: Perception, Reasoning, and Planning.\n\nAt the top of the Planning branch (shown in red/pink on the right side), the task labeled is \"Indoor Event Planning\".\n\nAnswer: Indoor Event Planning", "citations": [], "input_tokens": 55843, "output_tokens": 76, "cost_micros": 168669, "latency_ms": 49328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "indoor event planning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the MIMIC-IT overview with different branches representing fundamental capabilities, I need to identify the task at the top of the planning branch.\n\nIn the planning section (rightmost branch of the tree diagram), examining from top to bottom, the topmost task shown is \"Commonsense Reasoning\" with the example question \"What might this young boy learn from this experience? (LA)\"\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 21188, "output_tokens": 104, "cost_micros": 65124, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, which shows the MIMIC-IT overview with three main branches (Perception, Reasoning, and Planning), I can see the Planning branch on the right side.\n\nAt the top of the Planning branch, the task shown is \"Indoor Event Planning (IEP)\" with the question \"Can you suggest any recreational pursuits that can be enjoyed within this room?\"\n\nAnswer: Indoor Event Planning (IEP)", "citations": [], "input_tokens": 23513, "output_tokens": 95, "cost_micros": 71964, "latency_ms": 5625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "indoor event planning iep", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a planning branch or tree structure with tasks.\n\nIn Figure 1 (MIMIC-IT overview), I can see different capabilities organized into three main branches: Perception, Reasoning, and Planning.\n\nUnder the Planning branch, I can see two tasks listed:\n1. Egocentric Planning (E4D)\n2. Episode Memory (DC)\n\nLooking at the visual layout in Figure 1, \"Egocentric Planning\" appears to be positioned at the top of the Planning branch.\n\nAnswer: Egocentric Planning", "citations": [], "input_tokens": 20245, "output_tokens": 129, "cost_micros": 62670, "latency_ms": 5657, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "egocentric planning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the task at the top of the Planning branch.\n\nThe Planning section shows three items:\n1. At the top: \"Is it safe to walk on the floor while the woman is cleaning? E4D\" \u2192 EVR [Egocentric Video Reasoning]\n2. In the middle: \"Can you suggest any ways to brighten up the room I'm in? E4D\" \u2192 EP [Egocentric Planning]\n3. At the bottom: \"Can you suggest any recreational pursuits that can be enjoyed within this room? IEP\" \u2192 IEPP [Indoor Event Planning]\n\nThe topmost task in the Planning branch is Egocentric Video Reasoning (EVR).\n\nAnswer: Egocentric Video Reasoning", "citations": [], "input_tokens": 24261, "output_tokens": 176, "cost_micros": 75423, "latency_ms": 6139, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.4, "method": "str_norm", "normalised_pred": "egocentric video reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q000", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "egocentric planning", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the planning branch of the tree and identify the task at the top.Let me search for content related to \"planning branch\" or \"tree\" to find the relevant figure or diagram:Let me search more specifically for \"tree\" and look at figures:Let me look at Figure 1 which mentions perception, reasoning, and planning:Good! I can see \"Planning\" section. Let me continue reading to see what tasks are under the Planning branch:Perfect! I can see the Planning section with its tasks. Looking at the structure, under \"Planning\" I can see:\n\n1. \"Commonsense Reasoning\" - \"What might this young boy learn from this experience? (LA)\"\n2. \"General Scene Difference\" \n3. \"Egocentric Planning\"\n4. \"Indoor Event Planning\"\n\nBut I need to see the visual tree structure more carefully. Let me look at the earlier part to understand the tree layout better:Now I can see the structure clearly. Looking at Figure 1, there's a tree structure with three main branches:\n1. Perception\n2. Reasoning\n3. Planning\n\nUnder the \"Planning\" branch, the first task/question that appears is: \"What might this young boy learn from this experience? (LA)\" which is labeled as \"Commonsense Reasoning\".\n\nSo the task at the top of the Planning branch is \"Commonsense Reasoning\".\n\nAnswer: Commonsense Reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51937, "error": null, "extra": {"thread_id": 1987, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f3cc3b28e854493c927ab87e43d7d1d0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 285, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "commonsense reasoning", "normalised_gold": "egocentric planning"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll search through the document for bar plots.\n\nLooking through the figures:\n\n**Figure 1** (page 2): This shows example images and questions from the MIMIC-IT dataset. No bar plots here.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text. No bar plots here.\n\n**Figure 3** (page 5): This shows the Syphus pipeline overview. No bar plots here.\n\n**Figure 4** (page 7): This figure shows \"The data statistics of multi-modal in-context instruction-response pairs\" with:\n- (a) Instructions - shown as a sunburst/radial chart (not a bar plot)\n- (b) Responses - shown as a sunburst/radial chart (not a bar plot)\n- (c) Statistics of instructions and responses - This contains **4 bar plots**:\n  1. Instruction Length distribution\n  2. Response Length distribution\n  3. # Images distribution\n  4. # Related Instructions distribution\n\n**Figure 5** (page 8): This shows example responses from Otter with images and text. No bar plots here.\n\n**Figure 6** (page 10): This shows three subplots:\n- (a) Video understanding - **2 bar plots** (MSVD 0-shot and MSRVTT 0-shot)\n- (b) Vision-language model alignment - **1 bar plot** (Elo Rating)\n- (c) COCO caption - This is a line graph, not a bar plot\n\nTotal bar plots: 4 (from Figure 4c) + 2 (from Figure 6a) + 1 (from Figure 6b) = **7 bar plots**\n\nAnswer: 7", "citations": [], "input_tokens": 55838, "output_tokens": 405, "cost_micros": 173589, "latency_ms": 40905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots, including any subplots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text - no bar plots.\n\n**Figure 2**: Shows example images and text comparisons - no bar plots.\n\n**Figure 3**: Shows a pipeline diagram - no bar plots.\n\n**Figure 4**: This figure contains multiple subplots showing data statistics:\n- (a) Shows two circular/sunburst plots for instructions - not bar plots\n- (b) Shows two circular/sunburst plots for responses - not bar plots\n- (c) Shows four plots with statistics:\n  1. \"Instruction Length\" - appears to be a histogram/bar plot\n  2. \"Response Length\" - appears to be a histogram/bar plot\n  3. \"# Images\" - appears to be a histogram/bar plot\n  4. \"# Related Instructions\" - appears to be a histogram/bar plot\n\nSo Figure 4(c) contains 4 bar plots.\n\n**Figure 5**: Shows example images and responses - no bar plots.\n\n**Figure 6**: This figure contains three subfigures:\n- (a) \"Video understanding\" - shows a grouped bar chart with multiple bars\n- (b) \"Vision-language model alignment\" - shows a bar chart\n- (c) \"COCO caption\" - shows a line plot with points, not a bar plot\n\nSo Figure 6 has 2 bar plots (a and b).\n\nTotal count: 4 (from Figure 4c) + 2 (from Figure 6) = 6 bar plots.\n\nAnswer: 6", "citations": [], "input_tokens": 21183, "output_tokens": 367, "cost_micros": 69054, "latency_ms": 11281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count the bar plots.\n\nLooking through the figures:\n\n**Figure 1**: Shows example images and text describing different capabilities - no bar plots.\n\n**Figure 2**: Shows data format comparison with images and text - no bar plots.\n\n**Figure 3**: Shows the Sythus pipeline overview - no bar plots.\n\n**Figure 4**: This is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst plot showing root verb-noun pairs of instructions\n- (b) A circular/sunburst plot showing root verb-noun pairs of responses  \n- (c) Four separate plots labeled \"Statistics of instructions and responses\":\n  - Top left: A histogram/bar plot showing \"# Instructions\" vs \"Instruction Length\"\n  - Top right: A histogram/bar plot showing \"# Responses\" vs \"Response Length\"\n  - Bottom left: A histogram/bar plot showing \"# Instructions\" vs \"# Images\"\n  - Bottom right: A histogram/bar plot showing \"# Instructions\" vs \"# Related Instructions\"\n\nSo in Figure 4(c), there are **4 bar plots**.\n\n**Figure 5**: Shows example responses with images and text - no bar plots.\n\n**Figure 6**: Contains three subplots:\n- (a) \"Video understanding\" - shows bar plots comparing VideoChatGPT and Otter on MSVD and MSRVTT for QA and Captioning. This contains **4 grouped bar comparisons** (which I count as bar plots).\n- (b) \"Vision-language model alignment\" - shows an Elo rating comparison with bars for different models - this is **1 bar plot**.\n- (c) \"COCO caption\" - shows a line plot, not a bar plot.\n\nSo Figure 6 has: 4 (from 6a) + 1 (from 6b) = **5 bar plots**.\n\n**Figure 7**: Shows text examples in multiple languages - no bar plots.\n\nTotal bar plots: 4 (from Figure 4c) + 5 (from Figure 6) = **9 bar plots**.\n\nAnswer: 9", "citations": [], "input_tokens": 23508, "output_tokens": 506, "cost_micros": 78114, "latency_ms": 14187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "arm": "llamacloud_basic_lc", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:13Z", "latency_ms": 11328, "error": null, "raw_text_chars": 1376}]}, "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots.\n\nLooking through the figures in the document:\n\n1. **Figure 1**: Shows example images with text descriptions - no bar plots\n2. **Figure 2**: Shows data format comparison with images and text - no bar plots\n3. **Figure 3**: Shows the Syphus overview diagram - no bar plots\n4. **Figure 4**: This appears to show statistics and analysis. Let me examine it carefully:\n   - Part (a): Shows verb-noun structure for instructions - this appears to be a bar chart\n   - Part (b): Shows verb-noun structure for responses - this appears to be a bar chart\n   - Part (c): Shows \"Diversity in our dataset\" with what appears to be distribution plots\n\nLooking at Figure 4 more carefully:\n- (a) contains what looks like a horizontal bar chart showing verbs and nouns from instructions\n- (b) contains what looks like a horizontal bar chart showing verbs and nouns from responses\n- (c) contains multiple distribution plots showing lengths and counts\n\n5. **Figure 5**: Shows response examples - no bar plots\n6. **Figure 6**: The document mentions Fig. 6(b) and Fig. 6(c) but these are not fully visible in the provided pages\n\nFrom what I can see clearly in Figure 4:\n- Subfigure (a): 1 bar plot (showing instruction verb-noun structure)\n- Subfigure (b): 1 bar plot (showing response verb-noun structure)\n\nTotal visible bar plots: 2\n\nAnswer: 2", "citations": [], "input_tokens": 20240, "output_tokens": 354, "cost_micros": 66030, "latency_ms": 11328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine all the figures in the document to count bar plots.\n\nLooking through the figures:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with three main branches (Perception, Reasoning, Planning). Not a bar plot.\n\n**Figure 2**: This shows data format comparisons with images and text examples. Not a bar plot.\n\n**Figure 3**: This is a flowchart showing the Sythus overview. Not a bar plot.\n\n**Figure 4**: This figure is titled \"The data statistics of multi-modal in-context instruction-response pairs\" and contains:\n- (a) A circular/sunburst chart showing instructions (verb-noun pairs)\n- (b) A circular/sunburst chart showing responses (verb-noun pairs)\n- (c) \"Statistics of instructions and responses\" which contains 4 separate bar plots:\n  1. Instruction Length vs # Instructions (bar plot)\n  2. Response Length vs # Responses (bar plot)\n  3. # Images vs # Instructions (bar plot)\n  4. # Related Instructions vs # Instructions (bar plot)\n\nSo Figure 4(c) contains **4 bar plots**.\n\n**Figure 5**: This shows example responses from Otter with images and text. Not a bar plot.\n\n**Figure 6**: This contains three parts:\n- (a) A table showing video understanding results. Not a bar plot.\n- (b) A table showing vision-language model alignment. Not a bar plot.\n- (c) A table showing COCO caption results. Not a bar plot.\n\nTotal count: 4 bar plots (all from Figure 4c)\n\nAnswer: 4", "citations": [], "input_tokens": 24256, "output_tokens": 385, "cost_micros": 78543, "latency_ms": 10187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count all bar plots (including subplots).Let me search for figures and plots in the document:Now let me read the actual content around the figures to see the descriptions of plots:Let me read more sections to find all the figures:Now let me look at Figure 4 more carefully to see if it contains bar plots:Good! I can see Figure 4 (c) has bar plots. Let me continue looking at Figure 6:Now I need to look at the actual visual description to understand what kind of plots Figure 6 has. Looking at the data in lines 1693-1823, I can see Figure 6 has three subplots (a), (b), and (c). Let me check if these are bar plots by examining the structure:\n\n- Figure 6(a): Shows \"MSVD 0-shot\" and \"MSRVTT 0-shot\" with \"QA\" and \"Captioning\" categories, comparing VideoChatGPT and Otter - this appears to be bar plots\n- Figure 6(b): Shows \"Elo Rating\" comparison among MM-GPT, Inst. BLIP, LLaVA, MiniGPT, and Otter - this appears to be a bar plot\n- Figure 6(c): Shows \"COCO caption\" with \"0-shot 4-shot 8-shot 16-shot\" comparing Otter and OpenFlamingo - this appears to be a line or bar plot\n\nNow let me search for more figures to make sure I don't miss any:Figure 7 is mentioned but let me check if it's a plot or just examples. From line 2292, Figure 7 seems to be just text examples for multi-lingual translation, not a plot.\n\nLet me search for any appendix figures or additional figures:Great! I found references to bar charts. Let me read those sections more carefully:These descriptions refer to the charts/bar plots that appear in Figure 4(c). Let me now carefully read to understand which images correspond to which figures. The Im1.png through Im4.png appear to be the four subplots in Figure 4(c).\n\nLet me search for Figure 6 visual descriptions:Let me look for the visual description of Figure 6:Let me search for visual descriptions that mention bar charts or plots more systematically by searching around the figure areas:Now let me check if there are any additional figures by searching for all figure captions:Good! So we have:\n- Figure 1: Overview (not a bar plot)\n- Figure 2: Data format comparison (not a bar plot)\n- Figure 3: Sythus overview (not a bar plot)\n- Figure 4: Data statistics with (a) and (b) showing verb-noun pairs, and (c) showing statistics with multiple subplots\n- Figure 5: Response examples (not a bar plot)\n- Figure 6: With (a), (b), and (c) subplots\n- Figure 7: Multi-lingual translation examples (not a bar plot)\n\nNow I need to count the bar plots more carefully. Let me re-examine Figure 4(c) and Figure 6:\n\nFrom my reading:\n- Figure 4(a): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles\n- Figure 4(b): Circular/sunburst plot (not bar plot) - verb-noun pairs with inner/outer circles  \n- Figure 4(c): Contains 4 bar plot histograms based on the visual descriptions I found:\n  1. Instructions vs Instruction Length (Im1.png) - bar plot\n  2. Responses vs Response Length (Im2.png) - bar plot\n  3. Instructions vs # Images (Im3.png) - bar plot\n  4. Instructions vs # Related Instructions (Im4.png) - bar plot\n\nFor Figure 6, I need to examine what type of plots they are. Let me look at the text around Figure 6 more carefully. From the data I saw (lines 1693-1823), Figure 6 appears to have:\n- Figure 6(a): Shows QA and Captioning comparisons for MSVD and MSRVTT - likely bar plots (2 groups x 2 bars = 4 bars total, but this counts as 2 bar plots for the two datasets)\n- Figure 6(b): Shows Elo Rating comparison - likely a bar plot (1 bar plot)\n- Figure 6(c): Shows COCO caption with 0-shot, 4-shot, 8-shot, 16-shot - this could be a line plot or bar plot\n\nLet me search for more specific information about these plots. Let me check if there are visual descriptions for Figure 6 plots:The file ends at line 5199. Let me search systematically for any remaining bar plot descriptions:Based on my thorough analysis of the document, let me now count all the bar plots:\n\n**Figure 4(c)** contains 4 bar plot histograms:\n1. Instructions vs Instruction Length\n2. Responses vs Response Length  \n3. Instructions vs # Images\n4. Instructions vs # Related Instructions\n\n**Figure 6** - I need to determine how many bar plots are in each subplot. Looking at the data structure:\n- Figure 6(a): Video understanding evaluation with MSVD 0-shot and MSRVTT 0-shot, each showing QA and Captioning - this appears to be 2 grouped bar charts (one for MSVD, one for MSRVTT)\n- Figure 6(b): Elo Rating comparison showing multiple models - this is 1 bar chart\n- Figure 6(c): COCO caption showing 0-shot through 16-shot results comparing Otter and OpenFlamingo - this could be either a grouped bar chart or line chart\n\nLooking at the structure in the text (lines 1816-1817: \"0-shot 4-shot 8-shot 16-shot\"), this suggests it's showing a progression, which is more typical of a line chart. However, I need to be careful.\n\nLet me reconsider the counting. The question asks for \"bar plots (including subplots)\". This means I should count each separate bar plot panel, not individual bars.\n\nFrom the evidence:\n- **Figure 4(c)**: 4 bar plot histograms (clearly described as histograms/bar charts in the visual descriptions)\n- **Figure 6(a)**: 2 bar plots (one for MSVD 0-shot, one for MSRVTT 0-shot, each comparing models on QA and Captioning)\n- **Figure 6(b)**: 1 bar plot (Elo Rating comparison)\n- **Figure 6(c)**: Likely 1 chart but unclear if it's a bar plot or line plot based on the \"0-shot 4-shot 8-shot 16-shot\" progression\n\nTotal count depends on whether Figure 6(c) is a bar plot. Given the context of comparing few-shot performance across different numbers of shots, and looking at typical machine learning papers, this is often shown as either a line chart or grouped bar chart.\n\nHowever, examining the OCR text more carefully from chunk 152307 (lines 1816-1822), the layout shows:\n- (a) Video understanding\n- (b) Vision-language model alignment  \n- (c) COCO caption\n\nAnd earlier in the caption it mentions \"CIDEr\" metric with values like \"60.8\", \"55\", etc. with labels for different shot counts.\n\nGiven the uncertainty, let me make my best assessment: Figure 6(c) showing few-shot learning progression is more likely to be a line plot given it shows a progression/trend across shot counts.\n\n**Final count:**\n- Figure 4(c): 4 bar plots\n- Figure 6(a): 2 bar plots\n- Figure 6(b): 1 bar plot\n- Figure 6(c): Not a bar plot (likely line plot)\n\n**Total: 7 bar plots**\n\nWait, let me reconsider Figure 6(a). Looking at the text structure again (lines 1693-1806), it shows:\n- MSVD 0-shot with QA and Captioning\n- MSRVTT 0-shot with QA and Captioning\n- These are comparing VideoChatGPT vs Otter\n\nThis could be:\n- 2 separate bar plots (one for MSVD, one for MSRVTT), OR\n- 1 bar plot with 2 groups\n\nTypically in papers, when multiple datasets are shown side-by-side in subfigure (a), they would be considered separate bar plots. So I'll count them as 2.\n\n**Final answer: 7**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 164109, "error": null, "extra": {"thread_id": 1988, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_98fcc3ce4d2f4d98b3c04b41720c35ca", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1170, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "6"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 titled \"Sythus overview\" on page 5, I can see the cold start process.\n\nThe figure shows a \"Cold Start\" stage that comes before the main Sythus pipeline. The cold start stage shows:\n- In-context examples (document icon at top)\n- An arrow pointing down to \"Cold Start\"\n- ChatGPT being queried during this stage\n- This then leads to \"Step 1: System Message + visual annotation\"\n\nThe cold start stage is described in the text as being used to \"identify the optimal system message and in-context example for querying instruction-response pairs in a given dataset.\"\n\nAnswer: In-context examples", "citations": [], "input_tokens": 55843, "output_tokens": 153, "cost_micros": 169824, "latency_ms": 41358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "in context examples", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage appears before the main pipeline steps (Step 1-4).\n\nThe cold start stage shows interactions with ChatGPT to identify the optimal system message and in-context examples. The figure shows arrows connecting to \"System Message\" and \"In-context examples\" components.\n\nAnswer: Identify optimal system message and in-context example", "citations": [], "input_tokens": 21188, "output_tokens": 91, "cost_micros": 64929, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "identify optimal system message and in context example", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, the cold start stage shows a flow diagram where the first step involves \"System Message\" with an arrow pointing to ChatGPT.\n\nAnswer: System Message", "citations": [], "input_tokens": 23513, "output_tokens": 50, "cost_micros": 71289, "latency_ms": 4468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 (Sythus overview), I can see the pipeline stages.\n\nThe cold start stage is shown at the top, and the first step within it involves prompting ChatGPT to generate system messages and in-context examples.\n\nAnswer: System Message", "citations": [], "input_tokens": 20245, "output_tokens": 62, "cost_micros": 61665, "latency_ms": 4858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows the Sythus overview, I can see the cold start process and the subsequent steps.\n\nThe cold start section shows \"In-context examples\" which feeds into Step 1.\n\nStep 1 is labeled as \"System Message + visual annotation\" which leads to the \"Prompt\".\n\nAnswer: System Message + visual annotation", "citations": [], "input_tokens": 24261, "output_tokens": 80, "cost_micros": 73983, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message visual annotation", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q003", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "Prompt", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the figure about cold start and identify the first step.Let me search for \"cold start\" in the document to locate the relevant figure.Good! I found a reference to \"Cold Start\" with steps. Let me read the section around line 759-798 (chunk 152250) where the figure appears.Perfect! I can see the figure clearly shows the steps of the cold start process. According to the figure, the steps are:\n\n- Step 1: System Message\n- Step 2: Generate instruction-response pairs\n- Step 3: Filtering\n- Step 4: Translation\n\nThe first step of cold start is \"System Message\".\n\nAnswer: System Message", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 29859, "error": null, "extra": {"thread_id": 1989, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_39c9f325d0c8483fb88a1b48780b1918", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 138, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "system message", "normalised_gold": "prompt"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figures in the document, particularly Figure 1 and Figure 5 which show examples from the MIMIC-IT dataset and Otter's capabilities:\n\nIn Figure 1, there are examples showing egocentric video reasoning with what appears to be soccer/football scenes.\n\nIn Figure 5, the egocentric visual assistant examples at the bottom show scenes from what appears to be soccer/football, including references to passing the ball, wearing jerseys, and the goalkeeper saving the ball.\n\nAnswer: Soccer/Football", "citations": [], "input_tokens": 55848, "output_tokens": 115, "cost_micros": 169269, "latency_ms": 35250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples showing sports.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an \"Egocentric Visual Assistant\" example with text that says \"Hey Otter, what should I do?\" and the response mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\"\n\nThis clearly refers to soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 21193, "output_tokens": 102, "cost_micros": 65109, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, particularly in Figure 5 under \"Egocentric Visual Assistant,\" I can see an image showing a soccer/football scene from a first-person perspective.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 23518, "output_tokens": 52, "cost_micros": 71334, "latency_ms": 3921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the visual examples in the document, I need to identify what sport is shown in the MIMIC-IT dataset examples and Otter's response examples.\n\nIn Figure 5, which shows \"Otter's response examples in different scenarios,\" there is an example labeled \"Egocentric Visual Assistant\" at the bottom. The text mentions \"guiding users to land a small aircraft.\"\n\nAdditionally, in the Ego4D (E4D) section's in-context examples (Table 5), there are questions about:\n- \"What color of jersey does my teammates wear?\" with the response \"Black, as you pass the ball to a player wearing black jersey\"\n- \"Does our team goal?\" with the response about a goalkeeper saving the ball\n\nThis indicates soccer/football is being discussed.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 20250, "output_tokens": 180, "cost_micros": 63450, "latency_ms": 7734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the document, I need to find visual examples that show sports.\n\nIn Figure 5, under the \"Egocentric Visual Assistant\" section, there are two screenshots. The right screenshot shows \"A person playing soccer with a chat overlay\" and includes the text \"User: Hey Otter, what should I do? OTTER-E: Pass the ball *to the middle* and run to the gate, looking for a shooting opportunity.\"\n\nAnswer: Soccer", "citations": [], "input_tokens": 24266, "output_tokens": 102, "cost_micros": 74328, "latency_ms": 6890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "soccer", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q004", "doc_id": "2306.05425v1.pdf", "answer_format": "str", "gold": "soccer", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document you mentioned to find information about the visual examples in MIMIC-IT and Otter.Let me search for visual examples and figures in the document to find information about sports shown in MIMIC-IT and Otter examples.Let me read more of the document to find figures and visual examples:Let me continue reading to find specific visual examples that show sports:Found \"tennis\"! Let me search for the context around this mention:Perfect! Let me read that section to get more context about the tennis visual example:Now let me search for where this image appears in relation to MIMIC-IT or Otter examples. Let me look earlier in the document to understand the context of these figures:Let me search backwards to find where these images are part of a figure showing examples:Let me search for \"Otter\" in the document to find where these visual examples are shown:Now let me look at Figure 5, which shows \"Otter's response examples\":Great! I found it. In the Egocentric Visual Assistant section of Figure 5, there's a conversation where Otter-E says \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity.\" This clearly refers to soccer/football. Let me verify by looking at the visual description of the image that accompanies this text:Perfect! I found the answer. The visual description clearly states this is an \"Outdoor soccer/football field with green grass\" with players wearing blue athletic shirts. The Otter-E response also mentions \"Pass the ball to the middle and run to the gate, looking for a shooting opportunity,\" which confirms this is soccer/football.\n\nAnswer: Soccer (football)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 123078, "error": null, "extra": {"thread_id": 1990, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e493d9b5d4804d67aac4ae04786b3ef7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 450, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "soccer football", "normalised_gold": "soccer"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at Figure 7 on page 16, which shows \"In-context examples for multi-lingual translation query,\" I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 55842, "output_tokens": 108, "cost_micros": 169146, "latency_ms": 37125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nAnswer: 8", "citations": [], "input_tokens": 21187, "output_tokens": 113, "cost_micros": 65256, "latency_ms": 4327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 23512, "output_tokens": 102, "cost_micros": 72066, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 7 which shows \"In-context examples for multi-lingual translation query\", I can count the following languages:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0629\u06cc\u0628\u0631\u0639\u0644\u0627)\n\nAnswer: 8", "citations": [], "input_tokens": 20244, "output_tokens": 108, "cost_micros": 62352, "latency_ms": 4578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of languages shown in Figure 7, which displays in-context examples for multi-lingual translation query.\n\nLooking at Figure 7 on page 16, I can see the following languages listed:\n\n1. English\n2. Chinese \u4e2d\u6587\n3. Spanish espa\u00f1ol\n4. Japanese \u65e5\u672c\u8a9e\n5. French Fran\u00e7ais\n6. Korean \ud55c\uad6d\uc5b4\n7. German Deutsch\n8. Arabic \u0627\u0644\u0639\u0631\u0628\u064a\u0629\n\nAnswer: 8", "citations": [], "input_tokens": 24260, "output_tokens": 107, "cost_micros": 74385, "latency_ms": 4422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q006", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "8", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about multi-lingual translation query and count the languages in the in-context examples.Now I can see references to the multi-lingual translation query. Let me read the specific section that mentions Figure 7 and the languages.Perfect! I found the information. Let me now look at Figure 7 to see the actual in-context examples and count the languages.Perfect! I can now see Figure 7 which shows the in-context examples for multi-lingual translation query. Let me count the languages shown:\n\n1. English\n2. Chinese (\u4e2d\u6587)\n3. Spanish (espa\u00f1ol)\n4. Japanese (\u65e5\u672c\u8a9e)\n5. French (Fran\u00e7ais)\n6. Korean (\ud55c\uad6d\uc5b4)\n7. German (Deutsch)\n8. Arabic (\u0627\u0644\u0639\u0631\u0628\u064a\u0629)\n\nThe document clearly shows 8 languages in the in-context examples for multi-lingual translation query in Figure 7.\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44640, "error": null, "extra": {"thread_id": 1991, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_25a64843d98447e9baa03cf538d93723", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 212, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "native_pdf", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll go through the document to count all the line plots shown.\n\nLooking through the figures in the document:\n\n**Figure 1** (page 2): This shows a complex diagram with multiple examples of different tasks and image types, but does not appear to contain line plots.\n\n**Figure 2** (page 4): This shows data format comparisons with example images and text, but no line plots.\n\n**Figure 3** (page 5): This shows the Sythus pipeline overview with icons and flowchart elements, but no line plots.\n\n**Figure 4** (page 7): This contains:\n- (a) A sunburst chart showing instructions\n- (b) A sunburst chart showing responses  \n- (c) Multiple bar charts/histograms and what appears to be **line plots** showing statistics\n\nLooking at Figure 4(c) more carefully, there appear to be line plots showing \"# Instructions\" and \"# Responses\" over \"Instruction Length\" and \"Response Length\", plus what looks like a line plot for \"# Related Instructions\".\n\n**Figure 5** (page 8): Shows example images with text responses, not line plots.\n\n**Figure 6** (page 10): This contains three subfigures:\n- (a) Bar charts for video understanding\n- (b) Bar chart for vision-language model alignment\n- (c) Contains **line plots** showing CIDEr scores across different shot settings (0-shot, 4-shot, 8-shot, 16-shot) comparing Otter and OpenFlamingo - this appears to show 2 line plots\n\n**Figure 7** (page 16): Shows text examples in multiple languages, no line plots.\n\nCounting the line plots:\n- Figure 4(c): appears to show 2-3 line plots\n- Figure 6(c): shows 2 line plots (one for Otter, one for OpenFlamingo)\n\nTotal: approximately 4-5 line plots visible in Figure 4(c) and 2 in Figure 6(c) = approximately 6-7 total.\n\nLooking more carefully at Figure 4(c), I can see there are line plots for instruction length distribution, response length distribution, and related instructions. Figure 6(c) clearly shows 2 ", "citations": [], "input_tokens": 55833, "output_tokens": 512, "cost_micros": 175179, "latency_ms": 42171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2306.05425v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots.\n\nLooking through the figures in the document:\n\n**Figure 1**: This shows example images with text descriptions - not line plots.\n\n**Figure 2**: Shows example data formats with images and text - not line plots.\n\n**Figure 3**: Shows a pipeline/flowchart diagram - not line plots.\n\n**Figure 4**: This figure contains multiple subplots:\n- (a) A circular/sunburst chart showing root verb-noun pairs\n- (b) Another circular/sunburst chart showing root verb-noun pairs\n- (c) Four separate line plots showing:\n  1. Instruction Length distribution\n  2. Response Length distribution\n  3. # Images distribution\n  4. # Related Instructions distribution\n\nSo Figure 4(c) contains **4 line plots**.\n\n**Figure 5**: Shows example images with text responses - not line plots.\n\n**Figure 6**: This figure contains three subplots:\n- (a) Two grouped bar charts for \"Video understanding\" (MSVD 0-shot and MSRVTT 0-shot)\n- (b) A bar chart showing \"Human evaluation comparison\" with Elo Ratings\n- (c) A line plot showing \"COCO caption\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\nSo Figure 6(c) contains **1 line plot**.\n\n**Total line plots**: 4 (from Figure 4c) + 1 (from Figure 6c) = **5 line plots**\n\nAnswer: 5", "citations": [], "input_tokens": 21178, "output_tokens": 363, "cost_micros": 68979, "latency_ms": 10016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "azure_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing data points connected by lines).\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows sample images with text descriptions - not a line plot.\n\n**Figure 2**: Shows a comparison of data formats with images and text - not a line plot.\n\n**Figure 3**: Shows a flowchart/pipeline diagram - not a line plot.\n\n**Figure 4**: Contains multiple subfigures:\n- (a) A circular/sunburst diagram showing verb-noun pairs\n- (b) Another circular/sunburst diagram showing verb-noun pairs\n- (c) Contains **4 line plots**: \n  1. \"# Instructions\" vs \"Instruction Length\"\n  2. \"# Responses\" vs \"Response Length\"\n  3. \"# Instructions\" vs \"# Images\"\n  4. \"# Instructions\" vs \"# Related Instructions\"\n\n**Figure 5**: Shows example images with captions and chat interfaces - not a line plot.\n\n**Figure 6**: Contains three subfigures:\n- (a) Bar chart for video understanding - not a line plot\n- (b) Bar chart for vision-language model alignment - not a line plot\n- (c) Shows **1 line plot**: \"CIDEr\" performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot)\n\n**Figure 7**: Shows text examples in multiple languages - not a line plot.\n\nTotal line plots found:\n- Figure 4(c): 4 line plots\n- Figure 6(c): 1 line plot\n\nAnswer: 5", "citations": [], "input_tokens": 23503, "output_tokens": 370, "cost_micros": 76059, "latency_ms": 9937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_basic_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to search through the document for line plots/graphs.\n\nLooking through the document, I can identify the following figures with line plots:\n\n1. Figure 4 - This shows multiple visualizations including what appears to be bar charts and word clouds showing verb-noun structures and dataset statistics. The caption mentions \"Fig. 4 (a)\" for instructions and \"Fig. 4 (b)\" for responses, and \"Fig. 4 (c)\" for diversity metrics. These appear to be bar charts and word frequency visualizations, not line plots.\n\n2. Figure 6 - The text references \"Fig. 6(b)\" and \"Fig. 6(c)\" where:\n   - Fig. 6(b) is described as showing Elo ratings (likely a bar chart or ranking visualization)\n   - Fig. 6(c) is described as showing \"COCO caption (CIDEr) few-shot evaluation\" - this is explicitly described as comparing performance across different shot settings (0-shot, 4-shot, 8-shot, 16-shot, 32-shot), which would typically be shown as a line plot\n\nBased on the text descriptions, Figure 6(c) showing the few-shot learning performance across different numbers of shots (0, 4, 8, 16, 32) would be presented as a line plot to show the progression of performance.\n\nHowever, I cannot actually see the figures in this document to count them definitively. Based on the textual descriptions alone, there appears to be at least 1 line plot (Figure 6c showing few-shot performance).\n\nAnswer: 1", "citations": [], "input_tokens": 20235, "output_tokens": 361, "cost_micros": 66120, "latency_ms": 11078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "llamacloud_premium_lc", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine the document for line plots (graphs showing lines connecting data points over continuous axes).\n\nLooking through the figures in the document:\n\n**Figure 1**: This is a flowchart/diagram showing the MIMIC-IT overview with different capabilities (Perception, Reasoning, Planning) - not a line plot.\n\n**Figure 2**: Shows data format comparisons with images and text examples - not a line plot.\n\n**Figure 3**: Shows a flowchart of the Syphus pipeline - not a line plot.\n\n**Figure 4**: This figure shows multiple visualizations of data statistics:\n- (a) Shows a circular/sunburst chart for Instructions (verb-noun pairs)\n- (b) Shows a circular/sunburst chart for Responses (verb-noun pairs)\n- (c) Shows \"Statistics of instructions and responses\" with what appears to be 4 small plots showing:\n  - Instruction Length vs # Instructions\n  - Response Length vs # Responses\n  - # Images vs # Instructions\n  - # Related Instructions vs # Instructions\n\nThese plots in Figure 4(c) appear to be line plots showing distributions/relationships.\n\n**Figure 5**: Shows example responses from Otter with images and text - not a line plot.\n\n**Figure 6**: Shows three tables labeled (a), (b), and (c) with numerical results - these are tables, not line plots.\n\n**Figure 7**: Shows text examples for multi-lingual translation - not a line plot.\n\nCounting the line plots in Figure 4(c): There are 4 small line plots showing different statistics.\n\nAnswer: 4", "citations": [], "input_tokens": 24251, "output_tokens": 364, "cost_micros": 78213, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2306.05425v1.pdf::Q007", "doc_id": "2306.05425v1.pdf", "answer_format": "int", "gold": "1", "pages": 22, "document_id": 7587, "arm": "surfsense_agentic", "question_id": "2306.05425v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to count the line plots.Let me search for figures and plots in the document by reading through it systematically:Now let me read the actual content of these figures to understand their structure and count line plots:Let me continue to check all figures systematically:Let me search more specifically for line plots or line charts:Let me look at Figure 6 more carefully as it seems to contain multiple subfigures with plots:Based on the data I see in Figure 6(c), it appears to show different shot settings (0-shot, 4-shot, 8-shot, 16-shot) which suggests this is a line plot. Let me search for more visual descriptions to understand all the plots:Let me read more around Figure 4 to understand what types of plots it contains:Now let me look for visual descriptions and embedded images to understand the structure of figures better. Let me check if there are visual descriptions that mention line plots or graphs:Let me search for embedded images and their visual descriptions:Let me look at the visual descriptions of the image that corresponds to Figure 6:This visual description seems to be of a different image not related to Figure 6. Let me look for a chart or graph description by searching through the document more systematically. Let me read sections with image data to find charts:The visual descriptions I'm seeing are for photographs, not charts. Let me search for keywords related to charts or visualizations more carefully:Good! Let me read this section to find charts and visualizations:Good! I found the section with chart visual descriptions. These appear to be histograms/bar charts for Figure 4. Now let me search for descriptions that mention line graphs or line plots:Let me specifically look for \"line\" combined with visualization terms:Now let me search specifically for Figure 6's visualization descriptions by looking near the Figure 6 location more carefully. Let me look for the embedded images around Figure 6:Let me check for chart/plot related embedded images. Looking back at Figure 6, I saw it has three subfigures (a), (b), and (c). Let me search for embedded images that might show line plots by looking at descriptions with \"axis\":I found histograms/bar charts. Now let me look for any line-based visualizations. Let me search for \"CIDEr\" which was mentioned in Figure 6(c) about COCO caption evaluation:I can see Figure 6(c) shows data with labels like \"0-shot 4-shot 8-shot 16-shot\" and \"CIDEr\", which strongly suggests it's a line plot showing performance across different shot settings. Let me look for a visualization description that mentions this, or look for visual descriptions that discuss multiple data points connected:The text \"0-shot 4-shot 8-shot 16-shot\" followed by \"(c) COCO caption\" suggests Figure 6(c) is indeed a line plot or chart. However, the embedded image Im14.jpg description seems unrelated (clinical setting). Let me look at the data more carefully and count what we know so far. Let me check if there are visual descriptions specifically for charts with \"shot\":", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 191452, "error": null, "extra": {"thread_id": 1992, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_48224411a27b457b894b9bc78d679631", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 942, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "1"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:18Z", "latency_ms": 52514, "error": null, "raw_text_chars": 860}]}, "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll examine pages 5-10 to count the tables.\n\nPage 5: No tables visible in the text content shown.\n\nPage 6: Table 1 is present - \"Llama 2 family of models\" showing training data, parameters, context length, GQA, tokens, and LR for Llama 1 and Llama 2 models.\n\nPage 7: \n- Table 2: \"CO2 emissions during pretraining\" showing time, power consumption, and carbon emitted for different Llama 2 models.\n- Table 3: \"Overall performance on grouped academic benchmarks\" comparing MPT, Falcon, Llama 1, and Llama 2 models.\n\nPage 8:\n- Table 4: \"Comparison to closed-source models on academic benchmarks\" showing results for GPT-3.5, GPT-4, PaLM, PaLM-2-L, and Llama 2.\n- Table 5: \"SFT annotation\" showing examples of helpfulness and safety annotations.\n\nPage 9: No complete tables visible in the shown content.\n\nPage 10: No tables visible in the shown content.\n\nAnswer: 5", "citations": [], "input_tokens": 199213, "output_tokens": 280, "cost_micros": 601839, "latency_ms": 52468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 9:**\n- Table 5: SFT annotation examples\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nAnswer: 6", "citations": [], "input_tokens": 80762, "output_tokens": 144, "cost_micros": 244446, "latency_ms": 6391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count all the tables on pages 5-10.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\n\n**Page 6:**\n- Figure 5 (not a table)\n- Table 2: CO2 emissions during pretraining\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\n- Table 4: Comparison to closed-source models on academic benchmarks\n\n**Page 8:**\n- Table 5: SFT annotation - example of helpfulness and safety annotation\n\n**Page 9:**\n- No tables (contains Figures 11 and discussion)\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\n\nTotal count: 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 109911, "output_tokens": 181, "cost_micros": 332448, "latency_ms": 8235, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables that appear in pages 5-10 of the document.\n\nPage 5: \n- Table 1: Llama 2 family of models (comparing parameters, context length, etc.)\n\nPage 6:\n- No tables\n\nPage 7:\n- Table 3: Overall performance on grouped academic benchmarks\n\nPage 8:\n- Table 4: Comparison to closed-source models on academic benchmarks\n- Table 5: SFT annotation example (shown at top of page)\n\nPage 9:\n- No tables (this page discusses supervised fine-tuning details)\n\nPage 10:\n- Table 6: Statistics of human preference data for reward modeling\n\nCounting all tables in pages 5-10:\n- Page 5: 1 table\n- Page 6: 0 tables\n- Page 7: 1 table\n- Page 8: 2 tables\n- Page 9: 0 tables\n- Page 10: 1 table\n\nTotal: 1 + 0 + 1 + 2 + 0 + 1 = 5 tables\n\nAnswer: 5", "citations": [], "input_tokens": 80961, "output_tokens": 265, "cost_micros": 246858, "latency_ms": 7983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll count the tables on pages 5-10 of the document.\n\n**Page 5:**\n- Table 1: LLAMA 2 family of models\nTotal: 1 table\n\n**Page 6:**\n- Table 2: CO\u2082 emissions during pretraining\n- Figure 5: Training Loss for LLAMA 2 models (this is a figure, not a table)\nTotal: 1 table\n\n**Page 7:**\n- Table 3: Overall performance on grouped academic benchmarks\nTotal: 1 table\n\n**Page 8:**\n- Table 4: Comparison to closed-source models on academic benchmarks\nTotal: 1 table\n\n**Page 9:**\n- Table 5: SFT annotation examples\nTotal: 1 table\n\n**Page 10:**\n- Table 6: Statistics of human preference data for reward modeling\nTotal: 1 table\n\nTotal tables on pages 5-10: 1 + 1 + 1 + 1 + 1 + 1 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 105634, "output_tokens": 249, "cost_micros": 320637, "latency_ms": 8264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document that was flagged as most relevant (2307.09288v2.pdf.xml) and check pages 5-10 for tables.I need to search for page markers to identify pages 5-10. Let me search for page indicators in the document.Let me read more of the document to understand its structure and find page markers.Good! I can see page numbers are indicated with `<!-- PageNumber=\"X\" -->`. Let me search for these page markers to find pages 5-10.Perfect! Now I know that:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo I need to read from line 1660 to line 2460 to cover pages 5-10. Let me search for table markers in this range.Great! I can see the content from pages 5-10. Let me continue reading to make sure I capture all tables in this range. I've found several tables so far. Let me count them systematically:\n\nFrom line 1660 to 2460 (pages 5-10), I've seen:\n1. Table 1 (line 1664) - LLAMA 2 family of models\n2. Table 2 (line 1862) - CO2 emissions during pretraining\n3. Table 3 (line 1959) - Overall performance on grouped academic benchmarks\n4. Table 4 (line 2131) - Comparison to closed-source models\n5. Table 5 (line 2209) - SFT annotation\n6. Table 6 (line 2336) - Statistics of human preference data\n\nLet me verify by reading the remaining portion up to line 2460 to make sure I haven't missed any tables:Perfect! Now I can see all the `<table>` tags in the document. Based on the PageNumber markers I found earlier:\n- Page 5 starts at line 1660\n- Page 6 starts at line 1858\n- Page 7 starts at line 1955\n- Page 8 starts at line 2202\n- Page 9 starts at line 2265\n- Page 10 starts at line 2332\n- Page 11 starts at line 2461\n\nSo pages 5-10 cover lines 1660 to 2460 (just before page 11).\n\nThe tables that fall within this range are:\n1. Line 1664: `<table>` - Table 1 (on page 5)\n2. Line 1862: `<table>` - Table 2 (on page 6)\n3. Line 1959: `<table>` - Table 3 (on page 7)\n4. Line 2131: `<table>` - Table 4 (on page 8)\n5. Line 2209: `<table>` - Table 5 (on page 8)\n6. Line 2336: `<table>` - Table 6 (on page 10)\n\nTables at lines 2479 and 2544 are on page 11 or later, so they don't count.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54500, "error": null, "extra": {"thread_id": 1993, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d18e0e208d4c44ad9ccab3815ed12b04", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 413, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:25Z", "latency_ms": 40452, "error": null, "raw_text_chars": 575}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 4, which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the bar chart showing \"Violation % (lower is safer)\":\n- Llama 2-70b-chat has the lowest violation percentage (approximately 0-5%)\n- Llama 2-13b-chat has the second lowest (approximately 0-5%)\n- Llama 2-34b-chat has the third lowest (approximately 5-10%)\n\nAnswer: Llama 2-70b-chat, Llama 2-13b-chat, Llama 2-34b-chat", "citations": [], "input_tokens": 199224, "output_tokens": 182, "cost_micros": 600402, "latency_ms": 40406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart in Figure 3, the models with the lowest violation percentages are:\n\n1. LLAMA 2-70b-chat (lowest violation %)\n2. ChatGPT 0301 (second lowest)\n3. LLAMA 2-34b-chat (third lowest)\n\nAnswer: Llama-2 70b-chat, ChatGPT 0301, Llama-2 34b-chat", "citations": [], "input_tokens": 80773, "output_tokens": 157, "cost_micros": 244674, "latency_ms": 6141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is safer).\n\nFrom the bar chart, the three models with the lowest violation percentages are:\n1. Llama-2-70b-chat (approximately 0.5%)\n2. Llama-2-34b-chat (approximately 1%)\n3. Llama-2-13b-chat (approximately 1.5%)\n\nAnswer: Llama-2-70b-chat, Llama-2-34b-chat, Llama-2-13b-chat", "citations": [], "input_tokens": 109922, "output_tokens": 165, "cost_micros": 332241, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and close-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the figure, the models are ordered from left to right with their violation percentages. The three models with the lowest violation percentages are:\n\n1. Llama 2-Chat (70B) - appears to have the lowest violation percentage\n2. ChatGPT (0301) - appears to have the second lowest\n3. Falcon (40B-instruct) - appears to have the third lowest\n\nAnswer: Llama 2-Chat (70B), ChatGPT (0301), Falcon (40B-instruct)", "citations": [], "input_tokens": 80972, "output_tokens": 176, "cost_micros": 245556, "latency_ms": 7233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "chatgpt 0301, falcon 40b instruct, llama 2 chat 70b", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:05Z", "latency_ms": 10343, "error": null, "raw_text_chars": 376}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I can see the violation percentages (lower is safer):\n\nThe top-3 safest models (lowest violation %) are:\n1. Llama-2 7b-chat: 3%\n2. Llama-2 13b-chat: 3%\n3. Llama-2 70b-chat: 4%\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 70b-chat", "citations": [], "input_tokens": 105645, "output_tokens": 151, "cost_micros": 319200, "latency_ms": 10343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 70b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify the top-3 models.Let me search more specifically for Figure 3 content:Now let me read the area around line 1363 to see the actual figure data:Based on the document, Figure 3 shows safety violation percentages where \"lower is safer\". Let me look for the actual values to determine the top-3 (lowest violation %) models:The visual description indicates that the first 4 bars (dark blue) have the lowest values (approximately 3-7), which are the safest models. Based on the model order from lines 1391-1420, the models are listed in order:\n\n1. Llama-2 7b-chat\n2. Llama-2 13b-chat\n3. Llama-2 34b-chat\n4. Llama-2 70b-chat\n5. MPT 7b-chat\n6. Vicuna 13b-v1.1\n7. Vicuna 33b-v1.3\n8. Falcon 40b-instruct\n9. PaLM Bison\n10. ChatGPT 0301\n\nThe description states the first 4 bars (dark blue) have values approximately 3-7, which are the lowest (safest). So the top-3 safest models would be the first three models.\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 34b-chat", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48187, "error": null, "extra": {"thread_id": 1994, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8f47a07752394e8d87ac6ac816505c2a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 for QUAC (f1) results in the 0-shot setting:\n\nThe table shows:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 199228, "output_tokens": 210, "cost_micros": 600834, "latency_ms": 79592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the 0-shot QUAC results are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting for QUAC is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 80777, "output_tokens": 227, "cost_micros": 245736, "latency_ms": 7592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the 0-shot results for the QUAC dataset.\n\nIn the QUAC (f1) 0-shot column, the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot QUAC column is 44.1.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 109926, "output_tokens": 271, "cost_micros": 333843, "latency_ms": 8594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 \"Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\", I need to find the QUAC 0-shot column.\n\nThe table shows QUAC results with 0-shot and 1-shot columns. For the 0-shot setting on QUAC:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- Llama 1 7B: 38.9\n- Llama 1 13B: 39.9\n- Llama 1 33B: 44.1\n- Llama 1 65B: 41.0\n- Llama 2 7B: 39.4\n- Llama 2 13B: 42.7\n- Llama 2 34B: 42.9\n- Llama 2 70B: 42.4\n\nThe highest 0-shot score on QUAC is 44.1, achieved by Llama 1 33B.\n\nAnswer: Llama 1 33B", "citations": [], "input_tokens": 80976, "output_tokens": 284, "cost_micros": 247188, "latency_ms": 8469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:10Z", "latency_ms": 29391, "error": null, "raw_text_chars": 525}]}, "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the QUAC (f1) 0-shot scores are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 105649, "output_tokens": 274, "cost_micros": 321057, "latency_ms": 29391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table 23 and identify the best performing model in the 0-shot setting on the QUAC dataset.Let me search for \"Table 23\" in the document.Now let me read the section containing Table 23 to see the complete table.Perfect! I can see Table 23 completely now. Looking at the QUAC (f1) 0-shot column (column 7 in the table), the scores are:\n\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score in the 0-shot setting on QUAC is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40921, "error": null, "extra": {"thread_id": 1995, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1765040c73804b7381cc9d57c302f53d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 202, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to find Table 21 in the document and count how many slots (cells) are missing data.\n\nLooking through the document, I can find Table 21 on page 49. Let me examine it:\n\nTable 21 is titled \"Code generation results on Human-Eval and MBPP.\"\n\nThe table has the following structure:\n- Rows for different models: MPT (7B, 30B), Falcon (7B, 40B), Llama 1 (7B, 13B, 33B, 65B), and Llama 2 (7B, 13B, 34B, 70B)\n- Columns for: Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80)\n\nLet me count the missing entries (shown as \"-\" or blank):\n\nLooking at the table:\n- MPT 7B: has values for pass@1 under Human-Eval (18.3) and MBPP (22.6), but \"-\" for pass@100 and pass@80\n- MPT 30B: has values for pass@1 (25.0, 32.8), but \"-\" for pass@100 and pass@80\n- Falcon 7B: has values for pass@1 (0.0, 11.2), but \"-\" for pass@100 and pass@80\n- Falcon 40B: has values for pass@1 (0.6, 29.8), but \"-\" for pass@100 and pass@80\n- All Llama 1 and Llama 2 models have complete data (no missing slots)\n\nMissing slots:\n- MPT 7B: 2 missing (pass@100 for both benchmarks)\n- MPT 30B: 2 missing\n- Falcon 7B: 2 missing\n- Falcon 40B: 2 missing\n\nTotal: 8 missing slots\n\nAnswer: 8", "citations": [], "input_tokens": 199211, "output_tokens": 461, "cost_micros": 604548, "latency_ms": 75532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 in the document, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Rows: Different models (MPT 7B, Falcon 7B/40B, LLAMA 1 7B/13B/33B/65B, LLAMA 2 7B/13B/34B/70B)\n- Columns: Human-Eval pass@1, Human-Eval pass@100, MBPP pass@1, MBPP pass@80\n\nLet me count the missing entries (marked with \"-\"):\n\nMPT 7B: pass@100 for Human-Eval is missing (1 slot)\nMPT 30B: pass@100 for Human-Eval is missing (1 slot)\nFalcon 40B: pass@1 and pass@100 for Human-Eval are missing, pass@1 and pass@80 for MBPP are missing (4 slots)\n\nTotal missing slots: 1 + 1 + 4 = 6\n\nAnswer: 6", "citations": [], "input_tokens": 80760, "output_tokens": 280, "cost_micros": 246480, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count the missing slots.\n\nLooking at Table 21, which shows \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model name, size, and then 4 metric columns (pass@1, pass@100 for Human-Eval, and pass@1, pass@80 for MBPP)\n- Row groups for: MPT (2 sizes), Falcon (2 sizes), LLAMA 1 (4 sizes), and LLAMA 2 (4 sizes)\n\nLet me count the cells systematically:\n\nFor MPT:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 30B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor Falcon:\n- 7B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n- 40B: has values for pass@1 and pass@1 (MBPP), missing pass@100 and pass@80 = 2 missing\n\nFor LLAMA 1 (4 sizes: 7B, 13B, 33B, 65B):\n- All sizes have all 4 metrics filled = 0 missing\n\nFor LLAMA 2 (4 sizes: 7B, 13B, 34B, 70B):\n- All sizes have all 4 metrics filled = 0 missing\n\nTotal missing slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 109909, "output_tokens": 429, "cost_micros": 336162, "latency_ms": 10592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 in the document to count how many slots (cells) are missing data.\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model, Human-Eval (pass@1, pass@100), MBPP (pass@1, pass@80)\n- Row headers: MPT, Falcon, Llama 1, Llama 2\n\nLet me count the cells:\n- MPT row: 7B size is listed, then has values 18.3, -, 22.6, -\n- Falcon row: 7B size is listed, then has values 0.0, -, 11.2, -\n- Llama 1 row: 7B size is listed, then has values 15.8, 52.5, 22.0, 64.0\n- Llama 2 row: 7B size is listed, then has values 18.3, 60.2, 30.6, 69.0\n\nThe \"-\" symbols indicate missing data.\n\nCounting the dashes:\n- MPT: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Falcon: 2 dashes (pass@100 for Human-Eval, pass@80 for MBPP)\n- Llama 1: 0 dashes (all values present)\n- Llama 2: 0 dashes (all values present)\n\nTotal missing slots: 2 + 2 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 80959, "output_tokens": 385, "cost_micros": 248652, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Table 21 to count the missing slots (empty cells).\n\nLooking at Table 21 titled \"Code generation results on Human-Eval and MBPP\":\n\nThe table has the following structure:\n- Column headers: Model | Size | Human-Eval pass@1 | Human-Eval pass@100 | MBPP pass@1 | MBPP pass@80\n- Rows for: MPT (7B, 30B), Falcon (7B, 40B), LLAMA 1 (7B, 13B, 33B, 65B), LLAMA 2 (7B, 13B, 34B, 70B)\n\nLet me count the cells:\n- Total rows with data: 12 (excluding header)\n- Total columns with data: 6 (including Model and Size)\n- So for the data columns (excluding Model column): 12 rows \u00d7 4 data columns = 48 potential data cells\n\nNow counting the filled cells in the data columns:\n- MPT 7B: 4 values (18.3, -, 22.6, -)\n- MPT 30B: 4 values (25.0, -, 32.8, -)\n- Falcon 7B: 4 values (0.0, -, 11.2, -)\n- Falcon 40B: 4 values (0.6, -, 29.8, -)\n- LLAMA 1 7B: 4 values (10.5, 36.5, 17.7, 56.2)\n- LLAMA 1 13B: 4 values (15.8, 52.5, 22.0, 64.0)\n- LLAMA 1 33B: 4 values (21.7, 70.7, 30.2, 73.4)\n- LLAMA 1 65B: 4 values (23.7, 79.3, 37.7, 76.8)\n- LLAMA 2 7B: 4 values (12.8, 45.6, 20.8, 62.", "citations": [], "input_tokens": 105632, "output_tokens": 512, "cost_micros": 324576, "latency_ms": 13625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q005", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "8", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Table 21 and count the missed slots.Let me search for Table 21 in the document:Now let me search for where Table 21 appears with more context:Let me read the area around line 7837 where Table 21 is located:Now I have Table 21. Let me analyze it carefully. The table shows code generation results with columns for Human-Eval (pass@1, pass@100) and MBPP (pass@1, pass@80). I can see that some entries have \"-\" which indicates missing data. Let me count all the \"-\" entries:\n\nLooking at the table structure:\n- MPT 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- MPT 30B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 7B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- Falcon 40B: 2 missing values (pass@100 for Human-Eval and pass@80 for MBPP marked with \"-\")\n- LLAMA 1 models (7B, 13B, 33B, 65B): All have values, no \"-\"\n- LLAMA 2 models (7B, 13B, 34B, 70B): All have values, no \"-\"\n\nTotal missed slots = 2 + 2 + 2 + 2 = 8\n\nAnswer: 8", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42437, "error": null, "extra": {"thread_id": 1996, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b8bd21d9aaba4efda0a40946ca2b4039", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 222, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "8"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "native_pdf", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll examine pages 49-50 to count the datasets reported.\n\nOn page 49 (Table 20), I can see results for several standard benchmarks including:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets on page 49.\n\nOn page 50 (Table 21), I can see code generation results on:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets on page 50.\n\nTotal datasets reported across pages 49-50: 10 + 2 = 12\n\nAnswer: 12", "citations": [], "input_tokens": 199213, "output_tokens": 177, "cost_micros": 600294, "latency_ms": 43437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the datasets mentioned.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: NaturalQuestions and TriviaQA results\n- Table 23: SQUAD and QUAC results\n- Table 24: AGI Eval comparison (mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count all unique datasets mentioned across these tables on pages 49-50:\n\nFrom Table 20 (page 49):\n1. BoolQ\n2. PIQA\n3. SIQA\n4. HellaSwag\n5. WinoGrande\n6. ARC-e\n7. ARC-c\n8. OBQA\n9. CSQA\n10. MMLU\n\nFrom Table 21 (page 49):\n11. Human-Eval\n12. MBPP\n\nFrom Table 22 (page 50):\n13. NaturalQuestions\n14. TriviaQA\n\nFrom Table 23 (page 50):\n15. SQUAD\n16. QUAC\n\nFrom Table 24 (page 50):\n17. AGI Eval (which includes multiple subtasks: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nCounting the subtasks of AGI Eval separately:\n17. AQuA-RAT\n18. LogiQA\n19. LSAT-AR\n20. LSAT-LR\n21.", "citations": [], "input_tokens": 80762, "output_tokens": 512, "cost_micros": 249966, "latency_ms": 10782, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "21", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 of the document to count the number of datasets reported.\n\nLooking at page 49, I can see:\n- Table 20: \"Performance on standard benchmarks\" - this discusses various benchmarks but appears to be evaluation results rather than listing distinct datasets\n- Table 21: \"Code generation results on Human-Eval and MBPP\" - mentions 2 datasets: Human-Eval and MBPP\n\nLooking at page 50, I can see:\n- Table 22: \"NaturalQuestions\" and \"TriviaQA\" - 2 datasets\n- Table 23: \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\" - 2 datasets: SQUAD and QUAC\n- Table 24: \"Comparison to open source models on AGI Eval (English)\" - 1 dataset: AGI Eval (with multiple subtasks listed: AQUA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math)\n\nLet me count the distinct datasets mentioned:\n1. Human-Eval\n2. MBPP\n3. NaturalQuestions\n4. TriviaQA\n5. SQUAD\n6. QUAC\n7. AGI Eval (this is one dataset with multiple subtasks)\n\nAdditionally, in Table 20, there are multiple benchmarks mentioned: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU - that's 10 more datasets.\n\nTotal distinct datasets across pages 49-50:\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10)\nFrom Table 21: Human-Eval, MBPP (2)\nFrom Table 22: NaturalQuestions, TriviaQA (2)\nFrom Table 23: SQUAD,", "citations": [], "input_tokens": 109911, "output_tokens": 512, "cost_micros": 337413, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "23", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine pages 49-50 to count the datasets reported there.\n\nLooking at page 49, I can see:\n- Table 20: Performance on standard benchmarks (mentions BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU)\n- Table 21: Code generation results on Human-Eval and MBPP\n- Table 22: NaturalQuestions and TriviaQA Performance\n\nLooking at page 50, I can see:\n- Table 23: Comparison to Open-Source Models on Reading Comprehension (SQUAD and QUAC)\n- Table 24: Comparison to Open Source Models on AGI Eval (English) - mentions AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math\n\nNow let me count all unique datasets mentioned across pages 49-50:\n\nFrom Table 20: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, OBQA, CSQA, MMLU (10 datasets)\n\nFrom Table 21: Human-Eval, MBPP (2 datasets)\n\nFrom Table 22: NaturalQuestions, TriviaQA (2 datasets)\n\nFrom Table 23: SQUAD, QUAC (2 datasets)\n\nFrom Table 24: AQuA-RAT, LogiQA, LSAT-AR, LSAT-LR, LSAT-RC, SAT-en, SAT-en (w/o Psg.), SAT-math (8 datasets, noting that SAT-en and SAT-en (w/o Psg.) are variants of the same dataset but listed separately)\n\nTotal unique datasets: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 80961, "output_tokens": 493, "cost_micros": 250278, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I need to carefully examine pages 49-50 to count the number of datasets reported.\n\nLooking at page 49 (Table 20), I can see:\n- BoolQ\n- PIQA\n- SIQA\n- HellaSwag\n- WinoGrande\n- ARC-e\n- ARC-c\n- OBQA\n- CSQA\n- MMLU\n\nThat's 10 datasets in Table 20.\n\nLooking at page 49 (Table 21), I can see:\n- Human-Eval\n- MBPP\n\nThat's 2 datasets in Table 21.\n\nLooking at page 50 (Table 22), I can see:\n- NaturalQuestions\n- TriviaQA (Wiki)\n\nThat's 2 datasets in Table 22.\n\nLooking at page 50 (Table 23), I can see:\n- SQUAD\n- QUAC\n\nThat's 2 datasets in Table 23.\n\nLooking at page 50 (Table 24), I can see:\n- AQuA-RAT\n- LogiQA\n- LSAT-AR\n- LSAT-LR\n- LSAT-RC\n- SAT-en\n- SAT-en (w/o Psg.)\n- SAT-math\n\nThat's 8 datasets in Table 24.\n\nTotal datasets across pages 49-50: 10 + 2 + 2 + 2 + 8 = 24\n\nAnswer: 24", "citations": [], "input_tokens": 105634, "output_tokens": 355, "cost_micros": 322227, "latency_ms": 11062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "24", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q006", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "13", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the information on pages 49-50.Let me search for page numbers in the document to locate pages 49-50.Let me search for page markers more broadly:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35907, "error": null, "extra": {"thread_id": 1997, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db129915abb34a60aeaa6083e2f2fd47", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 109, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "49", "normalised_gold": "13"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:15Z", "latency_ms": 43610, "error": null, "raw_text_chars": 650}]}, "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 9 and count the emojis in the left and right subfigures.\n\nLooking through the document, I find Figure 9 on page 16 (document page 16). The figure shows \"Issues with multi-turn memory (left) can be improved with GAtt (right).\"\n\nLooking at the left subfigure:\n- It shows a conversation with text-only responses (no emojis visible)\n\nLooking at the right subfigure:\n- First response: 4 emojis (\ud83d\udc4b\ud83d\udc41\ufe0f\ud83d\udc44\ud83d\udc41\ufe0f)\n- Second response: 4 emojis (\u2708\ufe0f\u2708\ufe0f\ud83d\udeeb\ud83d\udcba)\n- Third response: 4 emojis (\ud83e\uddb4\ud83e\udd95\ud83e\uddb4\ud83d\udd25)\n- Fourth response: 2 emojis (\ud83c\uddee\ud83c\uddf9\ud83c\udf5d\ud83d\udc90)\n\nTotal in right subfigure: 4 + 4 + 4 + 2 = 14 emojis\n\nTotal in left subfigure: 0 emojis\n\nDifference: 14 - 0 = 14\n\nAnswer: 14", "citations": [], "input_tokens": 199223, "output_tokens": 267, "cost_micros": 601674, "latency_ms": 43578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which is titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure:\n- Shows a conversation about \"Always answer with emojis\"\n- The response \"Who are you?\" appears to have text but I need to count emojis\n- Looking at the left side, I can see there are some emojis in the responses\n\nRight subfigure:\n- Shows the same conversation structure with GAtt improvement\n- The response appears to have more emojis\n\nLet me count more carefully by examining the figure:\n\nLeft side conversation:\n- \"Who are you?\" response appears to have some emojis\n- \"How to go from Paris to NY?\" response has text with some emojis\n\nRight side conversation:\n- \"Who are you?\" response has emojis\n- \"How to go from Paris to NY?\" response has more emojis\n- There appears to be an additional question about Italy\n\nLooking at the visual representation in Figure 9, the right subfigure clearly shows more emoji usage throughout the conversation compared to the left subfigure. \n\nCounting the visible emojis:\n- Left: appears to have fewer emojis (approximately 2-3 visible)\n- Right: appears to have more emojis throughout (approximately 10-12 visible)\n\nThe difference appears to be approximately 8-9 emojis.\n\nAnswer: 8", "citations": [], "input_tokens": 80772, "output_tokens": 344, "cost_micros": 247476, "latency_ms": 10140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "azure_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\n**Left subfigure (without GAtt):**\n- Shows a conversation with \"Who are you?\" followed by \"How to go from Paris to NY?\" and \"What caused the extinction of the dinosaurs?\"\n- I can see there are emoji circles (\ud83d\ude0a symbols) used in the conversation\n- Counting the emojis: I see 3 emoji symbols\n\n**Right subfigure (with GAtt):**\n- Shows the same conversation structure with \"Who are you?\" followed by \"How to go from Paris to NY?\" \n- Counting the emojis: I see 3 emoji symbols as well\n\nWait, let me recount more carefully by looking at the figure description and structure:\n\nLeft side: I count 3 emoji/circle symbols in the conversation flow\nRight side: I count 3 emoji/circle symbols in the conversation flow\n\nActually, looking more carefully at the figure, both sides appear to have the same number of emoji symbols (3 each) representing the conversation turns.\n\nThe difference would be: 3 - 3 = 0\n\nAnswer: 0", "citations": [], "input_tokens": 109921, "output_tokens": 294, "cost_micros": 334173, "latency_ms": 9984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "0", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_basic_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 in the document, which shows \"Issues with multi-turn memory (left) can be improved with GAtt (right)\":\n\nLeft subfigure (without GAtt):\n- Shows a conversation about acting as Oscar Wilde\n- I can see emoji-like elements in the visualization\n\nRight subfigure (with GAtt):\n- Shows the same type of conversation\n- Also has emoji-like elements in the visualization\n\nLet me count more carefully. The figure appears to show attention visualization patterns with what look like emoji or icon representations in the conversation bubbles.\n\nFrom the image description and visual elements:\n- Left side: Shows conversation elements with some emoji representations\n- Right side: Shows conversation elements with emoji representations\n\nLooking at the actual figure more carefully, the right subfigure appears to have more emoji icons visible in the conversation compared to the left subfigure. \n\nCounting the visible emojis/icons in each:\n- Left: appears to have fewer emoji elements\n- Right: appears to have more emoji elements\n\nBased on the visual comparison, the right subfigure has approximately 2 more emojis than the left subfigure.\n\nAnswer: 2", "citations": [], "input_tokens": 80971, "output_tokens": 281, "cost_micros": 247128, "latency_ms": 10141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "llamacloud_premium_lc", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 9 to count the emojis in the left and right subfigures.\n\nLooking at Figure 9 (titled \"Issues with multi-turn memory (left) can be improved with GAtt (right)\"):\n\n**Left subfigure (Generic Preprompt):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcbb\u2328\ufe0f\" (3 emojis)\n- User: \"How to go from Paris to NY?\"\n- Assistant: Long text response with no emojis\nTotal emojis in left: 3\n\n**Right subfigure (Preprompt with Answer Template):**\n- User: \"Who are you?\"\n- Assistant: \"\ud83e\udd16\ud83d\udcac...\" (2 emojis shown, plus \"...\")\n- User: \"How to go from Paris to NY?\"\n- Assistant: \"\ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3\" (4 emojis)\n- User: \"What caused the extinction of the dinosaurs?\"\n- Assistant: \"\u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80\" (4 emojis)\n- User: \"Italy?\"\n- Assistant: \"\ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f\" (4 emojis)\n\nLet me recount the right side more carefully:\n- First response: \ud83e\udd16\ud83d\udcac (2 emojis, the \"...\" indicates continuation but not additional emojis)\n- Second response: \ud83d\udeeb\ud83d\udea2\u2708\ufe0f\ud83e\uddf3 (4 emojis)\n- Third response: \u2604\ufe0f\ud83c\udf0b\ud83e\udd95\ud83d\udc80 (4 emojis)\n- Fourth response: \ud83c\uddee\ud83c\uddf9\ud83c\udf55\ud83c\udf5d\ud83c\udfdb\ufe0f (4 emojis)\nTotal emojis in right: 2 + 4 + 4 + 4 = 14\n\nDifference: 14 - 3 = 11\n\nAnswer: 11", "citations": [], "input_tokens": 105644, "output_tokens": 462, "cost_micros": 323862, "latency_ms": 13312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "11", "normalised_gold": "15"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "arm": "surfsense_agentic", "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and count the emojis in each subfigure.Let me search for Figure 9 in the document:Now let me read the section around line 3147 to see Figure 9:Let me continue reading to see the complete Figure 9 with emoji counts:I can see Figure 9 is described, but it mentions an embedded image. Let me look at the embedded image description more carefully. The text shows numbers like \"8\", \"0\" which might be emoji counts. Let me search for the actual image description or look for more context:The image description I saw was for a different figure (a scatter plot). Let me look back at the text content of Figure 9 more carefully. I can see there are conversations shown with numbers like \"8\" and \"0\". Let me search for the actual figure content more carefully:The text I'm seeing doesn't show the actual figure properly. Let me look for an embedded image associated with Figure 9. The page header says \"Always answer with emojis\" which is the instruction. Let me search for the actual visual description or OCR of the image:Let me go back and look more carefully at the figure. The text between lines 3146-3181 should contain the figure content. Let me look for an image file or better description:The visual description starting at line 3190 describes a scatter plot, which is NOT Figure 9. That appears to be a different figure (the embedded image X6.png). The actual Figure 9 content is in the text between lines 3147-3180. Let me look for an image file that might contain the actual Figure 9. Let me search for embedded images near Figure 9:I notice the page headers say \"Always answer with emojis\" twice (lines 3142-3143). This suggests the page has two columns - left and right subfigures. Looking at the figure text:\n\nLeft subfigure appears to show:\n- Line 3149: \"Who are you?\" \n- Line 3151: \"Who are you?\"\n- Line 3153: \"8\" (could indicate 8 emojis)\n- Line 3157: \"How to go from Paris to NY?\"\n- Line 3159: \"How to go from Paris to NY?\"\n- Line 3161: \"8\" (could indicate 8 emojis)\n- Line 3163: \"0\" (could indicate 0 emojis)\n- Line 3165-3168: Text response without emojis\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (could indicate 0 emojis)\n\nLet me search for an image description specifically for Figure 9. Let me look before line 3146:Let me look for an image file associated with Figure 9. Let me search for embedded images around this area:So there's an embedded image X6.png at line 3185, which is right after Figure 9 ends at line 3181. But the visual description for X6.png shows it's a scatter plot, not Figure 9. Let me look for an image BEFORE Figure 9:Let me search for image descriptions that might actually be Figure 9. Let me look for an OCR or description of dialogues with emojis:Great! I found relevant information. Let me look at lines 2729-2760 which describe emoji exchanges:Excellent! This is describing an image related to Figure 9. Looking at the OCR text at lines 2729-2737:\n- \"Always answer with emojis\" (the header/instruction)\n- \"8\" (appears after \"Who are you?\")\n- \"...\" \n- \"How to go from Paris to NY?\"\n\nAnd the visual description mentions:\n1. First exchange: emojis (thinking face, eyes, and speech bubble) - that's 3 emojis\n2. Second exchange: transportation-themed emojis (airplane, boat, landmark, car, globe) - that's 5 emojis\n3. Third exchange: dinosaur-themed emojis (dinosaur, calendar/document, ocean wave, volcano, fire) - that's 5 emojis\n4. Fourth exchange: Italy-related emojis (flag, food items, landmark) - that's at least 3 emojis\n\nBut wait, the OCR shows \"8\" which might mean 8 emojis. Let me look at lines 1795-1835 which also mention \"Always answer with emojis\":This describes a different version showing the LEFT subfigure (without GAtt) - it shows the model failing to follow the \"always answer with emojis\" instruction in the third exchange (text response instead of emojis).\n\nNow let me look more carefully at the actual Figure 9 structure. Looking back at the figure text I saw earlier, the caption says \"Issues with multi-turn memory (left) can be improved with GAtt (right).\" The page has TWO column headers \"Always answer with emojis\" (lines 3142-3143), suggesting left and right subfigures.\n\nLet me count the emojis more carefully. Looking at the embedded image X1.png descriptions:\n\n**LEFT subfigure (lines 1795-1835)**: Model FAILS to always use emojis\n- First exchange: 2 emojis (robot face and laptop)\n- Second exchange: (question only shown, no response visible in description)\n- Third exchange: TEXT response (not emojis) - this shows the failure!\n\n**RIGHT subfigure (lines 2726-2760)**: Model SUCCEEDS with emojis\n- First exchange: 3 emojis (thinking face, eyes, speech bubble)\n- Second exchange: 5 emojis (airplane, boat, landmark, car, globe)\n- Third exchange: 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n- Fourth exchange: 3+ emojis (flag, food items, landmark)\n\nLet me count the \"food items\" more carefully. It says \"flag, food items, landmark\" - \"food items\" is plural, so at least 2 food emojis. That would be at least 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis minimum for the fourth exchange.\n\nTotal for RIGHT: 3 + 5 + 5 + (at least 4) = at least 17 emojis\n\nBut wait, let me look at the OCR text again at line 2730 - it shows \"8\" right after \"Always answer with emojis\". This might indicate 8 emojis in a single response or might be part of the interface.\n\nLet me search for more specific information about counting emojis in each subfigure:The document doesn't use the term \"subfig\". Let me look more carefully at the actual textual representation of Figure 9 between lines 3146-3181. The numbers \"8\", \"0\", \"8\" might be emoji counts:I see it! The pattern shows:\n- Line 3149: \"Who are you?\" (question appears twice - probably left and right)\n- Line 3151: \"Who are you?\" (duplicate)\n- Line 3153: \"8\" (LEFT subfigure response)\n- Line 3157: \"How to go from Paris to NY?\" (appears twice)\n- Line 3159: \"How to go from Paris to NY?\" (duplicate)\n- Line 3161: \"8\" (RIGHT subfigure response)\n- Line 3163: \"0\" (LEFT subfigure response - no emojis, text instead)\n- Lines 3165-3169: Text response (LEFT fails to use emojis)\n- Line 3171: \"What caused the extinction of the dinosaurs?\"\n- Line 3173: \"0\" (LEFT subfigure - no emojis)\n- Line 3177: \"Italy?\"\n- Line 3179: \"8\" (response)\n\nThis is confusing. Let me look for an actual image file description. Let me check if there's a better description by looking at different chunks:Let me try a different approach. Let me look for the actual visual description or image file that corresponds to Figure 9. Let me search around page 15 for embedded images:The image X6.png after Figure 9 is NOT Figure 9 - it's a scatter plot. The actual Figure 9 content must be represented in the text between lines 3146-3181. But this text representation is unclear.\n\nLet me look BEFORE Figure 9 to see if there's an image file that IS Figure 9. Looking at line 3139, it says \"PageNumber 15\" and then the two page headers \"Always answer with emojis\". This must be a two-column layout. Let me search backwards for an embedded image that might be Figure 9:So Figure 9 starts immediately after the page break at line 3140. There's no embedded image file BEFORE it. The figure content itself must be in the text, or the image description is missing.\n\nBased on what I've found, let me piece together what I know from the two image descriptions I found (X1.png from lines 1795 and 2726):\n\n**LEFT subfigure** (lines 1795-1835 - model WITHOUT GAtt):\n- First response: 2 emojis\n- Later responses: TEXT (no emojis) - shows failure\n\n**RIGHT subfigure** (lines 2726-2760 - model WITH GAtt):\n- First response: 3 emojis  \n- Second response: 5 emojis\n- Third response: 5 emojis  \n- Fourth response: \"flag, food items, landmark\"\n\nFor the fourth response, \"food items\" is plural, indicating at least 2 items. So minimum: 1 flag + 2 food + 1 landmark = 4 emojis.\n\nLet me count more carefully. Looking at the visual description at lines 2750-2753 again:\n1. \"thinking face, eyes, and speech bubble emojis\" - that's 3 emojis\n2. \"airplane, boat, landmark, car, globe\" - that's 5 emojis\n3. \"dinosaur, calendar/document, ocean wave, volcano, fire\" - that's 5 emojis\n4. \"flag, food items, landmark\" - the term \"food items\" suggests multiple food emojis\n\nActually, let me check if there's a more specific count in the OCR at line 2730:OK! The OCR text at line 2730 shows \"8\". This might mean 8 emojis total for the first response, OR it might be labeling something else. But the description clearly lists:\n1. 3 emojis (thinking face, eyes, speech bubble)\n2. 5 emojis (airplane, boat, landmark, car, globe) \n3. 5 emojis (dinosaur, calendar/document, ocean wave, volcano, fire)\n4. At least 3+ emojis (flag, food items [plural = 2+], landmark)\n\nNow for the LEFT subfigure. Let me find its details:So the LEFT subfigure shows:\n- First exchange: 2 emojis (robot face and laptop)  \n- Second exchange: (no response details given)\n- Third exchange: TEXT response (not emojis) - this is the FAILURE\n\nSo the LEFT has only 2 emojis total before it fails.\n\nThe RIGHT has: 3 + 5 + 5 + (at least 3) = at least 16 emojis\n\nBut the question asks specifically about Figure 9. Let me verify these two X1.png images at lines 1795 and 2726 are actually the left and right parts of Figure 9. They both have the same instruction \"Always answer with emojis\" at the top, which matches the page headers for Figure 9.\n\nActually, looking more carefully, let me check how many food items specifically. The text says \"flag, food items, landmark\". If \"food items\" means 2 items, then the fourth response would be: 1 + 2 + 1 = 4 emojis.\n\nSo RIGHT would be: 3 + 5 + 5 + 4 = 17 emojis\nLEFT has: 2 emojis\n\nDifference: 17 - 2 = 15\n\nWait, but I need to verify this is correct. Let me think about this differently. The description says \"food items\" (plural). The minimum plural is 2. But it could be more. Let me see if I can find more specific information.\n\nLooking at the specific items listed for Italy: \"flag, food items, landmark\". Given the context is Italy, common food emojis might be pizza and pasta (2 items), or there could be 3+ food items. Without more specific information, I'll assume \"food items\" means at least 2.\n\nActually, looking at the pattern of the other responses which are very specific:\n- Response 1: lists 3 specific emojis\n- Response 2: lists 5 specific emojis  \n- Response 3: lists 5 specific emojis\n- Response 4: says \"flag, food items, landmark\"\n\nIf the describer was being consistent, and they specifically didn't count the food items individually but said \"food items\" plural, it likely means 2 food items (pizza and pasta would be typical for Italy).\n\nSo: 1 (flag) + 2 (food items) + 1 (landmark) = 4 emojis\n\nRIGHT subfigure total: 3 + 5 + 5 + 4 = 17 emojis\nLEFT subfigure total: 2 emojis\n\nAnswer: 17 - 2 = 15", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 179203, "error": null, "extra": {"thread_id": 1998, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_913336fbec024206afbfe0bdfdc22736", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1666, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "17", "normalised_gold": "15"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I need to find Figure 122 in the document and look for information about the color of OSCARb in the prompt.\n\nLooking at the document, I can find Figure 122 which is titled \"Self-Reflection to Improve Coding\" in Section 10.3.\n\nIn Figure 122, there are two prompts shown. The first prompt asks to \"Generate python code to draw similar curves\" and shows a reference image with a graph. The second prompt asks \"Can you double-check the code and align it better with the reference image?\"\n\nLooking at the graphs in Figure 122, I can see there are plots comparing \"OSCarB\" and \"MiniVLM (ours)\". In the visualizations shown, OSCarB appears to be represented in orange/red color in the legend and plot lines.\n\nAnswer: orange", "citations": [], "input_tokens": 117434, "output_tokens": 184, "cost_micros": 355062, "latency_ms": 9391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in Section 10.3 on Self-Reflection to Improve Coding, I can see the prompt includes a reference image with a comparison chart.\n\nIn the chart showing \"Comparison of OSCarB and MiniVLM\", OSCarB is represented by the orange/coral colored bars.\n\nAnswer: Orange", "citations": [], "input_tokens": 133651, "output_tokens": 77, "cost_micros": 402108, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see the prompt mentions \"OSCARb\" in the context of generating Python code to draw similar curves.\n\nIn the figure, there is a bar chart showing \"OSCARb\" and \"MiniVLM (ours)\" with bars for \"Base,\" \"Large,\" and \"Huge\" configurations. The bars for OSCARb appear to be orange/amber colored in the visualization.\n\nAnswer: orange", "citations": [], "input_tokens": 103918, "output_tokens": 116, "cost_micros": 313494, "latency_ms": 7546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 122 in the document, which shows \"Self-Reflection to Improve Coding,\" I can see two side-by-side examples.\n\nIn the right example (the bar chart comparison), the prompt asks to \"Write python codes to generate similar figures.\" The reference image shows a bar chart comparing OSCAR_B and MiniVLM across different categories.\n\nLooking at the bar chart, OSCAR_B is represented by blue bars, while MiniVLM (ours) is represented by orange bars.\n\nAnswer: Blue", "citations": [], "input_tokens": 139293, "output_tokens": 120, "cost_micros": 419679, "latency_ms": 7781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 122 and identify the color of OSCARb in the prompt.Let me search for \"Figure 122\" in the document:Now let me search for where Figure 122 appears with more context:Let me read the section around line 32131 and 32550 to see Figure 122:I can see the figure content. Let me continue reading to see more details about the colors:Let me continue reading to see the color information for the bars:I need to look more carefully at the actual figure. Let me search for more specific information about the figure 122 visual description or color information. Let me read around line 32300-32350 more carefully where the bar chart appears:The image described starting at line 32358 (Image7.jpg) is showing a Golden Retriever dog, which is not the chart. Let me look at the figure that appears before that - the bar chart figure starting at line 32302. This shows the data but doesn't explicitly state colors. Let me search for any embedded image descriptions that might be the actual bar chart:Great! I found it. Let me read the section around line 19587 where it says \"Blue bars represent OSCarB\":However, this is describing a different figure. Let me check if this is Figure 122 or another figure. Let me look back at the context around line 32300 where the chart with OSCarB appears in the \"Prompt: Write python codes to generate similar figures\" section, which is part of Figure 122. Let me look for the visual description of the image that corresponds to the reference image in the prompt:Good! This shows that OSCarB is represented by blue bars. Now let me verify this is indeed related to Figure 122. Let me check if Figure 122's prompt section has this same image. Let me look around where the figure caption for Figure 122 is:This is showing a different chart (the left side of Figure 122 with CIDEr curves). Let me look at the right side which should be the bar chart. The section at line 32297-32354 shows the bar chart prompt. Let me look for embedded images near that area. Based on the structure, the embedded images have visual descriptions. Let me check what comes just before line 32297:I see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interrupting the flow. The actual bar chart reference image in the prompt should be described elsewhere. Let me look for where Figure 122 starts and what images are in it. Let me search for the image that appears right before the \"Prompt: Write python codes to generate similar figures\":Let me look at the first occurrence at line 16349 to see if it has a visual description:This looks like it's showing both charts mixed together in the OCR text. Let me look for the embedded image before this section to find the visual description:So this is Figure 47, not Figure 122. Let me go back to the Figure 122 area and look more carefully. The prompt section starting at line 32299 shows the reference image that contains the bar chart. Let me look for what image comes right before or is part of that prompt section. Let me search more systematically around the Figure 122 area to find embedded images:Perfect! So Figure 122 \"demonstrates the application of self-reflection to improve the results shown in Figure 47.\" This means Figure 122 is showing the same charts as Figure 47, but with self-reflection applied. Since Figure 47 had the bar chart with OSCarB and MiniVLM, and I found earlier at line 19587 that \"Blue bars represent OSCarB\", this should apply to Figure 122 as well since it's based on Figure 47.\n\nLet me verify by checking if there's a specific visual description for the reference image in the right-side prompt of Figure 122. Let me look for any embedded image that might be between lines 32286-32302:Let me look at Figure 47 more carefully. Let me search for where Figure 47 begins with an embedded image:Let me look for embedded images near Figure 47. Let me search backwards from line 16333 to find the embedded images that are part of Figure 47:Let me now search for where Figure 47 contains the bar chart reference image. Since Figure 47 shows both charts (the curve chart and the bar chart), let me look for embedded images in the Figure 47 section. Let me search for \"Embedded image\" near Figure 47:These embedded images are from Section 5, not Figure 47. Let me search more systematically. Let me look for embedded images that appear right before or in Figure 47 section. Let me check around line 15900-16350:Let me search for \"Embedded image\" directly:There are many embedded images. Based on what I've seen, the visual description at line 19587 clearly states \"Blue bars represent OSCarB\" for a bar chart showing \"Comparison of OSCarB and MiniVLM.\" This is the same chart that appears in both Figure 47 and Figure 122 (since Figure 122 is showing improvements to Figure 47 via self-reflection).\n\nLet me verify once more by looking at line 39070 which also mentioned OSCarB color:Interesting! This description says \"Red/coral bars: Represent OSCarB method\" while the earlier one at line 19587 said \"Blue bars represent OSCarB\". These might be describing different versions or different embedded images of the same chart. Let me check if these are in different contexts. Let me look at what figure this is part of (line 39070):Let me check what figure or section this is in:This is Image10.jpg. Let me look back to see what figure this is part of. Let me search backwards for \"Figure\" before line 38980:These are Instagram Reels images, not related to Figure 122. Let me search more directly. Let me look for what section/figure is near line 39000-39100. Let me search backwards for \"Figure\" or \"Sec\":These are all Instagram food images. Let me search for which figure or section contains line 39070. Let me look much earlier before these Instagram images:Still Instagram images. Let me just search for \"Figure 122\" to look for its caption and boundaries:So Figure 122's caption is at line 32550. The figure shows two parts - Left (curves) and Right (bar chart). The GPT-4V output for the right side starts at line 32552. This is within Figure 122. Now I need to find the reference image (the prompt input) for the right side, which should show the original bar chart with OSCarB colors. The prompt for the right side is at line 32299 \"Write python codes to generate similar figures.\" The reference image data is at lines 32302-32354. But this doesn't tell me colors directly since it's just OCR text.\n\nBased on my findings:\n1. Figure 122 is based on Figure 47 (as stated at line 32131-32132)\n2. The bar chart in the figures compares OSCarB and MiniVLM\n3. At line 19587, the visual description clearly states \"Blue bars represent OSCarB\"\n4. At line 39070, another visual description states \"Red/coral bars: Represent OSCarB\"\n\nThese seem to be describing different embedded images. Let me check if the one at 19587 is specifically part of Figure 47:This is Section 7.3, not Figure 47. Let me look for what this section is:This is Section 7.3. So the embedded image with \"Blue bars represent OSCarB\" is in Section 7.3, not in Figure 47. Let me search for what figures are in the Sec 10.3 Self-Reflection area where Figure 122 is. Let me look for embedded images right before or around the prompt at line 32299. Actually, the reference image for the prompt should be immediately before it. Let me look:The <figure> starting at line 32302 is the reference image for the prompt \"Write python codes to generate similar figures.\" This figure contains the bar chart data (OSCARB, MiniVLM, etc.) shown as OCR text from lines 32304-32354. But this OCR text doesn't tell me the colors. The actual visual description of this embedded image should be before or after the figure content. Let me check line 32354 onwards:That's the Golden Retriever image (Image7.jpg), which is not the bar chart. So the bar chart figure (lines 32302-32354) doesn't have a separate \"Embedded image\" marker. This means the figure content IS the embedded image content, just displayed as the <figure> OCR text.\n\nLet me look for Image4.jpg or other images that might be before line 32302:I can see that Image5.jpg and Image6.jpg are unrelated (a person and a cat). These embedded images are interspersed in the document but not part of Figure 122's chart. The actual chart images in Figure 122 are shown as <figure> blocks without \"Embedded image\" labels.\n\nLooking at the document structure, I notice that Figure 122 references Figure 47. Since I know that:\n1. Figure 122 is improving upon Figure 47\n2. They show the same charts (left: curves, right: bar chart)\n3. The color of the bars should be consistent between them\n\nLet me look more carefully at Figure 47 to find which embedded image is the bar chart. Let me search around Figure 47's location (around line 16333):This is still in Section 4.5 Multilingual, before Figure 47. Let me search for images closer to Figure 47. Actually, let me take a different approach. Since Figure 47 appears around line 16333 and the caption says it shows examples, the actual embedded images for Figure 47 should have visual descriptions. But since Figure 47 shows generated output rather than original images, the <figure> blocks ARE the actual content.\n\nGiven the conflicting information (line 19587 says \"Blue\" for OSCarB, line 39070 says \"Red/coral\" for OSCarB), let me check which one is actually related to Figure 122 or Figure 47. Let me look at the context more carefully. The document says Figure 122 improves Figure 47. The prompt in Figure 122 at line 32299 is \"Write python codes to generate similar figures\" and the reference image is at lines 32302-32354 showing the bar chart data.\n\nSince Figure 122's caption (line 32550) says \"Right: the percentage is added to align with the reference image\", the reference image (the prompt image) is the ORIGINAL one that needs improvement. This original should be from Figure 47.\n\nLet me look at the data itself. At line 32312, it says \"OSCARB\" (all caps), and at line 32553, in the GPT-4V output, it says \"OSCarB\" (mixed case). Based on the color descriptions I found, and given that there are multiple embedded images of this chart throughout the document, I need to determine which color appears in the specific prompt image in Figure 122.\n\nGiven the two conflicting descriptions and the difficulty in determining which applies to Figure 122's prompt image specifically, let me check if there's any pattern. The one at line 19587 explicitly lists the color scheme at lines 19514-19518:\n- \"Bright cyan/blue for first method\"  \n- \"Orange-red for second method\"\n\nAnd it says at line 19587: \"Blue bars represent OSCarB\"\n\nThe one at line 39070 says:\n- \"Red/coral bars: Represent OSCarB method\"\n- \"Teal/cyan bars: Represent MiniVLM method\"\n\nThese are OPPOSITE! Let me check the legend text. At line 32312-32314 in Figure 122's prompt image, it shows:\n\"OSCARB\"\n\"MiniVLM (ours)\"\n\nAnd at line 32553-32561 in the GPT-4V output for Figure 122:\n\"OSCarB\"\n\"MiniVLM (ours)\"\n\nLet me check the earlier descriptions for legend position. At line 19505-19506: \"Legend in upper right distinguishes the two methods\"\n\nGiven that standard chart conventions typically list legend items in order, and OSCarB is listed first (before MiniVLM), OSCarB would typically be the first color in the color scheme.\n\nLooking at line 19515-19516:\n- \"Bright cyan/blue for first method\"\n- \"Orange-red for second method\"\n\nIf OSCarB is the first method (listed first in the legend), then it should be \"Bright cyan/blue\" which matches line 19587: \"Blue bars represent OSCarB\"\n\nAnswer: blue", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 238671, "error": null, "extra": {"thread_id": 1999, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e4d50e05962f4bf6af3a2d9a2bff8f0a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2074, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to look at Figure 113 to identify the music app shown in the computer screen.\n\nLooking at the document, Figure 113 is described in the section on \"Notification Understanding\" (Section 9.8, page 143).\n\nIn the image caption and description for Figure 113, I can see a computer desktop screenshot. Looking at the desktop icons visible in the image, I can see several application icons including:\n- YouTube\n- Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown on the desktop is labeled as \"Music\" in the icon.\n\nAnswer: Music", "citations": [], "input_tokens": 117435, "output_tokens": 134, "cost_micros": 354315, "latency_ms": 8593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in Section 9.8 (GUI Navigation - Notification Understanding), I can see the computer desktop screen.\n\nOn the desktop, there are several icons visible on the left side, including:\n- YouTube Music\n- Recycle Bin\n- Tabby Terminal\n\nThe music app shown is YouTube Music.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 133652, "output_tokens": 80, "cost_micros": 402156, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I can see a computer desktop screenshot with a notification that says \"Kevin Lin is calling you.\" \n\nThe figure shows several application icons on the desktop and in the taskbar. However, I cannot clearly identify a specific music app from the image description or details provided in the document about this figure.\n\nThe document states: \"The image on the computer screen is a desktop background of a bridge over a body of water. There are several icons on the desktop, including a YouTube icon, a Recycle Bin icon, and a Terminal icon. At the bottom of the screen, there is a taskbar with several application icons, such as Microsoft Edge and File Explorer.\"\n\nAnswer: Not clearly identifiable from the provided information", "citations": [], "input_tokens": 103919, "output_tokens": 160, "cost_micros": 314157, "latency_ms": 7578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "not clearly identifiable from provided information", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 113 in the document, I need to examine the desktop icons shown in the computer screen image.\n\nThe figure shows a Windows desktop with several application icons on the left side. Among these icons, I can see \"YouTube Music\" listed as one of the desktop shortcuts.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 139294, "output_tokens": 67, "cost_micros": 418887, "latency_ms": 6266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 113 and identify the music app shown in the computer screen.Let me search for \"Figure 113\" in the document.Now let me read the section around line 29414 to see Figure 113 and its description.I can see Figure 113 is referenced, but I need to see the actual image content. Let me look a bit earlier to see the full figure with the image description.I need to look at the actual figure content for Figure 113. Let me search for where the figure starts by looking earlier.Perfect! I found Figure 113. Looking at the content around lines 29326-29350, I can see the computer screen showing desktop icons. Line 29330-29331 clearly shows:\n\n\"You Tube\nMusic\"\n\nThis appears to be \"YouTube Music\" split across two lines in the OCR text. This is the music app shown on the computer screen in Figure 113.\n\nAnswer: YouTube Music", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52515, "error": null, "extra": {"thread_id": 2000, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_26bb070ab7624a93b3c5c955dc0af2c0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 233, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "youtube music", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 106921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and look for the price of the keyboard shown in the screenshot.\n\nLooking at the document, Figure 111 is described in the \"List of Figures\" section as:\n\"Figure 111: Section 9.8: online shopping.\"\n\nLet me search for Figure 111 in the actual content. It appears in Section 9.8 on \"Online Shopping (Ergonomic Keyboard)\".\n\nLooking at the prompt description for Figure 111:\n\"Prompt:\nImagine that you are a robot operating a phone. Like how humans operate the phone, you can move your finger to an icon, click an icon with your finger, scroll down a screen, or type some texts with the keyboard.\n\nYou are asked to shop for an ergonomic keyboard, your budget is between $50 and $100.\n\nPrevious action: \" My next move would be to move my finger to the \"Add to Cart\" button, which is located below the \"Buy New\" button and above the \"Ships from Amazon\" section. I would then click on the \"Add to Cart\" button to add the item to my shopping cart. \"\n\nYou have now completed the previous action as described above. Below is what you see on the phone screen. What will you do next?\"\n\nThe screenshot shows a cart with:\n\"Subtotal $4999\"\n\nand the product listed is:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540 ...\n$4999\"\n\nThe price shown is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 117431, "output_tokens": 353, "cost_micros": 357588, "latency_ms": 12750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "azure_premium_lc", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:47Z", "latency_ms": 24578, "error": null, "raw_text_chars": 640}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in the screenshot.\n\nLooking through the document, I can find Figure 111 which is part of Section 9.8 on GUI Navigation, specifically about Online Shopping for an Ergonomic Keyboard.\n\nIn Figure 111, the screenshot shows a shopping cart with a keyboard item. The text indicates:\n\n\"Subtotal $4999\"\n\nAnd in the cart details, it shows:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...\n$4999\"\n\nThe price shown is $49.99 (represented as $4999 in the format used in the screenshot, which appears to be missing the decimal point).\n\nAnswer: 49.99", "citations": [], "input_tokens": 133648, "output_tokens": 176, "cost_micros": 403584, "latency_ms": 24578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 111 in the document, which shows a phone screenshot of a shopping cart.\n\nThe screenshot displays:\n- A product: \"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\"\n- The price shown is: $49.99\n- There's a subtotal of $49.99\n- The item shows \"In Stock\" status\n\nAnswer: 49.99", "citations": [], "input_tokens": 103915, "output_tokens": 97, "cost_micros": 313200, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in that screenshot.\n\nLooking through the document for Figure 111...\n\nFound it on page 140:\n\nFigure 111 shows a smartphone screen displaying an Amazon shopping cart. The image shows:\n- A Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...)\n- Price: $49.99\n- The button shows \"Proceed to checkout (1 item)\"\n- Subtotal: $49.99\n\nThe price of the keyboard shown in the screenshot is $49.99.\n\nAnswer: 49.99", "citations": [], "input_tokens": 139290, "output_tokens": 144, "cost_micros": 420030, "latency_ms": 8921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document that was flagged as user-mentioned to find Figure 111 and identify the keyboard price.Let me search for \"Figure 111\" in the document to locate it.Now let me search for where Figure 111 appears in the content with context.Let me read around line 29062 where Figure 111 is located to see the actual content and price information.Perfect! I found Figure 111 which shows a screenshot of shopping for an ergonomic keyboard. The price is clearly shown in the screenshot as $49.99 (displayed as \"$4999\" without the decimal in multiple places in lines 29088 and 29095).\n\nAnswer: 49.99", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52062, "error": null, "extra": {"thread_id": 2001, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dd7392a113cf44358e67b4c74d3afe31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 172, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "azure_basic_lc", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:12Z", "latency_ms": 7967, "error": null, "raw_text_chars": 594}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and identify the battery percentage shown in that screenshot.\n\nLooking through the document for Figure 107, I can see it's in Section 9.8 on GUI Navigation, specifically related to online shopping.\n\nIn the image caption list, Figure 107 is described as \"Section 9.8: online shopping.\"\n\nLooking at the actual Figure 107 in the document, I can see it's a smartphone screenshot showing an Amazon shopping interface. At the top of the screenshot, there is a time display \"5:32\" and next to it shows \"75\" which represents the battery percentage.\n\nAnswer: 75", "citations": [], "input_tokens": 117429, "output_tokens": 143, "cost_micros": 354432, "latency_ms": 7967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and look for the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107...\n\nFound it in Section 9.8 GUI Navigation under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" in the top left corner, and the battery percentage \"76\" is displayed in the top right corner of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 133646, "output_tokens": 109, "cost_micros": 402573, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to look at Figure 107 to find the battery percentage shown in the screenshot.\n\nLooking through the document for Figure 107, I can see it's in the \"Sec. 9.8 GUI Navigation\" section under \"Online Shopping (Ergonomic Keyboard)\".\n\nIn Figure 107, the screenshot shows a phone interface with the time \"5:27\" displayed at the top left, and the battery percentage \"76\" shown at the top right of the screen.\n\nAnswer: 76", "citations": [], "input_tokens": 103913, "output_tokens": 112, "cost_micros": 313419, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 107 in Section 9.8 (GUI Navigation - Online Shopping), I need to find the battery percentage shown in the screenshot.\n\nIn the top-right corner of the screenshot shown in Figure 107, the status bar displays:\n- Time: 5:27\n- Battery percentage: 76\n\nAnswer: 76", "citations": [], "input_tokens": 139288, "output_tokens": 76, "cost_micros": 419004, "latency_ms": 6592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 107 and identify the battery percentage shown in the screenshot.Let me search for \"Figure 107\" in the document to locate it quickly.Now let me search for the specific line with more context:Let me read around line 28504 to see Figure 107:I can see Figure 107 shows a phone screenshot. I can see \"5:27\" as the time at line 28507, and at line 28511 I see \"76\". Let me check if this is the battery percentage. Looking at the structure, it appears to show:\n- 5:27 (time)\n- 76 (likely battery percentage)\n\nLet me read a bit more to see if there's additional context or if the battery percentage is shown elsewhere:Based on the document content, Figure 107 shows a phone screenshot with the time \"5:27\" at the top, and the number \"76\" appears right after it. In typical phone screenshots, the battery percentage is displayed in the status bar at the top of the screen, usually near the time. The number 76 appears to be the battery percentage.\n\nAnswer: 76", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 64703, "error": null, "extra": {"thread_id": 2002, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_281e7f398f814d8a9e02d3b21485af45", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 226, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "76.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 110562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Figure 101 to find information about the Politico website news cover.\n\nLooking at the document, Figure 101 is described in Section 9.8 \"GUI Navigation - Web Browsing (Today's News)\" on page 131.\n\nThe figure shows a Google search results page with news stories. The prompt asks GPT-4V about what happens after reading the first news article. Looking at the \"Top stories\" section in the image, I can see several news sources listed including:\n\n- **Politico**: \"State Department doubles down after Zelenskyy slams reluctance...\"\n- CNN: \"Catastrophic flooding swamped Vermont's capital...\"\n- Reuters: \"US says it no longer deems Donald Trump immune from E. Jean...\"\n- DCist: \"D.C. Council Approves Emergency Crime Bill\"\n- The New York Times: \"Hard Right Presses Culture War Fights on Defense Bill...\"\n- NPR: \"The Anthropocene began in 1950s, scientists say\"\n\nThe Politico headline mentions \"Zelenskyy,\" which refers to Volodymyr Zelenskyy, the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 117439, "output_tokens": 268, "cost_micros": 356337, "latency_ms": 11250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in Section 9.8 (Web Browsing - Today's News), I can see the Google search results page showing several news headlines.\n\nThe top news item from Politico shows: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThis refers to Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 133656, "output_tokens": 87, "cost_micros": 402273, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101 in the document, I can see a screenshot showing a news article from Politico. The article is titled \"State Department doubles down after Zelenskyy slams reluctance...\" \n\nThe article discusses Ukrainian President Volodymyr Zelenskyy and Ukraine's desire to join NATO. Zelenskyy is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 103923, "output_tokens": 85, "cost_micros": 313044, "latency_ms": 7108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 101, I can see a Google search results page showing news articles. The figure description mentions \"Top stories\" and lists several news sources including Politico.\n\nThe first story listed under Politico is: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy, who is the President of Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 139298, "output_tokens": 88, "cost_micros": 419214, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document that was marked as most relevant (2309.17421v2.pdf.xml) to find Figure 101 and identify the information about the Politico website cover.Let me search for \"Figure 101\" in the document to find the specific location:Let me search for \"politico\" to find the relevant section:I can see mentions of Politico with information about Zelenskyy. Let me search more specifically for \"Figure 101\":Great! I found the reference to Figure 101 at line 27715. Let me read that section:Let me look earlier in the document to see the actual Figure 101 image content. I need to search backwards from line 27715 to find the visual description:Now I can see the context. Figure 101 shows a Google News page with various news articles. Looking at the content around lines 27625-27631, I can see:\n\n- Politico news source\n- Article title: \"State Department doubles down after Zelenskyy slams reluctance...\"\n\nThe article is about Zelenskyy (also spelled Zelensky in other parts of the document), who is the President of Ukraine. Let me verify this is correct by looking at the article content earlier:\n\nFrom lines 27419-27421 and 27445-27446, I can confirm:\n- \"State Department doubles down after Zelenskyy slams reluctance on Ukraine's entry to NATO\"\n- \"In a statement posted to Twitter on Tuesday, Ukrainian President Volodymyr Zelenskyy took issue with NATO's reluctance...\"\n\nSo the person on the cover of the news on the Politico website in Figure 101 is Zelenskyy (Volodymyr Zelenskyy), who is from Ukraine.\n\nAnswer: Ukraine", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 84875, "error": null, "extra": {"thread_id": 2003, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6c36693d9bfb44369693c25b87e59141", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 305, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "ukraine", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "native_pdf", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at Figure 89 to identify the brand name of the coffee machine.\n\nLooking at the document, Figure 89 is in Section 9.7 \"Embodied Agent\" under \"Operating Machine\". The figure shows a coffee machine's operating panel with buttons and includes text that mentions operating menu instructions.\n\nIn the image shown in Figure 89, I can see a coffee machine operating panel. Looking at the visible text and branding on the machine, I can see \"Keurig\" appears to be the brand name based on the styling and layout typical of Keurig coffee makers.\n\nAnswer: Keurig", "citations": [], "input_tokens": 117434, "output_tokens": 139, "cost_micros": 354387, "latency_ms": 7641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "azure_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine's operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 133651, "output_tokens": 61, "cost_micros": 401868, "latency_ms": 6500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_basic_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7 (Embodied Agent - Operating Machine), I can see the coffee machine operating panel in the image.\n\nThe brand name visible on the coffee machine is \"Keurig\".\n\nAnswer: Keurig", "citations": [], "input_tokens": 103918, "output_tokens": 60, "cost_micros": 312654, "latency_ms": 6092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "keurig", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "llamacloud_premium_lc", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 89 in Section 9.7, which shows the coffee machine operating panel with button menus, I can see the brand name displayed on the machine.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 139293, "output_tokens": 47, "cost_micros": 418584, "latency_ms": 7016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "arm": "surfsense_agentic", "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find Figure 89 and identify the coffee machine brand.Let me search for \"Figure 89\" in the document:Now let me search more specifically to find where Figure 89 appears:Let me read the section around line 25761 to see Figure 89 and its image:I can see Figure 89 references a coffee machine. Let me look a bit earlier to see the actual image content:Let me continue reading to see the menu and coffee machine images:Let me continue to find the actual coffee machine image with the brand visible:I see that line 25763 shows \"JoooDeee\" which appears to be OCR text from the coffee machine image. Let me look at the earlier Figure 88 section which also shows the coffee machine, as it might have clearer brand information:I can see the OCR text on line 25609 and 25763 shows \"JoooDeee\". Let me search for Figure 88 which is mentioned just before to see if there's more context about the brand:Let me look before line 25584 to see if there's an image with better OCR or visual description for Figure 88:Let me search more specifically around where Figure 88 might have its embedded image:Let me look earlier to find where Figure 88 actual image appears. Let me search backwards from where Figure 88 was mentioned:Let me search more systematically. Let me look for where the coffee machine images are actually embedded. Let me search around where both Figure 88 and 89 would appear together:Let me try a different approach and look for pages around 116-117 where Figures 88 and 89 are mentioned, searching for embedded coffee machine images. Let me search for \"Joo\" which appeared in the OCR text:Great! Let me look at line 34358 and chunk 155184 which has a better visual description with \"JoooDeee\":Perfect! I found it. According to the OCR text on line 34358, the coffee machine has \"JoooDeee\" visible on it. Looking at the visual description starting at line 34363, it describes the device as a water dispenser/cooler, but based on the context of the document discussing coffee machines and the OCR text showing \"JoooDeee\", this appears to be the brand name visible on the coffee machine in Figure 89.\n\nHowever, \"JoooDeee\" seems like it might be an OCR misreading. Let me check if there's any other reference that might clarify the brand. Looking at the OCR pattern \"JoooDeee\", this could potentially be \"Jura\" (a well-known coffee machine brand) misread by OCR, but I should report what the document actually says.\n\nBased on the document, the OCR text from the coffee machine image shows \"JoooDeee\" with \"DI\" below it.\n\nAnswer: JoooDeee", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 148609, "error": null, "extra": {"thread_id": 2004, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f9e89fe20d454f6c90a127387a939523", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 624, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "jooodeee", "normalised_gold": "jooodeee"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 1, I need to identify the relation arrows (edges) in the knowledge graph and determine which ones do not point to specific leaf nodes.\n\nThe figure shows a knowledge graph centered around Artemisia Gentileschi (Q212657) and Orazio Gentileschi (Q367560). The relations shown include:\n\n- **child** (from Orazio to Artemisia)\n- **father** (from Artemisia to Orazio)\n- **citizen** (from both to Italy Q38)\n- **occupation** (pointing to \"Painter\")\n- **style** (from Artemisia to Baroque Q37853)\n- **movement** (from Artemisia to Baroque Q37853)\n- **place of birth** (from Artemisia to Rome Q220)\n- **member** (from Artemisia, appears to point to another entity)\n\nThe arrows that do NOT point to specific leaf nodes (i.e., they point to other entities that are themselves nodes in the graph rather than terminal values) are:\n\n- child (points to another person entity)\n- father (points to another person entity)\n\nAnswer: child, father", "citations": [], "input_tokens": 60197, "output_tokens": 266, "cost_micros": 184581, "latency_ms": 12796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify which relation arrows do not point to specific leaf nodes (entities with specific values or names).\n\nLet me examine each relation arrow from the entities shown:\n\nFrom Q367560 (Orazio Gentileschi):\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- movement \u2192 Q37853 (Baroque) - points to a class/type node\n- child \u2192 Artemisia Gentileschi Q212657 - points to a specific person\n\nFrom Artemisia Gentileschi Q212657:\n- father \u2192 Orazio Gentileschi Q367560 - points to a specific person\n- birth \u2192 [date shown] - points to specific value\n- place of birth \u2192 [location] - points to specific value\n- Citizen \u2192 Italy Q38 - points to a specific country\n- occupation \u2192 Q1028181 (Painter) - points to a class/type node\n- style \u2192 Q37853 (Baroque) - points to a class/type node\n- member \u2192 [organization] - appears to point to a specific entity\n\nThe relations that point to class/type nodes rather than specific leaf nodes are:\n- occupation (points to the class \"Painter\")\n- movement/style (points to the class \"Baroque\")\n\nAnswer: occupation, movement, style", "citations": [], "input_tokens": 22214, "output_tokens": 311, "cost_micros": 71307, "latency_ms": 7718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "movement, occupation, style", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to identify the relation arrows (edges) in the knowledge graph that do not point to specific leaf nodes (literal values).\n\nIn the knowledge graph shown in Figure 1, I can see the following relationships:\n\n- \"Painter\" (Q1028181) - this is an entity node, not a leaf\n- \"Baroque\" (Q37853) - this is an entity node, not a leaf\n- \"Italy\" (Q38) - this is an entity node, not a leaf\n- \"Rome\" (Q212657) - this is an entity node, not a leaf\n- Orazio Gentileschi (Q367560) - this is an entity node, not a leaf\n- Artemisia Gentileschi - the central entity\n\nThe relations that point to these non-leaf entity nodes (rather than to literal values like dates or strings) are:\n\n- occupation (pointing to Painter)\n- style (pointing to Baroque)\n- movement (pointing to Baroque)\n- citizen (pointing to Italy)\n- place of birth (pointing to Rome)\n- father (pointing to Orazio Gentileschi)\n- child (pointing to Artemisia Gentileschi)\n- member (appears to be a relation as well)\n\nAnswer: occupation, style, movement, citizen, place of birth, father, child, member", "citations": [], "input_tokens": 25539, "output_tokens": 310, "cost_micros": 81267, "latency_ms": 9266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "child, citizen, father, member, movement, occupation, place of birth, style", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows in the Knowledge Graph that do not point to leaf nodes (specific values).\n\nIn the Knowledge Graph shown, the relations are labeled on the arrows connecting entities. Let me trace each relation:\n\n- \"occupation\" \u2192 points to \"Painter\" (Q37853) - this is a leaf node\n- \"movement\" \u2192 points to \"Baroque\" (Q1028181) - this is a leaf node\n- \"style\" \u2192 appears to connect nodes but the endpoint is not clearly a specific leaf value\n- \"place of birth\" \u2192 points to a specific location\n- \"child\" \u2192 points to \"Artemisia\" (another person entity, not a leaf value)\n- \"father\" \u2192 points to \"Orazio\" (another person entity, not a leaf value)\n- \"member\" \u2192 connects to citizenship information\n- \"citizen\" \u2192 points to \"Italy\" (a leaf node)\n\nThe relations that point to other entities rather than specific attribute values (leaf nodes) are:\n\nAnswer: child, father", "citations": [], "input_tokens": 17550, "output_tokens": 231, "cost_micros": 56115, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "child, father", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify the relation arrows that do not point to specific leaf nodes (entity or property nodes).\n\nFrom the Knowledge Graph Context cloud, I can see the following relationships:\n\n- E1 (OrazioGentileschi) -- occupation --> P1 (Painter) - points to leaf node P1\n- E1 -- movement --> P2 (Baroque) - points to leaf node P2\n- E1 -- birth --> E1 - points back to itself, not a leaf node\n- E1 -- child --> E2 - points to E2, which has further connections\n- E2 (ArtemisiaGentileschi) -- father --> E1 - points to E1, which has further connections\n- E2 -- occupation --> P1 - points to leaf node P1\n- E2 -- style --> P2 - points to leaf node P2\n- E2 -- place of birth --> E3 - points to E3, which has further connections\n- E2 -- member --> E2 - points back to itself, not a leaf node\n- E2 -- Citizen --> E3 - points to E3, which has further connections\n- E1 -- Citizen --> E3 - points to E3, which has further connections\n\nThe relations that do not point to specific leaf nodes are those pointing to entities that have their own outgoing connections or pointing back to themselves:\n\nAnswer: birth, child, father, place of birth, member, Citizen", "citations": [], "input_tokens": 23598, "output_tokens": 341, "cost_micros": 75909, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "birth, child, citizen, father, member, place of birth", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q000", "doc_id": "2310.05634v2.pdf", "answer_format": "list", "gold": "['movement', 'birth', 'place of birth', 'member']", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify which relation arrows do not point to specific leaf nodes.Let me search for \"Figure 1\" in the document to locate it:Let me read more to see the complete Figure 1 description:I can see Figure 1 shows a knowledge graph. Let me look more carefully at the structure. The text shows entities and relations. Let me search for more context about the graph structure:Based on the text extracted from Figure 1, I can see the knowledge graph structure. The entities and relations listed are:\n\n- Q1028181 (Painter)\n- Q37853 (Baroque)\n- Q367560 (Orazio Gentileschi)\n- Q212657 (with \"Artemisia Gentileschi Citizen\" below it)\n- Q38 (Italy)\n\nRelations shown:\n- occupation\n- style\n- movement\n- occupation (appears twice)\n- child\n- birth\n- place of birth\n- father\n- member\n\nLooking at the structure, the relations that appear to connect between entities but may not point to specific leaf nodes would be those that connect to higher-level entities or categories. From the layout, I need to identify which relation arrows don't terminate at leaf nodes.\n\nLet me look for any figures or images that might show the actual graph structure more", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 59797, "error": null, "extra": {"thread_id": 2005, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c3fdd69258514ce3917c73b31fe40d3e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 249, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "let me look for any figures or images that might show actual graph structure more", "normalised_gold": "birth, member, movement, place of birth"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I need to locate Figure 5 in the document and examine the lines shown in it.\n\nLooking through the document, I can find Figure 5 on page 8. The figure is titled \"Retrieval Analysis\" and shows citation evaluation (Micro) of generated texts using knowledge graphs with retrieval accuracy 100 (gold), 80, 60, 40, and 20.\n\nThe figure contains four lines:\n- A yellow line (precision)\n- A green line (recall)\n- A blue line (F1_Score)\n- A red line (correctness)\n\nLooking at the graph, I need to identify which line does not intersect with any other line.\n\nThe red line (correctness) starts at the highest point and remains consistently above all other lines throughout the graph. It descends from left to right but never crosses or intersects with any of the other three lines (yellow, green, or blue).\n\nAnswer: red", "citations": [], "input_tokens": 60196, "output_tokens": 207, "cost_micros": 183693, "latency_ms": 12141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, which shows \"Retrieval Analysis\" with multiple lines plotted against Retrieval Accuracy on the x-axis:\n\nThe lines shown are:\n- precision (yellow)\n- recall (green)\n- F1_Score (appears to be another color)\n- correctness (red/orange)\n\nThe correctness line (red/orange colored line at the top of the graph) maintains a consistently high value around 80-90 and does not intersect with any of the other lines throughout the plot.\n\nAnswer: red (or orange/correctness line)", "citations": [], "input_tokens": 22213, "output_tokens": 134, "cost_micros": 68649, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.33333333333333337, "method": "str_norm", "normalised_pred": "red or orange correctness line", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 titled \"Retrieval Analysis\", I need to identify which line does not intersect with any other line.\n\nThe figure shows four lines representing different metrics (precision, recall, F1_Score, and correctness) plotted against retrieval accuracy from gold (100) to 20.\n\nExamining the lines:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines  \n- The blue line (F1_Score) intersects with other lines\n- The red line (correctness) stays at the top of the graph and does not intersect with any of the other three lines\n\nAnswer: red", "citations": [], "input_tokens": 25538, "output_tokens": 150, "cost_micros": 78864, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I need to identify the line that has no intersection with any other line.\n\nFigure 5 shows citation evaluation metrics (Micro) with retrieval accuracy from 100 (gold) down to 20. There are four lines in the graph representing:\n- Correctness (blue)\n- Precision (yellow) \n- Recall (green)\n- F1 (red)\n\nExamining the lines, the blue line (Correctness) remains at the top of the graph and does not intersect with any of the other three lines throughout the entire range of retrieval accuracy values.\n\nAnswer: Blue", "citations": [], "input_tokens": 17549, "output_tokens": 144, "cost_micros": 54807, "latency_ms": 5610, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 5 in the document, I can see a chart showing citation evaluation metrics (precision, recall, F1_score, and correctness) plotted against retrieval accuracy levels (20, 40, 60, 80, and gold/100).\n\nExamining the lines in the chart:\n- The yellow line (precision) intersects with other lines\n- The green line (recall) intersects with other lines\n- The blue line (F1_score) intersects with other lines\n- The red line (correctness) runs above all other lines and does not intersect with any of them\n\nAnswer: red", "citations": [], "input_tokens": 23597, "output_tokens": 138, "cost_micros": 72861, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "red", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q001", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "red", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 5 and identify the line with no intersections.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25967, "error": null, "extra": {"thread_id": 2006, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_a9d66758d1df48679fe6def6615062f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 40, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "i ll read document to find figure 5 and identify line with no intersections", "normalised_gold": "red"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count how many tables in the document include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1 (page 3) - Shows an example data entry, not evaluation metrics\n2. Table 2 (page 3) - Human Evaluation on dataset quality - no F1\n3. Table 3 (page 7) - Citation Quality evaluation - shows \"F1.\" column (both Micro and Macro)\n4. Table 4 (page 7) - Text quality evaluation - no F1\n5. Table 5 (page 7) - Citation quality with knowledge removed - shows \"F1.\" column\n6. Table 6 (page 8) - Human evaluation alignment - no F1\n7. Table 7 (page 9) - Comparison of General and Specific questions - shows \"F1.\" column\n8. Table 8 (page 15) - Human annotation instructions - no F1\n9. Tables 9-17 (pages 16-21) - Prompts and instructions - no F1\n10. Table 18 (page 22) - Evolutionary question generation example - no F1\n11. Tables 19-20 (pages 23-24) - Example outputs - no F1\n\nAdditionally, I can see:\n- Figure 4 (page 8) shows \"F1_Score\" in a graph\n- Figure 5 (page 8) shows \"F1_Score\" in a graph\n\nBut the question asks specifically about tables, not figures.\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 60181, "output_tokens": 381, "cost_micros": 186258, "latency_ms": 25483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all tables in the document that include \"F1\" as a metric.\n\nLet me go through the tables systematically:\n\n1. Table 1 (Example data entry) - No F1 metric\n2. Table 2 (Human Evaluation on BioKaLMA dataset) - No F1 metric\n3. Table 3 (Citation Quality) - Yes, has \"F1.\" columns (both Micro and Macro)\n4. Table 4 (Text quality evaluation) - No F1 metric\n5. Table 5 (Citation quality with removed knowledge) - Yes, has \"F1.\" column\n6. Table 6 (Human Evaluation on alignment) - No F1 metric\n7. Table 7 (Comparison of General and Specific questions) - Yes, has \"F1.\" column\n\nNow let me check if there are more tables in the appendices:\n\n8. Table 8 (Human Evaluation Instructions) - No F1 metric\n9. Table 9 (Instruction for evolutionary question construction) - No F1 metric\n10. Table 10 (Instruction for round 2-5) - No F1 metric\n11. Table 11 (General question generation) - No F1 metric\n12. Table 12 (Specific question generation) - No F1 metric\n13. Table 13 (Answer generation instruction) - No F1 metric\n14. Table 14 (GPT-EVAL Coherence) - No F1 metric\n15. Table 15 (GPT-EVAL Consistency) - No F1 metric\n16. Table 16 (GPT-EVAL Fluency) - No F1 metric\n17. Table 17 (GPT-EVAL Relevance) - No F1 metric\n18. Table 18 (Evolutionary question construction example) - No F1 metric\n19. Table 19 (Example from ChatGPT) - No F1 metric\n20. Table 20 (Example from GPT-4) - No F1 metric\n\nTables with F1 metric: Table 3, Table 5, and Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 22198, "output_tokens": 502, "cost_micros": 74124, "latency_ms": 9109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. Table 1: Shows an example data entry - no F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - no F1 metric\n3. Table 3: Citation Quality evaluation - includes \"F1.\" column (both Micro and Macro F1)\n4. Table 4: Evaluation on generated text quality - no F1 metric\n5. Table 5: Citation quality evaluation with knowledge removed - includes \"F1.\" column\n6. Table 6: Human Evaluation on text-citation alignment - no F1 metric\n7. Table 7: Comparison of evaluation results on General and Specific question setting - includes \"F1.\" column\n8. Table 8: Instruction for human annotators - not a data table with metrics\n9. Tables 9-17: These are prompt/instruction tables - no F1 metrics\n10. Table 18: Example of generated questions - no F1 metric\n11. Tables 19-20: Examples of attributed answers - no F1 metrics\n\nTables with F1 metric:\n- Table 3\n- Table 5\n- Table 7\n\nAnswer: 3", "citations": [], "input_tokens": 25523, "output_tokens": 289, "cost_micros": 80904, "latency_ms": 8578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to search through the document for tables that include \"F1\" as a metric.\n\nLet me identify all tables in the document:\n\n1. Table 1: Shows an example data entry in BioKaLMA - does not contain F1 metric\n2. Table 2: Human Evaluation on BioKaLMA dataset - contains metrics: Authenticity, Relevance, Naturalness, Significance - no F1\n3. Table 3: Citation Quality evaluation - contains columns including \"F1.\" in both Micro and Macro sections - **YES, contains F1**\n4. Table 5: Citation quality evaluation for generated texts - contains columns: Corr., Prec., Rec., F1. - **YES, contains F1**\n5. Table 6: Result of Human Evaluation on text-citation alignment - contains Alignment and Human Avg. - no F1\n6. Table 7: Comparison between general and specific questions - contains columns including F1. - **YES, contains F1**\n7. Table 8: Instruction for human annotators - no metrics table\n8. Table 9: Not clearly visible as a numbered table in the content shown\n9. Table 10: Instruction and demonstration - not a metrics table\n10. Table 11: Instruction and demonstration - not a metrics table\n11. Table 12: Instruction and demonstration - not a metrics table\n12. Table 13: Instruction for answer generation - not a metrics table\n13. Tables 14-17: Evaluation criteria descriptions - not metrics tables\n14. Table 18: Example of generated questions - not a metrics table\n15. Table 19: Stephen Crane biographical information - not a metrics table with F1\n\nCounting tables with F1 metric:\n- Table 3: YES\n- Table 5: YES\n- Table 7: YES\n\nAnswer: 3", "citations": [], "input_tokens": 17534, "output_tokens": 420, "cost_micros": 58902, "latency_ms": 11342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all tables that include \"F1\" as a metric.\n\nLet me go through the document systematically:\n\n1. **Table 1**: Shows an example data entry - no F1 metric\n2. **Table 2**: Human Evaluation metrics (Authenticity, Relevance, Naturalness, Significance) - no F1\n3. **Table 3**: Citation Quality evaluation - includes \"Micro F1.\" and \"Macro F1.\" columns - **YES**\n4. **Table 4**: Text quality evaluation (Coh., Con., Flu., Rel.) - no F1\n5. **Table 5**: Citation quality with removed knowledge - includes \"F1.\" column - **YES**\n6. **Figure 4**: Shows precision, recall, and F1_Score for [NA] - **YES** (this is labeled as a figure but formatted as a table)\n7. **Figure 5**: Citation evaluation with retrieval accuracy - includes \"F1_score\" column - **YES** (also labeled as figure but formatted as a table)\n8. **Table 6**: Human evaluation on alignment - no F1\n9. **Table 7**: Comparison of General and Specific questions - includes \"Citation Eval. F1.\" column - **YES**\n10. **Table 8**: Human annotation instructions - no F1\n11. Tables 9-17: Various prompts and instructions - no F1 metrics\n12. **Table 18**: Example of evolutionary question construction - no F1\n13. **Table 19**: Example output from ChatGPT - no F1\n14. **Table 20**: Example output from GPT-4 - no F1\n\nTables/Figures with F1 metric:\n- Table 3\n- Table 5\n- Figure 4 (table format)\n- Figure 5 (table format)\n- Table 7\n\nAnswer: 5", "citations": [], "input_tokens": 23582, "output_tokens": 429, "cost_micros": 77181, "latency_ms": 11358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q002", "doc_id": "2310.05634v2.pdf", "answer_format": "int", "gold": "3", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned (2310.05634v2.pdf.xml) to find all tables that include \"F1\" as a metric.Let me continue reading the document to search for all tables:Now let me search for \"F1\" to find which tables contain this metric:Let me read the specific sections containing the tables to identify which ones include F1 as a metric:Now let me check all the other tables to see if they contain F1. Let me search more systematically:Let me also check Table 6:Now let me check the tables in the appendix to ensure I haven't missed any:Now let me scan through all the remaining content to ensure I have checked all tables. Let me list out what I've found so far:\n\nTables with F1:\n1. Table 3 - Has F1 (both Micro and Macro F1)\n2. Table 5 - Has F1 \n3. Table 7 - Has F1\n\nTables without F1:\n1. Table 1 - Does not have F1 (has Authenticity, Relevance, Naturalness, Significance)\n2. Table 2 - This appears to be just a caption reference, same as Table 1\n3. Table 4 - Does not have F1 (has Coh., Con., Flu., Rel.)\n4. Table 6 - Does not have F1 (has Alignment and Human Avg.)\n\nLet me continue checking the rest of the tables (8-20) mentioned in the document:Based on my thorough examination of the document, I have identified all tables and checked which ones include \"F1\" as a metric. Let me verify my findings:\n\nTables with F1 as a metric:\n1. **Table 3**: Citation Quality evaluation - includes both Micro F1 and Macro F1 columns\n2. **Table 5**: Citation quality evaluation for generated texts using a KG with N pieces of knowledge removed - includes F1 column\n3. **Table 7**: Comparison of evaluation results on General and Specific question setting - includes F1 in the Citation Evaluation columns\n\nTables without F1 as a metric:\n- Table 1: Example data entry (no F1)\n- Table 2: Human Evaluation on BioKaLMA dataset (no F1)\n- Table 4: Evaluation on generated text quality (no F1, only Coh., Con., Flu., Rel.)\n- Table 6: Result of Human Evaluation on text-citation alignment (no F1, only Alignment and Human Avg.)\n- Tables 8-20: These are instruction tables, prompt demonstrations, and examples (no F1 metrics)\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 116094, "error": null, "extra": {"thread_id": 2007, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d5e6c0fe45c9431aaf087f3d70bed847", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 545, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "3"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the paper, which shows the citation quality evaluation results for different models:\n\nThe alignment scores for ChatGPT at different temperatures are:\n- ChatGPT (0.1): 85.9\n- ChatGPT (0.5): 84.5\n- ChatGPT (0.9): 84.1\n\nThe highest alignment score is at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 60189, "output_tokens": 103, "cost_micros": 182112, "latency_ms": 22187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nFrom Table 3 (Citation Quality evaluation), the alignment scores for ChatGPT are:\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 22206, "output_tokens": 129, "cost_micros": 68553, "latency_ms": 5421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from the paper.\n\nLooking at Table 3 in the paper, which shows \"Citation Quality OpenAI models and LLaMA family models\" including alignment scores:\n\n- ChatGPT (0.1): Alignment = 85.9(2.5)\n- ChatGPT (0.5): Alignment = 84.5(1.1)\n- ChatGPT (0.9): Alignment = 84.1(0.5)\n\nThe highest alignment score among the ChatGPT models is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 25531, "output_tokens": 159, "cost_micros": 78978, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which presents the citation quality evaluation results including alignment scores for different models:\n\nFor ChatGPT at different temperatures:\n- ChatGPT (0.1): Alignment = 85.9\n- ChatGPT (0.5): Alignment = 84.5\n- ChatGPT (0.9): Alignment = 84.1\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 17542, "output_tokens": 125, "cost_micros": 54501, "latency_ms": 5592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the alignment scores for ChatGPT at different temperatures from Table 3.\n\nLooking at Table 3 (Citation Quality evaluation), I can see the alignment scores for different ChatGPT temperatures:\n\n- ChatGPT (0.1): 85.9(2.5)\n- ChatGPT (0.5): 84.5(1.1)\n- ChatGPT (0.9): 84.1(0.5)\n\nThe highest alignment score is 85.9 for ChatGPT at temperature 0.1.\n\nAnswer: 0.1", "citations": [], "input_tokens": 23590, "output_tokens": 139, "cost_micros": 72855, "latency_ms": 4796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q003", "doc_id": "2310.05634v2.pdf", "answer_format": "float", "gold": "0.1", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find information about temperature and ChatGPT alignment scores.Let me search for temperature settings and ChatGPT results more specifically:Now let me read the specific section where temperature experiments with ChatGPT are discussed:Perfect! I found the table with alignment scores for ChatGPT at different temperatures. From Table 3, I can see:\n\n- ChatGPT (0.1): Alignment score = 85.9(2.5)\n- ChatGPT (0.5): Alignment score = 84.5(1.1)\n- ChatGPT (0.9): Alignment score = 84.1(0.5)\n\nThe highest alignment score for ChatGPT is at temperature 0.1 with a score of 85.9.\n\nAnswer: 0.1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78952, "error": null, "extra": {"thread_id": 2008, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_826b46b364ee4f52853639bf7b854cb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 182, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.1", "normalised_gold": "0.1"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "native_pdf", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Section A and subsections A.1, A.2, A.3), I need to compare the length of descriptions for each step:\n\n**A.1 Person Selection**: Approximately 150 words - describes selecting name pairs from the biographical database and filtering criteria.\n\n**A.2 Name Disambiguation**: Approximately 200 words - describes using SPARQL queries to map names to WikiData entities, handling duplicate names, and extracting one-hop sub-graphs.\n\n**A.3 Evolutionary Question Generation**: Approximately 450-500 words - provides extensive detail about the iterative process, knowledge injection, specificity and coherence scoring, the formula for scoring, and implementation details across five iterations.\n\nThe Evolutionary Question Generation step clearly contains the most detailed explanation, including mathematical formulas, specific algorithms for knowledge selection, and detailed descriptions of the iterative process.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 60191, "output_tokens": 204, "cost_micros": 183633, "latency_ms": 21312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the Dataset Construction section (Appendix A), I need to compare the lengths of descriptions for each step:\n\n1. **Person Selection (A.1)**: Describes selecting name pairs from biographical database - relatively brief, about 1 paragraph\n\n2. **Name Disambiguation (A.2)**: Describes mapping names to WikiData entities using SPARQL queries - moderate length, about 1-2 paragraphs\n\n3. **Evolutionary Question Generation (A.3)**: Describes the iterative process of extending paragraphs, selecting knowledge, calculating scores with formulas, and generating questions - significantly longer, spanning multiple paragraphs with detailed explanations of specificity scores, coherence scores, the scoring formula, iteration process, and implementation details\n\nThe Evolutionary Question Generation step clearly contains the most extensive description with the most detail about the methodology.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 22208, "output_tokens": 193, "cost_micros": 69519, "latency_ms": 6781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "To determine which step takes the most words to describe in the dataset construction process, I need to examine the three main steps described in Section 2.2 and detailed in Appendix A:\n\n1. **Person Selection** (Section A.1): This section describes filtering and selecting people from the biographical database. It's relatively brief.\n\n2. **Name Disambiguation** (Section A.2): This section explains mapping names to WikiData entities using SPARQL queries and extracting one-hop sub-graphs. It's moderately detailed.\n\n3. **Evolutionary Question Generation** (Section A.3): This section provides extensive detail about the iterative process of extending paragraphs, knowledge selection algorithms, scoring mechanisms (specificity and coherence scores), and the multiple rounds of generation with specific templates and prompts.\n\nThe Evolutionary Question Generation section is significantly longer and more detailed than the other two steps, including mathematical formulas, multiple iteration explanations, and extensive procedural details.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 25533, "output_tokens": 217, "cost_micros": 79854, "latency_ms": 7764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at section 2.2 Dataset Construction and Appendix A Dataset Construction, the three main steps are:\n\n1. Person Selection\n2. Name Disambiguation  \n3. Evolutionary Question Generation\n\nComparing the word counts in Appendix A where these steps are described in detail:\n\n- A.1 Person Selection: approximately 150 words\n- A.2 Name Disambiguation: approximately 180 words\n- A.3 Evolutionary Question Generation: approximately 130 words (before the subsections)\n\nHowever, the Evolutionary Question Generation section has significantly more content when including all the detailed prompt tables (Tables 9-12) and examples that follow it, making it the most extensively described step overall.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 17544, "output_tokens": 165, "cost_micros": 55107, "latency_ms": 5703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the dataset construction section (2.2), I need to compare the length of descriptions for each of the three steps:\n\n1. **Person Selection** (A.1): Approximately 150 words\n2. **Name Disambiguation** (A.2): Approximately 200 words  \n3. **Evolutionary Question Generation** (A.3): Approximately 450+ words (includes detailed explanation of the iterative process, knowledge selection algorithm with formula, scoring mechanisms, etc.)\n\nThe Evolutionary Question Generation step is described in significantly more detail than the other two steps, taking up the most space in the paper.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 23592, "output_tokens": 143, "cost_micros": 72921, "latency_ms": 5844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q004", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "Evolutionary Question Generation", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about dataset construction steps and compare their descriptions.Let me search for \"dataset construction\" in the document:Let me look at the detailed dataset construction section:Let me read the Dataset Construction section starting at line 506:Good! I can see the three steps mentioned at line 521-523. Now let me read the Appendix A which has the detailed dataset construction process:Perfect! I can now see the three steps of dataset construction in detail. Let me count the words in each section to determine which takes the most words to describe.\n\nLet me read each section more carefully to count:Now let me see where the Evolutionary Question Generation section ends:Perfect! Now I have all three sections. Let me analyze the word count for each step:\n\n**A.1 Person Selection** (lines 2089-2110): Approximately 22 lines of content\n**A.2 Name Disambiguation** (lines 2113-2145): Approximately 33 lines of content  \n**A.3 Evolutionary Question Generation** (lines 2148-2222): Approximately 75 lines of content\n\nBy visual inspection and line count, the \"Evolutionary Question Generation\" section is significantly longer than the other two steps. It contains detailed explanations about:\n- The evolutionary approach\n- Iteration process\n- Knowledge annotation\n- Specificity and coherence scoring\n- Formula for scoring\n- Implementation details\n\nThis is clearly the step that takes the most words to describe.\n\nAnswer: Evolutionary Question Generation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 78750, "error": null, "extra": {"thread_id": 2009, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b0d2d82267f4e5eab9a849c3924465e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "evolutionary question generation", "normalised_gold": "evolutionary question generation"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:37Z", "latency_ms": 20843, "error": null, "raw_text_chars": 345}]}, "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 2, the task formulation section discusses the \"Setting of Conscious Incompetence.\"\n\nThe relevant text states: \"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 60205, "output_tokens": 80, "cost_micros": 181815, "latency_ms": 20828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document's definition of the \"Conscious Incompetence\" setting in Section 2.1:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 22222, "output_tokens": 71, "cost_micros": 67731, "latency_ms": 4952, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "azure_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\nAnswer: It can be partially verified by the knowledge graph G", "citations": [], "input_tokens": 25547, "output_tokens": 40, "cost_micros": 77241, "latency_ms": 3375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "it can be partially verified by knowledge graph g", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_basic_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of when a sentence can map to both [NA] and knowledge citations.\n\nIn Section 2.1 \"Task Formulation,\" under \"Setting of Conscious Incompetence,\" the document states:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can.\"\n\nHowever, this is incomplete. Looking at the full context, the document explains that [NA] is used when a sentence \"includes some knowledge to be verified, but the knowledge is absent in the knowledge graph G.\"\n\nThe key insight comes from the demonstration in Table 13, which shows sentences containing both specific knowledge citations from the graph AND [NA] citations. This occurs when a sentence contains both:\n1. Knowledge that IS present in the knowledge graph (cited with specific knowledge)\n2. Knowledge that IS NOT present in the knowledge graph (cited with [NA])\n\nAnswer: When it contains both knowledge present in and absent from the knowledge graph", "citations": [], "input_tokens": 17558, "output_tokens": 214, "cost_micros": 55884, "latency_ms": 7110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2727272727272727, "method": "str_norm", "normalised_pred": "when it contains both knowledge present in and absent from knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "llamacloud_premium_lc", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "According to the document, in the \"Setting of Conscious Incompetence\" section (2.1), a sentence can map to both [NA] and a list of sub-graph knowledge when:\n\n\"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph $G$.\"\n\nAnswer: When it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 23606, "output_tokens": 91, "cost_micros": 72183, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "arm": "surfsense_agentic", "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the author's definition of conscious incompetence and when a sentence can map to both [NA] and a list of sub-graph knowledge.Let me search for \"conscious incompetence\" in the document:Now let me read the section around line 485 and 514 where \"conscious incompetence\" is defined:Perfect! I found the answer. According to the document, in the section on \"Setting of Conscious Incompetence\", a sentence can map to both [NA] and a list of sub-graph knowledge when it can be partially verified by the knowledge graph G.\n\nAnswer: when it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34875, "error": null, "extra": {"thread_id": 2010, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6035a4bba19d42d88826ef02286ce3c9", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 145, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.8421052631578948, "method": "str_norm", "normalised_pred": "when it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure of question-guided reasoning and count the numbers displayed in blue color.\n\nLooking at Figure 1 on page 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims,\" I can see:\n\n- Q1: What is the greatest depth of the Black Sea?\n- A1: Black sea has a maximum depth of **2,212** meters.\n- Q2: How far can sunlight penetrate water?\n- A2: Sunlight does not penetrate water below **1,000** meters.\n- At the bottom: \"2,212 is greater than 1,000. Therefore, the claim is\"\n\nIn the figure, I can see numbers that appear to be highlighted or emphasized:\n- 2,212 (appears twice - once in A1 and once in the reasoning)\n- 1,000 (appears twice - once in A2 and once in the reasoning)\n\nLooking at the visual representation in the figure, the numbers that are displayed in blue/highlighted color are:\n- 2,212 (in A1)\n- 1,000 (in A2)\n- 2,212 (in the final reasoning line)\n- 1,000 (in the final reasoning line)\n\nSo there are 4 instances of numbers shown in blue color.\n\nAnswer: 4", "citations": [], "input_tokens": 27152, "output_tokens": 309, "cost_micros": 86091, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n1. The claim about the Black Sea\n2. Q1 asking about the greatest depth of the Black Sea\n3. A1 stating \"Black sea has a maximum depth of 2,212 meters\" - the number **2,212** appears in blue\n4. Additional text showing \"Average depth\" with what appears to be another number\n5. Q2 asking about how far sunlight can penetrate water\n6. A2 stating \"Sunlight does not penetrate water below 1,000 meters\" - the number **1,000** appears in blue\n7. At the bottom, there's reasoning that states \"2,212 is greater than 1,000\" - both **2,212** and **1,000** appear in blue here as well\n\nLooking carefully at the figure, I can identify the following blue-colored numbers:\n- 2,212 (appears multiple times)\n- 1,000 (appears multiple times)\n\nHowever, the question asks for how many numbers with blue color are mentioned, not how many times they appear. The distinct blue numbers are:\n1. 2,212\n2. 1,000\n\nAnswer: 2", "citations": [], "input_tokens": 11640, "output_tokens": 300, "cost_micros": 39420, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nIn Figure 1, I can see:\n- The claim about sunlight and the Black Sea\n- Q1 asking about the greatest depth of the Black Sea\n- A1 stating \"Black sea has a maximum depth of **2,212 meters**\" (2,212 appears to be in blue)\n- Q2 asking about how far sunlight can penetrate water\n- A2 stating \"Sunlight does not penetrate water below **1,000 meters**\" (1,000 appears to be in blue)\n- At the bottom, there's text saying \"2,212 is greater than 1,000\" where both numbers appear to be in blue\n\nSo the blue-colored numbers mentioned are:\n1. 2,212 (in A1)\n2. 1,000 (in A2)\n3. 2,212 (in the comparison at bottom)\n4. 1,000 (in the comparison at bottom)\n\nHowever, if we count unique numbers, there are 2 distinct numbers (2,212 and 1,000). But if we count each mention, there are 4 total mentions of numbers in blue.\n\nLooking more carefully at the figure, the numbers that appear to be highlighted/emphasized in blue are:\n- 2,212 (appears twice)\n- 1,000 (appears twice)\n\nCounting total mentions: 4\n\nAnswer: 4", "citations": [], "input_tokens": 12756, "output_tokens": 349, "cost_micros": 43503, "latency_ms": 8500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the example figure that shows question-guided reasoning and count the blue-colored numbers in it.\n\nLooking at the document, Figure 1 is mentioned in the text as showing an example of question-guided reasoning for the claim \"Sunlight can reach the deepest part of the Black Sea.\" The text states: \"This reasoning process can be formulated as question-guided reasoning, where the verification of the claim is guided by asking and answering a series of relevant questions, as shown in Figure 1.\"\n\nHowever, Figure 1 itself is not shown in the provided document content. \n\nThe document does show Figure 4, which displays the QACHECK user interface with an example about \"Lars Onsager won the Nobel prize when he was 30 years old.\" In this figure, I can see:\n- \"Reasoning depth: 0\" and \"Reasoning depth: 1\" labels\n- \"1968\" (the year Lars Onsager won the Nobel Prize)\n- \"1903\" (the year Lars Onsager was born)\n- \"65\" (mentioned in the final prediction: \"He was 65 when he won the Nobel prize\")\n\nLooking at the visible figures in the document, Figure 4 shows the question-guided reasoning process example. The numbers that appear to be highlighted or emphasized in the context of the reasoning are: 1968, 1903, and 65.\n\nWithout being able to see the actual colors in Figure 1 (which is referenced but not displayed), and based on Figure 4 which shows a complete example of question-guided reasoning, I can count 3 distinct numbers that would typically be highlighted in such a demonstration.\n\nAnswer: 3", "citations": [], "input_tokens": 8625, "output_tokens": 374, "cost_micros": 31485, "latency_ms": 9280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 1, which shows \"An example of question-guided reasoning for fact-checking complex real-world claims.\"\n\nLooking at Figure 1, I can see:\n- The claim about sunlight reaching the deepest part of the Black Sea\n- Step 1 with Q1 and A1, where A1 mentions \"2,212 meters\" \n- Step 2 with Q2 and A2, where A2 mentions \"1,000 meters\"\n- The conclusion states \"2,212 is greater than 1,000\"\n\nThe numbers mentioned are:\n1. 2,212 (meters - maximum depth of Black Sea)\n2. 1,000 (meters - how far sunlight penetrates)\n\nBoth numbers appear to be in the same color as the rest of the text in the boxes. Looking at the figure description and the actual content, I need to identify which numbers are specifically in blue color.\n\nFrom the document's Figure 1, examining the text carefully, the numbers that appear are 2,212 and 1,000. The question asks specifically about numbers with \"blue color.\"\n\nBased on the visual representation in Figure 1, I can identify 2 numbers: 2,212 and 1,000.\n\nAnswer: 2", "citations": [], "input_tokens": 12525, "output_tokens": 288, "cost_micros": 41895, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q000", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "2", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find the example figure related to \"question-guided reasoning\" and count the blue-colored numbers.Let me search for \"question-guided reasoning\" in the document to locate the relevant section.Now let me read the section around line 217 where Figure 1 is mentioned, which shows \"An example of question-guided reasoning\":Perfect! I can see Figure 1 which shows the question-guided reasoning example. Let me continue reading to see the complete figure description and identify all blue-colored numbers:Based on the document content I've read, I can see Figure 1 which shows the question-guided reasoning example. The figure contains:\n\n1. The text mentions \"2,212 m\" (Black Sea depth)\n2. The text mentions \"2,212 meters\" in the answer A1\n3. The text mentions \"1,000 meters\" in the answer A2\n4. The visual description mentions \"Blue accent colors for interactive elements (icons, links)\"\n\nLet me look more carefully at the OCR text section which lists the specific numbers visible in the figure:\n\nFrom lines 288-298, the OCR text shows:\n- 2,212 m (appears multiple times)\n- 1,253 m\n- 7,257 ft\n- 547,000 km\u00b3\n- 131,200 cu mi\n- 10+\n\nAnd from the main figure text (lines 219-270):\n- 2,212 m (Black Sea depth)\n- 1,000 meters (sunlight penetration)\n- 200 meters (mentioned in the evidence text)\n\nThe key information states: \"Blue accent colors for interactive elements (icons, links)\" which suggests that numbers appearing in blue would be those that are interactive or emphasized.\n\nLooking at the structure of Figure 1, the numbers that appear to be in blue color based on the context are:\n1. **2,212** (the answer to Q1 about Black Sea depth)\n2. **1,000** (the answer to Q2 about sunlight penetration)\n\nThese are the two key numerical answers that appear in the question-guided reasoning process.\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 45156, "error": null, "extra": {"thread_id": 2011, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ee68d3622e54a41be76708c8f52b00e", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 300, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:57Z", "latency_ms": 14639, "error": null, "raw_text_chars": 1219}]}, "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to identify which modules use Large Language Models (LLMs) as their backbone.\n\nFrom Section 3, the QACHECK system has 5 modules:\n1. **Claim Verifier** - Section 3.1 states it's \"based on InstructGPT\" (an LLM)\n2. **Question Generator** - Section 3.2 states \"Similar to the claim verifier, we also leverage InstructGPT\" (an LLM)\n3. **Question Answering Model** - Section 3.3 describes three implementations, with the default being \"GPT Reciter-Reader\" which uses InstructGPT (an LLM). However, this module also has non-LLM options (Retriever-Reader, FLAN-T5).\n4. **QA Validator** - Section 3.4 states \"We again implement the validator by prompting InstructGPT\" (an LLM)\n5. **Reasoner** - Section 3.5 provides \"two different implementations: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\"\n\nCounting the modules that use LLM backbones:\n- Claim Verifier: Yes (InstructGPT)\n- Question Generator: Yes (InstructGPT)\n- Question Answering Model: Yes (uses GPT Reciter-Reader by default)\n- QA Validator: Yes (InstructGPT)\n- Reasoner: Yes (InstructGPT is one of the two implementations)\n\nAll 5 modules have LLM implementations, with some offering alternatives.\n\nAnswer: 5", "citations": [], "input_tokens": 27149, "output_tokens": 403, "cost_micros": 87492, "latency_ms": 14639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system are implemented using Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in Section 3:\n\n1. **Claim Verifier (Section 3.1)**: \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator (Section 3.2)**: \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Model (Section 3.3)**: This module has three different implementations:\n   - Retriever-Reader: Uses BM25 retriever and RoBERTa-large reader (not primarily LLM-based)\n   - FLAN-T5: This is an LLM\n   - GPT Reciter-Reader: Uses InstructGPT (LLM)\n   \n   The text states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default implementation uses LLM.\n\n4. **QA Validator (Section 3.4)**: \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner (Section 3.5)**: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nCounting the modules that are implemented with LLM backbone:\n1. Claim Verifier - Yes (InstructGPT)\n2. Question Generator - Yes (InstructGPT)\n3. Question Answering Model - Yes (default is GPT Reciter-Reader with InstructGPT)\n4. QA Validator - Yes (InstructGPT)\n5. Reasoner - Yes (FLAN-T5", "citations": [], "input_tokens": 11637, "output_tokens": 512, "cost_micros": 42591, "latency_ms": 10953, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): Uses InstructGPT with in-context learning\n   - LLM-based \u2713\n\n2. **Question Generator** (Section 3.2): Uses InstructGPT for in-context learning\n   - LLM-based \u2713\n\n3. **Question Answering Model** (Section 3.3): Has three implementations:\n   - Retriever-Reader: Uses BM25 + RoBERTa-large fine-tuned on SQUAD\n   - FLAN-T5: Uses FLAN-T5 model\n   - GPT Reciter-Reader: Uses InstructGPT\n   - Since the system uses one of these at a time, and the default is GPT Reciter-Reader (mentioned in the text), this module uses LLM \u2713\n\n4. **QA Validator** (Section 3.4): Uses InstructGPT with demonstrations\n   - LLM-based \u2713\n\n5. **Reasoner** (Section 3.5): Has two implementations:\n   - FLAN-T5 (end-to-end QA model)\n   - InstructGPT\n   - Both options use LLMs \u2713\n\nAll five modules are implemented with Large Language Models as their backbone (either InstructGPT or FLAN-T5, both of which are LLMs).\n\nAnswer: 5", "citations": [], "input_tokens": 12753, "output_tokens": 375, "cost_micros": 43884, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules described in the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - This uses LLM (InstructGPT).\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - This uses LLM (InstructGPT).\n\n3. **Question Answering Module** (Section 3.3): This module has three different implementations:\n   - Retriever-Reader: Uses RoBERTa-large (which is a language model)\n   - FLAN-T5: \"we also incorporate the FLAN-T5 model\" - This is an LLM\n   - GPT Reciter-Reader: Uses InstructGPT - This is an LLM\n   \n   The default implementation uses GPT Reciter-Reader (LLM-based).\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - This uses LLM (InstructGPT).\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs.\n\nAll five modules are implemented with Large Language Models as their backbone (either as the primary or available implementation).\n\nAnswer: 5", "citations": [], "input_tokens": 8622, "output_tokens": 425, "cost_micros": 32241, "latency_ms": 10062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to identify which modules in the QACHECK system use Large Language Models (LLMs) as their backbone.\n\nLet me go through each of the five modules:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT (Ouyang et al., 2022), utilizing its powerful in-context learning ability.\" - Uses LLM \u2713\n\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning.\" - Uses LLM \u2713\n\n3. **Question Answering Model** (Section 3.3): This module has three different implementations:\n   - Retriever-Reader: Uses RoBERTa-large fine-tuned on SQuAD (this is a reader model, but the main approach is retrieval-based)\n   - FLAN-T5: This is an LLM \u2713\n   - GPT Reciter-Reader: Uses InstructGPT as the reciter \u2713\n   \n   The document states \"In the following, we use GPT Reciter-Reader as the default implementation for our QA module.\" So the default uses LLM.\n\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT with a suite of ten demonstrations\" - Uses LLM \u2713\n\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Both implementations use LLMs \u2713\n\nCounting the modules that use LLMs:\n1. Claim Verifier - LLM\n2. Question Generator - LLM\n3. Question Answering Model - LLM (default implementation)\n4. QA Validator - LLM\n5. Reasoner - LLM\n\nAll 5 modules use LLMs in their implementation (either exclusively or as the default/primary option).\n\nAnswer: 5", "citations": [], "input_tokens": 12522, "output_tokens": 503, "cost_micros": 45111, "latency_ms": 11405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find information about modules implemented with Large Language Models backbone.Let me search for information about modules implemented with LLM backbone in the document.Now let me search for information about which modules use LLM backbone:Let me read more of the document to find details about the system architecture and which modules use Large Language Models:Now let me search for information specifically about which modules are implemented with Large Language Models:Based on the document, I can now identify which modules are implemented with Large Language Models (specifically InstructGPT). Let me verify this information:\n\nFrom the document, I can see that the QACHECK system has **five modules**:\n1. **Claim Verifier** - implemented with InstructGPT (Large Language Model)\n2. **Question Generator** - implemented with InstructGPT (Large Language Model)\n3. **Question Answering Model** - has three implementations, one of which (GPT Reciter-Reader) uses InstructGPT (Large Language Model)\n4. **QA Validator** - implemented with InstructGPT (Large Language Model)\n5. **Reasoner** - has two implementations, one of which uses InstructGPT (Large Language Model)\n\nHowever, I need to count how many modules are implemented with LLM backbone. Looking at the text more carefully:\n\n- **Claim Verifier**: Uses InstructGPT (LLM) \u2713\n- **Question Generator**: Uses InstructGPT (LLM) \u2713\n- **Question Answering Model**: Has 3 options (Retriever-Reader, FLAN-T5, GPT Reciter-Reader), but the QA module itself is not purely LLM-based in all implementations\n- **QA Validator**: Uses InstructGPT (LLM) \u2713\n- **Reasoner**: Has 2 implementations (FLAN-T5 and InstructGPT), one uses LLM \u2713\n\nLet me re-read to get the precise count of modules that ARE implemented with LLM backbone (not just have an option for it):\n\nFrom the text:\n- Claim Verifier: \"We build the claim verifier based on InstructGPT\" - **LLM**\n- Question Generator: \"we also leverage InstructGPT for in-context learning\" - **LLM**\n- QA Validator: \"We again implement the validator by prompting InstructGPT\" - **LLM**\n- Reasoner: \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - has LLM option but also non-LLM option\n\nThe Question Answering Model has multiple implementations, and the document states the system uses \"GPT Reciter-Reader as the default implementation\" which uses InstructGPT.\n\nSo counting modules that are implemented with LLM backbone: Claim Verifier (1), Question Generator (2), QA Validator (3), and considering the default implementations, the Reasoner could be 4.\n\nLet me check if there's a clearer statement in the document about this specific question.Let me read the sections more carefully to understand which modules specifically use LLM backbone:\n\nBased on my reading of the document:\n\n1. **Claim Verifier** (Section 3.1): \"We build the claim verifier based on InstructGPT\" - Uses LLM \u2713\n2. **Question Generator** (Section 3.2): \"Similar to the claim verifier, we also leverage InstructGPT for in-context learning\" - Uses LLM \u2713\n3. **Question Answering Model** (Section 3.3): Multiple implementations, NOT all use LLM\n4. **QA Validator** (Section 3.4): \"We again implement the validator by prompting InstructGPT\" - Uses LLM \u2713\n5. **Reasoner** (Section 3.5): \"We provide two different implementations for the reasoner: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\" - Has LLM option\n\nThe modules that are definitively implemented with LLM (InstructGPT) backbone are:\n1. Claim Verifier\n2. Question Generator  \n3. QA Validator\n\nThat's **3 modules** that are implemented with Large Language Models backbone.\n\nHowever, if we consider that the Reasoner also has an InstructGPT implementation and FLAN-T5 is also a Large Language Model, it could be 4. Let me verify if FLAN-T5 counts as an LLM.\n\nFLAN-T5 is described in the document as \"a Seq2Seq model pre-trained on more than 1.8K tasks with instruction tuning\" - this is indeed a Large Language Model.\n\nSo if the question is asking about modules that ARE implemented (not optionally, but actually) with LLM backbone:\n- Claim Verifier: InstructGPT (LLM) \u2713\n- Question Generator: InstructGPT (LLM) \u2713\n- QA Validator: InstructGPT (LLM) \u2713\n- Reasoner: Has two implementations, both are LLMs (FLAN-T5 and InstructGPT) \u2713\n\nThat would be **4 modules**.\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 100172, "error": null, "extra": {"thread_id": 2012, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5a2a7a1ab0194a9ba5958368d2c54734", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 686, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find the figure at the top of page 5 and identify the final step shown in it.\n\nLooking at page 5, I can see Figure 4, which shows a screenshot of the QACHECK user interface with numbered annotations.\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) is clearly labeled as \"The final prediction result with rationale\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 27168, "output_tokens": 141, "cost_micros": 83619, "latency_ms": 10530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 on page 5, which shows the QACHECK user interface screenshot, the final step (labeled as \"4\") shows:\n\n\"The final prediction result with rationale\"\n\nThis section displays:\n- \"Prediction with rationale:\"\n- The reasoning explanation\n- \"Finish\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 11656, "output_tokens": 80, "cost_micros": 36168, "latency_ms": 4530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 4 (the screenshot at the top of page 5), I can see the interface is annotated with 4 numbered sections:\n\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe fourth and final step shows \"Prediction with rationale:\" followed by the reasoning and final answer.\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 12772, "output_tokens": 111, "cost_micros": 39981, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at the figure at the top of page 5 (Figure 4), I can see the system interface showing the fact-checking process for the claim \"Lars Onsager won the Nobel prize when he was 30 years old.\"\n\nThe final step shown in the figure is labeled \"The final prediction result with rationale\" which displays:\n\n\"Lars Onsager won the Nobel prize in 1968. Lars Onsager was born in 1903. He was 65 when he won the Nobel prize. Therefore, the final answer is: False.\"\n\nAnswer: The final prediction result with rationale", "citations": [], "input_tokens": 8641, "output_tokens": 135, "cost_micros": 27948, "latency_ms": 5422, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.9090909090909091, "method": "str_norm", "normalised_pred": "final prediction result with rationale", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 5, I need to identify the final step in the architecture diagram.\n\nThe flow shows:\n1. Claim \u2192 Claim Verifier\n2. Question Generator \u2192 QA Model \u2192 Validator \u2192 Relevant Context (in a loop)\n3. Claim Verifier \u2192 Reasoner\n4. Reasoner \u2192 Label\n\nThe final step in the process is when the Reasoner outputs the Label.\n\nAnswer: Label", "citations": [], "input_tokens": 12541, "output_tokens": 104, "cost_micros": 39183, "latency_ms": 4155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "label", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q004", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "4. The final prediction result with rationale.", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find the figure at the top of page 5 and identify the final step.Let me search for page 5 content in the document:Let me search for page markers and figures in the document:Let me read around line 819 where Figure 4 is mentioned, as this might be on page 5:Perfect! I can see Figure 4 which appears to be on page 5 (after the PageBreak marker at line 815). Looking at the figure, I can see it shows a step-by-step process. Let me examine the final step more carefully:\n\nThe figure shows 4 numbered steps:\n1. Select or input a custom claim\n2. Submit to fact-check the input claim\n3. Visualize the question-answering guided reasoning process\n4. The final prediction result with rationale\n\nThe final step (step 4) shows: \"Prediction with rationale:\" followed by the text and ending with \"**Finish**\".\n\nLooking at the structure, the final step in the figure is marked as \"**Finish**\" at line 888.\n\nAnswer: Finish", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33953, "error": null, "extra": {"thread_id": 2013, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca5f20bedd764740aef245cbc4ae41c8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 195, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finish", "normalised_gold": "4 final prediction result with rationale"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 2 and Figure 3 on page 4:\n\nIn Figure 2 (the system architecture), the \"Reasoner\" module is shown in a blue rectangle.\n\nIn Figure 3 (showing the three QA model implementations), the \"Reader\" component appears in orange/coral rectangles in parts (a) and (c).\n\nHowever, the question asks about a model rectangle that appears in BOTH the QA model and Reasoner modules. Looking at the figures more carefully:\n\n- In Figure 3(b), the \"FLAN-T5\" box appears in yellow\n- In Figure 3(c), the \"Reader\" boxes appear in orange\n- The \"Reasoner\" in the main architecture uses similar model components\n\nThe \"Reader\" model component appears in an orange/coral colored rectangle in both the QA module implementations and is used in the reasoning process.\n\nAnswer: Orange", "citations": [], "input_tokens": 27172, "output_tokens": 206, "cost_micros": 84606, "latency_ms": 10108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "orange", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 that shows the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 4, which shows \"The architecture of our QACHECK system,\" I can see the system architecture with various modules including:\n- Claim Verifier\n- Question Generator\n- QA Model\n- Validator\n- Reasoner\n\nThe figure shows these modules connected with arrows. However, this figure does not clearly show colored rectangles that would indicate a specific model appearing in both the QA Model and Reasoner modules.\n\nLooking more carefully at the document, Figure 3 on page 5 shows \"Illustrations of the three different implementations of the Question Answering module in QACHECK\" with three subfigures (a), (b), and (c). In subfigure (a) \"Retriever-Reader\" and subfigure (c) \"GPT Reciter-Reader\", there is a \"Reader\" component shown.\n\nAccording to the text in Section 3.5, the Reasoner \"is a special question-answering model\" and the text mentions that both the QA module (Figure 3) and potentially the Reasoner use a \"Reader\" component.\n\nHowever, the document is in grayscale/black and white, so I cannot determine colors from the PDF provided.\n\nAnswer: Cannot determine - document appears to be in grayscale", "citations": [], "input_tokens": 11660, "output_tokens": 312, "cost_micros": 39660, "latency_ms": 9907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot determine document appears to be in grayscale", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Figure 2 on page 4, I need to identify the rectangles that appear in both the \"QA Model\" and \"Reasoner\" modules.\n\nIn the architecture diagram (Figure 2), the QA Model outputs \"(Q, A)\" pairs, and the Reasoner outputs \"Label\". \n\nThe component that connects both modules is the \"Wikipedia Corpus\" which is shown as a cylinder/database shape that provides evidence to the QA Model, and the accumulated context from Q&A pairs feeds into the Reasoner.\n\nHowever, looking more carefully at the diagram structure, both the QA Model and Reasoner are shown as rectangular boxes in the flowchart. The rectangles representing these modules themselves appear to be gray/neutral colored boxes in the system architecture.\n\nBut examining the actual visual elements more carefully, there isn't a colored rectangle that appears in both specific modules as separate components. The modules themselves are represented as boxes in the flow diagram.\n\nAnswer: gray", "citations": [], "input_tokens": 12776, "output_tokens": 212, "cost_micros": 41508, "latency_ms": 7203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "gray", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to look at the figure on page 4 to identify the color of the model rectangle that appears in both the QA model and Reasoner modules.\n\nLooking at Figure 2 on page 3 (which shows the system architecture), I can see the general architecture diagram. However, the question asks specifically about page 4.\n\nOn page 4, I can see Figure 3, which shows three different QA model implementations:\n(a) Retriever-Reader\n(b) FLAN-T5\n(c) GPT Reciter-Reader\n\nLooking at the diagram, I need to identify which model appears in both the QA module and the Reasoner module. According to the text in section 3.5, the Reasoner has two implementations: \"1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model.\"\n\nIn Figure 3, I can see rectangles representing different models. The FLAN-T5 model is shown in Figure 3(b), and this same model is mentioned as being used in the Reasoner module.\n\nLooking at the visual representation in Figure 3(b), the FLAN-T5 rectangle appears to be blue in color.\n\nAnswer: blue", "citations": [], "input_tokens": 8645, "output_tokens": 287, "cost_micros": 30240, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "blue", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine Figure 2 on page 4, which shows \"The architecture of our QACHECK system.\"\n\nLooking at the architecture diagram, I can see several modules represented as rectangles. The question asks about a rectangle that appears in both the QA Model and Reasoner modules.\n\nIn the diagram, I can see:\n- A \"QA Model\" box in the loop section\n- A \"Reasoner\" box after the loop\n- Both of these modules appear to have the same visual representation\n\nHowever, looking more carefully at the figure description and the actual diagram components, the modules that represent model components (like the Question Answering module and Reasoner) are shown as rectangles in the flowchart.\n\nThe document shows these as standard flowchart boxes, but I cannot definitively determine a specific color from the description provided. Looking at typical diagram conventions and the figure as presented, these model components appear to be represented in standard boxes.\n\nWithout being able to see the actual colors in the PDF image clearly described in the text, I cannot make a definitive statement about the specific color.\n\nAnswer: Cannot be determined from the provided text", "citations": [], "input_tokens": 12545, "output_tokens": 247, "cost_micros": 41340, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "cannot be determined from provided text", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q005", "doc_id": "2310.07609v1.pdf", "answer_format": "str", "gold": "Yellow", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the figure on page 4 and identify the color of the model rectangle that appears in both the QA model and Reasoner module.Let me search for page 4 content and figures in the document:Let me search for figure-related content and read more of the document to find page 4:Let me read the section containing Figure 3 which appears to show different implementations of the QA module:This is Figure 3. Let me continue reading to find more figures and look for page markers:Let me search for page numbers or section breaks to locate page 4:Let me read around the fourth PageBreak (line 815) which should be around page 4:This is Figure 4, which shows a user interface. Let me continue reading to see if there are more visual elements described:Now I need to go back and look at Figure 2, which shows the architecture of the QACHECK system. That figure should show both the QA model and Reasoner module. Let me read around line 416 where Figure 2 is mentioned:I see Figure 2 is described, but I need to look at more details about the visual representation. Let me search for information about colors in the figure or read earlier to see the full figure description:The embedded image description in Figure 2 only describes a yellow/golden amber circular icon with a question mark. But the question asks about a model rectangle that appears in both QA model and Reasoner modules. Let me search for more complete visual descriptions or look for other figures. Let me check if there's more detailed architecture visualization:Let me read that chunk:Let me read that section:This doesn't seem to be the right figure. Let me search more specifically for information about the architecture diagram or look for mentions of colors in model components:Let me search for \"green\" or other colors that might appear in the architecture diagram:The green icons appear to be information icons. Let me look at the text structure of Figure 2 more carefully. The figure shows \"QA Model\" and \"Reasoner\" as separate boxes. Let me search for \"orange\" or \"red\" colors:Let me try searching for \"model\" and \"reasoner\" together or look at the complete Figure 2 text more carefully:Let me look at a different approach. The question mentions \"page 4\", so let me count the PageBreaks more carefully. Let me look between the 3rd and 4th PageBreak which should be page 4:So Figure 3 is on page 4 (after the 3rd PageBreak at line 648). Figure 3 shows the three different implementations of the Question Answering module. The visual description mentions a green circular icon with \"i\". But this doesn't show both QA model and Reasoner module together.\n\nLet me look for if there are more images or visual descriptions in Figure 3:I see in Figure 3 there are three parts (a), (b), and (c) showing different QA implementations. Each shows \"Reader\" as a component, and there's a green circular icon. But I need to understand the question better - it asks about a \"model rectangle\" that appears in both QA model and Reasoner module.\n\nLet me re-read Figure 2 which shows the overall architecture, and look for visual elements that might represent both components. Let me search for mentions of \"Reader\" since that appears in both:I notice that \"Reader\" appears in Figure 3 as a component. In the figure, the description says there's a green circular icon with \"i\". The \"Reader\" component in the Retriever-Reader, FLAN-T5, and GPT Reciter-Reader architectures likely uses the same visual element. But the question asks about something that appears in both \"QA model\" and \"Reasoner module\".\n\nWait - let me re-read the architecture description. The Reasoner is described as \"a special question-answering model\". So both might use the same underlying reader model. Let me look at where the reader model is used in Figure 3. The green \"i\" icon might be the answer indicator. But the question asks about a \"model rectangle\" not an icon.\n\nLet me think about this differently. Perhaps the document has better visual descriptions elsewhere. Let me search for all embedded images:There are multiple embedded images. The question specifically mentions Figure on page 4. Based on the PageBreaks, page 4 would be between line 648 and 815. Let me look more carefully at what figures or diagrams might show architectural components with rectangles:\n\nLooking back at Figure 3 (lines 651-707), it shows three sub-figures (a), (b), and (c) representing different QA implementations. Each has components like \"Retriever\", \"Reader\", \"FLAN-T5\", \"InstructGPT\". These might be shown as rectangles in the original figure.\n\nIn Figure 3, all three QA implementations show a \"Reader\" component. And the text mentions that the Reasoner module (section 3.5, lines 919-933) is \"a special question-answering model\" that can be implemented using \"FLAN-T5\" or \"InstructGPT model\".\n\nSo FLAN-T5 appears in both:\n- In Figure 3(b) as the QA model implementation  \n- In the Reasoner as one of the two implementation options\n\nLet me check if there's a visual description that mentions FLAN-T5 as a colored rectangle. Let me search for \"FLAN\" in visual descriptions:Perfect! So FLAN-T5 is used in BOTH:\n1. Figure 3(b) - as a QA Model implementation\n2. Section 3.5 (Reasoner) - as one of the two reasoner implementations\n\nNow I need to find the visual description showing what color the FLAN-T5 rectangle is. Let me look more carefully at the embedded images around Figure 3. However, the visual description at line 719 only describes the green \"i\" icon, not the model boxes themselves.\n\nLet me check if there are more visual descriptions or embedded images in the Figure 3 section. The document might have multiple embedded images for the same figure. Let me read more around that area:These are descriptions of other images. Let me look at the specific section where Im3.png first appears (around line 711) and see if there are additional visual descriptions I missed:Still showing the green \"i\" icon. Let me look at Im5.jpg which mentions GPT3 - this might be describing the model boxes:Im5.jpg shows a GPT3 logo with purple and pink/magenta colors. But I need to find descriptions of the model rectangles/boxes in Figure 3. Let me search for descriptions that mention \"rectangle\" or \"box\" with colors:Purple appears in the GPT3 logo. Since the question asks about a component that appears in BOTH QA Model and Reasoner, and we know FLAN-T5 is used in both, let me look for visual descriptions that might show FLAN-T5 box. Let me search for \"yellow\" since that was mentioned earlier:Yellow appears in question mark icons. Let me try a different approach. Let me look at all the visual descriptions more systematically. Let me check descriptions that mention model names or rectangular shapes directly. Let me search for visual descriptions that mention boxes or rectangles with specific colors:Not helpful. Let me think differently. The embedded images are specific parts of the figure. Maybe there are multiple embedded images within Figure 3 that I haven't found yet. Let me look at the embedded images that appear chronologically around Figure 3 content. Figure 3 is around lines 651-720. Let me check what other images might be part of it:\n\nActually, wait - let me reconsider. Looking at the OCR text, Im3.png shows \"i\", Im5.jpg shows \"GPT3\". These are components within the figures. Let me look for images that might show \"FLAN-T5\" or \"FLAN\" text:FLANonly appears as text, not in embedded image OCR. Let me look for embedded images with OCR text that might contain model names. Let me check if there are images showing \"T5\" or looking at what other embedded images show:All I'm finding are green \"i\" icons. Let me try a completely different approach. Perhaps I need to look at the actual figure diagram structure more carefully. In Figure 3, there are three sub-diagrams showing different QA implementations. Each has boxes/rectangles for components. Looking at the text structure:\n\nFigure 3(a): Retriever-Reader has \"Retriever\" and \"Reader\" boxes\nFigure 3(b): FLAN-T5 has \"FLAN-T5\" box  \nFigure 3(c): GPT Reciter-Reader has \"InstructGPT\" and \"Reader\" boxes\n\nThe common element between Figure 3 and the Reasoner would be the model itself. Since both QA module and Reasoner can use FLAN-T5, I should look for a visual description of the FLAN-T5 box/rectangle.\n\nLet me search through all the visual descriptions more systematically for anything mentioning model boxes or FLAN. Actually, let me look for embedded images that have model-related OCR text. Let me look for embedded images around the areas discussing models:\n\nWait, I should look for the actual image file names and descriptions that might show text boxes or model components. Let me search for descriptions that mention \"text\" and colors:Turquoise/cyan blue! Let me read that chunk:This turquoise/cyan blue button shows \"Finish\" - that's from Figure 4 (the UI screenshot). Let me look systematically for all visual descriptions that mention rectangular shapes or boxes. Actually, let me search for specific model component descriptions. Let me look for \"RoBERTa\" which is mentioned as the Reader model:RoBERTa is used in the Reader model but there's no visual description mentioning it. Let me try to systematically go through the embedded images and look for any that might show architectural component boxes. Let me look at the images that haven't been described yet. Let me check Im2.png more thoroughly since that's Figure 2:These are still just the icons. Let me try a completely different approach. Maybe the XML parser extracted images separately from the diagram. Let me look at the beginning of the document to see if there are more detailed figure descriptions I'm missing:This is Figure 1. The visual content shows search icons and question marks. Let me consider: maybe the XML extraction treats architectural diagram components differently. Perhaps the model boxes themselves don't have detailed color descriptions, or the color information is embedded in a way I haven't found yet.\n\nLet me reconsider what the question is actually asking. It says \"the model rectangle in the figure of page 4 that appears both in QA model and Reasoner module\". If Figure 3 is on page 4, and Figure 3 shows three different QA implementations, then the component that would also appear in the Reasoner would be the UNDERLYING MODEL.\n\nLooking back at the text:\n- Section 3.3 (QA module) describes three implementations, one being FLAN-T5 (line 675-686)\n- Section 3.5 (Reasoner) says it can be implemented using \"the end-to-end QA model based on FLAN-T5\" (line 931-932)\n\nSo FLAN-T5 is the model that appears in both. Now I need to find what color the FLAN-T5 rectangle is shown in Figure 3. Since the visual descriptions I've found so far only show icons (green \"i\", yellow \"?\", orange \"?\"), maybe the model boxes themselves are not described with specific colors in the extracted XML, or I need to look elsewhere.\n\nLet me try to find if there's any systematic description of the figure architecture. Maybe there's a legend or color scheme description:Turquoise appears as a \"Finish\" button and footer element. But wait, let me read chunk 155824 which mentions a layout with multiple colored sections:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 328655, "error": null, "extra": {"thread_id": 2014, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4736292b32d6461e98620f394ccee4da", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 2072, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "let me try to find if there s any systematic description of figure architecture maybe there s legend or color scheme description turquoise appears as finish button and footer element but wait let me read chunk 155824 which mentions layout with multiple colored sections", "normalised_gold": "yellow"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompt instruction examples shown in the main body of the paper (not in the appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction example:\n   - \"Claim = CLAIM / We already know the following: CONTEXT / Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instruction examples:\n   - Initial question: \"Claim = CLAIM / To verify the above claim, we can first ask a simple question:\"\n   - Follow-up question: \"Claim = CLAIM / We already know the following: CONTEXT / To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction example:\n   - \"Claim = CLAIM / We already know the following: CONTEXT / Now we further know: NEW QA PAIR / Does the QA pair have additional knowledge useful for verifying the claim?\"\n\n4. **Section 3.5 (Reasoner)** - The text mentions prompts are in \"Appendix A.4\" but doesn't show the full prompt in the main body.\n\nTotal prompts shown in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 27159, "output_tokens": 335, "cost_micros": 86502, "latency_ms": 9625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper.\n\nLooking through the document, I can find the following prompt instructions in the main body (not in the Appendix):\n\n1. **Claim Verifier prompt** (Section 3.1):\n```\nClaim = CLAIM We already know the following:\nCONTEXT\nCan we know whether the claim is true or false now? Yes or no?\n```\n\n2. **Question Generator - Initial question prompt** (Section 3.2):\n```\nClaim = CLAIM\nTo verify the above claim, we can first ask a simple question:\n```\n\n3. **Question Generator - Follow-up question prompt** (Section 3.2):\n```\nClaim = CLAIM\nWe already know the following: CONTEXT\nTo verify the claim, what is the next question we need to know the answer to?\n```\n\n4. **QA Validator prompt** (Section 3.4):\n```\nClaim = CLAIM\nWe already know the following:\nCONTEXT\nNow we further know: NEW QA PAIR\nDoes the QA pair have additional knowledge useful for verifying the claim?\n```\n\nThat gives us 4 prompt instruction examples in the main body of the paper.\n\nAnswer: 4", "citations": [], "input_tokens": 11647, "output_tokens": 289, "cost_micros": 39276, "latency_ms": 5532, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the prompts/instruction examples that are explicitly shown in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - Shows 1 prompt instruction:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   Can we know whether the claim is true or false now? Yes or no?\n   ```\n\n2. **Section 3.2 (Question Generator)** - Shows 2 prompt instructions:\n   - For initial question generation:\n   ```\n   Claim = CLAIM\n   To verify the above claim, we can first ask a simple question:\n   ```\n   \n   - For follow-up questions:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   To verify the claim, what is the next question we need to know the answer to?\n   ```\n\n3. **Section 3.4 (QA Validator)** - Shows 1 prompt instruction:\n   ```\n   Claim = CLAIM\n   We already know the following:\n   CONTEXT\n   Now we further know:\n   NEW QA PAIR\n   Does the QA pair have additional knowledge useful for verifying the claim?\n   ```\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12763, "output_tokens": 336, "cost_micros": 43329, "latency_ms": 6578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (excluding the Appendix).\n\nLet me go through the main body of the paper systematically:\n\n1. **Section 3.1 (Claim Verifier)**: The text mentions \"We prompt InstructGPT with ten distinct in-context examples as detailed in Appendix A.1\" - but no examples are shown in the main body, only referenced.\n\n2. **Section 3.2 (Question Generator)**: Two prompts are shown:\n   - Initial question generation prompt (1 prompt)\n   - Follow-up question generation prompt (1 prompt)\n\n3. **Section 3.3 (Question Answering Model)**: No complete prompt examples are shown in the main body.\n\n4. **Section 3.4 (QA Validator)**: One prompt instruction is shown for the validator module (1 prompt)\n\n5. **Section 3.5 (Reasoner)**: The text mentions \"the InstructGPT model with the prompts given in Appendix A.4\" - but no examples are shown in the main body.\n\nCounting the prompts actually displayed in the main body:\n- Section 3.2: 2 prompts (initial question generation + follow-up question generation)\n- Section 3.4: 1 prompt (QA validator)\n\nTotal: 3 prompts in the main body\n\nAnswer: 3", "citations": [], "input_tokens": 8632, "output_tokens": 331, "cost_micros": 30861, "latency_ms": 7125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to count the number of prompt instruction examples listed in the main body of the paper (not in the Appendix).\n\nLooking through the main body of the paper:\n\n1. **Section 3.1 (Claim Verifier)** - 1 prompt instruction:\n   - \"Claim = CLAIM... Can we know whether the claim is true or false now? Yes or no?\"\n\n2. **Section 3.2 (Question Generator)** - 2 prompt instructions:\n   - Initial question generation: \"Claim = CLAIM... To verify the above claim, we can first ask a simple question:\"\n   - Follow-up questions: \"Claim = CLAIM... We already know the following: CONTEXT... To verify the claim, what is the next question we need to know the answer to?\"\n\n3. **Section 3.4 (QA Validator)** - 1 prompt instruction:\n   - \"Claim = CLAIM... We already know the following: CONTEXT... Now we further know: NEW QA PAIR... Does the QA pair have additional knowledge useful for verifying the claim?\"\n\nTotal prompts in the main body: 1 + 2 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 12532, "output_tokens": 279, "cost_micros": 41781, "latency_ms": 5687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q006", "doc_id": "2310.07609v1.pdf", "answer_format": "int", "gold": "4", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about instruction examples in the main body of the paper.Let me continue reading to find information about instruction examples in the main body of the paper.Let me read the Appendix section to find the prompts and instruction examples.Perfect! I can now see the prompts section clearly. Let me count the instruction examples mentioned. Looking at the document, I can see:\n\n1. **A.1 Prompts for Claim Verifier** - Shows \"<10 demonstrations in total>\"\n2. **A.2 Prompts for Question Generation** (initial question) - Shows \"<10 demonstrations in total>\"\n3. **A.2 Prompts for follow-up question generation** - Shows \"<10 demonstrations in total>\"\n4. **A.3 Prompts for Validator** - Shows \"<10 demonstrations in total>\"\n5. **A.4 Prompts for Reasoner** - Shows \"<10 demonstrations in total>\"\n\nHowever, the question asks about \"instruction examples listed in the main body of the paper.\" Let me check if there's more specific information in the main body about how many examples are actually shown.Let me search more specifically for mentions of how many examples are shown in the main body.Great! I found it. Let me read line 582 more carefully. The main body states: \"We prompt InstructGPT with **ten distinct in-context examples** as detailed in Appendix A.1\"\n\nLet me verify this is the answer by checking if there are other mentions of specific numbers in the main body.Perfect! I found another mention in line 790: \"with a suite of **ten demonstrations** shown in Appendix A.3\"\n\nLet me search for all instances where numbers of examples/demonstrations are mentioned in the main body.Based on my reading of the document, I found that in the main body of the paper, there are **two explicit mentions** of the number of instruction examples/demonstrations:\n\n1. Line 582: \"We prompt InstructGPT with **ten distinct in-context examples**\" (for the Claim Verifier)\n2. Line 790: \"with a suite of **ten demonstrations**\" (for the Validator)\n\nHowever, the question asks specifically \"How many prompts instruction examples are listed in the main body of the paper?\" - it asks for the COUNT/NUMBER that is mentioned, not how many times it's mentioned.\n\nAccording to the main body:\n- 10 examples for Claim Verifier (mentioned on line 582)\n- 10 demonstrations for Validator (mentioned on line 790)\n\nSince both mention \"10\" and the question asks for the number in float format and wants a single integer answer, the answer is:\n\n**10**\n\nAnswer: 10", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 77358, "error": null, "extra": {"thread_id": 2015, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_302989d449c9437a85f8635948950b78", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 438, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "10", "normalised_gold": "4"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "native_pdf", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance scores for ProgramFC and QACheck (the proposed system) in the HOVER dataset from Table 1.\n\nFrom Table 1 on page 6:\n\n**HOVER dataset:**\n- ProgramFC scores: 54.27 (2-hop), 54.18 (3-hop), 52.88 (4-hop)\n- QACheck scores: 55.67 (2-hop), 54.67 (3-hop), 52.35 (4-hop)\n\nNow I'll calculate the performance gap for each:\n- 2-hop: 55.67 - 54.27 = 1.40\n- 3-hop: 54.67 - 54.18 = 0.49\n- 4-hop: 52.35 - 52.88 = -0.53\n\nAverage performance gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.453333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 27174, "output_tokens": 268, "cost_micros": 85542, "latency_ms": 9796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nLooking at Table 1 in the document, I can find the F1 scores for both systems on HOVER:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nTo calculate the average performance gap across all three HOVER subsets:\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 11662, "output_tokens": 275, "cost_micros": 39111, "latency_ms": 5983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "azure_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12778, "output_tokens": 247, "cost_micros": 42039, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_basic_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck on the HOVER dataset.\n\nFrom Table 1 in the document, I can see the performance results:\n\nFor HOVER dataset:\n- ProgramFC: 54.27\n- QACheck: 55.67\n\nFor 2-hop:\n- ProgramFC: 54.18\n- QACheck: 54.67\n\nFor 3-hop:\n- ProgramFC: 52.88\n- QACheck: 52.35\n\nFor 4-hop:\n- ProgramFC: 59.66\n- QACheck: 59.47\n\nThe HOVER dataset has three subsets (2-hop, 3-hop, 4-hop) based on reasoning depth.\n\nTo calculate the average performance gap:\n- 2-hop gap: 54.67 - 54.18 = 0.49\n- 3-hop gap: 52.35 - 52.88 = -0.53\n- 4-hop gap: 59.47 - 59.66 = -0.19\n\nAverage gap = (0.49 + (-0.53) + (-0.19)) / 3 = -0.23 / 3 = -0.0767\n\nRounded to 2 decimal places: -0.08\n\nAnswer: -0.08", "citations": [], "input_tokens": 8647, "output_tokens": 319, "cost_micros": 30726, "latency_ms": 6530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-0.08", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "llamacloud_premium_lc", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I need to find the performance gap between ProgramFC and QACheck in the HOVER dataset from Table 1.\n\nFrom Table 1, the F1 scores are:\n\n**HOVER 2-hop:**\n- ProgramFC: 54.27\n- QACheck: 55.67\n- Gap: 55.67 - 54.27 = 1.40\n\n**HOVER 3-hop:**\n- ProgramFC: 54.18\n- QACheck: 54.67\n- Gap: 54.67 - 54.18 = 0.49\n\n**HOVER 4-hop:**\n- ProgramFC: 52.88\n- QACheck: 52.35\n- Gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimal places: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 12547, "output_tokens": 251, "cost_micros": 41406, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.07609v1.pdf::Q007", "doc_id": "2310.07609v1.pdf", "answer_format": "float", "gold": "0.45", "pages": 10, "document_id": 7591, "arm": "surfsense_agentic", "question_id": "2310.07609v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about ProgramFC performance and the proposed system on the HOVER dataset.Let me search for tables or sections containing performance comparisons on the HOVER dataset.Let me read more of the document to find the tables with performance results.Let me search for tables with results and performance metrics.Perfect! I found Table 1 with the performance results. Let me extract the exact values:\n\nFrom Table 1, for the HOVER dataset:\n- **ProgramFC**: \n  - 2-hop: 54.27\n  - 3-hop: 54.18\n  - 4-hop: 52.88\n\n- **QACheck (proposed system)**:\n  - 2-hop: 55.67\n  - 3-hop: 54.67\n  - 4-hop: 52.35\n\nNow I need to calculate the average performance gap between QACheck and ProgramFC:\n\nFor each hop level:\n- 2-hop gap: 55.67 - 54.27 = 1.40\n- 3-hop gap: 54.67 - 54.18 = 0.49\n- 4-hop gap: 52.35 - 52.88 = -0.53\n\nAverage gap = (1.40 + 0.49 + (-0.53)) / 3 = 1.36 / 3 = 0.45333...\n\nRounded to 2 decimals: 0.45\n\nAnswer: 0.45", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51640, "error": null, "extra": {"thread_id": 2016, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b7dbcb3c5c4d4293862871c529bf39fc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 231, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "0.45", "normalised_gold": "0.45"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify the main tasks the authors conducted experiments on by examining the document.\n\nFrom the abstract and introduction (page 1), the authors state:\n\"More in detail, we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 PRELIMINARIES (page 2), the document explicitly describes two main tasks:\n- Section 2.1: Event Relation Extraction\n- Section 2.2: Deductive Reasoning\n\nThese are consistently mentioned throughout the paper as the primary experimental tasks:\n- The pilot study (Section 3.1.1) uses \"MAVEN-ERE and ProofWriter\" \n- Main experiments (Section 4.1) evaluate on \"MAVEN-ERE, Causal-TimeBank, and ProofWriter\"\n- MAVEN-ERE and Causal-TimeBank are event relation extraction datasets\n- ProofWriter is a deductive reasoning dataset\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 65638, "output_tokens": 233, "cost_micros": 200409, "latency_ms": 34842, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks that the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This is extensively discussed throughout the paper, with experiments on datasets like MAVEN-ERE and Causal-TimeBank. The task involves identifying coreference, temporal, causal, and subevent relations between events.\n\n2. **Deductive Reasoning** - This is the second main task, with experiments conducted on the ProofWriter dataset. The task involves logical reasoning to prove, disprove, or determine if statements are unknown based on given facts and rules.\n\nThese two tasks are explicitly mentioned in the abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 28196, "output_tokens": 189, "cost_micros": 87423, "latency_ms": 6483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks where experiments were conducted.\n\nFrom the Abstract: \"we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning.\"\n\nFrom Section 2 (Preliminaries): \"In this section, we first introduce two tasks that this paper mainly explores.\" followed by Section 2.1 \"Event Relation Extraction\" and Section 2.2 \"Deductive Reasoning\"\n\nFrom the experimental setup sections, both tasks are consistently used throughout the paper for evaluation (Tables 1, 2, 3, and various figures reference MAVEN-ERE/Causal-TimeBank for ERE and ProofWriter for deductive reasoning).\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 34236, "output_tokens": 175, "cost_micros": 105333, "latency_ms": 7014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for the main experimental tasks:\n\nFrom the Introduction and throughout the paper, the authors clearly state they conduct investigations on two main tasks:\n\n1. **Event Relation Extraction (ERE)** - mentioned extensively, including datasets MAVEN-ERE and Causal-TimeBank\n2. **Deductive Reasoning** - mentioned alongside ERE, including the ProofWriter dataset\n\nThis is confirmed in multiple places:\n- Section 2 \"PRELIMINARIES\" has subsections 2.1 \"Event Relation Extraction\" and 2.2 \"Deductive Reasoning\"\n- Section 3.1.1 states: \"We conduct a manual evaluation on MAVEN-ERE and ProofWriter\"\n- The abstract mentions \"event relation extraction and deductive reasoning\"\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 26309, "output_tokens": 193, "cost_micros": 81822, "latency_ms": 6296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document, I can identify the two main tasks the authors conducted experiments on:\n\n1. **Event Relation Extraction (ERE)** - This task aims to identify relations (Coreference, Temporal, Causal, and Subevent) between two events in text. The authors used datasets like MAVEN-ERE and Causal-TimeBank for this task.\n\n2. **Deductive Reasoning** - This task involves beginning with known facts and rules, then iteratively making new inferences. The authors used the ProofWriter dataset for this task.\n\nThese two tasks are explicitly mentioned in Section 2 (PRELIMINARIES) where the authors state: \"In this section, we first introduce two tasks that this paper mainly explores.\" The section then proceeds with subsections 2.1 Event Relation Extraction and 2.2 Deductive Reasoning.\n\nAnswer: Event Relation Extraction, Deductive Reasoning", "citations": [], "input_tokens": 34535, "output_tokens": 211, "cost_micros": 106770, "latency_ms": 6907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q000", "doc_id": "2310.09158v1.pdf", "answer_format": "list", "gold": "['event relation extraction', 'deductive reasoning']", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify the two main tasks the author conducted experiments on.Let me continue reading to find the specific tasks mentioned:Based on the document, I can see that the author evaluated LLMs in two practical scenarios:\n\n1. Event relation extraction (lines 473)\n2. Deductive reasoning tasks (lines 480-481)\n\nAnswer: event relation extraction, deductive reasoning", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57891, "error": null, "extra": {"thread_id": 2017, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_fc47d9b3789248779051669f324d1694", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 114, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "deductive reasoning, event relation extraction", "normalised_gold": "deductive reasoning, event relation extraction"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find information about the number of possible combinations of relationships between two events.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the task involves identifying four types of relations between two events:\n\n1. **Coreference relations**: 2 possible labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 possible labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 possible labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 possible labels (NO_SUBEVENT, SUBEVENT)\n\nThe total number of combinations would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, let me verify this with Figure 1, which shows an example calculation. In Figure 1, it states:\n\n\"Logical Inconsistency (LI) = Number of Conflicts (1) / Number of Combinations (6) = 1/6 \u2248 16.7%\"\n\nThe text explains: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis is referring to pairwise combinations between the 4 relation types, not the total number of possible relationship combinations.\n\nFor the total number of possible combinations of relationships, the calculation is:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nAnswer: 84", "citations": [], "input_tokens": 65637, "output_tokens": 393, "cost_micros": 202806, "latency_ms": 38250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom the document, I can see in Section 2.1 (Event Relation Extraction) that the paper defines 4 types of relations between two events:\n\n1. **Coreference relations**: 2 labels (NO_COREFERENCE, COREFERENCE)\n2. **Temporal relations**: 7 labels (NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON)\n3. **Causal relations**: 3 labels (NO_CAUSAL, PRECONDITION, CAUSE)\n4. **Subevent relations**: 2 labels (NO_SUBEVENT, SUBEVENT)\n\nThe document also explicitly mentions in the logical inconsistency calculation (Figure 1 and Section 2.1):\n\n\"Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis refers to combinations between the 4 relation types (choosing 2 from 4 types to check for conflicts), not the total possible relationship combinations.\n\nHowever, looking more carefully at the document, particularly in Figure 1, it states:\n\"Number of Combinations (+ O) = 6\"\n\nThis is the number of pairwise combinations when checking for conflicts between the 4 different relation types: C(4,2) = 4!/(2!\u00d72!) = 6.\n\nBut the question asks about \"combinations of relationships between two events,\" which would be the total number of possible relationship combinations (one label from each of the 4 types):\n\nTotal combinations = 2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nHowever, re-reading the context in Figure 1 more carefully, the \"6\" refers to checking consistency between pairs of the 4 relation types, not the total possible labelings.\n\nLooking at the question again and the context in the paper where they discuss logical inconsistency, the relevant number appears to be **6** - the number of combinations when checking pairs of relation types for conflicts", "citations": [], "input_tokens": 28195, "output_tokens": 512, "cost_micros": 92265, "latency_ms": 12843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper states that for each pair of events, there are 4 types of relations to determine:\n\n1. **Coreference relations**: {NO_COREFERENCE, COREFERENCE} - 2 options\n2. **Temporal relations**: {NO_TEMPORAL, BEFORE, OVERLAP, CONTAINS, SIMULTANEOUS, ENDS-ON, BEGINS-ON} - 7 options\n3. **Causal relations**: {NO_CAUSAL, PRECONDITION, CAUSE} - 3 options\n4. **Subevent relations**: {NO_SUBEVENT, SUBEVENT} - 2 options\n\nThe paper also provides a specific formula in Figure 1 for calculating the \"total number of combinations\" when computing logical inconsistency:\n\nIn the example, it states: \"for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\"\n\nThis formula 4*(4-1)/2 = 6 represents the number of pairs of relation types (combinations of 2 from 4 types), which is used to check for conflicts between different relation types.\n\nHowever, if the question asks about the total possible combinations of relationship assignments (not pairs for conflict checking), it would be:\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84 possible combinations\n\nBut based on the context in Figure 1 where they explicitly calculate \"total number of combinations\" as 6 using the formula 4*(4-1)/2, the paper is referring to the number of pairs of relation types that need to be checked for logical consistency.\n\nAnswer: 6", "citations": [], "input_tokens": 34235, "output_tokens": 428, "cost_micros": 109125, "latency_ms": 11375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper.\n\nFrom Section 2.1 (Event Relation Extraction), the paper identifies four types of relations between two events:\n\n1. **Coreference relations**: 2 options\n   - NO_COREFERENCE\n   - COREFERENCE\n\n2. **Temporal relations**: 7 options\n   - NO_TEMPORAL\n   - BEFORE\n   - OVERLAP\n   - CONTAINS\n   - SIMULTANEOUS\n   - ENDS-ON\n   - BEGINS-ON\n\n3. **Causal relations**: 3 options\n   - NO_CAUSAL\n   - PRECONDITION\n   - CAUSE\n\n4. **Subevent relations**: 2 options\n   - NO_SUBEVENT\n   - SUBEVENT\n\nThe paper states in Section 2.1: \"We formulate ERE as a multi-classification problem, determining one label (relation) for each of these four relation types.\"\n\nSince we need to determine one label for each of the four relation types, and these are independent choices, the total number of possible combinations is:\n\n2 \u00d7 7 \u00d7 3 \u00d7 2 = 84\n\nThis is also confirmed in Section 2.1 where it mentions calculating logical inconsistency as \"the ratio of the number of conflicts to the total number of combinations (i.e., all combinations of each two relations).\"\n\nAnswer: 84", "citations": [], "input_tokens": 26308, "output_tokens": 335, "cost_micros": 83949, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "84", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I need to find how many possible combinations of relationships can exist between two events according to the paper's definition.\n\nFrom Section 2.1 (Event Relation Extraction), the paper discusses the Logical Inconsistency (LI) metric. In the example shown in Figure 1, there's a specific calculation:\n\n\"Among these, \"SIMULTANEOUS\" and \"CAUSE\" are identified as conflicting with each other based on the logical constraints we have defined, resulting in a single conflict. Now, regarding the total number of combinations: for each pair of events, we have 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: $4 * (4 - 1) / 2 = 6$.\"\n\nThis calculation shows that with 4 types of relations (Coreference, Temporal, Causal, and Subevent), the number of combinations between pairs of these relations is 6.\n\nThis is confirmed in the definition of Logical Inconsistency in Section 2.1:\n\"logical inconsistency is calculated as the ratio of the number of conflicts (i.e., the answers that conflict with the given logical constraints) to the total number of combinations (i.e., all combinations of each two relations).\"\n\nThe example explicitly states: \"Hence, the logical inconsistency in this example is computed as LI = 1/6 (or approximately 16.7%).\"\n\nThe denominator 6 represents the total number of possible combinations of relationships between two events.\n\nAnswer: 6", "citations": [], "input_tokens": 34534, "output_tokens": 346, "cost_micros": 108792, "latency_ms": 10032, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q001", "doc_id": "2310.09158v1.pdf", "answer_format": "int", "gold": "6", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the definition of relationships between two events.Let me search for more specific terms related to event relationships:Perfect! I found the answer. Let me read that specific chunk to get the complete information:Based on the document, I can see that for each pair of events, there are 4 types of relations to determine. The total combinations between these relations are calculated using the combinatorial formula: 4*(4-1)/2 = 6.\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36141, "error": null, "extra": {"thread_id": 2018, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_45c25fd9bb164100939e531a52487f98", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 140, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "native_pdf", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows \"Vicuna-13B and Llama2-13B's performance on MAVEN-ERE and Causal-TimeBank after pre-training on LLM-LR\":\n\nFor Vicuna-13B-PT on MAVEN-ERE, the results are:\n- vanilla ICL: 15.3% Micro-F1\n- vanilla ICL w. CoT: 15.8% Micro-F1\n- CoT w. logical constraints: 18.0% Micro-F1\n- w. all logical constraints: 16.3% Micro-F1\n- w. retrieved logical constraints: 16.1% Micro-F1\n- w. post-processing: 11.0% Micro-F1\n\nThe best performance is 18.0% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 65644, "output_tokens": 212, "cost_micros": 200112, "latency_ms": 35562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.09158v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B after pre-training (Vicuna-13B-PT) on MAVEN-ERE:\n\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest Micro-F1 score is 18.0%.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 28202, "output_tokens": 149, "cost_micros": 86841, "latency_ms": 5000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "azure_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, I need to find the best performance of Vicuna-13B-PT on MAVEN-ERE.\n\nThe table shows the following Micro-F1 scores for Vicuna-13B-PT on MAVEN-ERE:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34242, "output_tokens": 168, "cost_micros": 105246, "latency_ms": 5672, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_basic_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3, which shows the performance of Vicuna-13B-PT (pre-trained) on MAVEN-ERE:\n\nThe results show:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest micro-F1 score is 18.0% for \"CoT w. logical constraints\".\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 26315, "output_tokens": 151, "cost_micros": 81210, "latency_ms": 4937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "llamacloud_premium_lc", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 3 in the document, which shows the performance of Vicuna-13B and Llama2-13B after pre-training on LLM-LR:\n\nFor Vicuna-13B on MAVEN-ERE, the Micro-F1 scores are:\n- vanilla ICL: 15.3%\n- vanilla ICL w. CoT: 15.8%\n- CoT w. logical constraints: 18.0%\n- w. all logical constraints: 16.3%\n- w. retrieved logical constraints: 16.1%\n- w. post-processing: 11.0%\n\nThe highest performance is 18.0% with CoT w. logical constraints.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 34541, "output_tokens": 175, "cost_micros": 106248, "latency_ms": 5140, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2310.09158v1.pdf::Q004", "doc_id": "2310.09158v1.pdf", "answer_format": "str", "gold": "CoT w. logical constraints", "pages": 23, "document_id": 7592, "arm": "surfsense_agentic", "question_id": "2310.09158v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about Vicuna-13B's performance on MAVEN-ERE.Let me search for information about MAVEN-ERE and Vicuna-13B performance:Let me read the table that shows the performance results:Perfect! I found the table showing Vicuna-13B's performance on MAVEN-ERE. Looking at the table, I can see three different approaches for Vicuna (Vicuna-13B):\n\n1. vanilla ICL: 13.8% Micro-F1\n2. vanilla ICL w. CoT: 11.6% Micro-F1\n3. CoT w. logical constraints: 14.9% Micro-F1\n\nThe best performance was with \"CoT w. logical constraints\" at 14.9% Micro-F1.\n\nAnswer: CoT w. logical constraints", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38983, "error": null, "extra": {"thread_id": 2019, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b286a9a39b954fac8755dc6ce4c285c7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 166, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "cot w logical constraints", "normalised_gold": "cot w logical constraints"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking through the document for information about the Sociology subject...\n\nFrom Figure 3 on page 3, I can see that Sociology is listed under \"Humanities & Social Sci. (9%)\" with \"Sociology (287, 2.48%)\".\n\nFrom Table 8 on page 18 (Humanities & Social Science results), I can see that Sociology has 252 samples in the test set.\n\nAdding the validation and test samples:\n- Validation set appears to have a portion of the 947 total H&SS samples\n- Test set for Sociology: 252 samples\n\nLooking more carefully at the statistics in Table 1 on page 4, the total is 11,550 questions with a Dev:Validation:Test split of 150:900:10500.\n\nFrom the detailed breakdown in Figure 3, Sociology shows (287, 2.48%), which represents 287 total samples across all splits.\n\nAnswer: 287", "citations": [], "input_tokens": 259120, "output_tokens": 216, "cost_micros": 1559580, "latency_ms": 77812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in MMMU that belong to the sociology subject.\n\nLooking at the document, I can find this information in Figure 3, which shows the breakdown of questions by disciplines, subjects, and subfields.\n\nIn Figure 3, under \"Humanities & Social Sci. (9%)\", I can see:\n\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Sociology (287, 2.48%)\n- Psychology (340, 2.94%)\n\nThe number in parentheses after \"Sociology\" shows 287 questions, which represents 2.48% of the total.\n\nAnswer: 287", "citations": [], "input_tokens": 79424, "output_tokens": 155, "cost_micros": 240597, "latency_ms": 7077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the number of samples in the MMMU dataset that belong to the sociology subject.\n\nLooking through the document, I can find information about the distribution of samples across subjects in Figure 3 and Table 10.\n\nFrom Figure 3, which shows \"MMMU contains 11.5K multimodal questions covering six broad disciplines, 30 subjects, and 183 subfields,\" I can see the breakdown of subjects with their sample counts.\n\nUnder \"Humanities & Social Sci. (9%)\", I can see:\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- Sociology (287, 2.48%)\n\nThe number shown for Sociology is 287 samples.\n\nAnswer: 287", "citations": [], "input_tokens": 109175, "output_tokens": 183, "cost_micros": 330270, "latency_ms": 8484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the number of samples in the Sociology subject.\n\nIn Figure 3 on page 3, there is a breakdown table showing the distribution of samples across different subjects. Under \"Humanities & Social Sci. (9%)\", I can see:\n\nv Sociology (287, 2.48%)\n\nThe number 287 represents the number of samples in the Sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 83138, "output_tokens": 100, "cost_micros": 250914, "latency_ms": 6203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:12Z", "latency_ms": 6562, "error": null, "raw_text_chars": 406}]}, "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the sociology subject in MMMU.\n\nIn Figure 3, which shows the distribution of subjects across disciplines, I can see:\n\nUnder \"Humanities & Social Sci. (9%)\":\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- **Sociology (287, 2.48%)**\n\nThe number 287 represents the number of samples in the sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 103232, "output_tokens": 123, "cost_micros": 311541, "latency_ms": 6562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll search for information about MMMU and sociology samples in the provided documents.Let me search for information about sociology and sample counts in the document:I found the information! Let me read the specific section with the table that shows the sample counts:Perfect! I found the answer. According to line 1572, Sociology has 287 samples in MMMU, which represents 2.48% of the dataset.\n\nAnswer: 287", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 63671, "error": null, "extra": {"thread_id": 2020, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d6e9ac9023c84b0284ac8c1e7870f526", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 141, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and compare it to the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: According to Figure 3 on page 3, Public Health has **544 samples** (shown as \"Public Health (544, 4.7%)\").\n\n2. **Public Health samples showcased**: I need to count the case study figures related to Public Health from Table 10 on page 21. According to this table, Public Health has the following case study figures:\n   - Correct Case: 63\n   - Lack of Knowledge: 65\n   - Textual Understanding Error: 64\n\n   This gives us **3 showcased samples** from Public Health.\n\nThe ratio would be: 3/544 = 0.0055147...\n\nRounding to a reasonable number of decimal places: 0.0055\n\nAnswer: 0.0055", "citations": [], "input_tokens": 259140, "output_tokens": 214, "cost_micros": 1559655, "latency_ms": 83546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the ratio to the total Public Health samples.\n\nFrom Table 7 (Health & Medicine results), I can see that Public Health has 509 samples in the test set.\n\nNow I need to count how many Public Health samples are showcased in the paper (Appendix B - Case Study).\n\nLooking through the Case Study figures list and the actual figures:\n\nPublic Health samples shown:\n- Figure 63: Public Health 1 (Epidemiology) - Correct Case\n- Figure 64: Public Health 2 (Epidemiology) - Textual Understanding Error\n- Figure 65: Public Health 3 (Epidemiology) - Lack of Knowledge\n\nSo there are 3 Public Health samples showcased.\n\nThe ratio is: 3 / 509 = 0.005892...\n\nRounding to a reasonable number of decimal places:\n\nAnswer: 0.0059", "citations": [], "input_tokens": 79444, "output_tokens": 220, "cost_micros": 241632, "latency_ms": 8359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the percentage of Public Health samples that are showcased in the paper.\n\nFrom the document, I can find:\n\n1. Total number of Public Health samples in the test set:\n   - From Table 7, Public Health has 509 samples in the test set\n\n2. Number of Public Health samples showcased in the paper:\n   - I need to count the figures in Appendix B that are from Public Health\n   - From the List of Case Study Figures (pages 20-21):\n     - Figure 63: Public Health 1: Correct Case\n     - Figure 64: Public Health 2: Textual Understanding Error\n     - Figure 65: Public Health 3: Lack of Knowledge\n   \n   So there are 3 Public Health samples showcased.\n\n3. Calculate the percentage:\n   - Percentage = (3 / 509) \u00d7 100 = 0.589...\n\nWait, let me verify the total number. Looking at Table 1 (page 4), Public Health has 544 samples total. But the question asks about samples \"in this paper\", which could mean the test set.\n\nFrom Table 7 on page 17, Public Health (test set) = 509 samples.\n\nThe ratio would be: 3/509 = 0.00589...\n\nIf we want this as a percentage: 0.589%\n\nBut the question asks for \"a ratio\" as a decimal number, so:\n\n3/509 = 0.005893\n\nAnswer: 0.0059", "citations": [], "input_tokens": 109195, "output_tokens": 347, "cost_micros": 332790, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0059", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and calculate the percentage.\n\nFrom the document, I can find information about Public Health samples:\n\n1. First, let me find the total number of Public Health samples in the dataset.\nFrom Table 1 (Key statistics), I can see the total questions is 11,550.\n\nLooking at Figure 3 or the breakdown tables, I need to find Public Health information:\n- From Table 7 (Health & Medicine results), I can see Public Health is listed with 509 samples in the test set.\n- The validation set has 150 samples total for Health & Medicine.\n\nActually, looking more carefully at the tables:\n- Table 7 shows Health & Medicine breakdown with Public Health having 509 samples in the test set\n- There's also validation data\n\nLet me look at the case study section (Appendix B):\nFrom Table 10 (Table index of case study figures by subjects), I can count the Public Health cases:\n- Public Health 1: Correct Case (Figure 63)\n- Public Health 2: Textual Understanding Error (Figure 64)\n- Public Health 3: Lack of Knowledge (Figure 65)\n\nSo there are 3 Public Health cases showcased in the paper.\n\nNow I need to find the total number of Public Health samples:\nFrom Table 7, the Public Health row shows 509 samples in the test set.\n\nThe ratio would be: 3/509 = 0.00589...\n\nWait, let me verify this is asking for the percentage of showcased samples. \n\n3/509 \u00d7 100 = 0.589%\n\nAs a ratio (not percentage): 3/509 = 0.00589\n\nAnswer: 0.00589", "citations": [], "input_tokens": 83158, "output_tokens": 399, "cost_micros": 255459, "latency_ms": 11937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.00589", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I need to find the number of Public Health samples showcased in the paper and divide by the total number of Public Health samples.\n\nFrom the document:\n\n1. **Total Public Health samples**: Looking at Figure 3, Public Health has 544 samples (4.7% of total).\n\n2. **Public Health samples showcased in the paper**: Looking at Table 10 (Case Study Figures by subjects), I can find the Public Health figures:\n   - Figure 63: Public Health 1: Correct Case\n   - Figure 64: Public Health 2: Textual Understanding Error\n   - Figure 65: Public Health 3: Lack of Knowledge\n\nSo there are 3 Public Health case study figures shown in the paper.\n\nThe ratio is: 3 / 544 = 0.00551...\n\nAnswer: 0.0055", "citations": [], "input_tokens": 103252, "output_tokens": 191, "cost_micros": 312621, "latency_ms": 7812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "0.0055", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q001", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "0.55%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find information about 'Public Health' samples and calculate the percentage.Let me search for tables and figures that might contain information about Public Health samples:Now let me look at Figure 3 and tables that show the breakdown of subjects:Perfect! I found the information. Let me read more to make sure I have all the details:Perfect! I found the information in the table. From line 1505, I can see:\n\n\"Public Health (544, 4.7%)\"\n\nThis indicates that:\n- Public Health has 544 samples\n- This represents 4.7% of all samples in the dataset\n\nThe answer to the question \"Among all samples belonging to 'Public Health' subject, how many percentage samples showcased in this paper?\" is 4.7%.\n\nSince the question asks for a ratio (percentage) and wants a single decimal number, the answer is:\n\nAnswer: 4.7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53796, "error": null, "extra": {"thread_id": 2021, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8dc9bfdb2b5b47d48d7a6fa4dc0574a2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "4.7", "normalised_gold": "0.55"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:19Z", "latency_ms": 62062, "error": null, "raw_text_chars": 760}]}, "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Economics-related error cases that fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\nFrom the Case Study section (pages 20-21), I can see Table 10 which shows the index of case study figures by subjects with associated error categories.\n\nLooking at the Economics row in Table 10:\n- Economics has: Correct Case (22), Perceptual Error (23, 24)\n\nThis indicates that there are 2 Economics-related error cases in the Perceptual Error category (Figures 23 and 24).\n\nLet me verify by checking the actual figures:\n- Figure 23 (page 37): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n- Figure 24 (page 38): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n\nAnswer: 2", "citations": [], "input_tokens": 259127, "output_tokens": 212, "cost_micros": 1559532, "latency_ms": 62014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics-related figures with error categories.\n\nLooking through the case study figures in Appendix B:\n\n1. Figure 22 - Economics: Econometrics - This is a CORRECT case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n   This case shows GPT-4V making an error in correctly identifying numbers in the table.\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n   This case shows GPT-4V failing to correctly interpret the intersection point in the graph.\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n\nFrom Table 10, I can see:\n- Economics has entries for figures 22, 23, 24\n- Figure 22 is marked as \"Correct Case\"\n- Figure 23 is marked as \"Perceptual Error\"\n- Figure 24 is marked as \"Perceptual Error\"\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 79431, "output_tokens": 298, "cost_micros": 242763, "latency_ms": 8734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the document and count how many fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\n1. **Figure 23** - Economics: Macroeconomics - Error Category: Perceptual Error\n   - GPT-4V incorrectly attributes numbers to the correct objects for production\n\n2. **Figure 24** - Economics: Macroeconomics - Error Category: Perceptual Error\n   - GPT-4V fails to interpret the image correctly regarding the equilibrium interest rate\n\nLooking through the List of Case Study Figures table (Table 10), I can see:\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nSo there are **2** Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 109182, "output_tokens": 227, "cost_micros": 330951, "latency_ms": 8328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in the case study section and count how many fall into the \"Perceptual Error\" category.\n\nLooking through the case study figures, I'll identify Economics-related cases:\n\n1. Figure 22 - Economics: Econometrics - This is a correct case, not an error case.\n\n2. Figure 23 - Economics: Macroeconomics - Error Category: Perceptual Error\n\n3. Figure 24 - Economics: Macroeconomics - Error Category: Perceptual Error\n\nNow let me verify these are all the Economics cases by checking the Table of Contents in the case study section (Table 10):\n\nFrom Table 10:\n- Economics 1: Correct Case (Figure 22/36)\n- Economics 2: Perceptual Error (Figure 23/37)\n- Economics 3: Perceptual Error (Figure 24/38)\n\nSo there are 2 Economics-related error cases that fall into the Perceptual Error category.\n\nAnswer: 2", "citations": [], "input_tokens": 83145, "output_tokens": 237, "cost_micros": 252990, "latency_ms": 7797, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find all Economics-related error cases in Appendix B (Case Study) that are categorized as \"Perceptual Error\".\n\nLet me search through the Case Study section for Economics cases:\n\n1. **Figure 22** - Economics: Econometrics - This is a **correct case**, not an error case.\n\n2. **Figure 23** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\n3. **Figure 24** - Economics: Macroeconomics - Error Category: **Perceptual Error** \u2713\n\nLet me verify there are no other Economics cases by checking the Table of Contents (Table 10):\n- Economics 1: Correct Case (Figure 22)\n- Economics 2: Perceptual Error (Figure 23)\n- Economics 3: Perceptual Error (Figure 24)\n\nThat's all the Economics cases listed.\n\nTherefore, there are 2 Economics-related error cases that fall into the Error Category of Perceptual Error.\n\nAnswer: 2", "citations": [], "input_tokens": 103239, "output_tokens": 242, "cost_micros": 313347, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about Economics-related error cases and Perceptual Error categories in the provided documents.Let me search for content related to \"Economics\" and \"Perceptual Error\" in this document.Let me search for tables or error categories in the document:Now let me look for tables with error categories and subject areas. Let me search more broadly:Let me look at the specific area around line 7247 where there's a question about stocks (Economics-related):Let me search for a table that shows the distribution of error categories by subject area:Let me search for tables that might show error distribution:Let me look for error analysis tables. Let me search around the beginning of the document for methodology or error analysis sections:Let me search for \"Figure\" to find error analysis figures/tables:Great! Let me read the section with Figure 6 which shows error distribution:Good! I can see Figure 6 shows the overall error distribution, but I need to find a breakdown by subject area. Let me look for tables that show error categories by discipline:I can see there's a table showing different subjects and error cases. Let me read around line 5808 where there's an Accounting entry:Perfect! I can see the list of case studies. I can see:\n- Economics 1: Correct Case\n- Economics 2: Perceptual Error\n- Economics 3: Perceptual Error\n\nSo there are 2 Economics cases with Perceptual Error. Let me verify this is the complete list by checking if there are more Economics cases:Perfect! I found Table 10 which shows the case study figures by subjects with associated error categories. Looking at the Economics row:\n\n- Subject:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 94360, "error": null, "extra": {"thread_id": 2022, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ce7c6d6aee6641609ca2f09db6ff3175", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 531, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the subfields for the Energy & Power subject in the MMMU dataset.\n\nLooking at the document, I can find this information in Table 11 on page 111, which lists \"Subfields of each subject.\"\n\nUnder the \"Tech & Engineering\" discipline, I can see the \"Energy & Power\" subject with its subfields listed.\n\nFrom Table 11:\nEnergy & Power: Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 259135, "output_tokens": 118, "cost_micros": 1557465, "latency_ms": 60233, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists subfields of each subject:\n\nFor Energy & Power under the Tech & Engineering discipline, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 79439, "output_tokens": 52, "cost_micros": 239097, "latency_ms": 5375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists all subfields by subject:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 109190, "output_tokens": 35, "cost_micros": 328095, "latency_ms": 5171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_basic_lc", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:21Z", "latency_ms": 5093, "error": null, "raw_text_chars": 174}]}, "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists the subfields of each subject:\n\nFor Energy & Power, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 83153, "output_tokens": 47, "cost_micros": 250164, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document, which lists \"Subfields of each subject,\" I can find the subfields for Energy & Power.\n\nAccording to the table:\n\n**Energy & Power:** Thermodynamics, Heat Transfer, Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 103247, "output_tokens": 73, "cost_micros": 310836, "latency_ms": 4766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find the subfields in the Energy & Power subject.I found two subfields mentioned for Energy and Power. Let me search for more information to ensure I have a complete list.Let me search more specifically for all Energy and Power subfield mentions:Based on the document, I found two subfields explicitly mentioned for Energy and Power:\n1. Fluid Mechanics (Figure 89)\n2. Thermodynamics (Figure 90)\n\nLet me verify if there are any other subfields by looking at tables or lists that might contain more comprehensive information:Perfect! I found the information. In line 1545, it shows the subfields for Energy Power:\n\n\"Fluid Mechanics, Heat Transfer ...\"\n\nLet me search for more complete information about all the subfields:Excellent! I found the complete list. Let me verify this is in a table showing all subfields:Perfect! I found the complete list of subfields for Energy & Power in the table. The subfields are:\n- Thermodynamics\n- Heat Transfer\n- Fluid Mechanics\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 66234, "error": null, "extra": {"thread_id": 2023, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_db9765522b274b60ae516c6ef2df0d73", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 314, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU benchmark.\n\nLooking at the detailed subject breakdown tables in the appendix:\n\nFrom Table 9 (Tech & Engineering results on page 19):\n- Computer Science (Comp. Sci.) column shows GPT-4V(ision) (Playground) achieved **57.1%**\n\nFrom Table 6 (Science results on page 16):\n- Math column shows GPT-4V(ision) (Playground) achieved **45.0%**\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 259141, "output_tokens": 158, "cost_micros": 1558401, "latency_ms": 61328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can find Computer Science:\n- Computer Science (371): 57.1%\n\nLooking at Table 6 (Science results), I can find Math:\n- Math (505): 45.0%\n\nThe difference is: 57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 79445, "output_tokens": 114, "cost_micros": 240045, "latency_ms": 5546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects from the MMMU dataset.\n\nLooking through the tables in the document, I need to find the breakdown by subject for GPT-4V.\n\nFrom Table 6 (Science results), I can see:\n- Math: GPT-4V(ision) (Playground) achieves 45.0%\n\nFrom Table 9 (Tech & Engineering results), I can see:\n- Computer Science (Comp. Sci.): GPT-4V(ision) (Playground) achieves 57.1%\n\nThe performance difference is:\n57.1% - 45.0% = 12.1%\n\nAnswer: 12.1", "citations": [], "input_tokens": 109196, "output_tokens": 167, "cost_micros": 330093, "latency_ms": 7889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science (CS) and Math subjects in the MMMU dataset.\n\nLooking through the document for GPT-4V results by subject:\n\nIn Table 2 (page 6), I can see overall results, but I need to find the breakdown by specific subjects.\n\nLooking at the appendix sections for detailed subject breakdowns:\n\nFrom Table 9 (page 19) - Tech & Engineering results:\n- Computer Science is listed under Tech & Engineering\n- GPT-4V(ision) (Playground) shows: Comp. Sci. = 57.1%\n\nFrom Table 6 (page 16) - Science results:\n- Math is listed under Science\n- GPT-4V(ision) (Playground) shows: Math = 45.0%\n\nLet me verify these numbers are correct by checking the table again:\n\nTable 6 (Science results):\n- GPT-4V(ision) (Playground): Math column shows 45.0\n\nTable 9 (Tech & Engineering results):\n- GPT-4V(ision) (Playground): Comp. Sci. column shows 57.1\n\nThe performance difference is:\n57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 83159, "output_tokens": 300, "cost_micros": 253977, "latency_ms": 9016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I need to find GPT-4V's performance on Computer Science and Math subjects in the MMMU dataset.\n\nLooking at Table 9 (Tech & Engineering results), I can see Computer Science is listed with GPT-4V achieving **57.1%**.\n\nLooking at Table 6 (Science results), I can see Math is listed with GPT-4V achieving **45.0%**.\n\nThe performance difference is: 57.1 - 45.0 = 12.1\n\nAnswer: 12.1", "citations": [], "input_tokens": 103253, "output_tokens": 119, "cost_micros": 311544, "latency_ms": 6312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "12.1", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q004", "doc_id": "2311.16502v3.pdf", "answer_format": "float", "gold": "12.1%", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find GPT-4V's performance on CS and Math subjects in the MMMU dataset.Let me search for performance tables or results sections that would contain GPT-4V's performance by subject.Let me search for tables with performance metrics:Let me look for a results table that shows performance by subject. Let me read around line 5873 and 5978 where I saw Computer Science and Math mentioned:This appears to be a table of figure references. Let me search for actual performance data tables. Let me look for \"accuracy\" or percentage data:Let me read the beginning of the paper to find the main results table:Let me search for a table with actual performance numbers. Let me look for \"GPT-4V\" with numbers:Let me look for Table 2 which should contain the main results broken down by subject:Good! I found Table 2. Now I need to find the breakdown by individual subjects. The table shows broad disciplines but I need specific subjects like Computer Science and Math. Let me search for another table with subject-level breakdown:Table 9 should have Tech & Engineering which includes Computer Science. Let me read that table:Perfect! I found GPT-4V's Computer Science performance at 57.1% (line 5254). Now I need to find Math performance. Let me check Table 6 for Science subjects:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 132796, "error": null, "extra": {"thread_id": 2024, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b9c01f114dd641199d8ac115ae26b4c6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 506, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "-4.0", "normalised_gold": "12.1"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:26Z", "latency_ms": 64485, "error": null, "raw_text_chars": 1513}]}, "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 1 in the document and count the different icons shown as different image types.\n\nLooking at page 1, Figure 1 is titled \"Overview of the MMMU dataset\" and shows four main sections:\n\n1. **Comprehensive Disciplines** - Shows icons/images representing different disciplines (Engineering, Science, Art & Design, Business, Humanities & Social Sci., Medicine)\n\n2. **Heterogeneous Image Types** - Shows various image type icons including:\n   - Diagrams\n   - Tables\n   - Plots and Charts\n   - Photographs\n   - Chemical Structures\n   - Paintings\n   - Medical Images\n   - Sheet Music\n   - Geometric\n   - Pathology images\n   - Microscopic Images\n   - Comics\n\n3. **Interleaved Text and Images** - Shows a medical question example with 3 MRI images\n\n4. **Expert-level Skills Test** - Shows a diagram with three components: Perception, Knowledge, and Reasoning\n\nThe question asks specifically about \"different icon are shown as different image types\" in Figure 1. Looking at the \"Heterogeneous Image Types\" section, I can count the distinct image type icons shown.\n\nCounting the image types listed in the \"Heterogeneous Image Types\" section:\nDiagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, and there's an ellipsis (...) suggesting more types.\n\nBased on what's explicitly visible and labeled in Figure 1, I can count approximately 12 distinct image type representations shown.\n\nAnswer: 12", "citations": [], "input_tokens": 259123, "output_tokens": 378, "cost_micros": 1563243, "latency_ms": 64406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays various categories:\n\n1. **Comprehensive Disciplines** - showing a pie chart with percentages for different disciplines (Engineering 26%, Art & Design 11%, Business 14%, Medicine 17%, Science 23%, Humanities & Social Sci. 9%)\n\n2. **Heterogeneous Image Types** - listing various types: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\n\n3. **Interleaved Text and Images** - showing an example with 3 medical images\n\n4. **Expert-level Skills Test** - showing three categories:\n   - Perception (with an icon)\n   - Knowledge (with an icon)\n   - Reasoning (with an icon)\n\nThe question asks about \"different icons shown as different image types\" in Figure 1. Looking at the heterogeneous image types section, I can count the distinct image type categories mentioned (not just listed as text, but representing different types):\n\nFrom the visible list and description: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics (and the ellipsis suggests more).\n\nThat's 12 explicitly mentioned types, but the question asks about icons shown. The figure shows examples and visual representations rather than just listing them.\n\nLooking more carefully at Figure 1, I need to count the actual different image type examples/icons displayed, not just mentioned in text.\n\nAnswer: 12", "citations": [], "input_tokens": 79427, "output_tokens": 403, "cost_micros": 244326, "latency_ms": 11984, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which is titled \"Overview of the MMMU dataset,\" I can see it displays four main sections:\n\n1. **Comprehensive Disciplines** - showing a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - showing various image type icons\n3. **Interleaved Text and Images** - showing a sample question with images\n4. **Expert-level Skills Test** - showing three overlapping circles for Perception, Knowledge, and Reasoning\n\nIn the \"Heterogeneous Image Types\" section, I can count the following distinct icons representing different image types:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Chemical (structures)\n5. Photos/Photographs\n6. Paintings\n7. Geometric (shapes)\n8. Music (sheet music)\n9. Medical (images)\n\nLet me recount more carefully by examining the visual elements shown in that section. The figure shows various small icons/symbols representing different image types in a grid-like arrangement.\n\nCounting the distinct image type icons shown: 9 different icons are displayed representing different image types (Diagrams, Tables, Plots and Charts, Chemical Structures, Photographs, Paintings, Geometric shapes, Music/Sheet Music, and Medical Images).\n\nAnswer: 9", "citations": [], "input_tokens": 109178, "output_tokens": 314, "cost_micros": 332244, "latency_ms": 10828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to look at Figure 1 in the document to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see it displays four main characteristics:\n\n1. **Comprehensive Disciplines** - showing 6 disciplines with percentages:\n   - Engineering (26%)\n   - Art & Design (11%)\n   - Business (14%)\n   - Science (23%)\n   - Humanities & Social Sci. (9%)\n   - Medicine (17%)\n\n2. **Heterogeneous Image Types** - listing various types like: Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Music, Geometric, Pathology images, Microscopic Images, Comics, \u2026\n\n3. **Interleaved Text and Images** - showing an example question with images\n\n4. **Expert-level Skills Test** - listing various types of reasoning and knowledge\n\nThe figure shows icons/visual representations for these categories, but the question asks specifically about \"different icon shown as different image types.\"\n\nLooking at the \"Heterogeneous Image Types\" section in Figure 1, I can count the distinct image type categories mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nAnd there's an ellipsis (\u2026) indicating more types.\n\nHowever, the question asks about icons shown in Figure 1. Looking at the visual representation, the heterogeneous image types section appears to show example icons or representations of these different types.\n\nBased on the visible list in the \"Heterogeneous Image Types\" section of Figure 1, I can count 12 distinct image type categories explicitly listed before the ellipsis.\n\nAnswer: 12", "citations": [], "input_tokens": 83141, "output_tokens": 438, "cost_micros": 255993, "latency_ms": 11875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to carefully examine Figure 1 to count the different icons shown as different image types.\n\nLooking at Figure 1, which shows an \"Overview of the MMMU dataset,\" I can see four main sections:\n\n1. **Comprehensive Disciplines** - Shows a pie chart with 6 disciplines\n2. **Heterogeneous Image Types** - This section lists various image types\n3. **Interleaved Text and Images** - Shows an example question with images\n4. **Expert-level Skills Test** - Shows a diagram with arrows\n\nIn the \"Heterogeneous Image Types\" section, I can see a list of different image types mentioned:\n- Diagrams\n- Tables\n- Plots and Charts\n- Photographs\n- Chemical Structures\n- Paintings\n- Medical Images\n- Sheet Music\n- Geometric\n- Pathology images\n- Microscopic Images\n- Comics\n- (and the list continues with \"...\")\n\nHowever, the question asks about \"icons shown as different image types.\" Looking at the figure more carefully, I don't see actual icons representing each image type - rather, I see text labels listing the image types.\n\nLet me reconsider - perhaps the question is asking about visual representations or examples shown. In Figure 1, I can see:\n- A pie chart (1 type of visualization)\n- Example images in the \"Interleaved Text and Images\" section (3 medical scan images)\n- A flow diagram in the \"Expert-level Skills Test\" section\n\nBut this doesn't seem to match what the question is asking either.\n\nRe-reading the question: \"How many different icon are shown as different image types in Figure 1?\"\n\nLooking at the figure description more carefully, in the \"Heterogeneous Image Types\" section, there appears to be a list of image types. The text mentions: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nCounting the explicitly listed items before the \"...\": 12 different image types are mentioned.\n\nAnswer: 12", "citations": [], "input_tokens": 103235, "output_tokens": 475, "cost_micros": 316830, "latency_ms": 12858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and count the different icons shown as different image types.Let me search for \"Figure 1\" in the document:Now let me read the beginning of the document content to find Figure 1:Good, I can see Figure 1 information. Let me continue reading to see the complete description of the heterogeneous image types shown in Figure 1:Perfect! I can see the \"Heterogeneous Image Types\" section in Figure 1. The document lists the following image types:\n\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThe text shows \"...\" after Comics, but I need to verify if there are icons shown for each type. Let me search for more information about Figure 1 to see if there's a visual description that tells us how many icons are actually displayed:Looking back at the text from Figure 1, I can see it lists these image types under \"Heterogeneous Image Types\":\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nLet me count the distinct types mentioned:\n1. Diagrams\n2. Tables\n3. Plots and Charts (this appears to be one category)\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nHowever, I need to verify if the figure actually shows icons for each. Let me look for embedded images or visual descriptions around Figure 1 to see if there are actual icon images:I see that Im1.png is an embedded image showing a hierarchical flowchart. That's not the heterogeneous image types. Let me search more carefully through the document. Let me look for more embedded images around Figure 1:Let me look more carefully at the beginning of the document. Im1.png appeared right after the figure caption for Figure 1. Let me look at Figure 1 more systematically. Let me check if there are multiple images that are part of Figure 1:I see. The Figure 1 section ends at line 1160 with `</figure>`, and then there are embedded images. The text shows <image 1>, <image 2>, <image 3> within the figure. Let me search more systematically. The figure lists:\n\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nBut I need to see if there are icons displayed. Let me search for where the actual icons might be described. Let me look for \"icon\" in the document:Perfect! I found it. At line 1265, there's a reference to a camera icon. Let me read that section more carefully since it's right after Figure 1:Good! So now I can see there are embedded images that are icons representing different image types. Let me systematically go through and look for all the icons that are part of Figure 1. The document structure shows Im11.png is a camera icon and Im12.png is a geometric icon. Let me look back at the text of Figure 1 to understand the layout better. Looking at the document, it seems the figure starts at line 1097 and the text lists image types. Let me search for more icon descriptions nearby:I see that after the icon descriptions (Im11.png - camera, Im12.png - geometric), the document moves to Figure 3 (a table). So the icons are between Figure 1 and Figure 3. Let me go back and look more carefully at the images embedded right after Figure 1 ends. Let me look for all embedded images between line 1160 (where Figure 1 ends) and line 1455 (where Figure 3 starts):I see Im13.png is a germ/pathogen icon. That's another icon in the series. Let me systematically look at all embedded images between Figure 1 ending and where we transition to other figures. Let me search systematically from line 1160 to 1860:\n\nBased on what I've seen:\n- Im1.png (line 1164): Flowchart diagram\n- Im11.png (line 1256): Camera icon (Photographs)\n- Im12.png (line 1428): Geometric icon\n- Im13.png (line 1863): Germ/pathogen icon\n\nLet me continue looking for more icons. Let me search for the next embedded images:Good! Im14.png is a microscope icon. Let me continue to find more icons. Let me look at the next embedded images. Let me search more systematically. Based on what the abstract says: \"comprising 30 highly heterogeneous image types\" - but Figure 1 specifically lists these types. Let me look at the section with the image types more carefully. I need to look back at line 2501 which says \"Music\" and \"Medical\" - these might be labels for icons:Ah! Figure 5 shows different types of images: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical. That's 9 types. But this is Figure 5, not Figure 1. Let me go back and look very carefully at what exactly is in Figure 1. Let me re-read the text in Figure 1:\n\nFrom lines 1134-1138, the text explicitly states under \"Heterogeneous Image Types\":\n\"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nSo let me count these carefully:\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nBut the question asks about \"different icons shown as different image types in Figure 1\". I need to look back at Figure 1 itself more carefully. Let me check if there are actually icons displayed IN Figure 1, not just listed. Let me re-examine the structure. The figure tag starts at 1097 and ends at 1160. Let me look at what images are referenced within that figure:I see! Within the figure (lines 1097-1160), there are references to `<image 1>`, `<image 2>`, and `<image 3>` (lines 1113-1115 as part of a question, and again at lines 1154-1158). But the key section is \"Heterogeneous Image Types\" which lists the types. However, the question asks about icons shown as different image types. \n\nThe text lists image types but doesn't specify how many icons are actually displayed visually. Based on the embedded images that come right after Figure 1:\n- Im1.png (flowchart - but this appears to be an example diagram, not an icon representing a type)\n- Im11.png (camera icon - represents Photographs)\n- Im12.png (geometric shapes icon - represents Geometric)\n- Im13.png (germ icon - could represent Medical/Pathology/Microscopic)\n- Im14.png (microscope icon - represents Microscopic/Medical)\n\nBut I need to find ALL the icons that are specifically showing different image types in Figure 1. Let me look more systematically at what comes after Figure 1 but before Figure 2 (which starts at line 1289). All embedded images between line 1160 and line 1289 might be icons for Figure 1.\n\nFrom my reading:\n- Line 1164: Im1.png (flowchart diagram - this might be part of the figure illustration)\n- Line 1256: Im11.png (camera icon)\n- Line 1428: Im12.png (geometric shapes)\n\nLet me check if there are other embedded images I missed before Figure 2:\n\nLooking at the embedded image list I got earlier, between lines 1160 and 1289 I have:\n- Im1.png at line 1164\n- Im11.png at line 1256\n- Im12.png at line 1428 (but line 1289 is Figure 2, so this is after Figure 2 starts)\n\nWait, let me recheck. Figure 2 caption is at line 1289. So Im12.png at line 1428 is after Figure 2. Let me look more carefully:So between Figure 1 (ending at line 1160) and Figure 2 (starting at line 1288), there are only 2 embedded images:\n- Im1.png at line 1164\n- Im11.png at line 1256\n\nBut actually, looking at the visual descriptions, Im1.png is described as a \"Hierarchical flowchart/organizational diagram\" which seems to be an example diagram that's PART of Figure 1, not an icon representing an image type.\n\nIm11.png is clearly described as a \"Stylized camera icon\" which represents the Photographs image type.\n\nLet me think about this differently. Maybe the icons are all embedded within the actual Figure 1 structure, but the XML parsing has extracted them as separate images. Looking at the text in Figure 1, it lists types under \"Heterogeneous Image Types\". Perhaps in the original PDF, there are visual icons shown alongside or above these text labels, and those icons have been extracted as separate embedded images.\n\nGiven the timeframe and what I can see, let me search for a clearer pattern. Looking at Figure 5 (line 2475) which shows \"Performance of models on different types of images\" and lists: Diagrams, Tables, Charts, Chemical, Photos, Paintings, Geometric, Music, Medical - that's 9 types.\n\nBut I need to answer specifically about Figure 1. Let me think about the structure: Figure 1 has a section called \"Heterogeneous Image Types\" that lists multiple types. The question asks how many different icons are shown. \n\nLooking at the abstract again (line 1212): \"comprising 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures.\"\n\nBut in Figure 1's \"Heterogeneous Image Types\" section, it explicitly lists: \"Diagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, ...\"\n\nIf I count unique types (treating \"Plots and Charts\" as one type):\n1. Diagrams\n2. Tables\n3. Plots and Charts\n4. Photographs\n5. Chemical Structures\n6. Paintings\n7. Medical Images\n8. Sheet Music\n9. Geometric\n10. Pathology images\n11. Microscopic Images\n12. Comics\n\nThat's 12 distinct types listed (plus \"...\" suggesting more).\n\nHowever, Figure 5 later in the paper shows icons for 9 types. Perhaps Figure 1 shows icons for these 9 main types as well. Let me verify Figure 5 shows exactly 9 by re-reading:\n\nFrom line 2487-2503 in Figure 5:\n1. Diagrams\n2. Tables\n3. Charts\n4. Chemical\n5. Photos\n6. Paintings\n7. Geometric\n8. Music\n9. Medical\n\nThat's exactly 9 types in Figure 5.\n\nGiven the typical structure of such figures and the pattern I see, my best answer is that Figure 1 shows icons for **9** different image types.\n\nAnswer: 9", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 192280, "error": null, "extra": {"thread_id": 2025, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_f360703be6ba4537aef7760373d79d2f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1273, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:29Z", "latency_ms": 75843, "error": null, "raw_text_chars": 831}]}, "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts that appear in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Figure 4: A comparison chart showing benchmarks plotted on a 2D graph with \"Breadth (Knowledge)\" on x-axis and \"Depth (Reasoning)\" on y-axis\n- A table comparing different datasets\nTotal: 1 chart\n\n**Page 6:**\n- Table 2: Results table (not a chart)\nTotal: 0 charts\n\n**Page 7:**\n- Figure 5: Bar chart showing \"Performance of models on different types of images\"\n- Table 3: Results table (not a chart)\nTotal: 1 chart\n\n**Page 8:**\n- Figure 6: Pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\nTotal: 1 chart\n\n**Page 9:**\n- No charts, only text\nTotal: 0 charts\n\n**Page 10:**\n- No charts, only text (references section)\nTotal: 0 charts\n\nTotal charts in pages 5-10: 1 + 0 + 1 + 1 + 0 + 0 = 3\n\nAnswer: 3", "citations": [], "input_tokens": 259120, "output_tokens": 289, "cost_micros": 1561222, "latency_ms": 75796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts included in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Contains text and references but no charts visible\n\n**Page 6:**\n- Table 2: \"Overall results of different models on the MMMU validation and test set\" - This is a table, not a chart\n\n**Page 7:**\n- Figure 5: \"Performance of models on different types of images\" - This is a bar chart\n- Table 3: \"Result decomposition across question difficulty levels\" - This is a table, not a chart\n\n**Page 8:**\n- Figure 6: \"Error distribution over 150 annotated GPT-4V errors\" - This is a pie chart\n- Figure 7: Shows an example case with images and text - Not a chart\n\n**Page 9:**\n- Contains text discussion, no charts\n\n**Page 10:**\n- Contains text and references, no charts\n\nCounting the charts:\n- Page 7: Figure 5 (bar chart) = 1 chart\n- Page 8: Figure 6 (pie chart) = 1 chart\n\nTotal charts in pages 5-10: 2\n\nAnswer: 2", "citations": [], "input_tokens": 79424, "output_tokens": 276, "cost_micros": 242412, "latency_ms": 7796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll carefully count all charts in Pages 5-10.\n\n**Page 5:**\n- Figure 2: This shows sample MMMU examples in a table format, not a chart\n- No charts on this page\n\n**Page 6:**\n- Table 2: This is a table showing model results, not a chart\n- No charts on this page\n\n**Page 7:**\n- Figure 5: This is a bar chart showing \"Performance of models on different types of images\"\n- Table 3: This is a table, not a chart\n- Count: 1 chart\n\n**Page 8:**\n- Figure 6: This is a pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\n- Figure 7: This shows an example with illustrations but is not a chart\n- Count: 1 chart\n\n**Page 9:**\n- No figures or charts on this page (only references text)\n\n**Page 10:**\n- No figures or charts on this page (only references text)\n\nTotal charts in Pages 5-10: 1 (Figure 5) + 1 (Figure 6) = 2\n\nAnswer: 2", "citations": [], "input_tokens": 109175, "output_tokens": 264, "cost_micros": 331485, "latency_ms": 9110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts (plots, graphs, tables, diagrams) that appear in pages 5-10 of the document.\n\nLet me go through pages 5-10:\n\n**Page 5:**\n- Table 2: Overall results of different models (this is a large table)\n- Count: 1\n\n**Page 6:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a bar chart)\n- Count: 2\n\n**Page 7:**\n- Figure 6: Error analysis pie chart showing distribution of error types\n- Figure 7: Example error case with text and an image (not a chart)\n- Count: 1\n\n**Page 8:**\n- Continuation of references, no charts\n\n**Page 9:**\n- Continuation of references, no charts\n\n**Page 10:**\n- References continue, no charts\n\nTotal charts in pages 5-10:\n- Page 5: 1 (Table 2)\n- Page 6: 2 (Table 3, Figure 5)\n- Page 7: 1 (Figure 6)\n- Pages 8-10: 0\n\nTotal: 1 + 2 + 1 + 0 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 300, "cost_micros": 253914, "latency_ms": 8781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count all charts appearing in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Table 1: Key statistics of the MMMU benchmark\n\n**Page 6:**\n- Table 2: Overall results of different models on the MMMU validation and test set\n\n**Page 7:**\n- Table 3: Result decomposition across question difficulty levels\n- Figure 5: Performance of models on different types of images (this is a table/chart)\n\n**Page 8:**\n- Figure 6: Error distribution over 150 annotated GPT-4V errors (this is a table/chart)\n- Figure 7: A sample error case (this is an example, not a chart)\n\n**Page 9:**\n- No charts (this page discusses conclusion and references begin)\n\n**Page 10:**\n- No charts (continues references section)\n\nLet me recount more carefully by looking at what constitutes a \"chart\":\n\nPages 5-10:\n- Page 5: Table 1 (1 chart)\n- Page 6: Table 2 (1 chart)\n- Page 7: Table 3 (1 chart), Figure 5 which shows a table of performance data (1 chart)\n- Page 8: Figure 6 showing error distribution as a table (1 chart), Figure 7 is an example case study, not a chart\n- Pages 9-10: References, no charts\n\nTotal charts: 5\n\nAnswer: 5", "citations": [], "input_tokens": 103232, "output_tokens": 346, "cost_micros": 314886, "latency_ms": 10516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I'll read the document to count the charts in pages 5-10.Let me search for page markers to identify pages 5-10:Let me read through the document to find page markers:I can see page markers. Let me continue searching for pages 5-10:Perfect! Now I know the line numbers for pages 5-10. Let me read those pages to count the charts:Now let me search for charts/figures systematically in pages 5-10. Let me look for figure markers:Now let me carefully count the charts in pages 5-10. Let me search for \"Figure\" markers in this range:\n\nBased on my reading of pages 5-10 (lines 2071-3184), I can identify the following charts:\n\n**Page 5** (starts at line 2071):\n- No charts (only text and Table 2)\n\n**Page 6** (starts at line 2462):\n- **Figure 5**: \"Performance of models on different types of images\" - this is a bar chart showing performance metrics\n\n**Page 7** (starts at line 2687):\n- **Figure 6**: \"Error distribution over 150 annotated GPT-4V errors\" - this is a pie chart showing error percentages\n- **Figure 7**: Shows a perceptual error example (not a chart, but a diagram/illustration)\n\n**Page 8** (starts at line 2912):\n- No charts (only text starting with Conclusion section)\n\nLet me continue reading pages 9 and 10:**Page 9** (starts at line 3045): References section - no charts\n\n**Page 10** (starts at line 3184): References section - no charts\n\nNow let me verify my count by carefully reviewing what I found:\n\n**Summary of charts in Pages 5-10:**\n\n- **Page 5**: 0 charts (contains Table 2, which is a table, not a chart)\n- **Page 6**: 1 chart (Figure 5 - bar chart)\n- **Page 7**: 1 chart (Figure 6 - pie chart) + Figure 7 (which is an illustration/example, not a chart)\n- **Page 8**: 0 charts (text only)\n- **Page 9**: 0 charts (references)\n- **Page 10**: 0 charts (references)\n\nTotal charts in pages 5-10: **2 charts**\n\n(Figure 5 is a bar chart and Figure 6 is a pie chart. Figure 7 is not a chart but an illustrative example showing a perceptual error case.)\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 79750, "error": null, "extra": {"thread_id": 2026, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8497190a9f7e4c40b3088d1ce4cbc759", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 453, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across two dimensions - Breadth (Knowledge) and Depth (Reasoning).\n\nThe figure shows various datasets plotted on these two axes:\n- VQA, GQA, VisWiz (basic breadth and depth)\n- TextVQA, MMBench, SEED (moderate breadth)\n- MM-Vet, ScienceQA (moderate breadth)\n- OKVQA (moderate breadth)\n- MMMU (positioned highest on both breadth and depth)\n\nAccording to the figure and the accompanying table, MMMU covers:\n- 11.5K questions\n- 30 Types of images\n- 6 disciplines, 30 subjects, and 183 subfields\n\nThis is explicitly described in the caption as excelling \"in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nAnswer: MMMU", "citations": [], "input_tokens": 259139, "output_tokens": 228, "cost_micros": 1559964, "latency_ms": 60530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the paper, I need to examine which dataset covers the broadest range of disciplines and knowledge areas.\n\nFrom the figure, the datasets compared are:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nThe figure shows that MMMU covers:\n- 6 disciplines\n- 30 subjects\n- 183 subfields\n- 11.5K questions\n\nIn comparison, other datasets are more limited in scope. For example, ScienceQA covers specific subjects but is noted as being at elementary to middle school level. The other datasets (VQA, GQA, etc.) focus on specific task types rather than broad disciplinary coverage.\n\nThe paper explicitly states that MMMU \"covers 30 subjects across 6 disciplines\" and emphasizes its \"breadth\" across multiple disciplines including Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering.\n\nAnswer: MMMU", "citations": [], "input_tokens": 79443, "output_tokens": 256, "cost_micros": 242169, "latency_ms": 8655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, which compares MMMU with other existing benchmarks across breadth (knowledge) and depth (reasoning) dimensions:\n\nThe figure shows various datasets plotted, with MMMU positioned to excel in both breadth and depth. Among the nine different datasets shown (VQA, GQA, VizWiz, TextVQA, OKVQA, SEED, MMBench, MM-Vet, ScienceQA, and MMMU), MMMU is positioned highest on the breadth axis.\n\nAnswer: MMMU", "citations": [], "input_tokens": 109194, "output_tokens": 132, "cost_micros": 329562, "latency_ms": 7141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset covers the most breadth of knowledge.\n\nThe figure shows a comparison of different datasets along two axes:\n- Breadth (Knowledge) - horizontal axis\n- Depth (Reasoning) - vertical axis\n\nFrom the figure, the datasets shown include:\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- OKVQA\n- SEED\n- MMBench\n- MM-Vet\n- ScienceQA\n- MMMU\n\nLooking at the horizontal positioning (breadth), MMMU is positioned furthest to the right, indicating it has the greatest breadth of knowledge coverage among the datasets shown. This is also supported by the text which states MMMU covers \"30 Types\" of images and spans across 6 disciplines, 30 subjects, and 183 subfields.\n\nAnswer: MMMU", "citations": [], "input_tokens": 83157, "output_tokens": 210, "cost_micros": 252621, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which dataset represents the most breadth (knowledge) on the x-axis.\n\nThe scatter plot shows various datasets plotted with \"Breadth (Knowledge)\" on the x-axis and \"Depth (Reasoning)\" on the y-axis. MMMU is shown as a star at the highest position for both breadth and depth.\n\nAmong the datasets shown in Figure 4, MMMU is positioned furthest to the right on the Breadth axis, indicating it has the most breadth of knowledge coverage.\n\nAnswer: MMMU", "citations": [], "input_tokens": 103251, "output_tokens": 132, "cost_micros": 311733, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q011", "doc_id": "2311.16502v3.pdf", "answer_format": "str", "gold": "\"MMMU\"", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q011", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which dataset reflects the most breadth of knowledge.Let me search for Figure 4 in the document:Good! I found the caption for Figure 4. Now let me read the actual content around Figure 4 to see the details about which dataset reflects the most breadth of knowledge:Now let me look at the actual Figure 4 to see the visual comparison. Let me search for more context about breadth of knowledge in the figure:Let me read the section that shows the actual Figure 4 visualization:Perfect! I can see Figure 4 shows a scatter plot with \"Breadth (Knowledge)\" on one axis and \"Depth (Reasoning)\" on the other axis. The figure shows the following datasets plotted:\n- MMMU\n- VQA\n- GQA\n- VisWiz\n- TextVQA\n- MMBench\n- SEED\n- MM-Vet\n- ScienceQA\n- OKVQA\n\nFrom the figure structure (lines 1834-1858), I can see \"Breadth (Knowledge)\" is labeled as one of the axes. The caption states \"MMMU excels in both its breadth to cover a wide range of disciplines and its depth to test LMMs' reasoning abilities.\"\n\nLooking at the text in chunk 156205 (lines 2022-2029), it explicitly states: \"From the breadth perspective, the prior benchmarks are heavily focused on daily knowledge and common sense. The covered image format is also limited. Our benchmark aims to cover college-level knowledge with 30 image formats...\"\n\nThis indicates that **MMMU** reflects the most breadth of knowledge among the nine datasets shown in Figure 4.\n\nAnswer: MMMU", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 57452, "error": null, "extra": {"thread_id": 2027, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e1415f7fe2384e1ba85595cd09d85b89", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 282, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "mmmu", "normalised_gold": "mmmu"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:30Z", "latency_ms": 77217, "error": null, "raw_text_chars": 404}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 on page 8 of the document, which shows the \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe error categories and their percentages are:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 259144, "output_tokens": 133, "cost_micros": 1557856, "latency_ms": 77171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I need to look at Figure 6, which shows the error distribution over 150 annotated GPT-4V errors.\n\nFrom Figure 6, I can see the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 79448, "output_tokens": 174, "cost_micros": 240954, "latency_ms": 5937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "azure_premium_lc", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:45Z", "latency_ms": 7750, "error": null, "raw_text_chars": 387}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe pie chart shows the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 109199, "output_tokens": 125, "cost_micros": 329472, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 in the document, which shows the error analysis of 150 GPT-4V error cases:\n\nThe distribution shows:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding Error: 6%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 83162, "output_tokens": 122, "cost_micros": 251316, "latency_ms": 5827, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 which shows the error distribution over 150 annotated GPT-4V errors:\n\n1. Perceptual Error: 35%\n2. Lack of Knowledge: 29%\n3. Reasoning Error: 26%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 103256, "output_tokens": 77, "cost_micros": 310923, "latency_ms": 6108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "I'll read the document to find Figure 6 and identify the top-3 error types over 150 annotated GPT-4V errors.Let me search for Figure 6 in the document:Now let me read the section around line 2692 where Figure 6 is located:Perfect! I found Figure 6 which shows the error distribution over 150 annotated GPT-4V errors. Looking at the data:\n\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nThe top-3 error types are:\n1. Perceptual Error (35%)\n2. Lack of Knowledge (29%)\n3. Reasoning Error (26%)\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33125, "error": null, "extra": {"thread_id": 2028, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c246c6154fa7469892ccf49cfee50145", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 158, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:53Z", "latency_ms": 59125, "error": null, "raw_text_chars": 830}]}, "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 for error types mentioned in the case studies.\n\nFrom page 95 (Figure 80 - Architecture and Engineering: Surveying and Mapping):\n- This is a correct case, no error type mentioned\n\nFrom page 96 (Figure 82 - Architecture and Engineering: Civil Engineering):\n- Error Category: Reasoning Error\n\nFrom page 97 (Figure 83 - Computer Science: Data Structure and Algorithm):\n- This is a correct case, no error type mentioned\n\nFrom page 98 (Figure 84 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error, Lack of Knowledge\n\nFrom page 99 (Figure 85 - Computer Science: Operating System):\n- Error Category: Perceptual Error\n\nFrom page 100 (Figure 86 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 259136, "output_tokens": 226, "cost_micros": 1559901, "latency_ms": 59077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 (Figures 82-87) to identify all error types mentioned in the case studies.\n\nLooking at each figure in the specified range:\n\n**Figure 82** (Architecture and Engineering: Civil Engineering) - Page 96\n- Error Category: Reasoning Error\n\n**Figure 83** (Computer Science: Data Structure and Algorithm) - Page 97\n- This is a correct case, no error category\n\n**Figure 84** (Computer Science: Compiler Principle) - Page 98\n- Error Category: Perceptual Error, Lack of Knowledge\n\n**Figure 85** (Computer Science: Operating System) - Page 99\n- Error Category: Perceptual Error\n\n**Figure 86** (Computer Science: Compiler Principle) - Page 100\n- Error Category: Perceptual Error\n\n**Figure 87** (Electronics: Signal Processing) - Page 101\n- This is a correct case, no error category\n\nSo the error types mentioned in pages 95-100 are:\n- Reasoning Error\n- Perceptual Error\n- Lack of Knowledge\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 79440, "output_tokens": 265, "cost_micros": 242295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll carefully examine pages 95-100 of the document to identify all error types mentioned in the case studies.\n\nLooking through the figures on these pages:\n\n**Page 95 (Figure 81):** Architecture and Engineering - Correct case (no error type)\n\n**Page 96 (Figure 82):** Architecture and Engineering - **Reasoning Error**\n\n**Page 97 (Figure 83):** Computer Science - Correct case (no error type)\n\n**Page 98 (Figure 84):** Computer Science - **Perceptual Error, Lack of Knowledge**\n\n**Page 99 (Figure 85):** Computer Science - **Perceptual Error**\n\n**Page 100 (Figure 86):** Computer Science - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 109191, "output_tokens": 192, "cost_micros": 330453, "latency_ms": 8281, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 of the document for error types mentioned in case studies.\n\nLooking at the case studies in this range:\n\nPage 95 (Figure 81 - Architecture and Engineering): No error type specified (correct case)\n\nPage 96 (Figure 82 - Architecture and Engineering): **Reasoning Error**\n\nPage 97 (Figure 83 - Computer Science): No error type specified (correct case)\n\nPage 98 (Figure 84 - Computer Science): **Perceptual Error, Lack of Knowledge**\n\nPage 99 (Figure 85 - Computer Science): **Perceptual Error**\n\nPage 100 (Figure 86 - Computer Science): **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 83154, "output_tokens": 184, "cost_micros": 252222, "latency_ms": 6655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "llamacloud_premium_lc", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "Looking through pages 95-100 of the document (Figures 82-86 in the case studies):\n\nFigure 82 (Architecture and Engineering: Civil Engineering) - **Reasoning Error**\n\nFigure 83 (Computer Science: Data Structure and Algorithm) - No error type mentioned (this is a correct case)\n\nFigure 84 (Computer Science: Compiler Principle) - **Perceptual Error, Lack of Knowledge**\n\nFigure 85 (Computer Science: Operating System) - **Perceptual Error**\n\nFigure 86 (Computer Science: Compiler Principle) - **Perceptual Error**\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 103248, "output_tokens": 158, "cost_micros": 312114, "latency_ms": 11889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll read the document to find the error types mentioned in the case studies on pages 95-100.Let me search for page numbers to locate pages 95-100:Let me search for \"case stud\" to find the case studies section:Let me read around line 5765 where Table 10 mentions case study figures with error categories:Good! I found Table 10 which shows the error categories. The columns are: Correct Case, Perception, Lack of Knowledge, Reasoning, and Other. Let me continue reading to see the complete table:Good! I found that the error categories are: Perception, Lack of Knowledge, Reasoning, and Other (plus \"Correct Case\" which is not an error). Now I need to find the actual case study pages 95-100. Let me search for figure 95 or case 95:Let me search for the case studies section more specifically. Let me look for the pattern of case numbers 95-100:Figure 96 is something different. Let me check the structure - from the earlier search, I saw \"90 Energy and Power 2: Reasoning Error 104\" and \"95 Mechanical Engineering 3: Re\". Let me search for these patterns directly:Good! So these are Figure numbers 90-95. Let me read that section where it lists figures 90-100:I can see figures 90-95 here. Now I need to find what comes after figure 95. Let me search earlier in the document to see the complete list:This appears to be a table of contents or index. I need to understand better - these numbers (95-100) might refer to page numbers or figure numbers. Let me look at this list more carefully. I can see:\n\n- Page 95: Figure 81 Architecture and Engineering 1: Correct Case\n- Page 96: Figure 83 Computer Science 1: Correct Case  \n- Page 97: ?\n- Page 98: Figure 84 Computer Science 2: Perceptual Error, Lack of Knowledge\n- Page 99: Figure 85 Computer Science 3: Perceptual Error\n- Page 100: Figure 86 Computer Science 4: Perceptual Error\n\nBut I need to verify this interpretation. Let me search for where these actual figures appear in the document:Perfect! Now let me read from page 95 to page 100. Let me find line numbers around page 95:Good, I'm at page 95. Now let me continue reading through pages 95-100:Good! I'm now at page 97. Let me continue reading to get to page 100:Good! I'm at page 99. Let me continue to page 100:Good! Now let me continue to see the rest of page 100:Perfect! Now I have reviewed pages 95-100. Let me compile all the error types I found in the case studies on these pages:\n\nFrom the case studies I read on pages 95-100:\n\n**Page 95:**\n- Figure 81: Correct Case (not an error)\n\n**Page 96:**\n- Figure 82: Reasoning Error\n\n**Page 97:**\n- Figure 83: Correct Case (not an error)\n\n**Page 98:**\n- Figure 84: Perceptual Error, Lack of Knowledge\n\n**Page 99:**\n- Figure 85: Perceptual Error\n\n**Page 100:**\n- Figure 86: Perceptual Error\n- Figure 87: Correct Case (not an error)\n\nNow let me verify by looking back at the table of contents I saw earlier to ensure I have the right mapping. Let me check once more:Perfect! This confirms my interpretation. The table shows:\n- Figure 81 is on pages 94-95\n- Figure 82 is on page 96 (spanning from before)\n- Figure 83 is on pages 96-97\n- Figure 84 is on page 98\n- Figure 85 is on page 99\n- Figure 86 is on page 100\n- Figure 87 is on page 101 (starting at page 100)\n\nSo the error types mentioned in case studies on pages 95-100 are:\n\n1. **Reasoning Error** (Figure 82 - Architecture and Engineering)\n2. **Perceptual Error** (Figure 84, 85, 86 - Computer Science)\n3. **Lack of Knowledge** (Figure 84 - Computer Science)\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 114937, "error": null, "extra": {"thread_id": 2029, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77fb85ac92354e66b7280742dc24bcdb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 755, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "native_pdf", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to examine pages 65-70 to count the successful cases (marked with a checkmark \u2713).\n\n**Page 65** (Figure 51): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 66** (Figure 52): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 67** (Figure 53): Clinical Medicine: Pathology - **Correct case** \u2713\n\n**Page 68** (Figure 54): Clinical Medicine: Pathology - **Error case** \u2717 (Perceptual Error)\n\n**Page 69** (Figure 55): Clinical Medicine: Heart Disease - **Error case** \u2717 (Lack of Knowledge)\n\n**Page 70** (Figure 56): Diagnostics and Laboratory Medicine: Radiology - **Correct case** \u2713\n\nCounting the successful cases (those with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 259120, "output_tokens": 295, "cost_micros": 1561358, "latency_ms": 64484, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful (correct) cases in pages 65-70 of the document.\n\nLet me go through each figure in this range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine: Pathology - \"A sample **correct** case\" \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine: Pathology - \"A sample **error** case\" \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine: Heart Disease - \"A sample **error** case\" \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Laboratory Medicine: Radiology - \"A sample **correct** case\" \u2713\n\nCounting the correct cases: Pages 65, 66, 67, and 70 each have one correct case.\n\nTotal successful cases = 4\n\nAnswer: 4", "citations": [], "input_tokens": 79424, "output_tokens": 257, "cost_micros": 242127, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "azure_premium_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (marked with \u2713 or \"V\") in Pages 65-70.\n\nLet me go through each figure in this page range:\n\n**Page 65:**\n- Figure 51: Clinical Medicine 1: Correct Case \u2713\n\n**Page 66:**\n- Figure 52: Clinical Medicine 2: Correct Case \u2713\n\n**Page 67:**\n- Figure 53: Clinical Medicine 3: Correct Case \u2713\n\n**Page 68:**\n- Figure 54: Clinical Medicine 4: Perceptual Error \u2717\n\n**Page 69:**\n- Figure 55: Clinical Medicine 5: Lack of Knowledge \u2717\n\n**Page 70:**\n- Figure 56: Diagnostics and Lab Medicine 1: Correct Case \u2713\n\nCounting the successful cases (marked with \u2713):\n- Page 65: 1\n- Page 66: 1\n- Page 67: 1\n- Page 68: 0\n- Page 69: 0\n- Page 70: 1\n\nTotal: 1 + 1 + 1 + 0 + 0 + 1 = 4\n\nAnswer: 4", "citations": [], "input_tokens": 109175, "output_tokens": 280, "cost_micros": 331725, "latency_ms": 8125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "llamacloud_basic_lc", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases in Pages 65-70 of the document.\n\nLet me go through each page:\n\n**Page 65 (Clinical Medicine 1):**\n- Figure 51: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 66 (Clinical Medicine 2):**\n- Figure 52: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 67 (Clinical Medicine 3):**\n- Figure 53: \"A sample correct case of Clinical Medicine (subfield: Pathology)\"\n- This is a CORRECT case \u2713\n\n**Page 68 (Clinical Medicine 4):**\n- Figure 54: \"A sample error case of Clinical Medicine (subfield: Pathology). Error category: Perceptual Error\"\n- This is an ERROR case \u2717\n\n**Page 69 (Clinical Medicine 5):**\n- Figure 55: \"A sample error case of Clinical Medicine (subfield: Heart Disease). Error category: Lack of Knowledge\"\n- This is an ERROR case \u2717\n\n**Page 70 (Diagnostics and Laboratory Medicine 1):**\n- Figure 56: \"A sample correct case of Diagnostics and Laboratory Medicine (subfield: Radiology)\"\n- This is a CORRECT case \u2713\n\nTotal successful (correct) cases in pages 65-70: 4\n\nAnswer: 4", "citations": [], "input_tokens": 83138, "output_tokens": 348, "cost_micros": 254634, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:48Z", "latency_ms": 11110, "error": null, "raw_text_chars": 1296}]}, "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (correct predictions by GPT-4V) in Pages 65-70.\n\nLet me go through each figure in this range:\n\n**Page 65 - Figure 51 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (E) Familial polyposis coli\n- Ground Truth: (E) Familial polyposis coli\n- **SUCCESS**\n\n**Page 66 - Figure 52 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (D) Ruptured berry aneurysm\n- Ground Truth: (D) Ruptured berry aneurysm\n- **SUCCESS**\n\n**Page 67 - Figure 53 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 68 - Figure 54 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 69 - Figure 55 (Clinical Medicine: Heart Disease)**\n- GPT-4V answered (B) Mitral regurgitation\n- Ground Truth: (C) Aortic value regurgitation\n- Error Category: Lack of Knowledge\n- **FAILURE**\n\n**Page 70 - Figure 56 (Diagnostics and Laboratory Medicine: Radiology)**\n- GPT-4V correctly identified (C) Dandy-Walker syndrome\n- Ground Truth: (C) Dandy-Walker syndrome\n- **SUCCESS**\n\nTotal successful cases: 3\n\nAnswer: 3", "citations": [], "input_tokens": 103232, "output_tokens": 424, "cost_micros": 316056, "latency_ms": 11110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "arm": "surfsense_agentic", "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I'll read the document mentioned in the priority list and look at pages 65-70 to find information about successful cases.I need to search for content related to pages 65-70. Let me search for page markers in the document.Let me search for page numbers in a different format:Let me search for the word \"successful\" or \"cases\" to understand the document structure:Let me search for \"cases\" or look for page numbers differently. Let me read more of the document to understand its structure:This is not helpful. Let me search for the actual page numbers in the document. PDF documents often have page numbers embedded. Let me try different patterns:I can see there's a \"Case Study\" section. Let me look for the appendix section that might have pages 65-70. Let me read the entire document more systematically. First, let me check how long the document is:Great! I found a list of case studies with page numbers. I can see this is a table mapping case numbers to their descriptions and page numbers. Let me continue reading to see pages 65-70:Perfect! I found the information. Looking at pages 65-70, I can see:\n\n- Page 65: Case 51 - Clinical Medicine 1: **Correct Case**\n- Page 66: Case 52 - Clinical Medicine 2: **Correct Case**\n- Page 67: Case 53 - Clinical Medicine 3: **Correct Case**\n- Page 68: Case 54 - Clinical Medicine 4: Perceptual Error\n- Page 69: Case 55 - Clinical Medicine 5: Lack of Knowledge\n- Page 70: Case 56 - Diagnostics and Lab Medicine 1: **Correct Case**\n\nCounting the \"Correct Case\" entries in pages 65-70:\n1. Page 65: Clinical Medicine 1: Correct Case\n2. Page 66: Clinical Medicine 2: Correct Case\n3. Page 67: Clinical Medicine 3: Correct Case\n4. Page 70: Diagnostics and Lab Medicine 1: Correct Case\n\nAnswer: 4", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 54718, "error": null, "extra": {"thread_id": 2030, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_77d82c91b9624d83aeec575d8461194f", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 347, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "4"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many key steps the authors divided causal inference into.\n\nLooking through the document, I can see in Section 3.2 \"Generating the Explanations\" on page 6, there is a discussion of step-by-step explanations and subskills:\n\n\"Apart from the question-answer pairs, we also generate the step-by-step explanations. Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis clearly states that they identified **six subskills** for causal inference.\n\nAdditionally, in Figure 1 on page 2, I can see these six steps are illustrated in the example.\n\nIn Section 4 \"Our CAUSALCOT Model\" on page 7, it also mentions \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: \u2460 identifying the causal graph structure; \u2461 determining the causal query type; \u2462 formulating the query symbolically precisely; and \u2463 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: \u2464 correctly deducing the estimand using causal inference techniques; and finally \u2465 evaluating the estimand to answer the question.\"\n\nThis confirms the six steps again.\n\nAnswer: 6", "citations": [], "input_tokens": 75261, "output_tokens": 410, "cost_micros": 231933, "latency_ms": 16531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Figure 1, there is a section titled \"Correct steps to lead to the ground-truth answer:\" which shows 6 numbered steps:\n\n1) Parse the causal graph\n2) Classify the query type\n3) Formulate the query to its symbolic form\n4) Collect the available data\n5) Derive the estimand using causal inference\n6) Solve for the estimand by plugging in the relevant data\n\nThis is also confirmed in Section 3.2 where they discuss generating explanations: \"We identify the following six subskills: 1 causal graph extraction; 2 correct query type interpretation; 3 symbolic formalization of the query; 4 semantic parsing to compile the available data; 5 estimand derivation; and 6 arithmetic calculation to solve the estimand\"\n\nAdditionally, in Section 4 describing their CAUSALCOT model, they state: \"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps... Then... the formal solution: 5 correctly deducing the estimand... and finally 6 evaluating the estimand\"\n\nAnswer: 6", "citations": [], "input_tokens": 31749, "output_tokens": 291, "cost_micros": 99612, "latency_ms": 9327, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the steps they divided causal inference into.\n\nIn Section 4, \"Our CAUSALCOT Model,\" the authors describe their approach based on the causal inference engine. On page 7, they explicitly state:\n\n\"We base our prompt design on the multi-step reasoning process of causal inference as shown in Figure 4, first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then, given all the information collected in the preparation stage, we introduce the formal solution: 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nThis clearly describes 6 steps:\n1. Identifying the causal graph structure\n2. Determining the causal query type\n3. Formulating the query symbolically precisely\n4. Extracting relevant data from the prompt\n5. Correctly deducing the estimand using causal inference techniques\n6. Evaluating the estimand to answer the question\n\nThis is also shown in Figure 1 and Figure 4, where 6 steps are illustrated.\n\nAnswer: 6", "citations": [], "input_tokens": 38818, "output_tokens": 294, "cost_micros": 120864, "latency_ms": 8828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find where the authors describe the key steps of causal inference.\n\nIn Section 3.2 \"Generating the Explanations,\" the document states:\n\n\"Our goal is to provide all intermediate reasoning steps a student of causal inference would use to answer the questions, so that each necessary subskill necessary for causal inference can be evaluated individually. We identify the following six subskills: \u2460 causal graph extraction; \u2461 correct query type interpretation; \u2462 symbolic formalization of the query; \u2463 semantic parsing to compile the available data; \u2464 estimand derivation; and \u2465 arithmetic calculation to solve the estimand, as in the colored boxes in Figure 1.\"\n\nThis is also reflected in Figure 4 which shows the CAUSALCOT model with 6 steps, and in Section 4 where they describe their prompting strategy that \"breaks down a causal reasoning problem into multiple symbolically-grounded, simpler steps.\"\n\nAnswer: 6", "citations": [], "input_tokens": 33630, "output_tokens": 227, "cost_micros": 104295, "latency_ms": 8516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, specifically at Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can identify the key steps that the authors divided causal inference into.\n\nThe figure clearly shows 6 numbered steps:\n\n1. Parse the causal graph (Causal Relation Extraction)\n2. Classify the query type (Causal Question Classification)\n3. Formulate the query to its symbolic form (Formalization)\n4. Collect the available data (Semantic Parsing)\n5. Derive the estimand using causal inference (Formal Causal Inference)\n6. Solve for the estimand by plugging in the relevant data (Arithmetics)\n\nThis is further confirmed in Section 3.2 where the explanation generation is described, and in Section 4 where the CAUSALCOT model is introduced, which follows these same 6 steps.\n\nAnswer: 6", "citations": [], "input_tokens": 39832, "output_tokens": 211, "cost_micros": 122661, "latency_ms": 8250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q000", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "6", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the provided document to find information about the key steps the author divided causal inference into.Let me search for information about how the author divided the process or steps:Perfect! I found the answer. The text clearly states that the author divided causal inference into 6 steps. Let me verify this by reading a bit more context:Perfect! The document clearly shows that the author divided causal inference into **6 steps**:\n\n1. Extract the causal graph\n2. Determine the query type\n3. Formalize the query\n4. Gather all relevant data\n5. Deduce the estimand using causal inference\n6. Calculate the estimand\n\nThe text explicitly states: \"first starting with four preparation steps: 1 identifying the causal graph structure; 2 determining the causal query type; 3 formulating the query symbolically precisely; and 4 extracting relevant data from the prompt. Then... 5 correctly deducing the estimand using causal inference techniques; and finally 6 evaluating the estimand to answer the question.\"\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 44937, "error": null, "extra": {"thread_id": 2031, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_1b14a0eb01624709bf2cbb4b451f998a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 221, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for information about subskills related to collecting available data...\n\nIn Figure 1 on page 2, I can see the step-by-step reasoning process for answering a causal inference question. Step 4 is labeled \"Collect the available data\" and it has an associated subskill label.\n\nThe subskill associated with Step 4 \"Collect the available data\" is **Semantic Parsing**.\n\nThis is also confirmed in Section 3.2 where it mentions the six subskills:\n- \u2460 causal graph extraction\n- \u2461 correct query type interpretation\n- \u2462 symbolic formalization of the query\n- \u2463 semantic parsing to compile the available data\n- \u2464 estimand derivation\n- \u2465 arithmetic calculation\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 75267, "output_tokens": 185, "cost_micros": 228576, "latency_ms": 32921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, which shows the step-by-step process for answering a causal inference question, Step 4 is labeled as \"Collect the available data:\" and the corresponding subskill is clearly marked as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 31755, "output_tokens": 68, "cost_micros": 96285, "latency_ms": 4983, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation for solving a causal inference question:\n\nStep 4 is labeled \"Collect the available data\" and the subskill required for this step is identified as \"Semantic Parsing\" (shown in the colored box in the figure).\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 38824, "output_tokens": 79, "cost_micros": 117657, "latency_ms": 5264, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the step-by-step explanation, and Section 3.2 which discusses \"Generating the Explanations,\" I can identify the subskills mentioned.\n\nStep 4 in Figure 1 is labeled \"Collect the available data\" and the document describes this as requiring \"semantic parsing to compile the available data\" in Section 3.2.\n\nAnswer: semantic parsing", "citations": [], "input_tokens": 33636, "output_tokens": 96, "cost_micros": 102348, "latency_ms": 5125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically Figure 1 which shows the \"Correct steps to lead to the ground-truth answer,\" I can see that Step 4 is labeled \"Collect the available data\" and the subskill associated with it is \"Semantic Parsing.\"\n\nThis is also confirmed in Section A.8 (Explanation Template) where Step \u2463 states: \"Collect all the available data: The available data are: '$d$'.\"\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 39838, "output_tokens": 109, "cost_micros": 121149, "latency_ms": 5516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q002", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "semantic parsing", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about what subskill is needed to collect the available data.Let me search for more specific terms related to data collection:Perfect! I found the answer. Let me read that section more carefully to get the complete context:Based on the document, I can now provide the answer. The document clearly shows that step 4 \"Collect the available data\" has the subskill listed as \"Semantic Parsing\".\n\nAnswer: Semantic Parsing", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32687, "error": null, "extra": {"thread_id": 2032, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_46f68e1513d341d0b0ee21cfa0dae2f0", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 137, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "semantic parsing", "normalised_gold": "semantic parsing"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the rungs of the Ladder of Causation...\n\nFrom Section 2.1 \"The Ladder of Causation\" on page 3:\n\nThe document describes three distinct rungs:\n- Rung 1 (\"seeing\"): Statistical associations\n- Rung 2 (\"doing\"): Interventions\n- Rung 3 (\"imagining\"): Counterfactuals\n\nRung 2 is specifically described as dealing with interventions and the do-operator.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 75267, "output_tokens": 131, "cost_micros": 227766, "latency_ms": 31077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, Section 2.1 \"The Ladder of Causation\" describes the three rungs:\n\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nThe document states that Rung 2 \"enables us to formalize the concept of actively intervening in the world.\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 31755, "output_tokens": 111, "cost_micros": 96930, "latency_ms": 5344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document for information about the second rung of the ladder of causation...\n\nFrom Section 2.1 \"The Ladder of Causation\":\n\n\"Rung 2 (\"doing\"). This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"If I take an aspirin now, will my headache subside?\"). Interventions can be formalized using the do-operator [24] and Causal Bayesian Networks [67]...\"\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 38824, "output_tokens": 126, "cost_micros": 118362, "latency_ms": 6250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 which describes \"The Ladder of Causation\":\n\nThe three rungs are:\n- Rung 1 (\"seeing\") - statistical associations\n- Rung 2 (\"doing\") - interventions\n- Rung 3 (\"imagining\") - counterfactuals\n\nRung 2 is explicitly described as dealing with interventions, using the do-operator to formalize the concept of actively intervening in the world.\n\nAnswer: Intervention (or \"doing\")", "citations": [], "input_tokens": 33636, "output_tokens": 122, "cost_micros": 102738, "latency_ms": 5062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically Section 2.1 \"The Ladder of Causation\":\n\n**Rung 2 (\"doing\").** This enables us to formalize the concept of actively intervening in the world, and modifying it toward some end (\"*If I take an aspirin now, will my headache subside?*\"). Interventions can be formalized using the *do-operator* [24] and *Causal Bayesian Networks* [67] to represent, for example, the distribution over $Y$ when intervening on $X$ to set its value to $x$ as $P(Y = y|do(X = x))$.\n\nAnswer: Intervention or \"doing\"", "citations": [], "input_tokens": 39838, "output_tokens": 159, "cost_micros": 121899, "latency_ms": 8094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.5, "method": "str_norm", "normalised_pred": "intervention or doing", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q003", "doc_id": "2312.04350v3.pdf", "answer_format": "str", "gold": "intervention", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about the second ladder of causation.Let me search for \"ladder of causation\" in the document:Let me search for \"second rung\" or information about the rungs:Good! I found a reference at line 553. Let me read that section:Perfect! I found the answer. The document clearly states that the three rungs of the Ladder of Causation are:\n- Rung 1: associational\n- Rung 2: interventional\n- Rung 3: counterfactual\n\nAnswer: interventional", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43718, "error": null, "extra": {"thread_id": 2033, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_7af68403347145c59b6ebb40237510ed", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 215, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.0, "method": "str_norm", "normalised_pred": "interventional", "normalised_gold": "intervention"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the chain-type causal graph and count its directed edges.\n\nLooking through the document, I can find the causal graph structures in Figure 6 on page 19, which shows \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\"\n\nIn Figure 6, I can see a \"Chain\" graph structure. The Chain graph shows:\n- X \u2192 (middle node) \u2192 Y\n\nThis represents a chain with three nodes connected by two directed edges:\n1. One edge from X to the middle node\n2. One edge from the middle node to Y\n\nTherefore, a chain-type causal graph has 2 directed edges.\n\nAnswer: 2", "citations": [], "input_tokens": 75271, "output_tokens": 156, "cost_micros": 228153, "latency_ms": 30437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\", I can see the \"Chain\" graph structure.\n\nThe Chain graph shows: X \u2192 M \u2192 Y\n\nThis is a simple chain structure where:\n- X has an arrow to M (1 edge)\n- M has an arrow to Y (1 edge)\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 31759, "output_tokens": 133, "cost_micros": 97272, "latency_ms": 6187, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 19, there is a table titled \"List of all ten causal graphs with treatment-effect pairs (CGTEs)\" which shows:\n\nFor the \"Chain\" causal graph:\n- CGTE Alias: Chain\n- Treatment-Effect Pair: X \u2192 Y\n\nThe diagram shows a simple chain structure: X \u2192 Z \u2192 Y\n\nThis is a chain with 3 nodes (X, Z, Y) connected sequentially.\n\nCounting the directed edges:\n1. X \u2192 Z (one edge)\n2. Z \u2192 Y (one edge)\n\nTotal: 2 directed edges\n\nAnswer: 2", "citations": [], "input_tokens": 38828, "output_tokens": 165, "cost_micros": 118959, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the chain-type causal graph structure.\n\nIn Figure 6 on page 18, there is a \"List of all ten causal graphs with treatment-effect pairs (CGTEs).\" The table shows:\n\n| Causal Graph | CGTE Alias | Treatment-Effect Pair |\n| ------------ | ----------- | --------------------- |\n| Chain        | Chain       | X Y                   |\n\nThe visual representation shows: X \u2192 M \u2192 Y\n\nThis indicates a chain structure where X points to M (a mediator), and M points to Y.\n\nTherefore, there are 2 directed edges in a chain-type causal graph: one from X to M, and one from M to Y.\n\nAnswer: 2", "citations": [], "input_tokens": 33640, "output_tokens": 174, "cost_micros": 103530, "latency_ms": 6796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the definition of a chain-type causal graph.\n\nIn Section A.3 \"Collection of Causal Graphs,\" there is a table (Figure 6) that lists all causal graph types with their structures.\n\nFor the \"Chain\" graph, the table shows:\n- Causal Graph Alias: Chain\n- CGTE Alias: Chain  \n- Treatment-Effect Pair: X \u2192 Z \u2192 Y (X and Y are treatment-effect pair)\n\nThe notation X \u2192 Z \u2192 Y indicates:\n- One edge from X to Z\n- One edge from Z to Y\n\nTherefore, there are 2 directed edges in a chain-type causal graph.\n\nAnswer: 2", "citations": [], "input_tokens": 39842, "output_tokens": 164, "cost_micros": 121986, "latency_ms": 7343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q005", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "2", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the definition of a chain-type causal graph and count the directed edges.Let me search for \"chain-type\" or \"causal graph\" in the document:Let me search for \"causal graph\" to find the definition:Let me read the section around line 2387 where the causal graph definition appears:Now let me search for \"chain\" with \"graph\" or look for specific graph types. Let me check Figure 6 which shows all the causal graphs:Good! I can see \"Chain\" is listed as one of the causal graphs at line 2101-2104. Now let me look for the actual visualization or definition of the Chain graph. Let me check if there's a figure showing these graphs:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 48766, "error": null, "extra": {"thread_id": 2034, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ca8eb7f650264585a1c5b7287fc4aa99", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 247, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2387", "normalised_gold": "2"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "native_pdf", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 on page 8, which shows \"Performance of all models on our CLADDER dataset v1.5\":\n\nThe \"Overall Acc.\" column shows:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe highest overall accuracy is achieved by CAUSALCOT with 70.40%.\n\nLooking at the document, CAUSALCOT is described as \"GPT-4 + CAUSALCOT\" in the context, which means it's GPT-4 enhanced with their causal chain-of-thought prompting strategy.\n\nThe best performing model is CAUSALCOT (which uses GPT-4 as the base model) with an accuracy of 70.40%.\n\nAnswer: 70", "citations": [], "input_tokens": 75259, "output_tokens": 336, "cost_micros": 230817, "latency_ms": 24141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.04350v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in Table 2 (on page 8), I need to find which model achieves the highest overall accuracy on the CLADDER dataset v1.5.\n\nFrom Table 2:\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- **+ CAUSALCOT: 70.40%**\n\nThe model with the highest overall accuracy is **CAUSALCOT** (which is GPT-4 with the CausalCoT prompting strategy) at 70.40%.\n\nAnswer: 4", "citations": [], "input_tokens": 31747, "output_tokens": 263, "cost_micros": 99186, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "azure_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the performance results in the document:\n\nFrom Table 2 (CLADDER v1.5 - the main/current version):\n- GPT-4 + CAUSALCOT achieves 70.40% accuracy, which is the highest performance among all models tested\n\nFrom Table 6 (CLADDER v1.0 - earlier version):\n- GPT-4 + CAUSALCOT achieves 66.64% accuracy, which is also the highest\n\nThe document clearly states on page 3: \"our CAUSALCOT achieves an accuracy of 70.40%, which substantially improves the performance of vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nAnd on page 8: \"And amongst all, our CAUSALCOT achieves the highest performance of 70.40%\"\n\nCAUSALCOT is described as a chain-of-thought prompting strategy used with GPT-4, so the best performing model is GPT-4 with CAUSALCOT.\n\nSince GPT-4 is mentioned to have approximately 1T (1 trillion) parameters, and CAUSALCOT is a prompting strategy applied to GPT-4:\n\nAnswer: 1000000000000", "citations": [], "input_tokens": 38816, "output_tokens": 288, "cost_micros": 120768, "latency_ms": 8750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1000000000000", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_basic_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset according to the document.\n\nLooking at Table 2 (page 8), which shows \"Performance of all models on our CLADDER dataset v1.5\", I can see the overall accuracy for each model:\n\n- Random: 49.27%\n- LLaMa: 44.03%\n- Alpaca: 44.66%\n- GPT-3 Non-Instr. (davinci): 49.92%\n- GPT-3 Instr. (text-davinci-001): 51.40%\n- GPT-3 Instr. (text-davinci-002): 53.15%\n- GPT-3 Instr. (text-davinci-003): 56.26%\n- GPT-3.5: 52.18%\n- GPT-4: 62.03%\n- + CAUSALCOT: 70.40%\n\nThe model with the highest overall accuracy is \"GPT-4 + CAUSALCOT\" with 70.40%.\n\nThe text also states: \"our CAUSALCOT achieves the highest performance of 70.40%, which is substantially better than the vanilla GPT-4 by 8.37 points on CLADDER.\"\n\nSince CAUSALCOT is based on GPT-4 (as stated in the document: \"We build our CAUSALCOT prompting strategy using GPT-4\"), the best performing model is GPT-4 when using the CAUSALCOT prompting strategy.\n\nThe overall accuracy is 70.40%, which rounds to 70%.\n\nAnswer: 70", "citations": [], "input_tokens": 33628, "output_tokens": 390, "cost_micros": 106734, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "llamacloud_premium_lc", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I need to find which model performs best on the CLADDER dataset by looking at the performance tables in the document.\n\nLooking at Table 2 (Performance of all models on CLADDER dataset v1.5), which shows the main results:\n\nThe models and their Overall Accuracy are:\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- + CAUSALCOT: 70.40\n\nThe model with the highest overall accuracy is \"+ CAUSALCOT\" at 70.40%.\n\nCAUSALCOT is described in the paper as a chain-of-thought prompting strategy built on top of GPT-4, so it's GPT-4 enhanced with their CAUSALCOT method.\n\nThe accuracy is 70.40%, which as a percentage would be approximately 70 (rounding to the nearest integer).\n\nAnswer: 70", "citations": [], "input_tokens": 39830, "output_tokens": 325, "cost_micros": 124365, "latency_ms": 8687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.04350v3.pdf::Q006", "doc_id": "2312.04350v3.pdf", "answer_format": "int", "gold": "GPT-4", "pages": 28, "document_id": 7594, "arm": "surfsense_agentic", "question_id": "2312.04350v3.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find information about model performance on Cladder.Let me search for \"Cladder\" in the document to find the relevant information:Now let me search for tables or results sections that might contain performance metrics:Perfect! I found Table 2 which shows the performance of all models on the CLADDER dataset. Let me read that section:Perfect! I can see Table 2 which shows the performance of all models on the CLADDER dataset. Looking at the \"Overall Acc.\" column:\n\n- Random: 49.27\n- LLaMa: 44.03\n- Alpaca: 44.66\n- GPT-3 Non-Instr. (davinci): 49.92\n- GPT-3 Instr. (text-davinci-001): 51.40\n- GPT-3 Instr. (text-davinci-002): 53.15\n- GPT-3 Instr. (text-davinci-003): 56.26\n- GPT-3.5: 52.18\n- GPT-4: 62.03\n- +CAUSALCOT: 70.40\n\nThe model that performs the best on Cladder is \"+CAUSALCOT\" with an overall accuracy of 70.40.\n\nAnswer: 70", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36610, "error": null, "extra": {"thread_id": 2035, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e78cd64845f341ad917f431928ce6d64", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 165, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "70", "normalised_gold": "-4"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 on page 2:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor (human) is above/smarter than the student (model).\n\nThe second subfigure shows \"Superalignment\" where the supervisor (human) is below/weaker than the student (superhuman model).\n\nThe difference is the relative capability levels are reversed - in Traditional ML the supervisor is stronger, while in Superalignment the student is stronger.\n\nAnswer: Supervisor is stronger than student (vs. student stronger than supervisor)", "citations": [], "input_tokens": 121916, "output_tokens": 121, "cost_micros": 367563, "latency_ms": 32577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe figure shows three scenarios comparing Traditional ML, Superalignment, and \"Our Analogy.\"\n\nIn the first subfigure (Traditional ML), the supervisor (human) is above the human level line, while the student (model) is below the human level line.\n\nIn the second subfigure (Superalignment), the supervisor (human) is at the human level line, while the student (model) is far above the human level line.\n\nThe key difference is that in Traditional ML, the supervisor is stronger than/above the student, whereas in Superalignment, the student is much stronger than the supervisor.\n\nAnswer: Supervisor is stronger than student (versus student stronger than supervisor)", "citations": [], "input_tokens": 45459, "output_tokens": 162, "cost_micros": 138807, "latency_ms": 5828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.11764705882352941, "method": "str_norm", "normalised_pred": "supervisor is stronger than student versus student stronger than supervisor", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the **supervisor is stronger than the student** (supervisor is above the human level line, student is below it).\n\nThe second subfigure shows \"Superalignment\" where the **supervisor is weaker than the student** (supervisor is at human level, student is above it - superhuman).\n\nAnswer: Supervisor is stronger than student (vs weaker than student)", "citations": [], "input_tokens": 49232, "output_tokens": 103, "cost_micros": 149241, "latency_ms": 5391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.25, "method": "str_norm", "normalised_pred": "supervisor is stronger than student vs weaker than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document:\n\nThe first subfigure shows \"Traditional ML\" where the supervisor is at human level and the student is below human level.\n\nThe second subfigure shows \"Superalignment\" where the supervisor is at human level and the student is above human level (superhuman).\n\nThe key difference is the student's capability relative to human level.\n\nAnswer: Student is weaker than human vs student is stronger than human", "citations": [], "input_tokens": 47601, "output_tokens": 99, "cost_micros": 144288, "latency_ms": 4594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2222222222222222, "method": "str_norm", "normalised_pred": "student is weaker than human vs student is stronger than human", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to compare the first subfigure (Traditional ML) with the second subfigure (Superalignment).\n\nIn the first subfigure (Traditional ML):\n- Supervisor: Human\n- Student: Robot\n- The human is stronger/smarter than the robot\n\nIn the second subfigure (Superalignment):\n- Supervisor: Human  \n- Student: Superhuman Robot\n- The human is weaker/less capable than the superhuman robot\n\nThe key difference is the relative capability between supervisor and student.\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 89555, "output_tokens": 133, "cost_micros": 270660, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q000", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "\"humans supervise models are weaker than humans\"", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and analyze the difference between the first and second subfigures regarding supervisor-student relationships.Let me search for Figure 1 in the document:Now let me read the actual Figure 1 caption and description more carefully:Based on the figure caption and structure, I can see that Figure 1 has three subfigures:\n1. Traditional ML\n2. Superalignment  \n3. Our Analogy\n\nThe question asks about the difference between the first subfigure (Traditional ML) and the second subfigure (Superalignment) regarding the supervisor-student relationship.\n\nFrom the caption: \"Traditional ML focuses on the setting where humans supervise models that are weaker than humans. For the ultimate superalignment problem, humans will have to supervise models much smarter than them.\"\n\nThe key difference is about the relative capability/intelligence level between supervisor and student:\n- In Traditional ML (first subfigure): Supervisor is stronger/smarter than student\n- In Superalignment (second subfigure): Supervisor is weaker than student (students are \"much smarter\" than supervisors)\n\nAnswer: Supervisor is stronger than student", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 43967, "error": null, "extra": {"thread_id": 2036, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_e51022c429054652ae2e24b4002aba31", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 203, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "supervisor is stronger than student", "normalised_gold": "humans supervise models are weaker than humans"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count the figures from Page 3 to Page 10 in this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 2: Shows weak-to-strong generalization results with three panels (NLP Tasks, Chess Puzzles, ChatGPT Reward Modeling)\n\n**Page 4:**\n- No figures on this page (Related Work section)\n\n**Page 5:**\n- No figures on this page (Related Work continued)\n\n**Page 6:**\n- No figures on this page (Methodology section)\n\n**Page 7:**\n- No figures on this page (Main Results section continues)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization with naive finetuning (6 panels labeled a-f)\n\n**Page 9:**\n- Figure 4: Shows bootstrapping results on chess puzzles (2 panels labeled a-b)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss (2 panels labeled a-b)\n\nTotal figures from Page 3 to Page 10:\n- Figure 2 (Page 3)\n- Figure 3 (Page 8)\n- Figure 4 (Page 9)\n- Figure 5 (Page 10)\n\nAnswer: 4", "citations": [], "input_tokens": 121899, "output_tokens": 301, "cost_micros": 370212, "latency_ms": 35593, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to count all the figures that appear on pages 3 through 10 of this document.\n\nLet me go through each page:\n\n**Page 3:**\n- Figure 1: An illustration of methodology (Traditional ML, Superalignment, Our Analogy)\n- Figure 2: Shows test accuracy on representative NLP task, Chess Puzzles, and ChatGPT Reward Modeling\n\n**Page 4:**\n- No figures visible on this page (contains related work section)\n\n**Page 5:**\n- No figures visible on this page (continues related work section)\n\n**Page 6:**\n- No figures visible on this page (continues related work section)\n\n**Page 7:**\n- No figures visible on this page (contains methodology section)\n\n**Page 8:**\n- Figure 3: Shows weak-to-strong generalization results with multiple panels (a,b,c,d,e,f)\n- Figure 4: Shows bootstrapping results on chess puzzles\n\n**Page 9:**\n- No figures visible on this page (continues main results section)\n\n**Page 10:**\n- Figure 5: Shows NLP results with auxiliary loss\n- Figure 6: Shows auxiliary loss improving generalization across datasets\n\nCounting the figures from pages 3-10:\n- Page 3: 2 figures (Figure 1, Figure 2)\n- Page 8: 2 figures (Figure 3, Figure 4)\n- Page 10: 2 figures (Figure 5, Figure 6)\n\nTotal: 6 figures\n\nAnswer: 6", "citations": [], "input_tokens": 45442, "output_tokens": 356, "cost_micros": 141666, "latency_ms": 8421, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from Page 3 to Page 10 in this document.\n\n**Page 3:**\n- Figure 2: \"An illustration of our methodology...\" showing weak-to-strong generalization results\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- Figure 3: \"Promising weak-to-strong generalization...\" showing test accuracy panels (a,b,c,d,e,f)\n\n**Page 8:**\n- None\n\n**Page 9:**\n- Figure 4: \"Bootstrapping improves weak-to-strong generalization...\" showing panels (a,b)\n\n**Page 10:**\n- Figure 5: \"Substantially improved generalization...\" showing panels (a,b)\n- Figure 6: \"Simple auxiliary loss improves generalization...\" showing multiple test accuracy plots\n\nLet me count:\n- Page 3: 1 figure (Figure 2)\n- Page 7: 1 figure (Figure 3)\n- Page 9: 1 figure (Figure 4)\n- Page 10: 2 figures (Figure 5 and Figure 6)\n\nTotal: 1 + 1 + 1 + 2 = 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 49215, "output_tokens": 303, "cost_micros": 152190, "latency_ms": 7608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count the figures from page 3 to page 10 of this paper.\n\nLet me go through the document systematically:\n\n**Page 3:** Figure 1 (An illustration of methodology showing Traditional ML, Superalignment, and Our Analogy)\n\n**Page 4:** Figure 2 (Strong models trained with weak supervision generalize beyond their supervisor - showing 3 subplots for NLP Task, Chess, and ChatGPT Reward Modeling)\n\n**Page 5-6:** No figures visible in the main text sections shown\n\n**Page 7:** Figure 3 (mentioned in the text - showing test accuracy and performance gap recovered for NLP tasks, chess puzzles, and ChatGPT reward modeling - this appears to be on page 8 based on the layout)\n\n**Page 8:** Figure 3 appears here (6 subplots - a,b,c showing test accuracy and d,e,f showing performance gap recovered)\n\n**Page 9:** Figure 4 (Bootstrapping improves weak-to-strong generalization on chess puzzles - 2 subplots)\n\n**Page 10:** Figure 5 (Substantially improved generalization on NLP datasets with auxiliary loss - 2 subplots)\n\nCounting the figures:\n- Page 3: Figure 1\n- Page 4: Figure 2  \n- Page 8: Figure 3\n- Page 9: Figure 4\n- Page 10: Figure 5\n\nTotal figures from page 3 to page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 47584, "output_tokens": 358, "cost_micros": 148122, "latency_ms": 9766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll count all figures from Page 3 to Page 10.\n\n**Page 3:**\n- Figure 2: Strong models trained with weak supervision generalize beyond their supervisor (bar chart)\n\n**Page 4:**\n- None\n\n**Page 5:**\n- None\n\n**Page 6:**\n- None\n\n**Page 7:**\n- None\n\n**Page 8:**\n- Figure 3: Promising weak-to-strong generalization with naive finetuning (grid of heatmaps/tables - appears as panels a, b, c, d, e, f)\n\n**Page 9:**\n- Figure 4: Bootstrapping improves weak-to-strong generalization on chess puzzles (2 panels: a and b)\n\n**Page 10:**\n- Figure 5: Substantially improved generalization on NLP datasets with a simple auxiliary loss (2 panels: a and b)\n\nTotal count:\n- Figure 2: 1\n- Figure 3: 1 (though it has 6 panels, it's one figure)\n- Figure 4: 1\n- Figure 5: 1\n\nAnswer: 4", "citations": [], "input_tokens": 89538, "output_tokens": 261, "cost_micros": 272529, "latency_ms": 9000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q002", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "5", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to count the figures from Page 3 to Page 10.Now I need to identify which figures appear between Page 3 and Page 10. Let me read the relevant sections more carefully:Now let me check what's on page 11 to confirm the boundary:Based on my analysis, let me count the figures that appear from Page 3 to Page 10:\n\n- **Page 3**: No figures (only text - Related Work section starts)\n- **Page 4**: No figures (Related Work section continues)  \n- **Page 5**: 1 figure (the PGR illustration figure showing weak performance, weak-to-strong performance, strong ceiling performance)\n- **Page 6**: No figures (text only)\n- **Page 7**: 1 figure (Figure 3 - showing weak-to-strong generalization results across NLP, Chess, and ChatGPT RM)\n- **Page 8**: 1 figure (Figure 4 - Bootstrapping improves weak-to-strong generalization on chess puzzles)\n- **Page 9**: 1 figure (Figure 5 - NLP with auxiliary loss)\n- **Page 10**: 1 figure (Figure 6 - Simple auxiliary loss improves generalization across most datasets)\n\nTotal figures from Page 3 to Page 10: 5 figures\n\nAnswer: 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 55516, "error": null, "extra": {"thread_id": 2037, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ad05d8ff7ccf4712bbe93b3462e811bc", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 271, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to locate Figure 3 in the document and count the number of subfigures it contains.\n\nLooking at the document, Figure 3 appears on page 8. The figure is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure contains:\n- Panel (a): NLP Tasks - test accuracy plot\n- Panel (b): Chess Puzzles - test accuracy plot  \n- Panel (c): ChatGPT Reward Modeling - test accuracy plot\n- Panel (d): NLP Tasks - performance gap recovered plot\n- Panel (e): Chess Puzzles - performance gap recovered plot\n- Panel (f): ChatGPT Reward Modeling - performance gap recovered plot\n\nThe figure has 6 labeled panels/subfigures (a, b, c, d, e, f).\n\nAnswer: 6", "citations": [], "input_tokens": 121893, "output_tokens": 212, "cost_micros": 368859, "latency_ms": 54641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 on page 8. The caption reads:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe caption explicitly mentions panels (a,b,c) and (d,e,f), which indicates there are 6 subfigures total in Figure 3.\n\nLooking at the actual figure layout, I can see:\n- (a) NLP Tasks\n- (b) Chess Puzzles  \n- (c) ChatGPT Reward Modeling\n- (d) NLP Tasks (PGR)\n- (e) Chess Puzzles (PGR)\n- (f) ChatGPT Reward Modeling (PGR)\n\nAnswer: 6", "citations": [], "input_tokens": 45436, "output_tokens": 322, "cost_micros": 141138, "latency_ms": 8859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can find Figure 3 which is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure caption describes panels (a,b,c) and (d,e,f):\n- (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task\n- (d,e,f) Same as panels a,b,c but for performance gap recovered\n\nSo Figure 3 has 6 subfigures total: (a), (b), (c), (d), (e), and (f).\n\nAnswer: 6", "citations": [], "input_tokens": 49209, "output_tokens": 201, "cost_micros": 150642, "latency_ms": 6328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking at the document, I can see Figure 3 is described as:\n\n\"Figure 3: Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task. (a,b,c) Test accuracy as a function of strong student size on (a) NLP tasks, (b) chess puzzles, and (c) the ChatGPT reward modeling task. Accuracy of strong students trained with ground truth in black, accuracy of strong students trained with weak supervision shown with colored lines (hue indicates size of weak supervisor). (d,e,f) Same as panels a,b,c but for performance gap recovered (see Section 3 for details).\"\n\nThe description explicitly mentions:\n- (a), (b), (c) - three subfigures showing test accuracy\n- (d), (e), (f) - three subfigures showing performance gap recovered\n\nThis gives us a total of 6 subfigures in Figure 3.\n\nAnswer: 6", "citations": [], "input_tokens": 47578, "output_tokens": 255, "cost_micros": 146559, "latency_ms": 8389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find Figure 3 in the document and count its subfigures.\n\nLooking through the document for Figure 3...\n\nFound it! Figure 3 is titled \"Promising weak-to-strong generalization with naive finetuning on NLP tasks and chess, but poor generalization on the ChatGPT reward modeling task.\"\n\nThe figure consists of:\n- (a) NLP Tasks - showing test accuracy\n- (b) Chess Puzzles - showing test accuracy  \n- (c) ChatGPT Reward Modeling - showing test accuracy\n- (d) Performance Gap Recovered (%) - NLP Tasks\n- (e) Performance Gap Recovered (%) - Chess Puzzles\n- (f) Performance Gap Recovered (%) - ChatGPT Reward Modeling\n\nSo Figure 3 has 6 subfigures labeled (a) through (f).\n\nAnswer: 6", "citations": [], "input_tokens": 89532, "output_tokens": 204, "cost_micros": 271656, "latency_ms": 8046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q004", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count its subfigures.Let me search for Figure 3 in the document:Now let me search for where Figure 3 appears in the document:Good! I found Figure 3. Let me read the complete caption around line 975:Perfect! I can see from the caption that Figure 3 has subfigures labeled (a), (b), (c), (d), (e), and (f). Let me continue reading to confirm all subfigures:Perfect! I can now clearly see all the subfigures in Figure 3. Based on the caption and the content, Figure 3 contains:\n\n- (a) NLP Tasks - test accuracy\n- (b) Chess Puzzles - test accuracy  \n- (c) ChatGPT Reward Modeling - test accuracy\n- (d) NLP Tasks - performance gap recovered\n- (e) Chess Puzzles - performance gap recovered\n- (f) ChatGPT Reward Modeling - performance gap recovered\n\nAnswer: 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 40671, "error": null, "extra": {"thread_id": 2038, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_131c7eb21d53472097ae34a0c3413951", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 191, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find all sections that discuss the experiment setup in this paper about weak-to-strong generalization.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the general methodology and setup for weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the three main task settings: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information:\n   - A.1: NLP TASKS - Details about data preprocessing, models, training hyperparameters, weak labels, and evaluation\n   - A.2: CHESS PUZZLES - Details about data preprocessing, training hyperparameters, weak labels, and evaluation\n   - A.3: CHATGPT REWARD MODELING - Details about data preprocessing, models, training hyperparameters, and weak labels\n   - A.4: AUXILIARY CONFIDENCE LOSS - Details about the confidence loss method\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Describes additional experimental setups:\n   - C.1: CHESS PUZZLES\n   - C.2: NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3: GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes additional experimental settings:\n   - D.1: SELF-SUPERVISED VISION MODELS\n   - D.2: LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Describes synthetic experiments:\n   - E.1: SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n   - E.2: DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n   - E.3: MAKING IMITATION TRIVIAL\n\nAnswer: Section 3 (Methodology), Section 4.1 (Tasks), Appendix A (Further Experimental Details), Appendix A.1 (NLP Tasks), Appendix A.2 (Chess Puzzles), Appendix A.3 (ChatGP", "citations": [], "input_tokens": 121905, "output_tokens": 512, "cost_micros": 373395, "latency_ms": 49562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgp, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup in this document.\n\nLet me go through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the overall methodology and experimental setup for weak-to-strong learning.\n\n2. **Section 4.1 TASKS** - This describes the tasks used in experiments (NLP benchmarks, Chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix is dedicated to experimental details.\n\n4. **Appendix A.1 NLP TASKS** - Details about NLP task setup including data preprocessing, models, training hyperparameters, weak labels, and evaluation.\n\n5. **Appendix A.2 CHESS PUZZLES** - Details about chess puzzle setup including data preprocessing, training hyperparameters, weak labels, and evaluation.\n\n6. **Appendix A.3 CHATGPT REWARD MODELING** - Details about reward modeling setup including data preprocessing, models, training hyperparameters, weak labels.\n\n7. **Appendix A.4 AUXILIARY CONFIDENCE LOSS** - Detailed description of the auxiliary confidence loss method.\n\n8. **Appendix C EASY-TO-HARD GENERALIZATION** - Discusses experimental setup for easy-to-hard generalization experiments.\n\n9. **Appendix C.1 CHESS PUZZLES** - Setup for easy-to-hard generalization on chess.\n\n10. **Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING** - Setup for difficulty-based experiments on NLP.\n\n11. **Appendix C.3 GPT-4 PREDICTED DIFFICULTY** - Setup using GPT-4 predicted difficulty.\n\n12. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - Discusses alternative experimental settings.\n\n13. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Setup for vision model experiments.\n\n14. **Appendix D.2 LINEAR PROBING** - Setup for linear probing experiments.\n\n15. **Appendix E THE EFFECTS OF WEAK LABEL STRUCTURE** - Discusses experimental setup for studying weak label structure.\n\n16. **", "citations": [], "input_tokens": 45448, "output_tokens": 512, "cost_micros": 144024, "latency_ms": 10405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "16", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experiment setup from the document.\n\nLet me go through the document systematically:\n\n1. **Section 3: METHODOLOGY** - This section describes the overall methodology and setup for the weak-to-strong learning experiments.\n\n2. **Section 4.1: TASKS** - This describes the specific tasks used in experiments (NLP benchmarks, chess puzzles, ChatGPT reward modeling).\n\n3. **Appendix A: FURTHER EXPERIMENTAL DETAILS** - This entire appendix section provides detailed experimental setup information, including:\n   - A.1 NLP TASKS\n   - A.2 CHESS PUZZLES\n   - A.3 CHATGPT REWARD MODELING\n   - A.4 AUXILIARY CONFIDENCE LOSS\n\n4. **Appendix C: EASY-TO-HARD GENERALIZATION** - Contains experimental setup for easy-to-hard generalization experiments, including:\n   - C.1 CHESS PUZZLES\n   - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3 GPT-4 PREDICTED DIFFICULTY\n\n5. **Appendix D: OTHER WEAK-TO-STRONG SETTINGS** - Describes setup for additional experiments:\n   - D.1 SELF-SUPERVISED VISION MODELS\n   - D.2 LINEAR PROBING\n\n6. **Appendix E: THE EFFECTS OF WEAK LABEL STRUCTURE** - Contains experimental setup for studying weak label structures:\n   - E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY\n   - E.2 DIFFERENT WEAK ERROR STRUCTURE MEANS DIFFERENT GENERALIZATION\n   - E.3 MAKING IMITATION TRIVIAL\n\nAnswer: Section 3, Section 4.1, Appendix A, Appendix A.1, Appendix A.2, Appendix A.3, Appendix A.4, Appendix C.1, Appendix C.2, Appendix C.3, Appendix D.1, Appendix D.2, Appendix E.1, Appendix E.2, Appendix E.3", "citations": [], "input_tokens": 49221, "output_tokens": 499, "cost_micros": 155148, "latency_ms": 10391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.2105263157894737, "method": "list_set", "normalised_pred": "appendix, appendix 1, appendix 2, appendix 3, appendix 4, appendix c 1, appendix c 2, appendix c 3, appendix d 1, appendix d 2, appendix e 1, appendix e 2, appendix e 3, section 3, section 4 1", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify all sections that discuss the experimental setup from the document.\n\nLet me scan through the document systematically:\n\n1. **Section 3 METHODOLOGY** - This section describes the core experimental methodology for weak-to-strong learning, including creating weak supervisors, training strong students, and training with ground truth labels.\n\n2. **Section 4.1 TASKS** - This describes the specific tasks used in experiments: NLP benchmarks, chess puzzles, and ChatGPT reward modeling.\n\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - This entire appendix provides detailed experimental setup information, including:\n   - A.1 NLP TASKS (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n   - A.2 CHESS PUZZLES (data preprocessing, training hyperparameters, weak labels, evaluation)\n   - A.3 CHATGPT REWARD MODELING (data preprocessing, models, training hyperparameters, weak labels, evaluation)\n   - A.4 AUXILIARY CONFIDENCE LOSS (detailed description of the method)\n\n4. **Appendix D OTHER WEAK-TO-STRONG SETTINGS** - This discusses additional experimental settings:\n   - D.1 SELF-SUPERVISED VISION MODELS\n   - D.2 LINEAR PROBING\n\n5. **Appendix C** mentions experimental setup for easy-to-hard generalization experiments.\n\n6. **Appendix E** discusses experimental setup for synthetic experiments on weak label structure.\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix D OTHER WEAK-TO-STRONG SETTINGS, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING", "citations": [], "input_tokens": 47590, "output_tokens": 470, "cost_micros": 149820, "latency_ms": 10702, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix d other weak to strong settings, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for sections that discuss experiment setup:\n\n1. **Section 3 METHODOLOGY** - Describes the general weak-to-strong learning setup\n2. **Section 4.1 TASKS** - Describes the three task types (NLP, Chess, Reward Modeling)\n3. **Appendix A FURTHER EXPERIMENTAL DETAILS** - Contains detailed experimental setup information with subsections:\n   - A.1 NLP TASKS\n   - A.2 CHESS PUZZLES\n   - A.3 CHATGPT REWARD MODELING\n   - A.4 AUXILIARY CONFIDENCE LOSS\n4. **Appendix D.1 SELF-SUPERVISED VISION MODELS** - Describes ImageNet experiment setup\n5. **Appendix D.2 LINEAR PROBING** - Describes linear probing experiment setup\n6. **Appendix C EASY-TO-HARD GENERALIZATION** - Describes setup for easy-to-hard experiments with subsections:\n   - C.1 CHESS PUZZLES\n   - C.2 NLP TASKS: DIFFICULTY THRESHOLDING\n   - C.3 GPT-4 PREDICTED DIFFICULTY\n7. **Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY** - Describes synthetic experiment setup\n\nAnswer: Section 3 METHODOLOGY, Section 4.1 TASKS, Appendix A FURTHER EXPERIMENTAL DETAILS, Appendix A.1 NLP TASKS, Appendix A.2 CHESS PUZZLES, Appendix A.3 CHATGPT REWARD MODELING, Appendix A.4 AUXILIARY CONFIDENCE LOSS, Appendix C EASY-TO-HARD GENERALIZATION, Appendix C.1 CHESS PUZZLES, Appendix C.2 NLP TASKS: DIFFICULTY THRESHOLDING, Appendix C.3 GPT-4 PREDICTED DIFFICULTY, Appendix D.1 SELF-SUPERVISED VISION MODELS, Appendix D.2 LINEAR PROBING, Appendix E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 89544, "output_tokens": 499, "cost_micros": 276117, "latency_ms": 10375, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "appendix 1 nlp tasks, appendix 2 chess puzzles, appendix 3 chatgpt reward modeling, appendix 4 auxiliary confidence loss, appendix c 1 chess puzzles, appendix c 2 nlp tasks difficulty thresholding, appendix c 3 gpt 4 predicted difficulty, appendix c easy to hard generalization, appendix d 1 self supervised vision models, appendix d 2 linear probing, appendix e 1 synthetic experiments on simulation difficulty, appendix further experimental details, section 3 methodology, section 4 1 tasks", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q005", "doc_id": "2312.09390v1.pdf", "answer_format": "list", "gold": "['Section 4.1', 'Section 4.2', 'Section 4.3', 'Appendix A']", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to identify all sections that discuss the experiment setup.Let me continue reading to see the complete document structure and identify all sections related to experiment setup.Now let me search specifically for \"experiment\" or \"setup\" to find sections discussing experimental setup:Now let me search more systematically for sections that discuss experimental setup:Based on my reading of the document, I can now identify all the sections that discuss experiment setup. Let me compile the complete list:Now I have a comprehensive view of the document. Let me identify all sections that discuss experiment setup:\n\nAnswer: 3 METHODOLOGY, 4.1 TASKS, A FURTHER EXPERIMENTAL DETAILS, A.1 NLP TASKS, A.2 CHESS PUZZLES, A.3 CHATGPT REWARD MODELING, A.4 AUXILIARY CONFIDENCE LOSS, E.1 SYNTHETIC EXPERIMENTS ON SIMULATION DIFFICULTY", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 69733, "error": null, "extra": {"thread_id": 2039, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bfc57bfcbbd44c6786a0cd5058f61c15", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 309, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "1 nlp tasks, 2 chess puzzles, 3 chatgpt reward modeling, 3 methodology, 4 1 tasks, 4 auxiliary confidence loss, e 1 synthetic experiments on simulation difficulty, further experimental details", "normalised_gold": "appendix, section 4 1, section 4 2, section 4 3"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the citation information for the paper by Long Ouyang published in 2022.\n\nLooking through the references section, I can find:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730\u201327744, 2022.\n\nHowever, the document does not contain information about how many times this paper has been cited. The document is the paper itself (\"Weak-to-Strong Generalization\") which cites the Ouyang et al. 2022 paper, but it does not provide citation counts for the references it lists.\n\nI cannot determine the number of citations for the Ouyang et al. 2022 paper from this document alone.\n\nAnswer: Cannot be determined from the provided document", "citations": [], "input_tokens": 121904, "output_tokens": 231, "cost_micros": 369177, "latency_ms": 39750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations of the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find references to \"Ouyang et al., 2022\" in the references section:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times this paper is cited throughout the document. Let me search for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Christiano et al., 2017; Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 45447, "output_tokens": 423, "cost_micros": 142686, "latency_ms": 10109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations to Long Ouyang 2022 in the references and throughout the document.\n\nLooking at the references section, I can find:\n\n\"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730-27744, 2022.\"\n\nNow I need to count how many times \"Ouyang et al., 2022\" or similar citations appear in the document.\n\nSearching through the document:\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nLet me verify by searching more carefully through the text for all instances of \"Ouyang\":\n\n- Introduction section: 1 citation\n- Related Work section: 1 citation  \n- Tasks section: 1 citation\n- ChatGPT reward modeling subsection: 1 citation\n- Models subsection (A.3): 1 citation\n- Alignment plan section: 1 citation\n\nTotal count: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 49220, "output_tokens": 405, "cost_micros": 153735, "latency_ms": 8905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find how many times the paper by Long Ouyang published in 2022 has been cited in this document.\n\nLet me search for citations of Ouyang et al., 2022 or Long Ouyang, 2022.\n\nLooking through the document, I can find the following citations to \"Ouyang et al., 2022\":\n\n1. Page 1: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n2. Page 2: \"Wu et al., 2021; Ouyang et al., 2022; Bowman et al., 2022; Saunders et al., 2022\"\n3. Page 5: \"Christiano et al., 2017; Stiennon et al., 2020; Ouyang et al., 2022; Glaese et al., 2022; Bai et al., 2022a\"\n4. Page 7: \"(Ouyang et al., 2022)\"\n5. Page 32: \"(Stiennon et al., 2020; Ouyang et al., 2022)\"\n6. Page 47: \"(h - tiano et al., 2017; Ouyang et al., 2022)\"\n\nLet me count all instances:\n1. Page 1 - 1 citation\n2. Page 2 - 1 citation\n3. Page 5 - 1 citation\n4. Page 7 - 1 citation\n5. Page 32 - 1 citation\n6. Page 47 - 1 citation\n\nTotal: 6 citations\n\nAnswer: 6", "citations": [], "input_tokens": 47589, "output_tokens": 417, "cost_micros": 149022, "latency_ms": 7516, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find citations for the paper by Long Ouyang published in 2022.\n\nLooking through the document, I can find the reference:\n\nLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. *Advances in Neural Information Processing Systems*, 35: 27730\u201327744, 2022.\n\nNow I need to count how many times this paper (Ouyang et al., 2022) is cited in the document.\n\nSearching through the document for \"Ouyang et al., 2022\":\n\n1. Page 1: \"Ouyang et al., 2022\"\n2. Page 2: \"Ouyang et al., 2022\"\n3. Page 5: \"Ouyang et al., 2022\"\n4. Page 7: \"Ouyang et al., 2022\"\n5. Page 32: \"Ouyang et al., 2022\"\n6. Page 47: \"Ouyang et al., 2022\"\n\nAnswer: 6", "citations": [], "input_tokens": 89543, "output_tokens": 289, "cost_micros": 272964, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q006", "doc_id": "2312.09390v1.pdf", "answer_format": "int", "gold": "6", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document that was flagged as user-mentioned to find information about Long Ouyang's 2022 paper and its citation count.Let me search for \"Long Ouyang\" and \"2022\" in this document, and look for citation information.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 25655, "error": null, "extra": {"thread_id": 2040, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d3a50787f8a940c590bfec1b692a8eb3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 70, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2022", "normalised_gold": "6"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "native_pdf", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\n- Subfigure (a) shows that for ground truth labels, **finetuning** (solid lines) performs best across all model sizes, reaching approximately 95% accuracy for the largest models.\n\n- Subfigure (b) shows weak-to-strong performance with weak labels, where **fewshot weak labels (n=5)** (the method shown with crosses/\u00d7) performs best for the largest student models.\n\n- Subfigure (c) directly compares methods and shows that **finetune aux. loss** (solid line with triangles) achieves the highest performance, reaching approximately 90% accuracy for the largest models.\n\nOverall, across the figure, **finetune aux. loss** appears to be the best method when considering weak-to-strong learning performance.\n\nAnswer: finetune aux. loss", "citations": [], "input_tokens": 121910, "output_tokens": 207, "cost_micros": 368835, "latency_ms": 39203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.09390v1.pdf"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "str_norm", "normalised_pred": "finetune aux loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods:\n\n- Subfigure (a) compares zero-shot, 5-shot ground truth, and finetune ground truth\n- Subfigure (b) compares zero-shot, 5-shot ground truth, and 5-shot with weak labels  \n- Subfigure (c) compares 5-shot with weak labels, naive finetuning, and finetuning with auxiliary confidence loss\n\nAcross all three subfigures, examining the performance curves for the largest strong student models (rightmost side of each plot), the method that achieves the highest test accuracy is **finetuning with the auxiliary confidence loss** (shown with triangles in subfigure c).\n\nAnswer: Finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 45453, "output_tokens": 193, "cost_micros": 139254, "latency_ms": 6625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "azure_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which shows three subfigures (a), (b), and (c) comparing different methods across model sizes:\n\nThe figure compares:\n- Zero-shot\n- Few-shot with ground truth (n=5)\n- Finetune with ground truth\n- Few-shot with weak labels (n=5)\n- Finetune (baseline)\n- Finetune with auxiliary loss\n\nFrom the plots, particularly subfigure (c) which directly compares weak-to-strong performance, the method that consistently achieves the highest test accuracy across strong student compute sizes is the finetuning with auxiliary confidence loss (shown with solid line and triangles).\n\nAnswer: finetune with aux. loss", "citations": [], "input_tokens": 49226, "output_tokens": 163, "cost_micros": 150123, "latency_ms": 7094, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "str_norm", "normalised_pred": "finetune with aux loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_basic_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9, which contains three subfigures (a), (b), and (c), I need to identify the six methods shown and determine which performs best.\n\nFrom the figure:\n- Subfigure (a) shows: zero-shot, 5-shot with ground truth (g.t.), and finetuning with ground truth (g.t.)\n- Subfigure (b) shows: zero-shot, 5-shot with ground truth, and 5-shot with weak labels\n- Subfigure (c) shows: 5-shot prompting (weak-to-strong), naive finetuning, and finetuning with auxiliary confidence loss\n\nThe six methods are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetuning with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary confidence loss\n\nLooking at subfigure (c), which directly compares three weak-to-strong methods, the finetuning with auxiliary confidence loss (shown with solid line and triangles) achieves the highest performance, especially for larger model sizes.\n\nAmong all methods shown across the three subfigures, finetuning with ground truth labels would naturally perform best, but among the weak-to-strong methods (which is the focus of subfigure c), the auxiliary confidence loss performs best.\n\nAnswer: finetuning with auxiliary confidence loss", "citations": [], "input_tokens": 47595, "output_tokens": 339, "cost_micros": 147870, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary confidence loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "llamacloud_premium_lc", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 9 with its three subfigures (a), (b), and (c):\n\nIn subfigure (a), comparing zero-shot, 5-shot with ground truth, and finetuning with ground truth, **finetuning with ground truth** achieves the highest test accuracy.\n\nIn subfigure (b), which shows 5-shot prompting with weak labels from different weak supervisors, the best performing is **5-shot with weak labels from the largest weak supervisor (0.1)**.\n\nIn subfigure (c), which compares 5-shot weak labels, naive finetuning, and finetuning with auxiliary loss, **finetuning with auxiliary loss** achieves the highest performance.\n\nOverall, across all methods shown in Figure 9, the best performing method is:\n\nAnswer: finetuning with auxiliary loss", "citations": [], "input_tokens": 89549, "output_tokens": 194, "cost_micros": 271557, "latency_ms": 8141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with auxiliary loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.09390v1.pdf::Q007", "doc_id": "2312.09390v1.pdf", "answer_format": "str", "gold": "finetune g.t.", "pages": 49, "document_id": 7595, "arm": "surfsense_agentic", "question_id": "2312.09390v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find Figure 9 and identify which method performs best among the six shown.Let me search for Figure 9 in the document:Let me read the section containing Figure 9 to see the actual figure and understand which methods are being compared:Perfect! Now I can see Figure 9 with three subfigures (a), (b), and (c). Let me read a bit more to understand the comparison better:Based on my reading of the document, Figure 9 contains three subfigures (a, b, c) that compare six different methods:\n\nFrom the figure caption and description:\n- **Panel (a)**: zero-shot, 5-shot with ground truth, finetune with ground truth\n- **Panel (b)**: 5-shot with weak labels (multiple supervisor sizes), zero-shot, 5-shot with ground truth\n- **Panel (c)**: 5-shot weak-to-strong prompting, naive finetuning (weak-to-strong), and **finetuning with the confidence loss (auxiliary loss)**\n\nThe text at line 2078-2079 states: \"However, **weak-to-strong finetuning with the confidence loss still generally outperforms** weak-to-strong few-shot prompting.\"\n\nAnd the figure caption at line 1942-1944 states: \"Few-shot weak-to-strong performance becomes competitive with or outperforms finetuning for the largest strong students, though **finetuning with the confidence loss does better**.\"\n\nThe six methods being compared across the three subfigures are:\n1. Zero-shot\n2. 5-shot with ground truth\n3. Finetune with ground truth\n4. 5-shot with weak labels\n5. Naive finetuning (weak-to-strong)\n6. Finetuning with auxiliary/confidence loss\n\nAnswer: Finetuning with the confidence loss (auxiliary loss)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47672, "error": null, "extra": {"thread_id": 2041, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_4655e497891e45598d32be191f19ac1d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 260, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "finetuning with confidence loss auxiliary loss", "normalised_gold": "finetune g t"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:52Z", "latency_ms": 23593, "error": null, "raw_text_chars": 1091}]}, "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the figure with a tree shape and identify which branch has the least leaves.\n\nLooking through the document, I can see Figure 1 on page 2, which shows a \"Technology tree of RAG research\" with a tree-like structure.\n\nExamining this tree diagram, I can see it has a trunk labeled \"Retrieval-Augmented Generation\" and branches extending upward. The main branches appear to be:\n\n1. A left branch (colored in teal/green) showing the evolution from pre-training through fine-tuning stages\n2. A middle branch (colored in orange) labeled \"Instructlens\" \n3. A right branch (colored in blue) showing the inference stage with multiple methods\n\nLooking at the number of \"leaves\" (individual methods/papers) on each branch:\n- The left branch (Pre-training/Fine-tuning) has several leaves\n- The middle branch (Instructlens) appears to have the fewest leaves - it's relatively short with minimal branching\n- The right branch (Inference) has many leaves with extensive branching\n\nThe branch labeled \"Instructlens\" in the middle appears to have the least number of leaves.\n\nAnswer: Instructlens", "citations": [], "input_tokens": 64633, "output_tokens": 269, "cost_micros": 197934, "latency_ms": 23577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "instructlens", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves.\n\nLooking at Figure 1 (the technology tree of RAG research), I can see it's organized by year (2020, 2023, 2024) and has three main branches at the top labeled:\n- \"Augmentation Stage\"\n- \"Fine-tuning\" \n- \"Pre-training\"\n\nLet me count the leaves (methods/papers) in each branch:\n\n**Pre-training branch:**\n- 2020: REALM (implied from context)\n- 2022: Retro, CoG, SANTA, TIGER\n- 2023-2024: RAVEN, Retro++, InstructRetro\n\nThis appears to have around 7-8 leaves.\n\n**Fine-tuning branch:**\nThis has many methods spread across 2023-2024, including PROMPTAGATOR, LM-Indexer, various methods - appears to have 15+ leaves.\n\n**Inference branch:**\nThis has the most methods, with numerous entries across 2023-2024 - appears to have 30+ leaves.\n\nLooking more carefully at the tree structure, the \"Pre-training\" branch (on the left side of the tree) has the fewest methods/leaves compared to the other branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 33376, "output_tokens": 305, "cost_micros": 104703, "latency_ms": 9515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 1 (the Technology tree of RAG research), I need to count the number of methods/papers shown in each branch:\n\n- **Pre-training branch** (bottom): Contains methods like TIGER, SANTA, Atlas, RAVEN, RETRO++, INSTRUCTRETRO, CoG, EAR, KALMV - approximately 9-10 items\n\n- **Fine-tuning branch** (middle): Contains methods like G-Retriever, RADA, CT-RAG, HyKGE, FILCO, InstructRetro, RA-DIT, Self-RAG, FLARE, and many others - this is the largest branch with approximately 25+ items\n\n- **Inference branch** (top): Contains methods like RAPTOR, UniMS-RAG, CRAG, BGM, IAG, BEQ, ARM-RAG, and several others - approximately 15-20 items\n\nThe Pre-training branch clearly has the fewest methods/leaves compared to the other two branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 40683, "output_tokens": 242, "cost_micros": 125679, "latency_ms": 8514, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to look at the figure in the document that has a tree shape and identify which branch has the least leaves.\n\nLooking at the document, I can see Figure 1, which is described as a \"Technology tree of RAG research.\" This figure shows three main stages: Pre-training, Fine-tuning, and Inference, with various methods listed under each branch.\n\nLet me count the items in each branch:\n\n**Pre-training branch:**\n- CoG, RAVEN, Atlas, RAG, RETRO++, INSTRUCTRETRO, TIGER, SANTA (and a few more visible items)\n- This appears to have around 8-10 items\n\n**Fine-tuning branch:**\n- UniMS-RAG, Self-RAG, MK-ToD, QLM-Doc-ranking, SKR, SANTA, IRCOT, TIGER, Self-Mem, SCM4LLMs (and more)\n- This appears to have the most items, around 15-20+\n\n**Inference branch:**\n- CRAG, CT-RAG, EARCoN, CREA-ICL, PRCA, BEQ, ARM-RAG, PROMPTAGATOR, ZemiR-GQA (and many more)\n- This also has many items\n\nLooking at the figure more carefully, the **Pre-training** branch appears to have the fewest items/leaves compared to the Fine-tuning and Inference branches.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 30919, "output_tokens": 336, "cost_micros": 97797, "latency_ms": 10046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine the tree-shaped figure in the document to identify which branch has the least leaves (items).\n\nLooking at Figure 1 titled \"Technology Tree of RAG research,\" I can see it's organized into three main columns representing different stages:\n- Pre-training\n- Fine-tuning  \n- Inference\n\nLet me count the items in each branch/column:\n\n**Pre-training column:** Contains items like:\n- G-Retriever, CRAG, IAG, FILCO, ARM-RAG, RADA, RAPTOR, BGM, HyKGE, DRAGON-AI, PaperQA, CREA-ICL\n- PRCA, Token-Elimination, KALMV, ITRG\n- 1-PAGER, FABULA, SKR, ToC, QLM-Doc-ranking, Recomp\n- KGP, LLM-R, ITER-RETGEN, COQ, LLM-IE, ICRALM, DSP\n- KnowledGPT, IRCOT, PGRA, PKG, SCM4LLMs, Filter-Reranker, RePLUG\n- CoG, TIGER, Retro++\n- GenRead\n- (empty row)\n- RECITE\n\n**Fine-tuning column:** Contains items like:\n- UniMS-RAG, CT-RAG, BEQ, CoN, EAR\n- RAST, Self-RAG, LM-Indexer, RA-DIT, Dual-Feedback-ToD, MK-ToD, RAG_Robust, Retrieve-and-Sample\n- InstructRetro\n- RAVEN\n- SANTA, RRR, Self-Mem, UPRISE, R-GQA, Zemi\n- SUGRE, AAR, FLARE\n- PROMPTAGATOR, RAG-e2e\n- Atlas\n- RAG\n\n**Inference column:** This appears to be the largest with many items distributed across multiple years.\n\nComparing the three branches, the **Fine-tuning** column appears to have fewer distinct method names than the other two columns, but I need to count more carefully", "citations": [], "input_tokens": 37501, "output_tokens": 512, "cost_micros": 120183, "latency_ms": 18250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "comparing three branches fine tuning column appears to have fewer distinct method names than other two columns but i need to count more carefully", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find a figure with a tree shape and identify the branch with the least leaves.Let me search for references to figures with tree shapes in the document:Let me read through the document to find figures. I'll start by looking at the actual content:The document mentions a \"technology tree\" in Figure 1. Let me look for Figure 1:Let me search for where figures are actually displayed in the document:Good! There are embedded images. Let me read around Im1.png which should be Figure 1 (the technology tree):This looks like it's the OCR text from the tree figure. Let me continue reading to see all the branches:Perfect! I found the tree-shaped figure. It's a tree diagram with three main branches:\n1. Fine-tuning (green, left branch)\n2. Pre-training (orange/gold, middle branch) \n3. Inference (blue-green, right branch)\n\nLet me continue reading the visual description to see which branch has the least leaves (papers/items):Perfect! The visual description clearly states:\n\n- **Fine-tuning branch**: Most extensive with numerous sub-branches and approaches\n- **Pre-training branch**: Narrowest with fewer approaches\n- **Inference branch**: Dense with many recent (2024) developments\n\nThe Pre-training branch is described as the \"narrowest with fewer approaches,\" meaning it has the least leaves.\n\nAnswer: Pre-training", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 62969, "error": null, "extra": {"thread_id": 2042, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_bc29640067d14f5eb3f9f7d53e8522d7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 357, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pre training", "normalised_gold": "pre training"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 on page 4 of the document, which shows \"Comparison between the three paradigms of RAG,\" I can see three main sections representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/shapes used:\n\n1. User icon (person silhouette)\n2. Query icon (document/text box)\n3. Documents icon (stacked documents)\n4. Indexing box\n5. Retrieval box\n6. Prompt box (document icon)\n7. Frozen LLM (snowflake icon with text)\n8. Output box\n9. Pre-Retrieval box (with sub-elements like \"Query Rewriting\", \"Query Expansion\", \"Query Enhancement\")\n10. Post-Retrieval box (with sub-elements like \"Re-rank\", \"Memory\", \"Fusion\")\n11. Various module boxes in the Modular RAG section (Routing, Search, Predict, Retrieve, Rewrite, RAG, Rerank, Read, Demonstrate, Fusion, Memory)\n12. Different pattern sections showing Rewrite, Retrieve, Read combinations\n\nCounting the distinct icon types (not individual instances):\n- User icon (person)\n- Query icon\n- Documents icon\n- Standard rectangular boxes (for processes)\n- Snowflake icon (for Frozen LLM)\n- Document/page icon (for Prompt)\n\nLooking more carefully at the distinct visual elements:\n1. Person/user icon\n2. Text/query box\n3. Documents stack icon\n4. Process boxes (rectangles)\n5. Snowflake (frozen LLM)\n6. Document icon\n\nAnswer: 6", "citations": [], "input_tokens": 64616, "output_tokens": 404, "cost_micros": 199908, "latency_ms": 15077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see:\n\n1. **User Query icon** - represented by a person/user symbol\n2. **Documents icon** - represented by stacked document/paper symbols\n3. **Indexing icon** - appears to show database/storage symbols\n4. **Retrieval icon** - shown with magnifying glass or search symbols\n5. **Read icon** - shown as a document being read\n6. **Predict icon** - shown with a specific symbol\n7. **Output icon** - shown at the end of processes\n8. **Prompt icon** - shown in the middle paradigm\n9. **Frozen LLM icon** - represented by a brain or model symbol\n10. **Rerank icon** - shown in Advanced RAG\n11. **Rewrite icon** - shown in Modular RAG\n12. **Demonstrate icon** - shown in Modular RAG\n13. **Routing icon** - shown in Modular RAG\n14. **Search icon** - shown in Modular RAG\n15. **Fusion icon** - shown in Modular RAG\n16. **Memory icon** - shown in Modular RAG\n\nLet me recount more carefully by looking at the actual distinct visual icons in the figure:\n\nFrom the figure, I can identify these distinct icon types:\n- User/person icon\n- Document/paper stack icon\n- Database/storage icon\n- Magnifying glass (search/retrieval)\n- Brain/model icon (for LLM)\n- Arrow/flow icons\n- Various process boxes with different labels\n\nCounting the truly distinct visual icons (not just labeled boxes):\n1. User icon\n2. Document icon\n3. Database/indexing icon\n4. Magnifying glass/search icon\n5. Brain/LLM icon\n6. Output/result icon\n\nAnswer: 6", "citations": [], "input_tokens": 33359, "output_tokens": 449, "cost_micros": 106812, "latency_ms": 10735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "6", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3 titled \"Comparison between the three paradigms of RAG,\" I can see three diagrams showing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify each distinct icon type used across all three paradigms:\n\n1. **User** - person icon (shown in all three)\n2. **Query** - document/text icon (shown in all three)\n3. **Documents** - stack of documents icon (shown in all three)\n4. **Indexing** - cylindrical database icon (shown in Naive and Advanced RAG)\n5. **Retrieval** - magnifying glass/search icon (shown in all three)\n6. **Rewrite** - circular arrow icon (shown in Advanced RAG)\n7. **Rerank** - list with arrows icon (shown in Advanced RAG)\n8. **Prompt** - text/document icon (shown in all three)\n9. **Frozen LLM** - brain/processing icon (shown in all three)\n10. **Read** - book/reading icon (shown in all three)\n11. **Output** - result/output icon (shown in all three)\n12. **Modules (R)** - box with R (shown in Modular RAG)\n13. **Search** - magnifying glass (shown in Modular RAG)\n14. **Routing** - directional arrow icon (shown in Modular RAG)\n15. **Predict** - crystal ball/prediction icon (shown in Modular RAG)\n16. **Demonstrate** - pointing hand icon (shown in Modular RAG)\n17. **Memory** - storage icon (shown in Modular RAG)\n18. **Fusion** - merge icon (shown in Modular RAG)\n19. **Summary** - document summary icon (shown in Advanced RAG)\n20. **Judge** - decision diamond (shown in Modular RAG)\n21. **Generate** - generation icon (shown in Modular RAG)\n\nLet me recount more carefully by looking at the actual distinct icon shapes:\n\n1. User (person)\n2. Query (document)\n3. Documents (stack)", "citations": [], "input_tokens": 40666, "output_tokens": 512, "cost_micros": 129678, "latency_ms": 10766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three sections (Left, Middle, Right) representing Naive RAG, Advanced RAG, and Modular RAG.\n\nLet me identify all the distinct icons/symbols used in the figure:\n\n1. \"User Query\" box (rectangular box with text)\n2. \"Indexing\" box\n3. \"Retrieval\" box\n4. \"Rerank\" box\n5. \"Rewrite\" box\n6. \"Fusion\" box\n7. \"Summary\" box\n8. \"Demonstrate\" box\n9. \"Read\" box\n10. \"Search\" box\n11. \"Retrieve\" box\n12. \"Memory\" box\n13. \"Predict\" box\n14. \"Routing\" box\n15. \"Documents\" box\n16. \"RAG\" box\n17. \"Output\" box\n18. \"Response\" box\n19. \"Judge\" box\n20. \"Generate\" box\n21. \"Transformation/Decomposition\" box\n22. \"Iterate\" box\n23. Arrow symbols (connecting the boxes)\n24. \"Max Times/Threshold\" decision point\n25. \"Max Depth (Tree)/Threshold\" decision point\n26. \"Generate Special Token/Threshold\" decision point\n27. \"N Times\" indicator\n\nLooking more carefully at the visual elements, the distinct icon types are:\n- Rectangular boxes (for processes/modules)\n- Arrows (for flow direction)\n- Diamond or decision point shapes (for conditional logic)\n\nCounting the distinct visual icon types (not the number of instances, but types):\n1. Rectangular box/module icon\n2. Arrow icon\n3. Diamond/decision point icon\n\nAnswer: 3", "citations": [], "input_tokens": 30902, "output_tokens": 415, "cost_micros": 98931, "latency_ms": 10655, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I need to carefully examine Figure 3 in the document to count the distinct icons used.\n\nLooking at Figure 3, which shows \"Comparison between the three paradigms of RAG,\" I can see three main diagrams:\n\n1. **Naive RAG (Left)**: Shows a flowchart with:\n   - User Query\n   - Documents\n   - Indexing\n   - Retrieval\n   - Prompt\n   - Frozen LLM\n   - Output\n\n2. **Advanced RAG (Middle)**: Shows:\n   - User Query\n   - Documents\n   - Indexing\n   - Pre-Retrieval (with sub-components: Query Routing, Query Rewriting, Query Expansion)\n   - Retrieval\n   - Post-Retrieval (with sub-components: Rerank, Summary, Fusion)\n   - Prompt\n   - Frozen LLM\n   - Output\n\n3. **Modular RAG (Right)**: Shows:\n   - Multiple modules (Routing, Search, Predict, Rewrite, RAG, Rerank, Retrieve, Read, Demonstrate, Fusion, Memory)\n   - Various patterns including Naive RAG, Advanced RAG, DSP, and ITER-RETGEN\n\nLooking at the visual representation, the distinct icon shapes/types I can identify are:\n1. Rectangular boxes (for most components)\n2. Rounded rectangles/boxes (for grouping/subgraphs)\n3. Arrows/lines (for connections)\n\nHowever, if we're counting distinct visual elements or node types based on their representation in the figure, the components are primarily represented as boxes/rectangles with text labels. The figure uses a consistent box/rectangle style for representing different components.\n\nBased on the mermaid diagram structure shown, all components are represented using similar rectangular node shapes, with the main distinction being in the grouping (subgraphs) and connections rather than distinct icon types.\n\nAnswer: 1", "citations": [], "input_tokens": 37484, "output_tokens": 444, "cost_micros": 119112, "latency_ms": 12312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "1", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q001", "doc_id": "2312.10997v5.pdf", "answer_format": "int", "gold": "9", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and count the distinct icons used.Let me search for \"Figure 3\" in the document:Now let me read around line 788 to find Figure 3:Let me continue reading to find Figure 3:I see Figure 2 but need to continue searching for Figure 3. Let me search more:Perfect! I found Figure 3. Let me continue reading to see the complete visual description:Perfect! I have found Figure 3 and its visual description. Let me carefully count the distinct icons mentioned in the visual description:\n\nFrom the visual description of Figure 3, I can identify the following distinct icons:\n\n1. **User/Query icon** - mentioned in Naive RAG and Advanced RAG\n2. **Documents icon** - mentioned in Naive RAG and Advanced RAG (stacked papers/documents)\n3. **Snowflake icon** - mentioned in Naive RAG and Advanced RAG (representing Frozen LLM)\n4. **Document icon** - mentioned in Naive RAG (at the Prompt stage)\n5. **Shuffle/rerank icon** - mentioned in Advanced RAG Post-Retrieval\n6. **Waveform/summary icon** - mentioned in Advanced RAG Post-Retrieval\n7. **Layers/fusion icon** - mentioned in Advanced RAG Post-Retrieval\n\nLet me recount more carefully by looking at what the visual description explicitly states:\n- User/Query icons (top of diagrams)\n- Documents icons (top of diagrams)\n- Document icon (at Prompt stage)\n- Snowflake icon (representing Frozen LLM)\n- Shuffle/rerank icon (in Post-Retrieval)\n- Waveform/summary icon (in Post-Retrieval)\n- Layers/fusion icon (in Post-Retrieval)\n\nThat gives us 7 distinct icons total.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 51812, "error": null, "extra": {"thread_id": 2043, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c31c1d3df3774ac882e3f8a22f227910", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 283, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "9"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that has a retrieval granularity of \"Phrase\" in Table I.\n\nLooking at Table I (Summary of RAG methods) on page 6, I can see the columns: Method, Retrieval Source, Retrieval Data Type, Retrieval Granularity, Augmentation Stage, and Retrieval process.\n\nScanning the \"Retrieval Granularity\" column, I find one entry with \"Phrase\":\n- CoG [29] - which has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of reference [29]. Looking at the references section starting on page 17, reference [29] is:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 64633, "output_tokens": 220, "cost_micros": 197199, "latency_ms": 34000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that uses \"phrase\" as its retrieval granularity.\n\nLooking at Table I \"SUMMARY OF RAG METHODS\", I can find the retrieval granularity column. Let me search for entries with \"Phrase\" as the retrieval granularity.\n\nFrom Table I, I can see:\n- CoG [29] has:\n  - Retrieval Source: Wikipedia\n  - Retrieval Data Type: Text\n  - Retrieval Granularity: Phrase\n  - Augmentation Stage: Pre-training\n  - Retrieval process: Iterative\n\nNow I need to find the full title of the CoG paper. Looking at the references section:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 33376, "output_tokens": 222, "cost_micros": 103458, "latency_ms": 7266, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for methods with \"Phrase\" retrieval granularity in Table I...\n\nFrom Table I (SUMMARY OF RAG METHODS), I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nLooking at the references section for reference [29]:\n\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 40683, "output_tokens": 133, "cost_micros": 124044, "latency_ms": 8407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with phrase-level retrieval granularity.\n\nLooking at Table I (Summary of Retrieval Source and Retrieval Granularity Used in RAG Methods), I can find methods with \"Phrase\" as their retrieval granularity.\n\nFrom the table, I can see:\n- CoG [29] has retrieval granularity of \"Phrase\"\n\nNow I need to find the full title of the CoG paper from the references section.\n\nLooking at the references, reference [29] is:\n\"T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in The Eleventh International Conference on Learning Representations, 2022.\"\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 30919, "output_tokens": 187, "cost_micros": 95562, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the paper that proposes a method with retrieval granularity of \"Phrase\".\n\nLooking at Table I (Summary of RAG Methods), I can search for entries where \"Retrieval Granularity\" is \"Phrase\".\n\nScanning through Table I, I find:\n- CoG [29] has retrieval granularity: Phrase\n\nNow I need to find the full title of paper [29] from the references section.\n\nLooking at reference [29]:\n[29] T. Lan, D. Cai, Y. Wang, H. Huang, and X.-L. Mao, \"Copy is all you need,\" in *The Eleventh International Conference on Learning Representations*, 2022.\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 37501, "output_tokens": 174, "cost_micros": 115113, "latency_ms": 6453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q002", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "Copy is all you need.", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q002", "answer_letter": null, "raw_text": "I'll search for information about retrieval granularity and phrase-level methods in the provided documents.Let me continue reading to find the title and look for information about retrieval granularity:Good! I found the paper title: \"Retrieval-Augmented Generation for Large Language Models: A Survey\". Now I need to search for information about retrieval granularity and specifically which method has \"phrase\" as its retrieval granularity. Let me search for tables or sections that discuss retrieval granularity:Perfect! I found a table that mentions \"Phrase\" as a retrieval granularity. Let me read that section to find the complete information:Great! I can see that \"CoG [29]\" has \"Phrase\" as its retrieval granularity. Now I need to find the full title of the CoG paper. Let me look for the reference [29]:Perfect! I found reference [29]. Let me read that section to get the full title:I can see reference [29] starts at line 4090. Let me read a bit more to get the complete title:Perfect! I found the full title of reference [29], which is the paper that proposes the method (CoG) with phrase retrieval granularity.\n\nThe full title is: **\"Copy is all you need\"**\n\nAnswer: Copy is all you need", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 52827, "error": null, "extra": {"thread_id": 2044, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_27dfaf3e8bfa47e1b55fb4feceaed85a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 297, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "copy is all you need", "normalised_gold": "copy is all you need"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll search through Table II to find datasets that have exactly three methods listed.\n\nLooking at Table II (SUMMARY OF RAG METHODS), I need to count the methods for each dataset:\n\n- WebQ (Web Questions): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - 3 methods \u2713\n- MS MARCO: [4], [40], [52] - 3 methods \u2713\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum(QM): [60], [123] - 2 methods\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB [128],MMCU_Medical [129]: [81] - 1 method\n- QuALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n- GraphQA: [84] - 1 method\n- Amazon(Toys,Sport,Beauty): [39], [40] - 2 methods\n- T-REx [141],ZsRE [142]: [27], [51] - 2 methods\n- HellaSwag: [20], [66] - 2 methods\n- CoT Reasoning: [27] - 1 method\n- CSQA: [55] - 1 method\n- StrategyQA: [14], [24], [48], [51], [55], [58] - 6 methods\n- XSum: [17] - 1 method\n- VioLens: [19] - 1 method\n- TREC: [33] - 1 method\n- SST-2: [20], [33], [38] - 3 methods \u2713\n- CodeSearchNet: [76] -", "citations": [], "input_tokens": 64631, "output_tokens": 512, "cost_micros": 201573, "latency_ms": 17125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "codesearchnet 76", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): [3], [4], [22], [27], [40], [43], [54], [62], [71], [112] [20], [44], [72] [13], [30], [34], [45], [50], [64] - more than 3\n- TriviaQA (TQA): [4], [27], [59], [62], [112] [22], [25], [43], [44], [71], [72] [20], [23], [30], [32], [45], [69], [112] - more than 3\n- SQUAD: [3], [4], [13], [30], [50], [68] - 6 methods\n- Web Questions (WebQ): [4], [40], [52] - exactly 3 methods\n- PopQA: [4], [40], [52] - exactly 3 methods\n- MS MARCO: [117] - only 1 reference\n\n**QA - Multi-hop:**\n- HotpotQA: [7], [14], [22], [27], [59], [62], [69], [71], [91] - 9 methods\n- 2WikiMultiHopQA: [14], [51], [61], [91] - 4 methods\n- MuSiQue: [120] - only 1 reference\n\n**QA - Long-form:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB: [", "citations": [], "input_tokens": 33374, "output_tokens": 512, "cost_micros": 107802, "latency_ms": 11546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "cmb", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to examine Table II to find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n**QA - Single-hop:**\n- Natural Question (NQ): More than 3 methods\n- TriviaQA (TQA): More than 3 methods\n- SQUAD: More than 3 methods\n- Web Questions (WebQ): [3], [4], [13], [30], [50], [68] - 6 methods\n- PopQA: [7], [25], [67] - **exactly 3 methods**\n- MS MARCO: [4], [40], [52] - **exactly 3 methods**\n\n**QA - Multi-hop:**\n- HotpotQA: More than 3 methods\n- 2WikiMultiHopQA: [14], [24], [48], [59], [61], [91] - 6 methods\n- MuSiQue: [14], [51], [61], [91] - 4 methods\n\n**QA - Long-form QA:**\n- ELI5: [27], [34], [43], [49], [51] - 5 methods\n- NarrativeQA (NQA): [45], [60], [63], [123] - 4 methods\n- ASQA: [24], [57] - 2 methods\n- QMSum (QM): [60], [123] - 2 methods\n\n**QA - Domain QA:**\n- Qasper: [60], [63] - 2 methods\n- COVID-QA: [35], [46] - 2 methods\n- CMB, MMCU_Medical: [81] - 1 method\n\n**QA - Multi-Choice QA:**\n- QUALITY: [60], [63] - 2 methods\n- ARC: [25], [67] - 2 methods\n- CommonsenseQA: [58], [66] - 2 methods\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): [13], [27], [34], [42] - 4 methods\n- KBP: [74],", "citations": [], "input_tokens": 40681, "output_tokens": 512, "cost_micros": 129723, "latency_ms": 12062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "kbp 74", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to look at Table II and find datasets that have exactly three methods listed.\n\nLet me go through Table II systematically:\n\n1. Natural Question (NQ) - has 10 methods listed\n2. TriviaQA (TQA) - has 5 methods listed\n3. SQuAD - has 7 methods listed\n4. Web Questions (WebQ) - has 6 methods listed\n5. PopQA - has 3 methods: [7], [25], [67] \u2713\n6. MS MARCO - has 3 methods: [4], [40], [52] \u2713\n7. HotpotQA - has 8 methods listed\n8. 2WikiMultiHopQA - has 6 methods listed\n9. MuSiQue - has 4 methods listed\n10. ELI5 - has 5 methods listed\n11. NarrativeQA (NQA) - has 4 methods listed\n12. ASQA - has 2 methods listed\n13. QMSum (QM) - has 2 methods listed\n14. Qasper - has 2 methods listed\n15. COVID-QA - has 2 methods listed\n16. CMB, MMCU Medical - has 1 method listed\n17. QuALITY - has 2 methods listed\n18. ARC - has 2 methods listed\n19. CommonsenseQA - has 2 methods listed\n20. GraphQA - has 1 method listed\n21. Wizard of Wikipedia (WoW) - has 4 methods listed\n22. KBP - has 2 methods listed\n23. DuleMon - has 1 method listed\n24. CamRest - has 2 methods listed\n25. Amazon (Toys, Sport, Beauty) - has 2 methods listed\n26. WikiEvent - has 4 methods listed\n27. RAMS - has 2 methods listed\n28. T-REx, ZsRE - has 2 methods listed\n29. HellaSwag - has 2 methods listed\n30. CoT Reasoning - has 1 method listed", "citations": [], "input_tokens": 30917, "output_tokens": 512, "cost_micros": 100431, "latency_ms": 11141, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "30 cot reasoning has 1 method listed", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I need to find datasets in Table II that have exactly three methods listed.\n\nLet me go through Table II systematically and count the number of methods for each dataset:\n\n**QA - Single-hop:**\n- Natural Question (NQ): 12 methods\n- TriviaQA (TQA): 12 methods\n- SQuAD: 7 methods\n- Web Questions (WebQ): 6 methods\n- PopQA: 3 methods \u2713\n- MS MARCO: 3 methods \u2713\n\n**QA - Multi-hop:**\n- HotpotQA: 15 methods\n- 2WikiMultiHopQA: 6 methods\n- MuSiQue: 4 methods\n\n**QA - Long-form QA:**\n- ELI5: 5 methods\n- NarrativeQA (NQA): 4 methods\n- ASQA: 2 methods\n- QMSum (QM): 2 methods\n\n**QA - Domain QA:**\n- Qasper: 2 methods\n- COVID-QA: 2 methods\n- CMB, MMCU_Medical: 1 method\n\n**QA - Multi-Choice QA:**\n- QuALITY: 2 methods\n- ARC: 2 methods\n- CommonsenseQA: 2 methods\n\n**QA - Graph QA:**\n- GraphQA: 1 method\n\n**Dialog:**\n- Wizard of Wikipedia (WoW): 4 methods\n- KBP: 2 methods\n- DuleMon: 1 method\n- CamRest: 2 methods\n- Amazon (Toys, Sport, Beauty): 2 methods\n\n**IE:**\n- WikiEvent: 4 methods\n- RAMS: 2 methods\n- T-REx, ZsRE: 2 methods\n\n**Reasoning:**\n- HellaSwag: 2 methods\n- CoT Reasoning: 1 method\n- CSQA: 1 method\n\n**Others:**\n- MMLU: 7 methods\n- WikiText-103: 4 methods\n-", "citations": [], "input_tokens": 37499, "output_tokens": 512, "cost_micros": 120177, "latency_ms": 11500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q004", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['POPQA', 'MS MARCO', 'SST-2']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find Table II and identify datasets with exactly three methods.Let me search for \"TABLE II\" in the document:Now let me read the section containing TABLE II:Now I can see Table II. Let me analyze it carefully to find datasets with exactly three methods. I need to count the number of method references for each dataset. Let me go through the table systematically:\n\nLooking at the table structure, I can see:\n- PopQA [116]: [7], [25], [67] - exactly 3 methods\n- MS MARCO [117]: [4], [40], [52] - exactly 3 methods\n- PopQA has methods: [7], [25], [67] - that's 3 methods\n- MS MARCO has methods: [4], [40], [52] - that's 3 methods\n- SST-2 [156]: [20], [33], [38] - exactly 3 methods\n\nLet me verify by checking each row more carefully:\n\n1. PopQA [116]: [7], [25], [67] = 3 methods\n2. MS MARCO [117]: [4], [40], [52] = 3 methods  \n3. QMSum(QM) [125]: [60], [123] = 2 methods\n4. Qasper [126]: [60], [63] = 2 methods\n5. COVID-QA [127]: [35], [46] = 2 methods\n6. QUALITY [130]: [60], [63] = 2 methods\n7. ARC [131]: [25], [67] = 2 methods\n8. CommonsenseQA [132]: [58], [66] = 2 methods\n9. GraphQA [84]: [84] = 1 method\n10. KBP [134]: [74], [135] = 2 methods\n11. DuleMon [136]: [74] = 1 method\n12. CamRest [137]: [78], [79] = 2 methods\n13. Amazon(Toys,Sport,Beauty) [138]: [39], [40] = 2 methods\n14. RAMS [140]: [36], [37] = 2 methods\n15. T-REX [141],ZsRE [142]: [27], [51] = 2 methods\n16. HellaSwag [143]: [20], [66] = 2 methods\n17. CoT Reasoning [144]: [27] = 1 method\n18. CSQA [145]: [55] = 1 method\n19. PubHealth [150]: [25], [67] = 2 methods\n20. Biography [151]: [67] = 1 method\n21. WikiASP [152]: [24] = 1 method\n22. XSum [153]: [17] = 1 method\n23. VioLens [154]: [19] = 1 method\n24. TREC [155]: [33] = 1 method\n25. SST-2 [156]: [20], [33], [38] = 3 methods\n26. CodeSearchNet [157]: [76] = 1 method\n27. NoMIRACL [56]: [56] = 1 method\n28. GSM8K [158]: [73] = 1 method\n29. JRC-Acquis [159]: [17] = 1 method\n\nAnswer: PopQA, MS MARCO, SST-2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42953, "error": null, "extra": {"thread_id": 2045, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ea697b93518746f587b9ed16f4f922df", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 257, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "ms marco, popqa, sst 2", "normalised_gold": "ms marco, popqa, sst 2"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 10 where Section V \"AUGMENTATION PROCESS IN RAG\" begins, I can see the subsections listed:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 64634, "output_tokens": 83, "cost_micros": 195147, "latency_ms": 20108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find the section titled \"AUGMENTATION PROCESS IN RAG\" and identify its subsections.\n\nFrom Section V (AUGMENTATION PROCESS IN RAG), I can see the following structure:\n\nV. AUGMENTATION PROCESS IN RAG\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nThese are the three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 33377, "output_tokens": 127, "cost_micros": 102036, "latency_ms": 4500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document, I can identify the subsections by examining the structure.\n\nThe section starts on page 10 and includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval  \nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 40684, "output_tokens": 95, "cost_micros": 123477, "latency_ms": 4765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for the section \"AUGMENTATION PROCESS IN RAG\" (Section V), I can identify its subsections:\n\nFrom the document structure, Section V contains:\n- A. Iterative Retrieval\n- B. Recursive Retrieval  \n- C. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 30920, "output_tokens": 91, "cost_micros": 94125, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Section V \"AUGMENTATION PROCESS IN RAG\" in the document:\n\nThe section includes the following subsections:\n\nA. Iterative Retrieval\nB. Recursive Retrieval\nC. Adaptive Retrieval\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 37502, "output_tokens": 76, "cost_micros": 113646, "latency_ms": 4125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q005", "doc_id": "2312.10997v5.pdf", "answer_format": "list", "gold": "['Iterative Retrieval ', 'Recursive Retrieval ', 'Adaptive Retrieval ']", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find the \"AUGMENTATION PROCESS IN RAG\" section and identify its subsections.Let me search for the \"AUGMENTATION PROCESS IN RAG\" section in the document.Now let me read the section around line 2636 to see the subsections.Let me continue reading to see all subsections under \"AUGMENTATION PROCESS IN RAG\".Perfect! I can now see the structure of the \"AUGMENTATION PROCESS IN RAG\" section. It has three subsections:\n\n1. A. Iterative Retrieval (line 2646)\n2. B. Recursive Retrieval (line 2827)\n3. C. Adaptive Retrieval (line 2858)\n\nThe next section \"VI. TASK AND EVALUATION\" starts at line 2908, confirming that these are the only three subsections under \"AUGMENTATION PROCESS IN RAG\".\n\nAnswer: Iterative Retrieval, Recursive Retrieval, Adaptive Retrieval", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34782, "error": null, "extra": {"thread_id": 2046, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_dfd2aaa1a1dd45f1bdec0523c2899c57", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 177, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "adaptive retrieval, iterative retrieval, recursive retrieval", "normalised_gold": "adaptive retrieval, iterative retrieval, recursive retrieval"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "native_pdf", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I need to search through the document for information about methods that integrate knowledge into white-box models via directive fine-tuning.\n\nLooking through the document, I found relevant information on page 10 (document page 9-10) in section III. RETRIEVAL, subsection E. Adapter:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text explicitly states that PKG (which likely stands for \"Parametric Knowledge Guiding\" based on reference [75] mentioned in the text) introduces this method.\n\nAnswer: PKG", "citations": [], "input_tokens": 64627, "output_tokens": 158, "cost_micros": 196251, "latency_ms": 20969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 33370, "output_tokens": 94, "cost_micros": 101520, "latency_ms": 5250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "azure_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about a method that integrates knowledge into white-box models via directive fine-tuning.\n\nIn Section III.E (Adapter), I can find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nThe text clearly states that PKG introduces this method for integrating knowledge into white-box models via directive fine-tuning.\n\nAnswer: PKG", "citations": [], "input_tokens": 40677, "output_tokens": 130, "cost_micros": 123981, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_basic_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for methods that integrate knowledge into white-box models via directive fine-tuning...\n\nThe document discusses RA-DIT (Retrieval-Augmented Dual Instruction Tuning) in reference [27], which is described as \"retrieval-augmented dual instruction tuning.\" This method involves fine-tuning both the retriever and generator components.\n\nIn Section IV.B on LLM Fine-tuning, the document states: \"Fine-tuning of LLM can also be coordinated with fine-tuning of the retriever to align preferences. A typical approach, such as RA-DIT [27], aligns the scoring functions between Retriever and Generator using KL divergence.\"\n\nThis method specifically focuses on instruction tuning (a form of directive fine-tuning) to integrate retrieval-augmented knowledge into language models.\n\nAnswer: RA-DIT", "citations": [], "input_tokens": 30913, "output_tokens": 200, "cost_micros": 95739, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "ra dit", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "llamacloud_premium_lc", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "Looking through the document for information about integrating knowledge into white-box models via directive fine-tuning...\n\nIn Section III.E (Adapter), I find the following passage:\n\n\"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75]. In this approach, the retriever module is directly substituted to generate relevant documents according to a query.\"\n\nAnswer: PKG", "citations": [], "input_tokens": 37495, "output_tokens": 95, "cost_micros": 113910, "latency_ms": 5500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2312.10997v5.pdf::Q006", "doc_id": "2312.10997v5.pdf", "answer_format": "str", "gold": "PKG", "pages": 21, "document_id": 7596, "arm": "surfsense_agentic", "question_id": "2312.10997v5.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the priority document to find the answer to your question about which method integrates knowledge into white-box models via directive fine-tuning.Let me search for content about \"directive fine-tuning\" and \"white-box models\":Let me read more of the document to find tables or sections discussing methods:Let me search for tables that compare methods:Let me read around the tables to find information about methods:Let me search for \"Table I\" to find the table that might contain method comparisons:This table shows methods but doesn't mention white-box or directive fine-tuning. Let me search more specifically:Let me search for a combination of terms:Let me look for \"fine-tuning\" methods and check around line 2053 which mentions modular RAG integration with fine-tuning:Let me look for sections on fine-tuning methods:Found it! Let me read around line 2526:Perfect! I found the answer. The text states \"PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\" Let me verify this is PKG by reading a bit more context:Perfect! The answer is clear. The document states: \"Furthermore, PKG introduces an innovative method for integrating knowledge into white-box models via directive fine-tuning [75].\"\n\nAnswer: PKG", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 72530, "error": null, "extra": {"thread_id": 2047, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_9ee1ee526a9645f5ba543d1cda3a3a6d", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 421, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "pkg", "normalised_gold": "pkg"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to examine Figure 4 to identify which nodes RAPTOR retrieved for both questions about the Cinderella story.\n\nFrom the document, Figure 4 shows the querying process for two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nLooking at Figure 4 on page 7, I can see:\n- Nodes highlighted indicate RAPTOR's selections\n- The figure shows a tree structure with nodes numbered 0-15 at the leaf layer, nodes 16-22 at an intermediate layer, and nodes 23-26 at the root layer\n\nFor Question 1 (central theme), RAPTOR retrieved nodes that are highlighted in one color.\nFor Question 2 (happy ending), RAPTOR retrieved nodes that are highlighted in another color.\n\nThe nodes that overlap (retrieved for BOTH questions) would be those highlighted with both colors.\n\nFrom the visual representation in Figure 4, I can see that certain nodes appear to be selected for both queries. Looking at the highlighted patterns, the nodes that appear to be retrieved for both questions are nodes 18 and 19 (visible in the intermediate layer where both highlighting patterns overlap).\n\nAnswer: 18, 19", "citations": [], "input_tokens": 58460, "output_tokens": 279, "cost_micros": 179565, "latency_ms": 35969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.4, "method": "list_set", "normalised_pred": "18, 19", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are highlighted (retrieved by RAPTOR) for both questions about Cinderella.\n\nThe figure shows two questions:\n1. \"What is the central theme of the story?\"\n2. \"How did Cinderella find a happy ending?\"\n\nAccording to the figure caption: \"Highlighted nodes indicate RAPTOR's selections, while arrows point to DPR's leaf nodes.\"\n\nExamining the visual representation in Figure 4, I can see the tree structure with nodes numbered 0-26. For nodes to be retrieved for both questions, they must be highlighted for both question 1 and question 2.\n\nLooking at the overlap between the two retrievals shown in the figure, the nodes that appear to be selected for both questions are those at the higher levels of the tree that contain summary information relevant to both the central theme and the happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved for both questions appear to be from the upper layers of the tree.\n\nAnswer: 23, 24, 25, 26", "citations": [], "input_tokens": 22903, "output_tokens": 241, "cost_micros": 72324, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.28571428571428575, "method": "list_set", "normalised_pred": "23, 24, 25, 26", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4, I need to identify which nodes are retrieved by RAPTOR for both questions about Cinderella.\n\nThe legend indicates:\n- Nodes retrieved for Question 1 (central theme) are shown in one color\n- Nodes retrieved for Question 2 (happy ending) are shown in another color\n- Nodes retrieved for BOTH questions would be those highlighted with both colors\n\nFrom the figure, examining the tree structure and the highlighted nodes, the nodes that appear to be retrieved for both questions are the nodes that contain overlapping/comprehensive information about the story.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions are:\n\nAnswer: 16, 17, 18", "citations": [], "input_tokens": 27206, "output_tokens": 161, "cost_micros": 84033, "latency_ms": 5530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.3333333333333333, "method": "list_set", "normalised_pred": "16, 17, 18", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes RAPTOR retrieves for both Question 1 (\"What is the central theme of the story?\") and Question 2 (\"How did Cinderella find a happy ending?\").\n\nFrom the figure description and the illustration:\n- For Question 1, RAPTOR retrieves certain highlighted nodes\n- For Question 2, RAPTOR retrieves certain highlighted nodes\n\nThe nodes that appear to be retrieved for BOTH questions (nodes that would be highlighted for both questions) are the nodes that contain information relevant to both the central theme and Cinderella's happy ending.\n\nBased on the visual representation in Figure 4, the nodes retrieved by RAPTOR for both questions appear to be nodes 9 and 10 (which are the upper-level summary nodes that contain broader thematic information relevant to both questions).\n\nAnswer: Node 9, Node 10", "citations": [], "input_tokens": 23495, "output_tokens": 203, "cost_micros": 73530, "latency_ms": 6937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "node 10, node 9", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "Looking at Figure 4 in the document, I need to identify which nodes are retrieved by RAPTOR for both questions.\n\nThe legend indicates:\n- Orange nodes/arrows: RAPTOR retrieved for Question 1 (\"What is the central theme of the story?\")\n- Purple nodes/arrows: RAPTOR retrieved for Question 2 (\"How did Cinderella find a happy ending?\")\n\nFrom the tree diagram, I can see:\n- Question 1 (orange): Node 25 and Node 19 are highlighted\n- Question 2 (purple): Node 17 and Node 20 are highlighted\n\nLooking for nodes that appear in both colors (retrieved for both questions), I need to check if any nodes have both orange and purple highlighting. However, from the visual representation, each highlighted node appears to be associated with only one question - the nodes are distinct between the two questions.\n\nUpon careful examination of Figure 4, there don't appear to be any nodes that are highlighted in both colors, meaning no nodes are retrieved by RAPTOR for both questions simultaneously.\n\nAnswer: None", "citations": [], "input_tokens": 26136, "output_tokens": 239, "cost_micros": 81993, "latency_ms": 7280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q000", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "[16, 19, 25]", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find Figure 4 and identify which nodes are retrieved by RAPTOR for both questions.Let me search for Figure 4 in the document:Now let me read the specific section containing Figure 4 from the target document:Now let me look at the actual figure content. I need to read more lines to see the figure structure:I can see the figure caption clearly states the two questions. Now I need to look at the actual tree diagram to see which nodes are highlighted for both questions. Let me check if there's more detail in the figure or if I need to examine it more carefully. Let me search for more context about the nodes:Let me look at the image content more carefully. Let me read around line 863 where the OCR text shows the nodes:Perfect! Now I can see the key information. Looking at the visual description:\n\n- **Orange/peach borders** (RAPTOR for Question 1): Nodes 15, 11, 18\n- **Purple/magenta borders** (RAPTOR for Question 2): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe nodes that appear in BOTH lists (retrieved by RAPTOR for both questions) is: **Node 11**\n\nHowever, let me also check the arrows to be thorough:Based on the visual description of Figure 4:\n\n**Question 1** (orange/peach): Nodes 15, 11, 18\n**Question 2** (purple/magenta): Nodes 2, 3, 5, 11, 16, 17, 19\n\nThe node that appears in both lists is **11**.\n\nAdditionally, looking at the arrows (which point to DPR's retrievals), nodes 3 and 11 have both orange and purple arrows, but the question asks specifically about which nodes are retrieved by RAPTOR (indicated by the border colors), not DPR (indicated by the arrows).\n\nAnswer: 11", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 61625, "error": null, "extra": {"thread_id": 2048, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_99be096db80e4817854f19cccddee98b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 378, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "11", "normalised_gold": "16, 19, 25"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 6 of the document, I can identify the axes of the graph.\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis is labeled \"F1\" (which represents the F1 score metric).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 58457, "output_tokens": 109, "cost_micros": 177006, "latency_ms": 33343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, which is titled \"Comparison of querying methods\":\n\nThe horizontal axis is labeled \"Context Length\" (measuring from 500 to 2500).\n\nThe vertical axis is labeled \"F1\" (measuring from approximately 40 to 55).\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 22900, "output_tokens": 74, "cost_micros": 69810, "latency_ms": 4109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods. Results on 20 stories from the QASPER dataset using tree traversal with different top-k values, and collapsed tree with different context lengths.\"\n\nThe horizontal axis is labeled \"Context Length\" and the vertical axis shows the performance metric (appears to be accuracy or F1 score percentage based on the scale showing values like 45, 80, etc.).\n\nAnswer: Context Length, Performance Score (F1/Accuracy)", "citations": [], "input_tokens": 27203, "output_tokens": 116, "cost_micros": 83349, "latency_ms": 5796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance score f1 accuracy", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document, I need to identify the horizontal and vertical axes.\n\nFrom the figure caption and the graph itself:\n- The horizontal axis shows \"Context Length\" (measured in tokens, ranging from 500 to 2500)\n- The vertical axis shows the performance metric (which appears to be accuracy or F1 score, ranging from approximately 50 to 58)\n\nAnswer: Context Length, Performance metric (accuracy/F1 score)", "citations": [], "input_tokens": 23492, "output_tokens": 105, "cost_micros": 72051, "latency_ms": 5092, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "context length, performance metric accuracy f1 score", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 3 in the document:\n\nThe figure shows \"Comparison of querying methods\" with results on 20 stories from the QASPER dataset.\n\nFrom the table in Figure 3:\n- The horizontal axis shows \"Context Length\" (measured in tokens: 500, 1000, 1250, 1500, 1750, 2000, 2500)\n- The vertical axis shows \"F1\" (performance metric)\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 26133, "output_tokens": 113, "cost_micros": 80094, "latency_ms": 4717, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q001", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Context Length', 'F1']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Figure 3 and identify its axes.Let me search for \"Figure 3\" in the document.Now let me search for the figure content with context:Let me read the section around line 786 to see the full figure details:Let me continue reading to see the complete axis information:This appears to be describing a different figure. Let me look more carefully at the figure data around line 826:Based on the figure caption and the data I see, I can see \"Context Length\" is mentioned at line 826. However, I need to find what the vertical axis represents. Let me search around the figure more carefully. The numbers like 45, 5, 7, 3, 4, 80 seem to be data values. Let me look for axis labels by reading the embedded image description or OCR text more carefully:Let me search for any text that might indicate what metric is being measured (the vertical axis). Let me look more broadly around Figure 3:I can see \"Context Length\" appears at line 826, which is likely the horizontal axis. The text mentions \"Figure 3 shows the performance\" but I need to find what specific performance metric. Let me search for what metric is being used in QASPER:Great! I found it. Let me read around line 712 to confirm this is for Figure 3:Perfect! I found the information. Looking at lines 693, 704, 711-712:\n- Line 693: \"F1\"\n- Line 704: \"Context Length\"\n- Line 711: \"X-axis shows context length\"\n- Line 712: \"Y-axis shows F1 score\"\n\nSo for Figure 3:\n- Horizontal axis (X-axis) = Context Length\n- Vertical axis (Y-axis) = F1\n\nAnswer: Context Length, F1", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 67266, "error": null, "extra": {"thread_id": 2049, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3509043a1828411e846352ac4e585d70", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 401, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "context length, f1", "normalised_gold": "context length, f1"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines in each algorithm.\n\nLooking at Appendix F on page 18, I can see:\n\n**Algorithm 1: Tree Traversal Algorithm**\nLet me count the lines of code (excluding comments and function declarations):\n- Line 1: Scurrent \u2190 tree.layer[0]\n- Line 2: for layer in range(tree.num layers) do\n- Line 3: topk \u2190 []\n- Line 4: for node in Scurrent do\n- Line 5: score \u2190 dot product(query, node)\n- Line 6: top k.append((node, score))\n- Line 7: end for\n- Line 8: Slayer \u2190 sorted(top k)[:k].nodes\n- Line 9: Scurrent \u2190 Slayer\n- Line 10: end for\n- Line 11: return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\n\nThat's approximately 11 lines of code.\n\n**Algorithm 2: Collapsed Tree Algorithm** (page 19)\nLet me count the lines:\n- Line 1: tree \u2190 flatten(tree)\n- Line 2: top nodes \u2190 []\n- Line 3: for node in tree do\n- Line 4: top nodes.append((node, dot product(query, node))\n- Line 5: end for\n- Line 6: top nodes \u2190 sorted(top nodes)\n- Line 7: result \u2190 []\n- Line 8: total tokens \u2190 0\n- Line 9: for node in top nodes do\n- Line 10: if total tokens + node.token size < max tokens then\n- Line 11: result.append(node)\n- Line 12: end if\n- Line 13: total tokens \u2190 total tokens + node.token size\n- Line 14: end for\n- Line 15: return result\n\nThat's approximately 15 lines of code.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 58465, "output_tokens": 483, "cost_micros": 182640, "latency_ms": 36671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode section in Appendix F:\n\n**Algorithm 1: Tree Traversal Algorithm**\n- Lines: function declaration, initialization (Scurrent), for loop, topk initialization, nested for loop, score calculation, top_k.append, end for, Slayer assignment, Scurrent assignment, end for, return statement, end function\n- Total: **13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm**\n- Lines: function declaration, tree flatten, top_nodes initialization, for loop, top_nodes.append, end for, top_nodes sort, result initialization, total_tokens initialization, for loop, if statement, result.append, end if, total_tokens update, end for, return statement, end function\n- Total: **17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 22908, "output_tokens": 184, "cost_micros": 71484, "latency_ms": 5922, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "arm": "azure_premium_lc", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:59Z", "latency_ms": 7156, "error": null, "raw_text_chars": 752}]}, "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Appendix F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 Tree Traversal Algorithm:**\n- Lines: function declaration, initialization of Scurrent, for layer loop, initialization of topk, for node loop, score calculation, append to top_k, end for, Slayer assignment, Scurrent update, end for, return statement, end function\n- Total: 13 lines\n\n**Algorithm 2 Collapsed Tree Algorithm:**\n- Lines: function declaration, flatten tree, initialization of top_nodes, for node loop, append with dot product, end for, sort top_nodes, result initialization, total_tokens initialization, for node loop, if condition, result append, end if, total_tokens update, end for, return statement, end function\n- Total: 16 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 27211, "output_tokens": 190, "cost_micros": 84483, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Appendix F:\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines 1-13 (including function definition, loops, operations, and end statements)\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines 1-17 (including function definition, loops, operations, and end statements)\n\nCounting all lines including the function declarations, loop structures, operations, and end statements, Algorithm 2 (Collapsed Tree Algorithm) has more lines.\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 23500, "output_tokens": 124, "cost_micros": 72360, "latency_ms": 5562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the pseudocode in Section F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 (Tree Traversal Algorithm):**\n- Lines include: function declaration, variable initialization, for loop with nested for loop, operations inside loops, and return statement\n- Counting all lines: approximately 13 lines\n\n**Algorithm 2 (Collapsed Tree Algorithm):**\n- Lines include: function declaration, tree flattening, initialization, for loop for scoring, sorting, result initialization, token counter, for loop with conditional, and return statement\n- Counting all lines: approximately 15 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 26141, "output_tokens": 143, "cost_micros": 80568, "latency_ms": 6157, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document to find the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section and count the lines for each algorithm.Let me search for the \"PSEUDOCODE FOR RETRIEVAL METHODS\" section:Now let me search for more specific context around pseudocode:Let me read the section with the algorithms:Perfect! I can see both algorithms. Let me count the lines:\n\n**Algorithm 1: Tree Traversal Algorithm** (lines 2344-2356):\n- Line 2344: function TRAVERSETREE(tree, query, k)\n- Line 2345: Scurrent + tree.layer[0]\n- Line 2346: for layer in range(tree.num_layers) do\n- Line 2347: topk + []\n- Line 2348: for node in Scurrent do\n- Line 2349: score + dot_product(query, node)\n- Line 2350: top_k.append((node, score))\n- Line 2351: end for\n- Line 2352: Slayer + sorted(top_k)[:k].nodes\n- Line 2353: Scurrent + Slayer\n- Line 2354: end for\n- Line 2355: return So U S1 U S2 U . . . USk\n- Line 2356: end function\n\n**Total: 13 lines**\n\n**Algorithm 2: Collapsed Tree Algorithm** (lines 2375-2391):\n- Line 2375: function COLLAPSEDTREE(tree, query, k, max_tokens)\n- Line 2376: tree \u2039- flatten(tree)\n- Line 2377: top_nodes <- []\n- Line 2378: for node in tree do\n- Line 2379: top_nodes.append((node, dot_product(query, node))\n- Line 2380: end for\n- Line 2381: top_nodes \u2039- sorted(top_nodes)\n- Line 2382: result +- []\n- Line 2383: total_tokens <- 0\n- Line 2384: for node in top nodes do\n- Line 2385: if total_tokens + node.token_size < max_tokens then\n- Line 2386: result.append(node)\n- Line 2387: end if\n- Line 2388: total_tokens \u2039- total_tokens + node.token_size\n- Line 2389: end for\n- Line 2390: return result\n- Line 2391: end function\n\n**Total: 17 lines**\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 33921, "error": null, "extra": {"thread_id": 2050, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8432e22fe2074cf88b3f3fbd3ebd2c32", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 208, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1 on page 2, which shows the \"Formation of one tree layer\" process:\n\nThe figure shows the clustering step (labeled \"1. Clustering\") where text chunks numbered 1-5 are grouped. The diagram illustrates how nodes are clustered together, with some nodes appearing to be part of multiple clusters during the soft clustering process.\n\nIn the clustering visualization, I can see that:\n- The nodes are represented as numbered boxes (1, 2, 3, 4, 5)\n- Some nodes appear in multiple cluster groupings\n- Looking at the color coding in the diagram, nodes that belong to multiple clusters appear to be shown with a pink/red outline or highlighting\n\nExamining the \"Contents of a node\" box on the right side and the clustering diagram in the middle, nodes that appear in multiple clusters are highlighted in pink/red color.\n\nAnswer: pink, red", "citations": [], "input_tokens": 58465, "output_tokens": 221, "cost_micros": 178710, "latency_ms": 37828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink, red", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 in the document to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Tree construction process\" and \"Formation of one tree layer\":\n\nThe figure shows:\n- Text chunks numbered 1-5 at the bottom (leaf layer)\n- A clustering process in the middle showing how these chunks are grouped\n- The tree structure on the right\n\nIn the \"Formation of one tree layer\" section, I can see:\n- The clustering diagram shows nodes being grouped together\n- Looking at the clustering visualization, I need to identify which numbered nodes appear in multiple clusters\n\nExamining the clustering section carefully:\n- Node 1 appears to be in one cluster\n- Node 2 appears in multiple positions\n- Node 3 appears in multiple positions  \n- Node 4 appears to be in one cluster\n- Node 5 appears in multiple positions\n\nLooking at the colors used in the figure:\n- The nodes that appear in multiple clusters appear to be colored in shades that look like they could be blue, green, or orange/yellow tones in the clustering diagram.\n\nHowever, upon closer examination of Figure 1, the nodes shown in the \"1. Clustering\" section use different colors. The nodes that appear in overlapping or multiple cluster regions appear to use colors like blue and green based on the grayscale representation in the figure.\n\nLooking more carefully at the specific nodes (2, 3, 5) that appear in the clustering overlap, they appear to use blue and green colors.\n\nAnswer: blue, green", "citations": [], "input_tokens": 22908, "output_tokens": 347, "cost_micros": 73929, "latency_ms": 10734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "blue, green", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1, I need to identify nodes that appear in multiple clusters during the clustering step.\n\nIn the \"Formation of one tree layer\" section of Figure 1, I can see the clustering process where nodes 1-5 are being grouped. \n\nExamining the clusters:\n- One cluster contains nodes 2 and 3\n- Another cluster contains nodes 4 and 5\n\nLooking at the visual representation, the nodes that appear in the clusters are shown with different colors. The nodes that would appear in multiple clusters (if any existed) would need to be identified by their color.\n\nHowever, from the figure, each node (1-5) appears to belong to only one cluster. Node 3 appears in the cluster with node 2, and node 5 appears in the cluster with node 4. None of the nodes are shown appearing in more than one cluster simultaneously.\n\nUpon careful examination of the figure, there don't appear to be any nodes shown in multiple clusters at the same time - the soft clustering concept is mentioned in the text but not visually demonstrated in this particular figure with overlapping memberships.\n\nAnswer: None", "citations": [], "input_tokens": 27211, "output_tokens": 246, "cost_micros": 85323, "latency_ms": 7733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "none", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to examine Figure 1 carefully to identify nodes that appear in more than one cluster and determine their colors.\n\nLooking at Figure 1, which shows the \"Formation of one tree layer\" section:\n\nThe figure shows a clustering process where text chunks (numbered 1-5 at the bottom) are being clustered. \n\nIn the clustering visualization, I can see:\n- Text chunks numbered 1, 2, 3, 4, 5 at the bottom\n- The chunks are shown being grouped into clusters\n- After clustering (step 1), chunks 3 and 5 appear to be in multiple clusters based on the overlapping circles/groupings shown\n\nLooking at the visual representation, the nodes that appear in more than one cluster are shown in a yellow/gold color, while nodes that appear in only one cluster are shown in blue.\n\nSpecifically, nodes 3 and 5 appear in overlapping cluster regions and are colored yellow, indicating they belong to multiple clusters (soft clustering).\n\nAnswer: yellow", "citations": [], "input_tokens": 23500, "output_tokens": 224, "cost_micros": 73860, "latency_ms": 8203, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "yellow", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I need to examine the tree structure diagram to identify nodes that appear in multiple clusters.\n\nFrom the diagram showing \"RAPTOR Tree\" and \"Formation of one tree layer\":\n\nThe diagram shows:\n- Text chunks labeled 1, 2, 3, 4, 5 at the bottom (leaf nodes)\n- These are clustered into groups\n- Summary nodes 6, 7, 8 are created from clusters\n- Higher level nodes 9, 10 are created from further clustering\n\nExamining the tree structure:\n- Node 6 is connected to nodes 1 and 2\n- Node 7 is connected to node 3\n- Node 8 is connected to nodes 4 and 5\n- Node 9 is connected to nodes 6 and 7\n- Node 10 is connected to node 8\n\nLooking at the clustering process description, the figure shows standard hierarchical clustering where each node belongs to one cluster at each level. The nodes are represented in the diagram without specific color coding mentioned in the text, and the diagram appears to be a standard tree structure where nodes don't appear in multiple clusters simultaneously.\n\nHowever, based on the visual representation in the document, the nodes appear to be shown in a neutral/default color (likely gray or black outlines) in the standard tree diagram format.\n\nAnswer: The figure does not show nodes in multiple clusters with distinct colors", "citations": [], "input_tokens": 26141, "output_tokens": 317, "cost_micros": 83178, "latency_ms": 9719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure does not show nodes in multiple clusters with distinct colors", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q003", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['green', 'yellow']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find Figure 1 and identify the colors of nodes that appear in more than one cluster.Let me continue reading to see the full description of Figure 1:Based on my reading of the document, I can see the description of Figure 1. Let me look more carefully at the visual description section that describes the tree construction process and the colors of nodes. Looking at lines 426-456, I found the relevant information:\n\nIn the visual description of Figure 1:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nThe description states that in the tree traversal diagram:\n- \"Purple/lavender nodes at leaf positions\"\n- \"Pink nodes appear at internal/decision points\"\n\nAnd in the Part B (Collapsed Tree Retrieval) section, it mentions:\n- \"Mix of purple and pink boxes in linear sequence\"\n\nFrom the description of the tree formation process in lines 305-385, it shows nodes being clustered together, and the color coding explanation in lines 447-453 clearly indicates:\n- **Purple/Lavender**: Tree nodes (non-selected or leaf nodes)\n- **Pink**: Selected/relevant nodes and retrieved context\n\nSince the question asks about nodes that appear in more than one cluster, and based on the visual description which shows that pink nodes appear at internal/decision points while purple/lavender nodes are at leaf positions, the nodes that would appear in multiple clusters (as parent nodes summarizing multiple child clusters) would be the **pink** nodes.\n\nHowever, let me verify this interpretation by checking if there's more specific information about which colored nodes appear in multiple clusters in the demonstration.\n\nLooking at the figure caption (lines 302-303): \"Nodes clustered together are siblings; a parent node contains the text summary of that cluster.\"\n\nThis indicates that parent nodes (which would be pink based on the color coding) are the ones that span multiple clusters by summarizing them.\n\nAnswer: pink", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36328, "error": null, "extra": {"thread_id": 2051, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_50d7b2d924b34b7ab5e0bc22ccd0b3d6", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 237, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "pink", "normalised_gold": "green, yellow"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about the clustering algorithm used in this paper and what challenges it faces.\n\nFrom page 3-4, I can find the relevant information about the clustering algorithm:\n\nThe paper states: \"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nRegarding the challenge, it states: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces (Aggarwal et al., 2001).\"\n\nTo address this challenge, the paper mentions: \"To mitigate this, we employ Uniform Manifold Approximation and Projection (UMAP), a manifold learning technique for dimensionality reduction (McInnes et al., 2018).\"\n\nAnswer: Gaussian Mixture Models (GMMs), high dimensionality of vector embeddings", "citations": [], "input_tokens": 58463, "output_tokens": 208, "cost_micros": 178509, "latency_ms": 33342, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "gaussian mixture models gmms, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll search through the document for information about the clustering algorithm and its challenges.\n\nLooking at Section 3 \"METHODS\", specifically the \"Clustering Algorithm\" subsection:\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nAnd regarding the challenge:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 22906, "output_tokens": 134, "cost_micros": 70728, "latency_ms": 7625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", the document states:\n\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nIt then discusses the challenge:\n\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 27209, "output_tokens": 120, "cost_micros": 83427, "latency_ms": 5312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking through the document for information about the clustering algorithm...\n\nIn Section 3, under \"Clustering Algorithm\", I can find the relevant information:\n\nThe clustering algorithm is based on **Gaussian Mixture Models (GMMs)**.\n\nThe challenge presented to it is stated as: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces.\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 23498, "output_tokens": 114, "cost_micros": 72204, "latency_ms": 4781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the clustering algorithm and what presents a challenge to it.\n\nFrom Section 3 \"METHODS\" under \"Clustering Algorithm\":\n\nThe document states:\n\"Our clustering algorithm is based on Gaussian Mixture Models (GMMs), an approach that offers both flexibility and a probabilistic framework.\"\n\nThen it mentions:\n\"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\"\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 26139, "output_tokens": 134, "cost_micros": 80427, "latency_ms": 6639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q005", "doc_id": "2401.18059v1.pdf", "answer_format": "list", "gold": "['Gaussian Mixture Models', 'the high dimensionality of vector embeddings']", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about the clustering algorithm, what model it's based on, and what presents a challenge to it.Let me search for information about the clustering algorithm and its base model:Perfect! I found the information about the clustering algorithm. Let me verify by reading a bit more to ensure I have the complete answer:Based on the document, I have found the answer to the question:\n\n1. The clustering algorithm is based on **Gaussian Mixture Models (GMMs)**\n2. The challenge to it is **the high dimensionality of vector embeddings** (as stated: \"The high dimensionality of vector embeddings presents a challenge for traditional GMMs, as distance metrics may behave poorly when used to measure similarity in high-dimensional spaces\")\n\nAnswer: Gaussian Mixture Models, high dimensionality of vector embeddings", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34000, "error": null, "extra": {"thread_id": 2052, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_6459e8a5fde745a3b9337a10119c8000", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 161, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "gaussian mixture models, high dimensionality of vector embeddings", "normalised_gold": "gaussian mixture models, high dimensionality of vector embeddings"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "native_pdf", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I need to find the pseudocode for the tree traversal method in the appendix and identify step 5.\n\nLooking at Appendix F, I can see Algorithm 1 which is the Tree Traversal Algorithm.\n\nLet me examine the steps in the algorithm:\n\n```\nfunction TRAVERSETREE(tree, query, k)\n    Scurrent \u2190 tree.layer[0]\n    for layer in range(tree.num layers) do\n        topk \u2190 []\n        for node in Scurrent do\n            score \u2190 dot product(query, node)\n            top k.append((node, score))\n        end for\n        Slayer \u2190 sorted(top k)[:k].nodes\n        Scurrent \u2190 Slayer\n    end for\n    return S0 \u222a S1 \u222a S2 \u222a . . . \u222a Sk\nend function\n```\n\nCounting the steps:\n1. `Scurrent \u2190 tree.layer[0]`\n2. `for layer in range(tree.num layers) do`\n3. `topk \u2190 []`\n4. `for node in Scurrent do`\n5. `score \u2190 dot product(query, node)`\n\nStep 5 is: `score \u2190 dot product(query, node)`\n\nAnswer: score \u2190 dot product(query, node)", "citations": [], "input_tokens": 58458, "output_tokens": 318, "cost_micros": 180144, "latency_ms": 33764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2401.18059v1.pdf"}, "graded": {"correct": false, "f1": 0.15384615384615383, "method": "str_norm", "normalised_pred": "score \u2190 dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method steps in Section 3 of the document, step 5 states:\n\"5. Continue this process recursively for d layers, producing sets S1, S2, . .. , Sd.\"\n\nNow checking Algorithm 1 (Tree Traversal Algorithm) in Appendix F, the line that corresponds to this step is:\n\n```\nfor layer in range(tree.num_layers) do\n```\n\nThis is the loop that continues the process recursively through the layers.\n\nAnswer: for layer in range(tree.num_layers) do", "citations": [], "input_tokens": 22901, "output_tokens": 129, "cost_micros": 70638, "latency_ms": 5359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "azure_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the document, in Section 3 under \"Querying\", step 5 of the tree traversal method states:\n\n\"5. Continue this process recursively for d layers, producing sets S1, S2, ... , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. Step 5 corresponds to the for loop that iterates through layers:\n\n```\nfor layer in range(tree.num_layers) do\n    topk + []\n    for node in Scurrent do\n        score + dot_product(query, node)\n        top_k.append((node, score))\n    end for\n    Slayer + sorted(top_k)[:k].nodes\n    Scurrent + Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 27204, "output_tokens": 200, "cost_micros": 84612, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_basic_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (Querying section), step 5 states:\n\n\"5. Continue this process recursively for d layers, producing sets S\u2081, S\u2082, . . . , Sd.\"\n\nIn Appendix F, Algorithm 1 shows the Tree Traversal Algorithm pseudocode. The code that corresponds to step 5 is the for loop that iterates through the layers:\n\n```\nfor layer in range(tree.num_layers) do\n    top\u2096 \u2190 []\n    for node in Scurrent do\n        score \u2190 dot_product(query, node)\n        top_k.append((node, score))\n    end for\n    Slayer \u2190 sorted(top_k)[:k].nodes\n    Scurrent \u2190 Slayer\nend for\n```\n\nAnswer: for layer in range(tree.num_layers) do ... end for", "citations": [], "input_tokens": 23493, "output_tokens": 212, "cost_micros": 73659, "latency_ms": 7062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "for layer in range tree num layers do end for", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "llamacloud_premium_lc", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "Looking at the tree traversal method described in Section 3 (page 4-5), step 5 states:\n\n\"5. Continue this process recursively for $d$ layers, producing sets $S_1, S_2, \\dots, S_d$.\"\n\nNow looking at Algorithm 1 (Tree Traversal Algorithm) in Appendix F (page 18), the pseudo code line that corresponds to this step is:\n\n```\nS_current \u2190 S_layer\n```\n\nThis line updates the current set of nodes to continue the process to the next layer.\n\nAnswer: S_current \u2190 S_layer", "citations": [], "input_tokens": 26134, "output_tokens": 141, "cost_micros": 80517, "latency_ms": 5858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.16666666666666666, "method": "str_norm", "normalised_pred": "s current \u2190 s layer", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2401.18059v1.pdf::Q006", "doc_id": "2401.18059v1.pdf", "answer_format": "str", "gold": "Slayer \u2190 sorted(top k)[:k].nodes", "pages": 23, "document_id": 7597, "arm": "surfsense_agentic", "question_id": "2401.18059v1.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to find the pseudocode in the appendix related to step 5 of the tree traversal method.Let me search for \"tree traversal\" and look at the appendix section to find the pseudocode.Now let me find the actual appendix section with pseudocode. Let me search for \"step 5\" or look at the end of the document where appendices typically are.Let me continue looking for the appendix sections. Let me search further in the document.Good, I found Appendix A. Let me continue searching for Appendix F which was mentioned to contain the pseudocode for both methods.Perfect! Found Appendix F. Let me read that section:Perfect! I found Algorithm 1 for Tree Traversal. Now let me look closely at the steps. The pseudocode shows:\n\n1. Line 2345: `Scurrent + tree.layer[0]` (Initialize current set)\n2. Line 2346: `for layer in range(tree.num_layers) do` (Loop through layers)\n3. Line 2347: `topk + []` (Initialize topk list)\n4. Line 2348: `for node in Scurrent do` (Loop through nodes)\n5. Line 2349: `score + dot_product(query, node)` (Calculate score)\n\nStep 5 corresponds to line 2349 which calculates the score using dot_product.\n\nAnswer: score + dot_product(query, node)", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 60780, "error": null, "extra": {"thread_id": 2053, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_cbc9bd5486c544c293802a0ecf0d5bc7", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 329, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "score dot product query node", "normalised_gold": "slayer \u2190 sorted top k k nodes"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 18671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify which figures in the paper contain line plots.\n\nLet me go through the figures mentioned in the document:\n\n**Figure 1**: Shows the Chameleon architecture with image tokenizer, mixed-modal auto-regressive LM, and image de-tokenizer. This is a diagram, not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation. This is an example output, not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon. This is an example output, not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs. This is an example output, not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"output norm and training loss curves\" - this includes line plots\n- Figure 5b: \"An ablation with Chameleon-7B with and without QK-Norm\" - shows training loss, which is a line plot\n- Figure 5c: \"An ablation with Chameleon-7B with and without dropout\" - shows training loss, which is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Curves for 600k steps for Chameleon-7B and Chameleon-34B\" - this is a line plot\n- Figure 6b: Shows \"Training loss curve with image generation disabled\" - this is a line plot\n- Figure 6c: Shows training loss curves with and without norm-reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories. This appears to be text/table format, not line plots.\n\n**Figure 8**: Shows task categories and examples of prompts. This is not a line plot.\n\n**Figure 9**: Contains:\n- Figure 9a: Shows \"prompt task fulfillment rates\" as a horizontal bar chart\n- Figure 9b: Shows comparison results as a horizontal bar chart\n\n**Figure 10**: Shows \"inter-annotator agreement\" as a horizontal bar chart.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 25078, "output_tokens": 479, "cost_micros": 82419, "latency_ms": 11155, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram of the Chameleon architecture with image and text tokenization flow - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: Contains three subfigures:\n- Figure 5a: Shows \"Output Norm\" vs \"Step\" - this is a line plot\n- Figure 5b: Shows \"Training Loss\" vs \"Step\" with and without QK-Norm - this is a line plot\n- Figure 5c: Shows \"Training Loss\" vs \"Step\" with and without dropout - this is a line plot\n\n**Figure 6**: Contains three subfigures:\n- Figure 6a: Shows \"Training Loss\" vs \"Step\" for 7B and 34B models - this is a line plot\n- Figure 6b: Shows \"Training Loss\" vs \"Step\" for 7B without image generation - this is a line plot\n- Figure 6c: Shows \"Training Loss\" vs \"Step\" with and without norm reordering - this is a line plot\n\n**Figure 7**: Shows example alignment data - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts - not a line plot.\n\n**Figure 9**: Contains two subfigures:\n- Figure 9a: Shows a bar chart of task fulfillment rates - not a line plot\n- Figure 9b: Shows a horizontal bar chart comparing Chameleon vs baselines - not a line plot\n\n**Figure 10**: Shows a horizontal bar chart of inter-annotator agreement - not a line plot.\n\nAnswer: Figure 5a, Figure 5b, Figure 5c, Figure 6a, Figure 6b, Figure 6c", "citations": [], "input_tokens": 34447, "output_tokens": 457, "cost_micros": 110196, "latency_ms": 10469, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "figure 5a, figure 5b, figure 5c, figure 6a, figure 6b, figure 6c", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the document that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows the Chameleon architecture diagram with tokens - not a line plot.\n\n**Figure 2**: Sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Sample generation from Chameleon with images - not a line plot.\n\n**Figure 4**: Sample Chameleon outputs with recipe - not a line plot.\n\n**Figure 5**: This figure has three subfigures:\n- (a) Shows \"Uncontrolled growth of output norms\" with what appears to be line plots showing training progress\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - appears to be line plots\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - appears to be line plots\n\n**Figure 6**: This figure has parts showing training curves:\n- (a) \"Training loss curve with image generation disabled\" - line plot\n- (b) Shows training for Chameleon-34B - line plots\n\nThe caption mentions \"Training loss curves for Chameleon models under various settings.\"\n\n**Figure 7**: Example alignment data - not line plots.\n\n**Figure 8**: Task categories and examples of prompts - not line plots.\n\n**Figure 9**: The text mentions Figure 9a and 9b for task fulfillment and win rates - likely bar charts or similar, need to check the context. The text describes percentages but doesn't clearly indicate these are line plots.\n\n**Figure 10**: \"The inter-annotator agreement on the questions in the absolute evaluation\" - context suggests this might be a bar chart or similar visualization.\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 26087, "output_tokens": 410, "cost_micros": 84411, "latency_ms": 11625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I need to identify all figures in the paper that contain line plots.\n\nLet me go through the figures systematically:\n\n**Figure 1**: Shows a diagram with flowcharts for pre-training and generation processes - not a line plot.\n\n**Figure 2**: Shows sample interleaved image and text generation - not a line plot.\n\n**Figure 3**: Shows sample generation from Chameleon - not a line plot.\n\n**Figure 4**: Shows sample Chameleon outputs - not a line plot.\n\n**Figure 5**: This figure has three parts:\n- (a) Shows \"Output norm\" over \"Step\" - this is a line plot\n- (b) Shows training loss over steps with and without QK-Norm - this is a line plot\n- (c) Shows training loss over steps with and without dropout - this is a line plot\n\n**Figure 6**: This figure has three parts:\n- (a) Shows \"Training Curves\" with loss over steps - this is a line plot\n- (b) Shows \"Training loss curve\" - this is a line plot\n- (c) Shows training loss with different settings - this is a line plot\n\n**Figure 7**: Shows example alignment data for different categories - not a line plot.\n\n**Figure 8**: Shows task categories and example prompts in a table - not a line plot.\n\n**Figure 9**: Shows performance comparison tables - not line plots, these are tables.\n\n**Figure 10**: Shows inter-annotator agreement in a table format - not a line plot.\n\nSo the figures containing line plots are: Figure 5 (parts a, b, c) and Figure 6 (parts a, b, c).\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 32138, "output_tokens": 382, "cost_micros": 102144, "latency_ms": 8875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to identify which figures contain line plots.Let me search for more specific information about each figure and read the sections containing figure descriptions:Now let me look at Figure 5 more carefully and check other figures:Let me look at the beginning of Figure 5 to see the first part:Good! I can see Figure 5 has multiple line plots. Now let me check other figures. Let me search for more figure captions and descriptions:Let me look for Figure 7 and Figure 8:Let me continue reading Figure 8 and check if there are more figures:I can see Figure 9a is a bar chart. Let me continue examining more figures. Let me search for all figures systematically by searching for specific figure numbers:Now let me look at the actual content of each figure more carefully. Let me check Figure 1:Figure 1 appears to be a diagram/schematic, not a line plot. Let me check Figure 2 and 3:Figures 2 and 3 appear to be examples of text and image generation, not line plots. Let me check Figure 4:Figure 4 is also a sample output, not a line plot. Now let me look more carefully at Figure 10 which I saw earlier has a bar chart format:Figure 10 is a bar chart showing counts for different agreement levels. Let me check if there are any other figures I might have missed by searching for more figure references:Figure 8 shows task categories and examples (text-based). Now let me systematically review what I found:\n\nBased on my reading of the document:\n\n- **Figure 1**: Diagram/schematic showing the Chameleon architecture\n- **Figure 2**: Sample text and image generation (not a line plot)\n- **Figure 3**: Sample generation (not a line plot)  \n- **Figure 4**: Sample outputs (not a line plot)\n- **Figure 5**: Contains THREE line plots:\n  - (a) Output norm vs Step (line plot)\n  - (b) Training Loss vs Step with/without QK-Norm (line plot)\n  - (c) Training Loss vs Step with/without dropout (line plot)\n- **Figure 6**: Contains THREE line plots:\n  - (a) Training Loss vs Step for 7B and 34B models (line plot)\n  - (b) Training Loss vs Step without image generation (line plot)\n  - (c) Training Loss vs Step with/without norm reordering (line plot)\n- **Figure 7**: Example alignment data for different categories (not a line plot)\n- **Figure 8**: Task categories and examples of prompts (not a line plot)\n- **Figure 9**: Contains bar charts showing performance comparison (not line plots)\n- **Figure 10**: Bar chart showing inter-annotator agreement (not a line plot)\n\nAnswer: Figure 5, Figure 6", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 104391, "error": null, "extra": {"thread_id": 2054, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3ce67a1a47334e71b23dc25fd26d8b74", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 654, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "figure 5, figure 6", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 25075, "output_tokens": 41, "cost_micros": 75840, "latency_ms": 3905, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 34444, "output_tokens": 38, "cost_micros": 103902, "latency_ms": 3641, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 in the document, I can see the caption states:\n\n\"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 26084, "output_tokens": 41, "cost_micros": 78867, "latency_ms": 3483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "Looking at Figure 1 and its caption, I can see that the figure shows Chameleon's architecture with different colored elements representing different types of tokens.\n\nThe caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 32135, "output_tokens": 62, "cost_micros": 97335, "latency_ms": 4108, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find Fig 1 and determine what the green squares denote.Let me search for \"Fig 1\" in the document to locate the figure and its caption.Let me search for \"Figure 1\" instead:Good, I found Figure 1. Now let me read the section around line 354 to see the figure and find information about green squares:Perfect! I found the answer. In line 355, the figure caption explicitly states: \"Text tokens are represented in green and image tokens are represented in blue.\"\n\nAnswer: Text tokens", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31469, "error": null, "extra": {"thread_id": 2055, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_242072fc7b9a40f1ba1d0b58497f99ea", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 150, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "text tokens", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in chat example figures, excluding the Appendix.\n\nLet me go through the document systematically:\n\n**Figure 2** (page 3): Sample interleaved image and text generation from Chameleon\n- Shows 3 images (Keel-Billed Toucan, Puffin, Golden Pheasant)\n\n**Figure 3** (page 4): Sample generation from Chameleon\n- Shows 2 images (the STOP sign input image and the generated chameleon image)\n\n**Figure 4** (page 5): Sample Chameleon outputs\n- Shows 2 images (bananas input image and the banana bread output image)\n\n**Figure 7** (page 10): Example alignment data for different categories\n- This shows examples of different data types:\n  - (a) Text - no images\n  - (b) Code - no images\n  - (c) Image Generation - 1 image shown\n  - (d) Visual Chat - 1 image shown (flower arrangement)\n  - (e) Interleaved Text/Image Generation - 2 images shown (appears to be related to Gyumri/Armenian content)\n\n**Figure 8** (page 11): Task categories and examples of prompts\n- Shows 4 small example images in the prompt examples\n\nNow let me check the Appendix section (starts at page 23):\n- The Appendix section A \"Samples\" contains additional examples, but I should exclude these.\n\nTotal count (excluding Appendix):\n- Figure 2: 3 images\n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 4 images (1 + 1 + 2)\n- Figure 8: 4 images\n\nTotal: 3 + 2 + 2 + 4 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 25074, "output_tokens": 446, "cost_micros": 81912, "latency_ms": 11907, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows 3 bird images (Keel-Billed Toucan, Puffin, Golden Pheasant)\nTotal: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows 1 stop sign image in the prompt\n- Shows 1 chameleon image in the response\nTotal: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows 1 banana image in the prompt\n- Shows 1 banana bread image in the response\nTotal: 2 images\n\nThe Appendix starts at page 23 with \"A Samples\". The figures after that are in the Appendix and should be excluded.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 34443, "output_tokens": 263, "cost_micros": 107274, "latency_ms": 7530, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures/images used in the chat example figures in the main body of the document (excluding the Appendix).\n\nLet me go through the figures that show chat/conversation examples:\n\n1. **Figure 1**: Shows a recipe example with bananas image and banana bread image = 2 images\n\n2. **Figure 2**: \"Sample interleaved image and text generation from Chameleon\" - This shows a response about quirky-looking birds with 3 bird images (Keel-Billed Toucan, Puffin, and Golden Pheasant) = 3 images\n\n3. **Figure 3**: \"Sample generation from Chameleon\" - Shows a stop sign image and a chameleon image = 2 images\n\n4. **Figure 4**: \"Sample Chameleon outputs\" - Shows bananas image and banana bread image = 2 images\n\n5. **Figure 7**: \"Example alignment data for different categories\" - This shows multiple examples including:\n   - Poem (no image)\n   - Renaissance Square (1 image)\n   - Code example (no image)\n   - Gyumri text (no image)\n   - Flower arrangement (1 image)\n   = 2 images\n\n6. **Figure 8**: \"Task categories and examples of prompts\" - Shows 4 images in the examples (pastry, pony, construction site, and one more)\n\nWait, let me recount Figure 8 more carefully. Looking at the attributions at the bottom: \"Seguin (2010); Agriflanders (2009); Tuszy\u0144ski (2015); Sokolov (2022)\" - this indicates 4 images.\n\nTotal count:\n- Figure 1: 2 images\n- Figure 2: 3 images  \n- Figure 3: 2 images\n- Figure 4: 2 images\n- Figure 7: 2 images\n- Figure 8: 4 images\n\nTotal = 2 + 3 + 2 + 2 + 2 + 4 = 15 images\n\nAnswer: 15", "citations": [], "input_tokens": 26083, "output_tokens": 489, "cost_micros": 85584, "latency_ms": 11483, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I need to count the pictures used in the chat example figures, excluding the Appendix.\n\nLet me go through the figures that show chat examples:\n\n**Figure 2** (page 3): \"Sample interleaved image and text generation from Chameleon\"\n- Shows a conversation about quirky birds\n- Contains 3 images: Keel-Billed Toucan, Puffin, and Golden Pheasant\n- Count: 3 images\n\n**Figure 3** (page 4): \"Sample generation from Chameleon\"\n- Shows a conversation about a stop sign and chameleon camouflage\n- Contains 2 images: stop sign in wooded area, and a reddish-brown chameleon on tree branch\n- Count: 2 images\n\n**Figure 4** (page 5): \"Sample Chameleon outputs\"\n- Shows a conversation about baking with bananas\n- Contains 2 images: bowl of sliced bananas, and a loaf of banana bread\n- Count: 2 images\n\nThe Appendix section starts at \"Appendix\" on page 23. The example in Section A (page 24) with the Mountain Cur dog and the pothos plant are in the Appendix, so I should not count those.\n\nTotal pictures in chat example figures (excluding Appendix): 3 + 2 + 2 = 7\n\nAnswer: 7", "citations": [], "input_tokens": 32134, "output_tokens": 325, "cost_micros": 101277, "latency_ms": 8719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the document to find chat example figures and count the pictures in them, excluding any in the Appendix.Let me search for figures more specifically:Now let me look for chat examples and appendix sections. Let me read through the document systematically:Let me read the figures mentioned (Figures 2-4) which appear to be the chat example figures, and check where the Appendix starts:Now let me check Figure 4:Let me go back and read Figure 2 more carefully to count the images:Now let me look at the beginning of Figure 2 to see all images:I see there's a mislabeled image at the beginning. Let me count more carefully. Looking at Figure 2, I can see references to:\n- Im3.png (Toucan)\n- Im4.png (Puffin)  \n- Im5.png (Golden Pheasant mentioned with <img> marker)\n\nLet me check Figure 3 now:Let me look for where Figure 3 starts:I need to see if there's another image in Figure 3. Let me check after line 747:Good! So I see Im7.png is a chameleon image. Let me check if this is part of Figure 3. Looking at the page break, Im7.png appears after the section break at line 756 where \"2.1 Tokenization\" starts. Let me verify where Figure 3 ends and Figure 4 starts:Perfect! Now I can see Figure 4 ends at line 922. Let me look for more images in Figure 4. The prompt at line 822 mentions an <img> tag. Let me check if there's an image for that:Now let me verify where the Appendix starts to ensure I don't count figures from there:Good! The Appendix starts at line 3372. Now let me systematically count the images in Figures 2, 3, and 4.\n\nLet me go back and look at the full scope of each figure more carefully:\n\n**Figure 2** (lines 471-620):\n- Starts with prompt at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Im5.png - Golden Pheasant (line 641)\n- Figure 2 caption at line 619\n\nWait, Im5.png at line 641 seems to be after the Figure 2 caption. Let me re-check:I see! There's a STOP sign figure at line 633-637, and then Im5.png (the Golden Pheasant) at line 641. The STOP sign appears to be the beginning of Figure 3's prompt. Let me trace this more carefully:So the STOP sign at line 633-637 with Im5.png description is actually part of Figure 3's input (the <img> in the prompt at line 675 refers to it). Let me reorganize:\n\n**Figure 2** (chat example about birds):\n- Prompt starts at line 471\n- Im3.png - Toucan (line 543)\n- Im4.png - Puffin (line 579)\n- Golden Pheasant text mentions <img> at line 614, but the actual image appears to be missing from Figure 2 content\n- Figure 2 caption at line 619-620\n\nWait, I need to look more carefully at the structure. The embedded images seem to be labeled differently. Let me look for all embedded images in the main body before the Appendix:Now let me determine which images belong to Figures 2, 3, and 4 by checking the line ranges:\n\n- Figure 2 caption: line 619\n- Figure 3 caption: line 701  \n- Figure 4 caption: line 922\n- Appendix starts: line 3372\n\nImages before Appendix:\n- Image4.jpg (line 407) - before Figure 2\n- Image5.jpg (line 480) - This is the mislabeled banana bread image, but checking context it's BEFORE the birds prompt starts at line 471\n- Im3.png (line 543) - in Figure 2 range\n- Im4.png (line 579) - in Figure 2 range\n- Im5.png (line 641) - between Figure 2 (ends 620) and Figure 3 (ends 701)\n- Im6.png (line 711) - between Figure 3 (ends 701) and section 2.1 starts\n- Im7.png (line 777) - after section 2.1 starts (line 756)\n- Im8.png (line 876) - in Figure 4 range (Figure 4 caption at 922)\n- Im9.png (line 991) - after Figure 4 ends\n\nLet me read around line 480 to verify Image5.jpg:This Image5.jpg (banana bread) is clearly mislabeled/misplaced content. The prompt at line 471 asks for birds, but Image5.jpg shows banana bread. This seems to be a document processing error. However, it appears WITHIN the Figure 2 example (after the prompt, before the response text starts at line 518).\n\nLet me check where Figure 2 actually starts and what the structure is:So Figure 2 starts at line 471. Let me now carefully count all images that appear in the three chat example figures (2, 3, 4) before the Appendix:\n\nLooking at my grep results and the line ranges:\n\n**Figure 2** (lines 471-620):\n- Image5.jpg (line 480) - appears after the prompt but seems misplaced\n- Im3.png (line 543) - Toucan\n- Im4.png (line 579) - Puffin\n\nHowever, Image5.jpg shows banana bread which doesn't match the birds prompt. Let me check if there's a third bird image I'm missing. The text mentions three birds (Toucan, Puffin, Golden Pheasant) and has <img> markers. Let me look for where the third bird image might be:\n\nLooking at my earlier reading, Im5.png (line 641) shows a bird (Golden Pheasant based on the description at lines 649-668). The caption for Figure 2 is at line 619-620, so Im5.png at line 641 is AFTER the Figure 2 caption.\n\nLet me check if Im5.png is actually part of Figure 3:I see! Im5.png (the Golden Pheasant/colorful bird) appears at line 641, but the prompt for Figure 3 starts at line 675 which references \"<img>\" - and that <img> is referring to the STOP sign that appeared earlier. So Im5.png is actually the third bird from Figure 2, even though it appears after the Figure 2 caption.\n\nLooking back at the structure more carefully, the figure captions seem to come before all the images are shown. Let me look at where the STOP sign figure starts:\n\nFrom line 633-637, there's a figure with \"STOP\" as OCR text. This must be the input image for Figure 3.\n\nLet me now systematically list the images:\n\n**Before Figure 2 caption (line 619):**\n- Image5.jpg (line 480) - banana bread (misplaced/error)\n- Im3.png (line 543) - Toucan  \n- Im4.png (line 579) - Puffin\n\n**After Figure 2 caption but before Figure 3 caption (line 701):**\n- Im5.png (line 641) - Golden Pheasant (part of Figure 2 content)\n\n**Around Figure 3:**\n- STOP sign figure (lines 633-637, but before Im5.png structurally in the document)\n- Im6.png (line 711) - STOP sign (after Figure 3 caption)\n\nWait, I need to look at this more systematically. Let me check what the STOP sign figure at line 633 is:The figure at 633-637 has OCR text \"STOP\" but the embedded image Im5.png (line 641) has \"NO_CONTENT_HERE\" as OCR and shows a bird. This is confusing. Let me look at Im6.png which should be the STOP sign:Yes! Im6.png is the STOP sign. So the document structure has the figure tags and embedded images separated. The figure at line 633 with \"STOP\" corresponds to Im6.png (the actual embedded image file) at line 711.\n\nLet me now map this out properly by looking at the figure captions and the embedded images:\n\n**Figure 2** (caption at line 619): Chat about birds\n**Figure 3** (caption at line 701): Chat about chameleon camouflage  \n**Figure 4** (caption at line 922): Chat about baking with bananas\n\nNow let me find which embedded images belong to each:So Im6.png appears RIGHT AFTER the Figure 3 caption. Now let me check Figure 4:So Im8.png (bananas) appears before the Figure 4 caption at line 922, and there's an <img> marker at line 920 suggesting another image.\n\nNow let me look at Im7.png and Im9.png to see which figure they belong to:\n\nIm7.png is at line 777. Let me check the context:Im7.png (chameleon) is at line 777, which is AFTER section 2.1 starts at line 756 and after Figure 3 caption at line 701. But looking at the context, Im7.png shows a chameleon which matches Figure 3's theme. Let me check if this is part of the Figure 3 example by looking backwards:\n\nThe Figure 3 response at line 747 has an <img> marker, and the text before talks about generating a chameleon image. So Im7.png is part of Figure 3's output!\n\nNow let me check Im9.png:Im9.png shows a loaf of baked good (banana bread final result). This is at line 991, which is after Figure 4 caption at line 922. The <img> marker at line 920 in Figure 4 refers to this final dish image. So Im9.png is part of Figure 4.\n\nNow let me go back and check Im5.png more carefully. At line 641, the bird image appears after Figure 2 caption but before Figure 3 content. Looking at Figure 2's text, the Golden Pheasant is mentioned with <img> at line 614. So Im5.png (Golden Pheasant) is part of Figure 2.\n\nLet me now do a final count:\n\n**Figure 2** (Chat about birds):\n- Im3.png - Toucan\n- Im4.png - Puffin  \n- Im5.png - Golden Pheasant\nTotal: 3 images\n\n**Figure 3** (Chat about chameleon camouflage):\n- Im6.png - STOP sign (input)\n- Im7.png - Chameleon (generated output)\nTotal: 2 images\n\n**Figure 4** (Chat about baking with bananas):\n- Im8.png - Bananas (input)\n- Im9.png - Banana bread (generated output)\nTotal: 2 images\n\n**Total images in chat example figures (Figures 2-4), excluding Appendix: 3 + 2 + 2 = 7**\n\nWait, but I should double-check if Image5.jpg (the banana bread at line 480) is actually meant to be part of Figure 2 or if it's a document error. Let me look at the context again:Image5.jpg (banana bread) appears right after the birds prompt but before the response starts. This appears to be a document processing error or misplacement - it doesn't match the context at all. The response starting at line 518 talks about birds, not banana bread.\n\nGiven that this is clearly an error in the document, I should not count Image5.jpg as part of the chat example figures. The actual chat examples are:\n\n**Figure 2**: 3 bird images (Im3.png, Im4.png, Im5.png)\n**Figure 3**: 2 images (Im6.png STOP sign, Im7.png chameleon)  \n**Figure 4**: 2 images (Im8.png bananas, Im9.png banana bread)\n\n**Total: 7 images**\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 178546, "error": null, "extra": {"thread_id": 2056, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d55f4fd667ac403b901ea3294619b198", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1600, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 17687, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find subplots that show obvious loss spikes in the document.\n\nLet me examine the figures that show training loss curves:\n\n1. **Figure 5**: Contains 3 subplots:\n   - (a) Shows output norm with clear divergence/spike\n   - (b) Shows training loss comparison with and without QK-Norm - one curve diverges (spikes)\n   - (c) Shows training loss with and without dropout - appears stable\n\n2. **Figure 6**: Contains 3 subplots:\n   - (a) Shows training loss curves for 7B and 34B models - appears stable without obvious spikes\n   - (b) Shows training loss curve with image generation disabled - appears stable without obvious spikes\n   - (c) Shows training loss with and without norm reordering - shows clear divergence/spike in one curve\n\nLet me count the subplots with obvious loss spikes:\n- Figure 5(a): Shows norm growth leading to divergence (1)\n- Figure 5(b): Shows loss divergence/spike (1)\n- Figure 6(c): Shows loss divergence/spike (1)\n\nTotal: 3 subplots show obvious loss spikes or divergences.\n\nAnswer: 3", "citations": [], "input_tokens": 25071, "output_tokens": 284, "cost_micros": 79473, "latency_ms": 8016, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss curves with obvious spikes.\n\nLooking through the document:\n\n**Figure 5** (page 6-7):\n- (a) Shows \"Output norm\" vs Step - not a loss curve with spikes\n- (b) Shows \"Training Loss\" with and without QK-Norm - shows relatively smooth curves, one diverges but no obvious spikes\n- (c) Shows \"Training Loss\" with and without dropout - shows smooth curves\n\n**Figure 6** (page 7):\n- (a) Shows \"Training Loss\" for 7B and 34B models over 600k steps - smooth curves, no obvious spikes\n- (b) Shows \"Training Loss\" for 7B without image generation - smooth curve, no obvious spikes\n- (c) Shows \"Training Loss\" with and without norm reordering - one curve shows a clear spike/divergence around 8k-10k steps\n\nFrom Figure 6c, I can see one subplot that clearly shows an obvious loss spike (the curve without norm reordering that spikes dramatically).\n\nLooking more carefully at Figure 5b, the curve \"w/o QK-norm\" shows a sharp spike/divergence near the end.\n\nSo counting subplots with obvious loss spikes:\n- Figure 5b: 1 subplot (shows divergence/spike)\n- Figure 6c: 1 subplot (shows clear spike)\n\nAnswer: 2", "citations": [], "input_tokens": 34440, "output_tokens": 324, "cost_micros": 108180, "latency_ms": 9828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures in the document that show training loss curves with obvious loss spikes.\n\nLooking through the document:\n\n**Figure 5**: \"Output norm and training loss curves for Chameleon models under various settings\"\n- (a) Shows uncontrolled growth of output norms\n- (b) Shows \"An ablation with Chameleon-7B with and without QK-Norm\" - this shows training loss with a divergence/spike\n- (c) Shows \"An ablation with Chameleon-7B with and without dropout\" - this shows training loss with divergences/spikes\n\n**Figure 6**: \"Training loss curves for Chameleon models under various settings\"\n- (a) Shows \"Training loss curve with image generation disabled does not suffer from instability issues\" - smooth curves, no spikes\n- (b) Shows \"For Chameleon-34B, using dropout does not fix divergences, both with and without reordering\" - this shows loss spikes/divergences\n\nCounting subplots with obvious loss spikes:\n- Figure 5(b): 1 subplot with loss spike\n- Figure 5(c): 1 subplot with loss spike  \n- Figure 6(b): 1 subplot with loss spikes\n\nTotal: 3 subplots\n\nAnswer: 3", "citations": [], "input_tokens": 26080, "output_tokens": 301, "cost_micros": 82755, "latency_ms": 9171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I need to find figures that show loss spikes in the document.\n\nLooking through the document for figures showing training loss or related metrics:\n\n**Figure 5: \"Output norm and training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows output norms with curves that spike/diverge\n- (b) Shows training loss with and without QK-Norm - one curve diverges/spikes\n- (c) Shows training loss with and without dropout - one curve appears to spike\n\n**Figure 6: \"Training loss curves for Chameleon models under various settings\"**\nThis figure has three subplots:\n- (a) Shows smooth training curves for 7B and 34B models - no obvious spikes\n- (b) Shows training loss without image generation - smooth curve, no spikes\n- (c) Shows training with/without norm reordering where one diverges - this shows a spike\n\nSo counting subplots with obvious loss spikes:\n- Figure 5(a): 1 (shows divergence/spikes in output norms)\n- Figure 5(b): 1 (shows divergence in loss)\n- Figure 5(c): 1 (shows divergence in loss)\n- Figure 6(c): 1 (shows divergence)\n\nTotal: 4 subplots show obvious spikes/divergences\n\nAnswer: 4", "citations": [], "input_tokens": 32131, "output_tokens": 324, "cost_micros": 101253, "latency_ms": 9125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "4", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to find information about subplots related to obvious loss spikes.Let me search for content related to \"loss spikes\" in the document:Let me search for \"subplot\":Let me read through the document more systematically to look for figures and their descriptions:Now let me look at Figure 5 and Figure 6 which seem to show training loss curves. Let me read those sections:Now let me look more closely at Figure 6 which appears to have subplots. Let me search for any reference to \"loss spikies\" or similar terms:Let me look at Figure 5 more carefully, as it shows \"Output norm and training loss curves for Chameleon models under various settings\":I can see Figure 5 has three subplots (a, b, c). Now let me check the figure caption more carefully and look at the actual charts. The caption says \"Figure 5 Output norm and training loss curves for Chameleon models under various settings.\" Let me read the text more carefully to understand what \"obvious loss spikies\" might refer to. Let me search for variations:Let me look at the entire document more systematically. Let me check around Figure 5 and 6 descriptions:Let me look more carefully at the whole document. Maybe \"spikies\" is a colloquial term in the figure or caption. Let me check the full text around Figures 5 and 6:So Figure 5 has subplots (a), (b), and (c). Let me check Figure 6:So Figure 6 also has subplots (a), (b), and (c). Now let me look at the actual chart data more carefully. The question asks about \"obvious loss spikies\" which likely refers to spikes in the loss curves. Let me look at the loss curve data more closely. Let me search the document for any terms that might be misspelled or colloquial:Based on my reading of the document, I can see that:\n\n**Figure 5** has three subplots:\n- (a) Output norms plot showing growth over training steps\n- (b) Training loss with and without QK-Norm\n- (c) Training loss with and without dropout\n\n**Figure 6** has three subplots:\n- (a) Training curves for 600k steps for Chameleon-7B and Chameleon-34B\n- (b) Training loss curve with image generation disabled\n- (c) For Chameleon-34B, using dropout comparison with and without norm-reordering\n\nLooking at the descriptions and the actual chart data in the text, the question asks about \"obvious loss spikies\" (spikes). From examining the text descriptions:\n\n- Figure 5(a) shows \"Uncontrolled growth of output norms\" - this is about norms, not loss spikes\n- Figure 5(b) shows training loss curves with the one \"w/o QK-norm\" showing a divergence\n- Figure 5(c) shows training loss curves with and without dropout - the \"w/o dropout\" curve appears to have issues\n- Figure 6(a) shows smooth training curves for both 7B and 34B models\n- Figure 6(b) shows a smooth training loss curve without image generation\n- Figure 6(c) shows curves where \"w/o norm reordering\" appears to diverge/spike\n\nLooking at the subplot captions that mention instability or divergences, which would correspond to loss spikes:\n- Figure 5(b): \"w/o QK-norm\" diverges\n- Figure 5(c): \"w/o dropout\" has spikes\n- Figure 6(c): \"w/o norm reordering\" has divergences/spikes\n\nThat would be 3 subplots showing obvious loss spikes.\n\nAnswer: 3", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 88405, "error": null, "extra": {"thread_id": 2057, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_d21eabfb0c2b44d69030354b180154e2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 635, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 15734, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2405.09818v1.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a \"Summary of core architecture and optimization decisions made in Chameleon in contrast to LLaMa-1 and LLaMa-2\", I can see that:\n\n- LLaMa-1 7B has an \"\u00d7\" (cross mark) in the Qknorm column\n- LLaMa-1 33B has an \"\u00d7\" (cross mark) in the Qknorm column\n\nThis indicates that LLaMa-1 does NOT apply QKnorm.\n\nAnswer: no", "citations": [], "input_tokens": 25087, "output_tokens": 130, "cost_micros": 77211, "latency_ms": 5343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which compares core architecture and optimization decisions across models:\n\nFor LLaMa-1 models (both 7B and 33B), the Qknorm column shows \"\u00d7\" (which indicates no).\n\nAnswer: no", "citations": [], "input_tokens": 34456, "output_tokens": 63, "cost_micros": 104313, "latency_ms": 4297, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which summarizes the core architecture and optimization decisions:\n\nFor LLaMa-1 models (both 7B and 33B), the \"Qknorm\" column shows \"\u00d7\", which indicates that QK-Norm was not applied.\n\nAnswer: No", "citations": [], "input_tokens": 26096, "output_tokens": 71, "cost_micros": 79353, "latency_ms": 4531, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "Looking at Table 1 in the document, which shows a comparison of core architecture and optimization decisions:\n\nFor LLaMa-1 (both 7B and 33B models), the \"Qknorm\" column shows \"\u00d7\", which indicates that query-key normalization (QK-Norm) was **not** applied.\n\nAnswer: No", "citations": [], "input_tokens": 32147, "output_tokens": 81, "cost_micros": 97656, "latency_ms": 4671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the document to find information about whether llama-1 applies qknorm.Let me search for \"qknorm\" and \"llama\" in the document to find relevant information.Let me read the table area that contains information about qknorm and llama-1:Perfect! I found the table that shows whether llama-1 applies qknorm. In the table, for both LLaMa-1 models (7B and 33B), the Qknorm column shows \"\u00d7 \u2612\" which indicates \"no\".\n\nAnswer: no", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 30905, "error": null, "extra": {"thread_id": 2058, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2c3221de2294418da7d44837e6c67391", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 146, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "no", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "native_pdf", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm\", I can see the training loss curves.\n\nThe curve labeled \"w/o QK-norm\" (without QK-norm) shows a divergence/spike in the loss after approximately 20% of training progress, which appears to occur around step 20,000-25,000 based on the x-axis scale shown in the figure.\n\nAnswer: Around step 20,000-25,000", "citations": [], "input_tokens": 25078, "output_tokens": 122, "cost_micros": 77064, "latency_ms": 6000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around step 20 000 25 000", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "azure_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for both conditions.\n\nThe curve labeled \"w/o QK-norm\" (without QK-Norm) shows the training progressing normally until it experiences a sudden spike and divergence.\n\nAnswer: Around 125k steps", "citations": [], "input_tokens": 34447, "output_tokens": 89, "cost_micros": 104676, "latency_ms": 5452, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 125k steps", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_basic_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b in the document, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves comparing the two conditions.\n\nThe figure shows that the model without QK-Norm diverges (loss spikes) after approximately 20% of training progress.\n\nAnswer: After approximately 20% of training", "citations": [], "input_tokens": 26087, "output_tokens": 87, "cost_micros": 79566, "latency_ms": 5046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "after approximately 20 of training", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "llamacloud_premium_lc", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "Looking at Figure 5b, which shows \"An ablation with Chameleon-7B with and without QK-Norm,\" I can see the training loss curves for models with and without QK-norm.\n\nThe graph shows that the model without QK-norm or dropout (red line) diverges after approximately 20k-30k steps, where the loss spikes dramatically upward.\n\nAnswer: Around 20k-30k steps", "citations": [], "input_tokens": 32138, "output_tokens": 100, "cost_micros": 97914, "latency_ms": 5485, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "around 20k 30k steps", "normalised_gold": "150k"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "arm": "surfsense_agentic", "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find information about loss spikes when without QK-norm.Let me search for \"QK-norm\" in the document to find the relevant information.Now let me read the specific sections that mention loss spikes and QK-norm. Let me look at the chart/figure around lines 1028-1076 which seems to show training loss curves.I can see a figure showing training loss with and without QK-norm. The x-axis shows steps from 0k to 175k (approximately). Let me look more carefully at the surrounding text to understand where the spike occurs. Let me check the text before and after this figure.Let me read more to find the caption or description of this figure:Good! Now I can see that figure (b) shows training loss with and without QK-Norm. Let me look back at the chart more carefully to understand where the spike happens. Let me check the text around lines 938-1000 to see if there's more description:Now I need to look at the actual description of figure 5b more carefully. Let me read around the text that discusses the training loss spike:Perfect! I found the answer. The text clearly states:\n\n\"In Figure 5b, we show training loss curves for Chameleon-7B with and without QK-Norm, and the latter diverges after approximately 20% of a training epoch.\"\n\nLet me look at the figure again to verify the step count. Looking at the figure data from earlier, the x-axis goes from 0k to approximately 175k steps. The text mentions \"after approximately 20% of a training epoch\" for the divergence/spike.\n\nHowever, let me look more carefully at what specific step number might correspond to this. Let me check if there's more specific information:", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 73108, "error": null, "extra": {"thread_id": 2059, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_c22c13a6350947eda6562103bdc5468c", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 343, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "however let me look more carefully at what specific step number might correspond to this let me check if there s more specific information", "normalised_gold": "150k"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I need to find who verified and signed the complaint by looking at the verification section of the document.\n\nLooking at page 16 of the document, I can see the \"VERIFICATION\" section which states:\n\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury of the laws of the United States of America that the facts contained therein are true to the best of my knowledge and belief, based upon knowledge possessed by me and/or on information received from other law enforcement agents.\"\n\nThis is followed by a signature and \"Special Agent Marc Silski, Federal Bureau of Investigation\" with the date \"September 6, 2018\".\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 25011, "output_tokens": 176, "cost_micros": 77673, "latency_ms": 14359, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (PageID.16 Page 16 of 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5579, "output_tokens": 52, "cost_micros": 17517, "latency_ms": 3594, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "According to the verification section at the end of the document, the officer who verified and signed the complaint was:\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 6098, "output_tokens": 34, "cost_micros": 18804, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16):\n\nThe verification states:\n\"I, Marc Silski, am a Special Agent of the Federal Bureau of Investigation (\"FBI\"). I have read the foregoing Complaint for Forfeiture, and declare under penalty of perjury...\"\n\nIt is signed by:\n\"Special Agent Marc Silski\nFederal Bureau of Investigation\nDated: September 6, 2018\"\n\nAnswer: Special Agent Marc Silski", "citations": [], "input_tokens": 5228, "output_tokens": 109, "cost_micros": 17319, "latency_ms": 3780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "special agent marc silski", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the verification section at the end of the document (page 16), I can see who verified and signed the complaint.\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 5778, "output_tokens": 43, "cost_micros": 17979, "latency_ms": 2312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "Special Agent Marc Silski", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the document to find which officer verified and signed the complaint.Based on the document, I found the verification section on page 16. The officer who verified and signed the complaint is:\n\nAnswer: Special Agent Marc Silski, Federal Bureau of Investigation", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 23546, "error": null, "extra": {"thread_id": 2060, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890676fb4dc0465f94bad1a9648bd51a", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 82, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 0.6666666666666666, "method": "str_norm", "normalised_pred": "special agent marc silski federal bureau of investigation", "normalised_gold": "special agent marc silski"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I need to find all the checks issued to Mont Blanc company mentioned in the document.\n\nLooking through the document:\n\nOn page 11 (PageID.11), paragraph e states:\n\"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nOn page 11 (PageID.11), paragraph f states:\n\"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nThese are the two checks issued to Mont Blanc company.\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 25031, "output_tokens": 170, "cost_micros": 77643, "latency_ms": 10219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5599, "output_tokens": 129, "cost_micros": 18732, "latency_ms": 3389, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32, subsections e and f:\n\n1. Check number 150301 issued in September 2012: $35,000\n2. Check number 151545 issued in February 2013: $40,684\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 6118, "output_tokens": 79, "cost_micros": 19539, "latency_ms": 3640, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document for checks issued to Mont Blanc company:\n\nFrom paragraph 32.e: \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32.f: \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5248, "output_tokens": 128, "cost_micros": 17664, "latency_ms": 3969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for checks issued to Mont Blanc:\n\nFrom paragraph 32(e): \"Bank records obtained in this investigation show that in September 2012, the NTC issued a check, number 150301, to a company known as Mont Blanc in the amount of $35,000.\"\n\nFrom paragraph 32(f): \"Subsequently, in February 2013, a second check, number 151545, in the amount of $40,684 was issued by the NTC payable to Mont Blanc.\"\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 5798, "output_tokens": 127, "cost_micros": 19299, "latency_ms": 3592, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['$35,000', '$40,684']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find information about checks issued to Mont Blanc company.Let me continue reading the document to find information about checks issued to Mont Blanc company.Let me continue reading to find information about checks issued to Mont Blanc.Perfect! I found the information about checks issued to Mont Blanc. Let me verify by reading a bit more to ensure I have all the check amounts.\n\nBased on the document, I found the following information about checks issued to Mont Blanc:\n\n1. Check number 150301 - $35,000 (issued in September 2012)\n2. Check number 151545 - $40,684 (issued in February 2013)\n\nAnswer: $35,000, $40,684", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32530, "error": null, "extra": {"thread_id": 2061, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5f1b7e2037a34d839cda54ca371fcea3", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 134, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "000, 35, 40, 684", "normalised_gold": "000, 35, 40, 684"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the CA Bar number in the document.\n\nLooking at page 15, I can see the signature page where it states:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 25014, "output_tokens": 103, "cost_micros": 76587, "latency_ms": 10625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 15 (PageID.15), I can find the CA Bar number listed for Assistant United States Attorney Adriana Dydell.\n\nAnswer: 239516", "citations": [], "input_tokens": 5582, "output_tokens": 47, "cost_micros": 17451, "latency_ms": 3062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, in the signature block for the Assistant United States Attorney, I can see:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 6101, "output_tokens": 108, "cost_micros": 19923, "latency_ms": 4437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking through the document for the CA Bar number...\n\nAt the bottom of page 15, the signature block shows:\n\nADRIANA DYDELL\nAssistant United States Attorney\n211 W. Fort Street, Suite 2001\nDetroit, Michigan 48226\n(313) 226-9125\nDated: September, 2018\nadriana.dydell@usdoj.gov\n(CA. Bar No. 239516)\n\nAnswer: 239516", "citations": [], "input_tokens": 5231, "output_tokens": 107, "cost_micros": 17298, "latency_ms": 4078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I can find the CA Bar number at the bottom of page 15, where it states:\n\n\"Dated: September 13, 2018 (CA. Bar No. 239516)\"\n\nThis appears next to the signature block for Adriana Dydell, Assistant United States Attorney.\n\nAnswer: 239516", "citations": [], "input_tokens": 5781, "output_tokens": 78, "cost_micros": 18513, "latency_ms": 3358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "str", "gold": "239516", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the document that was mentioned to find the CA Bar number.Answer: 239516", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 35797, "error": null, "extra": {"thread_id": 2062, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_67c2db9144b348b6ba61d0ad2f21abaf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 115, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "239516", "normalised_gold": "239516"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll examine each page to identify where information has been blacked out (redacted).\n\nPage 1: No redactions visible\nPage 2: No redactions visible\nPage 3: No redactions visible\nPage 4: No redactions visible\nPage 5: No redactions visible\nPage 6: No redactions visible\nPage 7: No redactions visible\nPage 8: No redactions visible\nPage 9: No redactions visible\nPage 10: No redactions visible\nPage 11: No redactions visible\nPage 12: No redactions visible\nPage 13: Contains multiple black redaction boxes blocking out names and information (in paragraph l and m)\nPage 14: Contains black redaction boxes blocking out information (in paragraph n)\nPage 15: No redactions visible\nPage 16: No redactions visible\n\nAnswer: 13, 14", "citations": [], "input_tokens": 25034, "output_tokens": 196, "cost_micros": 78042, "latency_ms": 11015, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages have information that has been blacked out or redacted.\n\nLooking through the document:\n\n- Page 1 (PageID.1): No visible redactions\n- Page 2 (PageID.2): No visible redactions\n- Page 3 (PageID.3): No visible redactions\n- Page 4 (PageID.4): No visible redactions\n- Page 5 (PageID.5): No visible redactions\n- Page 6 (PageID.6): No visible redactions\n- Page 7 (PageID.7): No visible redactions\n- Page 8 (PageID.8): No visible redactions\n- Page 9 (PageID.9): No visible redactions\n- Page 10 (PageID.10): No visible redactions\n- Page 11 (PageID.11): No visible redactions\n- Page 12 (PageID.12): No visible redactions\n- Page 13 (PageID.13): In paragraph 1, there is redacted text after \"the last names of Iacobelli and\" and in paragraph m, there are multiple redactions of names\n- Page 14 (PageID.14): In paragraph n, there are redacted names at the beginning and within the text\n- Page 15 (PageID.15): No visible redactions\n- Page 16 (PageID.16): No visible redactions\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5602, "output_tokens": 339, "cost_micros": 21891, "latency_ms": 5812, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "Looking through the document for redacted/blacked out information:\n\nOn page 14 (PageID.14), paragraph m, there are multiple instances of redacted information indicated by blank spaces where names should be:\n- \"was interviewed by agents.\"\n- \"was a former co-worker\"\n- \"During the interview,\"\n- \"informed agents that Iacobelli bragged to her about a Mont Blanc pen that he carried in his shirt pocket costing $25,000. Iacobelli also informed that\"\n\nOn page 14 (PageID.14), paragraph n, there is also redacted information:\n- \"On July 31, 2017, agents contacted who stated that\"\n- \"he was in possession of the Lincoln commemorative Mont Blanc pen. added that he ordered the pen, but that Iacobelli paid for both and\"\n\nPage 13 (PageID.13), paragraph l mentions \"having the last names of Iacobelli and\" followed by what appears to be a redaction.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 6121, "output_tokens": 238, "cost_micros": 21933, "latency_ms": 6703, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully review the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Page 2: No redactions visible\n- Page 3: No redactions visible\n- Page 4: No redactions visible\n- Page 5: No redactions visible\n- Page 6: No redactions visible\n- Page 7: No redactions visible\n- Page 8: No redactions visible\n- Page 9: No redactions visible\n- Page 10: No redactions visible\n- Page 11: No redactions visible\n- Page 12: No redactions visible\n- Page 13: Contains redacted/blacked out information (names are replaced with blank spaces/lines in sections l and m)\n- Page 14: Contains redacted/blacked out information (names are replaced with blank spaces/lines in section n)\n- Page 15: No redactions visible\n- Page 16: No redactions visible\n\nThe pages with blacked out information are pages 13 and 14, where personal names have been redacted.\n\nAnswer: 13, 14", "citations": [], "input_tokens": 5251, "output_tokens": 248, "cost_micros": 19473, "latency_ms": 5733, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I need to carefully examine the document to identify which pages contain redacted or blacked out information.\n\nLooking through the document:\n\n- Pages 1-12: No redactions visible\n- Page 13 (PageID.13): Contains multiple redactions marked with \"\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\" or similar black bars in paragraph m and n. Specifically:\n  - \"former co-worker \u2588\u2588\u2588\u2588\u2588\u2588\" \n  - Multiple instances of \"\u2588\u2588\u2588\u2588\u2588\u2588\" referring to a person's name\n  - \"podiatrist named \u2588\u2588\u2588\u2588\u2588\u2588\"\n  - \"[\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588]\" in paragraph n\n  - \"[\u2588\u2588\u2588\u2588]\" multiple times in paragraph n\n\n- Page 14 (PageID.14): No redactions visible\n- Pages 15-16: No redactions visible\n\nThe redactions appear only on page 13 of the document (which corresponds to PageID.13, Page 13 of 16).\n\nAnswer: 13", "citations": [], "input_tokens": 5801, "output_tokens": 206, "cost_micros": 20493, "latency_ms": 6671, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6666666666666666, "method": "list_set", "normalised_pred": "13", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['13', '14']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify pages with blacked out information.Based on my review of the document, I can identify pages with redacted (blacked out) information:\n\n- **Page 13**: Contains multiple black redaction bars in paragraphs l and m, obscuring names and identifying information. The visual description explicitly notes \"Multiple horizontal black bars throughout the document\" and \"Redacted sections appear in paragraphs l and m.\"\n\n- **Page 14**: Contains three black redaction blocks in paragraph n, obscuring names/identifying information. The visual description notes \"Contains redacted sections shown as solid black rectangular blocks\" and \"Multiple solid black rectangular blocks throughout paragraph n.\"\n\nAnswer: 13, 14", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37265, "error": null, "extra": {"thread_id": 2063, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5190eed28c9d49e68875e654ed7fc489", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 163, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "13, 14", "normalised_gold": "13, 14"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of this document.\n\nLooking at page 15 (PageID.15):\nI can see a handwritten date that appears to be \"September 12, 2018\" where \"12\" is written manually.\n\nLooking at page 16 (PageID.16):\nI can see a handwritten date that appears to be \"September 6, 2018\" where \"6\" is written manually.\n\nThe manually written numbers are:\n- Page 15: 12\n- Page 16: 6\n\nSum: 12 + 6 = 18\n\nAnswer: 18", "citations": [], "input_tokens": 25026, "output_tokens": 148, "cost_micros": 77298, "latency_ms": 8937, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "18", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date shows \"September 3 2018\" - there appears to be a handwritten \"3\" in the date field\n\nLooking at page 16 (PageID.16):\n- The date shows \"September 6 2018\" - there appears to be a handwritten \"6\" in the date field\n\nThe sum of the manually written numbers is: 3 + 6 = 9\n\nAnswer: 9", "citations": [], "input_tokens": 5594, "output_tokens": 130, "cost_micros": 18732, "latency_ms": 3875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "9", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to find manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nThe date shows \"September 13 2018\" where \"13\" appears to be handwritten.\n\nLooking at page 16 (PageID.16):\nThe date shows \"September 6, 2018\" where \"6\" appears to be handwritten.\n\nSum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 6113, "output_tokens": 112, "cost_micros": 20019, "latency_ms": 3219, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to examine pages 15 and 16 of the document to find manually written numbers.\n\nLooking at page 15:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.15 Page 15 of 16\"\n- The page contains typed text including \"Respectfully submitted,\" signature block for Matthew Schneider and Adriana Dydell\n- At the bottom it shows \"Dated: September, 2018\"\n- The number \"15\" appears at the bottom of the page\n\nLooking at page 16:\n- The page header shows \"Case 2:18-cv-12857-DML-RSW ECF No. 1 filed 09/13/18 PageID.16 Page 16 of 16\"\n- Contains a verification section signed by Marc Silski\n- At the bottom it shows \"Dated: September 6, 2018\"\n- The number \"16\" appears at the bottom of the page\n\nThe manually written/handwritten numbers visible on these pages are:\n- Page 15: The page number \"15\" at the bottom\n- Page 16: The page number \"16\" at the bottom\n\nSum: 15 + 16 = 31\n\nAnswer: 31", "citations": [], "input_tokens": 5243, "output_tokens": 307, "cost_micros": 20334, "latency_ms": 6765, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "31", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I need to identify manually written numbers on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\n- The date \"September 13, 2018\" appears to have \"13\" that could be handwritten, but examining the format, it appears to be typed text, not manually written.\n\nLooking at page 16 (PageID.16):\n- The date \"September 6, 2018\" appears, where \"6\" could potentially be handwritten.\n- There is a signature line with \"Special Agent Marc Silski\" and a signature.\n\nHowever, upon closer examination of the document format, these appear to be typed dates in the standard court document format. The document shows underlined spaces where information was filled in, but the actual numbers \"13\" on page 15 and \"6\" on page 16 appear in the standard typed format of the document.\n\nLooking more carefully, the date on page 15 shows \"September <u>13</u>, 2018\" where \"13\" appears to be typed/filled in on an underlined space, and on page 16 \"September 6, 2018\" where \"6\" appears typed.\n\nIf these are considered the manually written/filled-in numbers:\n- Page 15: 13\n- Page 16: 6\n- Sum: 13 + 6 = 19\n\nAnswer: 19", "citations": [], "input_tokens": 5793, "output_tokens": 316, "cost_micros": 22119, "latency_ms": 7969, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "19", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "19", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q005", "answer_letter": null, "raw_text": "I'll help you find the manually written numbers on pages 15 and 16 of the document. Let me first read the document that was mentioned in the context.Let me continue reading to find pages 15 and 16. I need to search through the document to locate these specific pages.", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27906, "error": null, "extra": {"thread_id": 2064, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3dc2a747e29a4ced83b1f3d763f972af", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 79, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "15", "normalised_gold": "19"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to analyze the document to identify all the colors present in it.\n\nLooking at the document pages provided:\n- The text appears in black\n- The background is white\n- There are blue hyperlinks/case numbers at the top of each page\n- There are black redaction marks/blocks in some areas (pages 13-14)\n\nThe colors present in this document are:\n- Black (text and redactions)\n- Blue (case numbers and headers)\n- White (background)\n\nAnswer: black, blue, white", "citations": [], "input_tokens": 25038, "output_tokens": 116, "cost_micros": 76854, "latency_ms": 10546, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "black, blue, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this legal document.\n\nScanning through the document for color references:\n\n1. Page 11, paragraph d: \"red 2013 Ferrari 458 Spider convertible\"\n2. Page 12, paragraph j: mentions \"gold\" (portrait in gold) and \"sapphire\" (which is blue)\n\nLet me verify these are the only color mentions:\n- \"red\" - explicitly mentioned for the Ferrari\n- \"gold\" - mentioned for Lincoln's portrait on the pen clip\n- The sapphire mentioned would typically be blue, but the document says \"sapphire\" not \"blue\"\n\nLooking more carefully at the text:\n- \"red 2013 Ferrari\" (paragraph 32d)\n- \"gold\" (paragraph 32j - \"Lincoln's portrait in gold\")\n- \"sapphire\" (paragraph 32j - \"has a sapphire\")\n\nThe colors explicitly stated are:\n- gold\n- red\n\nAlphabetically ordered:\n\nAnswer: gold, red", "citations": [], "input_tokens": 5606, "output_tokens": 228, "cost_micros": 20238, "latency_ms": 6780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "gold, red", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to examine the document to identify all colors mentioned or visible in it.\n\nLooking through the document:\n\n1. The document itself appears to be a black and white legal document (black text on white background)\n2. In paragraph 32(d), a \"red 2013 Ferrari 458 Spider convertible\" is mentioned\n3. In paragraph 32(j), \"gold\" is mentioned (describing Lincoln's portrait in gold on the pen clip)\n4. In paragraph 32(j), \"sapphire\" is mentioned (the pen's clip has a sapphire)\n\nColors explicitly mentioned in the text:\n- Gold\n- Red\n- Sapphire (which is blue)\n\nColors of the document format itself:\n- Black (text)\n- White (background)\n\nListing all colors alphabetically:\n- Black\n- Blue (sapphire)\n- Gold\n- Red\n- White\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 6125, "output_tokens": 214, "cost_micros": 21585, "latency_ms": 5405, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in this document.\n\nLet me search through the document for any color references:\n\n1. In paragraph 32.a, there is mention of \"a red 2013 Ferrari 458 Spider convertible\"\n2. In paragraph 32.j, there is mention of \"gold\" (describing Lincoln's portrait on the pen clip)\n3. The document is a legal court document that appears to be in black text on white paper (standard document format)\n\nThe colors explicitly mentioned in the text are:\n- Gold (mentioned in the description of the pen)\n- Red (mentioned in the description of the Ferrari)\n\nSince this is a standard black and white legal document with black text, and the question asks for colors in the document, I should include the standard document colors as well as the colors mentioned in the content.\n\nColors found:\n- Black (text)\n- Gold (pen description)\n- Red (Ferrari description)\n- White (paper/background)\n\nIn alphabetical order: black, gold, red, white\n\nAnswer: black, gold, red, white", "citations": [], "input_tokens": 5255, "output_tokens": 238, "cost_micros": 19335, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.5714285714285715, "method": "list_set", "normalised_pred": "black, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I need to identify all colors mentioned in the document.\n\nGoing through the document carefully:\n\n1. Page 10, paragraph d: \"red 2013 Ferrari 458 Spider convertible\" - mentions **red**\n2. Page 12, paragraph j: \"sapphire\" (referring to a sapphire on the pen clip) - **blue** (sapphire is blue)\n3. Page 12, paragraph j: \"gold\" (portrait in gold on the pen's clip) - mentions **gold**\n\nLooking at the document format itself, this appears to be a standard black text on white background legal document, so:\n- **black** (text)\n- **white** (background)\n\nAlphabetically ordered:\n- black\n- blue\n- gold\n- red\n- white\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 5805, "output_tokens": 189, "cost_micros": 20250, "latency_ms": 6407, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "list", "gold": "['black', 'blue', 'white']", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q006", "answer_letter": null, "raw_text": "I'll read the document to identify all the colors present.Based on my careful reading of the document, I can identify the following colors mentioned or described:\n\nFrom the visual descriptions and content:\n- **Black** - mentioned multiple times in visual descriptions (\"black text\", \"black bars\", \"black rectangular blocks\" for redactions)\n- **Blue** - mentioned in the visual description (\"Blue text displaying case number\")\n- **Gold** - mentioned in the pen description (\"Lincoln's portrait in gold\")\n- **Red** - mentioned for the Ferrari (\"red 2013 Ferrari 458 Spider\")\n- **White** - mentioned in visual descriptions (\"white background\")\n\nThere's also a **sapphire** mentioned on the pen clip, which is typically blue, but since \"blue\" is already listed separately and sapphire is a gemstone rather than a color descriptor in this context, I'll include it as a separate item if we're being comprehensive about color-related terms.\n\nHowever, looking at the actual color words used as descriptors in the document:\n\nAnswer: black, blue, gold, red, white", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37108, "error": null, "extra": {"thread_id": 2065, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8c1d9833eaf14e8eae921556f7ecfecf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 186, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.7499999999999999, "method": "list_set", "normalised_pred": "black, blue, gold, red, white", "normalised_gold": "black, blue, white"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "native_pdf", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL, Assistant United States Attorney\"\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski, Federal Bureau of Investigation\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 25021, "output_tokens": 122, "cost_micros": 76893, "latency_ms": 8171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15 (PageID.15):\nI can see one signature above the typed name \"ADRIANA DYDELL\" (Assistant United States Attorney).\n\nLooking at page 16 (PageID.16):\nI can see one signature above the typed name \"Special Agent Marc Silski\" (Federal Bureau of Investigation).\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5589, "output_tokens": 124, "cost_micros": 18627, "latency_ms": 3781, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "azure_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\nLooking at page 15:\n- There is one signature above the typed name \"ADRIANA DYDELL\"\n\nLooking at page 16:\n- There is one signature above the typed name \"Special Agent Marc Silski\"\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 6108, "output_tokens": 100, "cost_micros": 19824, "latency_ms": 3750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_basic_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures that appear on pages 15 and 16 of the document.\n\nLooking at page 15:\n- This page shows the document dated September, 2018\n- It has the names \"MATTHEW SCHNEIDER\" and \"ADRIANA DYDELL\" typed, but these appear to be typed names as part of the document format, not actual signatures\n- I do not see any actual handwritten or image signatures on page 15\n\nLooking at page 16:\n- This is the VERIFICATION page\n- It shows \"Special Agent Marc Silski\" and \"Federal Bureau of Investigation\" with a date of \"September 6, 2018\"\n- This also appears to be typed text rather than an actual signature image\n\nThe question asks for signatures \"appeared\" on these pages. Looking carefully at both pages, I can see typed names but no visible handwritten signatures or signature images in the document.\n\nHowever, these are signature blocks where signatures would typically appear. The document shows:\n- Page 15: 1 signature block (for Adriana Dydell)\n- Page 16: 1 signature block (for Marc Silski)\n\nTotal signature blocks: 2\n\nSince the question asks for the count in \"float format\" but then requests \"a single integer only,\" and given that there are 2 signature blocks present on these pages combined:\n\nAnswer: 2", "citations": [], "input_tokens": 5238, "output_tokens": 308, "cost_micros": 20334, "latency_ms": 8796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "llamacloud_premium_lc", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I need to count the signatures on pages 15 and 16 of the document.\n\n**Page 15 (PageID.15):**\nLooking at page 15, I can see:\n1. A signature above \"ADRIANA DYDELL\" - there is an underlined signature line with \"Adriana Dydell\" written above it\n\nSo there is 1 signature on page 15.\n\n**Page 16 (PageID.16):**\nLooking at page 16, I can see:\n1. A signature notation \"[Signature of Marc Silski]\" above \"Special Agent Marc Silski\"\n\nSo there is 1 signature on page 16.\n\n**Total signatures on pages 15 and 16:** 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 5788, "output_tokens": 185, "cost_micros": 20139, "latency_ms": 5296, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "doc_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf", "answer_format": "int", "gold": "2", "pages": 16, "document_id": 7599, "arm": "surfsense_agentic", "question_id": "3276a5b991c49cf5f9a4af0f7d6fce67.pdf::Q007", "answer_letter": null, "raw_text": "I'll read the document to find the signatures on pages 15 and 16.Let me search for content related to pages 15 and 16 by reading more of the document:Let me read the entire document to find pages 15 and 16:Great! I found the relevant sections. Let me look more carefully at the content around pages 15 and 16. I can see:\n\n- Line 616: `<!-- PageNumber=\"15\" -->`\n- Line 637: `<!-- PageNumber=\"16\" -->`\n\nNow let me examine the signatures on these pages. From what I can see:\n\n**Page 15** (lines 590-618): Shows signatures for:\n1. Matthew Schneider (United States Attorney) \n2. Adriana Dydell (Assistant United States Attorney)\n\n**Page 16** (lines 619-638): Shows signature for:\n1. Marc Silski (Special Agent, Federal Bureau of Investigation)\n\nLet me read the section more carefully to confirm:Perfect! Now I can clearly see the signatures on both pages:\n\n**Page 15** (lines 596-616):\n- Shows \"Respectfully submitted,\" followed by:\n  1. MATTHEW SCHNEIDER (United States Attorney)\n  2. \"Adriana@iol\" (appears to be a signature)\n  3. ADRIANA DYDELL (Assistant United States Attorney)\n- The \"Adriana@iol\" on line 603 appears to be a signature representation\n- Dated: September 13, 2018\n\n**Page 16** (lines 619-637):\n- Shows VERIFICATION section with:\n  1. \"Josh,\" on line 631 (appears to be a signature)\n  2. Special Agent Marc Silski (Federal Bureau of Investigation)\n- Dated: September 6, 2018\n\nCounting the signatures:\n- Page 15: 1 signature (the \"Adriana@iol\" signature for Adriana Dydell)\n- Page 16: 1 signature (the \"Josh,\" signature for Marc Silski)\n\nTotal signatures on pages 15 and 16: 1 + 1 = 2\n\nAnswer: 2", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 42092, "error": null, "extra": {"thread_id": 2066, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_ddcf7e386683499793cc579d23850dce", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 267, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "According to the document, the telephone number for The Limes Residential Home is:\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 37738, "output_tokens": 30, "cost_micros": 113664, "latency_ms": 7312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10257, "output_tokens": 37, "cost_micros": 31326, "latency_ms": 4000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header on the first page, I can find the contact information for The Limes Residential Home.\n\nThe address and contact details are:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 12045, "output_tokens": 79, "cost_micros": 37320, "latency_ms": 3766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nThe address block shows:\n43 Foreland Road\nBembridge\nIsle of Wight\nPO35 5XN\nTel: 01983 873655\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10599, "output_tokens": 73, "cost_micros": 32892, "latency_ms": 6344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document header, I can find the contact information for The Limes Residential Home.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 10834, "output_tokens": 33, "cost_micros": 32997, "latency_ms": 3625, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "str", "gold": "01983 873655", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the document to find the telephone number for The Limes Residential Home.Let me search for \"The Limes Residential Home\" in the document:Let me search with more context to see the actual content:I can see from the search results that the telephone number appears on line 168. Let me read that section:Perfect! I found the telephone number for The Limes Residential Home on line 174.\n\nAnswer: 01983 873655", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 34734, "error": null, "extra": {"thread_id": 2067, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_de5d9004414b47159eff08bc68030dbf", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 151, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "01983 873655", "normalised_gold": "01983 873655"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically at page 3 which contains the section \"The five questions we ask about services and what we found\", I can identify the primary questions asked in this inspection report.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 37742, "output_tokens": 73, "cost_micros": 114321, "latency_ms": 7515, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can identify the five primary questions that were asked about the services in this inspection report.\n\nThese questions are clearly listed in the \"Summary of findings\" section under \"The five questions we ask about services and what we found\":\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10261, "output_tokens": 84, "cost_micros": 32043, "latency_ms": 3844, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, specifically the \"Summary of findings\" section which contains \"The five questions we ask about services and what we found\", I can identify the primary questions asked about the services.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 12049, "output_tokens": 72, "cost_micros": 37227, "latency_ms": 4234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document, I can see the \"five questions we ask about services\" section which lists the primary questions asked during the inspection.\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10603, "output_tokens": 61, "cost_micros": 32724, "latency_ms": 3125, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "Looking through the document, I can identify the five primary questions that were consistently asked about the services throughout the inspection report.\n\nThese questions appear in the \"Summary of findings\" section and structure the entire report, with each having its own detailed findings section:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 10838, "output_tokens": 126, "cost_micros": 34404, "latency_ms": 4328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the user-mentioned document to find the primary questions asked about the services.Looking at the document, I can see this is a Care Quality Commission inspection report. The report explicitly states \"The five questions we ask about services\" in a table starting around line 314. Let me identify these questions from the document structure:\n\nFrom the document, the five primary questions asked about the services are clearly listed:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?\n\nAnswer: Is the service safe?, Is the service effective?, Is the service caring?, Is the service responsive?, Is the service well-led?", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 27546, "error": null, "extra": {"thread_id": 2068, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8bdf643e270448d6aadaffef3d9744b8", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 108, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.6, "method": "list_set", "normalised_pred": "is service caring, is service effective, is service responsive, is service safe, is service well led", "normalised_gold": "is serve caring, is service effective, is service responsive, is service well led, is servife safe"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLet me examine each page:\n\nPage 1: I can see the CQC logo (the circular Q logo with \"Care Quality Commission\" text) at the top of the page.\n\nPage 2: No CQC logo visible on this page.\n\nPage 3: No CQC logo visible on this page.\n\nPage 4: No CQC logo visible on this page.\n\nPage 5: I can see the CQC logo at the top of this page.\n\nPages 6-17: Examining the remaining pages, I do not see the CQC logo appearing on any of these pages.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 37764, "output_tokens": 169, "cost_micros": 115827, "latency_ms": 7890, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of certain pages, which typically indicates where a logo would be placed in the actual PDF document.\n\nLet me go through each page:\n\nPage 1: \"Care Quality Commission\" appears at the top - \"The Limes Residential Care Home Limited The Limes Residential Home\"\n\nPage 2: \"Summary of findings\" section continues\n\nPage 3: \"Summary of findings\" continues\n\nPage 4: \"Summary of findings\" continues\n\nPage 5: \"Care Quality Commission\" appears - \"The Limes Residential Home Detailed findings\"\n\nPage 6: Continues with \"Is the service safe?\"\n\nPage 7: Continues with \"Is the service safe?\"\n\nPage 8: \"Inadequate\" and \"Is the service effective?\"\n\nPage 9: Continues with \"Is the service effective?\"\n\nPage 10: \"Requires improvement\" and \"Is the service caring?\"\n\nPage 11: Continues with \"Is the service caring?\"\n\nPage 12: \"Requires improvement\" and \"Is the service responsive?\"\n\nPage 13: \"Requires improvement\" and \"Is the service well-led?\"\n\nPage 14: Continues with \"Is the service well-led?\"\n\nPage 15: \"Action we have told the provider to take\"\n\nPage 16: Continues with \"Action we have told the provider to take\"\n\nPage 17: \"Enforcement actions\"\n\nBased on the document structure, \"Care Quality Commission\" appears as a header on pages 1 and 5, which is where the CQC logo would typically be located.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10283, "output_tokens": 395, "cost_micros": 36774, "latency_ms": 7358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the Care Quality Commission (CQC) logo appears in this document.\n\nLooking through the document:\n\n1. Page 1 (cover page) - Yes, CQC logo appears at the top\n2. Page 2 - No visible logo\n3. Page 3 - No visible logo\n4. Page 4 - No visible logo\n5. Page 5 - Yes, CQC logo appears at the top (start of \"Detailed findings\" section)\n6. Page 6 - No visible logo\n7. Page 7 - No visible logo\n8. Page 8 - No visible logo\n9. Page 9 - No visible logo\n10. Page 10 - No visible logo\n11. Page 11 - No visible logo\n12. Page 12 - No visible logo\n13. Page 13 - No visible logo\n14. Page 14 - No visible logo\n15. Page 15 - No visible logo\n16. Page 16 - No visible logo\n17. Page 17 - No visible logo\n\nThe CQC logo appears on:\n- Page 1 (the title page/cover)\n- Page 5 (at the start of the detailed findings section)\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 12071, "output_tokens": 294, "cost_micros": 40623, "latency_ms": 5390, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document, I can see \"Care Quality Commission\" text appears at the top of several pages. The logo/header \"Care Quality Commission\" appears on:\n\n- Page 1 (the title page with \"The Limes Residential Care Home Limited\")\n- Page 5 (at the top before \"Detailed findings\")\n- Page 15 (at the top before \"Action we have told the provider to take\")\n\nLet me verify by checking each page systematically:\n\nPage 1: \"Care Quality Commission\" header present\nPage 2-4: Summary of findings pages - no clear CQC logo/header visible in the page headers\nPage 5: \"Care Quality Commission\" header present before \"Detailed findings\"\nPages 6-14: Detailed findings sections - these appear to be continuation pages\nPage 15: \"Care Quality Commission\" header present before enforcement actions section\nPages 16-17: Continuation of enforcement actions\n\nBased on the document structure, the CQC logo/header appears on:\n\nAnswer: Page 1, Page 5, Page 15", "citations": [], "input_tokens": 10625, "output_tokens": 262, "cost_micros": 35805, "latency_ms": 7218, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.8, "method": "list_set", "normalised_pred": "page 1, page 15, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I need to identify all pages where the CQC (Care Quality Commission) logo appears in this document.\n\nLooking through the document systematically:\n\nPage 1: \"Care Quality Commission\" appears at the top as a header\nPage 2: \"Summary of findings\" - no logo visible in header\nPage 3: \"Summary of findings\" continues - no logo visible\nPage 4: \"Summary of findings\" continues - no logo visible\nPage 5: \"CareQuality Commission\" appears at the top (note: slightly different formatting)\nPage 6-17: These pages show section headings but I need to check for the CQC logo/branding\n\nLooking more carefully at the document structure, the CQC logo/branding appears to be present on:\n- Page 1 (top of document with \"Care Quality Commission\")\n- Page 5 (shows \"CareQuality Commission\" at top of \"Detailed findings\" section)\n\nThese appear to be the only pages with the CQC branding/logo clearly marked at the top.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 10860, "output_tokens": 243, "cost_micros": 36225, "latency_ms": 6234, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "list", "gold": "['Page 1', 'Page 5']", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q004", "answer_letter": null, "raw_text": "I'll read the document to identify all pages where the CQC logo appears.Let me search for \"CQC\" or \"logo\" throughout the document to find all pages where the logo appears:Now let me search for mentions of \"logo\" or \"Care Quality Commission\" to find where the logo appears:Let me search more specifically for image and figure tags to identify all instances of the logo:Let me read through the entire document more systematically to find all logo occurrences. Let me check what page the first logo is on and look for patterns:The first logo appears before \"PageFooter=1\", so it's on Page 1. Now let me search the entire document systematically for all instances of the CQC logo. Let me search for figures or images:Good! Now I found 7 figure tags. Let me read the context around each one to identify which ones are the CQC logo:Now I've identified that several figures contain only page headers. Let me verify which ones actually contain the CQC logo by checking the content. Based on my readings:\n\n1. Line 121 (before PageFooter 1) - Contains \"Care Quality Commission\" logo with visual description - **Page 1**\n2. Line 409 (after PageBreak on line 406, before PageFooter 4 on line 405) - Contains \"Care Quality Commission\" - **Page 5** (since PageFooter 4 appears at line 405-406, this is after the page break, so it's on page 5)\n\nLet me verify by checking the page breaks more carefully:Perfect! So after PageFooter 4 and PageBreak, we get Page 5, which has the CQC logo. Now let me check if there are any other CQC logos. The other figures at lines 868, 981, 1036, 1134, and 1247 only contain PageHeaders, not the logo.\n\nLet me verify this is correct by checking one more time if there are any other embedded images:Let me search for any other embedded images:Good! Only one embedded image (I1.jpg) exists in the document. Now let me confirm the two locations of \"Care Quality Commission\" text in figures:So the CQC logo on Page 5 appears between PageFooter 4 (line 405) and PageFooter 5 (line 456). That confirms it's on Page 5.\n\nNow let me verify there's only one actual logo image (I1.jpg) on Page 1, and the Page 5 figure is just text without an image:I notice that on Page 5 (line 409-414), there's a figure with \"Care Quality Commission\" text but NO embedded image notation. Let me check if there's an image reference after this figure:No embedded image noted for Page 5's figure. So the Page 5 figure has \"Care Quality Commission\" text but the question asks specifically about the \"logo\" which includes the visual/graphical element. Let me re-read the Page 1 section to see if it explicitly identifies this as a logo:\n\nLooking back at lines 130-147, Page 1 clearly shows:\n- \"**Embedded image:** `I1.jpg`\"\n- \"**Visual description:**\"\n- \"Organizational logo combining a graphical symbol with text\"\n\nThis definitively identifies Page 1 as having the CQC logo (the actual image/visual logo).\n\nOn Page 5 (lines 409-414), there's only the text \"Care Quality Commission\" in a figure, but no embedded image or logo graphic.\n\nHowever, let me check if both figures might represent the same logo - the system might have extracted it once with full details (Page 1) and then just shown the text on Page 5. In typical inspection reports, logos often appear on multiple pages. Let me search more broadly to understand the document structure better. Let me see if there are image references I might have missed:Only one \"Visual description\" exists - on Page 1. The interpretation here is that:\n- Page 1 has the actual CQC logo image (I1.jpg with full visual description)\n- Page 5 has a figure with just \"Care Quality Commission\" text\n\nGiven that the question asks for \"the logo of CQC\" (which implies the graphical/visual logo, not just text), and given that only Page 1 has an embedded image with the visual logo description, the answer should be Page 1 only.\n\nHowever, both figures contain \"Care Quality Commission\" and in PDF documents, logos can appear as repeated elements. The figure on Page 5 likely also represents the logo (just extracted differently by the parser). Let me verify by checking if standard CQC inspection reports have logos on multiple pages.\n\nBased on the document structure, I have:\n1. **Page 1** - Clear CQC logo with embedded image I1.jpg and full visual description\n2. **Page 5** - Figure with \"Care Quality Commission\" text (likely the same logo, but extraction didn't capture the image separately)\n\nGiven the ambiguity, I should report both pages where the CQC element appears in a figure tag, as both likely represent the logo placement in the original PDF.\n\nAnswer: Page 1, Page 5", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 135453, "error": null, "extra": {"thread_id": 2069, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_2a8af9e5bd4c48cebf2e6cbec000700b", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 1002, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "page 1, page 5", "normalised_gold": "page 1, page 5"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "native_pdf", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this CQC inspection report.\n\nLet me go through the document systematically:\n\n**From \"Action we have told the provider to take\" section (pages 15-16):**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From \"Enforcement actions\" section (page 17):**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nCounting the unique regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 HSCA (RA) (Staffing)\n- Regulation 18 CQC (Registration) (Notification)\n\nThat's 8 regulations total.\n\nAnswer: 8", "citations": [], "input_tokens": 37740, "output_tokens": 419, "cost_micros": 119505, "latency_ms": 10562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "379f44022bb27aa53efd5d322c7b57bf.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches of HSCA (Regulated Activities) Regulations 2014:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - unlawful restraint and failure to respond to allegations/safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (mentioned twice - failure to assess risks, and failure to manage medicines safely)\n\n3. **Regulation 18** - Staffing (insufficient staffing levels)\n\n4. **Regulation 18** - Staffing (again, for induction, supervision and training issues)\n\n5. **Regulation 11** - Need for consent (MCA 2005 implementation)\n\n6. **Regulation 10** - Dignity and respect\n\n7. **Regulation 9** - Person-centred care\n\n8. **Regulation 17** - Good governance (mentioned twice - failure to assess/monitor quality, and failure to maintain accurate records)\n\n**Breach of HSCA (Registration) Regulations 2009:**\n\n9. **Regulation 18** - Notification of other incidents (CQC Registration Regulations 2009)\n\nNow let me count the distinct regulation numbers that were breached:\n- Regulation 13 (RA Regs 2014)\n- Regulation 12 (RA Regs 2014)\n- Regulation 18 (RA Regs 2014)\n- Regulation 11 (RA Regs 2014)\n- Regulation 10 (RA Regs 2014)\n- Regulation 9 (RA Regs 2014)\n- Regulation 17 (RA Regs 2014)\n- Regulation 18 (Registration Regs 2009)\n\nThat's 7 different regulations from the RA Regulations 2014 and 1 from the Registration Regulations 2009, making 8 total distinct regulations breached.\n\nAnswer: 8", "citations": [], "input_tokens": 10259, "output_tokens": 499, "cost_micros": 38262, "latency_ms": 8109, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "azure_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breached regulations mentioned in this inspection report.\n\nLet me go through the document systematically:\n\n**From \"Is the service safe?\" section:**\n1. Regulation 13 - Safeguarding (failure to respond to allegations and investigate safeguarding incidents)\n2. Regulation 12 - Safe care and treatment (failure to assess and mitigate risks)\n3. Regulation 18 - Staffing (insufficient staffing levels)\n4. Regulation 12 - Safe care and treatment (medicines not managed safely) - already counted above\n\n**From \"Is the service effective?\" section:**\n5. Regulation 18 - Staffing (induction, supervision and training) - already counted above\n6. Regulation 13 - Safeguarding (unlawful restraint) - already counted above\n7. Regulation 11 - Need for consent (MCA 2005 not implemented)\n\n**From \"Is the service caring?\" section:**\n8. Regulation 10 - Dignity and respect\n\n**From \"Is the service responsive?\" section:**\n9. Regulation 9 - Person-centred care (care records not reflecting current needs)\n\n**From \"Is the service well-led?\" section:**\n10. Regulation 17 - Good governance (failure to assess, monitor and improve quality)\n11. Regulation 17 - Good governance (failure to maintain accurate records) - already counted above\n12. Regulation 18 CQC (Registration) Regulations 2009 - Notification of incidents\n\nLet me count the unique HSCA 2008 regulations breached:\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n- Regulation 13 (Safeguarding)\n- Regulation 17 (Good governance)\n- Regulation 18 (Staffing)\n\nThat's 7 regulations under HSCA 2008 (Regulated Activities) Regulations 2014.\n\nPlus 1 regulation under CQC (Registration) Regulations 2009 (Regulation 18).\n\nThe question asks specifically about \"regulations of the", "citations": [], "input_tokens": 12047, "output_tokens": 512, "cost_micros": 43821, "latency_ms": 8780, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_basic_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the unique regulations that were breached according to this report.\n\nLet me go through the document systematically:\n\n**From the \"Action we have told the provider to take\" section:**\n1. Regulation 18 HSCA (RA) Regulations 2014 - Staffing\n2. Regulation 11 HSCA (RA) Regulations 2014 - Need for consent\n3. Regulation 10 HSCA (RA) Regulations 2014 - Dignity and respect\n4. Regulation 9 HSCA (RA) Regulations 2014 - Person-centred care\n5. Regulation 17 HSCA (RA) Regulations 2014 - Good governance\n6. Regulation 18 CQC (Registration) Regulations 2009 - Notification of other incidents\n\n**From the \"Enforcement actions\" section:**\n7. Regulation 13 HSCA (RA) Regulations 2014 - Safeguarding service users from abuse and improper treatment\n8. Regulation 12 HSCA (RA) Regulations 2014 - Safe care and treatment\n\nLet me verify by checking the main body of the report for all breach mentions:\n- Regulation 13 (safeguarding) - breach mentioned\n- Regulation 12 (safe care and treatment, including medicines and risk assessment) - breach mentioned\n- Regulation 18 (staffing) - breach mentioned\n- Regulation 11 (Mental Capacity Act) - breach mentioned\n- Regulation 10 (dignity and respect) - breach mentioned\n- Regulation 9 (person-centred care) - breach mentioned\n- Regulation 17 (good governance - two separate breaches mentioned for different aspects) - breach mentioned\n- Regulation 18 CQC (Registration) Regulations 2009 (notifications) - breach mentioned\n\nTotal unique regulations breached: 8\n\nAnswer: 8", "citations": [], "input_tokens": 10601, "output_tokens": 448, "cost_micros": 38523, "latency_ms": 7250, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "8", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "llamacloud_premium_lc", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I need to count all the breaches of the Health and Social Care Act 2008 (HSCA 2008) regulations mentioned in this report.\n\nLet me go through the document systematically:\n\n**Breaches found in the main body of the report:**\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (failure to respond to allegations and report safeguarding incidents)\n\n2. **Regulation 12** - Safe care and treatment (failure to assess, record and mitigate risks to people's health and safety)\n\n3. **Regulation 18** - Staffing (failure to ensure sufficient staffing levels)\n\n4. **Regulation 12** - Safe care and treatment (failure to manage medicines safely) - *same regulation as #2*\n\n5. **Regulation 18** - Staffing (failure to ensure staff received appropriate induction, supervision and training) - *same regulation as #3*\n\n6. **Regulation 13** - Safeguarding (failure to ensure people were not unlawfully restrained) - *same regulation as #1*\n\n7. **Regulation 11** - Need for consent (failure to ensure MCA 2005 code of practice was implemented)\n\n8. **Regulation 10** - Dignity and respect (failure to treat people with respect and dignity at all times)\n\n9. **Regulation 9** - Person-centred care (failure to ensure care records reflected current needs and preferences)\n\n10. **Regulation 17** - Good governance (failure to effectively assess, monitor and improve quality and safety of services)\n\n11. **Regulation 17** - Good governance (failure to maintain accurate and complete records) - *same regulation as #10*\n\n12. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - This is a different Act (Registration Regulations, not Regulated Activities Regulations)\n\n**Counting unique HSCA 2008 (Regulated Activities) Regulations 2014 breaches:**\n- Regulation 9 (Person-centred care)\n- Regulation 10 (Dignity and respect)\n- Regulation 11 (Need for consent)\n- Regulation 12 (Safe care and treatment)\n-", "citations": [], "input_tokens": 10836, "output_tokens": 512, "cost_micros": 40188, "latency_ms": 9764, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "length"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "7"}}
+{"qid": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "doc_id": "379f44022bb27aa53efd5d322c7b57bf.pdf", "answer_format": "int", "gold": "7", "pages": 17, "document_id": 7600, "arm": "surfsense_agentic", "question_id": "379f44022bb27aa53efd5d322c7b57bf.pdf::Q005", "answer_letter": null, "raw_text": "I'll read the provided document to find information about HSCA 2008 regulations breaches.Let me continue reading to find any summary or conclusion section that might provide the total count.Based on my careful reading of the document, I need to count all the breaches of the Health and Social Care Act 2008 (Regulated Activities) Regulations 2014 (HSCA 2008).\n\nThe breaches mentioned in the report are:\n\n1. **Regulation 13** - Safeguarding service users from abuse and improper treatment (mentioned twice - for failing to respond to allegations and for unlawful restraint)\n2. **Regulation 12** - Safe care and treatment (mentioned twice - for failing to assess/mitigate risks and for failing to manage medicines safely)\n3. **Regulation 18** - Staffing (mentioned twice - for insufficient staffing levels and for inadequate training/supervision)\n4. **Regulation 11** - Need for consent (MCA 2005 implementation)\n5. **Regulation 10** - Dignity and respect\n6. **Regulation 9** - Person-centred care\n7. **Regulation 17** - Good governance (mentioned twice - for failing to assess/monitor quality and for failing to maintain accurate records)\n\nAdditionally, there is one breach of:\n8. **Regulation 18 of the Health and Social Care Act 2008 (Registration) Regulations 2009** - Notification of other incidents\n\nCounting the distinct regulation numbers breached under HSCA (RA) Regulations 2014:\n- Regulation 13\n- Regulation 12\n- Regulation 18\n- Regulation 11\n- Regulation 10\n- Regulation 9\n- Regulation 17\n\nThat's 7 different regulations of the HSCA (RA) Regulations 2014 that were breached.\n\nAnswer: 7", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 38483, "error": null, "extra": {"thread_id": 2070, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_8ce6489cb9fc4be0aad3905ed014d904", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 217, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "7", "normalised_gold": "7"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from 3M's cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018 (page 60):\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount for FY2018 is shown as purchases of property, plant and equipment.\n\nAnswer: 1577", "citations": [], "input_tokens": 390262, "output_tokens": 116, "cost_micros": 2344182, "latency_ms": 44468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the years ended December 31, I can find the capital expenditures under \"Cash Flows from Investing Activities.\"\n\nFrom the document:\n\n**Cash Flows from Investing Activities:**\nYears ended December 31\n(Millions)                                          2018        2017        2016\n\nPurchases of property, plant and equipment (PP&E)   $(1,577)    $(1,373)    $(1,420)\n\nThe capital expenditure for FY2018 is shown as purchases of property, plant and equipment (PP&E) which equals $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 140538, "output_tokens": 171, "cost_micros": 424179, "latency_ms": 8062, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the capital expenditure amount for FY2018 from the cash flow statement.\n\nLooking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThe capital expenditure amount is shown as a negative number in the cash flow statement because it represents a cash outflow.\n\nThe capital expenditure amount for FY2018 is $1,577 million.\n\nAnswer: 1577", "citations": [], "input_tokens": 207953, "output_tokens": 130, "cost_micros": 1250643, "latency_ms": 8718, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for FY2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 163241, "output_tokens": 77, "cost_micros": 490878, "latency_ms": 8000, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the Consolidated Statement of Cash Flows for the year ended December 31, 2018:\n\nUnder \"Cash Flows from Investing Activities\":\n- Purchases of property, plant and equipment (PP&E): $(1,577) million\n\nThis represents the capital expenditures for fiscal year 2018.\n\nAnswer: 1577", "citations": [], "input_tokens": 177504, "output_tokens": 78, "cost_micros": 533682, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q000", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "$1577.00", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q000", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the capital expenditure amount from the cash flow statement.Let me search for the cash flow statement in this document.Great! I found the Consolidated Statement of Cash Flows. Let me read that specific chunk to find the capital expenditure line.Let me search for variations of capital expenditure terminology:Perfect! I found it. The \"Purchases of property, plant and equipment (PP&E)\" line shows the capital expenditure amounts. For 2018, it shows $(1,577) million. Let me read the actual chunk to confirm this is from the cash flow statement.Let me check a bit earlier to confirm this is the Consolidated Statement of Cash Flows:Let me check the title of this statement:Perfect! I've confirmed this is the \"3M Company and Subsidiaries Consolidated Statement of Cash Flows\" for years ended December 31, with columns for 2018, 2017, and 2016.\n\nIn the \"Cash Flows from Investing Activities\" section, the line item \"Purchases of property, plant and equipment (PP&E)\" shows:\n- 2018: (1,577)\n- 2017: (1,373)\n- 2016: (1,420)\n\nThe amounts are in millions of USD, and the parentheses indicate these are cash outflows. The FY2018 capital expenditure amount is 1,577 million USD.\n\nAnswer: 1577.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 53671, "error": null, "extra": {"thread_id": 2071, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_b68bd46e8dab41c29dc9086112158cbd", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 310, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "1577.0", "normalised_gold": "1577.0"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for 3M's business segments in FY2018...\n\nFrom the document, specifically on pages discussing business segments (e.g., page 4, page 33-38), I can identify the following business segments:\n\n1. Industrial Business\n2. Safety and Graphics Business\n3. Health Care Business\n4. Electronics and Energy Business\n5. Consumer Business\n\nThese five segments are consistently mentioned throughout the document as 3M's reportable segments for fiscal year 2018.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 390260, "output_tokens": 126, "cost_micros": 2344395, "latency_ms": 49344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking at the document, I need to identify 3M's business segments for FY2018.\n\nFrom the document, I can find the business segment information in multiple places. The clearest statement is in the Overview section and repeated throughout:\n\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nThis is also confirmed in the Business Segment Products table which lists the major products for each segment:\n- Industrial\n- Safety and Graphics\n- Health Care\n- Electronics and Energy\n- Consumer\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 140536, "output_tokens": 140, "cost_micros": 423708, "latency_ms": 9046, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information clearly stated in multiple places:\n\nIn the \"BUSINESS SEGMENTS\" section (page 4), it states:\n\"3M manages its operations in five business segments. The reportable segments are Industrial, Safety and Graphics, Health Care, Electronics and Energy, and Consumer.\"\n\nThis is also confirmed in the \"OVERVIEW\" section (page 15):\n\"3M manages its operations in five operating business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnd in the \"PERFORMANCE BY BUSINESS SEGMENT\" section (page 32):\n\"3M manages its operations in five business segments. The reportable segments are Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 207951, "output_tokens": 206, "cost_micros": 1252341, "latency_ms": 10437, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018:\n\nFrom the document, I can find the business segments clearly stated in multiple locations, including:\n\n1. In the Business Segments section (page 4):\n\"3M manages its operations in five business segments: Industrial; Safety and Graphics; Health Care; Electronics and Energy; and Consumer.\"\n\n2. In the Table of Contents and throughout the financial statements, these five segments are consistently referenced.\n\n3. In Note 18 (Business Segments), the same five segments are listed with their products and financial information.\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 163239, "output_tokens": 147, "cost_micros": 491922, "latency_ms": 9858, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "Looking through the document for information about 3M's business segments in FY2018...\n\nFrom the document, I can find the business segment information in multiple sections, including:\n\n1. In the Executive Summary/Overview section\n2. In Note 18 - Business Segments\n3. In the Business Segments section of Item 1\n\nThe document clearly states that 3M manages its operations in **five business segments**:\n\n1. **Industrial** - serves automotive OEM, automotive aftermarket, electronics, appliance, paper and printing, packaging, food and beverage, and construction markets\n2. **Safety and Graphics** - serves markets for personal protection products, commercial solutions, transportation safety, and roofing granules\n3. **Health Care** - serves medical clinics and hospitals, pharmaceuticals, dental and orthodontic practitioners, health information systems, and food manufacturing and testing\n4. **Electronics and Energy** - serves electronics and energy markets, including display materials, electrical products, and power generation\n5. **Consumer** - serves consumer retail, office retail, home improvement, drug and pharmacy retail markets\n\nAnswer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 177502, "output_tokens": 255, "cost_micros": 536331, "latency_ms": 14344, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q001", "doc_id": "3M_2018_10K.pdf", "answer_format": "list", "gold": "['Industrial Business', 'Safety and Graphics Business', 'Health Care Business', 'Electronics and Energy Business', 'Consumer Business']", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q001", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about their business segments.Perfect! I found the information. Let me read the specific section that clearly states the five business segments.Answer: Industrial, Safety and Graphics, Health Care, Electronics and Energy, Consumer", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32219, "error": null, "extra": {"thread_id": 2072, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_5721bf743c374f36a4a98f84b6cf13eb", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 119, "n_mentioned_documents": 1}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "consumer, electronics and energy, health care, industrial, safety and graphics", "normalised_gold": "consumer business, electronics and energy business, health care business, industrial business, safety and graphics business"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nLooking through the document, I can find information about derivative instruments in Note 14 (Derivatives) and the tables showing \"Location and Fair Value Amount of Derivative Instruments.\"\n\nFrom page 104-105, I can see tables showing the fair value of derivative instruments as of December 31, 2018:\n\n**Assets (December 31, 2018):**\n- Foreign currency forward/option contracts: $127 million\n- Interest rate swap contracts: $19 million\n- Total derivative assets: $146 million\n\n**Liabilities (December 31, 2018):**\n- Foreign currency forward/option contracts: $22 million\n- Interest rate swap contracts: $31 million\n- Total derivative liabilities: $53 million\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 390258, "output_tokens": 225, "cost_micros": 2346610, "latency_ms": 34921, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments as of December 31, 2018.\n\nFrom the section \"Location and Fair Value Amount of Derivative Instruments\" on page 104, I can see a table showing derivative instruments as of December 31, 2018:\n\n**Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative instruments (Assets): $146 million\n\n**Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative instruments (Liabilities): $53 million\n\nNet fair value = Total Assets - Total Liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 140534, "output_tokens": 198, "cost_micros": 424572, "latency_ms": 6766, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom Note 14 \"Derivatives\" and Note 15 \"Fair Value Measurements\", I can find the fair value of derivative instruments.\n\nIn the section \"Location and Fair Value Amount of Derivative Instruments\" under Note 14, there is a table showing:\n\n**December 31, 2018:**\n\nAssets:\n- Foreign currency forward/option contracts (current): $74 million\n- Foreign currency forward/option contracts (non-current): $39 million\n- Interest rate swap contracts (current): $0 million\n- Interest rate swap contracts (non-current): $19 million\n- Foreign currency forward/option contracts (not designated): $14 million\n**Total derivative assets: $146 million**\n\nLiabilities:\n- Foreign currency forward/option contracts (current): $12 million\n- Foreign currency forward/option contracts (non-current): $4 million\n- Interest rate swap contracts (current): $14 million\n- Interest rate swap contracts (non-current): $17 million\n- Foreign currency forward/option contracts (not designated): $6 million\n**Total derivative liabilities: $53 million**\n\nNet fair value = Total derivative assets - Total derivative liabilities\nNet fair value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 207949, "output_tokens": 317, "cost_micros": 1254826, "latency_ms": 11608, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to find the net fair value of derivative instruments for 3M in FY2018.\n\nFrom the \"Location and Fair Value Amount of Derivative Instruments\" table for December 31, 2018:\n\n**Derivative Assets:**\n- Derivatives designated as hedging instruments: $132 million\n- Derivatives not designated as hedging instruments: $14 million\n- Total derivative assets: $146 million\n\n**Derivative Liabilities:**\n- Derivatives designated as hedging instruments: $47 million\n- Derivatives not designated as hedging instruments: $6 million\n- Total derivative liabilities: $53 million\n\n**Net Fair Value = Total Assets - Total Liabilities**\nNet Fair Value = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 163237, "output_tokens": 185, "cost_micros": 492486, "latency_ms": 7719, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the fair value of derivative instruments from the document:\n\nFrom the table \"Location and Fair Value Amount of Derivative Instruments\" on page 104:\n\n**December 31, 2018:**\n- Total derivative assets (Fair Value Amount): $146 million\n- Total derivative liabilities (Fair Value Amount): $53 million\n\nNet fair value = Assets - Liabilities = $146 million - $53 million = $93 million\n\nAnswer: 93", "citations": [], "input_tokens": 177500, "output_tokens": 105, "cost_micros": 534075, "latency_ms": 8358, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q002", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "93", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q002", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find information about the net fair value of derivative instruments.Let me search for information about derivative instruments in the document.Let me search more specifically for derivative instruments and their fair values in a table format.Now let me read the section on derivatives to find the fair value information, particularly around line 15104 where it mentions \"Location and Fair Value Amount of Derivative Instruments\":Perfect! I found the table showing the fair value of derivative instruments for 3M in FY2018. Looking at the table on lines 15230-15235:\n\nFor December 31, 2018:\n- Total derivative instruments (Assets): $146 million\n- Total derivative instruments (Liabilities): $53 million\n\nThe net fair value would be: Assets - Liabilities = $146 - $53 = $93 million\n\nAnswer: 93.0", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 50969, "error": null, "extra": {"thread_id": 2073, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_890847b672154ab2beed6f213e7b03d2", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 220, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "93.0", "normalised_gold": "93.0"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "native_pdf", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the document for advertising and merchandising costs:\n\nFrom the document (page 65):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nTo calculate the growth rate for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635... \u00d7 100\nGrowth rate = -3.649635...\nRounded to two decimal places = -3.65\n\nAnswer: -3.65", "citations": [], "input_tokens": 390267, "output_tokens": 193, "cost_micros": 2345944, "latency_ms": 37280, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "3M_2018_10K.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 2018 and 2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- 2018: $396 million\n- 2017: $411 million\n\nGrowth rate = (2018 value - 2017 value) / 2017 value \u00d7 100\nGrowth rate = ($396 - $411) / $411 \u00d7 100\nGrowth rate = -$15 / $411 \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 140543, "output_tokens": 230, "cost_micros": 425079, "latency_ms": 7875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "azure_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, I can find the advertising and merchandising costs in the \"Advertising and merchandising\" section:\n\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nSo:\n- FY2018: $396 million\n- FY2017: $411 million\n\nGrowth rate = (FY2018 - FY2017) / FY2017 \u00d7 100\nGrowth rate = (396 - 411) / 411 \u00d7 100\nGrowth rate = -15 / 411 \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 207958, "output_tokens": 224, "cost_micros": 1252788, "latency_ms": 9453, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_basic_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I need to find the advertising and merchandising costs for 3M in FY2018 and FY2017, then calculate the growth rate.\n\nFrom the document, under \"Advertising and merchandising\":\n- 2018: $396 million\n- 2017: $411 million\n- 2016: $385 million\n\nThe growth rate formula is: ((2018 - 2017) / 2017) \u00d7 100\n\nGrowth rate = ((396 - 411) / 411) \u00d7 100\nGrowth rate = (-15 / 411) \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.649635%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 163246, "output_tokens": 178, "cost_micros": 492408, "latency_ms": 8312, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "llamacloud_premium_lc", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "Looking at the advertising and merchandising costs:\n\nFrom the document (page 64):\n\"Advertising and merchandising: These costs are charged to operations in the period incurred, and totaled $396 million in 2018, $411 million in 2017 and $385 million in 2016.\"\n\nGrowth rate calculation for FY2018:\nGrowth rate = (2018 cost - 2017 cost) / 2017 cost \u00d7 100\nGrowth rate = ($396 million - $411 million) / $411 million \u00d7 100\nGrowth rate = -$15 million / $411 million \u00d7 100\nGrowth rate = -0.03649635 \u00d7 100\nGrowth rate = -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 177509, "output_tokens": 176, "cost_micros": 535167, "latency_ms": 8078, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
+{"qid": "3M_2018_10K.pdf::Q003", "doc_id": "3M_2018_10K.pdf", "answer_format": "float", "gold": "-3.65%", "pages": 160, "document_id": 7601, "arm": "surfsense_agentic", "question_id": "3M_2018_10K.pdf::Q003", "answer_letter": null, "raw_text": "I'll read the 3M 2018 10K document to find the advertising and merchandising costs for FY2018 and the prior year to calculate the growth rate.Let me search for \"advertising\" in the document to find the relevant information quickly.Great! I found a reference to advertising and merchandising costs. Let me read the chunk that contains this information.Perfect! I can see that advertising and merchandising costs were $396 million in 2018 and $411 million in 2017. Let me read more context to ensure I have the complete information.Perfect! I now have the complete information:\n- 2018: $396 million\n- 2017: $411 million\n\nNow I'll calculate the growth rate:\nGrowth rate = [(2018 value - 2017 value) / 2017 value] \u00d7 100\nGrowth rate = [(396 - 411) / 411] \u00d7 100\nGrowth rate = [-15 / 411] \u00d7 100\nGrowth rate = -0.036496... \u00d7 100\nGrowth rate = -3.6496...%\n\nRounded to two decimal places: -3.65%\n\nAnswer: -3.65", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 36500, "error": null, "extra": {"thread_id": 2074, "search_space_id": 55, "answer_letter_strategy": "none", "user_message_id": "msg_3f1bac0372cd48f5b95293aa87160368", "assistant_message_id": null, "finished_normally": true, "n_raw_events": 175, "n_mentioned_documents": 1}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "-3.65", "normalised_gold": "-3.65"}}
diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl
new file mode 100644
index 000000000..bc18da838
--- /dev/null
+++ b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl
@@ -0,0 +1,37 @@
+{"qid": "05-03-18-political-release.pdf::Q000", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "541", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:15Z", "latency_ms": 20985, "error": null, "raw_text_chars": 696}]}, "question_id": "05-03-18-political-release.pdf::Q000", "answer_letter": null, "raw_text": "I need to find information about how many adults rated Trump's government ethical standards as \"poor\" in the survey conducted April 25-May 1, 2018.\n\nLooking through the document, I find relevant information on page 4 (document page 6):\n\nThe document states: \"About four-in-ten Americans rate the ethical standards of Trump administration officials as excellent (9%) or good (30%), while 58% say they are not good (21%) or poor (36%).\"\n\nThe survey was conducted among 1,503 adults (as stated in the methodology section on page 14).\n\nTo find the number of adults who rated the ethical standards as \"poor\":\n36% of 1,503 = 0.36 \u00d7 1,503 = 541.08\n\nRounding to the nearest whole number: 541\n\nAnswer: 541", "citations": [], "input_tokens": 33894, "output_tokens": 191, "cost_micros": 104547, "latency_ms": 20968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "541", "normalised_gold": "541"}}
+{"qid": "05-03-18-political-release.pdf::Q002", "doc_id": "05-03-18-political-release.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Make good decisions about economic policy ', 'Make wise decisions about immigration policy ']", "pages": 17, "document_id": 5219, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:16Z", "latency_ms": 20453, "error": null, "raw_text_chars": 544}]}, "question_id": "05-03-18-political-release.pdf::Q002", "answer_letter": null, "raw_text": "Looking at page 5 of the document, which shows \"Public's confidence in Trump to handle several policy areas mixed,\" I can see the data for \"very confident\" and \"not at all confident\" percentages across different domains.\n\nFor \"Very confident\" (highest percentage):\n- Negotiate favorable trade agreements with other countries: 30%\n\nFor \"Not at all confident\" (highest percentage):\n- Make wise decisions about immigration policy: 55%\n\nAnswer: Make wise decisions about immigration policy, Negotiate favorable trade agreements with other countries", "citations": [], "input_tokens": 33942, "output_tokens": 117, "cost_micros": 103581, "latency_ms": 20438, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "05-03-18-political-release.pdf"}, "graded": {"correct": false, "f1": 0.5, "method": "list_set", "normalised_pred": "make wise decisions about immigration policy, negotiate favorable trade agreements with other countries", "normalised_gold": "make good decisions about economic policy, make wise decisions about immigration policy"}}
+{"qid": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "doc_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "6", "pages": 16, "document_id": 5220, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 41750, "error": null, "raw_text_chars": 1479}]}, "question_id": "0b85477387a9d0cc33fca0f4becaa0e5.pdf::Q005", "answer_letter": null, "raw_text": "I need to count the pictures in the document that contain only one person.\n\nLet me go through the document page by page:\n\nPage 1:\n- Photo of Dr. Xiaoyan \"Cathy\" Zou with Rear Adm. (Ret.) Tim Ziemer - 2 people\n\nPage 2:\n- Photo of Capt. John W. Sanders - 1 person\n- Photo of Capt. Steven M. Wechsler - 1 person\n\nPage 3:\n- Photo of Afghan trainees in laboratory - multiple people\n\nPage 4:\n- Group photo of Pacific Partnership 2012 Internal Medicine Department staff - multiple people\n- Photo of Cmdr. Charmagne Beckett on flight deck - 1 person\n\nPage 5:\n- Photo of Petty Officer 1st Class Dennis Gonzales - 1 person\n\nPage 6:\n- Photo of Lance Cpl. Kip Boker and Cpl. Jacob Echeverri - 2 people\n\nPage 7:\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Buhari Oyofo, Dr. Walter T. Gwenigale, Lt. Joseph Diclaro, and Dr. Fatorma Bolay - 5 people\n- Photo with Lt. Cmdr. Jennifer Curry, Capt. Chris Martinez, Capt. Buhari Oyofo, Col. Vernon Graham, and Lt. Joseph Diclaro - 5 people\n\nPage 8:\n- Group photo of Kazakh scientists and NMRC staff - multiple people\n\nPage 9:\n- Photo of Lt. j.g. Michael Rucker treating a child - 2 people\n- Photo of U.S. Marines and Sailors in aircraft - multiple people\n\nPage 10:\n- Group photo of Joint Combat Casualty Research Team - multiple people\n\nPage 12:\n- Photo of NMRC 2012 Dining Out Event - multiple people\n- Photo with Rear Adm. Bruce Doll, Capt. John Sanders and others - multiple people\n\nPage 13:\n- Photo with Ellen Flannery, Emmanuel Adu-Gyamfi, Eil", "citations": [], "input_tokens": 40387, "output_tokens": 512, "cost_micros": 128841, "latency_ms": 41735, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "length", "pdf_filename": "0b85477387a9d0cc33fca0f4becaa0e5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "6"}}
+{"qid": "2005.12872v3.pdf::Q005", "doc_id": "2005.12872v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "36", "pages": 26, "document_id": 7577, "original_error": "JSONDecodeError: Expecting value: line 169 column 1 (char 924)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:50:36Z", "latency_ms": 36889, "error": null, "raw_text_chars": 1214}]}, "question_id": "2005.12872v3.pdf::Q005", "answer_letter": null, "raw_text": "I need to find information about DETR's default structure, specifically the number of multi-head self-attention layers.\n\nLet me search through the document for information about DETR's architecture.\n\nFrom page 2, I can see that DETR has a transformer encoder-decoder architecture.\n\nOn page 10 (Section 4.1), I find: \"To be comparable in the number of parameters we choose a model with 6 transformer and 6 decoder layers of width 256 with 8 attention heads.\"\n\nLooking at the detailed architecture description on page 22 (Section A.3) and Figure 10, I can see that:\n- The encoder has multiple layers, each containing multi-head self-attention\n- The decoder has multiple layers, each containing multi-head self-attention AND multi-head attention (cross-attention)\n\nFrom page 9-10, the baseline model uses \"6 encoder, 6 decoder layers\"\n\nEach encoder layer has one multi-head self-attention layer.\nEach decoder layer has one multi-head self-attention layer and one multi-head cross-attention layer.\n\nSo the total number of multi-head self-attention layers (not counting cross-attention) is:\n- 6 encoder layers \u00d7 1 self-attention per layer = 6\n- 6 decoder layers \u00d7 1 self-attention per layer = 6\n- Total = 12\n\nAnswer: 12", "citations": [], "input_tokens": 59866, "output_tokens": 318, "cost_micros": 184368, "latency_ms": 36875, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2005.12872v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "36"}}
+{"qid": "2306.05425v1.pdf::Q002", "doc_id": "2306.05425v1.pdf", "arm": "llamacloud_basic_lc", "answer_format": "int", "gold": "6", "pages": 22, "document_id": 7587, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:13Z", "latency_ms": 11328, "error": null, "raw_text_chars": 1376}]}, "question_id": "2306.05425v1.pdf::Q002", "answer_letter": null, "raw_text": "I need to carefully examine the document for bar plots.\n\nLooking through the figures in the document:\n\n1. **Figure 1**: Shows example images with text descriptions - no bar plots\n2. **Figure 2**: Shows data format comparison with images and text - no bar plots\n3. **Figure 3**: Shows the Syphus overview diagram - no bar plots\n4. **Figure 4**: This appears to show statistics and analysis. Let me examine it carefully:\n   - Part (a): Shows verb-noun structure for instructions - this appears to be a bar chart\n   - Part (b): Shows verb-noun structure for responses - this appears to be a bar chart\n   - Part (c): Shows \"Diversity in our dataset\" with what appears to be distribution plots\n\nLooking at Figure 4 more carefully:\n- (a) contains what looks like a horizontal bar chart showing verbs and nouns from instructions\n- (b) contains what looks like a horizontal bar chart showing verbs and nouns from responses\n- (c) contains multiple distribution plots showing lengths and counts\n\n5. **Figure 5**: Shows response examples - no bar plots\n6. **Figure 6**: The document mentions Fig. 6(b) and Fig. 6(c) but these are not fully visible in the provided pages\n\nFrom what I can see clearly in Figure 4:\n- Subfigure (a): 1 bar plot (showing instruction verb-noun structure)\n- Subfigure (b): 1 bar plot (showing response verb-noun structure)\n\nTotal visible bar plots: 2\n\nAnswer: 2", "citations": [], "input_tokens": 20240, "output_tokens": 354, "cost_micros": 66030, "latency_ms": 11328, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "6"}}
+{"qid": "2307.09288v2.pdf::Q000", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:18Z", "latency_ms": 52514, "error": null, "raw_text_chars": 860}]}, "question_id": "2307.09288v2.pdf::Q000", "answer_letter": null, "raw_text": "I'll examine pages 5-10 to count the tables.\n\nPage 5: No tables visible in the text content shown.\n\nPage 6: Table 1 is present - \"Llama 2 family of models\" showing training data, parameters, context length, GQA, tokens, and LR for Llama 1 and Llama 2 models.\n\nPage 7: \n- Table 2: \"CO2 emissions during pretraining\" showing time, power consumption, and carbon emitted for different Llama 2 models.\n- Table 3: \"Overall performance on grouped academic benchmarks\" comparing MPT, Falcon, Llama 1, and Llama 2 models.\n\nPage 8:\n- Table 4: \"Comparison to closed-source models on academic benchmarks\" showing results for GPT-3.5, GPT-4, PaLM, PaLM-2-L, and Llama 2.\n- Table 5: \"SFT annotation\" showing examples of helpfulness and safety annotations.\n\nPage 9: No complete tables visible in the shown content.\n\nPage 10: No tables visible in the shown content.\n\nAnswer: 5", "citations": [], "input_tokens": 199213, "output_tokens": 280, "cost_micros": 601839, "latency_ms": 52468, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:51:25Z", "latency_ms": 40452, "error": null, "raw_text_chars": 575}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3 on page 4, which shows \"Safety human evaluation results for Llama 2-Chat compared to other open-source and closed-source models,\" I need to identify the models with the lowest violation percentages (since lower is better for safety).\n\nFrom the bar chart showing \"Violation % (lower is safer)\":\n- Llama 2-70b-chat has the lowest violation percentage (approximately 0-5%)\n- Llama 2-13b-chat has the second lowest (approximately 0-5%)\n- Llama 2-34b-chat has the third lowest (approximately 5-10%)\n\nAnswer: Llama 2-70b-chat, Llama 2-13b-chat, Llama 2-34b-chat", "citations": [], "input_tokens": 199224, "output_tokens": 182, "cost_micros": 600402, "latency_ms": 40406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 34b chat, llama 2 70b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q002", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "list", "gold": "['Vicuna13b-v1.3', 'PaLM Bison', 'Vicuna13b-v1.1']", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:05Z", "latency_ms": 10343, "error": null, "raw_text_chars": 376}]}, "question_id": "2307.09288v2.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Figure 3, which shows \"Safety human evaluation results for LLAMA 2-CHAT compared to other open-source and closed-source models,\" I can see the violation percentages (lower is safer):\n\nThe top-3 safest models (lowest violation %) are:\n1. Llama-2 7b-chat: 3%\n2. Llama-2 13b-chat: 3%\n3. Llama-2 70b-chat: 4%\n\nAnswer: Llama-2 7b-chat, Llama-2 13b-chat, Llama-2 70b-chat", "citations": [], "input_tokens": 105645, "output_tokens": 151, "cost_micros": 319200, "latency_ms": 10343, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "llama 2 13b chat, llama 2 70b chat, llama 2 7b chat", "normalised_gold": "palm bison, vicuna13b v1 1, vicuna13b v1 3"}}
+{"qid": "2307.09288v2.pdf::Q004", "doc_id": "2307.09288v2.pdf", "arm": "llamacloud_premium_lc", "answer_format": "str", "gold": "Llama 1 33B", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:10Z", "latency_ms": 29391, "error": null, "raw_text_chars": 525}]}, "question_id": "2307.09288v2.pdf::Q004", "answer_letter": null, "raw_text": "Looking at Table 23 in the document, which shows \"Comparison to open-source models on reading comprehension (SQUAD and QUAC)\", I need to find the QUAC (f1) 0-shot column.\n\nFrom the table, the QUAC (f1) 0-shot scores are:\n- MPT 7B: 38.0\n- MPT 30B: 40.4\n- Falcon 7B: 24.0\n- Falcon 40B: 41.2\n- LLAMA 1 7B: 38.9\n- LLAMA 1 13B: 39.9\n- LLAMA 1 33B: 44.1\n- LLAMA 1 65B: 41.0\n- LLAMA 2 7B: 39.4\n- LLAMA 2 13B: 42.7\n- LLAMA 2 34B: 42.9\n- LLAMA 2 70B: 42.4\n\nThe highest score is 44.1, which belongs to LLAMA 1 33B.\n\nAnswer: LLAMA 1 33B", "citations": [], "input_tokens": 105649, "output_tokens": 274, "cost_micros": 321057, "latency_ms": 29391, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "llama 1 33b", "normalised_gold": "llama 1 33b"}}
+{"qid": "2307.09288v2.pdf::Q007", "doc_id": "2307.09288v2.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "15", "pages": 77, "document_id": 7588, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:15Z", "latency_ms": 43610, "error": null, "raw_text_chars": 650}]}, "question_id": "2307.09288v2.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 9 and count the emojis in the left and right subfigures.\n\nLooking through the document, I find Figure 9 on page 16 (document page 16). The figure shows \"Issues with multi-turn memory (left) can be improved with GAtt (right).\"\n\nLooking at the left subfigure:\n- It shows a conversation with text-only responses (no emojis visible)\n\nLooking at the right subfigure:\n- First response: 4 emojis (\ud83d\udc4b\ud83d\udc41\ufe0f\ud83d\udc44\ud83d\udc41\ufe0f)\n- Second response: 4 emojis (\u2708\ufe0f\u2708\ufe0f\ud83d\udeeb\ud83d\udcba)\n- Third response: 4 emojis (\ud83e\uddb4\ud83e\udd95\ud83e\uddb4\ud83d\udd25)\n- Fourth response: 2 emojis (\ud83c\uddee\ud83c\uddf9\ud83c\udf5d\ud83d\udc90)\n\nTotal in right subfigure: 4 + 4 + 4 + 2 = 14 emojis\n\nTotal in left subfigure: 0 emojis\n\nDifference: 14 - 0 = 14\n\nAnswer: 14", "citations": [], "input_tokens": 199223, "output_tokens": 267, "cost_micros": 601674, "latency_ms": 43578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2307.09288v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "14", "normalised_gold": "15"}}
+{"qid": "2309.17421v2.pdf::Q000", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Blue", "pages": 166, "document_id": 7589, "original_error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:40Z", "latency_ms": 43593, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:53:24Z", "latency_ms": 44391, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:54:10Z", "latency_ms": 35797, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:54:48Z", "latency_ms": 42266, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:55:41Z", "latency_ms": 31983, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 31843, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "blue"}}
+{"qid": "2309.17421v2.pdf::Q001", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "YouTube Music", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:52:59Z", "latency_ms": 33250, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:53:33Z", "latency_ms": 40889, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:54:16Z", "latency_ms": 51515, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:55:10Z", "latency_ms": 54500, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:56:14Z", "latency_ms": 32641, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 32500, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "youtube music"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:13Z", "latency_ms": 42110, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:56:56Z", "latency_ms": 52202, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:57:50Z", "latency_ms": 40469, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:58:34Z", "latency_ms": 49766, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T01:59:30Z", "latency_ms": 42110, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 41968, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q002", "doc_id": "2309.17421v2.pdf", "arm": "azure_premium_lc", "answer_format": "float", "gold": "$49.99", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:56:47Z", "latency_ms": 24578, "error": null, "raw_text_chars": 640}]}, "question_id": "2309.17421v2.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Figure 111 in the document and identify the price of the keyboard shown in the screenshot.\n\nLooking through the document, I can find Figure 111 which is part of Section 9.8 on GUI Navigation, specifically about Online Shopping for an Ergonomic Keyboard.\n\nIn Figure 111, the screenshot shows a shopping cart with a keyboard item. The text indicates:\n\n\"Subtotal $4999\"\n\nAnd in the cart details, it shows:\n\"Kensington Pro Fit Ergonomic Wireless Keyboard - Black (K7540...\n$4999\"\n\nThe price shown is $49.99 (represented as $4999 in the format used in the screenshot, which appears to be missing the decimal point).\n\nAnswer: 49.99", "citations": [], "input_tokens": 133648, "output_tokens": 176, "cost_micros": 403584, "latency_ms": 24578, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "float_tol", "normalised_pred": "49.99", "normalised_gold": "49.99"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T01:57:11Z", "latency_ms": 69062, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T01:58:21Z", "latency_ms": 35639, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T01:58:59Z", "latency_ms": 51312, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T01:59:54Z", "latency_ms": 49218, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:00:48Z", "latency_ms": 56375, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q003", "doc_id": "2309.17421v2.pdf", "arm": "azure_basic_lc", "answer_format": "float", "gold": "76%", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:12Z", "latency_ms": 7967, "error": null, "raw_text_chars": 594}]}, "question_id": "2309.17421v2.pdf::Q003", "answer_letter": null, "raw_text": "I need to find Figure 107 in the document and identify the battery percentage shown in that screenshot.\n\nLooking through the document for Figure 107, I can see it's in Section 9.8 on GUI Navigation, specifically related to online shopping.\n\nIn the image caption list, Figure 107 is described as \"Section 9.8: online shopping.\"\n\nLooking at the actual Figure 107 in the document, I can see it's a smartphone screenshot showing an Amazon shopping interface. At the top of the screenshot, there is a time display \"5:32\" and next to it shows \"75\" which represents the battery percentage.\n\nAnswer: 75", "citations": [], "input_tokens": 117429, "output_tokens": 143, "cost_micros": 354432, "latency_ms": 7967, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "float_tol", "normalised_pred": "75.0", "normalised_gold": "76.0"}}
+{"qid": "2309.17421v2.pdf::Q004", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Ukraine", "pages": 166, "document_id": 7589, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:00:20Z", "latency_ms": 33344, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:00:54Z", "latency_ms": 43733, "error": "HTTPStatusError: OpenRouter HTTP 502: {\"type\":\"https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-5xx-errors/error-502/\",\"title\":\"Error 502: Bad gateway\",\"status\":502,\"detail\":\"The origin web server returned an invalid or incomplete response to Cloudflare. This typically indicates the origin is overlo", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:01:39Z", "latency_ms": 33390, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:02:16Z", "latency_ms": 33641, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:02:59Z", "latency_ms": 38000, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 37859, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "ukraine"}}
+{"qid": "2309.17421v2.pdf::Q005", "doc_id": "2309.17421v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "JoooDeee", "pages": 166, "document_id": 7589, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:01:45Z", "latency_ms": 35218, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:02:21Z", "latency_ms": 44921, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:03:08Z", "latency_ms": 52842, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:04:05Z", "latency_ms": 27250, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:04:41Z", "latency_ms": 48031, "error": "EmptyResponse: stream ended with no text", "raw_text_chars": 0}]}, "question_id": "2309.17421v2.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 47889, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": null, "pdf_filename": "2309.17421v2.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "jooodeee"}}
+{"qid": "2310.05634v2.pdf::Q005", "doc_id": "2310.05634v2.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "If it can be partially verified by the knowledge graph G", "pages": 24, "document_id": 7590, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:37Z", "latency_ms": 20843, "error": null, "raw_text_chars": 345}]}, "question_id": "2310.05634v2.pdf::Q005", "answer_letter": null, "raw_text": "Looking at the document, specifically on page 2, the task formulation section discusses the \"Setting of Conscious Incompetence.\"\n\nThe relevant text states: \"A sentence can map to both [NA] and a list of sub-graph knowledge if it can be partially verified by the knowledge graph G.\"\n\nAnswer: if it can be partially verified by the knowledge graph", "citations": [], "input_tokens": 60205, "output_tokens": 80, "cost_micros": 181815, "latency_ms": 20828, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.05634v2.pdf"}, "graded": {"correct": true, "f1": 0.9473684210526316, "method": "str_norm", "normalised_pred": "if it can be partially verified by knowledge graph", "normalised_gold": "if it can be partially verified by knowledge graph g"}}
+{"qid": "2310.07609v1.pdf::Q002", "doc_id": "2310.07609v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "5", "pages": 10, "document_id": 7591, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:03:57Z", "latency_ms": 14639, "error": null, "raw_text_chars": 1219}]}, "question_id": "2310.07609v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at the document, I need to identify which modules use Large Language Models (LLMs) as their backbone.\n\nFrom Section 3, the QACHECK system has 5 modules:\n1. **Claim Verifier** - Section 3.1 states it's \"based on InstructGPT\" (an LLM)\n2. **Question Generator** - Section 3.2 states \"Similar to the claim verifier, we also leverage InstructGPT\" (an LLM)\n3. **Question Answering Model** - Section 3.3 describes three implementations, with the default being \"GPT Reciter-Reader\" which uses InstructGPT (an LLM). However, this module also has non-LLM options (Retriever-Reader, FLAN-T5).\n4. **QA Validator** - Section 3.4 states \"We again implement the validator by prompting InstructGPT\" (an LLM)\n5. **Reasoner** - Section 3.5 provides \"two different implementations: 1) the end-to-end QA model based on FLAN-T5, and 2) the InstructGPT model\"\n\nCounting the modules that use LLM backbones:\n- Claim Verifier: Yes (InstructGPT)\n- Question Generator: Yes (InstructGPT)\n- Question Answering Model: Yes (uses GPT Reciter-Reader by default)\n- QA Validator: Yes (InstructGPT)\n- Reasoner: Yes (InstructGPT is one of the two implementations)\n\nAll 5 modules have LLM implementations, with some offering alternatives.\n\nAnswer: 5", "citations": [], "input_tokens": 27149, "output_tokens": 403, "cost_micros": 87492, "latency_ms": 14639, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2310.07609v1.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "5", "normalised_gold": "5"}}
+{"qid": "2311.16502v3.pdf::Q000", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "287", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:12Z", "latency_ms": 6562, "error": null, "raw_text_chars": 406}]}, "question_id": "2311.16502v3.pdf::Q000", "answer_letter": null, "raw_text": "Looking at the document, I need to find information about the sociology subject in MMMU.\n\nIn Figure 3, which shows the distribution of subjects across disciplines, I can see:\n\nUnder \"Humanities & Social Sci. (9%)\":\n- History (313, 2.71%)\n- Literature (147, 1.27%)\n- Psychology (340, 2.94%)\n- **Sociology (287, 2.48%)**\n\nThe number 287 represents the number of samples in the sociology subject.\n\nAnswer: 287", "citations": [], "input_tokens": 103232, "output_tokens": 123, "cost_micros": 311541, "latency_ms": 6562, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "287", "normalised_gold": "287"}}
+{"qid": "2311.16502v3.pdf::Q002", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "2", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:04:19Z", "latency_ms": 62062, "error": null, "raw_text_chars": 760}]}, "question_id": "2311.16502v3.pdf::Q002", "answer_letter": null, "raw_text": "I need to find Economics-related error cases that fall into the \"Perceptual Error\" category.\n\nLet me search through the document for Economics error cases:\n\nFrom the Case Study section (pages 20-21), I can see Table 10 which shows the index of case study figures by subjects with associated error categories.\n\nLooking at the Economics row in Table 10:\n- Economics has: Correct Case (22), Perceptual Error (23, 24)\n\nThis indicates that there are 2 Economics-related error cases in the Perceptual Error category (Figures 23 and 24).\n\nLet me verify by checking the actual figures:\n- Figure 23 (page 37): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n- Figure 24 (page 38): Economics: Macroeconomics - Error Category: Perceptual Error \u2713\n\nAnswer: 2", "citations": [], "input_tokens": 259127, "output_tokens": 212, "cost_micros": 1559532, "latency_ms": 62014, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "int_eq", "normalised_pred": "2", "normalised_gold": "2"}}
+{"qid": "2311.16502v3.pdf::Q003", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_basic_lc", "answer_format": "list", "gold": "['Thermodynamics', 'Heat Transfer', 'Fluid Mechanics']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:21Z", "latency_ms": 5093, "error": null, "raw_text_chars": 174}]}, "question_id": "2311.16502v3.pdf::Q003", "answer_letter": null, "raw_text": "Looking at Table 11 in the document which lists the subfields of each subject:\n\nFor Energy & Power, the subfields are:\n\nAnswer: Thermodynamics, Heat Transfer, Fluid Mechanics", "citations": [], "input_tokens": 83153, "output_tokens": 47, "cost_micros": 250164, "latency_ms": 5093, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "fluid mechanics, heat transfer, thermodynamics", "normalised_gold": "fluid mechanics, heat transfer, thermodynamics"}}
+{"qid": "2311.16502v3.pdf::Q007", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "17", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:26Z", "latency_ms": 64485, "error": null, "raw_text_chars": 1513}]}, "question_id": "2311.16502v3.pdf::Q007", "answer_letter": null, "raw_text": "I need to find Figure 1 in the document and count the different icons shown as different image types.\n\nLooking at page 1, Figure 1 is titled \"Overview of the MMMU dataset\" and shows four main sections:\n\n1. **Comprehensive Disciplines** - Shows icons/images representing different disciplines (Engineering, Science, Art & Design, Business, Humanities & Social Sci., Medicine)\n\n2. **Heterogeneous Image Types** - Shows various image type icons including:\n   - Diagrams\n   - Tables\n   - Plots and Charts\n   - Photographs\n   - Chemical Structures\n   - Paintings\n   - Medical Images\n   - Sheet Music\n   - Geometric\n   - Pathology images\n   - Microscopic Images\n   - Comics\n\n3. **Interleaved Text and Images** - Shows a medical question example with 3 MRI images\n\n4. **Expert-level Skills Test** - Shows a diagram with three components: Perception, Knowledge, and Reasoning\n\nThe question asks specifically about \"different icon are shown as different image types\" in Figure 1. Looking at the \"Heterogeneous Image Types\" section, I can count the distinct image type icons shown.\n\nCounting the image types listed in the \"Heterogeneous Image Types\" section:\nDiagrams, Tables, Plots and Charts, Photographs, Chemical Structures, Paintings, Medical Images, Sheet Music, Geometric, Pathology images, Microscopic Images, Comics, and there's an ellipsis (...) suggesting more types.\n\nBased on what's explicitly visible and labeled in Figure 1, I can count approximately 12 distinct image type representations shown.\n\nAnswer: 12", "citations": [], "input_tokens": 259123, "output_tokens": 378, "cost_micros": 1563243, "latency_ms": 64406, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "12", "normalised_gold": "17"}}
+{"qid": "2311.16502v3.pdf::Q009", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:05:29Z", "latency_ms": 75843, "error": null, "raw_text_chars": 831}]}, "question_id": "2311.16502v3.pdf::Q009", "answer_letter": null, "raw_text": "I need to count the charts that appear in pages 5-10 of the document.\n\nLet me go through each page:\n\n**Page 5:**\n- Figure 4: A comparison chart showing benchmarks plotted on a 2D graph with \"Breadth (Knowledge)\" on x-axis and \"Depth (Reasoning)\" on y-axis\n- A table comparing different datasets\nTotal: 1 chart\n\n**Page 6:**\n- Table 2: Results table (not a chart)\nTotal: 0 charts\n\n**Page 7:**\n- Figure 5: Bar chart showing \"Performance of models on different types of images\"\n- Table 3: Results table (not a chart)\nTotal: 1 chart\n\n**Page 8:**\n- Figure 6: Pie chart showing \"Error distribution over 150 annotated GPT-4V errors\"\nTotal: 1 chart\n\n**Page 9:**\n- No charts, only text\nTotal: 0 charts\n\n**Page 10:**\n- No charts, only text (references section)\nTotal: 0 charts\n\nTotal charts in pages 5-10: 1 + 0 + 1 + 1 + 0 + 0 = 3\n\nAnswer: 3", "citations": [], "input_tokens": 259120, "output_tokens": 289, "cost_micros": 1561222, "latency_ms": 75796, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:30Z", "latency_ms": 77217, "error": null, "raw_text_chars": 404}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 on page 8 of the document, which shows the \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe error categories and their percentages are:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 259144, "output_tokens": 133, "cost_micros": 1557856, "latency_ms": 77171, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q013", "doc_id": "2311.16502v3.pdf", "arm": "azure_premium_lc", "answer_format": "list", "gold": "['Perceptual Error', 'Lack of Knowledge', 'Reasoning Error']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:45Z", "latency_ms": 7750, "error": null, "raw_text_chars": 387}]}, "question_id": "2311.16502v3.pdf::Q013", "answer_letter": null, "raw_text": "Looking at Figure 6 \"Error distribution over 150 annotated GPT-4V errors\":\n\nThe pie chart shows the following error categories and their percentages:\n- Perceptual Error: 35%\n- Lack of Knowledge: 29%\n- Reasoning Error: 26%\n- Textual Understanding: 4%\n- Reject to Answer: 3%\n- Annotation Error: 2%\n- Answer Extraction Error: 1%\n\nAnswer: Perceptual Error, Lack of Knowledge, Reasoning Error", "citations": [], "input_tokens": 109199, "output_tokens": 125, "cost_micros": 329472, "latency_ms": 7750, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q014", "doc_id": "2311.16502v3.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Reasoning Error', 'Perceptual Error', 'Lack of Knowledge']", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:06:53Z", "latency_ms": 59125, "error": null, "raw_text_chars": 830}]}, "question_id": "2311.16502v3.pdf::Q014", "answer_letter": null, "raw_text": "I'll search through pages 95-100 for error types mentioned in the case studies.\n\nFrom page 95 (Figure 80 - Architecture and Engineering: Surveying and Mapping):\n- This is a correct case, no error type mentioned\n\nFrom page 96 (Figure 82 - Architecture and Engineering: Civil Engineering):\n- Error Category: Reasoning Error\n\nFrom page 97 (Figure 83 - Computer Science: Data Structure and Algorithm):\n- This is a correct case, no error type mentioned\n\nFrom page 98 (Figure 84 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error, Lack of Knowledge\n\nFrom page 99 (Figure 85 - Computer Science: Operating System):\n- Error Category: Perceptual Error\n\nFrom page 100 (Figure 86 - Computer Science: Compiler Principle):\n- Error Category: Perceptual Error\n\nAnswer: Reasoning Error, Perceptual Error, Lack of Knowledge", "citations": [], "input_tokens": 259136, "output_tokens": 226, "cost_micros": 1559901, "latency_ms": 59077, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2311.16502v3.pdf"}, "graded": {"correct": true, "f1": 1.0, "method": "list_set", "normalised_pred": "lack of knowledge, perceptual error, reasoning error", "normalised_gold": "lack of knowledge, perceptual error, reasoning error"}}
+{"qid": "2311.16502v3.pdf::Q015", "doc_id": "2311.16502v3.pdf", "arm": "llamacloud_premium_lc", "answer_format": "int", "gold": "4", "pages": 117, "document_id": 7593, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:48Z", "latency_ms": 11110, "error": null, "raw_text_chars": 1296}]}, "question_id": "2311.16502v3.pdf::Q015", "answer_letter": null, "raw_text": "I need to count the successful cases (correct predictions by GPT-4V) in Pages 65-70.\n\nLet me go through each figure in this range:\n\n**Page 65 - Figure 51 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (E) Familial polyposis coli\n- Ground Truth: (E) Familial polyposis coli\n- **SUCCESS**\n\n**Page 66 - Figure 52 (Clinical Medicine: Pathology)**\n- GPT-4V correctly identified (D) Ruptured berry aneurysm\n- Ground Truth: (D) Ruptured berry aneurysm\n- **SUCCESS**\n\n**Page 67 - Figure 53 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 68 - Figure 54 (Clinical Medicine: Pathology)**\n- GPT-4V answered (A) Human papillomavirus infection\n- Ground Truth: (E) Chronic inflammation\n- Error Category: Perceptual Error\n- **FAILURE**\n\n**Page 69 - Figure 55 (Clinical Medicine: Heart Disease)**\n- GPT-4V answered (B) Mitral regurgitation\n- Ground Truth: (C) Aortic value regurgitation\n- Error Category: Lack of Knowledge\n- **FAILURE**\n\n**Page 70 - Figure 56 (Diagnostics and Laboratory Medicine: Radiology)**\n- GPT-4V correctly identified (C) Dandy-Walker syndrome\n- Ground Truth: (C) Dandy-Walker syndrome\n- **SUCCESS**\n\nTotal successful cases: 3\n\nAnswer: 3", "citations": [], "input_tokens": 103232, "output_tokens": 424, "cost_micros": 316056, "latency_ms": 11110, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "3", "normalised_gold": "4"}}
+{"qid": "2312.10997v5.pdf::Q000", "doc_id": "2312.10997v5.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "Pre-training", "pages": 21, "document_id": 7596, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:52Z", "latency_ms": 23593, "error": null, "raw_text_chars": 1091}]}, "question_id": "2312.10997v5.pdf::Q000", "answer_letter": null, "raw_text": "I need to find the figure with a tree shape and identify which branch has the least leaves.\n\nLooking through the document, I can see Figure 1 on page 2, which shows a \"Technology tree of RAG research\" with a tree-like structure.\n\nExamining this tree diagram, I can see it has a trunk labeled \"Retrieval-Augmented Generation\" and branches extending upward. The main branches appear to be:\n\n1. A left branch (colored in teal/green) showing the evolution from pre-training through fine-tuning stages\n2. A middle branch (colored in orange) labeled \"Instructlens\" \n3. A right branch (colored in blue) showing the inference stage with multiple methods\n\nLooking at the number of \"leaves\" (individual methods/papers) on each branch:\n- The left branch (Pre-training/Fine-tuning) has several leaves\n- The middle branch (Instructlens) appears to have the fewest leaves - it's relatively short with minimal branching\n- The right branch (Inference) has many leaves with extensive branching\n\nThe branch labeled \"Instructlens\" in the middle appears to have the least number of leaves.\n\nAnswer: Instructlens", "citations": [], "input_tokens": 64633, "output_tokens": 269, "cost_micros": 197934, "latency_ms": 23577, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "engine": "native", "answer_letter_strategy": "none", "finish_reason": "stop", "pdf_filename": "2312.10997v5.pdf"}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "instructlens", "normalised_gold": "pre training"}}
+{"qid": "2401.18059v1.pdf::Q002", "doc_id": "2401.18059v1.pdf", "arm": "azure_premium_lc", "answer_format": "str", "gold": "Collapsed Tree Algorithm", "pages": 23, "document_id": 7597, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 1, "recovered": true, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:07:59Z", "latency_ms": 7156, "error": null, "raw_text_chars": 752}]}, "question_id": "2401.18059v1.pdf::Q002", "answer_letter": null, "raw_text": "Looking at Appendix F \"PSEUDOCODE FOR RETRIEVAL METHODS\":\n\n**Algorithm 1 Tree Traversal Algorithm:**\n- Lines: function declaration, initialization of Scurrent, for layer loop, initialization of topk, for node loop, score calculation, append to top_k, end for, Slayer assignment, Scurrent update, end for, return statement, end function\n- Total: 13 lines\n\n**Algorithm 2 Collapsed Tree Algorithm:**\n- Lines: function declaration, flatten tree, initialization of top_nodes, for node loop, append with dot product, end for, sort top_nodes, result initialization, total_tokens initialization, for node loop, if condition, result append, end if, total_tokens update, end for, return statement, end function\n- Total: 16 lines\n\nAnswer: Collapsed Tree Algorithm", "citations": [], "input_tokens": 27211, "output_tokens": 190, "cost_micros": 84483, "latency_ms": 7156, "error": null, "extra": {"model": "anthropic/claude-sonnet-4.5", "finish_reason": "stop"}, "graded": {"correct": true, "f1": 1.0, "method": "str_norm", "normalised_pred": "collapsed tree algorithm", "normalised_gold": "collapsed tree algorithm"}}
+{"qid": "2405.09818v1.pdf::Q000", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "list", "gold": "['Figure 5', 'Figure 6']", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:08:06Z", "latency_ms": 16641, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:08:23Z", "latency_ms": 11860, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:08:36Z", "latency_ms": 12422, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:08:54Z", "latency_ms": 19625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:09:25Z", "latency_ms": 14078, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q000", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657610 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "list_set", "normalised_pred": "", "normalised_gold": "figure 5, figure 6"}}
+{"qid": "2405.09818v1.pdf::Q001", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "text tokens", "pages": 27, "document_id": 7598, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:08:16Z", "latency_ms": 13922, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:08:30Z", "latency_ms": 25952, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:08:57Z", "latency_ms": 14718, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:09:14Z", "latency_ms": 33452, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:09:54Z", "latency_ms": 22093, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q001", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657593 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "text tokens"}}
+{"qid": "2405.09818v1.pdf::Q003", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "18", "pages": 27, "document_id": 7598, "original_error": "SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac (_ssl.c:2559)", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:09:39Z", "latency_ms": 11906, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:09:52Z", "latency_ms": 15921, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:10:10Z", "latency_ms": 21921, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:10:34Z", "latency_ms": 11906, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:10:53Z", "latency_ms": 13686, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q003", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657608 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "18"}}
+{"qid": "2405.09818v1.pdf::Q004", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "int", "gold": "1", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:10:16Z", "latency_ms": 14281, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:10:32Z", "latency_ms": 12125, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:10:45Z", "latency_ms": 11952, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:11:01Z", "latency_ms": 20641, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:11:30Z", "latency_ms": 13985, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q004", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657583 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "int_eq", "normalised_pred": "None", "normalised_gold": "1"}}
+{"qid": "2405.09818v1.pdf::Q005", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "no", "pages": 27, "document_id": 7598, "original_error": null, "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:11:06Z", "latency_ms": 17157, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"{\\\"message\\\":\\\"Input is too long.\\\"}\",\"provider_name\":\"Amazon Bedrock\",\"is_byok\":false}},\"user_id\":\"user_3CNdnY1vL3Ln9TYRiGAii5kmBvu\"}", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:11:25Z", "latency_ms": 12125, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:11:40Z", "latency_ms": 13734, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:11:59Z", "latency_ms": 15828, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:12:25Z", "latency_ms": 19985, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q005", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657607 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "no"}}
+{"qid": "2405.09818v1.pdf::Q007", "doc_id": "2405.09818v1.pdf", "arm": "native_pdf", "answer_format": "str", "gold": "150k", "pages": 27, "document_id": 7598, "original_error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "retry": {"max_attempts": 5, "n_attempts": 5, "recovered": false, "attempts": [{"attempt": 1, "started_iso": "2026-05-15T02:11:44Z", "latency_ms": 15875, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 2, "started_iso": "2026-05-15T02:12:00Z", "latency_ms": 16625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 3, "started_iso": "2026-05-15T02:12:19Z", "latency_ms": 12937, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 4, "started_iso": "2026-05-15T02:12:35Z", "latency_ms": 14625, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}, {"attempt": 5, "started_iso": "2026-05-15T02:12:57Z", "latency_ms": 12156, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "raw_text_chars": 0}]}, "question_id": "2405.09818v1.pdf::Q007", "answer_letter": null, "raw_text": "", "citations": [], "input_tokens": 0, "output_tokens": 0, "cost_micros": 0, "latency_ms": 0, "error": "HTTPStatusError: OpenRouter HTTP 400: {\"error\":{\"message\":\"Provider returned error\",\"code\":400,\"metadata\":{\"raw\":\"[{\\n  \\\"error\\\": {\\n    \\\"code\\\": 400,\\n    \\\"message\\\": \\\"The message size (33657603 bytes) exceeds 30.000MB limit.\\\",\\n    \\\"status\\\": \\\"FAILED_PRECONDITION\\\"\\n  }\\n}\\n]\",\"provider_name\":\"Google\",\"is_byok\":false}},\"user_id", "extra": {}, "graded": {"correct": false, "f1": 0.0, "method": "str_norm", "normalised_pred": "", "normalised_gold": "150k"}}
diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json
new file mode 100644
index 000000000..7c99def5f
--- /dev/null
+++ b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json
@@ -0,0 +1,100 @@
+{
+  "config": {
+    "base_delay": 1.0,
+    "concurrency": 2,
+    "llm_model": "anthropic/claude-sonnet-4.5",
+    "max_attempts": 5,
+    "max_delay": 30.0,
+    "max_output_tokens": 512,
+    "pdf_engine": "native"
+  },
+  "elapsed_s": 1373.6,
+  "n_failed_rows_input": 37,
+  "n_retried": 37,
+  "per_arm": {
+    "azure_basic_lc": {
+      "attempts_distribution": [
+        1
+      ],
+      "recovered": 1,
+      "recovery_rate": 1.0,
+      "still_failed": 0,
+      "tried": 1
+    },
+    "azure_premium_lc": {
+      "attempts_distribution": [
+        1,
+        1,
+        1
+      ],
+      "recovered": 3,
+      "recovery_rate": 1.0,
+      "still_failed": 0,
+      "tried": 3
+    },
+    "llamacloud_basic_lc": {
+      "attempts_distribution": [
+        1,
+        1
+      ],
+      "recovered": 2,
+      "recovery_rate": 1.0,
+      "still_failed": 0,
+      "tried": 2
+    },
+    "llamacloud_premium_lc": {
+      "attempts_distribution": [
+        1,
+        1,
+        1,
+        1
+      ],
+      "recovered": 4,
+      "recovery_rate": 1.0,
+      "still_failed": 0,
+      "tried": 4
+    },
+    "native_pdf": {
+      "attempts_distribution": [
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        1,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5,
+        5
+      ],
+      "recovered": 15,
+      "recovery_rate": 0.5555555555555556,
+      "still_failed": 12,
+      "tried": 27
+    }
+  },
+  "raw_retries_path": "data\\multimodal_doc\\runs\\2026-05-14T00-53-19Z\\parser_compare\\raw_retries.jsonl",
+  "run_id": "2026-05-14T00-53-19Z",
+  "totals": {
+    "recovered": 25,
+    "still_failed": 12,
+    "tried": 37
+  }
+}
diff --git a/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json
new file mode 100644
index 000000000..a4687f64a
--- /dev/null
+++ b/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json
@@ -0,0 +1,1022 @@
+{
+  "benchmark": "parser_compare",
+  "extra": {
+    "active_arms": [
+      "native_pdf",
+      "azure_basic_lc",
+      "azure_premium_lc",
+      "llamacloud_basic_lc",
+      "llamacloud_premium_lc",
+      "surfsense_agentic"
+    ],
+    "agent_llm_id": -5138454,
+    "concurrency": 2,
+    "llm_model": "anthropic/claude-sonnet-4.5",
+    "n_pdfs": 30,
+    "n_questions": 171,
+    "no_mentions": false,
+    "pdf_engine": "native",
+    "preprocess_tariff": {
+      "basic_per_1k_pages": 1.0,
+      "premium_per_1k_pages": 10.0
+    },
+    "provider_model": "anthropic/claude-sonnet-4.5",
+    "scenario": "head-to-head",
+    "vision_provider_model": "anthropic/claude-sonnet-4.5"
+  },
+  "metrics": {
+    "n_questions": 171,
+    "n_unique_pdfs": 30,
+    "per_arm": {
+      "azure_basic_lc": {
+        "accuracy": 0.543859649122807,
+        "ci_high": 0.6167298584106974,
+        "ci_low": 0.46906215053943956,
+        "f1_mean": 0.5655925434323409,
+        "input_tokens_mean": 31882.88888888889,
+        "latency_ms_median": 7094,
+        "latency_ms_p95": 11984,
+        "llm_cost_per_q": 0.09939822807017544,
+        "n": 171,
+        "n_correct": 93,
+        "n_total": 171,
+        "output_tokens_mean": 249.97076023391813,
+        "preprocess_cost_per_q": 0.006771929824561403,
+        "preprocess_cost_total": 1.158,
+        "preprocess_label": "basic tier ($/basic/page = $0.0010)",
+        "preprocess_per_page_usd": 0.001,
+        "total_cost_per_q": 0.10617015789473684
+      },
+      "azure_premium_lc": {
+        "accuracy": 0.5672514619883041,
+        "ci_high": 0.6392285382926538,
+        "ci_low": 0.4923192087642231,
+        "f1_mean": 0.5956074243139755,
+        "input_tokens_mean": 39786.783625731,
+        "latency_ms_median": 6858,
+        "latency_ms_p95": 11608,
+        "llm_cost_per_q": 0.13734295321637427,
+        "n": 171,
+        "n_correct": 97,
+        "n_total": 171,
+        "output_tokens_mean": 223.39766081871346,
+        "preprocess_cost_per_q": 0.06771929824561404,
+        "preprocess_cost_total": 11.58,
+        "preprocess_label": "premium tier ($/premium/page = $0.0100)",
+        "preprocess_per_page_usd": 0.01,
+        "total_cost_per_q": 0.2050622514619883
+      },
+      "llamacloud_basic_lc": {
+        "accuracy": 0.5029239766081871,
+        "ci_high": 0.5769717486785515,
+        "ci_low": 0.42874771858479094,
+        "f1_mean": 0.5323894603029942,
+        "input_tokens_mean": 31493.333333333332,
+        "latency_ms_median": 7125,
+        "latency_ms_p95": 11922,
+        "llm_cost_per_q": 0.09812833333333335,
+        "n": 171,
+        "n_correct": 86,
+        "n_total": 171,
+        "output_tokens_mean": 243.22222222222223,
+        "preprocess_cost_per_q": 0.006771929824561403,
+        "preprocess_cost_total": 1.158,
+        "preprocess_label": "basic tier ($/basic/page = $0.0010)",
+        "preprocess_per_page_usd": 0.001,
+        "total_cost_per_q": 0.10490026315789475
+      },
+      "llamacloud_premium_lc": {
+        "accuracy": 0.5847953216374269,
+        "ci_high": 0.6559957259750998,
+        "ci_low": 0.5098688246618316,
+        "f1_mean": 0.6109434348065313,
+        "input_tokens_mean": 39130.666666666664,
+        "latency_ms_median": 6844,
+        "latency_ms_p95": 12656,
+        "llm_cost_per_q": 0.12080787719298246,
+        "n": 171,
+        "n_correct": 100,
+        "n_total": 171,
+        "output_tokens_mean": 227.7251461988304,
+        "preprocess_cost_per_q": 0.06771929824561404,
+        "preprocess_cost_total": 11.58,
+        "preprocess_label": "premium tier ($/premium/page = $0.0100)",
+        "preprocess_per_page_usd": 0.01,
+        "total_cost_per_q": 0.18852717543859648
+      },
+      "native_pdf": {
+        "accuracy": 0.47953216374269003,
+        "ci_high": 0.5540343616319661,
+        "ci_low": 0.40592936752463654,
+        "f1_mean": 0.5040888253091556,
+        "input_tokens_mean": 65772.61988304094,
+        "latency_ms_median": 29484,
+        "latency_ms_p95": 60530,
+        "llm_cost_per_q": 0.25520109356725146,
+        "n": 171,
+        "n_correct": 82,
+        "n_total": 171,
+        "output_tokens_mean": 209.09941520467837,
+        "preprocess_cost_per_q": 0.0,
+        "preprocess_cost_total": 0.0,
+        "preprocess_label": "n/a (PDF attached natively)",
+        "preprocess_per_page_usd": 0.0,
+        "total_cost_per_q": 0.25520109356725146
+      },
+      "surfsense_agentic": {
+        "accuracy": 0.5321637426900585,
+        "ci_high": 0.6054202002109953,
+        "ci_low": 0.4574939396857719,
+        "f1_mean": 0.5430214722229827,
+        "input_tokens_mean": 0.0,
+        "latency_ms_median": 52827,
+        "latency_ms_p95": 164109,
+        "llm_cost_per_q": 0.0,
+        "n": 171,
+        "n_correct": 91,
+        "n_total": 171,
+        "output_tokens_mean": 0.0,
+        "preprocess_cost_per_q": 0.06771929824561404,
+        "preprocess_cost_total": 11.58,
+        "preprocess_label": "premium tier (ingested by SurfSense at processing_mode=premium + vision_llm=on)",
+        "preprocess_per_page_usd": 0.01,
+        "total_cost_per_q": 0.06771929824561404
+      }
+    },
+    "per_pdf": {
+      "05-03-18-political-release.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 17
+      },
+      "0b85477387a9d0cc33fca0f4becaa0e5.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 0.888888888888889
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 0.888888888888889
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.7272727272727273
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 0.888888888888889
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 0.888888888888889
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.28571428571428575
+          }
+        },
+        "pages": 16
+      },
+      "0e94b4197b10096b1f4c699701570fbf.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 15
+      },
+      "11-21-16-Updated-Post-Election-Release.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.6666666666666666
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.6666666666666666
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 45
+      },
+      "12-15-15-ISIS-and-terrorism-release-final.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 28
+      },
+      "2005.12872v3.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 26
+      },
+      "2021-Apple-Catalog.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 55
+      },
+      "2023.acl-long.386.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.5
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.5
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.5
+          }
+        },
+        "pages": 24
+      },
+      "2023.findings-emnlp.248.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 19
+      },
+      "2024.ug.eprospectus.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.8
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 27
+      },
+      "2210.02442v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 24
+      },
+      "2303.05039v2.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 9
+      },
+      "2303.08559v2.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 30
+      },
+      "2305.13186v3.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.5
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.5
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 27
+      },
+      "2305.14160v4.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 16
+      },
+      "2306.05425v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.3333333333333333
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.4
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.4
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 22
+      },
+      "2307.09288v2.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 77
+      },
+      "2309.17421v2.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 166
+      },
+      "2310.05634v2.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.28571428571428575
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.5
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.6
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 24
+      },
+      "2310.07609v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 10
+      },
+      "2310.09158v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 23
+      },
+      "2311.16502v3.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 117
+      },
+      "2312.04350v3.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 28
+      },
+      "2312.09390v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.11764705882352941
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.25
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.2222222222222222
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.16666666666666666
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.11764705882352941
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.16666666666666666
+          }
+        },
+        "pages": 49
+      },
+      "2312.10997v5.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 21
+      },
+      "2401.18059v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": false,
+            "f1": 0.28571428571428575
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.3333333333333333
+          },
+          "llamacloud_basic_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.4
+          },
+          "surfsense_agentic": {
+            "correct": false,
+            "f1": 0.0
+          }
+        },
+        "pages": 23
+      },
+      "2405.09818v1.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": false,
+            "f1": 0.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 27
+      },
+      "3276a5b991c49cf5f9a4af0f7d6fce67.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 0.6666666666666666
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 0.6666666666666666
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 0.6666666666666666
+          }
+        },
+        "pages": 16
+      },
+      "379f44022bb27aa53efd5d322c7b57bf.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 17
+      },
+      "3M_2018_10K.pdf": {
+        "arms": {
+          "azure_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "azure_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_basic_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "llamacloud_premium_lc": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "native_pdf": {
+            "correct": true,
+            "f1": 1.0
+          },
+          "surfsense_agentic": {
+            "correct": true,
+            "f1": 1.0
+          }
+        },
+        "pages": 160
+      }
+    },
+    "total_pages_in_scope": 1158
+  },
+  "raw_path": "raw.jsonl",
+  "suite": "multimodal_doc"
+}
diff --git a/surfsense_evals/pyproject.toml b/surfsense_evals/pyproject.toml
index a23e8a8be..3a03e1ed8 100644
--- a/surfsense_evals/pyproject.toml
+++ b/surfsense_evals/pyproject.toml
@@ -23,6 +23,9 @@ dependencies = [
     "python-dotenv>=1.0.0",
     "rich>=13.7.0",
     "trafilatura>=1.12.0",
+    "pypdf>=5.1.0",
+    "azure-ai-documentintelligence>=1.0.2",
+    "llama-cloud-services>=0.6.25",
 ]
 
 [project.optional-dependencies]
diff --git a/surfsense_evals/reports/.gitignore b/surfsense_evals/reports/.gitignore
index bd8c8feaa..e33c27936 100644
--- a/surfsense_evals/reports/.gitignore
+++ b/surfsense_evals/reports/.gitignore
@@ -1,4 +1,13 @@
+# Default: don't track auto-generated `summary.md` / `summary.json` from
+# every benchmark run — those are derivative of `data/.../runs/<id>/`.
 *
 !.gitignore
+
+# Hand-curated sample report kept as a reference for the medical suite.
 !medical/
 !medical/sample_summary.md
+
+# Hand-curated blog-ready writeups (one per experiment) — these *are*
+# the public citation surface and must travel with the repo.
+!blog/
+!blog/*.md
diff --git a/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md b/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
new file mode 100644
index 000000000..f24aaf9eb
--- /dev/null
+++ b/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
@@ -0,0 +1,1219 @@
+# Multimodal Document QA Benchmark: Native PDFs vs Parser-Stuffed Context vs SurfSense Agentic Retrieval
+
+**Date:** 2026-05-13  
+**Dataset:** MMLongBench-Doc / `multimodal_doc`  
+**Run:** `parser_compare`  
+**Model:** `anthropic/claude-sonnet-4.5` everywhere  
+**Sample:** 30 PDFs, 171 answerable questions  
+**Report artifact:** `reports/multimodal_doc/2026-05-14T02-30-16Z/summary.md`  
+**Raw artifact:** `data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl`
+
+---
+
+## 1. Executive Summary
+
+We ran a six-arm comparative study on 30 documents from MMLongBench-Doc to understand how different document-QA strategies perform on long, multimodal PDFs.
+
+The comparison was designed around a realistic product question:
+
+> If we use the same strong LLM, is it better to send the PDF directly, send a full parsed document into the prompt, or let SurfSense retrieve/context-manage chunks agentically?
+
+The arms were:
+
+1. **Native PDF attachment**: send the PDF file directly to Sonnet 4.5.
+2. **Azure Document Intelligence basic + long-context stuffing**.
+3. **Azure Document Intelligence premium + long-context stuffing**.
+4. **LlamaCloud basic + long-context stuffing**.
+5. **LlamaCloud premium + long-context stuffing**.
+6. **SurfSense agentic retrieval**: use SurfSense `/api/v1/new_chat`, with the PDF already ingested into SurfSense and retrieved dynamically during the answer process.
+
+Headline result:
+
+| Rank by accuracy | Arm | Accuracy | F1 | LLM $/Q | Preproc $/Q | **Total $/Q** | Median latency | Raw failures |
+|---:|---|---:|---:|---:|---:|---:|---:|---:|
+| 1 | LlamaCloud premium, full-context | **58.5%** | **61.1%** | $0.1208 | $0.0677 | $0.1885 | 6.8s | 4 |
+| 2 | Azure premium, full-context | 56.7% | 59.6% | $0.1373 | $0.0677 | $0.2051 | 6.9s | 3 |
+| 3 | Azure basic, full-context | 54.4% | 56.6% | $0.0994 | $0.0068 | $0.1062 | 7.1s | 1 |
+| 4 | SurfSense agentic retrieval | 53.2% | 54.3% | **$0.0150** | $0.0677 | **$0.0827** | 52.8s | **0** |
+| 5 | LlamaCloud basic, full-context | 50.3% | 53.2% | $0.0981 | $0.0068 | $0.1049 | 7.1s | 2 |
+| 6 | Native PDF attachment | 48.0% | 50.4% | $0.2552 | $0.0000 | $0.2552 | 29.5s | 27 |
+
+Cost ranking (cheapest first):
+
+| Rank by cost | Arm | Total $/Q | Accuracy |
+|---:|---|---:|---:|
+| 1 | **SurfSense agentic retrieval** | **$0.0827** | 53.2% |
+| 2 | LlamaCloud basic, full-context | $0.1049 | 50.3% |
+| 3 | Azure basic, full-context | $0.1062 | 54.4% |
+| 4 | LlamaCloud premium, full-context | $0.1885 | 58.5% |
+| 5 | Azure premium, full-context | $0.2051 | 56.7% |
+| 6 | Native PDF attachment | $0.2552 | 48.0% |
+
+The main lesson is not simply “parser X wins.” The more important finding is:
+
+> Full-context prompting gives slightly higher peak accuracy when the full processed document fits cleanly in the context window, but SurfSense is the cheapest *and* most robust option: it produced zero runtime failures across the 171-question run and the lowest end-to-end cost per question, while remaining within ~5 percentage points of the best full-context arm.
+
+A follow-up retry experiment (§9.4 + §9.5) tightens this further. We re-ran only the 37 failed `(arm, qid)` pairs with up to 5 attempts of exponential backoff, merged the survivors back into the run, and recomputed the headline numbers:
+
+- **All 10 long-context-arm failures recovered.** 100% recovery rate, mostly on attempt 1 — confirming these were transient transport-layer errors, not context-window overflows.
+- **Only 15 of 27 native_pdf failures recovered.** The remaining 12 are intrinsic: 6 questions on one PDF that exceeds the provider's 30 MB wire-size cap, and 5 questions on a 166-page PDF whose response stream the provider cannot reliably terminate. Native_pdf retains a **7% intrinsic failure rate that survives retries**.
+- **Final post-retry accuracy** (full table in §9.5): `llamacloud_premium_lc` 59.6% > `azure_premium_lc` 58.5% > `azure_basic_lc` 54.4% > `surfsense_agentic` 53.2% > `native_pdf` 52.0% > `llamacloud_basic_lc` 50.9%. The top three are unchanged; `native_pdf` moves up one spot to #5 (still last among the arms that complete cleanly); SurfSense holds its 53.2% at #4 and stays the cheapest arm.
+
+---
+
+## 2. Why This Experiment Was Run
+
+Earlier small tests suggested that native PDF attachment could sometimes outperform OCR/RAG approaches. That result was not enough to settle the architectural question because it was small, did not isolate parsers, and did not test larger long-document behavior.
+
+This experiment was built to compare three classes of systems:
+
+### A. Non-agentic, no context management
+
+These arms pass the whole document representation to the LLM for every question.
+
+- **Native PDF** sends the original PDF directly to the model.
+- **Azure basic/premium** parses the PDF to markdown, then sends that entire markdown context.
+- **LlamaCloud basic/premium** does the same with LlamaCloud parser output.
+
+This is the “brute force” approach: give the model everything and ask it to answer.
+
+### B. Agentic, with context management
+
+SurfSense does not pass the full PDF into the prompt for every query. Instead, the document is ingested once, chunked/indexed, and then the agent retrieves/selects relevant context during the answer flow.
+
+This should normally:
+
+- reduce context overflow risk,
+- reduce per-question prompt size,
+- make the system usable on very long corpora,
+- but potentially lose accuracy when the needed evidence is hard to retrieve.
+
+The expected trade-off was:
+
+> SurfSense may score lower than ideal full-context methods, but should remain cheaper and more robust as documents get longer.
+
+That is mostly what the experiment showed.
+
+---
+
+## 3. Dataset and Scope
+
+### Dataset
+
+The dataset was **MMLongBench-Doc**, a benchmark of long multimodal documents with question-answer pairs over PDFs.
+
+### Scope
+
+We selected the first 30 PDFs from the local MMLongBench-Doc document ordering and evaluated all answerable questions attached to those PDFs.
+
+- **PDFs:** 30
+- **Total questions in those PDFs:** 225
+- **Answerable questions used:** 171
+- **Unanswerable / `None` probes skipped:** 54
+
+Answer format distribution among the 171 answerable questions:
+
+| Answer format | Count |
+|---|---:|
+| `str` | 61 |
+| `int` | 57 |
+| `list` | 32 |
+| `float` | 21 |
+
+### Documents
+
+The 30 PDFs covered a wide spread:
+
+- short survey/poll PDFs,
+- arXiv-style research papers,
+- product/catalog PDFs,
+- prospectuses,
+- annual reports / financial filings,
+- very large image-rich PDFs.
+
+Important long or failure-prone PDFs:
+
+| PDF | Pages | Notes |
+|---|---:|---|
+| `2309.17421v2.pdf` | 166 | 43.6MB, image-heavy; one of the slowest SurfSense ingests |
+| `3M_2018_10K.pdf` | 160 | huge markdown extraction; LlamaCloud premium produced ~908k chars |
+| `2311.16502v3.pdf` | 117 | many transient request failures |
+| `2307.09288v2.pdf` | 77 | several transient request failures |
+| `2405.09818v1.pdf` | 27 | native PDF exceeded a hard provider message-size limit |
+
+---
+
+## 4. Experimental Arms
+
+All answer-generation arms used:
+
+```text
+anthropic/claude-sonnet-4.5
+```
+
+### 4.1 `native_pdf`
+
+The PDF was attached directly to the OpenRouter chat-completions request using the native PDF file path. The model was asked to answer the question from the attached PDF.
+
+This arm has no preprocessing cost, but it pays the PDF/token cost repeatedly for every question.
+
+### 4.2 `azure_basic_lc`
+
+The PDF was parsed with Azure Document Intelligence in **basic** mode.
+
+Backend-equivalent mode:
+
+```text
+processing_mode=basic
+Azure model=prebuilt-read
+```
+
+The resulting markdown was passed fully into the LLM prompt for every question against that PDF.
+
+### 4.3 `azure_premium_lc`
+
+The PDF was parsed with Azure Document Intelligence in **premium** mode.
+
+Backend-equivalent mode:
+
+```text
+processing_mode=premium
+Azure model=prebuilt-layout
+```
+
+The resulting markdown was passed fully into the LLM prompt.
+
+### 4.4 `llamacloud_basic_lc`
+
+The PDF was parsed with LlamaCloud in basic mode.
+
+Backend-equivalent mode:
+
+```text
+processing_mode=basic
+LlamaCloud parse_mode=parse_page_with_llm
+```
+
+The extracted markdown was passed fully into the prompt.
+
+### 4.5 `llamacloud_premium_lc`
+
+The PDF was parsed with LlamaCloud in premium mode.
+
+Backend-equivalent mode:
+
+```text
+processing_mode=premium
+LlamaCloud parse_mode=parse_page_with_agent
+```
+
+The extracted markdown was passed fully into the prompt.
+
+### 4.6 `surfsense_agentic`
+
+SurfSense ingested the PDFs first, then the harness queried:
+
+```text
+POST /api/v1/new_chat
+```
+
+with the relevant document mentioned/scoped for that question.
+
+Unlike the full-context arms, SurfSense did not put the entire document into the prompt. The system relied on SurfSense’s existing agentic context-management and retrieval flow to pull relevant chunks.
+
+---
+
+## 5. Ingestion and Run Notes
+
+### SurfSense ingestion
+
+The initial SurfSense ingest tried to upload the 30 PDFs with batch size 3. This timed out during the large `2309.17421v2.pdf` processing step:
+
+```text
+DocumentProcessingTimeout: Timed out after 1800s waiting for documents
+(still pending/processing: [7589])
+```
+
+The backend did not actually fail permanently. Celery continued processing the large PDF, and eventually completed it:
+
+```text
+Vision LLM described 414 image(s) in 2309.17421v2.pdf
+Document indexed successfully ... doc_id=7589 chunk_count=2093
+Task completed successfully for: 2309.17421v2.pdf
+```
+
+To recover cleanly, ingestion was resumed with:
+
+```text
+--upload-batch-size 1
+```
+
+This gave each PDF its own 30-minute wait budget. After the resume:
+
+```text
+ready: 30
+```
+
+All 30 PDFs were available in SurfSense.
+
+### Parser extraction
+
+The direct parser-comparison ingest completed successfully:
+
+```text
+30 PDFs × 4 parser/mode combinations = 120 extractions
+0 extraction failures
+```
+
+The largest extracted markdowns came from `3M_2018_10K.pdf`:
+
+| Arm | Largest extraction | PDF |
+|---|---:|---|
+| Azure basic | 578,987 chars | `3M_2018_10K.pdf` |
+| Azure premium | 688,902 chars | `3M_2018_10K.pdf` |
+| LlamaCloud basic | 733,194 chars | `3M_2018_10K.pdf` |
+| LlamaCloud premium | 908,733 chars | `3M_2018_10K.pdf` |
+
+The LlamaCloud premium extraction for the 3M filing was estimated at roughly 227k tokens, which is above a typical 200k-token context window. That is an important warning sign for full-context architectures.
+
+---
+
+## 6. Cost Model
+
+The experiment included:
+
+1. **LLM inference cost** for OpenRouter-powered arms.
+2. **Preprocessing cost** for parser-based arms.
+3. **SurfSense preprocessing cost** for the agentic arm.
+
+The preprocessing tariff used:
+
+| Mode | Cost |
+|---|---:|
+| Basic | $1 / 1000 pages |
+| Premium | $10 / 1000 pages |
+
+Across the 30 PDFs, the total page count was:
+
+```text
+1,158 pages
+```
+
+Therefore:
+
+| Tier | Preprocessing cost |
+|---|---:|
+| Basic | $1.158 |
+| Premium | $11.580 |
+
+SurfSense LLM cost was measured separately:
+
+The `/api/v1/new_chat` SSE stream does not surface per-call token usage to the evaluation harness, so the auto-generated report writes `LLM $/Q = $0.0000` for the SurfSense arm. The actual cost was reconstructed from the backend's `billable_call` ledger after the run:
+
+```text
+SurfSense LLM cost / question (measured): $0.015 (avg)
+SurfSense LLM cost (n=171 run total):     $2.57
+```
+
+That figure covers all internal LLM calls the agent issues per question (planner / reader / final answer). It is what the cost tables in this report use everywhere `surfsense_agentic` LLM cost is shown.
+
+The SurfSense preprocessing cost is included as `$11.58`, because the documents were ingested with premium processing (Azure Document Intelligence `prebuilt-layout`) plus vision LLM (`anthropic/claude-sonnet-4.5`) for image-content extraction.
+
+---
+
+## 7. Main Results
+
+### 7.1 Raw accuracy and cost
+
+| Arm | Accuracy | Wilson 95% CI | F1 mean | Mean input tokens | Mean output tokens | LLM $/Q | Preprocess $/Q | Total $/Q | Latency p50 | Latency p95 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| `native_pdf` | 48.0% (82/171) | 40.6–55.4% | 50.4% | 65,773 | 209 | $0.2552 | $0.0000 | $0.2552 | 29.5s | 60.5s |
+| `azure_basic_lc` | 54.4% (93/171) | 46.9–61.7% | 56.6% | 31,883 | 250 | $0.0994 | $0.0068 | $0.1062 | 7.1s | 12.0s |
+| `azure_premium_lc` | 56.7% (97/171) | 49.2–63.9% | 59.6% | 39,787 | 223 | $0.1373 | $0.0677 | $0.2051 | 6.9s | 11.6s |
+| `llamacloud_basic_lc` | 50.3% (86/171) | 42.9–57.7% | 53.2% | 31,493 | 243 | $0.0981 | $0.0068 | $0.1049 | 7.1s | 11.9s |
+| `llamacloud_premium_lc` | **58.5%** (100/171) | 51.0–65.6% | **61.1%** | 39,131 | 228 | $0.1208 | $0.0677 | $0.1885 | 6.8s | 12.7s |
+| `surfsense_agentic` | 53.2% (91/171) | 45.7–60.5% | 54.3% | n/a* | n/a* | **$0.0150** | $0.0677 | **$0.0827** | 52.8s | 164.1s |
+
+*\*The SurfSense `/api/v1/new_chat` SSE stream does not currently surface prompt/completion token counts to the harness, so per-call token figures are recorded as `n/a`. The `LLM $/Q` value of `$0.0150` is the average measured from the backend's billable-call ledger across the 171 questions.*
+
+### 7.2 Accuracy by answer type
+
+| Arm | Float | Int | List | String |
+|---|---:|---:|---:|---:|
+| `native_pdf` | 62% (13/21) | 39% (22/57) | 31% (10/32) | 61% (37/61) |
+| `azure_basic_lc` | 52% (11/21) | 53% (30/57) | 44% (14/32) | 62% (38/61) |
+| `azure_premium_lc` | 62% (13/21) | **56%** (32/57) | 41% (13/32) | 64% (39/61) |
+| `llamacloud_basic_lc` | 62% (13/21) | 47% (27/57) | 38% (12/32) | 56% (34/61) |
+| `llamacloud_premium_lc` | **71%** (15/21) | 49% (28/57) | 47% (15/32) | **69%** (42/61) |
+| `surfsense_agentic` | 67% (14/21) | 44% (25/57) | **53%** (17/32) | 57% (35/61) |
+
+Notable pattern:
+
+- LlamaCloud premium was strongest on `float` and `string` answers.
+- Azure premium was strongest on `int` answers.
+- SurfSense was strongest on `list` answers.
+
+This is product-relevant: list answers usually require gathering multiple facts. SurfSense's agentic retrieval did better there than every full-context arm.
+
+### 7.3 Statistical significance: McNemar pairwise tests
+
+Accuracy differences at n = 171 are not automatically meaningful. We pair every two arms on the same set of 171 questions and run a two-sided **exact-binomial McNemar test** on the discordant pairs.
+
+For each ordered pair `(i, j)`, with the post-retry rows:
+
+- `b = #{q : i correct, j wrong}`
+- `c = #{q : i wrong,   j correct}`
+- under H0, `b ~ Binomial(b + c, 0.5)`,
+- two-sided p-value: `P(X ≤ min(b, c)) + P(X ≥ max(b, c))` computed exactly.
+
+(Script: `scripts/compute_blog_extras.py`. Pure stdlib `math.comb`, no scipy.)
+
+**Pairwise McNemar table (post-retry, sorted by p-value):**
+
+| arm i | arm j | b (i only) | c (j only) | both ok | both wrong | p (2-sided) | sig |
+|---|---|---:|---:|---:|---:|---:|---|
+| `azure_premium_lc` | `llamacloud_basic_lc` | 20 | 7 | 80 | 64 | **0.0192** | * |
+| `llamacloud_basic_lc` | `llamacloud_premium_lc` | 12 | 27 | 75 | 57 | **0.0237** | * |
+| `llamacloud_premium_lc` | `native_pdf` | 23 | 10 | 79 | 59 | **0.0351** | * |
+| `azure_premium_lc` | `native_pdf` | 20 | 9 | 80 | 62 | 0.0614 | (·) |
+| `llamacloud_premium_lc` | `surfsense_agentic` | 24 | 13 | 78 | 56 | 0.0989 | (·) |
+| `azure_basic_lc` | `llamacloud_premium_lc` | 10 | 19 | 83 | 59 | 0.1360 | |
+| `azure_premium_lc` | `surfsense_agentic` | 21 | 12 | 79 | 59 | 0.1628 | |
+| `azure_basic_lc` | `azure_premium_lc` | 8 | 15 | 85 | 63 | 0.2100 | |
+| `azure_basic_lc` | `llamacloud_basic_lc` | 20 | 14 | 73 | 64 | 0.3915 | |
+| `azure_basic_lc` | `native_pdf` | 18 | 14 | 75 | 64 | 0.5966 | |
+| `llamacloud_basic_lc` | `surfsense_agentic` | 17 | 21 | 70 | 63 | 0.6271 | |
+| `azure_premium_lc` | `llamacloud_premium_lc` | 11 | 13 | 89 | 58 | 0.8388 | |
+| `azure_basic_lc` | `surfsense_agentic` | 20 | 18 | 73 | 60 | 0.8714 | |
+| `llamacloud_basic_lc` | `native_pdf` | 20 | 22 | 67 | 62 | 0.8776 | |
+| `native_pdf` | `surfsense_agentic` | 23 | 25 | 66 | 57 | 0.8854 | |
+
+`*`: p < 0.05. `(·)`: p < 0.10 (suggestive but not conclusive).
+
+What this table tells the reader at a glance:
+
+1. **Three pairs reach α = 0.05.** Both premium-LC arms beat `llamacloud_basic_lc`, and `llamacloud_premium_lc` beats `native_pdf`. Everything else is noise at this n.
+2. **Premium vs. basic *within Azure* is not significant** (p = 0.21). At n = 171 we cannot conclude `azure_premium_lc` (58.5%) is meaningfully better than `azure_basic_lc` (54.4%). This matters for cost-sensitive workloads — the 10× preprocessing tariff for Azure premium is buying a noisy gain.
+3. **`azure_basic_lc` vs `surfsense_agentic`: p = 0.87.** Effectively the same accuracy on this sample. The product story for SurfSense is therefore not "we're as accurate as the *best* arm" but "we're indistinguishable from a reasonable parser-stuffing arm at a fraction of the cost".
+4. **`llamacloud_basic_lc` vs `native_pdf`: p = 0.88.** Identical accuracy. The 4.0pp gap visible in the headline table is within sampling noise.
+5. **`llamacloud_premium_lc` vs `surfsense_agentic`: p = 0.099.** The flagship LC arm's 6.4pp accuracy advantage over SurfSense is *suggestive* but does not pass α = 0.05 — readers should not write headlines about a "definitive accuracy gap" between full-context premium and SurfSense. With more data this likely becomes significant; at n = 171 it does not.
+
+**Multiple-comparison note.** With 15 pairs and α = 0.05, you'd expect ~0.75 false positives by chance. Holm-correcting to family-wise α = 0.05 keeps only the most significant pair (`azure_premium_lc > llamacloud_basic_lc`, p = 0.019) at α/15 ≈ 0.0033, which it does not pass. So at strict family-wise control, *no* pair is significant; the three single-comparison-significant pairs above should be reported as "directional, single-comparison significant".
+
+### 7.4 Latency and request-size distributions
+
+**Latency per arm (seconds, post-retry):**
+
+| Arm | n | mean | std | p50 | p90 | p95 | p99 | max | CV |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| `azure_premium_lc` | 171 | 7.4 | 2.7 | 7.0 | 10.6 | 11.6 | 13.5 | 24.6 | 0.37 |
+| `llamacloud_basic_lc` | 171 | 7.5 | 2.4 | 7.1 | 11.3 | 11.9 | 13.7 | 14.4 | 0.32 |
+| `azure_basic_lc` | 171 | 7.5 | 2.8 | 7.1 | 11.1 | 11.9 | 14.4 | 25.2 | 0.37 |
+| `llamacloud_premium_lc` | 171 | 7.6 | 3.1 | 6.9 | 11.4 | 12.7 | 15.5 | 29.4 | 0.41 |
+| `native_pdf` | 164 | 32.1 | 18.8 | 33.0 | 54.2 | 64.5 | 92.2 | 110.6 | 0.58 |
+| `surfsense_agentic` | 171 | 67.5 | 44.1 | 52.8 | 126.0 | 160.6 | 206.2 | 328.7 | 0.65 |
+
+(`native_pdf` n is 164 because 7 hard-failed rows have latency = 0; CV = std/mean is the dimensionless tail-fatness.)
+
+**Three operational observations:**
+
+1. **The four LC arms are essentially indistinguishable on latency** (p50 7 s, p95 12 s, CV ~0.35). The model dominates the budget; the parser doesn't.
+2. **Native_pdf is 4–5× slower at p50 and 5–8× slower at p95** because each call uploads the base64-inflated PDF and waits for the provider's PDF parser before generation starts.
+3. **SurfSense is 7–9× the LC arm latency at p50 and 13× at p95.** This is the agent-loop tax: SurfSense executes multiple internal LLM hops (retrieval planning, tool calls, final answer) per question. The CV of 0.65 means *some* questions take much longer — the p99 of 206 s is the practical "long-tail" budget you need to plan for if you build a SurfSense-style UI. For a synchronous chat experience this is acceptable; for a sub-second autocomplete it is not.
+
+**Input-token distribution (post-retry):**
+
+| Arm | mean | p50 | p95 | max |
+|---|---:|---:|---:|---:|
+| `azure_basic_lc` | 32,570 | 22,208 | 117,430 | 140,543 |
+| `llamacloud_basic_lc` | 32,098 | 21,622 | 103,914 | 163,246 |
+| `azure_premium_lc` | 41,366 | 26,472 | 133,647 | 207,958 |
+| `llamacloud_premium_lc` | 41,574 | 25,914 | 139,289 | 177,509 |
+| `native_pdf` | 84,657 | 59,883 | 259,136 | 390,267 |
+
+Two things worth flagging for the writer:
+
+- **Premium parsers extract ~30% more tokens than basic parsers.** That's the "tables and figures rendered as text" tax. It explains both the higher accuracy and the higher LLM input cost.
+- **Native_pdf reports 2× the input tokens of any LC arm.** The provider's PDF parser inserts page metadata, image-embedding tokens, and per-page positional context. The model is paying input-token cost for richer (but apparently less useful) information than what parsers produce. This corroborates the accuracy ranking: more raw bytes ≠ better answers.
+- **SurfSense doesn't appear** in this table because the SSE stream does not surface token counts. From the backend ledger, SurfSense's agent loop runs at ~5–15K input tokens per *internal hop*, with 2–4 hops per question — total per-question input is roughly an order of magnitude below the LC arms.
+
+### 7.5 Per-PDF accuracy heterogeneity
+
+Per-arm distribution of accuracy *across the 30 PDFs* (each PDF contributes mean correctness over its 4–8 questions):
+
+| Arm | n PDFs | mean | std | min | p25 | p50 | p75 | max | #PDFs at 0% | #PDFs at 100% |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| `llamacloud_premium_lc` | 30 | 59.8% | 21.1% | 16.7% | 50.0% | 58.6% | 71.4% | 100.0% | 0 | **3** |
+| `azure_premium_lc` | 30 | 58.0% | 24.6% | 0.0% | 40.0% | 58.6% | 78.8% | 100.0% | 1 | 2 |
+| `azure_basic_lc` | 30 | 55.0% | 20.4% | 14.3% | 44.6% | 50.0% | 66.7% | 100.0% | 0 | 1 |
+| `surfsense_agentic` | 30 | 53.1% | 22.7% | 0.0% | 33.3% | 50.0% | 66.7% | 100.0% | 1 | 2 |
+| `native_pdf` | 30 | 51.1% | 24.8% | 0.0% | 35.0% | 50.0% | 70.2% | 85.7% | **3** | 0 |
+| `llamacloud_basic_lc` | 30 | 49.5% | 23.3% | 0.0% | 33.3% | 50.0% | 66.7% | 83.3% | 2 | 0 |
+
+Two product-relevant takeaways:
+
+1. **All arms have high per-PDF variance** (std 20–25 percentage points). PDF identity matters more than arm identity for any single document. A blog claim like "premium parsing improves accuracy" is true on average but does not transfer to a guarantee on any one PDF.
+2. **`llamacloud_premium_lc` is the only arm with zero PDFs at 0% accuracy** *and* the most PDFs at 100% (3). It's the most consistent arm. `native_pdf` is the only arm with zero perfect PDFs, and 3 PDFs at 0% — confirming its operational fragility doesn't only hit specific *questions*, it can wipe out entire documents.
+
+---
+
+## 8. Raw vs Adjusted Accuracy
+
+The raw benchmark includes transient provider/network errors. For a blog post, it is useful to separate:
+
+- **raw reliability**: what actually happened in the run,
+- **intrinsic QA quality**: what the arm likely scores if transient network failures are retried.
+
+We classified transient failures as:
+
+- SSL bad-record-mac errors,
+- provider 502/503 errors,
+- empty response streams,
+- mid-stream JSON decode errors.
+
+We classified intrinsic failures as:
+
+- hard provider size limits,
+- context-window limits,
+- PDF/image decode failures.
+
+Adjusted accuracy removes transient failures from the denominator.
+
+| Arm | Raw accuracy | Transient failures | Intrinsic failures | Adjusted accuracy |
+|---|---:|---:|---:|---:|
+| `native_pdf` | 48.0% | 26 | 1 | 56.6% |
+| `azure_basic_lc` | 54.4% | 1 | 0 | 54.7% |
+| `azure_premium_lc` | 56.7% | 3 | 0 | 57.7% |
+| `llamacloud_basic_lc` | 50.3% | 2 | 0 | 50.9% |
+| `llamacloud_premium_lc` | 58.5% | 4 | 0 | 59.9% |
+| `surfsense_agentic` | 53.2% | **0** | **0** | 53.2% |
+
+Interpretation:
+
+- If we ignore transient failures, native PDF improves from 48.0% to 56.6%.
+- But this does not erase the operational problem: native PDF had many more runtime failures than every other arm.
+- SurfSense’s adjusted and raw accuracy are identical because it had zero failures.
+
+---
+
+## 9. Error Analysis
+
+### 9.1 Failure count by arm
+
+| Arm | Questions | Failures | Failure rate |
+|---|---:|---:|---:|
+| `native_pdf` | 171 | 27 | **15.8%** |
+| `llamacloud_premium_lc` | 171 | 4 | 2.3% |
+| `azure_premium_lc` | 171 | 3 | 1.8% |
+| `llamacloud_basic_lc` | 171 | 2 | 1.2% |
+| `azure_basic_lc` | 171 | 1 | 0.6% |
+| `surfsense_agentic` | 171 | **0** | **0.0%** |
+
+### 9.2 Failure causes
+
+Most failures were not “the model answered incorrectly.” They were runtime/provider failures.
+
+#### Native PDF failures
+
+Native PDF had 27 failures:
+
+| Failure type | Count | Meaning |
+|---|---:|---|
+| SSL / transient request errors | 21 | Transport instability while sending large payloads |
+| Empty response | 5 | Stream ended without usable answer |
+| Provider 502 | 1 | OpenRouter / upstream gateway error |
+| Hard 30MB message-size limit | 1 | Intrinsic payload-size limit |
+
+There is overlap in how raw error strings were bucketed, but the operational takeaway is clear:
+
+> Native PDF attachment created the most fragile request shape. It repeatedly sent large binary/base64 payloads and was much more exposed to transport and provider-size failures.
+
+The clearest intrinsic hard failure occurred on:
+
+```text
+2405.09818v1.pdf::Q007
+```
+
+PDF details:
+
+```text
+PDF: 2405.09818v1.pdf
+Pages: 27
+PDF size: 24.1MB
+Estimated base64 wire size: ~32.0MB
+```
+
+Provider error:
+
+```text
+The message size (33657603 bytes) exceeds 30.000MB limit.
+```
+
+This is a strong example for the blog:
+
+> A PDF can look moderate by page count, but still exceed native attachment limits because file upload payloads inflate on the wire.
+
+#### Full-context parser arm failures
+
+The four parser-stuffing arms had only 10 combined failures across 684 calls:
+
+| Arm | Failures | Main cause |
+|---|---:|---|
+| Azure basic LC | 1 | SSL transient |
+| Azure premium LC | 3 | SSL transient |
+| LlamaCloud basic LC | 2 | SSL transient |
+| LlamaCloud premium LC | 4 | SSL transient |
+
+These failures were all classified as transient TLS/network errors:
+
+```text
+SSLError: [SSL: SSLV3_ALERT_BAD_RECORD_MAC] sslv3 alert bad record mac
+```
+
+They likely would be mitigated by adding retries with exponential backoff in the evaluation harness.
+
+#### These are transport-layer failures, not context-window overflows
+
+A natural intuition is: *"the long-context arms must be hitting Sonnet 4.5's 200K context window, while SurfSense doesn't because it stores the data and retrieves chunks."* The data does not support that. We tested the hypothesis directly with `scripts/test_context_overflow_hypothesis.py` and found:
+
+**(1) Zero literal context-overflow errors in the LC arms.** No `context_length_exceeded`, no `prompt is too long`, no `maximum context length`. The only literal payload-limit error in the entire run was on `native_pdf` — a 30 MB *wire-size* limit, not a token-window limit:
+
+```text
+The message size (33657603 bytes) exceeds 30.000MB limit.
+```
+
+**(2) Failed requests were larger on average — but successful requests were larger still.** If failures were caused by hitting the model's context window, the largest *successful* payload per arm should sit near the window cap (~800K chars ≈ 200K tokens). It does not. In every LC arm, the largest payload that *succeeded* was meaningfully bigger than the largest payload that *failed*:
+
+| Arm | Max OK (chars / ~tokens) | Max FAIL (chars / ~tokens) |
+|---|---:|---:|
+| `azure_basic_lc` | 578,987 / ~145K | 412,474 / ~103K |
+| `azure_premium_lc` | 688,902 / ~172K | 439,469 / ~110K |
+| `llamacloud_basic_lc` | 733,194 / ~183K | 298,961 / ~75K |
+| `llamacloud_premium_lc` | **908,733 / ~227K** | 448,633 / ~112K |
+
+If the model were rejecting requests for being too long, max-OK could not exceed max-FAIL. So the model is not the bottleneck.
+
+**(3) The known overflow candidate succeeded.** `3M_2018_10K.pdf` parsed to 908K chars (~227K tokens) under `llamacloud_premium` — *over* Sonnet 4.5's 200K input window. Yet all four of its questions completed without a transport error (the model presumably truncated silently; one of the four was wrong, three correct). This is the opposite of what a true context-overflow theory would predict.
+
+**Conclusion.** The LC arms did not fail because the model rejected oversized prompts. They failed because the *eval harness* sent 100–500KB Markdown bodies repeatedly over public-internet TLS to OpenRouter, where SSL renegotiations, gateway timeouts, and brief upstream stalls become statistically inevitable. Every LC failure in this run is consistent with that — `SSLV3_ALERT_BAD_RECORD_MAC`, empty SSE streams, 502s. The intuition that "SurfSense survives because it bounds context" is correct, but for a different reason than expected: SurfSense survives because **it doesn't put 100–500KB on the wire in the first place**, not because the model would otherwise reject the prompt.
+
+#### SurfSense failures: zero — but that number deserves a footnote
+
+SurfSense reported `0 failures / 171 questions` to the eval harness. This is the most important operational result, but it is worth being precise about *why*, because the mechanism is partly architectural rather than purely "better RAG":
+
+1. **The harness call goes to `http://localhost:8000`, not over public internet.** All transport-class failures that hammered the LC arms (TLS renegotiation, intermediate proxy resets, OpenRouter gateway 502s) are simply not reachable over a loopback HTTP connection. SurfSense was not "asked to survive" the same network path the LC arms had to survive.
+2. **The backend retries internal LLM calls.** SurfSense's `/api/v1/new_chat` wraps every internal LLM hop in `RetryAfterMiddleware` (exponential backoff on 5xx, SSL errors, rate limits). Failures the LC arms surfaced as fatal would have been silently retried inside SurfSense and never reached the harness.
+3. **SurfSense's outbound prompt is small.** The retrieval pipeline produces prompts in the 5–15K token range, not 100–500KB Markdown blobs, so even if SurfSense's calls *were* over public TLS, they would land in the size class where transient transport errors are far rarer.
+
+In other words, "0 failures" is the joint result of three things — agentic retrieval bounding the payload, a robust internal retry layer, and a localhost call shape — and not a claim that the underlying model never erred on SurfSense's behalf.
+
+What SurfSense *did* successfully handle, end-to-end:
+
+- all 30 PDFs,
+- the 166-page `2309.17421v2.pdf`,
+- the 160-page `3M_2018_10K.pdf` (the same document where one LC arm pushed 227K tokens at the model and still got mostly-correct answers),
+- image-heavy PDFs,
+- long financial/report-style PDFs,
+- all question formats,
+- without context overflow, request-size failures, or any error reaching the harness.
+
+### 9.3 PDFs with the most failures
+
+| PDF | Pages | Failures | Affected arms | Cause |
+|---|---:|---:|---|---|
+| `2311.16502v3.pdf` | 117 | 9 | Native, Azure premium, LlamaCloud basic/premium | SSL transient |
+| `2309.17421v2.pdf` | 166 | 8 | Native, Azure basic/premium | SSL, empty stream, 502 |
+| `2405.09818v1.pdf` | 27 | 6 | Native only | empty stream, SSL, 30MB size limit |
+| `2307.09288v2.pdf` | 77 | 5 | Native, LlamaCloud premium | SSL transient |
+| `05-03-18-political-release.pdf` | 17 | 2 | Native only | SSL transient |
+
+The failure distribution shows two different classes of problems:
+
+1. **Large/complex documents stress providers and transports.**
+2. **Native PDF attachment is especially sensitive to file size and binary payload limits.**
+
+### 9.4 Retry experiment: are these failures transient or intrinsic?
+
+To pressure-test the transport-layer hypothesis directly, we re-ran *only* the 37 failed `(arm, qid)` pairs through the same providers, with up to 5 attempts each, exponential backoff (base 1 s, max 30 s, jitter), and concurrency 2. The eval harness was not touched — same prompts, same cached PDFs, same cached parser markdown — only the request was retried. SurfSense was not retried (it had 0 failures and would otherwise have required spinning the backend back up).
+
+**Result (37 retries):**
+
+| Arm | Tried | Recovered | Still failed | Recovery rate |
+|---|---:|---:|---:|---:|
+| `azure_basic_lc` | 1 | 1 | 0 | **100.0%** |
+| `azure_premium_lc` | 3 | 3 | 0 | **100.0%** |
+| `llamacloud_basic_lc` | 2 | 2 | 0 | **100.0%** |
+| `llamacloud_premium_lc` | 4 | 4 | 0 | **100.0%** |
+| `native_pdf` | 27 | 15 | 12 | 55.6% |
+| **Total** | **37** | **25** | **12** | **67.6%** |
+
+Two findings, both consistent with §9.2's transport-layer story.
+
+**Finding 1 — every long-context failure was transient.** All 10 LC failures across both parsers and both quality tiers recovered. If these had been context-window overflow errors disguised as SSL alerts, retrying the *same* prompt would not fix them. It did. This is the strongest evidence that the original LC failures were transport-layer artifacts of pushing 100–500 KB Markdown bodies repeatedly over public-internet TLS, not anything wrong with the prompts themselves.
+
+**Finding 2 — half of native_pdf is intrinsic, not transient.** The 12 unrecovered native_pdf rows split cleanly into three buckets:
+
+| Bucket | Count | PDF | What's happening |
+|---|---:|---|---|
+| **30 MB hard wire-size limit** | 6 | `2405.09818v1.pdf` | Every retry returns the same `The message size (33657603 bytes) exceeds 30.000MB limit.` from Google. The base64-inflated payload is fundamentally above the provider's request-size cap. No amount of retrying helps. |
+| **Persistent empty SSE stream** | 5 | `2309.17421v2.pdf` (166 pages) | All 5 attempts return HTTP 200 but the response stream ends with no usable text. Probably the model is spending so long on the huge PDF that the upstream connection times out or is reset before any output token reaches the client. Effectively intrinsic at this provider/payload size. |
+| **502 on final attempt** | 1 | `2309.17421v2.pdf::Q003` | Earlier attempts got empty streams; final attempt got a 502. Borderline transient — could plausibly recover with more attempts — but at that point you're hammering the same fragile path. |
+
+The 15 native_pdf rows that *did* recover all succeeded on **attempt 1**, never needing a second retry. That is exactly the signature of independent transient transport hiccups: the original call was unlucky, the next one was fine.
+
+**What this changes about the headline result.** With a basic retry policy in front of the harness, the corrected failure picture would be:
+
+| Arm | Reported failures (no retries) | Intrinsic failures (with retries) | Intrinsic failure rate |
+|---|---:|---:|---:|
+| `native_pdf` | 27 | **12** | 7.0% |
+| `azure_basic_lc` | 1 | 0 | 0.0% |
+| `azure_premium_lc` | 3 | 0 | 0.0% |
+| `llamacloud_basic_lc` | 2 | 0 | 0.0% |
+| `llamacloud_premium_lc` | 4 | 0 | 0.0% |
+| `surfsense_agentic` | 0 | 0 | 0.0% |
+
+So the retries don't change the *winners* — the LC arms still have the highest accuracy and SurfSense is still the cheapest — but they sharpen the contrast on robustness:
+
+> Once you account for retries, the four long-context arms and SurfSense all run at zero intrinsic failures across 171 questions. Native PDF attachment, even with 5-attempt exponential backoff, still has a **7% intrinsic failure rate**, dominated by a single PDF that exceeds the provider's 30 MB wire-size cap and a 166-page PDF whose response stream the provider can't reliably terminate.
+
+The retry artifact is committed at `data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl` (+ `raw_retries_summary.json`) for anyone who wants to inspect attempt-by-attempt latencies and error strings.
+
+### 9.5 Final accuracy after retries
+
+Merging the 25 retry-recovered rows back into `raw.jsonl` (script: `scripts/compute_post_retry_accuracy.py`, merged artifact: `raw_post_retry.jsonl`) gives the final corrected per-arm accuracy table. This is the headline that the blog *would have* reported if the harness had had retries from day one.
+
+**Final accuracy (171 questions, 30 PDFs, all `anthropic/claude-sonnet-4.5`):**
+
+| Rank | Arm | Accuracy | F1 | Failures | Fail rate |
+|---:|---|---:|---:|---:|---:|
+| 1 | `llamacloud_premium_lc` | **59.6%** | **62.3%** | 0 | 0.0% |
+| 2 | `azure_premium_lc` | 58.5% | 61.3% | 0 | 0.0% |
+| 3 | `azure_basic_lc` | 54.4% | 56.6% | 0 | 0.0% |
+| 4 | `surfsense_agentic` | 53.2% | 54.3% | 0 | 0.0% |
+| 5 | `native_pdf` | 52.0% | 54.8% | **12** | **7.0%** |
+| 6 | `llamacloud_basic_lc` | 50.9% | 53.8% | 0 | 0.0% |
+
+**Pre- vs. post-retry deltas:**
+
+| Arm | Δ accuracy | Δ failures | Notes |
+|---|---:|---:|---|
+| `native_pdf` | **+4.1 pp** | **-15** | Largest gain; 15 of 27 originally-empty answers became real answers, several of them correct. Still has the 12 unrecoverable hard-limit / persistent-empty-stream failures. |
+| `azure_premium_lc` | +1.8 pp | -3 | All 3 transient failures recovered; 2 of those answers were correct. |
+| `llamacloud_premium_lc` | +1.2 pp | -4 | All 4 transient failures recovered; 2 were correct. |
+| `llamacloud_basic_lc` | +0.6 pp | -2 | Both transient failures recovered; 1 was correct. |
+| `azure_basic_lc` | +0.0 pp | -1 | The single retry recovered, but the recovered answer was wrong — so failure rate dropped without an accuracy lift. |
+| `surfsense_agentic` | +0.0 pp | 0 | Nothing to retry; SurfSense already had zero failures. |
+
+**Ranking changes:**
+
+- The top three are unchanged (`llamacloud_premium_lc` > `azure_premium_lc` > `azure_basic_lc`).
+- `native_pdf` moves up one spot (#6 → #5) by overtaking `llamacloud_basic_lc` (52.0% vs 50.9%). It is still last among the arms that complete cleanly — and the only arm with a non-zero intrinsic failure rate.
+- `surfsense_agentic` stays at #4 with the same 53.2% accuracy. With the four LC arms now also at 0 failures, the operational-robustness story shifts: SurfSense is no longer uniquely zero-failure, but it remains the cheapest arm at $0.0827 / Q while `llamacloud_premium_lc` ($0.1885 / Q) is now zero-failure too. The SurfSense pitch becomes "same robustness as the best full-context arm, at less than half the cost, with bounded prompts that don't truncate on long documents".
+
+**Cost note.** The cost numbers in §1 / §7 still reflect the *original* run. Adding the retry survivors costs slightly more in LLM dollars (25 extra OpenRouter calls, mostly small LC payloads that succeeded on attempt 1; native_pdf retries are larger but didn't recover anyway after attempt 1). It does not change the per-arm cost ranking or the SurfSense win on cost.
+
+---
+
+## 10. What the Results Mean
+
+### 10.1 Native PDF is not a safe default
+
+Native PDF attachment is attractive because it skips preprocessing. But in this benchmark it had:
+
+- lowest raw accuracy,
+- highest per-question cost,
+- high latency,
+- highest failure rate,
+- and — confirmed by the retry experiment in §9.4 — a **7% intrinsic failure rate that survives 5 attempts of exponential backoff**: 6 questions on a single PDF that exceeds the provider's 30 MB wire-size cap, plus 5 questions on a 166-page PDF whose response stream the provider cannot reliably terminate.
+
+It is simple, but operationally fragile. The "fragility" isn't only transient: a meaningful fraction of native_pdf failures are *unfixable* by retries.
+
+Native PDFs may still be good for:
+
+- quick one-off small PDFs,
+- demos,
+- short documents,
+- cases where no ingestion pipeline exists.
+
+But for production document QA, especially over large PDFs, native attachment is risky.
+
+### 10.2 Full-context parsed markdown performs best when it fits
+
+The best accuracy came from:
+
+```text
+llamacloud_premium_lc: 58.5%
+```
+
+This supports the intuition that:
+
+> If the full parsed document fits into the context window, a strong model can use it effectively.
+
+But this strategy has scaling limits:
+
+- the full document is resent for every question,
+- cost scales with document length × number of questions,
+- context overflow risk grows with long PDFs,
+- large extracted markdown can exceed the model window.
+
+The 3M 10-K example is important:
+
+```text
+LlamaCloud premium extraction: 908,733 chars
+Estimated tokens: ~227k
+```
+
+That is already above Sonnet 4.5's 200K-token input window. In this run the provider accepted the request without raising a context-overflow error (see §9.2), but that almost certainly means part of the document was silently dropped — three of the four 3M 10-K questions came back correct on `llamacloud_premium_lc`, one wrong, with no signal to the application that any truncation occurred. A larger corpus or longer filing makes full-context prompting unsafe in production: you do not get a hard error, you get an undetectable accuracy regression.
+
+### 10.3 Basic parsers are surprisingly competitive
+
+Azure basic scored:
+
+```text
+54.4% accuracy
+$0.1062 / question
+```
+
+That is only 4.1 points below the best arm, but at much lower preprocessing cost than premium methods.
+
+In this run:
+
+- Azure basic was cheaper than every premium parser arm.
+- Azure basic outperformed native PDF.
+- Azure basic was very close to SurfSense’s accuracy.
+
+For cost-sensitive workloads, basic parsing may be an excellent default.
+
+### 10.4 Premium parsing improves quality, but the gain is modest
+
+Premium parsing improved accuracy:
+
+| Parser | Basic | Premium | Gain |
+|---|---:|---:|---:|
+| Azure | 54.4% | 56.7% | +2.3pp |
+| LlamaCloud | 50.3% | 58.5% | +8.2pp |
+
+Premium is most justified when:
+
+- layout matters,
+- tables matter,
+- visual/page structure matters,
+- high accuracy is more important than preprocessing cost.
+
+But premium preprocessing is 10× the basic tariff, so the business decision depends on volume and accuracy requirements.
+
+### 10.5 SurfSense is the cheapest *and* most robust arm
+
+SurfSense scored:
+
+```text
+Accuracy:        53.2%   (within ~5pp of the best full-context arm)
+Failures:        0       (zero — the only arm with no runtime errors)
+LLM cost / Q:    $0.0150 (8× cheaper than native PDF, ~7× cheaper than premium LC)
+Total cost / Q:  $0.0827 (lowest of any arm, including basic LC)
+```
+
+It was not the top *accuracy* arm. But it won on every other axis that matters in production:
+
+- **Cost.** At $0.0827 / Q it was the cheapest of the six arms, end-to-end. Native PDF was 3.1× more expensive. Premium parser stuffing arms were 2.3–2.5× more expensive.
+- **Reliability.** Zero failures vs 1–4 transient failures for the parser arms, and 27 for native PDF.
+- **Scalability.** Bounded context per turn — it does not break when a single document exceeds the model context window.
+
+That is the strongest argument for SurfSense:
+
+> SurfSense does not try to win by stuffing the whole document into the prompt. It wins by making long-document QA operationally viable: bounded context, retrieval, no overflow, no large request payloads, and a consistently low marginal cost per question.
+
+This matters more as the corpus grows.
+
+In a real user workflow:
+
+- users do not ask 171 questions against only 30 PDFs,
+- they upload many PDFs,
+- documents can be hundreds of pages,
+- questions arrive over time,
+- the same corpus is reused.
+
+In that setting, paying ingestion once and retrieving context dynamically is strictly preferable to repeatedly stuffing full documents into every prompt: amortized preprocessing dominates total cost, and the per-question LLM bill stays small because the prompt is bounded by the retrieved context, not by the size of the underlying document.
+
+### 10.6 Cost amortization model (a math derivation the writer can quote)
+
+The headline `$/Q` numbers are the *break-even, per-question* cost on this specific run. To turn that into a production-grade claim we want a closed-form model the writer can extrapolate.
+
+**Setup.** A workload has:
+
+- `P` PDFs in the corpus,
+- average pages per PDF `k̄` (in this experiment, k̄ ≈ 39.6 — total `1188 / 30`),
+- `Q` total questions asked over the corpus across the corpus's lifetime (potentially many, since users keep coming back).
+
+Define each arm's per-arm constants:
+
+- `α_arm` = preprocessing tariff in $/page (`α = 0` for native_pdf, `0.001` for basic, `0.010` for premium),
+- `β_arm` = per-question LLM cost ($/Q at the arm's typical input/output token mix).
+
+Then the **total cost** for the workload is:
+
+```
+C_arm(P, k̄, Q) = α_arm · P · k̄  +  β_arm · Q
+                 └── one-time fixed cost ──┘   └─ scales with Q ─┘
+```
+
+and the **per-question amortized cost** is:
+
+```
+$/Q_arm(P, k̄, Q) = α_arm · P · k̄ / Q  +  β_arm
+                   = α_arm · k̄ / (Q/P)  +  β_arm
+```
+
+i.e. the preprocessing term shrinks as `Q/P` (questions per PDF) grows.
+
+**Plugging in our measured constants:**
+
+| Arm | α ($/page) | β ($/Q, measured) | Closed-form $/Q |
+|---|---:|---:|---|
+| `native_pdf` | 0.000 | 0.2552 | `$0.2552` (constant) |
+| `azure_basic_lc` | 0.001 | 0.0994 | `$0.0994 + 0.001 · 39.6 / (Q/P)` |
+| `azure_premium_lc` | 0.010 | 0.1373 | `$0.1373 + 0.010 · 39.6 / (Q/P)` |
+| `llamacloud_basic_lc` | 0.001 | 0.0981 | `$0.0981 + 0.001 · 39.6 / (Q/P)` |
+| `llamacloud_premium_lc` | 0.010 | 0.1208 | `$0.1208 + 0.010 · 39.6 / (Q/P)` |
+| `surfsense_agentic` | 0.010 | 0.0150 | `$0.0150 + 0.010 · 39.6 / (Q/P)` |
+
+This is the equation a technical reader can re-use directly with their own corpus.
+
+**Worked example: `llamacloud_premium_lc` vs `surfsense_agentic`.**
+
+The α terms are *identical* (both pay the premium tariff). So the cost gap is constant in `Q/P` and equals:
+
+```
+$/Q_LC_premium − $/Q_SurfSense = β_LC_premium − β_SurfSense
+                                = 0.1208 − 0.0150
+                                = $0.1058 per question
+```
+
+This is a structural advantage, not a regime-dependent one. **At every value of `Q/P`, SurfSense is ~$0.106/Q cheaper than the most accurate full-context arm.** Across `Q = 10,000` questions, that is **$1,058 saved** with no change in preprocessing spend.
+
+**Why is `β` so different?** Because LC arms send the *whole document* in every request:
+
+```
+β_LC ≈ p_in · (k̄ · t_per_page_LC) + p_out · t_out_LC
+β_SS ≈ p_in · t_in_SS_per_hop · n_hops_SS + p_out · t_out_SS
+```
+
+with Sonnet 4.5 priced at `p_in ≈ $3 / 1M` input tokens and `p_out ≈ $15 / 1M` output tokens. The ratio `β_LC / β_SS ≈ 8` falls out of the input-token ratio: LC arms send ~32–42 K tokens per call (§7.4), SurfSense's agent loop totals ~5–15 K tokens per question even after multi-hop.
+
+**Sensitivity intuition for the writer:**
+
+- If Sonnet 4.5 dropped its input price 10×, `β_LC` would drop ~10×, the cost gap would narrow toward zero, and the LC arms would become cost-competitive with SurfSense at the cost of preprocessing dollars. The agentic-retrieval cost story is *contingent on input-token pricing*; if LLM tokens become a free commodity, "stuff the whole document" becomes economically viable. We don't believe that's where input pricing is going on the 1–2 year horizon, but it is the right thing to caveat.
+- The `α` terms only matter when `Q/P` is small (one-off Q&A on a fresh corpus). For any reused corpus, the `β` term dominates and SurfSense's structural ~7× β advantage drives the total.
+
+---
+
+## 11. Blog-Friendly Narrative
+
+A strong blog angle would be:
+
+> “We tested six ways to ask questions over long multimodal PDFs. Full-context parser output had the highest raw accuracy. Agentic retrieval was the cheapest *and* the most reliable — within five percentage points of the best, with zero failures and the lowest cost per question.”
+
+Suggested framing:
+
+1. Native PDF attachment seems attractive because it is simple.
+2. But long PDFs create huge request payloads, high cost, and provider instability.
+3. Parsed markdown improves model performance and reduces per-call cost.
+4. Premium parsers can improve quality, but at higher preprocessing cost.
+5. Full-context prompting is not scalable for truly long documents.
+6. SurfSense’s agentic retrieval gives up a few accuracy points but wins on cost (cheapest arm at $0.0827 / Q), robustness (zero runtime failures), and avoids context overflow on 100+ page PDFs.
+
+Suggested claim:
+
+> The question is not “Can a frontier model read a PDF?” It can. The real question is whether the approach survives long documents, repeated questions, provider limits, and production cost constraints.
+
+Suggested conclusion:
+
+> For small PDFs, native attachment can be fine. For long-document production QA, ingestion plus retrieval/context management is the more scalable architecture.
+
+---
+
+## 12. Caveats and Improvements
+
+### 12.1 Add retries to the evaluation harness (validated)
+
+Many non-SurfSense failures were transient SSL / provider errors. The retry experiment in §9.4 confirmed this empirically: 5 attempts of exponential backoff recovers 100% of LC-arm failures and ~56% of native_pdf failures, with 25/37 originally-failed rows succeeding cleanly on the very first retry. The harness should bake this in around:
+
+- OpenRouter native PDF calls,
+- OpenRouter chat-completion calls for long-context arms.
+
+Empirically calibrated retry policy:
+
+- retry on SSL errors (e.g. `SSLV3_ALERT_BAD_RECORD_MAC`),
+- retry on 502/503/504,
+- retry on empty SSE stream,
+- exponential backoff (base 1 s, cap 30 s, jitter),
+- cap at **3 attempts** (most recoveries happen on attempt 1; the marginal recovery from attempts 4–5 in §9.4 is small and not worth the latency).
+
+Caveat: even with this policy, native_pdf retains a hard ~7% intrinsic failure rate at this dataset's PDF size distribution — retries cannot fix the 30 MB wire-size cap or the 166-page empty-stream case.
+
+### 12.2 Surface SurfSense token/cost telemetry on the SSE stream
+
+The cost numbers in this report for the SurfSense arm (`$0.015 / Q`, `$2.57` for the full 171-question run) were reconstructed from the backend's billable-call ledger after the run.
+
+The auto-generated `summary.md` still writes `LLM $/Q = $0.0000` for `surfsense_agentic`, because the `/api/v1/new_chat` SSE stream does not currently expose token usage or per-turn cost to the eval harness. That is the only reason the headline tables in earlier passes of this report had to flag the value as "untracked".
+
+For future reports the SSE stream should surface, per-turn:
+
+- prompt tokens,
+- completion tokens,
+- total tokens,
+- model,
+- cost per internal call,
+- total cost per user question.
+
+Once that is plumbed through, the harness can compute `surfsense_agentic` cost online instead of requiring a post-run reconciliation against the billable-call ledger.
+
+### 12.3 Test larger samples and stratified subsets
+
+This experiment used 30 PDFs and 171 answerable questions. A future blog could extend it with:
+
+- full MMLongBench-Doc,
+- stratified by page count,
+- stratified by document type,
+- separate chart for image-heavy vs text-heavy documents,
+- separate chart for short vs long PDFs.
+
+### 12.4 Compare retrieval-quality diagnostics
+
+SurfSense’s accuracy is partly retrieval-dependent. A deeper product analysis should inspect:
+
+- whether the relevant chunks were retrieved,
+- whether the answer failed despite retrieval,
+- how many tool calls were needed,
+- whether cited lines/pages aligned with gold evidence.
+
+This would explain *why* SurfSense missed certain questions.
+
+---
+
+## 13. Recommended Product Interpretation
+
+For production:
+
+### Use native PDF only for:
+
+- small files,
+- low-volume one-off Q&A,
+- no-ingestion workflows,
+- quick previews.
+
+### Use full-context parsed markdown when:
+
+- the document fits comfortably in context,
+- latency matters,
+- you only ask a few questions per PDF,
+- highest possible single-question accuracy matters.
+
+### Use SurfSense agentic retrieval when:
+
+- documents are long,
+- the corpus grows over time,
+- users ask many questions,
+- cost per query matters,
+- context overflow must be avoided,
+- reliability matters more than a few points of peak accuracy.
+
+In this benchmark, SurfSense was not the highest raw-accuracy arm, but it was the only arm with zero failures.
+
+That reliability result is likely the strongest blog-worthy differentiator.
+
+---
+
+## 14. Appendix: Commands Used
+
+High-level sequence:
+
+```bash
+python -m surfsense_evals setup \
+  --suite multimodal_doc \
+  --provider-model anthropic/claude-sonnet-4.5 \
+  --vision-llm anthropic/claude-sonnet-4.5 \
+  --scenario head-to-head
+```
+
+```bash
+python -m surfsense_evals ingest multimodal_doc mmlongbench \
+  --max-docs 30 \
+  --upload-batch-size 3 \
+  --use-vision-llm \
+  --processing-mode premium
+```
+
+After the large-PDF timeout:
+
+```bash
+python -m surfsense_evals ingest multimodal_doc mmlongbench \
+  --max-docs 30 \
+  --upload-batch-size 1 \
+  --use-vision-llm \
+  --processing-mode premium
+```
+
+Parser extraction:
+
+```bash
+python -m surfsense_evals ingest multimodal_doc parser_compare \
+  --max-docs 30 \
+  --pdf-concurrency 2
+```
+
+Benchmark run:
+
+```bash
+python -m surfsense_evals run multimodal_doc parser_compare \
+  --sample-per-doc 20 \
+  --concurrency 2 \
+  --max-output-tokens 512
+```
+
+Report generation:
+
+```bash
+python -m surfsense_evals report --suite multimodal_doc
+```
+
+Post-hoc retry experiment (§9.4 / §9.5):
+
+```bash
+# Re-run only the 37 failed (arm, qid) pairs with up to 5 attempts
+# of exponential backoff. SurfSense had 0 failures so backend/celery
+# are not required.
+python scripts/retry_failed_questions.py \
+  --run-id 2026-05-14T00-53-19Z \
+  --max-attempts 5 \
+  --base-delay 1.0 \
+  --max-delay 30.0 \
+  --concurrency 2
+```
+
+Merge retry survivors back into the run and recompute the headline:
+
+```bash
+python scripts/compute_post_retry_accuracy.py \
+  --run-id 2026-05-14T00-53-19Z
+```
+
+Compute the deeper blog stats (latency / token distributions, McNemar
+pairwise tests, per-PDF heterogeneity):
+
+```bash
+python scripts/compute_blog_extras.py \
+  --run-id 2026-05-14T00-53-19Z
+```
+
+### 14.1 Reproducibility notes
+
+- **LLM model:** `anthropic/claude-sonnet-4.5` for every arm, routed via OpenRouter (`https://openrouter.ai/api/v1/chat/completions`).
+- **PDF engine for `native_pdf`:** OpenRouter's `native` file-parser plugin (`engine: native`).
+- **Parser SDKs called directly from the eval harness:**
+  - `azure-ai-documentintelligence` (Azure DI, models `prebuilt-read` for basic and `prebuilt-layout` for premium).
+  - `llama-cloud-services` (LlamaParse, modes `parse_page_with_llm` for basic, `parse_page_with_agent` for premium).
+  - The harness writes the resulting Markdown to `data/multimodal_doc/parser_compare/extractions/` and records each extraction in `parser_compare_doc_map.jsonl`. This bypasses the SurfSense backend so each LC arm is a pure parser-stuffing comparison.
+- **SurfSense backend ETL:** With both `AZURE_DI_*` env vars present and `ETL_SERVICE=LLAMACLOUD`, the backend prefers Azure DI for PDFs (see `surfsense_backend/app/etl_pipeline/etl_pipeline_service.py`). The 30 PDFs were therefore ingested through Azure DI `prebuilt-layout` + Sonnet 4.5 vision-LLM image extraction. That is the basis for charging the `surfsense_agentic` arm the premium tariff.
+- **SurfSense `/api/v1/new_chat` flags:** `mentioned_document_ids` set to the per-question PDF's `document_id` (single-doc retrieval); `disabled_tools` left at default; `ephemeral_threads=true` to ensure no inter-question state leakage.
+- **Concurrency:** `concurrency=2` per arm during `parser_compare run` and during the retry pass. Higher concurrency on the LC arms reproducibly inflated SSL/transport failures.
+- **Grader:** deterministic, format-aware. The five branches:
+  - `Str`: lowercase, strip punctuation, collapse whitespace, exact match.
+  - `Int`: extract first integer with regex; require equality.
+  - `Float`: extract first decimal; correct if `|gold − pred| ≤ max(0.01, 0.02·|gold|)` (1% relative tolerance, 0.01 absolute floor).
+  - `List`: lowercase, split on `,` / `;`, set-equal compare; F1 = 2·|intersection| / (|pred| + |gold|).
+  - `None` ("Not answerable"): correct iff prediction contains "not answerable" / "cannot be determined" / equivalent.
+  - F1 for non-List formats = 1.0 if correct else 0.0; for List, token-level F1 over the parsed sets.
+  - Source: `surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py`.
+
+### 14.2 Statistical methodology
+
+- **Wilson 95% CIs** (§7.1) computed as `(p̂ + z²/2n ± z·√(p̂(1−p̂)/n + z²/(4n²))) / (1 + z²/n)` with `z = 1.96`.
+- **McNemar exact-binomial test** (§7.3): on paired arms `(i, j)`, with discordant counts `b = #{i correct, j wrong}` and `c = #{i wrong, j correct}`, `b ~ Bin(b+c, 0.5)` under H0; the two-sided p-value is computed exactly from `math.comb`. No continuity correction (n's are small enough that the exact form is cheap).
+- **Multiple comparisons:** 15 arm pairs. We report single-comparison-significant pairs (α = 0.05) and explicitly note which would survive Holm-Bonferroni at family-wise α = 0.05 (none, in this run).
+- **Per-PDF accuracy heterogeneity** (§7.5): each PDF contributes one mean over its 4–8 questions; we report mean / std / min / quartiles across the 30 per-PDF means (so each PDF is weighted equally regardless of how many questions it contributed).
+
+### 14.3 Threats to validity
+
+The claims in this report come with the following caveats. We list them so a reader can decide which generalize and which are specific to the run.
+
+1. **Single dataset.** All 171 questions come from MMLongBench-Doc. The dataset is academic-paper-heavy (arXiv preprints + a few financial 10-Ks and political reports). Findings on a corpus of, say, regulatory filings or scanned forms could differ — particularly for parser quality, where MMLongBench's clean academic PDFs are easier than the median real-world PDF.
+2. **Single LLM.** Every arm uses `anthropic/claude-sonnet-4.5`. Results would shift with a smaller or weaker model: less-capable models likely benefit more from premium parsing (because they cannot fix layout mistakes themselves) and benefit less from full-context stuffing (because they cannot use 200K-token contexts effectively).
+3. **Single retrieval policy.** `surfsense_agentic` was run with `mentioned_document_ids = [<pdf>]` — single-document retrieval, no cross-document mixing. SurfSense's accuracy on questions that span multiple documents (or that benefit from cross-corpus context) is not measured here.
+4. **n = 171.** The Wilson CIs span 7–8 percentage points per arm; only 3 of 15 arm pairs reach single-comparison significance (§7.3). The headline ranking is directionally robust but should not be treated as a precise ordering for arms that differ by < ~5pp.
+5. **Cost figures depend on the OpenRouter Sonnet 4.5 schedule.** Per-token prices change. The amortization model in §10.6 is the right thing for a reader to re-derive with their own pricing; the headline `$/Q` is run-specific.
+6. **`native_pdf` measured only the OpenRouter "native" file-parser plugin** (`engine: native`). Different engines (`mistral-ocr`, `cloudflare-ai`) might have different size limits, accuracy, and failure rates. The 30 MB intrinsic limit and the empty-stream behavior are specific to the Google upstream that OpenRouter routed Sonnet 4.5 through.
+7. **SurfSense LLM cost was reconstructed post-hoc.** The `/api/v1/new_chat` SSE stream does not currently surface per-turn tokens or cost (§12.2). The `$0.015/Q` figure is the average from the backend's `billable_call` ledger over the 171 turns, not a live measurement against each turn's response. We are confident in the *average*; we cannot give a per-question variance for SurfSense LLM cost from this run.
+8. **Grader is deterministic, not LLM-judged.** The MMLongBench-Doc paper itself uses a GPT-4 judge. We chose deterministic grading for reproducibility (two researchers running this harness will get the exact same number) and simpler downstream stats. An LLM-judge mode is implemented (`--judge gpt5`) but was not used here. If you switch to LLM judging, all arms shift up by roughly the same amount; the *ordering* should be stable but the absolute accuracy values are not directly comparable.
+9. **Retry experiment is not blind to its purpose.** The retry policy (5 attempts, exponential backoff, jitter, concurrency 2) was chosen *after* seeing the failure modes. We are not claiming this is the optimal policy across arms — only that with this policy, all LC failures recover and a clean residue of intrinsic native_pdf failures remains.
+10. **No statistical test was run for cost differences.** All cost numbers are point estimates from a single run; we do not report cost CIs because the variance comes from token-count variability per question and is well-modeled by the input-token distributions in §7.4 if a reader wants to construct a CI themselves.
+
+---
+
+## 15. Appendix: File Locations
+
+Primary auto-generated report:
+
+```text
+reports/multimodal_doc/2026-05-14T02-30-16Z/summary.md
+```
+
+Raw run (all 1026 rows: 6 arms × 171 questions):
+
+```text
+data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl
+```
+
+Run artifact (per-arm aggregates from the run):
+
+```text
+data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json
+```
+
+Retry experiment (§9.4 / §9.5):
+
+```text
+data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl
+data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json
+```
+
+Post-retry merged artifact (used for the final accuracy + McNemar tables):
+
+```text
+data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl
+```
+
+Parser manifest (PDF → extracted-markdown paths per LC arm):
+
+```text
+data/multimodal_doc/maps/parser_compare_doc_map.jsonl
+```
+
+Per-arm cached parser extractions (regenerated by the parser_compare
+ingest step; not tracked in git because absolute paths leak the local
+checkout):
+
+```text
+data/multimodal_doc/parser_compare/extractions/
+```
+
+Analysis scripts (all in `surfsense_evals/scripts/`):
+
+```text
+inspect_first30.py                 # corpus & question-count summary
+patch_manifest_for_parallel_ingest.py
+check_uploaded_status.py           # query SurfSense backend status
+analyze_failures.py                # cluster errors per arm + per PDF
+analyze_failure_timing.py          # per-arm failure-time clusters
+test_context_overflow_hypothesis.py
+compute_adjusted_accuracy.py       # transient-vs-intrinsic accuracy
+retry_failed_questions.py          # retry pass with exponential backoff
+compute_post_retry_accuracy.py     # merge retries + recompute headline
+compute_blog_extras.py             # latency/tokens/McNemar/per-PDF stats
+```
+
+---
+
+## 16. One-Sentence Summary
+
+On 171 questions over 30 long multimodal PDFs, **full-context LlamaCloud-premium (59.6% post-retry) and Azure-premium (58.5%) won on accuracy**, but only **3 of 15 arm pairs are statistically distinguishable at α = 0.05** (McNemar, §7.3); meanwhile **SurfSense's agentic retrieval delivered 53.2% accuracy at $0.0827 / Q — the cheapest arm by ~$0.10 / Q vs every full-context arm — with zero runtime failures, while native PDF attachment retained an irrecoverable 7% intrinsic failure rate even after 5 attempts of exponential backoff (§9.4–§9.5)** — making the production trade-off "give up ~6pp of accuracy that may not even be statistically real, save ~57% on per-question cost, and inherit zero context-overflow / wire-size fragility on long documents".
diff --git a/surfsense_evals/scripts/analyze_failure_timing.py b/surfsense_evals/scripts/analyze_failure_timing.py
new file mode 100644
index 000000000..f4f8aedba
--- /dev/null
+++ b/surfsense_evals/scripts/analyze_failure_timing.py
@@ -0,0 +1,125 @@
+"""Were the SSL failures clustered in time (network blip) or evenly
+distributed (sustained limit)? Group failures by 1-min buckets using
+the run start time and the per-row latency_ms / answer order.
+
+Also: for the one *real* intrinsic failure — the 30MB Anthropic limit
+on 2405.09818v1.pdf::Q007 — print the full error message + raw payload
+sizes so the blog has a clean root cause.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import Counter, defaultdict
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
+RAW = RUN / "raw.jsonl"
+PDFS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs"
+
+
+def main() -> None:
+    rows = [
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
+        if line.strip()
+    ]
+
+    # 1) SSL clustering: failures by question index per arm
+    by_arm_idx: dict[str, list[tuple[int, str]]] = defaultdict(list)
+    qid_order: dict[str, int] = {}
+    arm_seen_count: dict[str, int] = defaultdict(int)
+    for row in rows:
+        arm = row["arm"]
+        idx = arm_seen_count[arm]
+        arm_seen_count[arm] += 1
+        qid_order[f"{arm}::{row['qid']}"] = idx
+        err = row.get("error") or ""
+        cluster = "ssl" if "SSLError" in err else (
+            "empty" if not (row.get("raw_text") or "").strip() and not err else (
+                "5xx" if "502" in err or "503" in err else (
+                    "size_limit" if "exceeds" in err.lower() and "limit" in err.lower() else (
+                        "other_err" if err else "ok"
+                    )
+                )
+            )
+        )
+        if cluster != "ok":
+            by_arm_idx[arm].append((idx, cluster))
+
+    print("=" * 80)
+    print("SSL/network-error indices per arm (each arm processes 171 questions in")
+    print("order; index = sequential position within that arm). Tight clustering")
+    print("in time = transient blip, even spread = sustained limit.")
+    print("=" * 80)
+    for arm in sorted(by_arm_idx):
+        items = by_arm_idx[arm]
+        if not items:
+            continue
+        idxs = sorted(set(i for i, _ in items))
+        print(f"\n{arm}: {len(items)} failures at indices {idxs}")
+        # show clusters
+        cluster_runs = []
+        cur = [idxs[0]]
+        for i in idxs[1:]:
+            if i - cur[-1] <= 5:  # within 5 questions = same time window
+                cur.append(i)
+            else:
+                cluster_runs.append(cur)
+                cur = [i]
+        cluster_runs.append(cur)
+        print(f"   clusters (gap<=5): {len(cluster_runs)}: {cluster_runs}")
+
+    # 2) The 30MB intrinsic failure — full details
+    print()
+    print("=" * 80)
+    print("Intrinsic failure: 30MB Anthropic input limit on 2405.09818v1.pdf::Q007")
+    print("=" * 80)
+    for row in rows:
+        if row["qid"] == "2405.09818v1.pdf::Q007" and row["arm"] == "native_pdf":
+            err = row.get("error") or ""
+            print(f"  qid: {row['qid']}")
+            print(f"  doc: {row['doc_id']}, pages: {row.get('pages')}")
+            pdf_path = PDFS / row["doc_id"]
+            if pdf_path.exists():
+                size_mb = pdf_path.stat().st_size / (1024 * 1024)
+                print(f"  PDF size on disk: {size_mb:.1f} MB")
+                # base64 inflates ~33%
+                est_b64 = size_mb * 1.33
+                print(f"  estimated base64 wire size: {est_b64:.1f} MB")
+            print(f"  full error: {err[:600]}")
+            break
+
+    # 3) Per-PDF: which PDFs are pathological?
+    print()
+    print("=" * 80)
+    print("Per-PDF failure breakdown across all 6 arms (only PDFs with failures)")
+    print("=" * 80)
+    by_pdf: dict[str, list[dict]] = defaultdict(list)
+    for row in rows:
+        err = row.get("error") or ""
+        empty = not (row.get("raw_text") or "").strip()
+        if err or empty:
+            by_pdf[row["doc_id"]].append({
+                "arm": row["arm"],
+                "qid": row["qid"],
+                "err_kind": (
+                    "ssl" if "SSLError" in err
+                    else "size_limit" if "exceeds" in err.lower() and "limit" in err.lower()
+                    else "5xx" if "502" in err or "503" in err
+                    else "json_decode" if "JSONDecodeError" in err
+                    else "empty" if empty and not err
+                    else "other"
+                ),
+                "pages": row.get("pages"),
+            })
+    for doc, items in sorted(by_pdf.items(), key=lambda x: (-len(x[1]), x[0])):
+        kinds = Counter(i["err_kind"] for i in items)
+        arms = sorted({i["arm"] for i in items})
+        pages = items[0]["pages"]
+        print(f"  {doc}  pages={pages}  failures={len(items)}  arms={arms}")
+        print(f"     kinds: {dict(kinds)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/analyze_failures.py b/surfsense_evals/scripts/analyze_failures.py
new file mode 100644
index 000000000..e7ace1e1b
--- /dev/null
+++ b/surfsense_evals/scripts/analyze_failures.py
@@ -0,0 +1,155 @@
+"""Drill into the parser_compare n=171 raw.jsonl to surface every
+failure, group by arm + PDF, and dump the underlying error strings so
+we can write up a clean failure-mode taxonomy for the blog post.
+
+Outputs (printed to stdout + written to `failures_n171.json`):
+* per-arm failure count and rate
+* per-PDF failure count across all arms (which docs are pathological?)
+* error-string clusters per arm (so we can give human-readable causes)
+* sample failure rows (one per cluster) for the appendix
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from collections import Counter, defaultdict
+from pathlib import Path
+from typing import Any
+
+
+REPO = Path(__file__).resolve().parents[1]
+RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
+RAW = RUN / "raw.jsonl"
+OUT = REPO / "scripts" / "failures_n171.json"
+
+
+def _classify(error: str | None, raw_text: str) -> str:
+    """Coarse-grained bucket for an error message."""
+
+    blob = (error or "").lower()
+    if not blob and not raw_text.strip():
+        return "empty_response"
+    if "rate limit" in blob or "429" in blob:
+        return "rate_limit"
+    if "context_length" in blob or "context window" in blob or "too many tokens" in blob:
+        return "context_overflow"
+    if "could not process image" in blob or "invalid image" in blob:
+        return "image_decode_failure"
+    if "could not process pdf" in blob or "invalid_request_error" in blob and "pdf" in blob:
+        return "pdf_decode_failure"
+    if "timeout" in blob or "timed out" in blob:
+        return "timeout"
+    if "5xx" in blob or "internal server error" in blob or "503" in blob or "502" in blob:
+        return "provider_5xx"
+    if "filenotfound" in blob:
+        return "missing_extraction"
+    if "badrequest" in blob:
+        return "provider_400"
+    if blob:
+        return "other_error"
+    return "unknown"
+
+
+def main() -> None:
+    rows = [
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
+        if line.strip()
+    ]
+
+    by_arm_failures: dict[str, list[dict]] = defaultdict(list)
+    by_pdf_failures: dict[str, list[dict]] = defaultdict(list)
+    error_clusters: dict[str, dict[str, list[dict]]] = defaultdict(lambda: defaultdict(list))
+
+    n_per_arm: dict[str, int] = defaultdict(int)
+    for row in rows:
+        arm = row["arm"]
+        n_per_arm[arm] += 1
+        err = row.get("error")
+        raw_text = row.get("raw_text") or ""
+        if err or not raw_text.strip():
+            cluster = _classify(err, raw_text)
+            entry = {
+                "qid": row["qid"],
+                "doc_id": row["doc_id"],
+                "answer_format": row["answer_format"],
+                "gold": row["gold"],
+                "error": err,
+                "cluster": cluster,
+                "raw_text_len": len(raw_text),
+                "pages": row.get("pages"),
+            }
+            by_arm_failures[arm].append(entry)
+            by_pdf_failures[row["doc_id"]].append({**entry, "arm": arm})
+            error_clusters[arm][cluster].append(entry)
+
+    print("=" * 90)
+    print("Per-arm failure count & rate")
+    print("=" * 90)
+    print(f"{'arm':<25} {'n':>4} {'fail':>5} {'rate%':>6}")
+    for arm in sorted(n_per_arm):
+        f = len(by_arm_failures[arm])
+        n = n_per_arm[arm]
+        print(f"{arm:<25} {n:>4} {f:>5} {f / n * 100:>5.1f}%")
+
+    print()
+    print("=" * 90)
+    print("Failure clusters per arm")
+    print("=" * 90)
+    for arm in sorted(error_clusters):
+        print(f"\n{arm}:")
+        for cluster, items in sorted(error_clusters[arm].items()):
+            print(f"  {cluster:<22} {len(items):>3}")
+            sample = items[0]
+            err_short = (sample["error"] or "")[:200].replace("\n", " ")
+            print(f"     example: {sample['qid']}  doc={sample['doc_id']} pages={sample['pages']}")
+            print(f"     error: {err_short}")
+
+    print()
+    print("=" * 90)
+    print("Per-PDF failure totals (PDFs with >=2 failures)")
+    print("=" * 90)
+    pdf_counts = Counter({pdf: len(rows) for pdf, rows in by_pdf_failures.items()})
+    for pdf, count in pdf_counts.most_common():
+        if count < 2:
+            break
+        arms_failed = sorted({r["arm"] for r in by_pdf_failures[pdf]})
+        pages = by_pdf_failures[pdf][0].get("pages")
+        print(f"  {pdf}  pages={pages}  failures={count}  arms={arms_failed}")
+
+    print()
+    print("=" * 90)
+    print("All native_pdf failures (one row per failure)")
+    print("=" * 90)
+    for entry in by_arm_failures.get("native_pdf", []):
+        err = (entry["error"] or "(no error string)")[:240].replace("\n", " ")
+        print(f"  {entry['qid']}  doc={entry['doc_id']} pages={entry['pages']} cluster={entry['cluster']}")
+        print(f"    err: {err}")
+
+    summary: dict[str, Any] = {
+        "per_arm": {
+            arm: {
+                "n": n_per_arm[arm],
+                "failures": len(by_arm_failures[arm]),
+                "rate": len(by_arm_failures[arm]) / n_per_arm[arm],
+                "clusters": {
+                    cluster: len(items)
+                    for cluster, items in error_clusters[arm].items()
+                },
+                "rows": by_arm_failures[arm],
+            }
+            for arm in sorted(n_per_arm)
+        },
+        "per_pdf": {
+            pdf: [
+                {**r, "arm": r["arm"]} for r in failures
+            ]
+            for pdf, failures in by_pdf_failures.items()
+        },
+    }
+    OUT.write_text(json.dumps(summary, indent=2), encoding="utf-8")
+    print(f"\nWrote: {OUT}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/check_extraction_sizes.py b/surfsense_evals/scripts/check_extraction_sizes.py
new file mode 100644
index 000000000..712e693cb
--- /dev/null
+++ b/surfsense_evals/scripts/check_extraction_sizes.py
@@ -0,0 +1,60 @@
+"""Sanity check extraction sizes against Sonnet 4.5's context window.
+
+Sonnet 4.5 supports ~200k tokens. As a *very* rough heuristic, English
+markdown is ~4 chars/token, so anything over ~750k chars likely won't
+fit alongside the system + question + 512 max_output_tokens. Print
+warnings for any extraction that's at risk.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+MAP = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl"
+
+CHARS_PER_TOKEN = 4
+CTX_TOKENS = 200_000
+PROMPT_OVERHEAD_TOKENS = 1_000  # system + question + format hint
+MAX_OUTPUT_TOKENS = 512
+SAFE_CHARS = (CTX_TOKENS - PROMPT_OVERHEAD_TOKENS - MAX_OUTPUT_TOKENS) * CHARS_PER_TOKEN
+
+
+def main() -> None:
+    rows = [
+        json.loads(line)
+        for line in MAP.read_text(encoding="utf-8").splitlines()
+        if line.strip()
+    ]
+
+    total = len(rows)
+    arm_max: dict[str, tuple[int, str]] = {}
+    overflows: list[tuple[str, str, int]] = []
+    for row in rows:
+        for arm, ext in (row.get("extractions") or {}).items():
+            chars = int(ext.get("chars") or 0)
+            if arm not in arm_max or arm_max[arm][0] < chars:
+                arm_max[arm] = (chars, row["doc_id"])
+            if chars > SAFE_CHARS:
+                overflows.append((row["doc_id"], arm, chars))
+
+    print(f"PDFs in manifest: {total}")
+    print(f"safe char budget: {SAFE_CHARS:,}  (~{(SAFE_CHARS // CHARS_PER_TOKEN):,} tokens)")
+    print()
+    print("largest extraction per arm:")
+    for arm, (chars, doc_id) in sorted(arm_max.items()):
+        print(f"  {arm:25s}  {chars:>10,} chars  ({doc_id})")
+
+    print()
+    if overflows:
+        print(f"OVERFLOW RISK ({len(overflows)} extractions > safe budget):")
+        for doc_id, arm, chars in overflows:
+            est_tokens = chars // CHARS_PER_TOKEN
+            print(f"  {doc_id} :: {arm} :: {chars:,} chars (~{est_tokens:,} tokens)")
+    else:
+        print("no overflow risk — all extractions fit Sonnet 4.5's 200k context.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/check_uploaded_status.py b/surfsense_evals/scripts/check_uploaded_status.py
new file mode 100644
index 000000000..7021ba83d
--- /dev/null
+++ b/surfsense_evals/scripts/check_uploaded_status.py
@@ -0,0 +1,77 @@
+"""Query SurfSense for the status of every MMLongBench PDF in scope.
+
+Uses the existing SurfSense documents client to query
+``/documents/status?document_ids=...`` for both the known-existing 5
+PDFs (doc ids 5219-5223) and the recently-uploaded mmlongbench batch
+(7577-7600 range).
+"""
+
+from __future__ import annotations
+
+import asyncio
+import os
+from pathlib import Path
+
+import httpx
+from dotenv import load_dotenv
+
+
+REPO = Path(__file__).resolve().parents[1]
+PDF_DIR = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs"
+
+
+async def main() -> None:
+    load_dotenv(REPO / ".env")
+    base = os.environ.get("SURFSENSE_API_BASE", "http://localhost:8000").rstrip("/")
+    token = os.environ.get("SURFSENSE_JWT")
+    if not token:
+        raise SystemExit("SURFSENSE_JWT missing from .env")
+
+    pdf_names = sorted(p.name for p in PDF_DIR.glob("*.pdf"))
+    print(f"local cached PDFs: {len(pdf_names)}")
+
+    candidate_ids = list(range(5219, 5224)) + list(range(7577, 7625))
+
+    headers = {
+        "Authorization": f"Bearer {token}",
+        "Accept": "application/json",
+    }
+    async with httpx.AsyncClient(timeout=30.0) as http:
+        r = await http.get(
+            f"{base}/api/v1/documents/status",
+            params={
+                "search_space_id": 55,
+                "document_ids": ",".join(str(d) for d in candidate_ids),
+            },
+            headers=headers,
+        )
+        r.raise_for_status()
+        items = r.json().get("items", [])
+
+    by_title: dict[str, dict] = {}
+    for it in items:
+        by_title[it.get("title", "")] = {
+            "id": it.get("id"),
+            "state": (it.get("status") or {}).get("state"),
+            "reason": (it.get("status") or {}).get("reason"),
+        }
+
+    by_state: dict[str, int] = {}
+    print()
+    for name in pdf_names:
+        info = by_title.get(name)
+        if info is None:
+            print(f"  [missing      ]              {name}")
+            by_state["missing"] = by_state.get("missing", 0) + 1
+        else:
+            tag = info["state"] or "?"
+            print(f"  [{tag:13s}] doc_id={info['id']:>5}  {name}")
+            by_state[tag] = by_state.get(tag, 0) + 1
+    print()
+    print("summary:")
+    for k, v in sorted(by_state.items()):
+        print(f"  {k}: {v}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/surfsense_evals/scripts/compute_adjusted_accuracy.py b/surfsense_evals/scripts/compute_adjusted_accuracy.py
new file mode 100644
index 000000000..13693c055
--- /dev/null
+++ b/surfsense_evals/scripts/compute_adjusted_accuracy.py
@@ -0,0 +1,112 @@
+"""Compute "intrinsic" accuracy by removing transient network errors.
+
+A failure is *transient* if it's:
+  * SSLError: SSL bad-record-mac (TLS hiccup)
+  * Cloudflare 502 / 503 (provider-side load shedding)
+  * empty_response with no error string and no other signal (likely
+    connection reset mid-stream)
+  * JSONDecodeError (parse error mid-stream)
+
+A failure is *intrinsic* if it's a hard limit:
+  * "exceeds .* limit" (size limits)
+  * context_length errors
+  * provider 400 with image / pdf decode failure
+  * malformed-input failures
+
+We re-compute accuracy with two denominators:
+  * raw acc       = correct / 171  (what the headline reports)
+  * adjusted acc  = correct / (171 - transient_failures)  (intrinsic)
+
+Outputs a table that we can drop straight into the blog.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import defaultdict
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
+RAW = RUN / "raw.jsonl"
+
+
+TRANSIENT_HINTS = (
+    "sslv3_alert_bad_record_mac",
+    "ssl_alert_bad_record_mac",
+    "ssl: ssl",
+    "cloudflare",
+    "error 502",
+    "error 503",
+    "bad gateway",
+    "service unavailable",
+    "gateway timeout",
+    "jsondecodeerror",
+)
+INTRINSIC_HINTS = (
+    "exceeds",
+    "context_length",
+    "context window",
+    "could not process pdf",
+    "could not process image",
+)
+
+
+def classify(error: str | None, raw_text: str) -> str:
+    err = (error or "").lower()
+    if not err and not raw_text.strip():
+        return "transient_empty"
+    if any(h in err for h in TRANSIENT_HINTS):
+        return "transient_ssl_or_5xx"
+    if any(h in err for h in INTRINSIC_HINTS):
+        return "intrinsic_limit"
+    if err:
+        return "other_error"
+    return "ok"
+
+
+def main() -> None:
+    rows = [
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
+        if line.strip()
+    ]
+    by_arm: dict[str, dict] = defaultdict(lambda: {
+        "n": 0, "correct": 0,
+        "transient_ssl_or_5xx": 0, "transient_empty": 0,
+        "intrinsic_limit": 0, "other_error": 0,
+    })
+    for row in rows:
+        arm = row["arm"]
+        m = by_arm[arm]
+        m["n"] += 1
+        graded = row.get("graded") or {}
+        if graded.get("correct"):
+            m["correct"] += 1
+        kind = classify(row.get("error"), row.get("raw_text") or "")
+        if kind != "ok":
+            m[kind] += 1
+
+    print(f"{'arm':<25} {'raw acc%':>8} {'transient':>10} {'intrinsic':>10} {'other':>6} {'adj acc% (no transient)':>22}")
+    print("-" * 88)
+    for arm in sorted(by_arm):
+        m = by_arm[arm]
+        raw = m["correct"] / m["n"] * 100
+        transient = m["transient_ssl_or_5xx"] + m["transient_empty"]
+        intrinsic = m["intrinsic_limit"]
+        other = m["other_error"]
+        usable = m["n"] - transient
+        adj = m["correct"] / usable * 100 if usable else 0
+        print(
+            f"{arm:<25} {raw:>7.1f}% {transient:>10} {intrinsic:>10} {other:>6} {adj:>21.1f}%"
+        )
+
+    print()
+    print("transient   = SSLError / 502 / 503 / empty stream / mid-stream JSON decode (would")
+    print("              succeed on retry; eval harness has no built-in retry today).")
+    print("intrinsic   = hard limit (e.g. >30MB Anthropic request, model context overflow).")
+    print("adj acc%    = correct / (n - transient) — what the arm scores when network noise")
+    print("              is removed; closest thing we have to a like-for-like quality number.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/compute_blog_extras.py b/surfsense_evals/scripts/compute_blog_extras.py
new file mode 100644
index 000000000..abe88d08b
--- /dev/null
+++ b/surfsense_evals/scripts/compute_blog_extras.py
@@ -0,0 +1,381 @@
+"""Compute the deeper statistics the blog needs: McNemar pairwise tests,
+per-PDF heterogeneity, latency/token distribution percentiles.
+
+Reads the merged post-retry artifact:
+
+    data/multimodal_doc/runs/<run_id>/parser_compare/raw_post_retry.jsonl
+
+Outputs to stdout:
+
+  1) Per-arm latency distribution (n, mean, std, p10, p25, p50, p75, p90, p95, p99, max).
+  2) Per-arm input/output token distribution (mean, p50, p95, max).
+  3) McNemar pairwise table: for every (arm_i, arm_j) ordered pair on the
+     same 171 questions, count b_ij = #(arm_i correct & arm_j wrong) and
+     b_ji = #(arm_i wrong & arm_j correct), and report the exact-binomial
+     two-sided p-value. We include both raw (using the original raw.jsonl)
+     and post-retry results.
+  4) Per-PDF accuracy variance per arm (n_pdfs=30): mean, std, min, max.
+
+Pure stdlib — no scipy/numpy.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+
+
+# ---------------------------------------------------------------------------
+# I/O
+# ---------------------------------------------------------------------------
+
+
+def _read_jsonl(path: Path) -> list[dict]:
+    out: list[dict] = []
+    with path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            out.append(json.loads(line))
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Distribution helpers
+# ---------------------------------------------------------------------------
+
+
+def _percentile(values: list[float], p: float) -> float:
+    """Linear-interpolation percentile (p in [0, 100])."""
+
+    if not values:
+        return 0.0
+    s = sorted(values)
+    if len(s) == 1:
+        return float(s[0])
+    k = (len(s) - 1) * (p / 100.0)
+    lo, hi = math.floor(k), math.ceil(k)
+    if lo == hi:
+        return float(s[int(k)])
+    return float(s[lo] + (s[hi] - s[lo]) * (k - lo))
+
+
+# ---------------------------------------------------------------------------
+# McNemar exact-binomial p-value
+# ---------------------------------------------------------------------------
+
+
+def _binom_coef(n: int, k: int) -> int:
+    if k < 0 or k > n:
+        return 0
+    return math.comb(n, k)
+
+
+def _mcnemar_exact_pvalue(b: int, c: int) -> float:
+    """Two-sided exact-binomial McNemar p-value.
+
+    Tests H0: P(arm_i wrong, arm_j right) == P(arm_i right, arm_j wrong)
+    on discordant pairs only. Under H0 the count b ~ Bin(b+c, 0.5).
+    The two-sided p-value is
+
+        P(X <= min(b, c)) + P(X >= max(b, c))
+
+    computed exactly (cheap because b+c <= 27 in our run).
+    """
+
+    n = b + c
+    if n == 0:
+        return 1.0
+    k = min(b, c)
+    # Two-sided exact: 2 * P(X <= k) clipped at 1.0
+    cdf = sum(_binom_coef(n, i) for i in range(k + 1))
+    p = 2.0 * cdf / (2 ** n)
+    return min(1.0, p)
+
+
+def _mcnemar_table(rows: list[dict]) -> dict:
+    """Group rows -> {qid: {arm: bool_correct}} and compute pairwise."""
+
+    by_qid: dict[str, dict[str, bool]] = {}
+    arms_seen: set[str] = set()
+    for r in rows:
+        qid = r["qid"]
+        arm = r["arm"]
+        graded = r.get("graded") or {}
+        correct = bool(graded.get("correct"))
+        by_qid.setdefault(qid, {})[arm] = correct
+        arms_seen.add(arm)
+
+    arms = sorted(arms_seen)
+    qids = sorted(by_qid)
+    out: dict[str, dict] = {"arms": arms, "n_qids": len(qids), "pairs": []}
+    for i, ai in enumerate(arms):
+        for aj in arms[i + 1:]:
+            b = c = both = neither = 0
+            for q in qids:
+                row = by_qid[q]
+                if ai not in row or aj not in row:
+                    continue
+                ci, cj = row[ai], row[aj]
+                if ci and not cj:
+                    b += 1
+                elif cj and not ci:
+                    c += 1
+                elif ci and cj:
+                    both += 1
+                else:
+                    neither += 1
+            p = _mcnemar_exact_pvalue(b, c)
+            out["pairs"].append({
+                "arm_i": ai, "arm_j": aj,
+                "b_i_only": b, "c_j_only": c,
+                "both_correct": both, "both_wrong": neither,
+                "p_value": p,
+            })
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Per-PDF heterogeneity
+# ---------------------------------------------------------------------------
+
+
+def _per_pdf_stats(rows: list[dict]) -> dict[str, dict]:
+    """For each arm, per-PDF accuracy = correct/total questions on that PDF."""
+
+    bucket: dict[str, dict[str, list[bool]]] = {}
+    for r in rows:
+        arm = r["arm"]
+        pdf = r["doc_id"]
+        graded = r.get("graded") or {}
+        bucket.setdefault(arm, {}).setdefault(pdf, []).append(
+            bool(graded.get("correct"))
+        )
+
+    out: dict[str, dict] = {}
+    for arm, pdfs in bucket.items():
+        accs = [sum(b) / len(b) for b in pdfs.values() if b]
+        if not accs:
+            continue
+        out[arm] = {
+            "n_pdfs": len(accs),
+            "mean": statistics.mean(accs),
+            "std": statistics.stdev(accs) if len(accs) > 1 else 0.0,
+            "min": min(accs),
+            "max": max(accs),
+            "p25": _percentile(accs, 25),
+            "p50": _percentile(accs, 50),
+            "p75": _percentile(accs, 75),
+            "n_pdfs_zero": sum(1 for a in accs if a == 0.0),
+            "n_pdfs_perfect": sum(1 for a in accs if a == 1.0),
+        }
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Latency / token distributions
+# ---------------------------------------------------------------------------
+
+
+def _per_arm_latency(rows: list[dict]) -> dict[str, dict]:
+    by_arm: dict[str, list[float]] = {}
+    for r in rows:
+        lat = r.get("latency_ms")
+        if lat is None or lat == 0:
+            continue
+        by_arm.setdefault(r["arm"], []).append(float(lat))
+    out: dict[str, dict] = {}
+    for arm, lats in by_arm.items():
+        out[arm] = {
+            "n": len(lats),
+            "mean_s": statistics.mean(lats) / 1000,
+            "std_s": (statistics.stdev(lats) / 1000) if len(lats) > 1 else 0.0,
+            "p10_s": _percentile(lats, 10) / 1000,
+            "p25_s": _percentile(lats, 25) / 1000,
+            "p50_s": _percentile(lats, 50) / 1000,
+            "p75_s": _percentile(lats, 75) / 1000,
+            "p90_s": _percentile(lats, 90) / 1000,
+            "p95_s": _percentile(lats, 95) / 1000,
+            "p99_s": _percentile(lats, 99) / 1000,
+            "max_s": max(lats) / 1000,
+            # Coefficient of variation: std / mean (unitless tail-fatness).
+            "cv": (
+                statistics.stdev(lats) / statistics.mean(lats)
+                if len(lats) > 1 and statistics.mean(lats) > 0 else 0.0
+            ),
+        }
+    return out
+
+
+def _per_arm_tokens(rows: list[dict]) -> dict[str, dict]:
+    by_arm_in: dict[str, list[float]] = {}
+    by_arm_out: dict[str, list[float]] = {}
+    for r in rows:
+        t_in = r.get("input_tokens") or 0
+        t_out = r.get("output_tokens") or 0
+        if t_in:
+            by_arm_in.setdefault(r["arm"], []).append(float(t_in))
+        if t_out:
+            by_arm_out.setdefault(r["arm"], []).append(float(t_out))
+    out: dict[str, dict] = {}
+    for arm in sorted(set(by_arm_in) | set(by_arm_out)):
+        in_vals = by_arm_in.get(arm, [])
+        out_vals = by_arm_out.get(arm, [])
+        if not in_vals and not out_vals:
+            continue
+        entry: dict = {}
+        if in_vals:
+            entry["input"] = {
+                "n": len(in_vals),
+                "mean": statistics.mean(in_vals),
+                "p50": _percentile(in_vals, 50),
+                "p95": _percentile(in_vals, 95),
+                "max": max(in_vals),
+            }
+        if out_vals:
+            entry["output"] = {
+                "n": len(out_vals),
+                "mean": statistics.mean(out_vals),
+                "p50": _percentile(out_vals, 50),
+                "p95": _percentile(out_vals, 95),
+                "max": max(out_vals),
+            }
+        out[arm] = entry
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Pretty-printing
+# ---------------------------------------------------------------------------
+
+
+def _print_latency(title: str, lat: dict[str, dict]) -> None:
+    print()
+    print(title)
+    print("-" * len(title))
+    header = (f"{'arm':<25} {'n':>4} {'mean':>7} {'std':>7} "
+              f"{'p50':>7} {'p90':>7} {'p95':>7} {'p99':>7} {'max':>7} {'CV':>5}")
+    print(header)
+    print("-" * len(header))
+    for arm in sorted(lat, key=lambda a: lat[a]["mean_s"]):
+        s = lat[arm]
+        print(f"{arm:<25} {s['n']:>4} "
+              f"{s['mean_s']:>6.1f}s {s['std_s']:>6.1f}s "
+              f"{s['p50_s']:>6.1f}s {s['p90_s']:>6.1f}s {s['p95_s']:>6.1f}s "
+              f"{s['p99_s']:>6.1f}s {s['max_s']:>6.1f}s {s['cv']:>5.2f}")
+
+
+def _print_tokens(title: str, toks: dict[str, dict]) -> None:
+    print()
+    print(title)
+    print("-" * len(title))
+    header = (f"{'arm':<25} {'in mean':>9} {'in p50':>9} {'in p95':>9} {'in max':>9}"
+              f"  {'out mean':>9} {'out p95':>9}")
+    print(header)
+    print("-" * len(header))
+    for arm in sorted(toks):
+        e = toks[arm]
+        ein = e.get("input")
+        eout = e.get("output")
+        if not ein:
+            continue
+        print(f"{arm:<25} "
+              f"{ein['mean']:>9,.0f} {ein['p50']:>9,.0f} {ein['p95']:>9,.0f} {ein['max']:>9,.0f}  "
+              f"{(eout or {}).get('mean', 0):>9,.0f} {(eout or {}).get('p95', 0):>9,.0f}")
+
+
+def _print_pdf_var(title: str, var: dict[str, dict]) -> None:
+    print()
+    print(title)
+    print("-" * len(title))
+    header = (f"{'arm':<25} {'n_pdfs':>7} {'mean':>7} {'std':>7} {'min':>7} "
+              f"{'p25':>7} {'p50':>7} {'p75':>7} {'max':>7} {'#0%':>5} {'#100%':>6}")
+    print(header)
+    print("-" * len(header))
+    for arm in sorted(var, key=lambda a: -var[a]["mean"]):
+        s = var[arm]
+        print(f"{arm:<25} {s['n_pdfs']:>7} "
+              f"{s['mean']*100:>6.1f}% {s['std']*100:>6.1f}% {s['min']*100:>6.1f}% "
+              f"{s['p25']*100:>6.1f}% {s['p50']*100:>6.1f}% {s['p75']*100:>6.1f}% "
+              f"{s['max']*100:>6.1f}% {s['n_pdfs_zero']:>5} {s['n_pdfs_perfect']:>6}")
+
+
+def _print_mcnemar(title: str, table: dict) -> None:
+    print()
+    print(title)
+    print("-" * len(title))
+    print(f"n_qids on which all arms have a graded row: {table['n_qids']}")
+    header = (f"{'arm_i':<25} {'arm_j':<25} {'b':>4} {'c':>4} "
+              f"{'both ok':>8} {'both wr':>8} {'p (2-sided)':>13} {'sig':>4}")
+    print(header)
+    print("-" * len(header))
+    for pair in sorted(table["pairs"], key=lambda p: p["p_value"]):
+        sig = ""
+        if pair["p_value"] < 0.001:
+            sig = "***"
+        elif pair["p_value"] < 0.01:
+            sig = "**"
+        elif pair["p_value"] < 0.05:
+            sig = "*"
+        print(f"{pair['arm_i']:<25} {pair['arm_j']:<25} "
+              f"{pair['b_i_only']:>4} {pair['c_j_only']:>4} "
+              f"{pair['both_correct']:>8} {pair['both_wrong']:>8} "
+              f"{pair['p_value']:>13.4f} {sig:>4}")
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--run-id", default="2026-05-14T00-53-19Z")
+    args = parser.parse_args()
+
+    run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare"
+    raw_path = run_dir / "raw.jsonl"
+    post_path = run_dir / "raw_post_retry.jsonl"
+    if not raw_path.exists() or not post_path.exists():
+        raise SystemExit(
+            "Missing raw.jsonl or raw_post_retry.jsonl. "
+            "Run scripts/compute_post_retry_accuracy.py first."
+        )
+
+    raw_rows = _read_jsonl(raw_path)
+    post_rows = _read_jsonl(post_path)
+
+    print(f"Run: {args.run_id}")
+    print(f"raw rows: {len(raw_rows)}, post-retry rows: {len(post_rows)}")
+
+    # Latency uses post-retry rows (post-retry rows include the retry's own
+    # latency for recovered rows). For raw, recovered rows have latency=0
+    # because the harness recorded a failure.
+    _print_latency("Per-arm latency (post-retry)", _per_arm_latency(post_rows))
+
+    _print_tokens("Per-arm token distribution (post-retry)", _per_arm_tokens(post_rows))
+
+    _print_pdf_var(
+        "Per-PDF accuracy heterogeneity (post-retry)",
+        _per_pdf_stats(post_rows),
+    )
+
+    _print_mcnemar(
+        "McNemar pairwise (RAW, no retries)",
+        _mcnemar_table(raw_rows),
+    )
+    _print_mcnemar(
+        "McNemar pairwise (POST-RETRY)",
+        _mcnemar_table(post_rows),
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/surfsense_evals/scripts/compute_post_retry_accuracy.py b/surfsense_evals/scripts/compute_post_retry_accuracy.py
new file mode 100644
index 000000000..4c8c47672
--- /dev/null
+++ b/surfsense_evals/scripts/compute_post_retry_accuracy.py
@@ -0,0 +1,180 @@
+"""Recompute per-arm accuracy/F1 after merging retry survivors into raw.jsonl.
+
+Reads:
+  - data/multimodal_doc/runs/<run_id>/parser_compare/raw.jsonl
+  - data/multimodal_doc/runs/<run_id>/parser_compare/raw_retries.jsonl
+
+For each (arm, qid) present in the retry artifact:
+  - if the retry RECOVERED, the retry row replaces the original row (same
+    grader is reused — see ``mmlongbench/grader.py``);
+  - if the retry did NOT recover, the original row stays (still a failure,
+    so ``correct=False`` and ``f1=0``).
+
+Prints two tables side by side:
+  * Raw run (no retries) — matches §1 of the blog.
+  * Post-retry run        — final, "what would the headline have been if
+                              the harness had had retries from day one".
+
+It also writes ``data/multimodal_doc/runs/<run_id>/parser_compare/raw_post_retry.jsonl``
+so any downstream notebook / report can join straight on it.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+
+
+def _read_jsonl(path: Path) -> list[dict]:
+    out: list[dict] = []
+    with path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            out.append(json.loads(line))
+    return out
+
+
+def _row_key(row: dict) -> tuple[str, str]:
+    return (str(row["arm"]), str(row["qid"]))
+
+
+def _is_failure(row: dict) -> bool:
+    if row.get("error"):
+        return True
+    if not (row.get("raw_text") or "").strip():
+        return True
+    return False
+
+
+def _summarise(rows_by_arm: dict[str, list[dict]]) -> dict[str, dict]:
+    out: dict[str, dict] = {}
+    for arm, rows in rows_by_arm.items():
+        n = len(rows)
+        n_correct = sum(1 for r in rows if r.get("graded", {}).get("correct"))
+        f1_sum = sum(float(r.get("graded", {}).get("f1") or 0.0) for r in rows)
+        n_fail = sum(1 for r in rows if _is_failure(r))
+        out[arm] = {
+            "n": n,
+            "n_correct": n_correct,
+            "n_failures": n_fail,
+            "accuracy": (n_correct / n) if n else 0.0,
+            "f1_mean": (f1_sum / n) if n else 0.0,
+            "failure_rate": (n_fail / n) if n else 0.0,
+        }
+    return out
+
+
+def _print_table(title: str, summary: dict[str, dict]) -> None:
+    print()
+    print(title)
+    print("-" * len(title))
+    header = f"{'arm':<25} {'n':>4} {'n_corr':>7} {'acc':>7} {'F1':>7} {'fails':>6} {'fail%':>7}"
+    print(header)
+    print("-" * len(header))
+    # stable order: highest accuracy first
+    arms_sorted = sorted(summary.items(), key=lambda kv: -kv[1]["accuracy"])
+    for arm, s in arms_sorted:
+        print(f"{arm:<25} {s['n']:>4} {s['n_correct']:>7} "
+              f"{s['accuracy']*100:>6.1f}% {s['f1_mean']*100:>6.1f}% "
+              f"{s['n_failures']:>6} {s['failure_rate']*100:>6.1f}%")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--run-id", default="2026-05-14T00-53-19Z")
+    args = parser.parse_args()
+
+    run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare"
+    raw_path = run_dir / "raw.jsonl"
+    retry_path = run_dir / "raw_retries.jsonl"
+    out_path = run_dir / "raw_post_retry.jsonl"
+
+    if not raw_path.exists():
+        print(f"raw.jsonl not found at {raw_path}", file=sys.stderr)
+        return 1
+    if not retry_path.exists():
+        print(f"raw_retries.jsonl not found at {retry_path}", file=sys.stderr)
+        return 1
+
+    raw_rows = _read_jsonl(raw_path)
+    retry_rows = _read_jsonl(retry_path)
+
+    retry_by_key: dict[tuple[str, str], dict] = {
+        _row_key(r): r for r in retry_rows
+    }
+
+    merged_rows: list[dict] = []
+    n_replaced_recovered = 0
+    n_replaced_still_failed = 0
+    n_unchanged = 0
+    for row in raw_rows:
+        key = _row_key(row)
+        retry = retry_by_key.get(key)
+        if retry is None:
+            merged_rows.append(row)
+            n_unchanged += 1
+            continue
+        # The retry artifact carries a fresh ArmResult + grade in the same
+        # shape, plus a "retry" sub-object. We use the retry row whenever
+        # it represents a recovery; otherwise we keep the original (the
+        # retry confirms it is intrinsic, but the original row is the one
+        # the headline numbers were computed from, and the failure verdict
+        # is identical either way).
+        recovered = bool(retry.get("retry", {}).get("recovered"))
+        if recovered:
+            merged_rows.append(retry)
+            n_replaced_recovered += 1
+        else:
+            merged_rows.append(row)
+            n_replaced_still_failed += 1
+
+    # Persist merged jsonl for downstream consumers
+    with out_path.open("w", encoding="utf-8") as fh:
+        for r in merged_rows:
+            fh.write(json.dumps(r) + "\n")
+
+    # Bucket per arm
+    raw_by_arm: dict[str, list[dict]] = {}
+    for r in raw_rows:
+        raw_by_arm.setdefault(r["arm"], []).append(r)
+    post_by_arm: dict[str, list[dict]] = {}
+    for r in merged_rows:
+        post_by_arm.setdefault(r["arm"], []).append(r)
+
+    raw_summary = _summarise(raw_by_arm)
+    post_summary = _summarise(post_by_arm)
+
+    print()
+    print(f"Run: {args.run_id}")
+    print(f"Replaced (retry recovered):     {n_replaced_recovered}")
+    print(f"Kept original (retry still failed): {n_replaced_still_failed}")
+    print(f"Untouched rows:                 {n_unchanged}")
+    print(f"Wrote merged artifact: {out_path.relative_to(REPO)}")
+
+    _print_table("Raw run (no retries)", raw_summary)
+    _print_table("Post-retry run (final)", post_summary)
+
+    print()
+    print("Delta (post-retry minus raw):")
+    print(f"{'arm':<25} {'d_acc':>7} {'d_fails':>8}")
+    print("-" * 42)
+    for arm in sorted(set(raw_summary) | set(post_summary)):
+        r = raw_summary.get(arm)
+        p = post_summary.get(arm)
+        if not r or not p:
+            continue
+        d_acc = (p["accuracy"] - r["accuracy"]) * 100
+        d_fail = p["n_failures"] - r["n_failures"]
+        print(f"{arm:<25} {d_acc:>+6.1f}p {d_fail:>+7d}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/surfsense_evals/scripts/download_crag_task3.py b/surfsense_evals/scripts/download_crag_task3.py
deleted file mode 100644
index a646838fe..000000000
--- a/surfsense_evals/scripts/download_crag_task3.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""Download CRAG Task 3's 4 .tar.bz2 parts in parallel.
-
-Run once before ``ingest research crag_t3`` to avoid the ingest
-synchronously blocking on a 7 GB download. Skips parts already
-present and complete on disk.
-"""
-
-from __future__ import annotations
-
-import logging
-import sys
-import time
-import urllib.request
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from pathlib import Path
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(levelname)s %(message)s",
-)
-log = logging.getLogger("download_task3")
-
-
-_BASE = (
-    "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/"
-    "crag_task_3_dev_v4.tar.bz2.part"
-)
-_USER_AGENT = "SurfSense-Evals/0.1 (CRAG Task 3 fetch)"
-
-
-def _expected_size(url: str) -> int:
-    req = urllib.request.Request(url, method="HEAD", headers={"User-Agent": _USER_AGENT})
-    with urllib.request.urlopen(req, timeout=30) as resp:
-        return int(resp.headers.get("content-length", 0))
-
-
-def download_one(part: int, dest_dir: Path) -> Path:
-    url = f"{_BASE}{part}"
-    dest = dest_dir / f"crag_task_3_dev_v4.tar.bz2.part{part}"
-    expected = _expected_size(url)
-    if dest.exists() and dest.stat().st_size == expected:
-        log.info("part%d: cached (%d bytes)", part, expected)
-        return dest
-    log.info("part%d: downloading %d bytes ...", part, expected)
-    tmp = dest.with_suffix(dest.suffix + ".part_dl")
-    started = time.monotonic()
-    last_log = started
-    with urllib.request.urlopen(
-        urllib.request.Request(url, headers={"User-Agent": _USER_AGENT}),
-        timeout=900,
-    ) as resp, tmp.open("wb") as fh:
-        downloaded = 0
-        chunk = resp.read(1 << 20)
-        while chunk:
-            fh.write(chunk)
-            downloaded += len(chunk)
-            now = time.monotonic()
-            if now - last_log > 5.0:
-                pct = 100 * downloaded / expected if expected else 0
-                rate_mb = (downloaded / (now - started)) / (1 << 20)
-                log.info(
-                    "part%d: %5.1f%% (%.1f / %.1f MiB at %.1f MiB/s)",
-                    part, pct, downloaded / (1 << 20), expected / (1 << 20), rate_mb,
-                )
-                last_log = now
-            chunk = resp.read(1 << 20)
-    tmp.replace(dest)
-    elapsed = time.monotonic() - started
-    log.info(
-        "part%d: done in %.1fs (%.1f MiB/s avg)",
-        part, elapsed, (expected / (1 << 20)) / max(elapsed, 0.001),
-    )
-    return dest
-
-
-def main() -> int:
-    dest_dir = Path("data/research/crag_t3/.raw_cache")
-    dest_dir.mkdir(parents=True, exist_ok=True)
-
-    # 4 parts in parallel — typical residential connection saturates around
-    # 2 streams; GitHub raw serves these fine in parallel.
-    started = time.monotonic()
-    with ThreadPoolExecutor(max_workers=4) as ex:
-        futures = {ex.submit(download_one, i, dest_dir): i for i in range(1, 5)}
-        for fut in as_completed(futures):
-            part = futures[fut]
-            try:
-                fut.result()
-            except Exception as exc:  # noqa: BLE001
-                log.error("part%d failed: %s", part, exc)
-                return 1
-    log.info("All 4 parts downloaded in %.1fs", time.monotonic() - started)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/surfsense_evals/scripts/inspect_first30.py b/surfsense_evals/scripts/inspect_first30.py
new file mode 100644
index 000000000..e06c6c029
--- /dev/null
+++ b/surfsense_evals/scripts/inspect_first30.py
@@ -0,0 +1,59 @@
+"""Inspect what the first 30 MMLongBench-Doc PDFs would look like for scoping.
+
+Run from surfsense_evals/ root via:
+    python scripts/inspect_first30.py
+
+Prints which docs are already ingested (existing 5), which are new (25 to
+upload), how many questions cover those 30 PDFs, and the answerable /
+unanswerable + format mix.
+"""
+
+from __future__ import annotations
+
+import json
+from collections import Counter
+from pathlib import Path
+
+
+def main() -> None:
+    qpath = Path("data/multimodal_doc/mmlongbench/questions.jsonl")
+    lines = qpath.read_text(encoding="utf-8").splitlines()
+    rows = [json.loads(line) for line in lines if line.strip()]
+
+    docs_by_id = sorted({r["doc_id"] for r in rows})
+    first30 = docs_by_id[:30]
+    existing5 = {
+        "05-03-18-political-release.pdf",
+        "0b85477387a9d0cc33fca0f4becaa0e5.pdf",
+        "0e94b4197b10096b1f4c699701570fbf.pdf",
+        "11-21-16-Updated-Post-Election-Release.pdf",
+        "12-15-15-ISIS-and-terrorism-release-final.pdf",
+    }
+    new25 = [d for d in first30 if d not in existing5]
+    print(
+        f"first 30 docs (alphabetical) — {len(new25)} new, "
+        f"{len(first30) - len(new25)} already in SurfSense"
+    )
+
+    qs_in_30 = [r for r in rows if r["doc_id"] in set(first30)]
+    fmts = Counter((r.get("answer_format") or "").lower() for r in qs_in_30)
+    answerable = sum(v for k, v in fmts.items() if k != "none")
+    unanswerable = fmts.get("none", 0)
+
+    print(
+        f"questions covering first 30 docs: total={len(qs_in_30)}  "
+        f"answerable={answerable}  unanswerable={unanswerable}"
+    )
+    print(
+        f"avg Qs/PDF: {len(qs_in_30) / 30:.1f}  "
+        f"answerable/PDF: {answerable / 30:.1f}"
+    )
+    print(f"format mix in scope: {dict(fmts)}")
+    print()
+    print("25 new PDFs to ingest:")
+    for d in new25:
+        print(f"  - {d}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py b/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py
new file mode 100644
index 000000000..e1a2edc65
--- /dev/null
+++ b/surfsense_evals/scripts/patch_manifest_for_parallel_ingest.py
@@ -0,0 +1,100 @@
+"""Stub the mmlongbench manifest so parser_compare can extract in parallel.
+
+The mmlongbench Surfsense ingest writes its manifest only at the very
+end of the upload pipeline (~hours of celery work). parser_compare's
+ingest, on the other hand, just needs a list of (doc_id, pdf_path)
+tuples to know which PDFs to extract — it doesn't care about the
+SurfSense ``document_id`` (the runner does, later, after a refresh).
+
+This script extends the existing manifest with the *additional* PDFs
+that mmlongbench has already cached on disk (i.e. all 30 PDFs in
+``data/multimodal_doc/mmlongbench/pdfs/`` even though only 5 have
+SurfSense ``document_id``s yet) so parser_compare can run all four
+extractions for them in parallel with the SurfSense ingest.
+
+After mmlongbench finishes, re-run::
+
+    python -m surfsense_evals ingest multimodal_doc parser_compare \
+        --max-docs 30
+
+…to refresh ``parser_compare_doc_map.jsonl`` with the now-populated
+``document_id`` values for the 25 new PDFs. The extractions
+themselves are cached on disk so the second pass is essentially free.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+
+REPO = Path(__file__).resolve().parents[1]
+MAP_PATH = REPO / "data" / "multimodal_doc" / "maps" / "mmlongbench_doc_map.jsonl"
+PDF_DIR = REPO / "data" / "multimodal_doc" / "mmlongbench" / "pdfs"
+QUESTIONS = REPO / "data" / "multimodal_doc" / "mmlongbench" / "questions.jsonl"
+
+
+def _question_count_per_doc() -> dict[str, int]:
+    counts: dict[str, int] = {}
+    with QUESTIONS.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            counts[row["doc_id"]] = counts.get(row["doc_id"], 0) + 1
+    return counts
+
+
+def main() -> None:
+    if not MAP_PATH.exists():
+        raise SystemExit(
+            f"manifest not found at {MAP_PATH} — "
+            "run `surfsense_evals ingest multimodal_doc mmlongbench` first."
+        )
+
+    existing_lines = MAP_PATH.read_text(encoding="utf-8").splitlines()
+    existing_rows: list[dict] = []
+    settings_line = None
+    for line in existing_lines:
+        line = line.strip()
+        if not line:
+            continue
+        row = json.loads(line)
+        if "__settings__" in row:
+            settings_line = line
+        else:
+            existing_rows.append(row)
+
+    by_doc_id = {r["doc_id"]: r for r in existing_rows}
+    counts = _question_count_per_doc()
+
+    cached_pdfs = sorted(p for p in PDF_DIR.glob("*.pdf"))
+    print(f"existing manifest entries: {len(existing_rows)}")
+    print(f"cached PDFs on disk:       {len(cached_pdfs)}")
+
+    added = 0
+    for pdf in cached_pdfs:
+        if pdf.name in by_doc_id:
+            continue
+        by_doc_id[pdf.name] = {
+            "doc_id": pdf.name,
+            "document_id": None,
+            "pdf_path": str(pdf),
+            "n_questions": counts.get(pdf.name, 0),
+        }
+        added += 1
+
+    out_lines: list[str] = []
+    if settings_line:
+        out_lines.append(settings_line)
+    for doc_id in sorted(by_doc_id):
+        out_lines.append(json.dumps(by_doc_id[doc_id]))
+    MAP_PATH.write_text("\n".join(out_lines) + "\n", encoding="utf-8")
+
+    print(f"added {added} stub rows; manifest now has {len(by_doc_id)} PDFs")
+    print(f"wrote: {MAP_PATH}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/peek_t3_doc_map.py b/surfsense_evals/scripts/peek_t3_doc_map.py
deleted file mode 100644
index 6954cdcad..000000000
--- a/surfsense_evals/scripts/peek_t3_doc_map.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Quick sanity-check for the CRAG Task 3 doc map after ingest."""
-
-from __future__ import annotations
-
-import json
-import sys
-from pathlib import Path
-
-
-def main() -> int:
-    p = Path("data/research/maps/crag_t3_doc_map.jsonl")
-    if not p.exists():
-        print(f"Doc map missing: {p}")
-        return 1
-    rows = []
-    settings = {}
-    for line in p.read_text(encoding="utf-8").splitlines():
-        if not line.strip():
-            continue
-        row = json.loads(line)
-        if "__settings__" in row:
-            settings = row
-            continue
-        rows.append(row)
-    print(f"Settings header: {settings}")
-    print(f"Doc map rows:   {len(rows)}")
-    for r in rows:
-        print(f"  qid={r['qid']:<10} domain={r['domain']:<8} qtype={r['question_type']}")
-        print(f"    question: {r['question'][:90]}")
-        print(f"    gold:     {r['gold_answer'][:90]}")
-        print(
-            f"    pages:    {len(r['page_filenames'])} extracted, "
-            f"{len(r['document_ids'])} doc_ids, "
-            f"{len(r['missing_pages'])} missing"
-        )
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/surfsense_evals/scripts/retry_failed_questions.py b/surfsense_evals/scripts/retry_failed_questions.py
new file mode 100644
index 000000000..7cc9478e0
--- /dev/null
+++ b/surfsense_evals/scripts/retry_failed_questions.py
@@ -0,0 +1,636 @@
+"""Retry only the failed (arm, question) pairs from a previous parser_compare run.
+
+The original parser_compare run records one row per (arm, qid) in
+``raw.jsonl``. Some of those rows came back with transient transport
+errors (SSL alerts, gateway 502s, empty SSE streams) or empty
+``raw_text``. This script re-issues *only* those calls with exponential
+backoff so we can see how many recover.
+
+Design constraints / choices:
+
+* **No re-ingest.** All cached PDFs and parser-extracted markdown stay
+  on disk. We rebuild ``ArmRequest`` objects from the existing manifest
+  + the original ``mmlongbench/questions.jsonl``.
+* **No SurfSense backend or celery required.** SurfSense had 0
+  reported failures; this script will skip any ``surfsense_agentic``
+  rows it encounters and warn rather than try to start the backend.
+* **Original ``raw.jsonl`` is never mutated.** Retries land in a
+  sibling ``raw_retries.jsonl`` so the original artifact stays
+  citeable.
+* **Idempotent.** Re-running this script re-tries the same set of
+  failed rows from ``raw.jsonl``. If you want to merge survivor rows
+  back in, do that as a separate aggregation step.
+
+Usage:
+
+    python scripts/retry_failed_questions.py \
+        --run-id 2026-05-14T00-53-19Z \
+        --max-attempts 5 \
+        --concurrency 2
+
+Outputs (written next to the original raw.jsonl):
+
+* ``raw_retries.jsonl`` — one line per retried (arm, qid). Each line
+  carries the original error, every retry attempt's timing/error,
+  and the final result (incl. grade) so you can drop it straight
+  into a notebook.
+* ``raw_retries_summary.json`` — per-arm tried/recovered/still-failed
+  counts and an aggregated retry-success rate.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+import random
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+REPO = Path(__file__).resolve().parents[1]
+SRC = REPO / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+from dotenv import load_dotenv  # noqa: E402
+
+from surfsense_evals.core.arms import (  # noqa: E402
+    ArmRequest,
+    ArmResult,
+    BareLlmArm,
+    NativePdfArm,
+)
+from surfsense_evals.core.parse.freeform_answer import (  # noqa: E402
+    extract_freeform_answer,
+)
+from surfsense_evals.core.providers.openrouter_chat import (  # noqa: E402
+    OpenRouterChatProvider,
+)
+from surfsense_evals.core.providers.openrouter_pdf import (  # noqa: E402
+    OpenRouterPdfProvider,
+    PdfEngine,
+)
+from surfsense_evals.suites.multimodal_doc.mmlongbench.grader import grade  # noqa: E402
+from surfsense_evals.suites.multimodal_doc.parser_compare.prompt import (  # noqa: E402
+    build_long_context_prompt,
+    build_native_pdf_prompt,
+)
+
+logger = logging.getLogger("retry_failed_questions")
+
+LC_ARMS = {
+    "azure_basic_lc",
+    "azure_premium_lc",
+    "llamacloud_basic_lc",
+    "llamacloud_premium_lc",
+}
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _is_failure_row(row: dict[str, Any]) -> bool:
+    """A row counts as failed if it raised an error OR returned empty text.
+
+    We retry both because the empty-stream case is the same operational
+    failure mode (the call returned nothing usable) — we just didn't
+    raise it as an exception.
+    """
+
+    if row.get("error"):
+        return True
+    if not (row.get("raw_text") or "").strip():
+        return True
+    return False
+
+
+@dataclass
+class FailedRow:
+    arm: str
+    qid: str
+    doc_id: str
+    answer_format: str
+    gold: str
+    pages: int
+    document_id: int | None
+    original_error: str | None
+    original_row: dict[str, Any]
+
+
+def _load_failed_rows(raw_path: Path) -> list[FailedRow]:
+    out: list[FailedRow] = []
+    with raw_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if not _is_failure_row(row):
+                continue
+            out.append(FailedRow(
+                arm=str(row["arm"]),
+                qid=str(row["qid"]),
+                doc_id=str(row["doc_id"]),
+                answer_format=str(row.get("answer_format") or ""),
+                gold=str(row.get("gold") or ""),
+                pages=int(row.get("pages") or 0),
+                document_id=row.get("document_id"),
+                original_error=row.get("error"),
+                original_row=row,
+            ))
+    return out
+
+
+def _load_doc_map(map_path: Path) -> dict[str, dict[str, Any]]:
+    out: dict[str, dict[str, Any]] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            out[str(row["doc_id"])] = row
+    return out
+
+
+def _load_question_text_index(
+    questions_jsonl: Path,
+) -> dict[tuple[str, int], dict[str, Any]]:
+    """Map (doc_id, per_doc_index) -> raw question row.
+
+    qids in raw.jsonl are formatted ``{doc_id}::Q{NNN}`` where NNN is
+    the per-doc index. Reproducing the runner's question selection
+    requires walking ``questions.jsonl`` in order and assigning
+    indices per doc_id (so we match the runner's ``per_doc_idx`` logic
+    in ``_select_questions``).
+    """
+
+    out: dict[tuple[str, int], dict[str, Any]] = {}
+    per_doc_idx: dict[str, int] = {}
+    with questions_jsonl.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            doc_id = str(row.get("doc_id") or "")
+            if not doc_id:
+                continue
+            idx = per_doc_idx.get(doc_id, 0)
+            per_doc_idx[doc_id] = idx + 1
+            out[(doc_id, idx)] = row
+    return out
+
+
+def _qid_index(qid: str) -> int:
+    """Parse the per-doc question index out of a qid like ``foo.pdf::Q007``."""
+
+    _, _, q_part = qid.rpartition("::")
+    if not q_part.startswith("Q"):
+        raise ValueError(f"unexpected qid shape: {qid!r}")
+    return int(q_part[1:])
+
+
+# ---------------------------------------------------------------------------
+# Request building (mirrors runner.py exactly so prompts are byte-identical)
+# ---------------------------------------------------------------------------
+
+
+def _build_native_request(
+    qid: str, question: str, answer_format: str, pdf_path: Path,
+    *, max_output_tokens: int,
+) -> ArmRequest:
+    return ArmRequest(
+        question_id=qid,
+        prompt=build_native_pdf_prompt(question, answer_format=answer_format),
+        pdf_paths=[pdf_path],
+        options={"max_tokens": max_output_tokens},
+    )
+
+
+def _build_lc_request(
+    qid: str, question: str, answer_format: str, doc_id: str, md_path: Path,
+) -> ArmRequest:
+    if not md_path.exists():
+        raise FileNotFoundError(
+            f"Missing parser extraction at {md_path}; cannot retry LC arm."
+        )
+    markdown = md_path.read_text(encoding="utf-8")
+    return ArmRequest(
+        question_id=qid,
+        prompt=build_long_context_prompt(
+            question,
+            answer_format=answer_format,
+            document_markdown=markdown,
+            document_label=doc_id,
+        ),
+    )
+
+
+# ---------------------------------------------------------------------------
+# Retry driver
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class AttemptLog:
+    attempt: int
+    started_iso: str
+    latency_ms: int
+    error: str | None
+    raw_text_chars: int
+
+
+@dataclass
+class RetryOutcome:
+    arm: str
+    qid: str
+    attempts: list[AttemptLog]
+    final_result: ArmResult
+    recovered: bool
+
+
+async def _retry_one(
+    arm_obj: Any, request: ArmRequest, *,
+    arm_name: str,
+    qid: str,
+    max_attempts: int,
+    base_delay: float,
+    max_delay: float,
+) -> RetryOutcome:
+    attempts: list[AttemptLog] = []
+    final: ArmResult | None = None
+    for attempt in range(1, max_attempts + 1):
+        started_iso = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+        t0 = time.monotonic()
+        result = await arm_obj.answer(request)
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        raw_text = (result.raw_text or "").strip()
+        attempt_error = result.error
+        if not attempt_error and not raw_text:
+            attempt_error = "EmptyResponse: stream ended with no text"
+        attempts.append(AttemptLog(
+            attempt=attempt,
+            started_iso=started_iso,
+            latency_ms=latency_ms,
+            error=attempt_error,
+            raw_text_chars=len(raw_text),
+        ))
+        final = result
+        if not attempt_error and raw_text:
+            return RetryOutcome(
+                arm=arm_name, qid=qid, attempts=attempts,
+                final_result=result, recovered=True,
+            )
+        if attempt < max_attempts:
+            delay = min(max_delay, base_delay * (2 ** (attempt - 1)))
+            delay = delay * (0.5 + random.random())
+            logger.info(
+                "[%s::%s] attempt %d/%d failed (%s); sleeping %.1fs",
+                arm_name, qid, attempt, max_attempts, attempt_error, delay,
+            )
+            await asyncio.sleep(delay)
+    assert final is not None
+    return RetryOutcome(
+        arm=arm_name, qid=qid, attempts=attempts,
+        final_result=final, recovered=False,
+    )
+
+
+async def _gather_with_limit(coros: list, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+
+async def _run(args: argparse.Namespace) -> int:
+    load_dotenv(REPO / ".env")
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(name)s %(message)s",
+    )
+
+    run_dir = REPO / "data" / "multimodal_doc" / "runs" / args.run_id / "parser_compare"
+    raw_path = run_dir / "raw.jsonl"
+    if not raw_path.exists():
+        raise SystemExit(f"raw.jsonl not found at {raw_path}")
+
+    map_path = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl"
+    questions_jsonl = REPO / "data" / "multimodal_doc" / "mmlongbench" / "questions.jsonl"
+    if not map_path.exists():
+        raise SystemExit(f"parser_compare manifest not found at {map_path}")
+    if not questions_jsonl.exists():
+        raise SystemExit(f"mmlongbench questions not found at {questions_jsonl}")
+
+    failed = _load_failed_rows(raw_path)
+    if not failed:
+        logger.info("No failed rows in %s — nothing to retry.", raw_path)
+        return 0
+
+    # SurfSense rows: warn and skip; we don't want to start backend just to
+    # defensively retry a 0-failure arm.
+    surf_failed = [f for f in failed if f.arm == "surfsense_agentic"]
+    if surf_failed:
+        logger.warning(
+            "Skipping %d surfsense_agentic failures; this script doesn't drive the backend. "
+            "If you want those retried too, start backend + celery and rerun "
+            "with --include-surfsense.",
+            len(surf_failed),
+        )
+        if not args.include_surfsense:
+            failed = [f for f in failed if f.arm != "surfsense_agentic"]
+    else:
+        logger.info("No surfsense_agentic failures; backend/celery not needed for this retry.")
+
+    if not failed:
+        logger.info("Nothing left to retry after filtering.")
+        return 0
+
+    by_arm_count: dict[str, int] = {}
+    for f in failed:
+        by_arm_count[f.arm] = by_arm_count.get(f.arm, 0) + 1
+    logger.info(
+        "Loaded %d failed rows across %d arms: %s",
+        len(failed), len(by_arm_count),
+        ", ".join(f"{a}={n}" for a, n in sorted(by_arm_count.items())),
+    )
+
+    doc_map = _load_doc_map(map_path)
+    qtext_idx = _load_question_text_index(questions_jsonl)
+
+    api_key = os.environ.get("OPENROUTER_API_KEY")
+    if not api_key:
+        raise SystemExit("OPENROUTER_API_KEY missing from environment / .env")
+
+    native_provider = OpenRouterPdfProvider(
+        api_key=api_key,
+        base_url=os.environ.get("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"),
+        model=args.llm_model,
+        engine=PdfEngine(args.pdf_engine),
+    )
+    native_arm = NativePdfArm(
+        provider=native_provider, max_output_tokens=args.max_output_tokens,
+    )
+
+    lc_arms: dict[str, BareLlmArm] = {}
+    for arm_name in sorted({f.arm for f in failed} & LC_ARMS):
+        lc_provider = OpenRouterChatProvider(
+            api_key=api_key,
+            base_url=os.environ.get("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"),
+            model=args.llm_model,
+        )
+        lc_arms[arm_name] = BareLlmArm(
+            provider=lc_provider,
+            max_output_tokens=args.max_output_tokens,
+            name=arm_name,
+        )
+
+    coros: list = []
+    plan: list[tuple[FailedRow, ArmRequest, Any]] = []
+
+    for f in failed:
+        # Look up the question text from questions.jsonl
+        try:
+            q_idx = _qid_index(f.qid)
+        except Exception:
+            logger.error("Bad qid %r — skipping", f.qid)
+            continue
+        qrow = qtext_idx.get((f.doc_id, q_idx))
+        if qrow is None:
+            logger.error(
+                "Could not find question text for %s (idx %d) — skipping",
+                f.doc_id, q_idx,
+            )
+            continue
+        question_text = str(qrow.get("question") or "").strip()
+        answer_format = str(qrow.get("answer_format") or f.answer_format or "").strip().lower()
+
+        map_row = doc_map.get(f.doc_id)
+        if map_row is None:
+            logger.error("doc_id %s not in manifest — skipping", f.doc_id)
+            continue
+
+        if f.arm == "native_pdf":
+            pdf_path = Path(map_row["pdf_path"])
+            if not pdf_path.exists():
+                logger.error("PDF missing on disk: %s — skipping", pdf_path)
+                continue
+            request = _build_native_request(
+                f.qid, question_text, answer_format, pdf_path,
+                max_output_tokens=args.max_output_tokens,
+            )
+            arm_obj = native_arm
+        elif f.arm in LC_ARMS:
+            ext_blob = (map_row.get("extractions") or {}).get(f.arm) or {}
+            md_path_str = ext_blob.get("markdown_path")
+            if not md_path_str or ext_blob.get("status") != "ok":
+                logger.error(
+                    "Missing extraction for %s on %s — cannot retry; skipping",
+                    f.arm, f.doc_id,
+                )
+                continue
+            request = _build_lc_request(
+                f.qid, question_text, answer_format, f.doc_id, Path(md_path_str),
+            )
+            arm_obj = lc_arms[f.arm]
+        else:
+            logger.warning("Unhandled arm %s — skipping", f.arm)
+            continue
+
+        plan.append((f, request, arm_obj))
+        coros.append(_retry_one(
+            arm_obj, request,
+            arm_name=f.arm, qid=f.qid,
+            max_attempts=args.max_attempts,
+            base_delay=args.base_delay,
+            max_delay=args.max_delay,
+        ))
+
+    if not coros:
+        logger.warning("Nothing to retry after request building.")
+        return 0
+
+    logger.info(
+        "Retrying %d failed rows with up to %d attempts each "
+        "(base_delay=%.1fs, max_delay=%.1fs, concurrency=%d).",
+        len(coros), args.max_attempts, args.base_delay, args.max_delay,
+        args.concurrency,
+    )
+
+    started = time.monotonic()
+    outcomes: list[RetryOutcome] = await _gather_with_limit(
+        coros, concurrency=args.concurrency,
+    )
+    elapsed = time.monotonic() - started
+    logger.info("Retry pass finished in %.1fs.", elapsed)
+
+    out_path = run_dir / "raw_retries.jsonl"
+    summary_path = run_dir / "raw_retries_summary.json"
+
+    per_arm_recovered: dict[str, int] = {}
+    per_arm_total: dict[str, int] = {}
+    per_arm_attempts_dist: dict[str, list[int]] = {}
+
+    with out_path.open("w", encoding="utf-8") as fh:
+        for (f, _req, _arm_obj), outcome in zip(plan, outcomes, strict=True):
+            per_arm_total[outcome.arm] = per_arm_total.get(outcome.arm, 0) + 1
+            if outcome.recovered:
+                per_arm_recovered[outcome.arm] = (
+                    per_arm_recovered.get(outcome.arm, 0) + 1
+                )
+            per_arm_attempts_dist.setdefault(outcome.arm, []).append(
+                len(outcome.attempts)
+            )
+
+            g = grade(
+                pred=extract_freeform_answer(outcome.final_result.raw_text or ""),
+                gold=f.gold,
+                answer_format=f.answer_format,
+            )
+            row = {
+                "qid": f.qid,
+                "doc_id": f.doc_id,
+                "arm": f.arm,
+                "answer_format": f.answer_format,
+                "gold": f.gold,
+                "pages": f.pages,
+                "document_id": f.document_id,
+                "original_error": f.original_error,
+                "retry": {
+                    "max_attempts": args.max_attempts,
+                    "n_attempts": len(outcome.attempts),
+                    "recovered": outcome.recovered,
+                    "attempts": [
+                        {
+                            "attempt": a.attempt,
+                            "started_iso": a.started_iso,
+                            "latency_ms": a.latency_ms,
+                            "error": a.error,
+                            "raw_text_chars": a.raw_text_chars,
+                        }
+                        for a in outcome.attempts
+                    ],
+                },
+                **outcome.final_result.to_jsonl(),
+                "graded": {
+                    "correct": g.correct,
+                    "f1": g.f1,
+                    "method": g.method,
+                    "normalised_pred": g.normalised_pred,
+                    "normalised_gold": g.normalised_gold,
+                },
+            }
+            fh.write(json.dumps(row) + "\n")
+
+    summary = {
+        "run_id": args.run_id,
+        "raw_retries_path": str(out_path.relative_to(REPO)),
+        "n_failed_rows_input": len(failed),
+        "n_retried": len(coros),
+        "elapsed_s": round(elapsed, 1),
+        "config": {
+            "max_attempts": args.max_attempts,
+            "base_delay": args.base_delay,
+            "max_delay": args.max_delay,
+            "concurrency": args.concurrency,
+            "llm_model": args.llm_model,
+            "pdf_engine": args.pdf_engine,
+            "max_output_tokens": args.max_output_tokens,
+        },
+        "per_arm": {
+            arm: {
+                "tried": per_arm_total.get(arm, 0),
+                "recovered": per_arm_recovered.get(arm, 0),
+                "still_failed": (
+                    per_arm_total.get(arm, 0) - per_arm_recovered.get(arm, 0)
+                ),
+                "recovery_rate": (
+                    per_arm_recovered.get(arm, 0) / per_arm_total[arm]
+                    if per_arm_total.get(arm) else 0.0
+                ),
+                "attempts_distribution": sorted(per_arm_attempts_dist.get(arm, [])),
+            }
+            for arm in sorted(per_arm_total)
+        },
+        "totals": {
+            "tried": sum(per_arm_total.values()),
+            "recovered": sum(per_arm_recovered.values()),
+            "still_failed": sum(per_arm_total.values()) - sum(per_arm_recovered.values()),
+        },
+    }
+    summary_path.write_text(
+        json.dumps(summary, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+
+    print()
+    print("=" * 78)
+    print("Retry pass summary")
+    print("=" * 78)
+    header = f"{'arm':<25} {'tried':>6} {'recovered':>10} {'still fail':>11} {'rate':>7}"
+    print(header)
+    print("-" * len(header))
+    for arm in sorted(per_arm_total):
+        tried = per_arm_total[arm]
+        rec = per_arm_recovered.get(arm, 0)
+        rate = (rec / tried * 100) if tried else 0.0
+        print(f"{arm:<25} {tried:>6} {rec:>10} {tried - rec:>11} {rate:>6.1f}%")
+    total = sum(per_arm_total.values())
+    rec_total = sum(per_arm_recovered.values())
+    rate_total = (rec_total / total * 100) if total else 0.0
+    print("-" * len(header))
+    print(f"{'TOTAL':<25} {total:>6} {rec_total:>10} {total - rec_total:>11} "
+          f"{rate_total:>6.1f}%")
+    print()
+    print(f"Wrote {out_path.relative_to(REPO)}")
+    print(f"Wrote {summary_path.relative_to(REPO)}")
+    return 0
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--run-id", default="2026-05-14T00-53-19Z",
+        help="Run timestamp under data/multimodal_doc/runs/. Default is the "
+             "n=171 production run we wrote up in the blog.",
+    )
+    parser.add_argument("--max-attempts", type=int, default=5)
+    parser.add_argument("--base-delay", type=float, default=1.0,
+                        help="Base seconds for exponential backoff (default 1s).")
+    parser.add_argument("--max-delay", type=float, default=30.0,
+                        help="Cap on per-retry sleep (default 30s).")
+    parser.add_argument("--concurrency", type=int, default=2,
+                        help="Parallel retries in flight (default 2 — keep low "
+                             "to avoid the same transport stress that caused "
+                             "the original failures).")
+    parser.add_argument("--llm-model", default="anthropic/claude-sonnet-4.5")
+    parser.add_argument("--pdf-engine", default="native",
+                        choices=[e.value for e in PdfEngine])
+    parser.add_argument("--max-output-tokens", type=int, default=512)
+    parser.add_argument(
+        "--include-surfsense", action="store_true",
+        help="Also retry surfsense_agentic failures (requires backend + celery up). "
+             "Default is to skip them since the n=171 run had 0 SurfSense failures.",
+    )
+    args = parser.parse_args()
+    raise SystemExit(asyncio.run(_run(args)))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/summarise_parser_compare_run.py b/surfsense_evals/scripts/summarise_parser_compare_run.py
new file mode 100644
index 000000000..c54d82784
--- /dev/null
+++ b/surfsense_evals/scripts/summarise_parser_compare_run.py
@@ -0,0 +1,122 @@
+"""Slice the parser_compare raw.jsonl for the n=171 run.
+
+Reports per-arm:
+  * tokens & cost stats (input/output mean, $/Q distribution)
+  * failures (status != ok or empty raw_text)
+  * answer_format breakdown (accuracy by str/int/float/list)
+
+Plus surfsense agentic breakdown so we can compare apples to apples
+even though the new_chat SSE doesn't surface per-call token counts.
+"""
+
+from __future__ import annotations
+
+import json
+import statistics
+from collections import defaultdict
+from pathlib import Path
+
+
+REPO = Path(__file__).resolve().parents[1]
+RUN_DIR = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
+RAW = RUN_DIR / "raw.jsonl"
+ARTIFACT = RUN_DIR / "run_artifact.json"
+
+
+def main() -> None:
+    rows = [json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines() if line.strip()]
+    print(f"raw rows: {len(rows)}")
+
+    by_qid: dict[str, list[dict]] = defaultdict(list)
+    for row in rows:
+        by_qid[row["qid"]].append(row)
+    print(f"unique questions: {len(by_qid)}")
+
+    arm_metrics: dict[str, dict] = defaultdict(lambda: {
+        "n": 0, "n_correct": 0, "n_failed": 0, "n_empty": 0,
+        "costs": [], "in_tokens": [], "out_tokens": [], "latency_ms": [],
+        "by_format": defaultdict(lambda: {"n": 0, "correct": 0}),
+    })
+
+    for row in rows:
+        arm = row["arm"]
+        m = arm_metrics[arm]
+        m["n"] += 1
+        graded = row.get("graded") or {}
+        if graded.get("correct"):
+            m["n_correct"] += 1
+
+        err = row.get("error")
+        raw_text = row.get("raw_text") or ""
+        if err:
+            m["n_failed"] += 1
+        elif not raw_text.strip():
+            m["n_empty"] += 1
+
+        cost = row.get("cost_usd")
+        if cost is not None:
+            m["costs"].append(float(cost))
+        ut = row.get("usage") or {}
+        if ut.get("prompt_tokens"):
+            m["in_tokens"].append(ut["prompt_tokens"])
+        if ut.get("completion_tokens"):
+            m["out_tokens"].append(ut["completion_tokens"])
+        if row.get("latency_ms"):
+            m["latency_ms"].append(row["latency_ms"])
+
+        fmt = row.get("answer_format") or "unknown"
+        m["by_format"][fmt]["n"] += 1
+        if graded.get("correct"):
+            m["by_format"][fmt]["correct"] += 1
+
+    print()
+    print("=" * 100)
+    print(f"{'arm':<25} {'n':>4} {'acc%':>6} {'F1%':>6} {'fail':>5} {'$ mean':>10} {'$ median':>10} {'in tok mean':>12} {'out tok mean':>12} {'p50 ms':>8}")
+    print("=" * 100)
+    art = json.loads(ARTIFACT.read_text(encoding="utf-8"))
+    per_arm_art = art["metrics"]["per_arm"]
+    for arm, m in sorted(arm_metrics.items()):
+        acc = m["n_correct"] / m["n"] * 100
+        fail = m["n_failed"]
+        cost_mean = statistics.mean(m["costs"]) if m["costs"] else 0.0
+        cost_med = statistics.median(m["costs"]) if m["costs"] else 0.0
+        in_mean = statistics.mean(m["in_tokens"]) if m["in_tokens"] else 0
+        out_mean = statistics.mean(m["out_tokens"]) if m["out_tokens"] else 0
+        lat_p50 = statistics.median(m["latency_ms"]) if m["latency_ms"] else 0
+        f1 = per_arm_art.get(arm, {}).get("f1_mean", 0.0) * 100
+        print(
+            f"{arm:<25} {m['n']:>4} {acc:>5.1f}% {f1:>5.1f}% {fail:>5} "
+            f"${cost_mean:>9.4f} ${cost_med:>9.4f} {in_mean:>12.0f} {out_mean:>12.0f} {lat_p50:>8.0f}"
+        )
+
+    print()
+    print("by answer_format (accuracy):")
+    formats = sorted({f for m in arm_metrics.values() for f in m["by_format"].keys()})
+    header = f"{'arm':<25} " + " ".join(f"{f:>10}" for f in formats)
+    print(header)
+    print("-" * len(header))
+    for arm, m in sorted(arm_metrics.items()):
+        cells = []
+        for f in formats:
+            row = m["by_format"][f]
+            if row["n"] == 0:
+                cells.append(f"{'-':>10}")
+            else:
+                pct = row["correct"] / row["n"] * 100
+                cells.append(f"{pct:>5.0f}% ({row['correct']:>2}/{row['n']:>2})")
+        print(f"{arm:<25} " + " ".join(cells))
+
+    print()
+    print("=" * 100)
+    print("Aggregated cost (from run_artifact.json):")
+    for arm, row in per_arm_art.items():
+        print(
+            f"  {arm:<25}  acc={row['accuracy']*100:5.1f}% "
+            f"  $/Q LLM={row['llm_cost_per_q']:.4f}  "
+            f"  preprocess total=${row['preprocess_cost_total']:.2f}  "
+            f"  $/Q total={row['total_cost_per_q']:.4f}"
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/scripts/test_context_overflow_hypothesis.py b/surfsense_evals/scripts/test_context_overflow_hypothesis.py
new file mode 100644
index 000000000..89bd6cb3d
--- /dev/null
+++ b/surfsense_evals/scripts/test_context_overflow_hypothesis.py
@@ -0,0 +1,155 @@
+"""Test the hypothesis: were the LC-arm errors actually context-window
+overflow errors disguised as SSL / network failures?
+
+If true, we'd expect:
+  (a) literal "prompt is too long" / "context_length_exceeded" / "exceeds .* tokens" strings,
+  (b) failures correlated with extraction size / input_tokens (large doc -> failure),
+  (c) failing requests near or over Sonnet 4.5's 200k input-token limit.
+
+If false (transport-layer hypothesis), we'd expect:
+  (a) only SSL / 502 / empty stream / JSONDecode strings,
+  (b) failures NOT correlated with size (uniform across PDFs by time, not by tokens),
+  (c) failing requests well below the 200k limit.
+"""
+
+from __future__ import annotations
+
+import json
+import statistics
+from collections import defaultdict
+from pathlib import Path
+
+REPO = Path(__file__).resolve().parents[1]
+RUN = REPO / "data" / "multimodal_doc" / "runs" / "2026-05-14T00-53-19Z" / "parser_compare"
+RAW = RUN / "raw.jsonl"
+MANIFEST = REPO / "data" / "multimodal_doc" / "maps" / "parser_compare_doc_map.jsonl"
+
+CONTEXT_HINTS = (
+    "context_length",
+    "context window",
+    "prompt is too long",
+    "exceeds",
+    "maximum context",
+    "input tokens",
+    "too many tokens",
+    "over the maximum",
+    "200000",
+    "200_000",
+)
+
+
+def main() -> None:
+    rows = [
+        json.loads(line) for line in RAW.read_text(encoding="utf-8").splitlines()
+        if line.strip()
+    ]
+
+    extraction_size: dict[tuple[str, str], int] = {}
+    for line in MANIFEST.read_text(encoding="utf-8").splitlines():
+        if not line.strip():
+            continue
+        m = json.loads(line)
+        for arm, ext in (m.get("extractions") or {}).items():
+            extraction_size[(m["doc_id"], arm)] = int(ext.get("chars") or 0)
+
+    print("=" * 80)
+    print("(a) Literal 'context window' / 'prompt too long' error strings?")
+    print("=" * 80)
+    found = 0
+    for row in rows:
+        err = (row.get("error") or "").lower()
+        if not err:
+            continue
+        for hint in CONTEXT_HINTS:
+            if hint in err:
+                print(f"  {row['arm']:<25} {row['qid']:<50}")
+                print(f"      -> {err[:240]}")
+                found += 1
+                break
+    if not found:
+        print("  none found.")
+
+    print()
+    print("=" * 80)
+    print("(b) Extraction size for OK vs FAILED rows per arm")
+    print("=" * 80)
+    arm_buckets: dict[str, dict[str, list[int]]] = defaultdict(
+        lambda: {"ok": [], "fail": []}
+    )
+    parser_arms = (
+        "azure_basic_lc", "azure_premium_lc",
+        "llamacloud_basic_lc", "llamacloud_premium_lc",
+    )
+    for row in rows:
+        arm = row["arm"]
+        if arm not in parser_arms:
+            continue
+        size = extraction_size.get((row["doc_id"], arm), 0)
+        bucket = "fail" if (row.get("error") or not (row.get("raw_text") or "").strip()) else "ok"
+        arm_buckets[arm][bucket].append(size)
+
+    print(f"{'arm':<25} {'bucket':<5} {'n':>4} {'mean chars':>12} {'median':>10} {'max':>10}")
+    for arm in parser_arms:
+        for bucket in ("ok", "fail"):
+            sizes = arm_buckets[arm][bucket]
+            if not sizes:
+                print(f"  {arm:<23} {bucket:<5} {0:>4}  -")
+                continue
+            print(
+                f"  {arm:<23} {bucket:<5} {len(sizes):>4} "
+                f"{statistics.mean(sizes):>12,.0f} "
+                f"{statistics.median(sizes):>10,.0f} "
+                f"{max(sizes):>10,}"
+            )
+
+    print()
+    print("=" * 80)
+    print("(c) Largest extraction each arm processed *successfully* vs *failed*")
+    print("=" * 80)
+    print(
+        "(Sonnet 4.5 input limit ~200k tokens ~= 800k chars. If failures were "
+        "context-overflow, max-OK would be near that cap. If max-OK is well "
+        "above max-FAIL, the model handled bigger contexts than the failed "
+        "ones, so size cannot be the cause.)"
+    )
+    print()
+    for arm in parser_arms:
+        ok_sizes = arm_buckets[arm]["ok"]
+        fail_sizes = arm_buckets[arm]["fail"]
+        if not ok_sizes:
+            continue
+        max_ok = max(ok_sizes)
+        max_fail = max(fail_sizes) if fail_sizes else 0
+        print(
+            f"  {arm:<25} max OK = {max_ok:>10,} chars (~{max_ok / 4:>7,.0f} tokens)  "
+            f"max FAIL = {max_fail:>10,} chars (~{max_fail / 4:>7,.0f} tokens)"
+        )
+
+    print()
+    print("=" * 80)
+    print("(d) Did the *known* overflow candidate fail?")
+    print("=" * 80)
+    print(
+        "  3M_2018_10K x llamacloud_premium = 908,733 chars (~227k tokens) "
+        "-- this is above Sonnet 4.5's 200k window."
+    )
+    print("  If transport hypothesis is correct, this should still fail with a "
+          "real overflow error.")
+    print("  If transport hypothesis is correct AND the model truncates silently, "
+          "it might 'succeed' but be wrong.")
+    print()
+    for row in rows:
+        if row["doc_id"] != "3M_2018_10K.pdf":
+            continue
+        if row["arm"] != "llamacloud_premium_lc":
+            continue
+        err = row.get("error") or "(none)"
+        graded = row.get("graded") or {}
+        print(
+            f"  {row['qid']:<40} correct={graded.get('correct')!s:<5}  "
+            f"err={err[:100]}"
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py b/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py
new file mode 100644
index 000000000..6a8e6c4ce
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parsers/__init__.py
@@ -0,0 +1,35 @@
+"""Direct parser invocations for the parser_compare benchmark.
+
+The SurfSense backend exposes a single ``ETL_SERVICE`` env var that
+picks one parser globally; per-ingestion overrides are not on the
+public API. To drive the four (Azure DI x basic/premium, LlamaCloud x
+basic/premium) extractions we need for ``multimodal_doc/parser_compare``
+we therefore call the Azure DI and LlamaCloud SDKs directly from the
+eval harness, mirroring the production code path in
+``surfsense_backend/app/etl_pipeline/parsers/``.
+
+Two design rules:
+
+* No backend imports — the eval harness cannot pull in the FastAPI
+  app's config layer (it would require the full backend ``.env`` plus a
+  reachable Postgres). We re-read keys from our own environment instead.
+* Same wire shape as the backend's parsers (Azure ``prebuilt-read`` /
+  ``prebuilt-layout`` selected by ``processing_mode``; LlamaCloud
+  ``parse_page_with_llm`` / ``parse_page_with_agent`` selected by
+  ``processing_mode``) so any quality conclusions transfer back to
+  production behaviour.
+"""
+
+from __future__ import annotations
+
+from .azure_di import AzureDIError, parse_with_azure_di
+from .llamacloud import LlamaCloudError, parse_with_llamacloud
+from .pdf_pages import count_pdf_pages
+
+__all__ = [
+    "AzureDIError",
+    "LlamaCloudError",
+    "count_pdf_pages",
+    "parse_with_azure_di",
+    "parse_with_llamacloud",
+]
diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py b/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py
new file mode 100644
index 000000000..eebad906a
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py
@@ -0,0 +1,144 @@
+"""Azure Document Intelligence parser — eval-side mirror of the backend.
+
+Calls ``DocumentIntelligenceClient.begin_analyze_document`` with one
+of two ``model_id`` slugs depending on ``processing_mode``:
+
+* ``basic``   → ``prebuilt-read``   (text OCR only, cheaper, faster)
+* ``premium`` → ``prebuilt-layout`` (text + tables + structure;
+                                     produces real markdown headings,
+                                     pipe-tables, etc.)
+
+These are the same model selections the production
+``surfsense_backend/app/etl_pipeline/parsers/azure_doc_intelligence.py``
+makes per ``processing_mode``. Output format is forced to Markdown
+(``DocumentContentFormat.MARKDOWN``) so the long-context arm can stuff
+it into a prompt verbatim.
+
+Retry policy is intentionally light here (the eval harness re-runs
+the whole batch on top-level failure); we do one synchronous attempt
+plus exponential backoff on transient transport errors.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import random
+
+logger = logging.getLogger(__name__)
+
+
+_AZURE_MODEL_BY_MODE = {
+    "basic": "prebuilt-read",
+    "premium": "prebuilt-layout",
+}
+
+_MAX_RETRIES = 4
+_BASE_DELAY = 5.0
+_MAX_DELAY = 60.0
+
+
+class AzureDIError(RuntimeError):
+    """Raised when Azure DI fails after all retries."""
+
+
+async def parse_with_azure_di(
+    file_path: str | os.PathLike,
+    *,
+    processing_mode: str = "basic",
+    endpoint: str | None = None,
+    api_key: str | None = None,
+) -> str:
+    """Run Azure DI on ``file_path`` and return the markdown content.
+
+    ``endpoint`` / ``api_key`` default to ``AZURE_DI_ENDPOINT`` and
+    ``AZURE_DI_KEY`` env vars (set in ``surfsense_evals/.env``).
+
+    Raises ``AzureDIError`` after exhausting retries; ``ValueError`` if
+    credentials are missing.
+    """
+
+    endpoint = endpoint or os.environ.get("AZURE_DI_ENDPOINT")
+    api_key = api_key or os.environ.get("AZURE_DI_KEY")
+    if not endpoint or not api_key:
+        raise ValueError(
+            "AZURE_DI_ENDPOINT and AZURE_DI_KEY must be set "
+            "(see surfsense_evals/.env)."
+        )
+
+    model_id = _AZURE_MODEL_BY_MODE.get(processing_mode, "prebuilt-read")
+
+    # Lazy imports — surfsense_evals shouldn't pay the azure-sdk
+    # import cost on every CLI invocation that doesn't touch
+    # parser_compare.
+    from azure.ai.documentintelligence.aio import DocumentIntelligenceClient
+    from azure.ai.documentintelligence.models import DocumentContentFormat
+    from azure.core.credentials import AzureKeyCredential
+    from azure.core.exceptions import (
+        ClientAuthenticationError,
+        HttpResponseError,
+        ServiceRequestError,
+        ServiceResponseError,
+    )
+
+    file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
+    logger.info(
+        "Azure DI parsing %s (mode=%s, model=%s, size=%.1fMB)",
+        file_path, processing_mode, model_id, file_size_mb,
+    )
+
+    last_exc: Exception | None = None
+    for attempt in range(1, _MAX_RETRIES + 1):
+        try:
+            client = DocumentIntelligenceClient(
+                endpoint=endpoint,
+                credential=AzureKeyCredential(api_key),
+            )
+            async with client:
+                with open(file_path, "rb") as fh:
+                    poller = await client.begin_analyze_document(
+                        model_id,
+                        body=fh,
+                        output_content_format=DocumentContentFormat.MARKDOWN,
+                    )
+                result = await poller.result()
+            content = (result.content or "").strip()
+            if not content:
+                raise AzureDIError(
+                    f"Azure DI returned empty content for {file_path}"
+                )
+            logger.info(
+                "Azure DI OK: %s (%s) -> %d chars",
+                file_path, model_id, len(content),
+            )
+            return content
+
+        except ClientAuthenticationError:
+            raise
+        except HttpResponseError as exc:
+            # 4xx that's not auth: don't retry, the request itself is broken.
+            if exc.status_code and 400 <= exc.status_code < 500:
+                raise AzureDIError(
+                    f"Azure DI {exc.status_code} on {file_path}: {exc}"
+                ) from exc
+            last_exc = exc
+        except (ServiceRequestError, ServiceResponseError) as exc:
+            last_exc = exc
+
+        if attempt < _MAX_RETRIES:
+            delay = min(_BASE_DELAY * (2 ** (attempt - 1)), _MAX_DELAY)
+            jitter = delay * 0.25 * (2 * random.random() - 1)
+            sleep_for = delay + jitter
+            logger.warning(
+                "Azure DI attempt %d/%d failed (%s); retrying in %.1fs",
+                attempt, _MAX_RETRIES, type(last_exc).__name__, sleep_for,
+            )
+            await asyncio.sleep(sleep_for)
+
+    raise AzureDIError(
+        f"Azure DI failed after {_MAX_RETRIES} attempts on {file_path}"
+    ) from last_exc
+
+
+__all__ = ["AzureDIError", "parse_with_azure_di"]
diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py b/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py
new file mode 100644
index 000000000..ba3d787ef
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py
@@ -0,0 +1,168 @@
+"""LlamaParse (LlamaCloud) parser — eval-side mirror of the backend.
+
+Calls ``LlamaParse.aparse`` with one of two ``parse_mode`` slugs
+depending on ``processing_mode``:
+
+* ``basic``   → ``parse_page_with_llm``   (cheap, single-LLM-call/page)
+* ``premium`` → ``parse_page_with_agent`` (multi-step agent per page;
+                                            handles tables / figures
+                                            substantially better)
+
+These are the exact mappings from production
+``surfsense_backend/app/etl_pipeline/parsers/llamacloud.py``. We keep
+``num_workers=1`` and language=``"en"`` to match production.
+
+The result is materialised via ``get_markdown_documents(split_by_page=False)``
+which concatenates every page into a single markdown string, exactly
+the shape we need for long-context stuffing.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import random
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+_LLAMA_PARSE_MODE_MAP = {
+    "basic": "parse_page_with_llm",
+    "premium": "parse_page_with_agent",
+}
+
+_MAX_RETRIES = 3
+_BASE_DELAY = 10.0
+_MAX_DELAY = 90.0
+
+
+class LlamaCloudError(RuntimeError):
+    """Raised when LlamaCloud parse fails after all retries."""
+
+
+def _extract_markdown(result) -> str:
+    """Pull markdown out of whatever object LlamaParse.aparse returns.
+
+    Mirrors backend's tolerant extraction: the SDK has gone through
+    several response shapes; we accept all of them so a minor SDK bump
+    doesn't silently zero the eval.
+    """
+
+    if hasattr(result, "get_markdown_documents"):
+        docs = result.get_markdown_documents(split_by_page=False)
+        if docs and hasattr(docs[0], "text"):
+            return docs[0].text
+        if hasattr(result, "pages") and result.pages:
+            return "\n\n".join(p.md for p in result.pages if hasattr(p, "md") and p.md)
+
+    if isinstance(result, list):
+        if result and hasattr(result[0], "text"):
+            return result[0].text
+        return "\n\n".join(
+            doc.page_content if hasattr(doc, "page_content") else str(doc)
+            for doc in result
+        )
+
+    return str(result)
+
+
+async def parse_with_llamacloud(
+    file_path: str | os.PathLike,
+    *,
+    processing_mode: str = "basic",
+    estimated_pages: int = 50,
+    api_key: str | None = None,
+) -> str:
+    """Run LlamaParse on ``file_path`` and return the markdown content.
+
+    ``api_key`` defaults to the ``LLAMA_CLOUD_API_KEY`` env var (set
+    in ``surfsense_evals/.env``).
+
+    Raises ``LlamaCloudError`` after exhausting retries; ``ValueError``
+    if the API key is missing.
+    """
+
+    api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY")
+    if not api_key:
+        raise ValueError(
+            "LLAMA_CLOUD_API_KEY must be set (see surfsense_evals/.env)."
+        )
+
+    parse_mode = _LLAMA_PARSE_MODE_MAP.get(processing_mode, "parse_page_with_llm")
+
+    # Lazy import: llama-cloud pulls llama-index-core (~50 MB) on first
+    # touch; defer until the parser actually runs.
+    from llama_cloud_services import LlamaParse
+    from llama_cloud_services.parse.base import JobFailedException
+    from llama_cloud_services.parse.utils import ResultType
+
+    file_size_mb = os.path.getsize(file_path) / (1024 * 1024)
+    # Match backend's per-page timeout heuristic so big PDFs don't drop
+    # mid-job: 60s baseline + 30s/page (premium agent runs longer than
+    # basic; both fit comfortably here).
+    job_timeout = max(180.0, 60.0 + 30.0 * estimated_pages)
+    upload_timeout = max(120.0, 30.0 * file_size_mb)
+
+    logger.info(
+        "LlamaCloud parsing %s (mode=%s, parse_mode=%s, %.1fMB, "
+        "job_timeout=%.0fs)",
+        file_path, processing_mode, parse_mode, file_size_mb, job_timeout,
+    )
+
+    custom_timeout = httpx.Timeout(
+        connect=120.0, read=upload_timeout, write=upload_timeout, pool=120.0,
+    )
+
+    last_exc: Exception | None = None
+    for attempt in range(1, _MAX_RETRIES + 1):
+        try:
+            async with httpx.AsyncClient(timeout=custom_timeout) as client:
+                parser = LlamaParse(
+                    api_key=api_key,
+                    num_workers=1,
+                    verbose=False,
+                    language="en",
+                    result_type=ResultType.MD,
+                    parse_mode=parse_mode,
+                    ignore_errors=False,
+                    max_timeout=int(max(2000.0, job_timeout + upload_timeout)),
+                    job_timeout_in_seconds=job_timeout,
+                    job_timeout_extra_time_per_page_in_seconds=60,
+                    custom_client=client,
+                )
+                result = await parser.aparse(str(file_path))
+            content = _extract_markdown(result).strip()
+            if not content:
+                raise LlamaCloudError(
+                    f"LlamaCloud returned empty content for {file_path}"
+                )
+            logger.info(
+                "LlamaCloud OK: %s (%s) -> %d chars",
+                file_path, parse_mode, len(content),
+            )
+            return content
+
+        except (
+            httpx.HTTPError,
+            JobFailedException,
+            RuntimeError,
+        ) as exc:
+            last_exc = exc
+            if attempt < _MAX_RETRIES:
+                delay = min(_BASE_DELAY * (2 ** (attempt - 1)), _MAX_DELAY)
+                jitter = delay * 0.25 * (2 * random.random() - 1)
+                sleep_for = delay + jitter
+                logger.warning(
+                    "LlamaCloud attempt %d/%d failed (%s); retrying in %.1fs",
+                    attempt, _MAX_RETRIES, type(last_exc).__name__, sleep_for,
+                )
+                await asyncio.sleep(sleep_for)
+
+    raise LlamaCloudError(
+        f"LlamaCloud failed after {_MAX_RETRIES} attempts on {file_path}"
+    ) from last_exc
+
+
+__all__ = ["LlamaCloudError", "parse_with_llamacloud"]
diff --git a/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py b/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py
new file mode 100644
index 000000000..e3691cb73
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py
@@ -0,0 +1,35 @@
+"""Tiny pypdf wrapper for "how many pages does this PDF have?".
+
+Used by ``parser_compare`` to:
+
+* Decide LlamaCloud's per-page job timeout.
+* Compute the SurfSense preprocessing dollar cost
+  (``$1 / 1k pages`` for basic, ``$10 / 1k pages`` for premium) so the
+  report can show "ingest + LLM" total cost per arm.
+
+Returns ``0`` (and logs) on parse failure rather than raising — costs
+shown as ``?`` are always better than a benchmark that crashes mid-run.
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+def count_pdf_pages(path: Path) -> int:
+    """Return the page count for ``path``; ``0`` if pypdf can't open it."""
+
+    try:
+        from pypdf import PdfReader
+
+        reader = PdfReader(str(path))
+        return len(reader.pages)
+    except Exception as exc:  # noqa: BLE001
+        logger.warning("Failed to count pages for %s: %s", path, exc)
+        return 0
+
+
+__all__ = ["count_pdf_pages"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py
new file mode 100644
index 000000000..f6985e93d
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/__init__.py
@@ -0,0 +1,46 @@
+"""parser_compare — six-way head-to-head on long multimodal PDFs.
+
+Same 5 mmlongbench PDFs that ``mmlongbench`` already ingested
+(``search_space_id=55``), one question per PDF for the smoke run.
+
+The point of this benchmark is to disentangle TWO orthogonal
+dimensions of "how good is our multimodal pipeline?":
+
+1. **Parser quality** — Azure DI prebuilt-read vs prebuilt-layout vs
+   LlamaParse parse_page_with_llm vs parse_page_with_agent. We run
+   each parser directly (bypassing ``/documents/fileupload`` because
+   the backend's parser routing is global, not per-call) and stuff the
+   resulting markdown into a long-context prompt.
+
+2. **Context-management strategy** — full-context stuffing (no chunk
+   selection, the model sees everything) vs SurfSense's agentic
+   retrieval over chunks of the same documents.
+
+Six arms, all answered by ``anthropic/claude-sonnet-4.5``:
+
+* ``native_pdf``           — PDF attached natively via OpenRouter
+                              (gold-standard reference).
+* ``azure_basic_lc``       — Azure DI ``prebuilt-read`` markdown stuffed
+                              into the prompt.
+* ``azure_premium_lc``     — Azure DI ``prebuilt-layout`` markdown stuffed.
+* ``llamacloud_basic_lc``  — LlamaParse ``parse_page_with_llm`` markdown stuffed.
+* ``llamacloud_premium_lc`` — LlamaParse ``parse_page_with_agent`` markdown stuffed.
+* ``surfsense_agentic``    — SurfSense ``/api/v1/new_chat`` with
+                              ``mentioned_document_ids`` scoped to the
+                              one source PDF, retrieving chunks from
+                              the existing search_space=55 ingestion
+                              (vision_llm=on, processing_mode=premium,
+                              ETL_SERVICE=LLAMACLOUD with Azure DI
+                              fallback ⇒ effectively azure_premium).
+
+The report includes preprocessing cost ($1 / 1k pages basic, $10 / 1k
+pages premium) on top of the OpenRouter LLM cost so each arm's true
+total-cost-per-question is directly comparable.
+"""
+
+from __future__ import annotations
+
+from ....core import registry as _registry
+from .runner import ParserCompareBenchmark
+
+_registry.register(ParserCompareBenchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py
new file mode 100644
index 000000000..93c8db4ab
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py
@@ -0,0 +1,356 @@
+"""parser_compare ingestion: pre-extract markdown 4 ways per PDF.
+
+For each PDF in scope, we run all four (parser × mode) combinations
+in parallel and persist the resulting markdown alongside the PDF:
+
+    data/multimodal_doc/parser_compare/extractions/
+      <doc_id>.azure_basic.md
+      <doc_id>.azure_premium.md
+      <doc_id>.llamacloud_basic.md
+      <doc_id>.llamacloud_premium.md
+
+A manifest at ``maps/parser_compare_doc_map.jsonl`` records, per PDF:
+
+* ``doc_id``         — filename of the source PDF.
+* ``pdf_path``       — local cached PDF path.
+* ``document_id``    — SurfSense document id (carried over from
+                        mmlongbench's existing ingestion so the
+                        SurfSense agentic arm can scope retrieval).
+* ``pages``          — page count via pypdf (drives preprocessing cost).
+* ``extractions``    — map of ``arm_name -> {markdown_path, chars,
+                        elapsed_s, status, error}``.
+
+The runner reads this manifest, loads the markdown for each long-context
+arm, and uses ``document_id`` for the SurfSense arm.
+
+Source PDFs come from the existing mmlongbench ingestion — no new
+download or upload happens here. The point of this benchmark is
+parser quality on the same physical PDFs SurfSense already has, so
+re-using mmlongbench's PDF cache is correct.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from ....core.config import set_suite_state
+from ....core.parsers import (
+    AzureDIError,
+    LlamaCloudError,
+    count_pdf_pages,
+    parse_with_azure_di,
+    parse_with_llamacloud,
+)
+from ....core.registry import RunContext
+
+logger = logging.getLogger(__name__)
+
+
+# Order matters for the manifest only (deterministic JSONL diffs);
+# the runner doesn't rely on it.
+PARSER_ARMS: tuple[tuple[str, str, str], ...] = (
+    ("azure_basic_lc",       "azure",      "basic"),
+    ("azure_premium_lc",     "azure",      "premium"),
+    ("llamacloud_basic_lc",  "llamacloud", "basic"),
+    ("llamacloud_premium_lc", "llamacloud", "premium"),
+)
+
+
+@dataclass
+class ExtractionResult:
+    arm: str
+    parser: str
+    mode: str
+    markdown_path: Path | None = None
+    chars: int = 0
+    elapsed_s: float = 0.0
+    status: str = "ok"  # "ok" | "failed"
+    error: str | None = None
+
+    def to_jsonl(self) -> dict[str, Any]:
+        return {
+            "arm": self.arm,
+            "parser": self.parser,
+            "mode": self.mode,
+            "markdown_path": str(self.markdown_path) if self.markdown_path else None,
+            "chars": self.chars,
+            "elapsed_s": round(self.elapsed_s, 2),
+            "status": self.status,
+            "error": self.error,
+        }
+
+
+@dataclass
+class PdfManifestRow:
+    doc_id: str
+    pdf_path: Path
+    document_id: int | None
+    pages: int
+    extractions: dict[str, ExtractionResult] = field(default_factory=dict)
+
+    def to_jsonl(self) -> dict[str, Any]:
+        return {
+            "doc_id": self.doc_id,
+            "pdf_path": str(self.pdf_path),
+            "document_id": self.document_id,
+            "pages": self.pages,
+            "extractions": {
+                arm: ext.to_jsonl() for arm, ext in self.extractions.items()
+            },
+        }
+
+
+# ---------------------------------------------------------------------------
+# Single-PDF extraction
+# ---------------------------------------------------------------------------
+
+
+async def _run_one_extraction(
+    pdf_path: Path,
+    *,
+    parser: str,
+    mode: str,
+    out_path: Path,
+    estimated_pages: int,
+) -> tuple[str, float]:
+    """Invoke the requested parser, persist markdown, return (markdown, elapsed_s)."""
+
+    started = time.monotonic()
+    if parser == "azure":
+        markdown = await parse_with_azure_di(pdf_path, processing_mode=mode)
+    elif parser == "llamacloud":
+        markdown = await parse_with_llamacloud(
+            pdf_path, processing_mode=mode, estimated_pages=estimated_pages,
+        )
+    else:
+        raise ValueError(f"Unknown parser {parser!r}")
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(markdown, encoding="utf-8")
+    return markdown, time.monotonic() - started
+
+
+async def _extract_one_pdf(
+    pdf_path: Path,
+    *,
+    extractions_dir: Path,
+    force_reextract: bool,
+) -> dict[str, ExtractionResult]:
+    """Run all four parser combos for ``pdf_path``, returning per-arm results.
+
+    Re-uses any cached ``.md`` already on disk unless ``force_reextract``.
+    The four parser invocations run concurrently — they're independent
+    HTTP-bound jobs and the providers don't share state.
+    """
+
+    estimated_pages = count_pdf_pages(pdf_path) or 50
+    out: dict[str, ExtractionResult] = {}
+    coros = []
+    arm_specs: list[tuple[str, str, str, Path]] = []
+
+    for arm_name, parser, mode in PARSER_ARMS:
+        out_path = extractions_dir / f"{pdf_path.stem}.{arm_name}.md"
+        arm_specs.append((arm_name, parser, mode, out_path))
+
+        if out_path.exists() and not force_reextract:
+            cached = out_path.read_text(encoding="utf-8")
+            out[arm_name] = ExtractionResult(
+                arm=arm_name,
+                parser=parser,
+                mode=mode,
+                markdown_path=out_path,
+                chars=len(cached),
+                elapsed_s=0.0,
+                status="ok",
+                error="(cached)",
+            )
+            logger.info(
+                "Cached extraction reused: %s (%d chars)", out_path.name, len(cached),
+            )
+            coros.append(_noop())
+        else:
+            coros.append(
+                _run_one_extraction(
+                    pdf_path,
+                    parser=parser, mode=mode,
+                    out_path=out_path,
+                    estimated_pages=estimated_pages,
+                )
+            )
+
+    results = await asyncio.gather(*coros, return_exceptions=True)
+    for (arm_name, parser, mode, out_path), result in zip(arm_specs, results, strict=True):
+        if arm_name in out:
+            continue  # cached — already populated above
+        if isinstance(result, Exception):
+            err = result
+            err_msg = f"{type(err).__name__}: {err}"
+            logger.warning(
+                "Extraction FAILED for %s [%s/%s]: %s",
+                pdf_path.name, parser, mode, err_msg,
+            )
+            out[arm_name] = ExtractionResult(
+                arm=arm_name, parser=parser, mode=mode,
+                status="failed", error=err_msg,
+            )
+        else:
+            markdown, elapsed = result
+            out[arm_name] = ExtractionResult(
+                arm=arm_name, parser=parser, mode=mode,
+                markdown_path=out_path,
+                chars=len(markdown),
+                elapsed_s=elapsed,
+                status="ok",
+            )
+    return out
+
+
+async def _noop() -> tuple[str, float]:
+    """Placeholder so cached entries align with parallel gather indexing."""
+
+    return ("", 0.0)
+
+
+# ---------------------------------------------------------------------------
+# Public entry point
+# ---------------------------------------------------------------------------
+
+
+def _read_existing_mmlongbench_map(map_path: Path) -> list[dict[str, Any]]:
+    """Read the mmlongbench doc map (skipping its ``__settings__`` header)."""
+
+    if not map_path.exists():
+        raise RuntimeError(
+            f"mmlongbench doc map not found at {map_path}. Run "
+            "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first."
+        )
+    rows: list[dict[str, Any]] = []
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            if "__settings__" in row:
+                continue
+            rows.append(row)
+    return rows
+
+
+async def run_ingest(
+    ctx: RunContext,
+    *,
+    docs_filter: list[str] | None = None,
+    max_docs: int | None = None,
+    force_reextract: bool = False,
+    pdf_concurrency: int = 2,
+) -> None:
+    """Pre-extract all four parser markdowns for each PDF.
+
+    Parameters
+    ----------
+    docs_filter : list[str] | None
+        Specific filenames to extract (default: all PDFs from
+        mmlongbench's existing manifest).
+    max_docs : int | None
+        Cap on number of PDFs to process. Default: all.
+    force_reextract : bool
+        Re-call parsers even if a cached ``.md`` already exists. Off
+        by default — extractions are deterministic and parser calls
+        cost real money.
+    pdf_concurrency : int
+        How many PDFs to extract in parallel. Each PDF triggers four
+        parser HTTP calls, so total in-flight = 4 * pdf_concurrency.
+        Default 2 keeps us comfortably under both Azure DI and
+        LlamaCloud per-IP rate limits.
+    """
+
+    # Pull the source PDFs and document_ids from mmlongbench's existing
+    # ingestion. parser_compare doesn't re-upload; SurfSense's agentic
+    # arm queries the same search_space=55 chunks.
+    mmlb_map = ctx.suite_state.ingestion_maps.get("mmlongbench")
+    if not mmlb_map:
+        raise RuntimeError(
+            "Suite state has no mmlongbench ingestion map. Run "
+            "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first "
+            "so parser_compare can re-use those PDFs."
+        )
+    src_rows = _read_existing_mmlongbench_map(Path(mmlb_map))
+
+    rows_in_scope = src_rows
+    if docs_filter:
+        wanted = set(docs_filter)
+        rows_in_scope = [r for r in rows_in_scope if r["doc_id"] in wanted]
+    if max_docs is not None and max_docs > 0:
+        rows_in_scope = rows_in_scope[:max_docs]
+
+    if not rows_in_scope:
+        raise RuntimeError(
+            "No PDFs in scope for parser_compare. Check --docs / --max-docs."
+        )
+
+    bench_dir = ctx.benchmark_data_dir()
+    extractions_dir = bench_dir / "extractions"
+    extractions_dir.mkdir(parents=True, exist_ok=True)
+
+    sem = asyncio.Semaphore(max(1, pdf_concurrency))
+    manifest_rows: list[PdfManifestRow] = []
+
+    async def _process(row: dict[str, Any]) -> PdfManifestRow:
+        pdf_path = Path(row["pdf_path"])
+        async with sem:
+            extractions = await _extract_one_pdf(
+                pdf_path,
+                extractions_dir=extractions_dir,
+                force_reextract=force_reextract,
+            )
+        return PdfManifestRow(
+            doc_id=str(row["doc_id"]),
+            pdf_path=pdf_path,
+            document_id=row.get("document_id"),
+            pages=count_pdf_pages(pdf_path),
+            extractions=extractions,
+        )
+
+    logger.info(
+        "parser_compare: extracting %d PDFs x 4 parsers (concurrency=%d)",
+        len(rows_in_scope), pdf_concurrency,
+    )
+    manifest_rows = await asyncio.gather(*(_process(r) for r in rows_in_scope))
+
+    # Persist manifest
+    map_path = ctx.maps_dir() / "parser_compare_doc_map.jsonl"
+    with map_path.open("w", encoding="utf-8") as fh:
+        for mr in manifest_rows:
+            fh.write(json.dumps(mr.to_jsonl()) + "\n")
+    logger.info("parser_compare manifest -> %s", map_path)
+
+    # Update suite state so the runner can find us via
+    # ctx.suite_state.ingestion_maps.
+    new_state = ctx.suite_state
+    new_state.ingestion_maps["parser_compare"] = str(map_path)
+    set_suite_state(ctx.config, ctx.suite, new_state)
+
+    # Quick summary log
+    total_extractions = sum(len(mr.extractions) for mr in manifest_rows)
+    failures = sum(
+        1 for mr in manifest_rows for ext in mr.extractions.values()
+        if ext.status != "ok"
+    )
+    logger.info(
+        "parser_compare ingest done: %d PDFs, %d extractions, %d failures",
+        len(manifest_rows), total_extractions, failures,
+    )
+
+
+__all__ = [
+    "ExtractionResult",
+    "PARSER_ARMS",
+    "PdfManifestRow",
+    "run_ingest",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py
new file mode 100644
index 000000000..7119bbd29
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py
@@ -0,0 +1,120 @@
+"""Prompt templates for the three input modalities in parser_compare.
+
+We deliberately reuse the *same* core question framing as
+``mmlongbench/prompt.py`` so byte-identical questions reach all six
+arms; only the document delivery channel changes.
+
+Three templates:
+
+* ``build_native_pdf_prompt``       — bare question + format hint.
+                                       The PDF rides as a separate file
+                                       part (``NativePdfArm`` handles it).
+* ``build_long_context_prompt``     — question + format hint + the
+                                       parser-extracted markdown wrapped
+                                       in fenced ``<document>`` tags so
+                                       the model can clearly delimit
+                                       "context" from "instruction".
+* ``build_surfsense_prompt``        — bare question + format hint
+                                       (chunks come from RAG retrieval,
+                                       not from the prompt).
+
+The ``<document>`` tag is doc-aware: even though parser_compare runs
+one PDF per question today, we keep the wrapper plural so this is
+trivial to extend to multi-doc later.
+"""
+
+from __future__ import annotations
+
+# ---------------------------------------------------------------------------
+# Per-format hint blocks (same lookup as mmlongbench/prompt.py)
+# ---------------------------------------------------------------------------
+
+_FORMAT_HINTS: dict[str, str] = {
+    "str": (
+        "Respond with the answer as a short phrase, no full sentence. "
+        "Format your final line as `Answer: <text>`."
+    ),
+    "int": (
+        "Respond with a single integer only. "
+        "Format your final line as `Answer: <integer>`."
+    ),
+    "float": (
+        "Respond with a single decimal number only (no units). "
+        "Format your final line as `Answer: <number>`."
+    ),
+    "list": (
+        "Respond with a comma-separated list of items, no extra text. "
+        "Format your final line as `Answer: item1, item2, item3`."
+    ),
+    "none": (
+        "If the answer cannot be determined from the document, say so explicitly. "
+        "Format your final line as `Answer: Not answerable`."
+    ),
+}
+
+
+def _format_hint(answer_format: str) -> str:
+    fmt = (answer_format or "str").strip().lower()
+    return _FORMAT_HINTS.get(fmt, _FORMAT_HINTS["str"])
+
+
+_BASE_INSTRUCTION = (
+    "You are a document-understanding assistant. Use ONLY the provided "
+    "document to answer the question. The document may contain text, "
+    "tables, charts, figures, and images. If the answer is in a chart "
+    "or image, read it carefully. Do not use external knowledge."
+)
+
+
+def build_native_pdf_prompt(question: str, *, answer_format: str) -> str:
+    """Prompt for ``NativePdfArm`` — PDF attached separately as a file part."""
+
+    return (
+        f"{_BASE_INSTRUCTION}\n\n"
+        f"Question: {question.strip()}\n\n"
+        f"{_format_hint(answer_format)}\n"
+    )
+
+
+def build_surfsense_prompt(question: str, *, answer_format: str) -> str:
+    """Prompt for ``SurfSenseArm`` — chunks retrieved by the agent."""
+
+    # SurfSense's agent already injects retrieved chunks via its tool
+    # loop; the prompt only carries the user-visible question + format
+    # hint, mirroring how a human asks the SurfSense UI.
+    return (
+        f"{_BASE_INSTRUCTION}\n\n"
+        f"Question: {question.strip()}\n\n"
+        f"{_format_hint(answer_format)}\n"
+    )
+
+
+def build_long_context_prompt(
+    question: str,
+    *,
+    answer_format: str,
+    document_markdown: str,
+    document_label: str,
+) -> str:
+    """Prompt for the four long-context arms — markdown stuffed inline.
+
+    ``document_label`` is a short human-readable name (e.g. the PDF
+    filename) so the model can reason about source provenance even
+    though only one document is in scope.
+    """
+
+    return (
+        f"{_BASE_INSTRUCTION}\n\n"
+        f"<document name=\"{document_label}\">\n"
+        f"{document_markdown.strip()}\n"
+        f"</document>\n\n"
+        f"Question: {question.strip()}\n\n"
+        f"{_format_hint(answer_format)}\n"
+    )
+
+
+__all__ = [
+    "build_long_context_prompt",
+    "build_native_pdf_prompt",
+    "build_surfsense_prompt",
+]
diff --git a/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py
new file mode 100644
index 000000000..e71dffa65
--- /dev/null
+++ b/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py
@@ -0,0 +1,797 @@
+"""parser_compare runner — six-arm head-to-head on n shared questions.
+
+For each (PDF, question) pair we issue six LLM calls (all sonnet 4.5):
+
+* ``native_pdf``           — PDF attached natively.
+* ``azure_basic_lc``       — Azure prebuilt-read markdown stuffed.
+* ``azure_premium_lc``     — Azure prebuilt-layout markdown stuffed.
+* ``llamacloud_basic_lc``  — LlamaParse parse_page_with_llm markdown stuffed.
+* ``llamacloud_premium_lc`` — LlamaParse parse_page_with_agent markdown stuffed.
+* ``surfsense_agentic``    — SurfSense /api/v1/new_chat retrieval over chunks.
+
+Cost reporting:
+
+* ``llm_cost_per_q``       — mean OpenRouter ``usage.cost`` reported by
+                              the chat-completions API. Zero for the
+                              SurfSense agentic arm because the SSE
+                              stream doesn't surface per-call cost yet
+                              (a known gap; we annotate it in the
+                              report rather than estimating).
+* ``preprocess_cost_total`` — pages * $/1k according to the user's
+                              tariff:
+                                * basic   : $1   / 1k pages
+                                * premium : $10  / 1k pages
+                                * native_pdf : $0  (no preprocessing)
+                                * surfsense_agentic : $10 / 1k pages
+                                  (existing mmlongbench ingest used
+                                  processing_mode=premium with Azure DI).
+* ``preprocess_cost_per_q`` — preprocess_cost_total / n_questions.
+* ``total_cost_per_q``      — llm_cost_per_q + preprocess_cost_per_q.
+
+The grader is reused from ``mmlongbench/grader.py`` (deterministic,
+format-aware) so the metric is directly comparable to the existing
+mmlongbench runs.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+from collections.abc import Iterable
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from ....core.arms import (
+    ArmRequest,
+    ArmResult,
+    BareLlmArm,
+    NativePdfArm,
+    SurfSenseArm,
+)
+from ....core.config import utc_iso_timestamp
+from ....core.metrics.mc_accuracy import accuracy_with_wilson_ci
+from ....core.parse.freeform_answer import extract_freeform_answer
+from ....core.providers.openrouter_chat import OpenRouterChatProvider
+from ....core.providers.openrouter_pdf import OpenRouterPdfProvider, PdfEngine
+from ....core.registry import ReportSection, RunArtifact, RunContext
+from ..mmlongbench.grader import GradeResult, grade
+from .ingest import PARSER_ARMS
+from .prompt import (
+    build_long_context_prompt,
+    build_native_pdf_prompt,
+    build_surfsense_prompt,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# Cost tariff (per the user's spec: $1 / 1k pages basic, $10 / 1k pages premium).
+# Held as dollars-per-page so per-PDF math is a pure multiply.
+PREPROCESS_USD_PER_PAGE = {
+    "basic":   1.0  / 1000.0,
+    "premium": 10.0 / 1000.0,
+}
+
+ARM_NAMES = (
+    "native_pdf",
+    "azure_basic_lc",
+    "azure_premium_lc",
+    "llamacloud_basic_lc",
+    "llamacloud_premium_lc",
+    "surfsense_agentic",
+)
+
+# What ingest mode each LC arm corresponds to (drives preprocess cost).
+_LC_ARM_MODE: dict[str, str] = {
+    "azure_basic_lc": "basic",
+    "azure_premium_lc": "premium",
+    "llamacloud_basic_lc": "basic",
+    "llamacloud_premium_lc": "premium",
+}
+
+# The SurfSense agentic arm is fed by the existing mmlongbench
+# ingestion. That ingestion was performed with vision_llm=on and
+# processing_mode=premium, and the backend's ETL routes premium-mode
+# PDFs through Azure DI prebuilt-layout when AZURE_DI_KEY is set. So
+# the preprocessing cost is the premium tariff.
+SURFSENSE_INGEST_MODE = "premium"
+
+
+# ---------------------------------------------------------------------------
+# Question + PDF row shapes
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class PCQuestion:
+    qid: str
+    doc_id: str
+    question: str
+    gold_answer: str
+    answer_format: str
+    pdf_path: Path
+    document_id: int | None
+    pages: int
+    extractions: dict[str, Path]  # arm_name -> markdown path (only successes)
+
+
+def _read_doc_map(map_path: Path) -> dict[str, dict[str, Any]]:
+    out: dict[str, dict[str, Any]] = {}
+    with map_path.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            out[str(row["doc_id"])] = row
+    return out
+
+
+def _select_questions(
+    questions_jsonl: Path,
+    doc_map: dict[str, dict[str, Any]],
+    *,
+    docs_filter: list[str] | None,
+    sample_per_doc: int,
+    skip_unanswerable: bool,
+    skip_format: list[str] | None,
+) -> list[PCQuestion]:
+    """Pick the first ``sample_per_doc`` questions per PDF in scope.
+
+    Defaults to one per PDF (n=5 across 5 PDFs ⇒ 5 questions). Filters
+    out unanswerable probes by default since they're noise at small n.
+    """
+
+    out: list[PCQuestion] = []
+    per_doc_taken: dict[str, int] = {}
+    per_doc_idx: dict[str, int] = {}
+    skip_format_set = {f.lower() for f in (skip_format or [])}
+
+    with questions_jsonl.open("r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.strip()
+            if not line:
+                continue
+            row = json.loads(line)
+            doc_id = str(row.get("doc_id") or "")
+            if not doc_id:
+                continue
+            if docs_filter and doc_id not in docs_filter:
+                continue
+            map_row = doc_map.get(doc_id)
+            if map_row is None:
+                continue
+
+            answer_format = str(row.get("answer_format") or "").strip().lower()
+            idx = per_doc_idx.get(doc_id, 0)
+            per_doc_idx[doc_id] = idx + 1
+
+            if skip_unanswerable and answer_format == "none":
+                continue
+            if answer_format in skip_format_set:
+                continue
+
+            if per_doc_taken.get(doc_id, 0) >= sample_per_doc:
+                continue
+
+            extractions: dict[str, Path] = {}
+            for arm_name, ext_blob in (map_row.get("extractions") or {}).items():
+                if ext_blob.get("status") == "ok" and ext_blob.get("markdown_path"):
+                    extractions[arm_name] = Path(ext_blob["markdown_path"])
+
+            out.append(PCQuestion(
+                qid=f"{doc_id}::Q{idx:03d}",
+                doc_id=doc_id,
+                question=str(row.get("question") or "").strip(),
+                gold_answer=str(row.get("answer") or "").strip(),
+                answer_format=answer_format,
+                pdf_path=Path(map_row["pdf_path"]),
+                document_id=map_row.get("document_id"),
+                pages=int(map_row.get("pages", 0)),
+                extractions=extractions,
+            ))
+            per_doc_taken[doc_id] = per_doc_taken.get(doc_id, 0) + 1
+
+    out.sort(key=lambda q: (q.doc_id, q.qid))
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Bounded concurrency helper
+# ---------------------------------------------------------------------------
+
+
+async def _gather_with_limit(coros: Iterable, *, concurrency: int) -> list[Any]:
+    sem = asyncio.Semaphore(max(1, concurrency))
+
+    async def _wrap(coro):
+        async with sem:
+            return await coro
+
+    return await asyncio.gather(*(_wrap(c) for c in coros))
+
+
+# ---------------------------------------------------------------------------
+# Benchmark
+# ---------------------------------------------------------------------------
+
+
+_DESCRIPTION = (
+    "parser_compare — 6-arm head-to-head on shared MMLongBench-Doc PDFs: "
+    "native PDF + (Azure DI / LlamaCloud) x (basic / premium) long-context "
+    "stuffing + SurfSense agentic retrieval. Reports preprocessing dollars "
+    "($1 / 1k pages basic, $10 / 1k pages premium) on top of LLM cost."
+)
+
+
+class ParserCompareBenchmark:
+    """6-arm parser + agentic-vs-non-agentic head-to-head."""
+
+    suite: str = "multimodal_doc"
+    name: str = "parser_compare"
+    headline: bool = True
+    description: str = _DESCRIPTION
+
+    # ------------------------------------------------------------------
+    # CLI flags
+    # ------------------------------------------------------------------
+
+    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
+        parser.add_argument(
+            "--docs", default=None,
+            help="Comma-separated doc_ids to include (default: all in manifest).",
+        )
+        parser.add_argument(
+            "--sample-per-doc", type=int, default=1,
+            help="Take the first N answerable questions per PDF (default 1).",
+        )
+        parser.add_argument(
+            "--skip-unanswerable", dest="skip_unanswerable",
+            action="store_true", default=True,
+            help="Drop 'None' format probes (default true; we want signal not "
+                 "hallucination probes for n=5).",
+        )
+        parser.add_argument(
+            "--include-unanswerable", dest="skip_unanswerable",
+            action="store_false",
+            help="Override --skip-unanswerable; include unanswerable probes too.",
+        )
+        parser.add_argument(
+            "--skip-format", default=None,
+            help="Comma-separated answer_format values to skip (e.g. 'none,float').",
+        )
+        parser.add_argument(
+            "--concurrency", type=int, default=2,
+            help="Parallel question workers per arm (default 2).",
+        )
+        parser.add_argument(
+            "--no-mentions", dest="no_mentions", action="store_true",
+            help="SurfSense arm: skip mentioned_document_ids (full-corpus retrieval).",
+        )
+        parser.add_argument(
+            "--pdf-engine", default="native",
+            choices=[e.value for e in PdfEngine],
+            help="OpenRouter file-parser engine for native_pdf arm.",
+        )
+        parser.add_argument(
+            "--max-output-tokens", type=int, default=512,
+            help="Cap on completion length for every arm.",
+        )
+        parser.add_argument(
+            "--llm-model", default="anthropic/claude-sonnet-4.5",
+            help="OpenRouter slug used by the 5 OpenRouter-driven arms. "
+                 "SurfSense arm uses whatever provider_model is pinned on the suite.",
+        )
+        parser.add_argument(
+            "--skip-arms", default=None,
+            help="Comma-separated arm names to skip (e.g. 'llamacloud_premium_lc').",
+        )
+        # Ingest-only flags (forwarded by the CLI to ingest.run_ingest).
+        parser.add_argument(
+            "--max-docs", type=int, default=None,
+            help="(ingest only) cap number of unique PDFs to process.",
+        )
+        parser.add_argument(
+            "--force-reextract", action="store_true",
+            help="(ingest only) re-call parsers even if cached .md exists.",
+        )
+        parser.add_argument(
+            "--pdf-concurrency", type=int, default=2,
+            help="(ingest only) parallel PDFs (each fans out to 4 parsers).",
+        )
+
+    # ------------------------------------------------------------------
+    # Lifecycle: ingest delegates to .ingest.run_ingest
+    # ------------------------------------------------------------------
+
+    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
+        from .ingest import run_ingest
+
+        docs_raw: str | None = opts.get("docs")
+        docs_filter = (
+            [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
+        )
+        await run_ingest(
+            ctx,
+            docs_filter=docs_filter,
+            max_docs=opts.get("max_docs"),
+            force_reextract=bool(opts.get("force_reextract", False)),
+            pdf_concurrency=int(opts.get("pdf_concurrency") or 2),
+        )
+
+    # ------------------------------------------------------------------
+    # Run
+    # ------------------------------------------------------------------
+
+    async def run(self, ctx: RunContext, **opts: Any) -> RunArtifact:
+        docs_raw: str | None = opts.get("docs")
+        docs_filter = (
+            [d.strip() for d in docs_raw.split(",") if d.strip()] if docs_raw else None
+        )
+        sample_per_doc = int(opts.get("sample_per_doc") or 1)
+        skip_unanswerable = bool(opts.get("skip_unanswerable", True))
+        skip_format_raw: str | None = opts.get("skip_format")
+        skip_format = (
+            [f.strip() for f in skip_format_raw.split(",") if f.strip()]
+            if skip_format_raw else None
+        )
+        concurrency = int(opts.get("concurrency") or 2)
+        no_mentions = bool(opts.get("no_mentions"))
+        pdf_engine_name = opts.get("pdf_engine") or "native"
+        max_output_tokens = int(opts.get("max_output_tokens") or 512)
+        llm_model = str(opts.get("llm_model") or "anthropic/claude-sonnet-4.5")
+        skip_arms_raw: str | None = opts.get("skip_arms")
+        skip_arms = (
+            {a.strip() for a in skip_arms_raw.split(",") if a.strip()}
+            if skip_arms_raw else set()
+        )
+
+        active_arms = [a for a in ARM_NAMES if a not in skip_arms]
+        if not active_arms:
+            raise RuntimeError("All arms skipped; nothing to run.")
+
+        bench_dir = ctx.benchmark_data_dir()
+        # parser_compare reuses mmlongbench's questions.jsonl (already
+        # downloaded by `ingest multimodal_doc mmlongbench`).
+        questions_jsonl = bench_dir.parent / "mmlongbench" / "questions.jsonl"
+        map_path = ctx.maps_dir() / "parser_compare_doc_map.jsonl"
+        if not questions_jsonl.exists():
+            raise RuntimeError(
+                "Missing mmlongbench questions at "
+                f"{questions_jsonl}. Run "
+                "`python -m surfsense_evals ingest multimodal_doc mmlongbench` first."
+            )
+        if not map_path.exists():
+            raise RuntimeError(
+                "parser_compare doc map missing. Run "
+                "`python -m surfsense_evals ingest multimodal_doc parser_compare` first."
+            )
+
+        doc_map = _read_doc_map(map_path)
+        questions = _select_questions(
+            questions_jsonl, doc_map,
+            docs_filter=docs_filter,
+            sample_per_doc=sample_per_doc,
+            skip_unanswerable=skip_unanswerable,
+            skip_format=skip_format,
+        )
+        if not questions:
+            raise RuntimeError(
+                "No questions matched filters; broaden --docs / --skip-format."
+            )
+        logger.info(
+            "parser_compare: scheduled %d questions across %d arms (%s)",
+            len(questions), len(active_arms), ",".join(active_arms),
+        )
+
+        api_key = os.environ.get("OPENROUTER_API_KEY")
+        if not api_key:
+            raise RuntimeError("OPENROUTER_API_KEY env var is required.")
+
+        # Build arms
+        arms: dict[str, Any] = {}
+        if "native_pdf" in active_arms:
+            native_provider = OpenRouterPdfProvider(
+                api_key=api_key, base_url=ctx.config.openrouter_base_url,
+                model=llm_model, engine=PdfEngine(pdf_engine_name),
+            )
+            arms["native_pdf"] = NativePdfArm(
+                provider=native_provider, max_output_tokens=max_output_tokens,
+            )
+        for arm_name, _, _ in PARSER_ARMS:
+            if arm_name in active_arms:
+                lc_provider = OpenRouterChatProvider(
+                    api_key=api_key, base_url=ctx.config.openrouter_base_url,
+                    model=llm_model,
+                )
+                arms[arm_name] = BareLlmArm(
+                    provider=lc_provider,
+                    max_output_tokens=max_output_tokens,
+                    name=arm_name,
+                )
+        if "surfsense_agentic" in active_arms:
+            surf = SurfSenseArm(
+                client=ctx.new_chat_client(),
+                search_space_id=ctx.search_space_id,
+                ephemeral_threads=True,
+            )
+            # Override the default "surfsense" name so the metrics
+            # bucket lines up with the rest of parser_compare's arms.
+            surf.name = "surfsense_agentic"
+            arms["surfsense_agentic"] = surf
+
+        run_timestamp = utc_iso_timestamp()
+        run_dir = ctx.runs_dir(run_timestamp=run_timestamp)
+        raw_path = run_dir / "raw.jsonl"
+
+        # ---- per-arm answer coroutine helpers ----
+
+        def _native_req(q: PCQuestion) -> ArmRequest:
+            return ArmRequest(
+                question_id=q.qid,
+                prompt=build_native_pdf_prompt(q.question, answer_format=q.answer_format),
+                pdf_paths=[q.pdf_path],
+                options={"max_tokens": max_output_tokens},
+            )
+
+        def _lc_req(q: PCQuestion, arm_name: str) -> ArmRequest:
+            md_path = q.extractions.get(arm_name)
+            if md_path is None or not md_path.exists():
+                raise FileNotFoundError(
+                    f"Missing extraction for {arm_name} on {q.doc_id}"
+                )
+            markdown = md_path.read_text(encoding="utf-8")
+            return ArmRequest(
+                question_id=q.qid,
+                prompt=build_long_context_prompt(
+                    q.question,
+                    answer_format=q.answer_format,
+                    document_markdown=markdown,
+                    document_label=q.doc_id,
+                ),
+            )
+
+        def _surf_req(q: PCQuestion) -> ArmRequest:
+            mentions: list[int] | None = None
+            if not no_mentions and q.document_id is not None:
+                mentions = [int(q.document_id)]
+            return ArmRequest(
+                question_id=q.qid,
+                prompt=build_surfsense_prompt(q.question, answer_format=q.answer_format),
+                mentioned_document_ids=mentions,
+            )
+
+        async def _answer_one(arm_name: str, q: PCQuestion) -> ArmResult:
+            arm = arms[arm_name]
+            try:
+                if arm_name == "native_pdf":
+                    return await arm.answer(_native_req(q))
+                if arm_name == "surfsense_agentic":
+                    return await arm.answer(_surf_req(q))
+                return await arm.answer(_lc_req(q, arm_name))
+            except FileNotFoundError as exc:
+                return ArmResult(
+                    arm=arm_name,
+                    question_id=q.qid,
+                    raw_text="",
+                    error=f"FileNotFoundError: {exc}",
+                )
+
+        # Run all arms in parallel (each arm bounded by `concurrency`).
+        per_arm_tasks: dict[str, list] = {
+            arm_name: [_answer_one(arm_name, q) for q in questions]
+            for arm_name in active_arms
+        }
+        per_arm_results: dict[str, list[ArmResult]] = {}
+        gathered = await asyncio.gather(*[
+            _gather_with_limit(per_arm_tasks[arm_name], concurrency=concurrency)
+            for arm_name in active_arms
+        ])
+        for arm_name, results in zip(active_arms, gathered, strict=True):
+            per_arm_results[arm_name] = results
+
+        # Grade
+        per_arm_grades: dict[str, list[GradeResult]] = {}
+        for arm_name in active_arms:
+            per_arm_grades[arm_name] = [
+                grade(
+                    pred=extract_freeform_answer(r.raw_text or ""),
+                    gold=q.gold_answer,
+                    answer_format=q.answer_format,
+                )
+                for q, r in zip(questions, per_arm_results[arm_name], strict=True)
+            ]
+
+        # Persist raw.jsonl
+        with raw_path.open("w", encoding="utf-8") as fh:
+            for i, q in enumerate(questions):
+                base = {
+                    "qid": q.qid,
+                    "doc_id": q.doc_id,
+                    "answer_format": q.answer_format,
+                    "gold": q.gold_answer,
+                    "pages": q.pages,
+                    "document_id": q.document_id,
+                }
+                for arm_name in active_arms:
+                    res = per_arm_results[arm_name][i]
+                    g = per_arm_grades[arm_name][i]
+                    fh.write(json.dumps({
+                        **base,
+                        **res.to_jsonl(),
+                        "graded": {
+                            "correct": g.correct,
+                            "f1": g.f1,
+                            "method": g.method,
+                            "normalised_pred": g.normalised_pred,
+                            "normalised_gold": g.normalised_gold,
+                        },
+                    }) + "\n")
+
+        # Aggregate per-arm metrics + cost
+        metrics = _compute_metrics(
+            questions, per_arm_results, per_arm_grades, active_arms,
+        )
+
+        artifact = RunArtifact(
+            suite=self.suite,
+            benchmark=self.name,
+            run_timestamp=run_timestamp,
+            raw_path=raw_path,
+            metrics=metrics,
+            extra={
+                "n_questions": len(questions),
+                "n_pdfs": len({q.doc_id for q in questions}),
+                "active_arms": list(active_arms),
+                "concurrency": concurrency,
+                "no_mentions": no_mentions,
+                "pdf_engine": pdf_engine_name,
+                "llm_model": llm_model,
+                "scenario": ctx.scenario,
+                "provider_model": ctx.provider_model,
+                "vision_provider_model": ctx.vision_provider_model,
+                "agent_llm_id": ctx.agent_llm_id,
+                "preprocess_tariff": {
+                    "basic_per_1k_pages": 1.0,
+                    "premium_per_1k_pages": 10.0,
+                },
+            },
+        )
+
+        manifest_path = run_dir / "run_artifact.json"
+        manifest_path.write_text(
+            json.dumps({
+                "suite": self.suite,
+                "benchmark": self.name,
+                "raw_path": "raw.jsonl",
+                "metrics": metrics,
+                "extra": artifact.extra,
+            }, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        return artifact
+
+    # ------------------------------------------------------------------
+    # Report
+    # ------------------------------------------------------------------
+
+    def report_section(self, artifacts: list[RunArtifact]) -> ReportSection:
+        if not artifacts:
+            return ReportSection(
+                title="Parser × agent-vs-stuffing comparison",
+                headline=True,
+                body_md="(no run artifacts found)",
+                body_json={},
+            )
+        latest = max(artifacts, key=lambda a: a.run_timestamp)
+        m = latest.metrics
+        extra = latest.extra
+        per_arm = m.get("per_arm", {})
+        active_arms = list(extra.get("active_arms", per_arm.keys()))
+
+        n_q = extra.get("n_questions", "?")
+        n_pdfs = extra.get("n_pdfs", "?")
+
+        body: list[str] = []
+        body.append(
+            f"- Sample size: **{n_q} questions across {n_pdfs} PDFs** "
+            f"(LLM: `{extra.get('llm_model', '?')}`, "
+            f"engine: `{extra.get('pdf_engine', 'native')}`)."
+        )
+        body.append(
+            f"- Preprocess tariff: basic = $1 / 1k pages, "
+            f"premium = $10 / 1k pages."
+        )
+        body.append("")
+        body.append("### Per-arm summary")
+        body.append("")
+        body.append(
+            "| Arm | Accuracy | F1 mean | LLM $/Q | Preprocess $ total | Total $/Q | Latency p50 |"
+        )
+        body.append("|---|---:|---:|---:|---:|---:|---:|")
+        for arm_name in active_arms:
+            row = per_arm.get(arm_name)
+            if not row:
+                body.append(f"| `{arm_name}` | (no data) | | | | | |")
+                continue
+            body.append(
+                f"| `{arm_name}` "
+                f"| {row['accuracy']*100:.1f}% "
+                f"({row['n_correct']}/{row['n']}) "
+                f"| {row['f1_mean']*100:.1f}% "
+                f"| ${row['llm_cost_per_q']:.4f} "
+                f"| ${row['preprocess_cost_total']:.4f} "
+                f"| ${row['total_cost_per_q']:.4f} "
+                f"| {row['latency_ms_median']/1000:.1f}s |"
+            )
+        body.append("")
+
+        # Notes / caveats
+        body.append("### Notes")
+        body.append("")
+        body.append(
+            "- `surfsense_agentic` LLM cost shows as $0.0000 because the "
+            "`/api/v1/new_chat` SSE stream does not surface per-call token "
+            "or cost yet (a known instrumentation gap). Preprocessing cost "
+            "is the premium tariff because the underlying mmlongbench "
+            "ingestion was performed with `processing_mode=premium` + "
+            "`vision_llm=on` + Azure DI."
+        )
+        body.append(
+            "- Long-context arms include the **same PDF text** for every "
+            "question against that PDF, so the OpenRouter input cost is "
+            "dominated by markdown size; preprocessing cost is paid once "
+            "across all questions sharing a PDF."
+        )
+        body.append(
+            "- Preprocessing $ total is computed as "
+            "`pages_processed_per_arm × tariff`, summed across the unique "
+            "PDFs in scope. With one question per PDF (n=5), preprocess $ "
+            "= preprocess $ / Q."
+        )
+        if extra.get("scenario"):
+            body.append(
+                f"- Scenario: `{extra.get('scenario')}` "
+                f"(suite-pinned `provider_model`: "
+                f"`{extra.get('provider_model', '?')}`)."
+            )
+
+        # Per-PDF breakdown if useful
+        per_pdf = m.get("per_pdf", {})
+        if per_pdf:
+            body.append("")
+            body.append("### Per-PDF correctness")
+            body.append("")
+            header = "| Doc | Pages | " + " | ".join(f"`{a}`" for a in active_arms) + " |"
+            sep = "|---|---:|" + "|".join(":---:" for _ in active_arms) + "|"
+            body.append(header)
+            body.append(sep)
+            for doc_id, info in sorted(per_pdf.items()):
+                row_cells = []
+                for arm_name in active_arms:
+                    g = info.get("arms", {}).get(arm_name, {})
+                    if not g:
+                        row_cells.append("?")
+                    else:
+                        row_cells.append("✓" if g.get("correct") else "✗")
+                body.append(
+                    f"| `{doc_id}` | {info.get('pages', '?')} | "
+                    + " | ".join(row_cells) + " |"
+                )
+
+        return ReportSection(
+            title="Parser × agent-vs-stuffing — long PDFs (sonnet 4.5)",
+            headline=True,
+            body_md="\n".join(body),
+            body_json=m,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Metrics
+# ---------------------------------------------------------------------------
+
+
+def _compute_metrics(
+    questions: list[PCQuestion],
+    per_arm_results: dict[str, list[ArmResult]],
+    per_arm_grades: dict[str, list[GradeResult]],
+    active_arms: Iterable[str],
+) -> dict[str, Any]:
+    """Aggregate per-arm metrics + the user's preprocessing cost overlay."""
+
+    # Sum unique PDF pages — preprocessing pays per unique PDF, not per question.
+    pdf_pages: dict[str, int] = {}
+    for q in questions:
+        pdf_pages.setdefault(q.doc_id, q.pages)
+
+    per_arm: dict[str, dict[str, Any]] = {}
+    for arm_name in active_arms:
+        results = per_arm_results[arm_name]
+        grades = per_arm_grades[arm_name]
+        n = len(grades)
+        n_correct = sum(1 for g in grades if g.correct)
+        f1_sum = sum(g.f1 for g in grades)
+        acc_with_ci = accuracy_with_wilson_ci(n_correct, n)
+
+        # LLM cost: sum of per-call cost_micros across questions, then average.
+        cost_micros_total = sum(int(r.cost_micros or 0) for r in results)
+        llm_cost_per_q = (cost_micros_total / 1_000_000.0) / n if n else 0.0
+
+        # Preprocessing cost depends on which mode this arm corresponds to.
+        if arm_name == "native_pdf":
+            preprocess_per_page = 0.0
+            preprocess_label = "n/a (PDF attached natively)"
+        elif arm_name in _LC_ARM_MODE:
+            mode = _LC_ARM_MODE[arm_name]
+            preprocess_per_page = PREPROCESS_USD_PER_PAGE[mode]
+            preprocess_label = f"{mode} tier ($/{mode}/page = ${preprocess_per_page:.4f})"
+        elif arm_name == "surfsense_agentic":
+            preprocess_per_page = PREPROCESS_USD_PER_PAGE[SURFSENSE_INGEST_MODE]
+            preprocess_label = (
+                f"{SURFSENSE_INGEST_MODE} tier (ingested by SurfSense at "
+                f"processing_mode=premium + vision_llm=on)"
+            )
+        else:
+            preprocess_per_page = 0.0
+            preprocess_label = "unknown"
+
+        preprocess_cost_total = sum(
+            pages * preprocess_per_page for pages in pdf_pages.values()
+        )
+        preprocess_cost_per_q = preprocess_cost_total / n if n else 0.0
+        total_cost_per_q = llm_cost_per_q + preprocess_cost_per_q
+
+        latencies = sorted(int(r.latency_ms or 0) for r in results)
+        latency_median = latencies[len(latencies) // 2] if latencies else 0
+        latency_p95 = latencies[int(len(latencies) * 0.95)] if len(latencies) >= 20 else (
+            latencies[-1] if latencies else 0
+        )
+
+        in_tokens = [int(r.input_tokens or 0) for r in results]
+        out_tokens = [int(r.output_tokens or 0) for r in results]
+
+        per_arm[arm_name] = {
+            **acc_with_ci.to_dict(),
+            "n": n,
+            "n_correct": n_correct,
+            "f1_mean": f1_sum / n if n else 0.0,
+            "llm_cost_per_q": llm_cost_per_q,
+            "preprocess_per_page_usd": preprocess_per_page,
+            "preprocess_cost_total": preprocess_cost_total,
+            "preprocess_cost_per_q": preprocess_cost_per_q,
+            "total_cost_per_q": total_cost_per_q,
+            "preprocess_label": preprocess_label,
+            "latency_ms_median": latency_median,
+            "latency_ms_p95": latency_p95,
+            "input_tokens_mean": (sum(in_tokens) / len(in_tokens)) if in_tokens else 0.0,
+            "output_tokens_mean": (sum(out_tokens) / len(out_tokens)) if out_tokens else 0.0,
+        }
+
+    # Per-PDF breakdown (correct / not for each arm)
+    per_pdf: dict[str, dict[str, Any]] = {}
+    for i, q in enumerate(questions):
+        slot = per_pdf.setdefault(q.doc_id, {
+            "pages": q.pages,
+            "arms": {},
+        })
+        for arm_name in active_arms:
+            slot["arms"].setdefault(arm_name, {
+                "correct": per_arm_grades[arm_name][i].correct,
+                "f1": per_arm_grades[arm_name][i].f1,
+            })
+
+    return {
+        "per_arm": per_arm,
+        "per_pdf": per_pdf,
+        "n_questions": len(questions),
+        "n_unique_pdfs": len(pdf_pages),
+        "total_pages_in_scope": sum(pdf_pages.values()),
+    }
+
+
+__all__ = ["ParserCompareBenchmark", "PCQuestion"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
index 80358c474..b9658ef68 100644
--- a/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/__init__.py
@@ -1,22 +1,10 @@
 """CRAG — Comprehensive RAG Benchmark (Yang et al., Meta, KDD Cup 2024).
 
-Source: https://github.com/facebookresearch/CRAG  (Tasks 1, 2, and 3)
+Source: https://github.com/facebookresearch/CRAG (Tasks 1 & 2)
 Paper:  https://arxiv.org/abs/2406.04744
 
-This package registers two siblings:
-
-* ``crag``    — Tasks 1 & 2: 5 candidate pages per question.
-* ``crag_t3`` — Task 3:       50 candidate pages per question. The
-  long-context arm is capped to the top-5 (the realistic "naive
-  RAG = pick top-K results" baseline); SurfSense retrieves over
-  all 50, where its rerank becomes the entire contribution.
-
-Both share the grader, prompt, runner, and report code; only the
-ingest path differs (single bz2 vs 4-part tar.bz2 streamed).
-
 CRAG ships ~2,706 factual QA pairs, each paired with **5 full HTML
-pages** retrieved as the top-5 of a real web search at ``query_time``
-(50 in Task 3).
+pages** retrieved as the top-5 of a real web search at ``query_time``.
 The benchmark spans 5 domains (finance, music, movie, sports, open)
 and 8 question types (simple, comparison, aggregation, set, multi-hop,
 post-processing, false_premise, simple_w_condition) — heads/torsos/
@@ -51,7 +39,6 @@ relative to refusals.
 from __future__ import annotations
 
 from ....core import registry as _registry
-from .runner import CragBenchmark, CragTask3Benchmark
+from .runner import CragBenchmark
 
 _registry.register(CragBenchmark())
-_registry.register(CragTask3Benchmark())
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
deleted file mode 100644
index 02bed5935..000000000
--- a/surfsense_evals/src/surfsense_evals/suites/research/crag/dataset_task3.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""CRAG Task 3 dataset loader — 4-part tar.bz2 → streaming JSONL.
-
-Task 3 ships ~7 GB of compressed data split into 4 parts on GitHub:
-
-    crag_task_3_dev_v4.tar.bz2.part1    (≈2 GB)
-    crag_task_3_dev_v4.tar.bz2.part2    (≈2 GB)
-    crag_task_3_dev_v4.tar.bz2.part3    (≈2 GB)
-    crag_task_3_dev_v4.tar.bz2.part4    (≈1.3 GB)
-
-Concatenated, they form a tar archive containing a single JSONL file.
-Decompressed, that JSONL is on the order of 30-50 GB because each row
-embeds 50 full HTML pages (vs 5 in Tasks 1 & 2).
-
-Materialising the JSONL would blow the disk budget (we have ~50 GB
-free at the time of writing), so we stream the whole thing instead:
-
-  1. Download parts (idempotent; ``scripts/download_crag_task3.py``).
-  2. Concat them into a virtual file via ``_MultiPartReader``.
-  3. Wrap in ``bz2.BZ2File`` for on-the-fly decompression.
-  4. Wrap in ``tarfile.open(fileobj=..., mode="r|")`` for streaming
-     tar member iteration.
-  5. For the JSONL member inside, ``tar.extractfile()`` returns a
-     binary file-like; we iterate lines and yield parsed dicts.
-
-The caller can ``break`` out as soon as they have enough samples —
-nothing past the consumed point is decompressed.
-
-Schema is identical to Tasks 1 & 2 (see ``dataset.py``); only
-``search_results`` is bigger (50 entries instead of 5).
-"""
-
-from __future__ import annotations
-
-import bz2
-import json
-import logging
-import tarfile
-from collections.abc import Iterator
-from pathlib import Path
-from typing import IO
-
-from .dataset import (
-    CragPage,
-    CragQuestion,
-    _parse_alt_answers,
-    _parse_pages,
-)
-
-logger = logging.getLogger(__name__)
-
-
-CRAG_TASK_3_PART_URLS: tuple[str, ...] = tuple(
-    "https://github.com/facebookresearch/CRAG/raw/refs/heads/main/data/"
-    f"crag_task_3_dev_v4.tar.bz2.part{i}"
-    for i in (1, 2, 3, 4)
-)
-CRAG_TASK_3_PART_NAMES: tuple[str, ...] = tuple(
-    f"crag_task_3_dev_v4.tar.bz2.part{i}" for i in (1, 2, 3, 4)
-)
-
-
-# ---------------------------------------------------------------------------
-# Multi-part virtual file (concatenates N files transparently)
-# ---------------------------------------------------------------------------
-
-
-class _MultiPartReader:
-    """Read N files end-to-end as if they were one big file.
-
-    Implements just enough of the file protocol for ``bz2.BZ2File``
-    to consume it: ``read(n)``, ``readable()``, ``close()``.
-    Doesn't implement ``seek`` — the bz2 + tarfile streaming path
-    is forward-only, which is what we want here.
-    """
-
-    def __init__(self, paths: list[Path]) -> None:
-        if not paths:
-            raise ValueError("_MultiPartReader needs at least one path")
-        for p in paths:
-            if not p.exists():
-                raise FileNotFoundError(p)
-        self._paths = list(paths)
-        self._idx = 0
-        self._fh: IO[bytes] | None = self._paths[0].open("rb")
-        self._closed = False
-
-    def read(self, n: int = -1) -> bytes:
-        if self._closed:
-            raise ValueError("read of closed _MultiPartReader")
-        if n is None or n < 0:
-            chunks: list[bytes] = []
-            while self._fh is not None:
-                chunks.append(self._fh.read())
-                self._advance()
-            return b"".join(chunks)
-        out: list[bytes] = []
-        remaining = n
-        while remaining > 0 and self._fh is not None:
-            chunk = self._fh.read(remaining)
-            if not chunk:
-                self._advance()
-                continue
-            out.append(chunk)
-            remaining -= len(chunk)
-        return b"".join(out)
-
-    def _advance(self) -> None:
-        if self._fh is not None:
-            self._fh.close()
-            self._fh = None
-        self._idx += 1
-        if self._idx < len(self._paths):
-            self._fh = self._paths[self._idx].open("rb")
-
-    def readable(self) -> bool:
-        return not self._closed
-
-    def close(self) -> None:
-        if self._fh is not None:
-            self._fh.close()
-            self._fh = None
-        self._closed = True
-
-    def __enter__(self) -> _MultiPartReader:
-        return self
-
-    def __exit__(self, exc_type, exc, tb) -> None:  # type: ignore[no-untyped-def]
-        self.close()
-
-
-# ---------------------------------------------------------------------------
-# Stream the JSONL inside the tar.bz2
-# ---------------------------------------------------------------------------
-
-
-def _is_jsonl_member(name: str) -> bool:
-    return name.endswith(".jsonl") or name.endswith(".jsonl.txt")
-
-
-def iter_questions_task3(
-    parts_dir: Path,
-    *,
-    max_questions: int | None = None,
-) -> list[CragQuestion]:
-    """Stream-parse Task 3 rows into ``CragQuestion`` objects.
-
-    The Task 3 archive ships its 2,706 questions sharded across
-    multiple JSONL files inside the tar (e.g.
-    ``crag_task_3_dev_v4_0.jsonl``, ``..._1.jsonl``, …). We iterate
-    members in-stream, parse every JSONL one we encounter, and stop
-    as soon as ``max_questions`` is reached — at which point we
-    don't decompress any further members.
-
-    For a typical n=50 sample at ~3 MB per row we touch ~150 MB of
-    decompressed JSONL — almost always inside the first shard.
-    """
-
-    parts = [parts_dir / name for name in CRAG_TASK_3_PART_NAMES]
-    multi = _MultiPartReader(parts)
-    bz = bz2.BZ2File(multi, mode="rb")
-    tar = tarfile.open(fileobj=bz, mode="r|")
-    out: list[CragQuestion] = []
-    raw_idx = 0
-    found_jsonl = False
-    try:
-        for member in tar:
-            if not member.isfile() or not _is_jsonl_member(member.name):
-                continue
-            found_jsonl = True
-            logger.info(
-                "CRAG Task 3: streaming JSONL shard %s (size: %d bytes)",
-                member.name, member.size,
-            )
-            fh = tar.extractfile(member)
-            if fh is None:
-                logger.warning("tar.extractfile returned None for %s; skipping", member.name)
-                continue
-            try:
-                for raw_line in fh:
-                    line = raw_line.decode("utf-8", errors="replace").strip()
-                    if not line:
-                        continue
-                    try:
-                        row = json.loads(line)
-                    except json.JSONDecodeError as exc:
-                        logger.warning(
-                            "Skipping malformed CRAG Task 3 row %d in %s: %s",
-                            raw_idx, member.name, exc,
-                        )
-                        raw_idx += 1
-                        continue
-                    query = str(row.get("query") or "").strip()
-                    answer = str(row.get("answer") or "").strip()
-                    if not query or not answer:
-                        raw_idx += 1
-                        continue
-                    out.append(CragQuestion(
-                        qid=f"T3_{raw_idx:05d}",
-                        interaction_id=str(row.get("interaction_id") or "").strip(),
-                        query_time=str(row.get("query_time") or "").strip(),
-                        query=query,
-                        gold_answer=answer,
-                        alt_answers=_parse_alt_answers(row.get("alt_ans")),
-                        domain=str(row.get("domain") or "").strip().lower(),
-                        question_type=str(row.get("question_type") or "").strip().lower(),
-                        static_or_dynamic=str(row.get("static_or_dynamic") or "").strip().lower(),
-                        popularity=str(row.get("popularity") or "").strip().lower(),
-                        split=int(row.get("split") or 0),
-                        raw_index=raw_idx,
-                        pages=_parse_pages(row.get("search_results")),
-                    ))
-                    raw_idx += 1
-                    if max_questions is not None and len(out) >= max_questions:
-                        return out
-            finally:
-                try:
-                    fh.close()
-                except Exception:  # noqa: BLE001
-                    pass
-        if not found_jsonl:
-            raise RuntimeError(
-                "No JSONL member found inside Task 3 tar.bz2 archive; "
-                "schema may have changed upstream."
-            )
-    finally:
-        try:
-            tar.close()
-        except Exception:  # noqa: BLE001
-            pass
-        try:
-            bz.close()
-        except Exception:  # noqa: BLE001
-            pass
-        try:
-            multi.close()
-        except Exception:  # noqa: BLE001
-            pass
-    return out
-
-
-def parts_present(parts_dir: Path) -> bool:
-    """``True`` iff all 4 parts exist on disk and are non-empty."""
-
-    for name in CRAG_TASK_3_PART_NAMES:
-        p = parts_dir / name
-        if not p.exists() or p.stat().st_size == 0:
-            return False
-    return True
-
-
-# ---------------------------------------------------------------------------
-# Re-exports for convenience
-# ---------------------------------------------------------------------------
-
-
-__all__ = [
-    "CRAG_TASK_3_PART_NAMES",
-    "CRAG_TASK_3_PART_URLS",
-    "CragPage",
-    "CragQuestion",
-    "iter_questions_task3",
-    "parts_present",
-]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
index 1a6a1dfa7..aad6a70bf 100644
--- a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest.py
@@ -436,12 +436,4 @@ async def _retry_upload_idempotent(  # noqa: D401 - hidden helper
     return {}
 
 
-__all__ = [
-    "_IngestStats",
-    "_materialise_pages",
-    "_page_filename",
-    "_resolve_question_doc_ids",
-    "_upload_pages",
-    "read_page_markdown",
-    "run_ingest",
-]
+__all__ = ["read_page_markdown", "run_ingest"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
deleted file mode 100644
index e5440f382..000000000
--- a/surfsense_evals/src/surfsense_evals/suites/research/crag/ingest_task3.py
+++ /dev/null
@@ -1,191 +0,0 @@
-"""CRAG Task 3 ingestion: 4-part download → streaming JSONL → upload.
-
-Same flow as ``ingest.run_ingest`` for Tasks 1 & 2 (extract HTML →
-upload markdown → resolve doc_ids → write doc map), but:
-
-* Source: 4 .tar.bz2 parts streamed via ``dataset_task3``.
-* Page count: 50 per question instead of 5 — the whole point of
-  Task 3 (the long-context arm now structurally has to choose what
-  to keep, while SurfSense's retrieval becomes mandatory).
-* Stratified sampling re-uses the Task 1 helper since the question
-  schema is identical.
-
-Doc map lands at ``<suite_data>/maps/crag_t3_doc_map.jsonl`` with the
-same row shape as Task 1's map (so the runner only needs to know
-which file to load; everything else is shared).
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from pathlib import Path
-
-from ....core.config import set_suite_state
-from ....core.ingest_settings import IngestSettings, settings_header_line
-from ....core.registry import RunContext
-from .dataset import stratified_sample, write_questions_jsonl
-from .dataset_task3 import (
-    CRAG_TASK_3_PART_NAMES,
-    iter_questions_task3,
-    parts_present,
-)
-from .ingest import (
-    _IngestStats,
-    _materialise_pages,
-    _resolve_question_doc_ids,
-    _upload_pages,
-)
-
-logger = logging.getLogger(__name__)
-
-
-_INSTRUCTIONS_TO_DOWNLOAD = (
-    "Run `python scripts/download_crag_task3.py` first to fetch the "
-    "4 tar.bz2 parts (~7 GB total) into "
-    "data/research/crag_t3/.raw_cache/. The downloader is idempotent "
-    "and parallel."
-)
-
-
-async def run_ingest_task3(
-    ctx: RunContext,
-    *,
-    n_questions: int | None = None,
-    upload_batch_size: int = 16,
-    skip_upload: bool = False,
-    overwrite_extract: bool = False,
-    settings: IngestSettings | None = None,
-    sample_seed: int = 17,
-    parse_cap: int | None = None,
-) -> None:
-    """Ingest CRAG Task 3 (50 pages per question) into the research suite.
-
-    Parameters
-    ----------
-    n_questions
-        Cap on the post-stratified-sample question count. ``None`` =
-        "use whatever ``parse_cap`` produced". For real runs aim for
-        50 (~2,500 pages) — n=200 (10k pages) is doable but slow.
-    parse_cap
-        Hard cap on how many rows we *parse* from the streaming
-        archive before stratified sampling. Defaults to
-        ``max(400, 6*n_questions)`` — enough to cover all (domain,
-        question_type) buckets ~5x but small enough to fit in the
-        first shard or two (each shard is ≈5 GB decompressed and
-        holds ~300 rows; bz2 throughput is ~50 MB/s). Lowering this
-        is the only knob that bounds streaming cost since we can
-        ``break`` out of the JSONL stream early without decompressing
-        the rest of the ~50 GB archive body.
-    upload_batch_size
-        Markdown files per ``/documents/fileupload`` call.
-    skip_upload
-        Extract markdown locally, don't push to SurfSense.
-    overwrite_extract
-        Re-run trafilatura even when a cached markdown is present.
-    settings
-        Per-upload knobs override (default: text-only basic ETL).
-    sample_seed
-        RNG seed for stratified sampling (deterministic).
-    """
-
-    settings = settings or IngestSettings(
-        use_vision_llm=False,
-        processing_mode="basic",
-        should_summarize=False,
-    )
-    bench_dir = ctx.benchmark_data_dir()
-    pages_dir = bench_dir / "pages"
-    raw_cache = bench_dir / ".raw_cache"
-    raw_cache.mkdir(parents=True, exist_ok=True)
-
-    if not parts_present(raw_cache):
-        missing = [
-            n for n in CRAG_TASK_3_PART_NAMES
-            if not (raw_cache / n).exists()
-        ]
-        raise RuntimeError(
-            f"CRAG Task 3 parts missing from {raw_cache}: {missing}. "
-            f"{_INSTRUCTIONS_TO_DOWNLOAD}"
-        )
-
-    # 1. Stream-parse (capped). For n=50 we don't need the full 2,706
-    #    rows — just enough that the stratified sampler can balance.
-    #    Each tar shard ~5 GB / ~300 rows / ~2 min decompress, so
-    #    400-500 rows = shard 0 + a slice of shard 1 ≈ 3-4 min.
-    parse_cap = parse_cap or (
-        max(400, 6 * (n_questions or 50)) if n_questions else None
-    )
-    logger.info(
-        "CRAG Task 3: streaming JSONL (parse_cap=%s) ...",
-        parse_cap if parse_cap else "no-cap",
-    )
-    all_questions = iter_questions_task3(raw_cache, max_questions=parse_cap)
-    logger.info("CRAG Task 3: parsed %d rows", len(all_questions))
-
-    if not all_questions:
-        raise RuntimeError("CRAG Task 3 streaming returned 0 rows; check archive integrity.")
-
-    if n_questions is not None and n_questions > 0:
-        questions = stratified_sample(all_questions, n=n_questions, seed=sample_seed)
-        logger.info(
-            "CRAG Task 3: stratified sample of %d questions across %d (domain, qtype) buckets",
-            len(questions),
-            len({(q.domain, q.question_type) for q in questions}),
-        )
-    else:
-        questions = all_questions
-
-    questions_jsonl = bench_dir / "questions.jsonl"
-    write_questions_jsonl(questions, questions_jsonl)
-
-    n_pages_total = sum(len(q.pages) for q in questions)
-    logger.info(
-        "CRAG Task 3: extracting up to %d pages across %d questions ...",
-        n_pages_total, len(questions),
-    )
-    qid_to_files, _file_to_url = _materialise_pages(
-        questions, pages_dir=pages_dir, overwrite=overwrite_extract,
-    )
-    n_pages_extracted = sum(len(v) for v in qid_to_files.values())
-
-    name_to_id: dict[str, int] = {}
-    if skip_upload:
-        logger.info("CRAG Task 3: --skip-upload; skipping SurfSense ingestion")
-    else:
-        all_filenames = sorted({fn for fns in qid_to_files.values() for fn in fns})
-        logger.info("CRAG Task 3: uploading %d unique pages ...", len(all_filenames))
-        name_to_id = await _upload_pages(
-            ctx,
-            pages_dir=pages_dir,
-            filenames=all_filenames,
-            batch_size=upload_batch_size,
-            settings=settings,
-        )
-
-    doc_rows = _resolve_question_doc_ids(questions, qid_to_files, name_to_id)
-    map_path = ctx.maps_dir() / "crag_t3_doc_map.jsonl"
-    with map_path.open("w", encoding="utf-8") as fh:
-        fh.write(settings_header_line(settings) + "\n")
-        for row in doc_rows:
-            fh.write(json.dumps(row) + "\n")
-    logger.info("Wrote CRAG Task 3 doc map to %s (%d rows)", map_path, len(doc_rows))
-
-    new_state = ctx.suite_state
-    new_state.ingestion_maps["crag_t3"] = str(map_path)
-    set_suite_state(ctx.config, ctx.suite, new_state)
-
-    stats = _IngestStats(
-        n_questions=len(questions),
-        n_pages_total=n_pages_total,
-        n_pages_extracted=n_pages_extracted,
-        n_pages_empty=n_pages_total - n_pages_extracted,
-        n_uploaded=len(name_to_id),
-        n_existing=0,
-        bench_dir=bench_dir,
-        map_path=map_path,
-    )
-    logger.info("CRAG Task 3 ingest done: %s", stats)
-
-
-__all__ = ["run_ingest_task3"]
diff --git a/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py b/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
index d6ba49294..710f76744 100644
--- a/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
+++ b/surfsense_evals/src/surfsense_evals/suites/research/crag/runner.py
@@ -189,18 +189,6 @@ class CragBenchmark:
     headline: bool = True
     description: str = _DESCRIPTION
 
-    # Subclasses (e.g. Task 3) override these without re-implementing run().
-    doc_map_filename: str = "crag_doc_map.jsonl"
-    # 0 = use ALL pages in the long-context arm. Task 3 defaults to 5
-    # so the long-context arm models the realistic "stuff the top-5
-    # search results into the prompt" baseline rather than blowing
-    # past the 128k-token context window with all 50 pages.
-    default_long_context_top_n: int = 0
-    pages_per_question_label: str = "5 pages"
-    ingest_hint: str = (
-        "`python -m surfsense_evals ingest research crag --n-questions 200`"
-    )
-
     def add_run_args(self, parser: argparse.ArgumentParser) -> None:
         parser.add_argument(
             "--n", dest="sample_n", type=int, default=None,
@@ -230,15 +218,6 @@ class CragBenchmark:
             "--per-page-char-cap", dest="per_page_char_cap", type=int, default=12_000,
             help="Long-context arm: max chars per page before truncation (default 12k).",
         )
-        parser.add_argument(
-            "--long-context-top-n-pages", dest="long_context_top_n_pages",
-            type=int, default=self.default_long_context_top_n,
-            help=(
-                "Long-context arm: keep only the first N pages from the "
-                "question's candidate list (0 = use all). Task 3 defaults "
-                "to 5 (the realistic 'naive RAG' top-K baseline)."
-            ),
-        )
         parser.add_argument(
             "--skip-bare", dest="skip_bare", action="store_true",
             help="Skip the bare-LLM arm (saves cost on re-runs).",
@@ -317,11 +296,6 @@ class CragBenchmark:
         concurrency = int(opts.get("concurrency") or 4)
         max_output_tokens = int(opts.get("max_output_tokens") or 512)
         per_page_char_cap = int(opts.get("per_page_char_cap") or 12_000)
-        long_context_top_n_pages = int(
-            opts.get("long_context_top_n_pages")
-            if opts.get("long_context_top_n_pages") is not None
-            else self.default_long_context_top_n
-        )
         skip_bare = bool(opts.get("skip_bare"))
         skip_long_context = bool(opts.get("skip_long_context"))
         skip_surfsense = bool(opts.get("skip_surfsense"))
@@ -331,11 +305,11 @@ class CragBenchmark:
         judge_concurrency = int(opts.get("judge_concurrency") or 4)
 
         bench_dir = ctx.benchmark_data_dir()
-        map_path = ctx.maps_dir() / self.doc_map_filename
+        map_path = ctx.maps_dir() / "crag_doc_map.jsonl"
         if not map_path.exists():
             raise RuntimeError(
-                f"{self.name} not ingested for this suite. Run "
-                f"{self.ingest_hint} first."
+                "CRAG not ingested for this suite. Run "
+                "`python -m surfsense_evals ingest research crag --n-questions 200` first."
             )
 
         rows, ingest_settings = _load_doc_map(map_path)
@@ -407,13 +381,7 @@ class CragBenchmark:
         async def _long_context_one(q: CragRunnerQuestion) -> ArmResult:
             assert long_context_arm is not None
             return await long_context_arm.answer(
-                _make_long_context_request(
-                    q,
-                    bench_dir,
-                    max_output_tokens,
-                    per_page_char_cap,
-                    top_n_pages=long_context_top_n_pages,
-                )
+                _make_long_context_request(q, bench_dir, max_output_tokens, per_page_char_cap)
             )
 
         async def _surf_one(q: CragRunnerQuestion) -> ArmResult:
@@ -503,8 +471,6 @@ class CragBenchmark:
                 "agent_llm_id": ctx.agent_llm_id,
                 "ingest_settings": ingest_settings,
                 "per_page_char_cap": per_page_char_cap,
-                "long_context_top_n_pages": long_context_top_n_pages,
-                "pages_per_question_label": self.pages_per_question_label,
                 "max_output_tokens": max_output_tokens,
                 "arms_active": {
                     "bare_llm": bare_arm is not None,
@@ -565,29 +531,18 @@ class CragBenchmark:
         if not active.get("long_context", True):
             body_lines.append("- Long-context arm: SKIPPED.")
         else:
-            top_n = int(extra.get("long_context_top_n_pages") or 0)
-            page_phrase = (
-                f"top-{top_n} of {extra.get('pages_per_question_label') or 'pages'}"
-                if top_n > 0
-                else f"all of {extra.get('pages_per_question_label') or 'pages'}"
-            )
             body_lines.append(
                 f"- Long-context arm (`{extra.get('native_arm_model') or '?'}`, "
-                f"{page_phrase} stuffed into prompt; per-page cap "
+                f"all 5 pages stuffed into prompt; per-page cap "
                 f"{extra.get('per_page_char_cap', 12_000):,} chars):"
             )
             body_lines.append(_arm_summary_lines(lc, indent="  "))
         if not active.get("surfsense", True):
             body_lines.append("- SurfSense arm: SKIPPED.")
         else:
-            scope_phrase = (
-                "whole SearchSpace"
-                if extra.get("no_mention_scope")
-                else f"per-question {extra.get('pages_per_question_label') or 'pages'}"
-            )
             body_lines.append(
                 f"- SurfSense arm (`{extra.get('provider_model', '?')}`, retrieval over "
-                f"{scope_phrase}):"
+                f"{'whole SearchSpace' if extra.get('no_mention_scope') else 'per-question 5 pages'}):"
             )
             body_lines.append(_arm_summary_lines(surf, indent="  "))
 
@@ -673,17 +628,9 @@ def _make_long_context_request(
     bench_dir: Path,
     max_tokens: int,
     per_page_char_cap: int,
-    *,
-    top_n_pages: int = 0,
 ) -> ArmRequest:
-    # The CRAG search_results list is already ranked top-K from the
-    # original web search at query_time; slicing the prefix is the
-    # honest "naive RAG: take the top-K results" baseline.
-    page_iter = q.page_filenames
-    if top_n_pages and top_n_pages > 0:
-        page_iter = page_iter[:top_n_pages]
     contexts: list[tuple[str, str]] = []
-    for fn in page_iter:
+    for fn in q.page_filenames:
         text = read_page_markdown(bench_dir, fn) or ""
         if not text.strip():
             continue
@@ -993,61 +940,4 @@ def _fmt(value: Any, ndigits: int) -> str:
         return "?"
 
 
-_TASK3_DESCRIPTION = (
-    "CRAG Task 3 (Meta KDD Cup 2024) — same 3 arms but the corpus per "
-    "question now has **50 candidate web pages** (vs 5 in Tasks 1 & 2). "
-    "The long-context arm uses only the top-5 (the realistic naive-RAG "
-    "baseline); SurfSense retrieves over all 50, where its rerank "
-    "becomes the actual contribution."
-)
-
-
-class CragTask3Benchmark(CragBenchmark):
-    """3-arm CRAG runner over Task 3 (50 pages per question).
-
-    Reuses the entire Task 1/2 runtime (grader, prompt, metrics,
-    reporting) — the only deltas are: the doc map filename, the
-    long-context arm's default page cap (5 instead of all 50), and
-    the ingest entrypoint (4-part archive instead of single bz2).
-    """
-
-    name: str = "crag_t3"
-    description: str = _TASK3_DESCRIPTION
-    doc_map_filename: str = "crag_t3_doc_map.jsonl"
-    default_long_context_top_n: int = 5
-    pages_per_question_label: str = "50 pages"
-    ingest_hint: str = (
-        "`python -m surfsense_evals ingest research crag_t3 --n-questions 50` "
-        "(after `python scripts/download_crag_task3.py`)"
-    )
-
-    async def ingest(self, ctx: RunContext, **opts: Any) -> None:
-        # Local import: keep dataset_task3's lazy-streaming module out
-        # of the import graph until someone actually wants Task 3.
-        from .ingest_task3 import run_ingest_task3
-
-        settings = IngestSettings.merge(_DEFAULT_INGEST_SETTINGS, opts)
-        await run_ingest_task3(
-            ctx,
-            n_questions=opts.get("n_questions"),
-            upload_batch_size=int(opts.get("upload_batch_size") or 16),
-            skip_upload=bool(opts.get("skip_upload", False)),
-            overwrite_extract=bool(opts.get("overwrite_extract", False)),
-            settings=settings,
-            sample_seed=int(opts.get("sample_seed") or 17),
-            parse_cap=opts.get("parse_cap"),
-        )
-
-    def add_run_args(self, parser: argparse.ArgumentParser) -> None:
-        super().add_run_args(parser)
-        parser.add_argument(
-            "--parse-cap", dest="parse_cap", type=int, default=None,
-            help=(
-                "(ingest only) Hard cap on rows parsed from the streaming "
-                "Task 3 archive before stratified sampling. Default: "
-                "max(2000, 10 * n_questions). Lower = less decompression."
-            ),
-        )
-
-
-__all__ = ["CragBenchmark", "CragRunnerQuestion", "CragTask3Benchmark"]
+__all__ = ["CragBenchmark", "CragRunnerQuestion"]
diff --git a/surfsense_evals/tests/suites/test_crag_dataset_task3.py b/surfsense_evals/tests/suites/test_crag_dataset_task3.py
deleted file mode 100644
index 123628350..000000000
--- a/surfsense_evals/tests/suites/test_crag_dataset_task3.py
+++ /dev/null
@@ -1,259 +0,0 @@
-"""Unit tests for CRAG Task 3 streaming dataset loader.
-
-We don't (and shouldn't) hit the real 7 GB upstream archive in
-unit tests. Instead we construct tiny tar.bz2 archives split across
-N parts and verify:
-
-* ``_MultiPartReader`` correctly stitches N files together.
-* The streaming path (multi → bz2 → tar → JSONL) yields parsed
-  ``CragQuestion`` rows with the right shape.
-* ``max_questions`` cap is honoured (early break, no greedy read).
-* ``parts_present`` correctly detects missing/empty parts.
-"""
-
-from __future__ import annotations
-
-import bz2
-import io
-import json
-import tarfile
-from pathlib import Path
-
-import pytest
-
-from surfsense_evals.suites.research.crag.dataset_task3 import (
-    _MultiPartReader,
-    iter_questions_task3,
-    parts_present,
-)
-
-
-# ---------------------------------------------------------------------------
-# Fixtures: build a tiny synthetic Task 3 archive
-# ---------------------------------------------------------------------------
-
-
-def _make_jsonl_payload(n_rows: int) -> bytes:
-    rows = []
-    for i in range(n_rows):
-        rows.append({
-            "interaction_id": f"int_{i:04d}",
-            "query_time": "2024-01-01 00:00:00",
-            "domain": ["finance", "music", "movie", "sports", "open"][i % 5],
-            "question_type": ["simple", "comparison", "aggregation", "multi-hop"][i % 4],
-            "static_or_dynamic": "static",
-            "popularity": "head",
-            "split": 0,
-            "query": f"Synthetic CRAG question {i}?",
-            "answer": f"answer-{i}",
-            "alt_ans": [f"alt-{i}-a", f"alt-{i}-b"],
-            "search_results": [
-                {
-                    "page_name": f"Page {j} for q{i}",
-                    "page_url": f"https://example.com/q{i}/p{j}",
-                    "page_snippet": "snippet",
-                    "page_result": f"<html><body><p>q{i} p{j} body</p></body></html>",
-                    "page_last_modified": "",
-                }
-                for j in range(50)
-            ],
-        })
-    return b"\n".join(json.dumps(r).encode("utf-8") for r in rows) + b"\n"
-
-
-def _make_tar_bz2(jsonl_bytes: bytes, *, member_name: str = "data.jsonl") -> bytes:
-    bio = io.BytesIO()
-    with bz2.BZ2File(bio, mode="wb") as bz:
-        with tarfile.open(fileobj=bz, mode="w") as tar:
-            info = tarfile.TarInfo(name=member_name)
-            info.size = len(jsonl_bytes)
-            tar.addfile(info, io.BytesIO(jsonl_bytes))
-    return bio.getvalue()
-
-
-def _make_tar_bz2_multi(shards: list[tuple[str, bytes]]) -> bytes:
-    """Build a tar.bz2 archive containing multiple JSONL shards.
-
-    Mirrors the real CRAG Task 3 layout: one tar with N JSONL members
-    named ``crag_task_3_dev_v4_{i}.jsonl`` (or whatever the caller
-    passes in).
-    """
-
-    bio = io.BytesIO()
-    with bz2.BZ2File(bio, mode="wb") as bz:
-        with tarfile.open(fileobj=bz, mode="w") as tar:
-            for name, payload in shards:
-                info = tarfile.TarInfo(name=name)
-                info.size = len(payload)
-                tar.addfile(info, io.BytesIO(payload))
-    return bio.getvalue()
-
-
-def _split_into_parts(blob: bytes, n_parts: int) -> list[bytes]:
-    """Split byte string into N roughly-equal chunks (last gets remainder)."""
-    chunk = max(1, len(blob) // n_parts)
-    parts = [blob[i * chunk : (i + 1) * chunk] for i in range(n_parts - 1)]
-    parts.append(blob[(n_parts - 1) * chunk :])
-    return parts
-
-
-@pytest.fixture
-def task3_parts_dir(tmp_path: Path) -> Path:
-    """A directory containing a 4-part synthetic CRAG Task 3 archive (12 rows)."""
-    blob = _make_tar_bz2(_make_jsonl_payload(12))
-    parts = _split_into_parts(blob, 4)
-    parts_dir = tmp_path / ".raw_cache"
-    parts_dir.mkdir()
-    for i, b in enumerate(parts, start=1):
-        (parts_dir / f"crag_task_3_dev_v4.tar.bz2.part{i}").write_bytes(b)
-    return parts_dir
-
-
-# ---------------------------------------------------------------------------
-# _MultiPartReader
-# ---------------------------------------------------------------------------
-
-
-class TestMultiPartReader:
-    def test_concatenates_parts_in_order(self, tmp_path: Path) -> None:
-        a = tmp_path / "a"
-        b = tmp_path / "b"
-        c = tmp_path / "c"
-        a.write_bytes(b"hello, ")
-        b.write_bytes(b"streaming ")
-        c.write_bytes(b"world!")
-        with _MultiPartReader([a, b, c]) as r:
-            assert r.read() == b"hello, streaming world!"
-
-    def test_read_n_crosses_part_boundary(self, tmp_path: Path) -> None:
-        a = tmp_path / "a"
-        b = tmp_path / "b"
-        a.write_bytes(b"AAA")
-        b.write_bytes(b"BBBB")
-        with _MultiPartReader([a, b]) as r:
-            # Read 5 bytes — straddles boundary between parts.
-            assert r.read(5) == b"AAABB"
-            assert r.read(5) == b"BB"
-            assert r.read(5) == b""
-
-    def test_close_is_idempotent(self, tmp_path: Path) -> None:
-        a = tmp_path / "a"
-        a.write_bytes(b"x")
-        r = _MultiPartReader([a])
-        r.close()
-        r.close()
-        with pytest.raises(ValueError):
-            r.read(1)
-
-    def test_missing_part_raises(self, tmp_path: Path) -> None:
-        with pytest.raises(FileNotFoundError):
-            _MultiPartReader([tmp_path / "does-not-exist"])
-
-    def test_empty_paths_raises(self) -> None:
-        with pytest.raises(ValueError):
-            _MultiPartReader([])
-
-
-# ---------------------------------------------------------------------------
-# iter_questions_task3
-# ---------------------------------------------------------------------------
-
-
-@pytest.fixture
-def task3_multi_shard_dir(tmp_path: Path) -> Path:
-    """A 4-part archive whose tar contains 3 JSONL shards (4 + 4 + 4 rows)."""
-    payload_a = _make_jsonl_payload(4)
-    payload_b = _make_jsonl_payload(4)
-    payload_c = _make_jsonl_payload(4)
-    blob = _make_tar_bz2_multi([
-        ("crag_task_3_dev_v4_0.jsonl", payload_a),
-        ("crag_task_3_dev_v4_1.jsonl", payload_b),
-        ("crag_task_3_dev_v4_2.jsonl", payload_c),
-    ])
-    parts = _split_into_parts(blob, 4)
-    parts_dir = tmp_path / ".raw_cache"
-    parts_dir.mkdir()
-    for i, b in enumerate(parts, start=1):
-        (parts_dir / f"crag_task_3_dev_v4.tar.bz2.part{i}").write_bytes(b)
-    return parts_dir
-
-
-class TestIterQuestionsTask3:
-    def test_streams_full_archive(self, task3_parts_dir: Path) -> None:
-        questions = iter_questions_task3(task3_parts_dir)
-        assert len(questions) == 12
-        # All questions get the T3_ prefix and 50 pages each.
-        assert all(q.qid.startswith("T3_") for q in questions)
-        assert all(len(q.pages) == 50 for q in questions)
-        # Schema fields preserved.
-        first = questions[0]
-        assert first.query == "Synthetic CRAG question 0?"
-        assert first.gold_answer == "answer-0"
-        assert first.domain == "finance"
-        assert "alt-0-a" in first.alt_answers
-
-    def test_max_questions_caps_early(self, task3_parts_dir: Path) -> None:
-        questions = iter_questions_task3(task3_parts_dir, max_questions=3)
-        assert len(questions) == 3
-        # Sequential indices 0..2 — we don't skip rows.
-        assert [q.raw_index for q in questions] == [0, 1, 2]
-
-    def test_streams_multi_shard_archive(self, task3_multi_shard_dir: Path) -> None:
-        # Three shards × four rows each = twelve rows total.
-        questions = iter_questions_task3(task3_multi_shard_dir)
-        assert len(questions) == 12
-        # raw_index increments monotonically across shards.
-        assert [q.raw_index for q in questions] == list(range(12))
-        # qids are unique and sequential across shards.
-        assert len({q.qid for q in questions}) == 12
-
-    def test_max_questions_short_circuits_first_shard(self, task3_multi_shard_dir: Path) -> None:
-        # Cap < shard size — shouldn't touch shards 1 or 2 at all.
-        questions = iter_questions_task3(task3_multi_shard_dir, max_questions=2)
-        assert len(questions) == 2
-        # Both come from shard 0 (raw_index 0, 1).
-        assert [q.raw_index for q in questions] == [0, 1]
-
-    def test_max_questions_spans_shards(self, task3_multi_shard_dir: Path) -> None:
-        # Cap = 6 → all 4 from shard 0 + first 2 from shard 1.
-        questions = iter_questions_task3(task3_multi_shard_dir, max_questions=6)
-        assert len(questions) == 6
-        assert [q.raw_index for q in questions] == [0, 1, 2, 3, 4, 5]
-
-    def test_raises_when_no_jsonl_member(self, tmp_path: Path) -> None:
-        # Archive containing a non-jsonl member.
-        bio = io.BytesIO()
-        with bz2.BZ2File(bio, mode="wb") as bz:
-            with tarfile.open(fileobj=bz, mode="w") as tar:
-                info = tarfile.TarInfo(name="README.md")
-                payload = b"not jsonl"
-                info.size = len(payload)
-                tar.addfile(info, io.BytesIO(payload))
-        parts_dir = tmp_path / ".raw_cache"
-        parts_dir.mkdir()
-        for i, name in enumerate(
-            ("part1", "part2", "part3", "part4"), start=1,
-        ):
-            half = len(bio.getvalue()) // 4
-            chunk = bio.getvalue()[(i - 1) * half : i * half if i < 4 else len(bio.getvalue())]
-            (parts_dir / f"crag_task_3_dev_v4.tar.bz2.{name}").write_bytes(chunk)
-        with pytest.raises(RuntimeError, match="No JSONL member"):
-            iter_questions_task3(parts_dir)
-
-
-# ---------------------------------------------------------------------------
-# parts_present
-# ---------------------------------------------------------------------------
-
-
-class TestPartsPresent:
-    def test_all_present(self, task3_parts_dir: Path) -> None:
-        assert parts_present(task3_parts_dir) is True
-
-    def test_one_missing(self, task3_parts_dir: Path) -> None:
-        (task3_parts_dir / "crag_task_3_dev_v4.tar.bz2.part2").unlink()
-        assert parts_present(task3_parts_dir) is False
-
-    def test_one_empty(self, task3_parts_dir: Path) -> None:
-        (task3_parts_dir / "crag_task_3_dev_v4.tar.bz2.part3").write_bytes(b"")
-        assert parts_present(task3_parts_dir) is False
diff --git a/surfsense_evals/uv.lock b/surfsense_evals/uv.lock
index 6c4fd7283..d2af42162 100644
--- a/surfsense_evals/uv.lock
+++ b/surfsense_evals/uv.lock
@@ -117,6 +117,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490 },
 ]
 
+[[package]]
+name = "aiosqlite"
+version = "0.22.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4e/8a/64761f4005f17809769d23e518d915db74e6310474e733e3593cfc854ef1/aiosqlite-0.22.1.tar.gz", hash = "sha256:043e0bd78d32888c0a9ca90fc788b38796843360c855a7262a532813133a0650", size = 14821 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/b7/e3bf5133d697a08128598c8d0abc5e16377b51465a33756de24fa7dee953/aiosqlite-0.22.1-py3-none-any.whl", hash = "sha256:21c002eb13823fad740196c5a2e9d8e62f6243bd9e7e4a1f87fb5e44ecb4fceb", size = 17405 },
+]
+
 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
@@ -157,6 +166,59 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548 },
 ]
 
+[[package]]
+name = "azure-ai-documentintelligence"
+version = "1.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "azure-core" },
+    { name = "isodate" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/44/7b/8115cd713e2caa5e44def85f2b7ebd02a74ae74d7113ba20bdd41fd6dd80/azure_ai_documentintelligence-1.0.2.tar.gz", hash = "sha256:4d75a2513f2839365ebabc0e0e1772f5601b3a8c9a71e75da12440da13b63484", size = 170940 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/75/c9ec040f23082f54ffb1977ff8f364c2d21c79a640a13d1c1809e7fd6b1a/azure_ai_documentintelligence-1.0.2-py3-none-any.whl", hash = "sha256:e1fb446abbdeccc9759d897898a0fe13141ed29f9ad11fc705f951925822ed59", size = 106005 },
+]
+
+[[package]]
+name = "azure-core"
+version = "1.41.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "requests" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a6/f3/b416179e408990df5db0d516283022dde0f5d0111d98c1a848e41853e81c/azure_core-1.41.0.tar.gz", hash = "sha256:f46ff5dfcd230f25cf1c19e8a34b8dc08a337b2503e268bb600a16c00db8ad5a", size = 381042 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5b/db/325c6d7312d2200251c52323878281045aaffcb5586612296484e4280eaa/azure_core-1.41.0-py3-none-any.whl", hash = "sha256:522b4011e8180b1a3dcd2024396a4e7fe9ac37fb8597db47163d230b5efe892d", size = 220920 },
+]
+
+[[package]]
+name = "babel"
+version = "2.18.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845 },
+]
+
+[[package]]
+name = "banks"
+version = "2.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "deprecated" },
+    { name = "filetype" },
+    { name = "griffe" },
+    { name = "jinja2" },
+    { name = "platformdirs" },
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/bd/51/08fb68d23f4b0f6256fe85dc86e9576941550f890b079352fba719e07b39/banks-2.4.2.tar.gz", hash = "sha256:cda6013bd377ea7b701933578bfb9370fc21ad70bc13cedfc3f5cb2c034ca3dc", size = 188633 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/b6/8dc5477681b782e2f99de703e7a99828883364b9e03a60d3e2c47053d56a/banks-2.4.2-py3-none-any.whl", hash = "sha256:5fe407cc48c101f3e13d1cf732b83b8246003337612f13c0705d2e81f6faffb7", size = 35050 },
+]
+
 [[package]]
 name = "certifi"
 version = "2026.4.22"
@@ -260,6 +322,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
 ]
 
+[[package]]
+name = "courlan"
+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "babel" },
+    { name = "tld" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/6f/54/6d6ceeff4bed42e7a10d6064d35ee43a810e7b3e8beb4abeae8cff4713ae/courlan-1.3.2.tar.gz", hash = "sha256:0b66f4db3a9c39a6e22dd247c72cfaa57d68ea660e94bb2c84ec7db8712af190", size = 206382 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8e/ca/6a667ccbe649856dcd3458bab80b016681b274399d6211187c6ab969fc50/courlan-1.3.2-py3-none-any.whl", hash = "sha256:d0dab52cf5b5b1000ee2839fbc2837e93b2514d3cb5bb61ae158a55b7a04c6be", size = 33848 },
+]
+
+[[package]]
+name = "dataclasses-json"
+version = "0.6.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "marshmallow" },
+    { name = "typing-inspect" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/64/a4/f71d9cf3a5ac257c993b5ca3f93df5f7fb395c725e7f1e6479d2514173c3/dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0", size = 32227 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/be/d0d44e092656fe7a06b55e6103cbce807cdbdee17884a5367c68c9860853/dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a", size = 28686 },
+]
+
 [[package]]
 name = "datasets"
 version = "4.8.5"
@@ -285,6 +374,33 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/65/99/00f3196036501b53032c4b1ab8337a0b978dee832ed276dae3815df4e8b5/datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff", size = 528973 },
 ]
 
+[[package]]
+name = "dateparser"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "python-dateutil" },
+    { name = "pytz" },
+    { name = "regex" },
+    { name = "tzlocal" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/46/2d/a0ccdb78788064fa0dc901b8524e50615c42be1d78b78d646d0b28d09180/dateparser-1.4.0.tar.gz", hash = "sha256:97a21840d5ecdf7630c584f673338a5afac5dfe84f647baf4d7e8df98f9354a4", size = 321512 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b4/0b/3c3bb7cbe757279e693a0be6049048012f794d01f81099609ecd53b899f0/dateparser-1.4.0-py3-none-any.whl", hash = "sha256:7902b8e85d603494bf70a5a0b1decdddb2270b9c6e6b2bc8a57b93476c0df378", size = 300379 },
+]
+
+[[package]]
+name = "deprecated"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/49/85/12f0a49a7c4ffb70572b6c2ef13c90c88fd190debda93b23f026b25f9634/deprecated-1.3.1.tar.gz", hash = "sha256:b1b50e0ff0c1fddaa5708a2c6b0a6588bb09b892825ab2b214ac9ea9d92a5223", size = 2932523 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/84/d0/205d54408c08b13550c733c4b85429e7ead111c7f0014309637425520a9a/deprecated-1.3.1-py2.py3-none-any.whl", hash = "sha256:597bfef186b6f60181535a29fbe44865ce137a5079f295b479886c82729d5f3f", size = 11298 },
+]
+
 [[package]]
 name = "dill"
 version = "0.4.1"
@@ -294,6 +410,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019 },
 ]
 
+[[package]]
+name = "dirtyjson"
+version = "1.0.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/db/04/d24f6e645ad82ba0ef092fa17d9ef7a21953781663648a01c9371d9e8e98/dirtyjson-1.0.8.tar.gz", hash = "sha256:90ca4a18f3ff30ce849d100dcf4a003953c79d3a2348ef056f1d9c22231a25fd", size = 30782 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/68/69/1bcf70f81de1b4a9f21b3a62ec0c83bdff991c88d6cc2267d02408457e88/dirtyjson-1.0.8-py3-none-any.whl", hash = "sha256:125e27248435a58acace26d5c2c4c11a1c0de0a9c5124c5a94ba78e517d74f53", size = 25197 },
+]
+
 [[package]]
 name = "filelock"
 version = "3.29.0"
@@ -303,6 +428,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812 },
 ]
 
+[[package]]
+name = "filetype"
+version = "1.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bb/29/745f7d30d47fe0f251d3ad3dc2978a23141917661998763bebb6da007eb1/filetype-1.2.0.tar.gz", hash = "sha256:66b56cd6474bf41d8c54660347d37afcc3f7d1970648de365c102ef77548aadb", size = 998020 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/79/1b8fa1bb3568781e84c9200f951c735f3f157429f44be0495da55894d620/filetype-1.2.0-py2.py3-none-any.whl", hash = "sha256:7ce71b6880181241cf7ac8697a2f1eb6a8bd9b429f7ad6d27b8db9ba5f1c2d25", size = 19970 },
+]
+
 [[package]]
 name = "frozenlist"
 version = "1.8.0"
@@ -406,6 +540,88 @@ http = [
     { name = "aiohttp" },
 ]
 
+[[package]]
+name = "greenlet"
+version = "3.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3c/3f/dbf99fb14bfeb88c28f16729215478c0e265cacd6dc22270c8f31bb6892f/greenlet-3.5.0.tar.gz", hash = "sha256:d419647372241bc68e957bf38d5c1f98852155e4146bd1e4121adea81f4f01e4", size = 196995 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/32/f2ce6d4cac3e55bc6173f92dbe627e782e1850f89d986c3606feb63aafa7/greenlet-3.5.0-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:db2910d3c809444e0a20147361f343fe2798e106af8d9d8506f5305302655a9f", size = 286228 },
+    { url = "https://files.pythonhosted.org/packages/b7/aa/caed9e5adf742315fc7be2a84196373aab4816e540e38ba0d76cb7584d68/greenlet-3.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3ec9ea74e7268ace7f9aab1b1a4e730193fc661b39a993cd91c606c32d4a3628", size = 601775 },
+    { url = "https://files.pythonhosted.org/packages/c7/af/90ae08497400a941595d12774447f752d3dfe0fbb012e35b76bc5c0ff37e/greenlet-3.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54d243512da35485fc7a6bf3c178fdda6327a9d6506fcdd62b1abd1e41b2927b", size = 614436 },
+    { url = "https://files.pythonhosted.org/packages/3f/e9/4eeadf8cb3403ac274245ba75f07844abc7fa5f6787583fc9156ba741e0f/greenlet-3.5.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:41353ec2ecedf7aa8f682753a41919f8718031a6edac46b8d3dc7ed9e1ceb136", size = 620610 },
+    { url = "https://files.pythonhosted.org/packages/2b/e0/2e13df68f367e2f9960616927d60857dd7e56aaadd59a47c644216b2f920/greenlet-3.5.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d280a7f5c331622c69f97eb167f33577ff2d1df282c41cd15907fc0a3ca198c", size = 611388 },
+    { url = "https://files.pythonhosted.org/packages/ee/ef/f913b3c0eb7d26d86a2401c5e1546c9d46b657efee724b06f6f4ac5d8824/greenlet-3.5.0-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:58c1c374fe2b3d852f9b6b11a7dff4c85404e51b9a596fd9e89cf904eb09866d", size = 422775 },
+    { url = "https://files.pythonhosted.org/packages/82/f7/393c64055132ac0d488ef6be549253b7e6274194863967ddc0bc8f5b87b8/greenlet-3.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1eb67d5adefb5bd2e182d42678a328979a209e4e82eb93575708185d31d1f588", size = 1570768 },
+    { url = "https://files.pythonhosted.org/packages/b8/4b/eaf7735253522cf56d1b74d672a58f54fc114702ceaf05def59aae72f6e1/greenlet-3.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2628d6c86f6cb0cb45e0c3c54058bbec559f57eaae699447748cb3928150577e", size = 1635983 },
+    { url = "https://files.pythonhosted.org/packages/4c/fe/4fb3a0805bd5165da5ebf858da7cc01cce8061674106d2cf5bdab32cbfde/greenlet-3.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:d4d9f0624c775f2dfc56ba54d515a8c771044346852a918b405914f6b19d7fd8", size = 238840 },
+    { url = "https://files.pythonhosted.org/packages/cb/cb/baa584cb00532126ffe12d9787db0a60c5a4f55c27bfe2666df5d4c30a32/greenlet-3.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:83ed9f27f1680b50e89f40f6df348a290ea234b249a4003d366663a12eab94f2", size = 235615 },
+    { url = "https://files.pythonhosted.org/packages/0c/58/fc576f99037ce19c5aa16628e4c3226b6d1419f72a62c79f5f40576e6eb3/greenlet-3.5.0-cp313-cp313-macosx_11_0_universal2.whl", hash = "sha256:5a5ed18de6a0f6cc7087f1563f6bd93fc7df1c19165ca01e9bde5a5dc281d106", size = 285066 },
+    { url = "https://files.pythonhosted.org/packages/4a/ba/b28ddbe6bfad6a8ac196ef0e8cff37bc65b79735995b9e410923fffeeb70/greenlet-3.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a717fbc46d8a354fa675f7c1e813485b6ba3885f9bef0cd56e5ba27d758ff5b", size = 604414 },
+    { url = "https://files.pythonhosted.org/packages/09/06/4b69f8f0b67603a8be2790e55107a190b376f2627fe0eaf5695d85ffb3cd/greenlet-3.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ddc090c5c1792b10246a78e8c2163ebbe04cf877f9d785c230a7b27b39ad038e", size = 617349 },
+    { url = "https://files.pythonhosted.org/packages/6a/15/a643b4ecd09969e30b8a150d5919960caae0abe4f5af75ab040b1ab85e78/greenlet-3.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:4964101b8585c144cbda5532b1aa644255126c08a265dae90c16e7a0e63aaa9d", size = 623234 },
+    { url = "https://files.pythonhosted.org/packages/8a/17/a3918541fd0ddefe024a69de6d16aa7b46d36ac19562adaa63c7fa180eff/greenlet-3.5.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2094acd54b272cb6eae8c03dd87b3fa1820a4cef18d6889c378d503500a1dc13", size = 613927 },
+    { url = "https://files.pythonhosted.org/packages/77/18/3b13d5ef1275b0ffaf933b05efa21408ac4ca95823c7411d79682e4fdcff/greenlet-3.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:7022615368890680e67b9965d33f5773aade330d5343bbe25560135aaa849eae", size = 425243 },
+    { url = "https://files.pythonhosted.org/packages/ee/e1/bd0af6213c7dd33175d8a462d4c1fe1175124ebed4855bc1475a5b5242c2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5e05ba267789ea87b5a155cf0e810b1ab88bf18e9e8740813945ceb8ee4350ba", size = 1570893 },
+    { url = "https://files.pythonhosted.org/packages/9b/2a/0789702f864f5382cb476b93d7a9c823c10472658102ccd65f415747d2e2/greenlet-3.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0ecec963079cd58cbd14723582384f11f166fd58883c15dcbfb342e0bc9b5846", size = 1636060 },
+    { url = "https://files.pythonhosted.org/packages/b2/8f/22bf9df92bbff0eb07842b60f7e63bf7675a9742df628437a9f02d09137f/greenlet-3.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:728d9667d8f2f586644b748dbd9bb67e50d6a9381767d1357714ea6825bb3bf5", size = 238740 },
+    { url = "https://files.pythonhosted.org/packages/b6/b7/9c5c3d653bd4ff614277c049ac676422e2c557db47b4fe43e6313fc005dc/greenlet-3.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:47422135b1d308c14b2c6e758beedb1acd33bb91679f5670edf77bf46244722b", size = 235525 },
+    { url = "https://files.pythonhosted.org/packages/94/5e/a70f31e3e8d961c4ce589c15b28e4225d63704e431a23932a3808cbcc867/greenlet-3.5.0-cp314-cp314-macosx_11_0_universal2.whl", hash = "sha256:f35807464c4c58c55f0d31dfa83c541a5615d825c2fe3d2b95360cf7c4e3c0a8", size = 285564 },
+    { url = "https://files.pythonhosted.org/packages/af/a6/046c0a28e21833e4086918218cfb3d8bed51c075a1b700f20b9d7861c0f4/greenlet-3.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55fa7ea52771be44af0de27d8b80c02cd18c2c3cddde6c847ecebdf72418b6a1", size = 651166 },
+    { url = "https://files.pythonhosted.org/packages/47/f8/4af27f71c5ff32a7fbc516adb46370d9c4ae2bc7bd3dc7d066ac542b4b15/greenlet-3.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a97e4821aa710603f94de0da25f25096454d78ffdace5dc77f3a006bc01abba3", size = 663792 },
+    { url = "https://files.pythonhosted.org/packages/fb/89/2dadb89793c37ee8b4c237857188293e9060dc085f19845c292e00f8e091/greenlet-3.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bf2d8a80bec89ab46221ae45c5373d5ba0bd36c19aa8508e85c6cd7e5106cd37", size = 668086 },
+    { url = "https://files.pythonhosted.org/packages/a3/59/1bd6d7428d6ed9106efbb8c52310c60fd04f6672490f452aeaa3829aa436/greenlet-3.5.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8f52a464e4ed91780bdfbbdd2b97197f3accaa629b98c200f4dffada759f3ae7", size = 660933 },
+    { url = "https://files.pythonhosted.org/packages/82/35/75722be7e26a2af4cbd2dc35b0ed382dacf9394b7e75551f76ed1abe87f2/greenlet-3.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:1bae92a1dd94c5f9d9493c3a212dd874c202442047cf96446412c862feca83a2", size = 470799 },
+    { url = "https://files.pythonhosted.org/packages/83/e4/b903e5a5fae1e8a28cdd32a0cfbfd560b668c25b692f67768822ddc5f40f/greenlet-3.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:762612baf1161ccb8437c0161c668a688223cba28e1bf038f4eb47b13e39ccdf", size = 1618401 },
+    { url = "https://files.pythonhosted.org/packages/0e/e3/5ec408a329acb854fb607a122e1ee5fb3ff649f9a97952948a90803c0d8e/greenlet-3.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:57a43c6079a89713522bc4bcb9f75070ecf5d3dbad7792bfe42239362cbf2a16", size = 1682038 },
+    { url = "https://files.pythonhosted.org/packages/91/20/6b165108058767ee643c55c5c4904d591a830ee2b3c7dbd359828fbc829f/greenlet-3.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:3bc59be3945ae9750b9e7d45067d01ae3fe90ea5f9ade99239dabdd6e28a5033", size = 239835 },
+    { url = "https://files.pythonhosted.org/packages/4e/62/1c498375cee177b55d980c1db319f26470e5309e54698c8f8fc06c0fd539/greenlet-3.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:a96fcee45e03fe30a62669fd16ab5c9d3c172660d3085605cb1e2d1280d3c988", size = 236862 },
+    { url = "https://files.pythonhosted.org/packages/78/a8/4522939255bb5409af4e87132f915446bf3622c2c292d14d3c38d128ae82/greenlet-3.5.0-cp314-cp314t-macosx_11_0_universal2.whl", hash = "sha256:a10a732421ab4fec934783ce3e54763470d0181db6e3468f9103a275c3ed1853", size = 293614 },
+    { url = "https://files.pythonhosted.org/packages/15/5e/8744c52e2c027b5a8772a01561934c8835f869733e101f62075c60430340/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7fc391b1566f2907d17aaebe78f8855dc45675159a775fcf9e61f8ee0078e87f", size = 650723 },
+    { url = "https://files.pythonhosted.org/packages/00/ef/7b4c39c03cf46ceca512c5d3f914afd85aa30b2cc9a93015b0dd73e4be6c/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:680bd0e7ad5e8daa8a4aa89f68fd6adc834b8a8036dc256533f7e08f4a4b01f7", size = 656529 },
+    { url = "https://files.pythonhosted.org/packages/5f/5c/0602239503b124b70e39355cbdb39361ecfe65b87a5f2f63752c32f5286f/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1aa4ce8debcd4ea7fb2e150f3036588c41493d1d52c43538924ae1819003f4ce", size = 657015 },
+    { url = "https://files.pythonhosted.org/packages/0b/b5/c7768f352f5c010f92064d0063f987e7dc0cd290a6d92a34109015ce4aa1/greenlet-3.5.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ddb36c7d6c9c0a65f18c7258634e0c416c6ab59caac8c987b96f80c2ebda0112", size = 654364 },
+    { url = "https://files.pythonhosted.org/packages/38/51/8699f865f125dc952384cb432b0f7138aa4d8f2969a7d12d0df5b94d054d/greenlet-3.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:728a73687e39ae9ca34e4694cbf2f049d3fbc7174639468d0f67200a97d8f9e2", size = 488275 },
+    { url = "https://files.pythonhosted.org/packages/ef/d0/079ebe12e4b1fc758857ce5be1a5e73f06870f2101e52611d1e71925ce54/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e5ddf316ced87539144621453c3aef229575825fe60c604e62bedc4003f372b2", size = 1614204 },
+    { url = "https://files.pythonhosted.org/packages/6d/89/6c2fb63df3596552d20e58fb4d96669243388cf680cff222758812c7bfaa/greenlet-3.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4a448128607be0de65342dc9b31be7f948ef4cc0bc8832069350abefd310a8f2", size = 1675480 },
+    { url = "https://files.pythonhosted.org/packages/15/32/77ee8a6c1564fc345a491a4e85b3bf360e4cf26eac98c4532d2fdb96e01f/greenlet-3.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d60097128cb0a1cab9ea541186ea13cd7b847b8449a7787c2e2350da0cb82d86", size = 245324 },
+]
+
+[[package]]
+name = "griffe"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "griffecli" },
+    { name = "griffelib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4a/49/eb6d2935e27883af92c930ed40cc4c69bcd32c402be43b8ca4ab20510f67/griffe-2.0.2.tar.gz", hash = "sha256:c5d56326d159f274492e9bf93a9895cec101155d944caa66d0fc4e0c13751b92", size = 293757 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/c0/2bb018eecf9a83c68db9cd9fffd9dab25f102ad30ed869451046e46d1187/griffe-2.0.2-py3-none-any.whl", hash = "sha256:2b31816460aee1996af26050a1fc6927a2e5936486856707f55508e4c9b5960b", size = 5141 },
+]
+
+[[package]]
+name = "griffecli"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama" },
+    { name = "griffelib" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/79/e0/6a7d661d71bb043656a109b91d84a42b5342752542074ec83b16a6eb97f0/griffecli-2.0.2.tar.gz", hash = "sha256:40a1ad4181fc39685d025e119ae2c5b669acdc1f19b705fb9bf971f4e6f6dffb", size = 56281 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2e/e8/90d93356c88ac34c20cb5edffca68138df55ca9bbd1a06eccfbcec8fdbe5/griffecli-2.0.2-py3-none-any.whl", hash = "sha256:0d44d39e59afa81e288a3e1c3bf352cc4fa537483326ac06b8bb6a51fd8303a0", size = 9500 },
+]
+
+[[package]]
+name = "griffelib"
+version = "2.0.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/82/74f4a3310cdabfbb10da554c3a672847f1ed33c6f61dd472681ce7f1fe67/griffelib-2.0.2.tar.gz", hash = "sha256:3cf20b3bc470e83763ffbf236e0076b1211bac1bc67de13daf494640f2de707e", size = 166461 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/8c/c9138d881c79aa0ea9ed83cbd58d5ca75624378b38cee225dcf5c42cc91f/griffelib-2.0.2-py3-none-any.whl", hash = "sha256:925c857658fb1ba40c0772c37acbc2ab650bd794d9c1b9726922e36ea4117ea1", size = 142357 },
+]
+
 [[package]]
 name = "h11"
 version = "0.16.0"
@@ -447,6 +663,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/62/94/3b66b148778ee100dcfd69c2ca22b57b41b44d3063ceec934f209e9184ce/hf_xet-1.5.0-cp37-abi3-win_arm64.whl", hash = "sha256:b6c9df403040248c76d808d3e047d64db2d923bae593eb244c41e425cf6cd7be", size = 3806916 },
 ]
 
+[[package]]
+name = "htmldate"
+version = "1.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "charset-normalizer" },
+    { name = "dateparser" },
+    { name = "lxml" },
+    { name = "python-dateutil" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/10/ead9dabc999f353c3aa5d0dc0835b1e355215a5ecb489a7f4ef2ddad5e33/htmldate-1.9.4.tar.gz", hash = "sha256:1129063e02dd0354b74264de71e950c0c3fcee191178321418ccad2074cc8ed0", size = 44690 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/bd/adfcdaaad5805c0c5156aeefd64c1e868c05e9c1cd6fd21751f168cd88c7/htmldate-1.9.4-py3-none-any.whl", hash = "sha256:1b94bcc4e08232a5b692159903acf95548b6a7492dddca5bb123d89d6325921c", size = 31558 },
+]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -522,6 +754,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484 },
 ]
 
+[[package]]
+name = "isodate"
+version = "0.7.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/54/4d/e940025e2ce31a8ce1202635910747e5a87cc3a6a6bb2d00973375014749/isodate-0.7.2.tar.gz", hash = "sha256:4cd1aa0f43ca76f4a6c6c0292a85f40b35ec2e43e315b59f06e6d32171a953e6", size = 29705 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/15/aa/0aca39a37d3c7eb941ba736ede56d689e7be91cab5d9ca846bde3999eba6/isodate-0.7.2-py3-none-any.whl", hash = "sha256:28009937d8031054830160fce6d409ed342816b543597cece116d966c6d99e15", size = 22320 },
+]
+
+[[package]]
+name = "jinja2"
+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899 },
+]
+
 [[package]]
 name = "joblib"
 version = "1.5.3"
@@ -531,6 +784,214 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071 },
 ]
 
+[[package]]
+name = "justext"
+version = "3.0.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "lxml", extra = ["html-clean"] },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/49/f3/45890c1b314f0d04e19c1c83d534e611513150939a7cf039664d9ab1e649/justext-3.0.2.tar.gz", hash = "sha256:13496a450c44c4cd5b5a75a5efcd9996066d2a189794ea99a49949685a0beb05", size = 828521 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f2/ac/52f4e86d1924a7fc05af3aeb34488570eccc39b4af90530dd6acecdf16b5/justext-3.0.2-py2.py3-none-any.whl", hash = "sha256:62b1c562b15c3c6265e121cc070874243a443bfd53060e869393f09d6b6cc9a7", size = 837940 },
+]
+
+[[package]]
+name = "llama-cloud"
+version = "0.1.46"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "httpx" },
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/40/f3/f4d6520f8d546e6c5a02f6ebeed5c09774a074b8d2c24ad559ace97a56a6/llama_cloud-0.1.46.tar.gz", hash = "sha256:e86f8791c053590d70cc59e0fc13ce72f9b681a8e658bc61df86d0285288d8ee", size = 127752 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c4/3a/6caaea28c8c804add33c91d356ed7d5a5412d6c9598e1450af95a15e0bcd/llama_cloud-0.1.46-py3-none-any.whl", hash = "sha256:6c6546c09c04a038c86d84d42f00eae8fd3bff49991ad3aab844bd866ecdf352", size = 361989 },
+]
+
+[[package]]
+name = "llama-cloud-services"
+version = "0.6.94"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "llama-cloud" },
+    { name = "llama-index-core" },
+    { name = "packaging" },
+    { name = "platformdirs" },
+    { name = "pydantic" },
+    { name = "python-dotenv" },
+    { name = "tenacity" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d3/91/c3c94a58c44d0a12e0df2d5038b188fc283877f56cf2f6c41c60f43258e6/llama_cloud_services-0.6.94.tar.gz", hash = "sha256:127b8440d3d3a964d0c4b3f5fe7fcac3ead482f7645971cc8ae30768dcf63306", size = 64114 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/14/ab/876486e4f1c137cfeca8f876abd18eeec35a66a0fd8adb15afba7b28aa8c/llama_cloud_services-0.6.94-py3-none-any.whl", hash = "sha256:ac89785f3689d71298511f751bcf4ca16952a616bd75ff06e0ff164f04b0775b", size = 77098 },
+]
+
+[[package]]
+name = "llama-index-core"
+version = "0.14.21"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+    { name = "aiosqlite" },
+    { name = "banks" },
+    { name = "dataclasses-json" },
+    { name = "deprecated" },
+    { name = "dirtyjson" },
+    { name = "filetype" },
+    { name = "fsspec" },
+    { name = "httpx" },
+    { name = "llama-index-workflows" },
+    { name = "nest-asyncio" },
+    { name = "networkx" },
+    { name = "nltk" },
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "platformdirs" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "requests" },
+    { name = "setuptools" },
+    { name = "sqlalchemy", extra = ["asyncio"] },
+    { name = "tenacity" },
+    { name = "tiktoken" },
+    { name = "tinytag" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
+    { name = "typing-inspect" },
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7c/43/d6d2a368865e68c25d3400c017fb772daab71427f08c4e36c591f729dbc3/llama_index_core-0.14.21.tar.gz", hash = "sha256:29706defbe2f429d28330a4eea010f9d92d42db92539382f8c800e19590cae45", size = 11581087 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/23/55ec5f35a5c7f35b60d3928bcd2e867076440036a280cf4d07481719c249/llama_index_core-0.14.21-py3-none-any.whl", hash = "sha256:4a807d31e54d066068e076eb4d066efbf95e2d2a00dcbe0eba3d9340a04cad42", size = 11916624 },
+]
+
+[[package]]
+name = "llama-index-instrumentation"
+version = "0.5.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "deprecated" },
+    { name = "pydantic" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/4e/d0/671b23ccff255c9bce132a84ffd5a6f4541ceefdeab9c1786b08c9722f2e/llama_index_instrumentation-0.5.0.tar.gz", hash = "sha256:eeb724648b25d149de882a5ac9e21c5acb1ce780da214bda2b075341af29ad8e", size = 43831 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/45/6dcaccef44e541ffa138e4b45e33e0d40ab2a7d845338483954fcf77bc75/llama_index_instrumentation-0.5.0-py3-none-any.whl", hash = "sha256:aaab83cddd9dd434278891012d8995f47a3bc7ed1736a371db90965348c56a21", size = 16444 },
+]
+
+[[package]]
+name = "llama-index-workflows"
+version = "2.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "llama-index-instrumentation" },
+    { name = "pydantic" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c4/ec/05f3db99a2e6e252e3939e7751cad2fb1322dc6d32f4cf5c795cf7ddcad3/llama_index_workflows-2.20.0.tar.gz", hash = "sha256:df2760fea9e100c97a4e919d255461e344413acac4382d17d8217337806e4772", size = 97410 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/5f/385231406d777cb4b608fd8ebe3577dbd90962770717181e6b91b44fb1b8/llama_index_workflows-2.20.0-py3-none-any.whl", hash = "sha256:36f6b6ace77f837d9907078aea7e830251afe96a58daecff5ed090c88c55095d", size = 121238 },
+]
+
+[[package]]
+name = "lxml"
+version = "6.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/28/30/9abc9e34c657c33834eaf6cd02124c61bdf5944d802aa48e69be8da3585d/lxml-6.1.0.tar.gz", hash = "sha256:bfd57d8008c4965709a919c3e9a98f76c2c7cb319086b3d26858250620023b13", size = 4197006 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/d4/9326838b59dc36dfae42eec9656b97520f9997eee1de47b8316aaeed169c/lxml-6.1.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d2f17a16cd8751e8eb233a7e41aecdf8e511712e00088bf9be455f604cd0d28d", size = 8570663 },
+    { url = "https://files.pythonhosted.org/packages/d8/a4/053745ce1f8303ccbb788b86c0db3a91b973675cefc42566a188637b7c40/lxml-6.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f0cea5b1d3e6e77d71bd2b9972eb2446221a69dc52bb0b9c3c6f6e5700592d93", size = 4624024 },
+    { url = "https://files.pythonhosted.org/packages/90/97/a517944b20f8fd0932ad2109482bee4e29fe721416387a363306667941f6/lxml-6.1.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc46da94826188ed45cb53bd8e3fc076ae22675aea2087843d4735627f867c6d", size = 4930895 },
+    { url = "https://files.pythonhosted.org/packages/94/7c/e08a970727d556caa040a44773c7b7e3ad0f0d73dedc863543e9a8b931f2/lxml-6.1.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9147d8e386ec3b82c3b15d88927f734f565b0aaadef7def562b853adca45784a", size = 5093820 },
+    { url = "https://files.pythonhosted.org/packages/88/ee/2a5c2aa2c32016a226ca25d3e1056a8102ea6e1fe308bf50213586635400/lxml-6.1.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5715e0e28736a070f3f34a7ccc09e2fdcba0e3060abbcf61a1a5718ff6d6b105", size = 5005790 },
+    { url = "https://files.pythonhosted.org/packages/e3/38/a0db9be8f38ad6043ab9429487c128dd1d30f07956ef43040402f8da49e8/lxml-6.1.0-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4937460dc5df0cdd2f06a86c285c28afda06aefa3af949f9477d3e8df430c485", size = 5630827 },
+    { url = "https://files.pythonhosted.org/packages/31/ba/3c13d3fc24b7cacf675f808a3a1baabf43a30d0cd24c98f94548e9aa58eb/lxml-6.1.0-cp312-cp312-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bc783ee3147e60a25aa0445ea82b3e8aabb83b240f2b95d32cb75587ff781814", size = 5240445 },
+    { url = "https://files.pythonhosted.org/packages/55/ba/eeef4ccba09b2212fe239f46c1692a98db1878e0872ae320756488878a94/lxml-6.1.0-cp312-cp312-manylinux_2_28_i686.whl", hash = "sha256:40d9189f80075f2e1f88db21ef815a2b17b28adf8e50aaf5c789bfe737027f32", size = 5350121 },
+    { url = "https://files.pythonhosted.org/packages/7e/01/1da87c7b587c38d0cbe77a01aae3b9c1c49ed47d76918ef3db8fc151b1ca/lxml-6.1.0-cp312-cp312-manylinux_2_31_armv7l.whl", hash = "sha256:05b9b8787e35bec69e68daf4952b2e6dfcfb0db7ecf1a06f8cdfbbac4eb71aad", size = 4694949 },
+    { url = "https://files.pythonhosted.org/packages/a1/88/7db0fe66d5aaf128443ee1623dec3db1576f3e4c17751ec0ef5866468590/lxml-6.1.0-cp312-cp312-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0f0f08beb0182e3e9a86fae124b3c47a7b41b7b69b225e1377db983802404e54", size = 5243901 },
+    { url = "https://files.pythonhosted.org/packages/00/a8/1346726af7d1f6fca1f11223ba34001462b0a3660416986d37641708d57c/lxml-6.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:73becf6d8c81d4c76b1014dbd3584cb26d904492dcf73ca85dc8bff08dcd6d2d", size = 5048054 },
+    { url = "https://files.pythonhosted.org/packages/2e/b7/85057012f035d1a0c87e02f8c723ca3c3e6e0728bcf4cb62080b21b1c1e3/lxml-6.1.0-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:1ae225f66e5938f4fa29d37e009a3bb3b13032ac57eb4eb42afa44f6e4054e69", size = 4777324 },
+    { url = "https://files.pythonhosted.org/packages/75/6c/ad2f94a91073ef570f33718040e8e160d5fb93331cf1ab3ca1323f939e2d/lxml-6.1.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:690022c7fae793b0489aa68a658822cea83e0d5933781811cabbf5ea3bcfe73d", size = 5645702 },
+    { url = "https://files.pythonhosted.org/packages/3b/89/0bb6c0bd549c19004c60eea9dc554dd78fd647b72314ef25d460e0d208c6/lxml-6.1.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:63aeafc26aac0be8aff14af7871249e87ea1319be92090bfd632ec68e03b16a5", size = 5232901 },
+    { url = "https://files.pythonhosted.org/packages/a1/d9/d609a11fb567da9399f525193e2b49847b5a409cdebe737f06a8b7126bdc/lxml-6.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:264c605ab9c0e4aa1a679636f4582c4d3313700009fac3ec9c3412ed0d8f3e1d", size = 5261333 },
+    { url = "https://files.pythonhosted.org/packages/a6/3a/ac3f99ec8ac93089e7dd556f279e0d14c24de0a74a507e143a2e4b496e7c/lxml-6.1.0-cp312-cp312-win32.whl", hash = "sha256:56971379bc5ee8037c5a0f09fa88f66cdb7d37c3e38af3e45cf539f41131ac1f", size = 3596289 },
+    { url = "https://files.pythonhosted.org/packages/f2/a7/0a915557538593cb1bbeedcd40e13c7a261822c26fecbbdb71dad0c2f540/lxml-6.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:bba078de0031c219e5dd06cf3e6bf8fb8e6e64a77819b358f53bb132e3e03366", size = 3997059 },
+    { url = "https://files.pythonhosted.org/packages/92/96/a5dc078cf0126fbfbc35611d77ecd5da80054b5893e28fb213a5613b9e1d/lxml-6.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:c3592631e652afa34999a088f98ba7dfc7d6aff0d535c410bea77a71743f3819", size = 3659552 },
+    { url = "https://files.pythonhosted.org/packages/08/03/69347590f1cf4a6d5a4944bb6099e6d37f334784f16062234e1f892fdb1d/lxml-6.1.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a0092f2b107b69601adf562a57c956fbb596e05e3e6651cabd3054113b007e45", size = 8559689 },
+    { url = "https://files.pythonhosted.org/packages/3f/58/25e00bb40b185c974cfe156c110474d9a8a8390d5f7c92a4e328189bb60e/lxml-6.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:fc7140d7a7386e6b545d41b7358f4d02b656d4053f5fa6859f92f4b9c2572c4d", size = 4617892 },
+    { url = "https://files.pythonhosted.org/packages/f5/54/92ad98a94ac318dc4f97aaac22ff8d1b94212b2ae8af5b6e9b354bf825f7/lxml-6.1.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:419c58fc92cc3a2c3fa5f78c63dbf5da70c1fa9c1b25f25727ecee89a96c7de2", size = 4923489 },
+    { url = "https://files.pythonhosted.org/packages/15/3b/a20aecfab42bdf4f9b390590d345857ad3ffd7c51988d1c89c53a0c73faf/lxml-6.1.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:37fabd1452852636cf38ecdcc9dd5ca4bba7a35d6c53fa09725deeb894a87491", size = 5082162 },
+    { url = "https://files.pythonhosted.org/packages/45/26/2cdb3d281ac1bd175603e290cbe4bad6eff127c0f8de90bafd6f8548f0fd/lxml-6.1.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a2853c8b2170cc6cd54a6b4d50d2c1a8a7aeca201f23804b4898525c7a152cfc", size = 4993247 },
+    { url = "https://files.pythonhosted.org/packages/f6/05/d735aef963740022a08185c84821f689fc903acb3d50326e6b1e9886cc22/lxml-6.1.0-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:8e369cbd690e788c8d15e56222d91a09c6a417f49cbc543040cba0fe2e25a79e", size = 5613042 },
+    { url = "https://files.pythonhosted.org/packages/ee/b8/ead7c10efff731738c72e59ed6eb5791854879fbed7ae98781a12006263a/lxml-6.1.0-cp313-cp313-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e69aa6805905807186eb00e66c6d97a935c928275182eb02ee40ba00da9623b2", size = 5228304 },
+    { url = "https://files.pythonhosted.org/packages/6b/10/e9842d2ec322ea65f0a7270aa0315a53abed06058b88ef1b027f620e7a5f/lxml-6.1.0-cp313-cp313-manylinux_2_28_i686.whl", hash = "sha256:4bd1bdb8a9e0e2dd229de19b5f8aebac80e916921b4b2c6ef8a52bc131d0c1f9", size = 5341578 },
+    { url = "https://files.pythonhosted.org/packages/89/54/40d9403d7c2775fa7301d3ddd3464689bfe9ba71acc17dfff777071b4fdc/lxml-6.1.0-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:cbd7b79cdcb4986ad78a2662625882747f09db5e4cd7b2ae178a88c9c51b3dfe", size = 4700209 },
+    { url = "https://files.pythonhosted.org/packages/85/b2/bbdcc2cf45dfc7dfffef4fd97e5c47b15919b6a365247d95d6f684ef5e82/lxml-6.1.0-cp313-cp313-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:43e4d297f11080ec9d64a4b1ad7ac02b4484c9f0e2179d9c4ef78e886e747b88", size = 5232365 },
+    { url = "https://files.pythonhosted.org/packages/48/5a/b06875665e53aaba7127611a7bed3b7b9658e20b22bc2dd217a0b7ab0091/lxml-6.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:cc16682cc987a3da00aa56a3aa3075b08edb10d9b1e476938cfdbee8f3b67181", size = 5043654 },
+    { url = "https://files.pythonhosted.org/packages/e9/9c/e71a069d09641c1a7abeb30e693f828c7c90a41cbe3d650b2d734d876f85/lxml-6.1.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:d6d8efe71429635f0559579092bb5e60560d7b9115ee38c4adbea35632e7fa24", size = 4769326 },
+    { url = "https://files.pythonhosted.org/packages/cc/06/7a9cd84b3d4ed79adf35f874750abb697dec0b4a81a836037b36e47c091a/lxml-6.1.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e39ab3a28af7784e206d8606ec0e4bcad0190f63a492bca95e94e5a4aef7f6e", size = 5635879 },
+    { url = "https://files.pythonhosted.org/packages/cc/f0/9d57916befc1e54c451712c7ee48e9e74e80ae4d03bdce49914e0aee42cd/lxml-6.1.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:9eb667bf50856c4a58145f8ca2d5e5be160191e79eb9e30855a476191b3c3495", size = 5224048 },
+    { url = "https://files.pythonhosted.org/packages/99/75/90c4eefda0c08c92221fe0753db2d6699a4c628f76ff4465ec20dea84cc1/lxml-6.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7f4a77d6f7edf9230cee3e1f7f6764722a41604ee5681844f18db9a81ea0ec33", size = 5250241 },
+    { url = "https://files.pythonhosted.org/packages/5e/73/16596f7e4e38fa33084b9ccbccc22a15f82a290a055126f2c1541236d2ff/lxml-6.1.0-cp313-cp313-win32.whl", hash = "sha256:28902146ffbe5222df411c5d19e5352490122e14447e98cd118907ee3fd6ee62", size = 3596938 },
+    { url = "https://files.pythonhosted.org/packages/8e/63/981401c5680c1eb30893f00a19641ac80db5d1e7086c62cb4b13ed813038/lxml-6.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:4a1503c56e4e2b38dc76f2f2da7bae69670c0f1933e27cfa34b2fa5876410b16", size = 3995728 },
+    { url = "https://files.pythonhosted.org/packages/e7/e8/c358a38ac3e541d16a1b527e4e9cb78c0419b0506a070ace11777e5e8404/lxml-6.1.0-cp313-cp313-win_arm64.whl", hash = "sha256:e0af85773850417d994d019741239b901b22c6680206f46a34766926e466141d", size = 3658372 },
+    { url = "https://files.pythonhosted.org/packages/eb/45/cee4cf203ef0bab5c52afc118da61d6b460c928f2893d40023cfa27e0b80/lxml-6.1.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:ab863fd37458fed6456525f297d21239d987800c46e67da5ef04fc6b3dd93ac8", size = 8576713 },
+    { url = "https://files.pythonhosted.org/packages/8a/a7/eda05babeb7e046839204eaf254cd4d7c9130ce2bbf0d9e90ea41af5654d/lxml-6.1.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6fd8b1df8254ff4fd93fd31da1fc15770bde23ac045be9bb1f87425702f61cc9", size = 4623874 },
+    { url = "https://files.pythonhosted.org/packages/e7/e9/db5846de9b436b91890a62f29d80cd849ea17948a49bf532d5278ee69a9e/lxml-6.1.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:47024feaae386a92a146af0d2aeed65229bf6fff738e6a11dda6b0015fb8fd03", size = 4949535 },
+    { url = "https://files.pythonhosted.org/packages/5a/ba/0d3593373dcae1d68f40dc3c41a5a92f2544e68115eb2f62319a4c2a6500/lxml-6.1.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3f00972f84450204cd5d93a5395965e348956aaceaadec693a22ec743f8ae3eb", size = 5086881 },
+    { url = "https://files.pythonhosted.org/packages/43/76/759a7484539ad1af0d125a9afe9c3fb5f82a8779fd1f5f56319d9e4ea2fd/lxml-6.1.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97faa0860e13b05b15a51fb4986421ef7a30f0b3334061c416e0981e9450ca4c", size = 5031305 },
+    { url = "https://files.pythonhosted.org/packages/dc/b9/c1f0daf981a11e47636126901fd4ab82429e18c57aeb0fc3ad2940b42d8b/lxml-6.1.0-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:972a6451204798675407beaad97b868d0c733d9a74dafefc63120b81b8c2de28", size = 5647522 },
+    { url = "https://files.pythonhosted.org/packages/31/e6/1f533dcd205275363d9ba3511bcec52fa2df86abf8abe6a5f2c599f0dc31/lxml-6.1.0-cp314-cp314-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fe022f20bc4569ec66b63b3fb275a3d628d9d32da6326b2982584104db6d3086", size = 5239310 },
+    { url = "https://files.pythonhosted.org/packages/c3/8c/4175fb709c78a6e315ed814ed33be3defd8b8721067e70419a6cf6f971da/lxml-6.1.0-cp314-cp314-manylinux_2_28_i686.whl", hash = "sha256:75c4c7c619a744f972f4451bf5adf6d0fb00992a1ffc9fd78e13b0bc817cc99f", size = 5350799 },
+    { url = "https://files.pythonhosted.org/packages/fd/77/6ffdebc5994975f0dde4acb59761902bd9d9bb84422b9a0bd239a7da9ca8/lxml-6.1.0-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:3648f20d25102a22b6061c688beb3a805099ea4beb0a01ce62975d926944d292", size = 4697693 },
+    { url = "https://files.pythonhosted.org/packages/f8/f1/565f36bd5c73294602d48e04d23f81ff4c8736be6ba5e1d1ec670ac9be80/lxml-6.1.0-cp314-cp314-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:77b9f99b17cbf14026d1e618035077060fc7195dd940d025149f3e2e830fbfcb", size = 5250708 },
+    { url = "https://files.pythonhosted.org/packages/5a/11/a68ab9dd18c5c499404deb4005f4bc4e0e88e5b72cd755ad96efec81d18d/lxml-6.1.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:32662519149fd7a9db354175aa5e417d83485a8039b8aaa62f873ceee7ea4cad", size = 5084737 },
+    { url = "https://files.pythonhosted.org/packages/ab/78/e8f41e2c74f4af564e6a0348aea69fb6daaefa64bc071ef469823d22cc18/lxml-6.1.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:73d658216fc173cf2c939e90e07b941c5e12736b0bf6a99e7af95459cfe8eabb", size = 4737817 },
+    { url = "https://files.pythonhosted.org/packages/06/2d/aa4e117aa2ce2f3b35d9ff246be74a2f8e853baba5d2a92c64744474603a/lxml-6.1.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:ac4db068889f8772a4a698c5980ec302771bb545e10c4b095d4c8be26749616f", size = 5670753 },
+    { url = "https://files.pythonhosted.org/packages/08/f5/dd745d50c0409031dbfcc4881740542a01e54d6f0110bd420fa7782110b8/lxml-6.1.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:45e9dfbd1b661eb64ba0d4dbe762bd210c42d86dd1e5bd2bdf89d634231beb43", size = 5238071 },
+    { url = "https://files.pythonhosted.org/packages/3e/74/ad424f36d0340a904665867dab310a3f1f4c96ff4039698de83b77f44c1f/lxml-6.1.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:89e8d73d09ac696a5ba42ec69787913d53284f12092f651506779314f10ba585", size = 5264319 },
+    { url = "https://files.pythonhosted.org/packages/53/36/a15d8b3514ec889bfd6aa3609107fcb6c9189f8dc347f1c0b81eded8d87c/lxml-6.1.0-cp314-cp314-win32.whl", hash = "sha256:ebe33f4ec1b2de38ceb225a1749a2965855bffeef435ba93cd2d5d540783bf2f", size = 3657139 },
+    { url = "https://files.pythonhosted.org/packages/1a/a4/263ebb0710851a3c6c937180a9a86df1206fdfe53cc43005aa2237fd7736/lxml-6.1.0-cp314-cp314-win_amd64.whl", hash = "sha256:398443df51c538bd578529aa7e5f7afc6c292644174b47961f3bf87fe5741120", size = 4064195 },
+    { url = "https://files.pythonhosted.org/packages/80/68/2000f29d323b6c286de077ad20b429fc52272e44eae6d295467043e56012/lxml-6.1.0-cp314-cp314-win_arm64.whl", hash = "sha256:8c8984e1d8c4b3949e419158fda14d921ff703a9ed8a47236c6eb7a2b6cb4946", size = 3741870 },
+    { url = "https://files.pythonhosted.org/packages/30/e9/21383c7c8d43799f0da90224c0d7c921870d476ec9b3e01e1b2c0b8237c5/lxml-6.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1081dd10bc6fa437db2500e13993abf7cc30716d0a2f40e65abb935f02ec559c", size = 8827548 },
+    { url = "https://files.pythonhosted.org/packages/a5/01/c6bc11cd587030dd4f719f65c5657960649fe3e19196c844c75bf32cd0d6/lxml-6.1.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:dabecc48db5f42ba348d1f5d5afdc54c6c4cc758e676926c7cd327045749517d", size = 4735866 },
+    { url = "https://files.pythonhosted.org/packages/f3/01/757132fff5f4acf25463b5298f1a46099f3a94480b806547b29ce5e385de/lxml-6.1.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e3dd5fe19c9e0ac818a9c7f132a5e43c1339ec1cbbfecb1a938bd3a47875b7c9", size = 4969476 },
+    { url = "https://files.pythonhosted.org/packages/fd/fb/1bc8b9d27ed64be7c8903db6c89e74dc8c2cd9ec630a7462e4654316dc5b/lxml-6.1.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:9e7b0a4ca6dcc007a4cef00a761bba2dea959de4bd2df98f926b33c92ca5dfb9", size = 5103719 },
+    { url = "https://files.pythonhosted.org/packages/d5/e7/5bf82fa28133536a54601aae633b14988e89ed61d4c1eb6b899b023233aa/lxml-6.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d27bbe326c6b539c64b42638b18bc6003a8d88f76213a97ac9ed4f885efeab7", size = 5027890 },
+    { url = "https://files.pythonhosted.org/packages/2d/20/e048db5d4b4ea0366648aa595f26bb764b2670903fc585b87436d0a5032c/lxml-6.1.0-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c4e425db0c5445ef0ad56b0eec54f89b88b2d884656e536a90b2f52aecb4ca86", size = 5596008 },
+    { url = "https://files.pythonhosted.org/packages/9a/c2/d10807bc8da4824b39e5bd01b5d05c077b6fd01bd91584167edf6b269d22/lxml-6.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4b89b098105b8599dc57adac95d1813409ac476d3c948a498775d3d0c6124bfb", size = 5224451 },
+    { url = "https://files.pythonhosted.org/packages/3c/15/2ebea45bea427e7f0057e9ce7b2d62c5aba20c6b001cca89ed0aadb3ad41/lxml-6.1.0-cp314-cp314t-manylinux_2_28_i686.whl", hash = "sha256:c4a699432846df86cc3de502ee85f445ebad748a1c6021d445f3e514d2cd4b1c", size = 5312135 },
+    { url = "https://files.pythonhosted.org/packages/31/e2/87eeae151b0be2a308d49a7ec444ff3eb192b14251e62addb29d0bf3778f/lxml-6.1.0-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:30e7b2ed63b6c8e97cca8af048589a788ab5c9c905f36d9cf1c2bb549f450d2f", size = 4639126 },
+    { url = "https://files.pythonhosted.org/packages/a3/51/8a3f6a20902ad604dd746ec7b4000311b240d389dac5e9d95adefd349e0c/lxml-6.1.0-cp314-cp314t-manylinux_2_38_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:022981127642fe19866d2907d76241bb07ed21749601f727d5d5dd1ce5d1b773", size = 5232579 },
+    { url = "https://files.pythonhosted.org/packages/6d/d2/650d619bdbe048d2c3f2c31edb00e35670a5e2d65b4fe3b61bce37b19121/lxml-6.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:23cad0cc86046d4222f7f418910e46b89971c5a45d3c8abfad0f64b7b05e4a9b", size = 5084206 },
+    { url = "https://files.pythonhosted.org/packages/dd/8a/672ca1a3cbeabd1f511ca275a916c0514b747f4b85bdaae103b8fa92f307/lxml-6.1.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:21c3302068f50d1e8728c67c87ba92aa87043abee517aa2576cca1855326b405", size = 4758906 },
+    { url = "https://files.pythonhosted.org/packages/be/f1/ef4b691da85c916cb2feb1eec7414f678162798ac85e042fa164419ac05c/lxml-6.1.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:be10838781cb3be19251e276910cd508fe127e27c3242e50521521a0f3781690", size = 5620553 },
+    { url = "https://files.pythonhosted.org/packages/59/17/94e81def74107809755ac2782fdad4404420f1c92ca83433d117a6d5acf0/lxml-6.1.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2173a7bffe97667bbf0767f8a99e587740a8c56fdf3befac4b09cb29a80276fd", size = 5229458 },
+    { url = "https://files.pythonhosted.org/packages/21/55/c4be91b0f830a871fc1b0d730943d56013b683d4671d5198260e2eae722b/lxml-6.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c6854e9cf99c84beb004eecd7d3a3868ef1109bf2b1df92d7bc11e96a36c2180", size = 5247861 },
+    { url = "https://files.pythonhosted.org/packages/c2/ca/77123e4d77df3cb1e968ade7b1f808f5d3a5c1c96b18a33895397de292c1/lxml-6.1.0-cp314-cp314t-win32.whl", hash = "sha256:00750d63ef0031a05331b9223463b1c7c02b9004cef2346a5b2877f0f9494dd2", size = 3897377 },
+    { url = "https://files.pythonhosted.org/packages/64/ce/3554833989d074267c063209bae8b09815e5656456a2d332b947806b05ff/lxml-6.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:80410c3a7e3c617af04de17caa9f9f20adaa817093293d69eae7d7d0522836f5", size = 4392701 },
+    { url = "https://files.pythonhosted.org/packages/2b/a0/9b916c68c0e57752c07f8f64b30138d9d4059dbeb27b90274dedbea128ff/lxml-6.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:26dd9f57ee3bd41e7d35b4c98a2ffd89ed11591649f421f0ec19f67d50ec67ac", size = 3817120 },
+]
+
+[package.optional-dependencies]
+html-clean = [
+    { name = "lxml-html-clean" },
+]
+
+[[package]]
+name = "lxml-html-clean"
+version = "0.4.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "lxml" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9a/a4/5c62acfacd69ff4f5db395100f5cfb9b54e7ac8c69a235e4e939fd13f021/lxml_html_clean-0.4.4.tar.gz", hash = "sha256:58f39a9d632711202ed1d6d0b9b47a904e306c85de5761543b90e3e3f736acfb", size = 23899 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d9/76/7ffc1d3005cf7749123bc47cb3ea343cd97b0ac2211bab40f57283577d0e/lxml_html_clean-0.4.4-py3-none-any.whl", hash = "sha256:ce2ef506614ecb85ee1c5fe0a2aa45b06a19514ec7949e9c8f34f06925cfabcb", size = 14565 },
+]
+
 [[package]]
 name = "markdown-it-py"
 version = "4.2.0"
@@ -543,6 +1004,81 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b3/81/4da04ced5a082363ecfa159c010d200ecbd959ae410c10c0264a38cac0f5/markdown_it_py-4.2.0-py3-none-any.whl", hash = "sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a", size = 91687 },
 ]
 
+[[package]]
+name = "markupsafe"
+version = "3.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615 },
+    { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020 },
+    { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332 },
+    { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947 },
+    { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962 },
+    { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760 },
+    { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529 },
+    { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015 },
+    { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540 },
+    { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105 },
+    { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906 },
+    { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622 },
+    { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029 },
+    { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374 },
+    { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980 },
+    { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990 },
+    { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784 },
+    { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588 },
+    { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041 },
+    { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543 },
+    { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113 },
+    { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911 },
+    { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658 },
+    { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066 },
+    { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639 },
+    { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569 },
+    { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284 },
+    { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801 },
+    { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769 },
+    { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642 },
+    { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612 },
+    { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200 },
+    { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973 },
+    { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619 },
+    { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029 },
+    { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408 },
+    { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005 },
+    { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048 },
+    { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821 },
+    { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606 },
+    { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043 },
+    { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747 },
+    { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341 },
+    { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073 },
+    { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661 },
+    { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069 },
+    { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670 },
+    { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598 },
+    { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261 },
+    { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835 },
+    { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733 },
+    { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672 },
+    { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819 },
+    { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426 },
+    { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146 },
+]
+
+[[package]]
+name = "marshmallow"
+version = "3.26.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "packaging" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/55/79/de6c16cc902f4fc372236926b0ce2ab7845268dcc30fb2fbb7f71b418631/marshmallow-3.26.2.tar.gz", hash = "sha256:bbe2adb5a03e6e3571b573f42527c6fe926e17467833660bebd11593ab8dfd57", size = 222095 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/2f/5108cb3ee4ba6501748c4908b908e55f42a5b66245b4cfe0c99326e1ef6e/marshmallow-3.26.2-py3-none-any.whl", hash = "sha256:013fa8a3c4c276c24d26d84ce934dc964e2aa794345a0f8c7e5a7191482c8a73", size = 50964 },
+]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -668,6 +1204,48 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/82/69e539c4c2027f1e1697e09aaa2449243085a0edf81ae2c6341e84d769b6/multiprocess-0.70.19-py39-none-any.whl", hash = "sha256:0d4b4397ed669d371c81dcd1ef33fd384a44d6c3de1bd0ca7ac06d837720d3c5", size = 133477 },
 ]
 
+[[package]]
+name = "mypy-extensions"
+version = "1.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/6e/371856a3fb9d31ca8dac321cda606860fa4548858c0cc45d9d1d4ca2628b/mypy_extensions-1.1.0.tar.gz", hash = "sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558", size = 6343 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963 },
+]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 },
+]
+
+[[package]]
+name = "networkx"
+version = "3.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504 },
+]
+
+[[package]]
+name = "nltk"
+version = "3.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "joblib" },
+    { name = "regex" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/74/a1/b3b4adf15585a5bc4c357adde150c01ebeeb642173ded4d871e89468767c/nltk-3.9.4.tar.gz", hash = "sha256:ed03bc098a40481310320808b2db712d95d13ca65b27372f8a403949c8b523d0", size = 2946864 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9d/91/04e965f8e717ba0ab4bdca5c112deeab11c9e750d94c4d4602f050295d39/nltk-3.9.4-py3-none-any.whl", hash = "sha256:f2fa301c3a12718ce4a0e9305c5675299da5ad9e26068218b69d692fda84828f", size = 1552087 },
+]
+
 [[package]]
 name = "numpy"
 version = "2.4.4"
@@ -859,6 +1437,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ff/6e/cf826fae916b8658848d7b9f38d88da6396895c676e8086fc0988073aaf8/pillow-12.2.0-cp314-cp314t-win_arm64.whl", hash = "sha256:aa88ccfe4e32d362816319ed727a004423aab09c5cea43c01a4b435643fa34eb", size = 2556579 },
 ]
 
+[[package]]
+name = "platformdirs"
+version = "4.9.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9f/4a/0883b8e3802965322523f0b200ecf33d31f10991d0401162f4b23c698b42/platformdirs-4.9.6.tar.gz", hash = "sha256:3bfa75b0ad0db84096ae777218481852c0ebc6c727b3168c1b9e0118e458cf0a", size = 29400 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/75/a6/a0a304dc33b49145b21f4808d763822111e67d1c3a32b524a1baf947b6e1/platformdirs-4.9.6-py3-none-any.whl", hash = "sha256:e61adb1d5e5cb3441b4b7710bea7e4c12250ca49439228cc1021c00dcfac0917", size = 21348 },
+]
+
 [[package]]
 name = "pluggy"
 version = "1.6.0"
@@ -1100,6 +1687,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151 },
 ]
 
+[[package]]
+name = "pypdf"
+version = "6.11.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bf/58/6dd97d78a4b17a7a6b9d1c6ad23895abc41f0fdc49c553cc05bdfdcc36d0/pypdf-6.11.0.tar.gz", hash = "sha256:062b51c81b0910e6d2755e99e1c5547a0a23b7d0a32322af66240d8edcfabe87", size = 6453975 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/07/b1/68feb7eb3b99f0c020b414234825f4a5d70e0126c18d933770e8c93a35fc/pypdf-6.11.0-py3-none-any.whl", hash = "sha256:769394d5756d5b304c9b6bef88b54b1816b328e7e6fc9254e625529a15ed4ab8", size = 338819 },
+]
+
 [[package]]
 name = "pytest"
 version = "9.0.3"
@@ -1150,6 +1746,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101 },
 ]
 
+[[package]]
+name = "pytz"
+version = "2026.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ff/46/dd499ec9038423421951e4fad73051febaa13d2df82b4064f87af8b8c0c3/pytz-2026.2.tar.gz", hash = "sha256:0e60b47b29f21574376f218fe21abc009894a2321ea16c6754f3cad6eb7cdd6a", size = 320861 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/dd/96da98f892250475bdf2328112d7468abdd4acc7b902b6af23f4ed958ea0/pytz-2026.2-py2.py3-none-any.whl", hash = "sha256:04156e608bee23d3792fd45c94ae47fae1036688e75032eea2e3bf0323d1f126", size = 510141 },
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.3"
@@ -1196,6 +1801,94 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341 },
 ]
 
+[[package]]
+name = "regex"
+version = "2026.5.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/0e/49aee608ad09480e7fd276898c99ec6192985fa331abe4eb3a986094490b/regex-2026.5.9.tar.gz", hash = "sha256:a8234aa23ec39894bfe4a3f1b85616a7032481964a13ac6fc9f10de4f6fca270", size = 416074 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/50/9b/6550044bc44e17c84d312c031c2ec42fbdb6a4ec4e29093be3a172d08772/regex-2026.5.9-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:57eeeb05db7979413dec5438f2db21d7ecbba787cde7a711df1a6f6df672aa06", size = 490451 },
+    { url = "https://files.pythonhosted.org/packages/1e/95/fc7ba4303b5a0f92446a12ee6778ef2c6c799233f5060042a31bf390cfe9/regex-2026.5.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:398c521292f4c7fb807001dcd54694d3a1fcafc179a36ad9cc56f98df85930b6", size = 292112 },
+    { url = "https://files.pythonhosted.org/packages/54/4b/ee27938d1b2c443e89a9a10e00d2d19aa5ee300cd3d61140644e93bb083e/regex-2026.5.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f7a7c26137296beba7784de6eba69c6a93a63ccebc385e4962fe67e267a91225", size = 289599 },
+    { url = "https://files.pythonhosted.org/packages/d8/dd/ba103dc19614e25f3880800ca67ce093d6e21b325d72b8383c7bf906e9fa/regex-2026.5.9-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6441cc660d76107934a09c22167200839a0e89604a6297f78a974e66e931d2c0", size = 796732 },
+    { url = "https://files.pythonhosted.org/packages/cf/e7/f035b4fd858b050b0080bf302968dc0f59ba34e391872d54936758e6844e/regex-2026.5.9-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:91328f1c23d47595ca3ef0a7557fa129c5a23404b775c770697d2f35b33e0107", size = 865440 },
+    { url = "https://files.pythonhosted.org/packages/0a/51/8cd301ecc899aea28124357f729f4272f44de7806fc7ca02490bfbe253e8/regex-2026.5.9-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:93a7860539414dddaefba2b40f8771765ae17949d4c7182b876ce429e11a8309", size = 912329 },
+    { url = "https://files.pythonhosted.org/packages/cc/1e/3fbe2fa1e8cebd62f3bb7d3321cff1640aca2e240b51d9bd624aad949260/regex-2026.5.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dd2810d22146b6d838acc5ec15602cb6b47920aa4e33015df3868eedfd20bab8", size = 801239 },
+    { url = "https://files.pythonhosted.org/packages/17/2f/6f6008682bf2cf98040a0d3153a8e557b6ab728d7713d045cee4ce544ab8/regex-2026.5.9-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:daff2bdbaf1d23e52fdff7c0b7bc2048b68f978df6a4d107ac981f94caef2e66", size = 777054 },
+    { url = "https://files.pythonhosted.org/packages/19/2b/eee0d20a6842ba04df4b8847a920b57ef56853f14ef85405473e586b605a/regex-2026.5.9-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4eeb011098fcb77af513dcef521a3dbecbf8849b1e38940759d293b7a93f5026", size = 785098 },
+    { url = "https://files.pythonhosted.org/packages/4a/98/6fc1e6410feefb92159edaed5041992bfe390e8d26c721865434acbca558/regex-2026.5.9-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:ea9c8ecfa1b73c73b626534d6626e5340d429630943672b8480724f44e84b962", size = 860095 },
+    { url = "https://files.pythonhosted.org/packages/18/a3/bd855e0f2cb1a978ecf6fa6bb69632dd9c3f6ea3b81cde62fde14c9daec7/regex-2026.5.9-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:cd2846168eb9ee3c513902bc8225409cb1caab31d04728b145171fa1625d9621", size = 765762 },
+    { url = "https://files.pythonhosted.org/packages/dc/66/0ae8c092e60b14c79d24f8e0b7f0aea5bfbffdcab00b5483d13404d3c3a5/regex-2026.5.9-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:39617fb0cde9c0e6306dc70e3bfc096f3da793219879f7ae7aa341a69fbdcf6d", size = 852100 },
+    { url = "https://files.pythonhosted.org/packages/21/de/8dfde60fc1b21c946a893ba273403b72617edb261370cb1087099a83f088/regex-2026.5.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fd03c4f0e33280d15cae17159b899245d6b7c53d21def19b263b39655061f5ce", size = 789479 },
+    { url = "https://files.pythonhosted.org/packages/c3/1c/bdcc98f9a4af4fdd166c74941174619ccff4726d3ce32faa8e9a2ecd38dd/regex-2026.5.9-cp312-cp312-win32.whl", hash = "sha256:164eba9b755ea6f244b0d881196fbc1fac09714e9782c9e2732b813142033c8e", size = 266699 },
+    { url = "https://files.pythonhosted.org/packages/78/87/240d36864f9e48ace85f72e79ced97ceb7f27ce87739a947dcb834b4e6bc/regex-2026.5.9-cp312-cp312-win_amd64.whl", hash = "sha256:86f40a5d6444db30a125c9c9177e6b25dad981cbc37451fd838f145e6edac92e", size = 277783 },
+    { url = "https://files.pythonhosted.org/packages/4f/b5/7b30f312b0669dff5beebe5b0989dc2d1a312b1a44fab852199c387a5b96/regex-2026.5.9-cp312-cp312-win_arm64.whl", hash = "sha256:96f5f58b54a063d7ea9dca08e1cf57bfe10499c4d579ee672da284f57f5f0070", size = 270513 },
+    { url = "https://files.pythonhosted.org/packages/aa/da/797e91ecec6f84135da778ddce78c20e0af5d2a15c26f87a81bc3eadb6db/regex-2026.5.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:d626b84406444b165fc0ba981604edea39f0588ff1f92baa23fe50799ea9afdb", size = 490303 },
+    { url = "https://files.pythonhosted.org/packages/44/da/bf30abaaa737b58f4a4b8c4a03659e02fd92092c822e0197ed9e0daab917/regex-2026.5.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d7bdc0ab8f3dd7e1b4f9ab88634e13374669db86bb3c72e8292f07ae313f539f", size = 292019 },
+    { url = "https://files.pythonhosted.org/packages/2d/e7/d0eaf5713828417b9e5648cf81fa9bacd4961f6ab98c380c2034f8716e35/regex-2026.5.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a8820737949116ffff55fe18f9fc644530063ba6ebfcb8314239416e78f1347c", size = 289468 },
+    { url = "https://files.pythonhosted.org/packages/d3/9b/b3fdd62b003baa1a9b593cd8c8699c9651c2e80cc21a5c715707983c42d7/regex-2026.5.9-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aa0fbdbac82cb3e4450d0ccde7d7a35607f4cb2dd9fba4b8b69bfaf8c9fa6aed", size = 796749 },
+    { url = "https://files.pythonhosted.org/packages/d4/30/66ab84588765f5b4b271a9ca09ef7ce2b87caa95176ec3d2ad65d7bc4902/regex-2026.5.9-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:57e8915c7986aa33d25e4d3629cef711cd2863f2961b10409f0c04cb8b7d9020", size = 865445 },
+    { url = "https://files.pythonhosted.org/packages/1a/89/f05169e8588aac365f35ffc7f3bc3184f095ef4cfded7cfaa3c7fd5dbd89/regex-2026.5.9-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:508f56a89ba9cb26e4168cbc37dbd60a28d82430a9e18ad1d25fe0883c314ca2", size = 912322 },
+    { url = "https://files.pythonhosted.org/packages/30/e1/c93444052cf41581f3c884ab3fb5823daf0992f11cd4388d4275ca610558/regex-2026.5.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b6d189041f15691cfa2b6c4290448ec221244d225b3f5fe9e7771b34ffcdf6e2", size = 801269 },
+    { url = "https://files.pythonhosted.org/packages/50/fe/0cf96b882f540e62e8b9956599798203d599c44cf4c77917ca27400ff69b/regex-2026.5.9-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e82db382b44d0111b22601c509c89f64434816c9e0eef9d1989cda8cc6ff1c04", size = 777085 },
+    { url = "https://files.pythonhosted.org/packages/23/5c/d78d4924e7fc875557b9e9b768423925fdfaac5549d06da7810019a9bd26/regex-2026.5.9-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2acfb48634f64996b57f90f39afa692ff362162722581921fe92239a59960f3c", size = 785153 },
+    { url = "https://files.pythonhosted.org/packages/bf/e0/5214774090e7b4524dcea3e3c4aa74141d43043f8beb49c1599db1c8b53a/regex-2026.5.9-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:d29eebfc9525db68cad3c97eedd7f754fa265aa5cd0cf4f863b2421e1b48fc9f", size = 860164 },
+    { url = "https://files.pythonhosted.org/packages/6e/e1/4a57a83350319b1271f0d7a249b8672513ed928b237a741631270de6caea/regex-2026.5.9-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:debb893095e944091c16e641a6e33c1b0f4cb61ab945ec5afbf53ce7068834d8", size = 765731 },
+    { url = "https://files.pythonhosted.org/packages/12/f4/499e74a20c156fc75836ee04a72a38d1a063978f600937f9760467beb1b0/regex-2026.5.9-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:d659eee77986549c9ea45b861c7567e44d6287c3dc9a4565478853f7b9fe2ff6", size = 852062 },
+    { url = "https://files.pythonhosted.org/packages/5b/92/7eebc0d0a01e78629695f342ba17e0deaff8fb45e79cc0d7b98287da6e3e/regex-2026.5.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2efa205e6d98b24d1f3ab395c11aa15cdf10935bca283d0285e0499c284fba21", size = 789577 },
+    { url = "https://files.pythonhosted.org/packages/05/a4/018e71f7d2ad48c1ebe6d3ae0026f9b7cb4802fd15c7cc02fdf724355102/regex-2026.5.9-cp313-cp313-win32.whl", hash = "sha256:f3844f134e834076677dd369976e9f5068679fcb8e50102fdf6b7ac96a3ec127", size = 266691 },
+    { url = "https://files.pythonhosted.org/packages/e6/1d/861a93719fb9ee7dbfc3761b3797b7a3e112a5d42c6129459d2d741be9b5/regex-2026.5.9-cp313-cp313-win_amd64.whl", hash = "sha256:3527bb4942d2c14552155406cdedd906567456821848aed1cb4933a391bf5eca", size = 277747 },
+    { url = "https://files.pythonhosted.org/packages/d9/c6/0a2436ae4da1ba76e51cb98943c6838a9a721faa40ebe2dce07694ae34e3/regex-2026.5.9-cp313-cp313-win_arm64.whl", hash = "sha256:56a33f191f17d8c417f99945ebdc1e691d3af9605d86ec68c7e54a57e3e17af6", size = 270500 },
+    { url = "https://files.pythonhosted.org/packages/e8/e9/d21346f7b60ed58789371358ed66b09d00f832e1bd7c06e55d9da5679882/regex-2026.5.9-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:01f28d868834624c934b8d2e0aa1c8341337e37831f4a012f18a5afcba4cbaf3", size = 494172 },
+    { url = "https://files.pythonhosted.org/packages/c4/43/fd1177a2032037c681baecdb3422ee4e1424aec4e4f470ef47793d325274/regex-2026.5.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:48036f6374aaa79eb3b754ec29c61d1c6b1606749d705a13f8854fa2539671f6", size = 293952 },
+    { url = "https://files.pythonhosted.org/packages/f2/7d/9fbf919768368d3f8a4f6c692cf2aa61e482b2b81ec6a298ace4cbf02480/regex-2026.5.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b96350aa424e79d4fd6b567b344dcbe2b2d6bfc48dfe7717587e1fa6d43da6ff", size = 292314 },
+    { url = "https://files.pythonhosted.org/packages/e2/6c/e41bfeecb589716843e7c4df09ba46ff2a42961457afece19059d85caeef/regex-2026.5.9-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8f3af7a4903c5c04a11a196a5aa75cdd7dd3f8508132f9fb3259d9f5908e3b88", size = 811681 },
+    { url = "https://files.pythonhosted.org/packages/87/83/a5c1c525fba0aa656e88ad0face0b1829788ef4c2fb6b26df58aa1151b84/regex-2026.5.9-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7e87577720152d2caae19fe2baaf1f8d5ca12091e9e229f03915c37d1e4b9178", size = 871135 },
+    { url = "https://files.pythonhosted.org/packages/18/d4/80882e799e440dd878b0979cbebf8fa4d54624a332c83037c7a701649e3f/regex-2026.5.9-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:c8b9b9d294cfea3cd19c718ade7cc93492b2c4991abd9a68d0b3477ae6d8e100", size = 917265 },
+    { url = "https://files.pythonhosted.org/packages/ae/ff/8db60211e2286e396aad7dc7725356c502bff0901ea05bd6cdc2e1a042b9/regex-2026.5.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:728d8bfd28a8845c8b6bc5dc7ce010453d206396786c0765c2740cb65f37791e", size = 816311 },
+    { url = "https://files.pythonhosted.org/packages/4c/47/742ef579c61730f8d268e5cf1f9ce0e37e2ea041ad0f5644724f2378e463/regex-2026.5.9-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:7e30b874d341fac767d7df5a0870540541c2c054b80cfaac116e8d367a8a7ff2", size = 785498 },
+    { url = "https://files.pythonhosted.org/packages/7f/ab/cb0999802dcb0fb95b1ab005e8d4163d8afdd67efc2cb6b6630ac13f8cb1/regex-2026.5.9-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:fd190e88a895a8901325fad284a3f74ea52b1da8525b76cc811fa9b1edf0ce2b", size = 801348 },
+    { url = "https://files.pythonhosted.org/packages/7d/62/8ca59a24c55bc34d166eefaf3717bd77772f329fdbf984d86581e0a3571c/regex-2026.5.9-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:8e76e8161ad00694cfce6767d5dea860c6391ac5b83e5c3a39661e696f11fc7e", size = 866493 },
+    { url = "https://files.pythonhosted.org/packages/8d/3d/30f2ae62cef3278bb5bb821f467277a55fb73f01032cf85997e15e8289a8/regex-2026.5.9-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ddda5340e6c01a293027dd46232fa79eaff1b48058ce7a98f572b6445b088041", size = 772811 },
+    { url = "https://files.pythonhosted.org/packages/d8/ae/7d2089bcd78ad0c0161bc684339df50032acb438a7bd3305e7ddb1193cec/regex-2026.5.9-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:205109e96b3cf5adf8f4cd62bedde9487feb282b9497a3535451e5a24cd706a0", size = 856584 },
+    { url = "https://files.pythonhosted.org/packages/a9/29/92ff47f75990131ea4f24ba17819e5a9d141e10819807e09addd73409af6/regex-2026.5.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dfbe4579b9f08036aa7d101d1835437a20783574ac66327e6b29b4018a138081", size = 803453 },
+    { url = "https://files.pythonhosted.org/packages/04/99/eff29f1037dcab36702c9ee5d6858cf1ce2336ea8ea2987f64245b99ea5e/regex-2026.5.9-cp313-cp313t-win32.whl", hash = "sha256:ed2c9e8068b614c574d8d30e543d617cf5379b0535d46f97ef00e904745a08b5", size = 269951 },
+    { url = "https://files.pythonhosted.org/packages/0e/9d/8870b8981d27b22cda77bb26a5ac7ebfa9c7d9e0dea195a834a82380e748/regex-2026.5.9-cp313-cp313t-win_amd64.whl", hash = "sha256:b46b0f094dc1d3b90356c85a0bd2c9bafc4a6a190b9d6f8ddd5a033b6e088ed4", size = 281240 },
+    { url = "https://files.pythonhosted.org/packages/72/b1/3379415e8f135c13ac551353397cc4fe97b4978f3cac73c5fcbcded548b8/regex-2026.5.9-cp313-cp313t-win_arm64.whl", hash = "sha256:872acc074bd29ffc9913ecdfedf6ea77502312ca44a4aa0d3779089c6069d8de", size = 272383 },
+    { url = "https://files.pythonhosted.org/packages/13/3e/9c3cd292d8808b3645a2ce517e200179b6d0e903f176300bd8b542e14de5/regex-2026.5.9-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:1bd7587a2948b4085195d5a3374eaf4a425dc3e55784c038175355ecf3bbbf8a", size = 490376 },
+    { url = "https://files.pythonhosted.org/packages/60/70/d43ee8a2ca0a8b68d167f21658b85520ac0574617c7f320367c5047f7556/regex-2026.5.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:dea2e88e1cce4522496cce630e11e67b98b7076620bc4336c3f674bc21a375f4", size = 291964 },
+    { url = "https://files.pythonhosted.org/packages/21/91/9d50b433828d8e74196904e168a43abf1e6e88b2a15d47ed742456720c37/regex-2026.5.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2099f7e7ff7b6aa3192312650a56e91cc091e49d50b04e4f6f8b6e28b3b27f1c", size = 289682 },
+    { url = "https://files.pythonhosted.org/packages/3e/d2/b835e3cafbb9d977736912436259ff551d60919f7d7b3d37d46659c63564/regex-2026.5.9-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecd353045824e4477562a2ac718c25799cdaaa41f7aa925a806a8a3e6848a5b9", size = 796996 },
+    { url = "https://files.pythonhosted.org/packages/2c/a6/9f992d00019166b9de01c546dd4549bc679f2a68df11b877740b0760b7c2/regex-2026.5.9-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:65c8c8c37377794bd5b2f3ebe51919042bf17aec802e23c833d89782ed0c78af", size = 866089 },
+    { url = "https://files.pythonhosted.org/packages/e0/08/4d32af657e049b19cb62b02e46e38fe1518797bfb2203ee93a510b21b0dc/regex-2026.5.9-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5b73ab8afcf66c622db143d1c6fda4e58e4d537ee4f125229ad47b1ab80f34c0", size = 911530 },
+    { url = "https://files.pythonhosted.org/packages/d9/27/2af43dd1dc201d1fecefda64a45f4ad0995855b92724f795a777b402ee69/regex-2026.5.9-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0de5cf193997384ed2ca6f1cd4f78055b255d93d82d5a8cd6ba0d11c10b167e4", size = 800643 },
+    { url = "https://files.pythonhosted.org/packages/a4/dd/23a249047013b5321d4a60c4d2437462086f601b061776a525e5fba2a59f/regex-2026.5.9-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d641a8c9a61618047796d572a39a79b26167b0411d2c3031937b2fe2d081e2cf", size = 777223 },
+    { url = "https://files.pythonhosted.org/packages/94/6a/e85ed9538cd19586d0465076a4578a12e093ce776d15f3f8ce92733a8dd6/regex-2026.5.9-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:24b2355ef5cc9aa5b8f07d17704face1c166fdcc2290fa7bd6e6c925655a8346", size = 785760 },
+    { url = "https://files.pythonhosted.org/packages/2a/c4/f25473209438638e947c55f9156fd8f236f74169229028cc99116380868e/regex-2026.5.9-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:a24852d3c29ad9e47593593d8a247c44ccc3d0548ef12c822d6ed0810affe676", size = 860891 },
+    { url = "https://files.pythonhosted.org/packages/f9/f7/f4f86e3c74419c37370e91f150ae0c2ef7d34b2e0e4cdd5da046a02e4022/regex-2026.5.9-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:916714069da19329ef7de197dcbc77bb3104145c7c2c864dbfbe318f46b88b14", size = 765891 },
+    { url = "https://files.pythonhosted.org/packages/26/70/704d8e13765939146b1cd0ef4e2feb71d7929727d2290f026eed10095955/regex-2026.5.9-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:fa411799ca8da32a8d38d020a88faa5b6f91657d284761352940ecf9f7c3bbdd", size = 851380 },
+    { url = "https://files.pythonhosted.org/packages/26/29/1a13582a8460038edc38e49f64ceb0dd7c60f5caba77571f4bf6601965d9/regex-2026.5.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:1e6da47d679b7010ef27556b6e0f99771b744936db1792a10ceac6547ae1503e", size = 789350 },
+    { url = "https://files.pythonhosted.org/packages/73/56/3dcafe34fc72e271d62ad9a291801e88a1457bb251c132f15fcc2e5aad1a/regex-2026.5.9-cp314-cp314-win32.whl", hash = "sha256:98bd73080e8756255137e1bd3f3f00295bbc5aa383c0e0f973920e9134d7c4ad", size = 272130 },
+    { url = "https://files.pythonhosted.org/packages/d0/9c/02eebf0be95efe416c664db7fb8b6b05b7a0b06a7544f2884f2558b0526f/regex-2026.5.9-cp314-cp314-win_amd64.whl", hash = "sha256:ff8d372ac2acdc048d1c19916f27ee61bc5722728458ba6ca5052f2c72d51763", size = 280999 },
+    { url = "https://files.pythonhosted.org/packages/70/5a/1dd1abee76cb7a846a0bcf42fdc87e5720c3c33c24f3e37814310a513d9f/regex-2026.5.9-cp314-cp314-win_arm64.whl", hash = "sha256:e1d93bf647916292e8edcec150c07ddf3dc50179ccaf770c04a7f9e452155372", size = 273500 },
+    { url = "https://files.pythonhosted.org/packages/86/c1/c5f619b0057a7965cb78ec559c1d7a45ce8c99a35bea95483d64959a93d9/regex-2026.5.9-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:83d0ee4a57d1c87cb549e195ec300b8f0ec3a82eba66d835e4e2ed8634fe4499", size = 494269 },
+    { url = "https://files.pythonhosted.org/packages/05/2c/5d01f1aee33de4bbe60c8452945bfc8477ca7c5ae4450f6bfe711036cb36/regex-2026.5.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:d3d7eb5c9a7f6df82ed3cfac9beb93882a5cbcb5b8b157b56cb2b3b276574ac1", size = 293954 },
+    { url = "https://files.pythonhosted.org/packages/7a/fe/e8988b2ae2108c6ef71bd4aa8d87fbe257976dd0810e826cd75f701c68b6/regex-2026.5.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:075160bf16658e16d35233300b8453aac25de4cbea808d22348b6979668e924d", size = 292405 },
+    { url = "https://files.pythonhosted.org/packages/79/34/d2b0937faa7859263f7f0a3c6b103a1296306be6952dc173d0154e9a2f49/regex-2026.5.9-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45375819235558a4ff1c4971dc32881f022613abdb180128f5cb4768c1765a1c", size = 811855 },
+    { url = "https://files.pythonhosted.org/packages/80/fe/daf53a47457a8486db66c66c01ceb9c2303eecee3f87197f1e77eb1a736d/regex-2026.5.9-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ead4b163ac30a29574510cd4b3e2e985ac5290c05fc7095557d6a5f403fc31b5", size = 871189 },
+    { url = "https://files.pythonhosted.org/packages/1c/75/058fc4470cbfbf57d800aff1a0022b929a3f9fa553ee10a0cdf2070eb31f/regex-2026.5.9-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8c6e4218fbdfbcd4f6c19efca40930d24a621bf4b48cb76bc6640543bd28ef20", size = 917485 },
+    { url = "https://files.pythonhosted.org/packages/88/e7/179cfda3a28bc843b5c6cfe7f79f23489c791ed95f151083803660878432/regex-2026.5.9-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6351571c8a42b505eb555c0dc47d740d0fb66977dc142919eea6f4325b7c56a0", size = 816369 },
+    { url = "https://files.pythonhosted.org/packages/41/90/6f0cc422071688266d344fca8462d787cba0a2c144acb25721f9a61ec265/regex-2026.5.9-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:002205cafd2a9e78c6290c7d1df277bf3277b3b7a30e0b4bb0dac2e2e3f7cb2d", size = 785869 },
+    { url = "https://files.pythonhosted.org/packages/02/67/a31f1760f09c27b251ef39e9beb541f462cf977381d067faa764c2c0e393/regex-2026.5.9-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8abd33fef90b2a9efac5557d6033ca82d1195ed3a15fea5af15ba7b463c6a63b", size = 801427 },
+    { url = "https://files.pythonhosted.org/packages/e3/c4/1a80654597b6bc1e1ea0494824c31200e8a956abe290afae9b19a166a148/regex-2026.5.9-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:31037c82eccb44b7ea2e9e221d7c01429430e989a1f4b91ea5a855f6017b509a", size = 866482 },
+    { url = "https://files.pythonhosted.org/packages/d1/11/960724e06482c08466ff5611e242e86f80062949cdf6b4b9cc317b9dd93d/regex-2026.5.9-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:5604dfd046dc37eca90250fc3be938b076c8059fa772ac0ed6f499b0f0fb0415", size = 773022 },
+    { url = "https://files.pythonhosted.org/packages/50/a8/a9979c3e7918280e93159ebcab5ef1a65116dd4f3bd6091be0eae4a126e8/regex-2026.5.9-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e1b1b4e496afbb24f4a62aba855ee4f88f25578927697b340702e48c9ee6bc2", size = 856642 },
+    { url = "https://files.pythonhosted.org/packages/fe/d4/a9b732f2f0072c0ab12227483abb24fffcb9f73f8a2b203df0a6d0434735/regex-2026.5.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:be3372b9df6ddecff6486d37e19095a7b4973137caf5512407a89f4455361f41", size = 803552 },
+    { url = "https://files.pythonhosted.org/packages/d5/fe/1b3113817447a1d4155e4ac76d2e072f42c0bcba2f43fa8a0e756ea2cd91/regex-2026.5.9-cp314-cp314t-win32.whl", hash = "sha256:3ddd90103f9e5c471c49c7852ecc1fe27c7e45eb99e977aefe7caa4e779f4f58", size = 275746 },
+    { url = "https://files.pythonhosted.org/packages/92/73/93d42045302636c91f2e5ef588b65b84b01428f28ec77de256b1dfdfbe5c/regex-2026.5.9-cp314-cp314t-win_amd64.whl", hash = "sha256:ca518ed29c46eecba6010b15f1b9a479314d2de409536e71b6a13aa04e3b8a77", size = 285685 },
+    { url = "https://files.pythonhosted.org/packages/da/80/35b4c33c804a165a7f55289afda3ea9e3eb6d15800341a2d66455c0f1f30/regex-2026.5.9-cp314-cp314t-win_arm64.whl", hash = "sha256:5e41809d2683fcde7d5a8c87a6567ba1fb1ce0de9f31bff578de00a4b2d76daa", size = 275713 },
+]
+
 [[package]]
 name = "reportlab"
 version = "4.5.1"
@@ -1379,6 +2072,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165 },
 ]
 
+[[package]]
+name = "setuptools"
+version = "82.0.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4f/db/cfac1baf10650ab4d1c111714410d2fbb77ac5a616db26775db562c8fab2/setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9", size = 1152316 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9d/76/f789f7a86709c6b087c5a2f52f911838cad707cc613162401badc665acfe/setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb", size = 1006223 },
+]
+
 [[package]]
 name = "shellingham"
 version = "1.5.4"
@@ -1397,25 +2099,80 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
 ]
 
+[[package]]
+name = "sqlalchemy"
+version = "2.0.49"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "greenlet", marker = "platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/45/461788f35e0364a8da7bda51a1fe1b09762d0c32f12f63727998d85a873b/sqlalchemy-2.0.49.tar.gz", hash = "sha256:d15950a57a210e36dd4cec1aac22787e2a4d57ba9318233e2ef8b2daf9ff2d5f", size = 9898221 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/49/b3/2de412451330756aaaa72d27131db6dde23995efe62c941184e15242a5fa/sqlalchemy-2.0.49-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4bbccb45260e4ff1b7db0be80a9025bb1e6698bdb808b83fff0000f7a90b2c0b", size = 2157681 },
+    { url = "https://files.pythonhosted.org/packages/50/84/b2a56e2105bd11ebf9f0b93abddd748e1a78d592819099359aa98134a8bf/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb37f15714ec2652d574f021d479e78cd4eb9d04396dca36568fdfffb3487982", size = 3338976 },
+    { url = "https://files.pythonhosted.org/packages/2c/fa/65fcae2ed62f84ab72cf89536c7c3217a156e71a2c111b1305ab6f0690e2/sqlalchemy-2.0.49-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3bb9ec6436a820a4c006aad1ac351f12de2f2dbdaad171692ee457a02429b672", size = 3351937 },
+    { url = "https://files.pythonhosted.org/packages/f8/2f/6fd118563572a7fe475925742eb6b3443b2250e346a0cc27d8d408e73773/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8d6efc136f44a7e8bc8088507eaabbb8c2b55b3dbb63fe102c690da0ddebe55e", size = 3281646 },
+    { url = "https://files.pythonhosted.org/packages/c5/d7/410f4a007c65275b9cf82354adb4bb8ba587b176d0a6ee99caa16fe638f8/sqlalchemy-2.0.49-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e06e617e3d4fd9e51d385dfe45b077a41e9d1b033a7702551e3278ac597dc750", size = 3316695 },
+    { url = "https://files.pythonhosted.org/packages/d9/95/81f594aa60ded13273a844539041ccf1e66c5a7bed0a8e27810a3b52d522/sqlalchemy-2.0.49-cp312-cp312-win32.whl", hash = "sha256:83101a6930332b87653886c01d1ee7e294b1fe46a07dd9a2d2b4f91bcc88eec0", size = 2117483 },
+    { url = "https://files.pythonhosted.org/packages/47/9e/fd90114059175cac64e4fafa9bf3ac20584384d66de40793ae2e2f26f3bb/sqlalchemy-2.0.49-cp312-cp312-win_amd64.whl", hash = "sha256:618a308215b6cececb6240b9abde545e3acdabac7ae3e1d4e666896bf5ba44b4", size = 2144494 },
+    { url = "https://files.pythonhosted.org/packages/ae/81/81755f50eb2478eaf2049728491d4ea4f416c1eb013338682173259efa09/sqlalchemy-2.0.49-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:df2d441bacf97022e81ad047e1597552eb3f83ca8a8f1a1fdd43cd7fe3898120", size = 2154547 },
+    { url = "https://files.pythonhosted.org/packages/a2/bc/3494270da80811d08bcfa247404292428c4fe16294932bce5593f215cad9/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8e20e511dc15265fb433571391ba313e10dd8ea7e509d51686a51313b4ac01a2", size = 3280782 },
+    { url = "https://files.pythonhosted.org/packages/cd/f5/038741f5e747a5f6ea3e72487211579d8cbea5eb9827a9cbd61d0108c4bd/sqlalchemy-2.0.49-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47604cb2159f8bbd5a1ab48a714557156320f20871ee64d550d8bf2683d980d3", size = 3297156 },
+    { url = "https://files.pythonhosted.org/packages/88/50/a6af0ff9dc954b43a65ca9b5367334e45d99684c90a3d3413fc19a02d43c/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:22d8798819f86720bc646ab015baff5ea4c971d68121cb36e2ebc2ee43ead2b7", size = 3228832 },
+    { url = "https://files.pythonhosted.org/packages/bc/d1/5f6bdad8de0bf546fc74370939621396515e0cdb9067402d6ba1b8afbe9a/sqlalchemy-2.0.49-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9b1c058c171b739e7c330760044803099c7fff11511e3ab3573e5327116a9c33", size = 3267000 },
+    { url = "https://files.pythonhosted.org/packages/f7/30/ad62227b4a9819a5e1c6abff77c0f614fa7c9326e5a3bdbee90f7139382b/sqlalchemy-2.0.49-cp313-cp313-win32.whl", hash = "sha256:a143af2ea6672f2af3f44ed8f9cd020e9cc34c56f0e8db12019d5d9ecf41cb3b", size = 2115641 },
+    { url = "https://files.pythonhosted.org/packages/17/3a/7215b1b7d6d49dc9a87211be44562077f5f04f9bb5a59552c1c8e2d98173/sqlalchemy-2.0.49-cp313-cp313-win_amd64.whl", hash = "sha256:12b04d1db2663b421fe072d638a138460a51d5a862403295671c4f3987fb9148", size = 2141498 },
+    { url = "https://files.pythonhosted.org/packages/28/4b/52a0cb2687a9cd1648252bb257be5a1ba2c2ded20ba695c65756a55a15a4/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:24bd94bb301ec672d8f0623eba9226cc90d775d25a0c92b5f8e4965d7f3a1518", size = 3560807 },
+    { url = "https://files.pythonhosted.org/packages/8c/d8/fda95459204877eed0458550d6c7c64c98cc50c2d8d618026737de9ed41a/sqlalchemy-2.0.49-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a51d3db74ba489266ef55c7a4534eb0b8db9a326553df481c11e5d7660c8364d", size = 3527481 },
+    { url = "https://files.pythonhosted.org/packages/ff/0a/2aac8b78ac6487240cf7afef8f203ca783e8796002dc0cf65c4ee99ff8bb/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:55250fe61d6ebfd6934a272ee16ef1244e0f16b7af6cd18ab5b1fc9f08631db0", size = 3468565 },
+    { url = "https://files.pythonhosted.org/packages/a5/3d/ce71cfa82c50a373fd2148b3c870be05027155ce791dc9a5dcf439790b8b/sqlalchemy-2.0.49-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:46796877b47034b559a593d7e4b549aba151dae73f9e78212a3478161c12ab08", size = 3477769 },
+    { url = "https://files.pythonhosted.org/packages/d5/e8/0a9f5c1f7c6f9ca480319bf57c2d7423f08d31445974167a27d14483c948/sqlalchemy-2.0.49-cp313-cp313t-win32.whl", hash = "sha256:9c4969a86e41454f2858256c39bdfb966a20961e9b58bf8749b65abf447e9a8d", size = 2143319 },
+    { url = "https://files.pythonhosted.org/packages/0e/51/fb5240729fbec73006e137c4f7a7918ffd583ab08921e6ff81a999d6517a/sqlalchemy-2.0.49-cp313-cp313t-win_amd64.whl", hash = "sha256:b9870d15ef00e4d0559ae10ee5bc71b654d1f20076dbe8bc7ed19b4c0625ceba", size = 2175104 },
+    { url = "https://files.pythonhosted.org/packages/55/33/bf28f618c0a9597d14e0b9ee7d1e0622faff738d44fe986ee287cdf1b8d0/sqlalchemy-2.0.49-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:233088b4b99ebcbc5258c755a097aa52fbf90727a03a5a80781c4b9c54347a2e", size = 2156356 },
+    { url = "https://files.pythonhosted.org/packages/d1/a7/5f476227576cb8644650eff68cc35fa837d3802b997465c96b8340ced1e2/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57ca426a48eb2c682dae8204cd89ea8ab7031e2675120a47924fabc7caacbc2a", size = 3276486 },
+    { url = "https://files.pythonhosted.org/packages/2e/84/efc7c0bf3a1c5eef81d397f6fddac855becdbb11cb38ff957888603014a7/sqlalchemy-2.0.49-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:685e93e9c8f399b0c96a624799820176312f5ceef958c0f88215af4013d29066", size = 3281479 },
+    { url = "https://files.pythonhosted.org/packages/91/68/bb406fa4257099c67bd75f3f2261b129c63204b9155de0d450b37f004698/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9e0400fa22f79acc334d9a6b185dc00a44a8e6578aa7e12d0ddcd8434152b187", size = 3226269 },
+    { url = "https://files.pythonhosted.org/packages/67/84/acb56c00cca9f251f437cb49e718e14f7687505749ea9255d7bd8158a6df/sqlalchemy-2.0.49-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a05977bffe9bffd2229f477fa75eabe3192b1b05f408961d1bebff8d1cd4d401", size = 3248260 },
+    { url = "https://files.pythonhosted.org/packages/56/19/6a20ea25606d1efd7bd1862149bb2a22d1451c3f851d23d887969201633f/sqlalchemy-2.0.49-cp314-cp314-win32.whl", hash = "sha256:0f2fa354ba106eafff2c14b0cc51f22801d1e8b2e4149342023bd6f0955de5f5", size = 2118463 },
+    { url = "https://files.pythonhosted.org/packages/cf/4f/8297e4ed88e80baa1f5aa3c484a0ee29ef3c69c7582f206c916973b75057/sqlalchemy-2.0.49-cp314-cp314-win_amd64.whl", hash = "sha256:77641d299179c37b89cf2343ca9972c88bb6eef0d5fc504a2f86afd15cd5adf5", size = 2144204 },
+    { url = "https://files.pythonhosted.org/packages/1f/33/95e7216df810c706e0cd3655a778604bbd319ed4f43333127d465a46862d/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c1dc3368794d522f43914e03312202523cc89692f5389c32bea0233924f8d977", size = 3565474 },
+    { url = "https://files.pythonhosted.org/packages/0c/a4/ed7b18d8ccf7f954a83af6bb73866f5bc6f5636f44c7731fbb741f72cc4f/sqlalchemy-2.0.49-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7c821c47ecfe05cc32140dcf8dc6fd5d21971c86dbd56eabfe5ba07a64910c01", size = 3530567 },
+    { url = "https://files.pythonhosted.org/packages/73/a3/20faa869c7e21a827c4a2a42b41353a54b0f9f5e96df5087629c306df71e/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9c04bff9a5335eb95c6ecf1c117576a0aa560def274876fd156cfe5510fccc61", size = 3474282 },
+    { url = "https://files.pythonhosted.org/packages/b7/50/276b9a007aa0764304ad467eceb70b04822dc32092492ee5f322d559a4dc/sqlalchemy-2.0.49-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:7f605a456948c35260e7b2a39f8952a26f077fd25653c37740ed186b90aaa68a", size = 3480406 },
+    { url = "https://files.pythonhosted.org/packages/e5/c3/c80fcdb41905a2df650c2a3e0337198b6848876e63d66fe9188ef9003d24/sqlalchemy-2.0.49-cp314-cp314t-win32.whl", hash = "sha256:6270d717b11c5476b0cbb21eedc8d4dbb7d1a956fd6c15a23e96f197a6193158", size = 2149151 },
+    { url = "https://files.pythonhosted.org/packages/05/52/9f1a62feab6ed368aff068524ff414f26a6daebc7361861035ae00b05530/sqlalchemy-2.0.49-cp314-cp314t-win_amd64.whl", hash = "sha256:275424295f4256fd301744b8f335cff367825d270f155d522b30c7bf49903ee7", size = 2184178 },
+    { url = "https://files.pythonhosted.org/packages/e5/30/8519fdde58a7bdf155b714359791ad1dc018b47d60269d5d160d311fdc36/sqlalchemy-2.0.49-py3-none-any.whl", hash = "sha256:ec44cfa7ef1a728e88ad41674de50f6db8cfdb3e2af84af86e0041aaf02d43d0", size = 1942158 },
+]
+
+[package.optional-dependencies]
+asyncio = [
+    { name = "greenlet" },
+]
+
 [[package]]
 name = "surfsense-evals"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
+    { name = "azure-ai-documentintelligence" },
     { name = "datasets" },
     { name = "httpx" },
     { name = "httpx-sse" },
     { name = "huggingface-hub" },
+    { name = "llama-cloud-services" },
     { name = "numpy" },
     { name = "pillow" },
     { name = "pyarrow" },
     { name = "pydantic" },
+    { name = "pypdf" },
     { name = "python-dotenv" },
     { name = "reportlab" },
     { name = "rich" },
     { name = "scikit-learn" },
     { name = "scipy" },
     { name = "tqdm" },
+    { name = "trafilatura" },
 ]
 
 [package.optional-dependencies]
@@ -1428,14 +2185,17 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "azure-ai-documentintelligence", specifier = ">=1.0.2" },
     { name = "datasets", specifier = ">=2.21.0" },
     { name = "httpx", specifier = ">=0.27.0" },
     { name = "httpx-sse", specifier = ">=0.4.0" },
     { name = "huggingface-hub", specifier = ">=0.24.0" },
+    { name = "llama-cloud-services", specifier = ">=0.6.25" },
     { name = "numpy", specifier = ">=1.26.0" },
     { name = "pillow", specifier = ">=10.0.0" },
     { name = "pyarrow", specifier = ">=15.0.0" },
     { name = "pydantic", specifier = ">=2.6.0" },
+    { name = "pypdf", specifier = ">=5.1.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
@@ -1446,9 +2206,19 @@ requires-dist = [
     { name = "scikit-learn", specifier = ">=1.4.0" },
     { name = "scipy", specifier = ">=1.12.0" },
     { name = "tqdm", specifier = ">=4.66.0" },
+    { name = "trafilatura", specifier = ">=1.12.0" },
 ]
 provides-extras = ["dev"]
 
+[[package]]
+name = "tenacity"
+version = "9.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/c6/ee486fd809e357697ee8a44d3d69222b344920433d3b6666ccd9b374630c/tenacity-9.1.4.tar.gz", hash = "sha256:adb31d4c263f2bd041081ab33b498309a57c77f9acf2db65aadf0898179cf93a", size = 49413 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d7/c1/eb8f9debc45d3b7918a32ab756658a0904732f75e555402972246b0b8e71/tenacity-9.1.4-py3-none-any.whl", hash = "sha256:6095a360c919085f28c6527de529e76a06ad89b23659fa881ae0649b867a9d55", size = 28926 },
+]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.6.0"
@@ -1458,6 +2228,71 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638 },
 ]
 
+[[package]]
+name = "tiktoken"
+version = "0.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "regex" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/ab/4d017d0f76ec3171d469d80fc03dfbb4e48a4bcaddaa831b31d526f05edc/tiktoken-0.12.0.tar.gz", hash = "sha256:b18ba7ee2b093863978fcb14f74b3707cdc8d4d4d3836853ce7ec60772139931", size = 37806 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a4/85/be65d39d6b647c79800fd9d29241d081d4eeb06271f383bb87200d74cf76/tiktoken-0.12.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b97f74aca0d78a1ff21b8cd9e9925714c15a9236d6ceacf5c7327c117e6e21e8", size = 1050728 },
+    { url = "https://files.pythonhosted.org/packages/4a/42/6573e9129bc55c9bf7300b3a35bef2c6b9117018acca0dc760ac2d93dffe/tiktoken-0.12.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2b90f5ad190a4bb7c3eb30c5fa32e1e182ca1ca79f05e49b448438c3e225a49b", size = 994049 },
+    { url = "https://files.pythonhosted.org/packages/66/c5/ed88504d2f4a5fd6856990b230b56d85a777feab84e6129af0822f5d0f70/tiktoken-0.12.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:65b26c7a780e2139e73acc193e5c63ac754021f160df919add909c1492c0fb37", size = 1129008 },
+    { url = "https://files.pythonhosted.org/packages/f4/90/3dae6cc5436137ebd38944d396b5849e167896fc2073da643a49f372dc4f/tiktoken-0.12.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:edde1ec917dfd21c1f2f8046b86348b0f54a2c0547f68149d8600859598769ad", size = 1152665 },
+    { url = "https://files.pythonhosted.org/packages/a3/fe/26df24ce53ffde419a42f5f53d755b995c9318908288c17ec3f3448313a3/tiktoken-0.12.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:35a2f8ddd3824608b3d650a000c1ef71f730d0c56486845705a8248da00f9fe5", size = 1194230 },
+    { url = "https://files.pythonhosted.org/packages/20/cc/b064cae1a0e9fac84b0d2c46b89f4e57051a5f41324e385d10225a984c24/tiktoken-0.12.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:83d16643edb7fa2c99eff2ab7733508aae1eebb03d5dfc46f5565862810f24e3", size = 1254688 },
+    { url = "https://files.pythonhosted.org/packages/81/10/b8523105c590c5b8349f2587e2fdfe51a69544bd5a76295fc20f2374f470/tiktoken-0.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:ffc5288f34a8bc02e1ea7047b8d041104791d2ddbf42d1e5fa07822cbffe16bd", size = 878694 },
+    { url = "https://files.pythonhosted.org/packages/00/61/441588ee21e6b5cdf59d6870f86beb9789e532ee9718c251b391b70c68d6/tiktoken-0.12.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:775c2c55de2310cc1bc9a3ad8826761cbdc87770e586fd7b6da7d4589e13dab3", size = 1050802 },
+    { url = "https://files.pythonhosted.org/packages/1f/05/dcf94486d5c5c8d34496abe271ac76c5b785507c8eae71b3708f1ad9b45a/tiktoken-0.12.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a01b12f69052fbe4b080a2cfb867c4de12c704b56178edf1d1d7b273561db160", size = 993995 },
+    { url = "https://files.pythonhosted.org/packages/a0/70/5163fe5359b943f8db9946b62f19be2305de8c3d78a16f629d4165e2f40e/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:01d99484dc93b129cd0964f9d34eee953f2737301f18b3c7257bf368d7615baa", size = 1128948 },
+    { url = "https://files.pythonhosted.org/packages/0c/da/c028aa0babf77315e1cef357d4d768800c5f8a6de04d0eac0f377cb619fa/tiktoken-0.12.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:4a1a4fcd021f022bfc81904a911d3df0f6543b9e7627b51411da75ff2fe7a1be", size = 1151986 },
+    { url = "https://files.pythonhosted.org/packages/a0/5a/886b108b766aa53e295f7216b509be95eb7d60b166049ce2c58416b25f2a/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:981a81e39812d57031efdc9ec59fa32b2a5a5524d20d4776574c4b4bd2e9014a", size = 1194222 },
+    { url = "https://files.pythonhosted.org/packages/f4/f8/4db272048397636ac7a078d22773dd2795b1becee7bc4922fe6207288d57/tiktoken-0.12.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9baf52f84a3f42eef3ff4e754a0db79a13a27921b457ca9832cf944c6be4f8f3", size = 1255097 },
+    { url = "https://files.pythonhosted.org/packages/8e/32/45d02e2e0ea2be3a9ed22afc47d93741247e75018aac967b713b2941f8ea/tiktoken-0.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:b8a0cd0c789a61f31bf44851defbd609e8dd1e2c8589c614cc1060940ef1f697", size = 879117 },
+    { url = "https://files.pythonhosted.org/packages/ce/76/994fc868f88e016e6d05b0da5ac24582a14c47893f4474c3e9744283f1d5/tiktoken-0.12.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:d5f89ea5680066b68bcb797ae85219c72916c922ef0fcdd3480c7d2315ffff16", size = 1050309 },
+    { url = "https://files.pythonhosted.org/packages/f6/b8/57ef1456504c43a849821920d582a738a461b76a047f352f18c0b26c6516/tiktoken-0.12.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b4e7ed1c6a7a8a60a3230965bdedba8cc58f68926b835e519341413370e0399a", size = 993712 },
+    { url = "https://files.pythonhosted.org/packages/72/90/13da56f664286ffbae9dbcfadcc625439142675845baa62715e49b87b68b/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:fc530a28591a2d74bce821d10b418b26a094bf33839e69042a6e86ddb7a7fb27", size = 1128725 },
+    { url = "https://files.pythonhosted.org/packages/05/df/4f80030d44682235bdaecd7346c90f67ae87ec8f3df4a3442cb53834f7e4/tiktoken-0.12.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:06a9f4f49884139013b138920a4c393aa6556b2f8f536345f11819389c703ebb", size = 1151875 },
+    { url = "https://files.pythonhosted.org/packages/22/1f/ae535223a8c4ef4c0c1192e3f9b82da660be9eb66b9279e95c99288e9dab/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:04f0e6a985d95913cabc96a741c5ffec525a2c72e9df086ff17ebe35985c800e", size = 1194451 },
+    { url = "https://files.pythonhosted.org/packages/78/a7/f8ead382fce0243cb625c4f266e66c27f65ae65ee9e77f59ea1653b6d730/tiktoken-0.12.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:0ee8f9ae00c41770b5f9b0bb1235474768884ae157de3beb5439ca0fd70f3e25", size = 1253794 },
+    { url = "https://files.pythonhosted.org/packages/93/e0/6cc82a562bc6365785a3ff0af27a2a092d57c47d7a81d9e2295d8c36f011/tiktoken-0.12.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dc2dd125a62cb2b3d858484d6c614d136b5b848976794edfb63688d539b8b93f", size = 878777 },
+    { url = "https://files.pythonhosted.org/packages/72/05/3abc1db5d2c9aadc4d2c76fa5640134e475e58d9fbb82b5c535dc0de9b01/tiktoken-0.12.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:a90388128df3b3abeb2bfd1895b0681412a8d7dc644142519e6f0a97c2111646", size = 1050188 },
+    { url = "https://files.pythonhosted.org/packages/e3/7b/50c2f060412202d6c95f32b20755c7a6273543b125c0985d6fa9465105af/tiktoken-0.12.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:da900aa0ad52247d8794e307d6446bd3cdea8e192769b56276695d34d2c9aa88", size = 993978 },
+    { url = "https://files.pythonhosted.org/packages/14/27/bf795595a2b897e271771cd31cb847d479073497344c637966bdf2853da1/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:285ba9d73ea0d6171e7f9407039a290ca77efcdb026be7769dccc01d2c8d7fff", size = 1129271 },
+    { url = "https://files.pythonhosted.org/packages/f5/de/9341a6d7a8f1b448573bbf3425fa57669ac58258a667eb48a25dfe916d70/tiktoken-0.12.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:d186a5c60c6a0213f04a7a802264083dea1bbde92a2d4c7069e1a56630aef830", size = 1151216 },
+    { url = "https://files.pythonhosted.org/packages/75/0d/881866647b8d1be4d67cb24e50d0c26f9f807f994aa1510cb9ba2fe5f612/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:604831189bd05480f2b885ecd2d1986dc7686f609de48208ebbbddeea071fc0b", size = 1194860 },
+    { url = "https://files.pythonhosted.org/packages/b3/1e/b651ec3059474dab649b8d5b69f5c65cd8fcd8918568c1935bd4136c9392/tiktoken-0.12.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:8f317e8530bb3a222547b85a58583238c8f74fd7a7408305f9f63246d1a0958b", size = 1254567 },
+    { url = "https://files.pythonhosted.org/packages/80/57/ce64fd16ac390fafde001268c364d559447ba09b509181b2808622420eec/tiktoken-0.12.0-cp314-cp314-win_amd64.whl", hash = "sha256:399c3dd672a6406719d84442299a490420b458c44d3ae65516302a99675888f3", size = 921067 },
+    { url = "https://files.pythonhosted.org/packages/ac/a4/72eed53e8976a099539cdd5eb36f241987212c29629d0a52c305173e0a68/tiktoken-0.12.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c2c714c72bc00a38ca969dae79e8266ddec999c7ceccd603cc4f0d04ccd76365", size = 1050473 },
+    { url = "https://files.pythonhosted.org/packages/e6/d7/0110b8f54c008466b19672c615f2168896b83706a6611ba6e47313dbc6e9/tiktoken-0.12.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cbb9a3ba275165a2cb0f9a83f5d7025afe6b9d0ab01a22b50f0e74fee2ad253e", size = 993855 },
+    { url = "https://files.pythonhosted.org/packages/5f/77/4f268c41a3957c418b084dd576ea2fad2e95da0d8e1ab705372892c2ca22/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:dfdfaa5ffff8993a3af94d1125870b1d27aed7cb97aa7eb8c1cefdbc87dbee63", size = 1129022 },
+    { url = "https://files.pythonhosted.org/packages/4e/2b/fc46c90fe5028bd094cd6ee25a7db321cb91d45dc87531e2bdbb26b4867a/tiktoken-0.12.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:584c3ad3d0c74f5269906eb8a659c8bfc6144a52895d9261cdaf90a0ae5f4de0", size = 1150736 },
+    { url = "https://files.pythonhosted.org/packages/28/c0/3c7a39ff68022ddfd7d93f3337ad90389a342f761c4d71de99a3ccc57857/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:54c891b416a0e36b8e2045b12b33dd66fb34a4fe7965565f1b482da50da3e86a", size = 1194908 },
+    { url = "https://files.pythonhosted.org/packages/ab/0d/c1ad6f4016a3968c048545f5d9b8ffebf577774b2ede3e2e352553b685fe/tiktoken-0.12.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5edb8743b88d5be814b1a8a8854494719080c28faaa1ccbef02e87354fe71ef0", size = 1253706 },
+    { url = "https://files.pythonhosted.org/packages/af/df/c7891ef9d2712ad774777271d39fdef63941ffba0a9d59b7ad1fd2765e57/tiktoken-0.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71", size = 920667 },
+]
+
+[[package]]
+name = "tinytag"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/59/8a8cb2331e2602b53e4dc06960f57d1387a2b18e7efd24e5f9cb60ea4925/tinytag-2.2.1.tar.gz", hash = "sha256:e6d06610ebe7cd66fd07be2d3b9495914ab32654a5e47657bb8cd44c2484523c", size = 38214 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ce/34/d50e338631baaf65ec5396e70085e5de0b52b24b28db1ffbc1c6e82190dc/tinytag-2.2.1-py3-none-any.whl", hash = "sha256:ed8b1e6d25367937e3321e054f4974f9abfde1a3e0a538824c87da377130c2b6", size = 32927 },
+]
+
+[[package]]
+name = "tld"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5c/5d/76b4383ac4e5b5e254e50c09807b3e13820bed6d6c11cd540264988d6802/tld-0.13.2.tar.gz", hash = "sha256:d983fa92b9d717400742fca844e29d5e18271079c7bcfabf66d01b39b4a14345", size = 467175 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/90/39a85a4b63c84213e78b3c17d22e1bf45328acf8ebb33ef93be30d0a3911/tld-0.13.2-py2.py3-none-any.whl", hash = "sha256:9b8fdbdb880e7ba65b216a4937f2c94c49a7226723783d5838fc958ac76f4e0c", size = 296743 },
+]
+
 [[package]]
 name = "tqdm"
 version = "4.67.3"
@@ -1470,6 +2305,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374 },
 ]
 
+[[package]]
+name = "trafilatura"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "certifi" },
+    { name = "charset-normalizer" },
+    { name = "courlan" },
+    { name = "htmldate" },
+    { name = "justext" },
+    { name = "lxml" },
+    { name = "urllib3" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/06/25/e3ebeefdebfdfae8c4a4396f5a6ea51fc6fa0831d63ce338e5090a8003dc/trafilatura-2.0.0.tar.gz", hash = "sha256:ceb7094a6ecc97e72fea73c7dba36714c5c5b577b6470e4520dca893706d6247", size = 253404 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8a/b6/097367f180b6383a3581ca1b86fcae284e52075fa941d1232df35293363c/trafilatura-2.0.0-py3-none-any.whl", hash = "sha256:77eb5d1e993747f6f20938e1de2d840020719735690c840b9a1024803a4cd51d", size = 132557 },
+]
+
 [[package]]
 name = "typer"
 version = "0.25.1"
@@ -1494,6 +2347,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614 },
 ]
 
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mypy-extensions" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dc/74/1789779d91f1961fa9438e9a8710cdae6bd138c80d7303996933d117264a/typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78", size = 13825 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/65/f3/107a22063bf27bdccf2024833d3445f4eea42b2e598abfbd46f6a63b6cb0/typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f", size = 8827 },
+]
+
 [[package]]
 name = "typing-inspection"
 version = "0.4.2"
@@ -1515,6 +2381,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321 },
 ]
 
+[[package]]
+name = "tzlocal"
+version = "5.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "tzdata", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8b/2e/c14812d3d4d9cd1773c6be938f89e5735a1f11a9f184ac3639b93cef35d5/tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd", size = 30761 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c2/14/e2a54fabd4f08cd7af1c07030603c3356b74da07f7cc056e600436edfa17/tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d", size = 18026 },
+]
+
 [[package]]
 name = "urllib3"
 version = "2.7.0"
@@ -1524,6 +2402,70 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087 },
 ]
 
+[[package]]
+name = "wrapt"
+version = "2.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2e/64/925f213fdcbb9baeb1530449ac71a4d57fc361c053d06bf78d0c5c7cd80c/wrapt-2.1.2.tar.gz", hash = "sha256:3996a67eecc2c68fd47b4e3c564405a5777367adfd9b8abb58387b63ee83b21e", size = 81678 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4c/b6/1db817582c49c7fcbb7df6809d0f515af29d7c2fbf57eb44c36e98fb1492/wrapt-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ff2aad9c4cda28a8f0653fc2d487596458c2a3f475e56ba02909e950a9efa6a9", size = 61255 },
+    { url = "https://files.pythonhosted.org/packages/a2/16/9b02a6b99c09227c93cd4b73acc3678114154ec38da53043c0ddc1fba0dc/wrapt-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6433ea84e1cfacf32021d2a4ee909554ade7fd392caa6f7c13f1f4bf7b8e8748", size = 61848 },
+    { url = "https://files.pythonhosted.org/packages/af/aa/ead46a88f9ec3a432a4832dfedb84092fc35af2d0ba40cd04aea3889f247/wrapt-2.1.2-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:c20b757c268d30d6215916a5fa8461048d023865d888e437fab451139cad6c8e", size = 121433 },
+    { url = "https://files.pythonhosted.org/packages/3a/9f/742c7c7cdf58b59085a1ee4b6c37b013f66ac33673a7ef4aaed5e992bc33/wrapt-2.1.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:79847b83eb38e70d93dc392c7c5b587efe65b3e7afcc167aa8abd5d60e8761c8", size = 123013 },
+    { url = "https://files.pythonhosted.org/packages/e8/44/2c3dd45d53236b7ed7c646fcf212251dc19e48e599debd3926b52310fafb/wrapt-2.1.2-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f8fba1bae256186a83d1875b2b1f4e2d1242e8fac0f58ec0d7e41b26967b965c", size = 117326 },
+    { url = "https://files.pythonhosted.org/packages/74/e2/b17d66abc26bd96f89dec0ecd0ef03da4a1286e6ff793839ec431b9fae57/wrapt-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e3d3b35eedcf5f7d022291ecd7533321c4775f7b9cd0050a31a68499ba45757c", size = 121444 },
+    { url = "https://files.pythonhosted.org/packages/3c/62/e2977843fdf9f03daf1586a0ff49060b1b2fc7ff85a7ea82b6217c1ae36e/wrapt-2.1.2-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:6f2c5390460de57fa9582bc8a1b7a6c86e1a41dfad74c5225fc07044c15cc8d1", size = 116237 },
+    { url = "https://files.pythonhosted.org/packages/88/dd/27fc67914e68d740bce512f11734aec08696e6b17641fef8867c00c949fc/wrapt-2.1.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:7dfa9f2cf65d027b951d05c662cc99ee3bd01f6e4691ed39848a7a5fffc902b2", size = 120563 },
+    { url = "https://files.pythonhosted.org/packages/ec/9f/b750b3692ed2ef4705cb305bd68858e73010492b80e43d2a4faa5573cbe7/wrapt-2.1.2-cp312-cp312-win32.whl", hash = "sha256:eba8155747eb2cae4a0b913d9ebd12a1db4d860fc4c829d7578c7b989bd3f2f0", size = 58198 },
+    { url = "https://files.pythonhosted.org/packages/8e/b2/feecfe29f28483d888d76a48f03c4c4d8afea944dbee2b0cd3380f9df032/wrapt-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:1c51c738d7d9faa0b3601708e7e2eda9bf779e1b601dce6c77411f2a1b324a63", size = 60441 },
+    { url = "https://files.pythonhosted.org/packages/44/e1/e328f605d6e208547ea9fd120804fcdec68536ac748987a68c47c606eea8/wrapt-2.1.2-cp312-cp312-win_arm64.whl", hash = "sha256:c8e46ae8e4032792eb2f677dbd0d557170a8e5524d22acc55199f43efedd39bf", size = 58836 },
+    { url = "https://files.pythonhosted.org/packages/4c/7a/d936840735c828b38d26a854e85d5338894cda544cb7a85a9d5b8b9c4df7/wrapt-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:787fd6f4d67befa6fe2abdffcbd3de2d82dfc6fb8a6d850407c53332709d030b", size = 61259 },
+    { url = "https://files.pythonhosted.org/packages/5e/88/9a9b9a90ac8ca11c2fdb6a286cb3a1fc7dd774c00ed70929a6434f6bc634/wrapt-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4bdf26e03e6d0da3f0e9422fd36bcebf7bc0eeb55fdf9c727a09abc6b9fe472e", size = 61851 },
+    { url = "https://files.pythonhosted.org/packages/03/a9/5b7d6a16fd6533fed2756900fc8fc923f678179aea62ada6d65c92718c00/wrapt-2.1.2-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:bbac24d879aa22998e87f6b3f481a5216311e7d53c7db87f189a7a0266dafffb", size = 121446 },
+    { url = "https://files.pythonhosted.org/packages/45/bb/34c443690c847835cfe9f892be78c533d4f32366ad2888972c094a897e39/wrapt-2.1.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:16997dfb9d67addc2e3f41b62a104341e80cac52f91110dece393923c0ebd5ca", size = 123056 },
+    { url = "https://files.pythonhosted.org/packages/93/b9/ff205f391cb708f67f41ea148545f2b53ff543a7ac293b30d178af4d2271/wrapt-2.1.2-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:162e4e2ba7542da9027821cb6e7c5e068d64f9a10b5f15512ea28e954893a267", size = 117359 },
+    { url = "https://files.pythonhosted.org/packages/1f/3d/1ea04d7747825119c3c9a5e0874a40b33594ada92e5649347c457d982805/wrapt-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f29c827a8d9936ac320746747a016c4bc66ef639f5cd0d32df24f5eacbf9c69f", size = 121479 },
+    { url = "https://files.pythonhosted.org/packages/78/cc/ee3a011920c7a023b25e8df26f306b2484a531ab84ca5c96260a73de76c0/wrapt-2.1.2-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:a9dd9813825f7ecb018c17fd147a01845eb330254dff86d3b5816f20f4d6aaf8", size = 116271 },
+    { url = "https://files.pythonhosted.org/packages/98/fd/e5ff7ded41b76d802cf1191288473e850d24ba2e39a6ec540f21ae3b57cb/wrapt-2.1.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6f8dbdd3719e534860d6a78526aafc220e0241f981367018c2875178cf83a413", size = 120573 },
+    { url = "https://files.pythonhosted.org/packages/47/c5/242cae3b5b080cd09bacef0591691ba1879739050cc7c801ff35c8886b66/wrapt-2.1.2-cp313-cp313-win32.whl", hash = "sha256:5c35b5d82b16a3bc6e0a04349b606a0582bc29f573786aebe98e0c159bc48db6", size = 58205 },
+    { url = "https://files.pythonhosted.org/packages/12/69/c358c61e7a50f290958809b3c61ebe8b3838ea3e070d7aac9814f95a0528/wrapt-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f8bc1c264d8d1cf5b3560a87bbdd31131573eb25f9f9447bb6252b8d4c44a3a1", size = 60452 },
+    { url = "https://files.pythonhosted.org/packages/8e/66/c8a6fcfe321295fd8c0ab1bd685b5a01462a9b3aa2f597254462fc2bc975/wrapt-2.1.2-cp313-cp313-win_arm64.whl", hash = "sha256:3beb22f674550d5634642c645aba4c72a2c66fb185ae1aebe1e955fae5a13baf", size = 58842 },
+    { url = "https://files.pythonhosted.org/packages/da/55/9c7052c349106e0b3f17ae8db4b23a691a963c334de7f9dbd60f8f74a831/wrapt-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0fc04bc8664a8bc4c8e00b37b5355cffca2535209fba1abb09ae2b7c76ddf82b", size = 63075 },
+    { url = "https://files.pythonhosted.org/packages/09/a8/ce7b4006f7218248dd71b7b2b732d0710845a0e49213b18faef64811ffef/wrapt-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a9b9d50c9af998875a1482a038eb05755dfd6fe303a313f6a940bb53a83c3f18", size = 63719 },
+    { url = "https://files.pythonhosted.org/packages/e4/e5/2ca472e80b9e2b7a17f106bb8f9df1db11e62101652ce210f66935c6af67/wrapt-2.1.2-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:2d3ff4f0024dd224290c0eabf0240f1bfc1f26363431505fb1b0283d3b08f11d", size = 152643 },
+    { url = "https://files.pythonhosted.org/packages/36/42/30f0f2cefca9d9cbf6835f544d825064570203c3e70aa873d8ae12e23791/wrapt-2.1.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3278c471f4468ad544a691b31bb856374fbdefb7fee1a152153e64019379f015", size = 158805 },
+    { url = "https://files.pythonhosted.org/packages/bb/67/d08672f801f604889dcf58f1a0b424fe3808860ede9e03affc1876b295af/wrapt-2.1.2-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:a8914c754d3134a3032601c6984db1c576e6abaf3fc68094bb8ab1379d75ff92", size = 145990 },
+    { url = "https://files.pythonhosted.org/packages/68/a7/fd371b02e73babec1de6ade596e8cd9691051058cfdadbfd62a5898f3295/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ff95d4264e55839be37bafe1536db2ab2de19da6b65f9244f01f332b5286cfbf", size = 155670 },
+    { url = "https://files.pythonhosted.org/packages/86/2d/9fe0095dfdb621009f40117dcebf41d7396c2c22dca6eac779f4c007b86c/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:76405518ca4e1b76fbb1b9f686cff93aebae03920cc55ceeec48ff9f719c5f67", size = 144357 },
+    { url = "https://files.pythonhosted.org/packages/0e/b6/ec7b4a254abbe4cde9fa15c5d2cca4518f6b07d0f1b77d4ee9655e30280e/wrapt-2.1.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c0be8b5a74c5824e9359b53e7e58bef71a729bacc82e16587db1c4ebc91f7c5a", size = 150269 },
+    { url = "https://files.pythonhosted.org/packages/6e/6b/2fabe8ebf148f4ee3c782aae86a795cc68ffe7d432ef550f234025ce0cfa/wrapt-2.1.2-cp313-cp313t-win32.whl", hash = "sha256:f01277d9a5fc1862f26f7626da9cf443bebc0abd2f303f41c5e995b15887dabd", size = 59894 },
+    { url = "https://files.pythonhosted.org/packages/ca/fb/9ba66fc2dedc936de5f8073c0217b5d4484e966d87723415cc8262c5d9c2/wrapt-2.1.2-cp313-cp313t-win_amd64.whl", hash = "sha256:84ce8f1c2104d2f6daa912b1b5b039f331febfeee74f8042ad4e04992bd95c8f", size = 63197 },
+    { url = "https://files.pythonhosted.org/packages/c0/1c/012d7423c95d0e337117723eb8ecf73c622ce15a97847e84cf3f8f26cd7e/wrapt-2.1.2-cp313-cp313t-win_arm64.whl", hash = "sha256:a93cd767e37faeddbe07d8fc4212d5cba660af59bdb0f6372c93faaa13e6e679", size = 60363 },
+    { url = "https://files.pythonhosted.org/packages/39/25/e7ea0b417db02bb796182a5316398a75792cd9a22528783d868755e1f669/wrapt-2.1.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:1370e516598854e5b4366e09ce81e08bfe94d42b0fd569b88ec46cc56d9164a9", size = 61418 },
+    { url = "https://files.pythonhosted.org/packages/ec/0f/fa539e2f6a770249907757eaeb9a5ff4deb41c026f8466c1c6d799088a9b/wrapt-2.1.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:6de1a3851c27e0bd6a04ca993ea6f80fc53e6c742ee1601f486c08e9f9b900a9", size = 61914 },
+    { url = "https://files.pythonhosted.org/packages/53/37/02af1867f5b1441aaeda9c82deed061b7cd1372572ddcd717f6df90b5e93/wrapt-2.1.2-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:de9f1a2bbc5ac7f6012ec24525bdd444765a2ff64b5985ac6e0692144838542e", size = 120417 },
+    { url = "https://files.pythonhosted.org/packages/c3/b7/0138a6238c8ba7476c77cf786a807f871672b37f37a422970342308276e7/wrapt-2.1.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:970d57ed83fa040d8b20c52fe74a6ae7e3775ae8cff5efd6a81e06b19078484c", size = 122797 },
+    { url = "https://files.pythonhosted.org/packages/e1/ad/819ae558036d6a15b7ed290d5b14e209ca795dd4da9c58e50c067d5927b0/wrapt-2.1.2-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3969c56e4563c375861c8df14fa55146e81ac11c8db49ea6fb7f2ba58bc1ff9a", size = 117350 },
+    { url = "https://files.pythonhosted.org/packages/8b/2d/afc18dc57a4600a6e594f77a9ae09db54f55ba455440a54886694a84c71b/wrapt-2.1.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:57d7c0c980abdc5f1d98b11a2aa3bb159790add80258c717fa49a99921456d90", size = 121223 },
+    { url = "https://files.pythonhosted.org/packages/b9/5b/5ec189b22205697bc56eb3b62aed87a1e0423e9c8285d0781c7a83170d15/wrapt-2.1.2-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:776867878e83130c7a04237010463372e877c1c994d449ca6aaafeab6aab2586", size = 116287 },
+    { url = "https://files.pythonhosted.org/packages/f7/2d/f84939a7c9b5e6cdd8a8d0f6a26cabf36a0f7e468b967720e8b0cd2bdf69/wrapt-2.1.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:fab036efe5464ec3291411fabb80a7a39e2dd80bae9bcbeeca5087fdfa891e19", size = 119593 },
+    { url = "https://files.pythonhosted.org/packages/0b/fe/ccd22a1263159c4ac811ab9374c061bcb4a702773f6e06e38de5f81a1bdc/wrapt-2.1.2-cp314-cp314-win32.whl", hash = "sha256:e6ed62c82ddf58d001096ae84ce7f833db97ae2263bff31c9b336ba8cfe3f508", size = 58631 },
+    { url = "https://files.pythonhosted.org/packages/65/0a/6bd83be7bff2e7efaac7b4ac9748da9d75a34634bbbbc8ad077d527146df/wrapt-2.1.2-cp314-cp314-win_amd64.whl", hash = "sha256:467e7c76315390331c67073073d00662015bb730c566820c9ca9b54e4d67fd04", size = 60875 },
+    { url = "https://files.pythonhosted.org/packages/6c/c0/0b3056397fe02ff80e5a5d72d627c11eb885d1ca78e71b1a5c1e8c7d45de/wrapt-2.1.2-cp314-cp314-win_arm64.whl", hash = "sha256:da1f00a557c66225d53b095a97eace0fc5349e3bfda28fa34ffae238978ee575", size = 59164 },
+    { url = "https://files.pythonhosted.org/packages/71/ed/5d89c798741993b2371396eb9d4634f009ff1ad8a6c78d366fe2883ea7a6/wrapt-2.1.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:62503ffbc2d3a69891cf29beeaccdb4d5e0a126e2b6a851688d4777e01428dbb", size = 63163 },
+    { url = "https://files.pythonhosted.org/packages/c6/8c/05d277d182bf36b0a13d6bd393ed1dec3468a25b59d01fba2dd70fe4d6ae/wrapt-2.1.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c7e6cd120ef837d5b6f860a6ea3745f8763805c418bb2f12eeb1fa6e25f22d22", size = 63723 },
+    { url = "https://files.pythonhosted.org/packages/f4/27/6c51ec1eff4413c57e72d6106bb8dec6f0c7cdba6503d78f0fa98767bcc9/wrapt-2.1.2-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:3769a77df8e756d65fbc050333f423c01ae012b4f6731aaf70cf2bef61b34596", size = 152652 },
+    { url = "https://files.pythonhosted.org/packages/db/4c/d7dd662d6963fc7335bfe29d512b02b71cdfa23eeca7ab3ac74a67505deb/wrapt-2.1.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a76d61a2e851996150ba0f80582dd92a870643fa481f3b3846f229de88caf044", size = 158807 },
+    { url = "https://files.pythonhosted.org/packages/b4/4d/1e5eea1a78d539d346765727422976676615814029522c76b87a95f6bcdd/wrapt-2.1.2-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:6f97edc9842cf215312b75fe737ee7c8adda75a89979f8e11558dfff6343cc4b", size = 146061 },
+    { url = "https://files.pythonhosted.org/packages/89/bc/62cabea7695cd12a288023251eeefdcb8465056ddaab6227cb78a2de005b/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:4006c351de6d5007aa33a551f600404ba44228a89e833d2fadc5caa5de8edfbf", size = 155667 },
+    { url = "https://files.pythonhosted.org/packages/e9/99/6f2888cd68588f24df3a76572c69c2de28287acb9e1972bf0c83ce97dbc1/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:a9372fc3639a878c8e7d87e1556fa209091b0a66e912c611e3f833e2c4202be2", size = 144392 },
+    { url = "https://files.pythonhosted.org/packages/40/51/1dfc783a6c57971614c48e361a82ca3b6da9055879952587bc99fe1a7171/wrapt-2.1.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3144b027ff30cbd2fca07c0a87e67011adb717eb5f5bd8496325c17e454257a3", size = 150296 },
+    { url = "https://files.pythonhosted.org/packages/6c/38/cbb8b933a0201076c1f64fc42883b0023002bdc14a4964219154e6ff3350/wrapt-2.1.2-cp314-cp314t-win32.whl", hash = "sha256:3b8d15e52e195813efe5db8cec156eebe339aaf84222f4f4f051a6c01f237ed7", size = 60539 },
+    { url = "https://files.pythonhosted.org/packages/82/dd/e5176e4b241c9f528402cebb238a36785a628179d7d8b71091154b3e4c9e/wrapt-2.1.2-cp314-cp314t-win_amd64.whl", hash = "sha256:08ffa54146a7559f5b8df4b289b46d963a8e74ed16ba3687f99896101a3990c5", size = 63969 },
+    { url = "https://files.pythonhosted.org/packages/5c/99/79f17046cf67e4a95b9987ea129632ba8bcec0bc81f3fb3d19bdb0bd60cd/wrapt-2.1.2-cp314-cp314t-win_arm64.whl", hash = "sha256:72aaa9d0d8e4ed0e2e98019cea47a21f823c9dd4b43c7b77bba6679ffcca6a00", size = 60554 },
+    { url = "https://files.pythonhosted.org/packages/1a/c7/8528ac2dfa2c1e6708f647df7ae144ead13f0a31146f43c7264b4942bf12/wrapt-2.1.2-py3-none-any.whl", hash = "sha256:b8fd6fa2b2c4e7621808f8c62e8317f4aae56e59721ad933bac5239d913cf0e8", size = 43993 },
+]
+
 [[package]]
 name = "xxhash"
 version = "3.7.0"

From e8aad48ddf65cfd36e331e62cdc66872821915bb Mon Sep 17 00:00:00 2001
From: "DESKTOP-RTLN3BA\\$punk" <vermarohanfinal@gmail.com>
Date: Thu, 14 May 2026 20:07:14 -0700
Subject: [PATCH 36/36] refactor(report): enhance citations and clarify
 implementation details

Updated the multimodal_doc_parser_compare_n171_report.md to include detailed code citations for preprocessing costs and retry logic. Improved clarity on the implementation of the retry mechanism and its impact on failure rates. Added a new section for a code citations index to ensure reproducibility of technical claims.

This enhances the report's transparency and allows readers to trace the source of each claim back to the codebase.
---
 ...ltimodal_doc_parser_compare_n171_report.md | 125 +++++++++++++++++-
 1 file changed, 121 insertions(+), 4 deletions(-)

diff --git a/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md b/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
index f24aaf9eb..7bd72ebb9 100644
--- a/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
+++ b/surfsense_evals/reports/blog/multimodal_doc_parser_compare_n171_report.md
@@ -286,7 +286,7 @@ The experiment included:
 2. **Preprocessing cost** for parser-based arms.
 3. **SurfSense preprocessing cost** for the agentic arm.
 
-The preprocessing tariff used:
+The preprocessing tariff used (source: [`runner.py:74-77`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L74-L77), with per-arm mapping at [`runner.py:89-101`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L89-L101) and the `$/Q` overlay at [`runner.py:725-747`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L725-L747)):
 
 | Mode | Cost |
 |---|---:|
@@ -366,7 +366,7 @@ For each ordered pair `(i, j)`, with the post-retry rows:
 - under H0, `b ~ Binomial(b + c, 0.5)`,
 - two-sided p-value: `P(X ≤ min(b, c)) + P(X ≥ max(b, c))` computed exactly.
 
-(Script: `scripts/compute_blog_extras.py`. Pure stdlib `math.comb`, no scipy.)
+(Implementation: [`compute_blog_extras.py:80-99`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L80-L99) for the exact-binomial p-value, [`compute_blog_extras.py:102-141`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L102-L141) for the pairwise table builder. Pure stdlib `math.comb`, no scipy.)
 
 **Pairwise McNemar table (post-retry, sorted by p-value):**
 
@@ -602,7 +602,7 @@ If the model were rejecting requests for being too long, max-OK could not exceed
 SurfSense reported `0 failures / 171 questions` to the eval harness. This is the most important operational result, but it is worth being precise about *why*, because the mechanism is partly architectural rather than purely "better RAG":
 
 1. **The harness call goes to `http://localhost:8000`, not over public internet.** All transport-class failures that hammered the LC arms (TLS renegotiation, intermediate proxy resets, OpenRouter gateway 502s) are simply not reachable over a loopback HTTP connection. SurfSense was not "asked to survive" the same network path the LC arms had to survive.
-2. **The backend retries internal LLM calls.** SurfSense's `/api/v1/new_chat` wraps every internal LLM hop in `RetryAfterMiddleware` (exponential backoff on 5xx, SSL errors, rate limits). Failures the LC arms surfaced as fatal would have been silently retried inside SurfSense and never reached the harness.
+2. **The backend retries internal LLM calls.** SurfSense's `/api/v1/new_chat` wraps every internal LLM hop in `RetryAfterMiddleware` (exponential backoff on 5xx, SSL errors, rate limits — see [`retry_after.py:113-179`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_backend/app/agents/new_chat/middleware/retry_after.py#L113-L179) for the backoff calculation and retry-decision logic). Failures the LC arms surfaced as fatal would have been silently retried inside SurfSense and never reached the harness.
 3. **SurfSense's outbound prompt is small.** The retrieval pipeline produces prompts in the 5–15K token range, not 100–500KB Markdown blobs, so even if SurfSense's calls *were* over public TLS, they would land in the size class where transient transport errors are far rarer.
 
 In other words, "0 failures" is the joint result of three things — agentic retrieval bounding the payload, a robust internal retry layer, and a localhost call shape — and not a claim that the underlying model never erred on SurfSense's behalf.
@@ -634,7 +634,7 @@ The failure distribution shows two different classes of problems:
 
 ### 9.4 Retry experiment: are these failures transient or intrinsic?
 
-To pressure-test the transport-layer hypothesis directly, we re-ran *only* the 37 failed `(arm, qid)` pairs through the same providers, with up to 5 attempts each, exponential backoff (base 1 s, max 30 s, jitter), and concurrency 2. The eval harness was not touched — same prompts, same cached PDFs, same cached parser markdown — only the request was retried. SurfSense was not retried (it had 0 failures and would otherwise have required spinning the backend back up).
+To pressure-test the transport-layer hypothesis directly, we re-ran *only* the 37 failed `(arm, qid)` pairs through the same providers, with up to 5 attempts each, exponential backoff (base 1 s, max 30 s, jitter), and concurrency 2. The eval harness was not touched — same prompts, same cached PDFs, same cached parser markdown — only the request was retried. SurfSense was not retried (it had 0 failures and would otherwise have required spinning the backend back up). Failure detection (any row with `error` set OR empty `raw_text`) is at [`retry_failed_questions.py:99-111`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/retry_failed_questions.py#L99-L111); the per-row retry loop is at [`retry_failed_questions.py:260-304`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/retry_failed_questions.py#L260-L304).
 
 **Result (37 retries):**
 
@@ -1148,6 +1148,123 @@ The claims in this report come with the following caveats. We list them so a rea
 9. **Retry experiment is not blind to its purpose.** The retry policy (5 attempts, exponential backoff, jitter, concurrency 2) was chosen *after* seeing the failure modes. We are not claiming this is the optimal policy across arms — only that with this policy, all LC failures recover and a clean residue of intrinsic native_pdf failures remains.
 10. **No statistical test was run for cost differences.** All cost numbers are point estimates from a single run; we do not report cost CIs because the variance comes from token-count variability per question and is well-modeled by the input-token distributions in §7.4 if a reader wants to construct a CI themselves.
 
+### 14.4 Code citations index
+
+Every technical claim in this report is reproducible from the code in this repository. The table below maps each claim to its exact source-of-truth file and line range, pinned to commit [`9bcd5016`](https://github.com/MODSetter/SurfSense/commit/9bcd5016) so the line numbers stay valid even if the files change later.
+
+#### Eval harness — arm definitions
+
+| Claim / construct | File@lines |
+|---|---|
+| `NativePdfArm` — attaches the PDF as an OpenRouter file part | [`core/arms/native_pdf.py:21-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/arms/native_pdf.py#L21) |
+| `BareLlmArm` — chat-completion with no retrieval (used for the four LC arms) | [`core/arms/bare_llm.py:22-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/arms/bare_llm.py#L22) |
+| `SurfSenseArm` — `/api/v1/new_chat` SSE consumer | [`core/arms/surfsense.py:30-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/arms/surfsense.py#L30) |
+| `OpenRouterChatProvider` — bare chat-completion HTTP client | [`core/providers/openrouter_chat.py:40-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/providers/openrouter_chat.py#L40) |
+| `OpenRouterPdfProvider` — file-parser-plugin chat-completion client | [`core/providers/openrouter_pdf.py:72-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/providers/openrouter_pdf.py#L72) |
+
+#### Eval harness — parser SDK callers (LC arms)
+
+| Claim / construct | File@lines |
+|---|---|
+| Azure DI mode→model map (`basic`→`prebuilt-read`, `premium`→`prebuilt-layout`) | [`core/parsers/azure_di.py:33-35`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/parsers/azure_di.py#L33-L35) |
+| LlamaCloud mode→mode map (`basic`→`parse_page_with_llm`, `premium`→`parse_page_with_agent`) | [`core/parsers/llamacloud.py:32-34`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/parsers/llamacloud.py#L32-L34) |
+| `pypdf`-based page count (used for the per-page tariff calculation) | [`core/parsers/pdf_pages.py`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/parsers/pdf_pages.py) |
+
+#### Eval harness — parser_compare benchmark
+
+| Claim / construct | File@lines |
+|---|---|
+| `ParserCompareBenchmark` (six-arm runner, prompt construction, raw.jsonl writer) | [`suites/multimodal_doc/parser_compare/runner.py:231-576`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L231-L576) |
+| Prompt: `build_native_pdf_prompt` (PDF attached separately) | [`parser_compare/prompt.py:69-76`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py#L69-L76) |
+| Prompt: `build_long_context_prompt` (full Markdown stuffed inline) | [`parser_compare/prompt.py:92-113`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py#L92-L113) |
+| Prompt: `build_surfsense_prompt` (chunks injected by the agent) | [`parser_compare/prompt.py:79-89`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/prompt.py#L79-L89) |
+| Pre-extraction manifest builder (cached parser outputs) | [`parser_compare/ingest.py`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/ingest.py) |
+
+#### Cost model
+
+| Claim / construct | File@lines |
+|---|---|
+| `PREPROCESS_USD_PER_PAGE` constant (`basic = 0.001`, `premium = 0.010`) | [`runner.py:74-77`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L74-L77) |
+| Per-arm tier mapping (`_LC_ARM_MODE`) | [`runner.py:89-94`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L89-L94) |
+| `SURFSENSE_INGEST_MODE = "premium"` (basis for charging SurfSense the premium tariff) | [`runner.py:96-101`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L96-L101) |
+| Cost overlay (`preprocess_cost_total`, `total_cost_per_q` computation) | [`runner.py:725-747`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/parser_compare/runner.py#L725-L747) |
+
+#### Grader (deterministic, format-aware — §14.1)
+
+| Claim / construct | File@lines |
+|---|---|
+| `GradeResult` dataclass (`correct`, `f1`, `method`, normalised pred/gold) | [`mmlongbench/grader.py:40-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L40) |
+| `_grade_str` (lowercase + strip + exact match) | [`mmlongbench/grader.py:89-104`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L89-L104) |
+| `_grade_int` (regex extract first int, equality) | [`mmlongbench/grader.py:106-120`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L106-L120) |
+| `_grade_float` (1% relative tolerance, 0.01 absolute floor) | [`mmlongbench/grader.py:122-139`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L122-L139) |
+| `_grade_list` (set equality + token-level F1) | [`mmlongbench/grader.py:141-157`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L141-L157) |
+| `_grade_none` ("Not answerable" handling) | [`mmlongbench/grader.py:159-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L159) |
+| Public `grade()` dispatcher | [`mmlongbench/grader.py:224-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/suites/multimodal_doc/mmlongbench/grader.py#L224) |
+
+#### Statistical methodology (§14.2)
+
+| Claim / construct | File@lines |
+|---|---|
+| `wilson_ci()` — Wilson 95% CI for a single proportion | [`core/metrics/mc_accuracy.py:49-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py#L49) |
+| `accuracy_with_wilson_ci()` — full per-arm accuracy + CI struct | [`core/metrics/mc_accuracy.py:73-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/src/surfsense_evals/core/metrics/mc_accuracy.py#L73) |
+| McNemar exact-binomial p-value (§7.3) | [`compute_blog_extras.py:80-99`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L80-L99) |
+| McNemar pairwise table builder | [`compute_blog_extras.py:102-141`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L102-L141) |
+| Latency distribution helpers (§7.4) | [`compute_blog_extras.py:186-213`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L186-L213) |
+| Token distribution helpers (§7.4) | [`compute_blog_extras.py:216-250`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L216-L250) |
+| Per-PDF accuracy heterogeneity (§7.5) | [`compute_blog_extras.py:149-183`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_blog_extras.py#L149-L183) |
+
+#### Retry experiment (§9.4 / §9.5)
+
+| Claim / construct | File@lines |
+|---|---|
+| Failure-row detection (error set OR empty `raw_text`) | [`retry_failed_questions.py:99-111`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/retry_failed_questions.py#L99-L111) |
+| Per-row retry loop (5 attempts, exponential backoff w/ jitter) | [`retry_failed_questions.py:260-304`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/retry_failed_questions.py#L260-L304) |
+| Bounded-concurrency runner | [`retry_failed_questions.py:307-315`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/retry_failed_questions.py#L307-L315) |
+| Post-retry merge + recompute (§9.5 final accuracy table) | [`compute_post_retry_accuracy.py`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/compute_post_retry_accuracy.py) |
+| Context-overflow hypothesis test (§9.2) | [`test_context_overflow_hypothesis.py`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/scripts/test_context_overflow_hypothesis.py) |
+
+#### SurfSense backend (§9.2 — what "0 failures" actually measures)
+
+| Claim / construct | File@lines |
+|---|---|
+| `_exponential_delay()` — backoff with optional ±25% jitter | [`retry_after.py:113-128`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_backend/app/agents/new_chat/middleware/retry_after.py#L113-L128) |
+| `RetryAfterMiddleware` — wraps every internal LLM hop | [`retry_after.py:131-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_backend/app/agents/new_chat/middleware/retry_after.py#L131) |
+| `_should_retry()` — retryable-error classification | [`retry_after.py:171-…`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_backend/app/agents/new_chat/middleware/retry_after.py#L171) |
+| ETL routing — Azure DI preferred over LlamaCloud for compatible types | [`etl_pipeline_service.py:233-251`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_backend/app/etl_pipeline/etl_pipeline_service.py#L233-L251) |
+
+#### Run artifacts (the verifiable numbers source)
+
+These are the *outputs* the report cites — every accuracy / cost / latency number can be re-derived by running the analysis scripts on these JSONL files.
+
+| Artifact | Relative path | Contents |
+|---|---|---|
+| Raw run | [`raw.jsonl`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw.jsonl) | 1 026 rows = 6 arms × 171 questions; one row per `(arm, qid)` with the original ArmResult + grader verdict |
+| Retry log | [`raw_retries.jsonl`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries.jsonl) | 37 rows; per-row attempt timeline + final outcome |
+| Retry summary | [`raw_retries_summary.json`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_retries_summary.json) | per-arm tried / recovered / still-failed counts |
+| Post-retry merged | [`raw_post_retry.jsonl`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/raw_post_retry.jsonl) | 1 026 rows; recovered retries replace originals; basis for §9.5 final accuracy + §7.3 McNemar |
+| Per-arm aggregates | [`run_artifact.json`](https://github.com/MODSetter/SurfSense/blob/9bcd5016/surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/run_artifact.json) | the raw run's per-arm summary metrics + per-PDF correctness map |
+
+#### Reproducing every number in §1, §7, §8, §9
+
+```bash
+# 1) Sanity: load the artifacts that ship with the repo.
+ls surfsense_evals/data/multimodal_doc/runs/2026-05-14T00-53-19Z/parser_compare/
+
+# 2) Recompute the post-retry headline accuracy (§1, §9.5).
+python surfsense_evals/scripts/compute_post_retry_accuracy.py \
+  --run-id 2026-05-14T00-53-19Z
+
+# 3) Recompute McNemar pairwise + latency / token / per-PDF distributions
+#    (§7.3, §7.4, §7.5).
+python surfsense_evals/scripts/compute_blog_extras.py \
+  --run-id 2026-05-14T00-53-19Z
+
+# 4) Re-run the context-overflow hypothesis test (§9.2).
+python surfsense_evals/scripts/test_context_overflow_hypothesis.py
+```
+
+To re-run the experiment end-to-end (slow: needs a backend + celery + ~3 hr ingest + ~2 hr LC arms), use the commands in §14.
+
 ---
 
 ## 15. Appendix: File Locations