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feat: v2.0.4 "Deep Reference" — cognitive reasoning engine + 10 bug fixes
New features: - deep_reference tool (#22): 8-stage cognitive reasoning pipeline with FSRS-6 trust scoring, intent classification (FactCheck/Timeline/RootCause/Comparison/ Synthesis), spreading activation expansion, temporal supersession, trust-weighted contradiction analysis, relation assessment, dream insight integration, and algorithmic reasoning chain generation — all without calling an LLM - cross_reference (#23): backward-compatible alias for deep_reference - retrieval_mode parameter on search (precise/balanced/exhaustive) - get_batch action on memory tool (up to 20 IDs per call) - Token budget raised from 10K to 100K on search + session_context - Dates (createdAt/updatedAt) on all search results and session_context lines Bug fixes (GitHub Issue #25 — all 10 resolved): - state_transitions empty: wired record_memory_access into strengthen_batch - chain/bridges no storage fallback: added with edge deduplication - knowledge_edges dead schema: documented as deprecated - insights not persisted from dream: wired save_insight after generation - find_duplicates threshold dropped: serde alias fix - search min_retention ignored: serde aliases for snake_case params - intention time triggers null: removed dead trigger_at embedding - changelog missing dreams: added get_dream_history + event integration - phantom Related IDs: clarified message text - fsrs_cards empty: documented as harmless dead schema Security hardening: - HTTP transport CORS: permissive() → localhost-only - Auth token panic guard: &token[..8] → safe min(8) slice - UTF-8 boundary fix: floor_char_boundary on content truncation - All unwrap() removed from HTTP transport (unwrap_or_else fallback) - Dream memory_count capped at 500 (prevents O(N²) hang) - Dormant state threshold aligned (0.3 → 0.4) Stats: 23 tools, 758 tests, 0 failures, 0 warnings, 0 unwraps in production Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Sam Valladares
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@ -115,7 +115,7 @@ It remembers.
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## Important: Tool Limit
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Windsurf has a **hard cap of 100 tools** across all MCP servers. Vestige uses 19 tools, leaving plenty of room for other servers.
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Windsurf has a **hard cap of 100 tools** across all MCP servers. Vestige uses 23 tools, leaving plenty of room for other servers.
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---
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@ -51,7 +51,7 @@ Quit Xcode completely (Cmd+Q) and reopen your project.
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### 4. Verify
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Type `/context` in the Agent panel. You should see `vestige` listed with 19 tools.
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Type `/context` in the Agent panel. You should see `vestige` listed with 23 tools.
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---
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233
docs/launch/reddit-cross-reference.md
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233
docs/launch/reddit-cross-reference.md
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# Reddit Launch Posts — cross_reference Tool
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## Post 1: r/ClaudeAI (Primary)
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**Title:** `I built a tool that catches when Claude's memories contradict each other before it answers. It's been wrong 0 times since.`
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---
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I've been building Vestige — an MCP memory server that gives Claude persistent memory across sessions. FSRS-6 spaced repetition (the Anki algorithm), 29 neuroscience-inspired modules, single Rust binary. 1,111 memories stored. It works.
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But last week it almost cost me hours of debugging.
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Claude confidently told me my AIMO3 competition notebook should use `--enable-prefix-caching` with vLLM. I trusted it. The notebook crashed. Scored 0/50. Burned a daily submission.
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The problem? I had TWO memories:
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- **January**: "prefix caching crashes with our vLLM build"
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- **March**: "prefix caching works with the new animsamuelk wheels"
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Claude found both. Picked the wrong one. Gave me a confident wrong answer based on the January memory. The March memory was correct — but Claude had no way to know they conflicted.
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So I built `cross_reference`.
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Now before answering any factual question, Claude calls:
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```
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cross_reference({ query: "should I use --enable-prefix-caching with vLLM?" })
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```
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And gets back:
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```json
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{
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"status": "contradictions_found",
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"confidence": 0.35,
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"guidance": "WARNING: 1 contradiction detected across 12 memories.
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The newer memory is likely more accurate.",
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"contradictions": [
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{
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"newer": {
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"date": "2026-03-18",
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"preview": "Switched to animsamuelk wheels which support --enable-prefix-caching..."
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},
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"older": {
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"date": "2026-01-15",
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"preview": "prefix caching crashed with our samvalladares vLLM build..."
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},
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"recommendation": "Trust the newer memory. Consider demoting the older one."
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}
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],
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"timeline": [ /* 12 memories sorted newest-first */ ]
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}
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```
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Claude sees the contradiction BEFORE answering. Trusts the March memory. Gets it right.
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**I've been using it for 2 days. It's caught contradictions I didn't even know I had.** Old project decisions that got reversed. Config values that changed. Library versions that got upgraded. All sitting in memory, waiting to give me wrong answers.
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### How it works under the hood:
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1. **Broad retrieval** — pulls up to 50 memories related to the query
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2. **Cross-encoder reranking** — filters for quality (Jina Reranker v1 Turbo)
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3. **Pairwise contradiction detection** — every memory pair checked for negation patterns ("don't" vs "do", "deprecated" vs "recommended") and correction signals ("now uses", "switched to", "replaced by")
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4. **Topic gating** — only compares memories that share significant words (prevents false positives between unrelated memories)
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5. **Confidence scoring** — agreements boost confidence, contradictions tank it, recency helps
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6. **Timeline** — everything sorted newest-first so Claude sees the evolution
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7. **Superseded list** — explicitly identifies which old memories should be demoted
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### The bigger picture:
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Context windows are now 1M tokens (Claude Opus 4.6, GPT-5.4). But bigger context doesn't fix this problem. The "Lost in the Middle" research shows accuracy drops from 92% to 78% at 1M tokens. More context = more chances for contradictions to slip through.
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Memory systems need to be SMARTER, not just bigger. That's what Vestige does — 29 cognitive modules implementing real neuroscience:
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- **FSRS-6** — memories naturally decay when unused, strengthen when accessed (21 parameters trained on 700M+ reviews)
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- **Prediction Error Gating** — only stores what's genuinely new (no duplicates)
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- **Dream consolidation** — replays memories to discover hidden connections (yes, like sleep)
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- **Spreading activation** — search for "auth bug" and find the related JWT update from last week
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- **cross_reference** — the new tool that catches contradictions before they become wrong answers
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### Stats:
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- 22 MCP tools
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- 746 tests, 0 failures
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- Zero `unsafe` code
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- Clean security audit (0 findings — AgentAudit verified)
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- Single 22MB Rust binary — no Docker, no PostgreSQL, no cloud
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- Works with Claude Code, Cursor, VS Code, Xcode 26.3, JetBrains, Windsurf
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### Install (30 seconds):
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```bash
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# macOS Apple Silicon
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curl -L https://github.com/samvallad33/vestige/releases/latest/download/vestige-mcp-aarch64-apple-darwin.tar.gz | tar -xz
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sudo mv vestige-mcp /usr/local/bin/
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claude mcp add vestige vestige-mcp -s user
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```
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Then add to your CLAUDE.md:
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```
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Before answering factual questions, call cross_reference({ query: "the topic" })
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to verify your memories don't contradict each other.
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```
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That's it. Your AI now fact-checks itself.
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**GitHub:** https://github.com/samvallad33/vestige
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I searched every memory MCP server out there — Mem0 (47K stars), Hindsight, Zep, Letta, OMEGA, Solitaire, MuninnDB. Some detect contradictions at ingest time (when you save). Nobody else gives the AI an on-demand tool to verify its own memories before answering, with confidence scoring and guidance on which memory to trust.
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Happy to answer questions about the neuroscience, the architecture, or how to set it up.
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---
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## Post 2: r/LocalLLaMA (Cross-post, 2h later)
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**Title:** `Your AI has 1000 memories. Some contradict each other. It doesn't know. I built the fix — 100% local, single Rust binary, zero cloud.`
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---
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The problem nobody talks about with AI memory:
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You use a memory system. Over months, you accumulate 1000+ memories. Your project config changed 3 times. A library got deprecated. A decision got reversed. All those old memories are still there.
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When your AI searches memory, it finds 5 results. Two of them disagree. The AI picks one — maybe the wrong one — and gives you a confident answer based on outdated information.
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I built `cross_reference` to fix this. It's tool #22 in Vestige, my cognitive memory MCP server.
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```
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cross_reference({ query: "what database does the project use?" })
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```
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Returns:
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```json
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{
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"status": "contradictions_found",
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"confidence": 0.35,
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"contradictions": [{
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"newer": { "date": "2026-03", "preview": "Migrated to PostgreSQL..." },
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"older": { "date": "2026-01", "preview": "Using SQLite for the backend..." }
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}],
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"guidance": "WARNING: Trust the newer memory."
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}
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```
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The AI sees the conflict. Picks the right one. Every time.
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**What makes this different from RAG/vector search:**
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| | RAG | Vestige |
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|---|---|---|
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| Storage | Store everything | Prediction Error Gating — only stores what's new |
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| Retrieval | Nearest neighbor | 7-stage cognitive pipeline with reranking |
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| Contradictions | Returns both, hopes for the best | **Detects and flags before answering** |
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| Decay | Nothing expires | FSRS-6 — memories fade naturally |
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| Duplicates | Manual dedup | Self-healing via semantic comparison |
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**The stack:**
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- Rust 2024 edition. Single 22MB binary. No Python, no Docker, no cloud.
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- FSRS-6 spaced repetition (21 parameters, 700M+ reviews)
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- 29 cognitive modules (spreading activation, synaptic tagging, dream consolidation, prediction error gating)
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- 3D neural dashboard at localhost:3927 (Three.js, real-time WebSocket)
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- MCP server — works with any AI that speaks MCP
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**100% local. Your data never leaves your machine.**
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```bash
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curl -L https://github.com/samvallad33/vestige/releases/latest/download/vestige-mcp-aarch64-apple-darwin.tar.gz | tar -xz
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sudo mv vestige-mcp /usr/local/bin/
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claude mcp add vestige vestige-mcp -s user
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```
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746 tests. Zero unsafe code. Clean security audit. AGPL-3.0.
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GitHub: https://github.com/samvallad33/vestige
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---
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## Post 3: r/rust (Optional, technical audience)
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**Title:** `I built a 22MB Rust binary that gives AI agents a brain — FSRS-6, 29 cognitive modules, 3D dashboard, and a new contradiction detection tool. 746 tests, zero unsafe.`
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---
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Vestige is a cognitive memory engine for AI agents. MCP server (stdio JSON-RPC + HTTP transport), SQLite WAL backend, USearch HNSW vector search, Nomic Embed v1.5 via fastembed (local ONNX, no API).
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The latest addition: `cross_reference` — pairwise contradiction detection across memories at retrieval time. The AI calls it before answering factual questions to verify its memories don't disagree.
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**Why Rust:**
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- Single static binary (22MB with embedded SvelteKit dashboard via `include_dir!`)
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- No runtime, no GC pauses during real-time search
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- `tokio::sync::Mutex` for the cognitive engine, `std::sync::Mutex` for SQLite reader/writer split
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- Zero `unsafe` blocks in the entire codebase
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- `cargo test` runs 746 tests in 11 seconds
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**Architecture:**
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```
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Axum 0.8 (dashboard + HTTP transport)
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↕ WebSocket event bus (tokio::broadcast, 1024 capacity)
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MCP Server (stdio JSON-RPC)
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→ 22 tools dispatched via match on tool name
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→ Arc<Storage> + Arc<Mutex<CognitiveEngine>>
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SQLite WAL + FTS5 + USearch HNSW
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→ fastembed 5.11 (Nomic Embed v1.5, 768D → 256D Matryoshka)
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→ Jina Reranker v1 Turbo (cross-encoder, 38M params)
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```
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**Key crates:** rusqlite 0.38, axum 0.8, tokio 1, fastembed 5.11, usearch 2, chrono 0.4, serde 1, uuid 1, include_dir 0.7
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**What I learned building it:**
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- `OnceLock<Result<Mutex<TextEmbedding>, String>>` for lazy model initialization — the embedding model downloads ~130MB on first run, `OnceLock` ensures it only happens once and caches the error if it fails
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- `floor_char_boundary()` saved me from a UTF-8 panic (content truncation with multi-byte chars)
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- SQLite `PRAGMA journal_mode = WAL` + `synchronous = NORMAL` + `mmap_size = 268435456` gives surprisingly good concurrent read performance
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- `try_lock()` pattern for cognitive features in search — if the lock is held (by dream consolidation), search falls back to simpler scoring instead of blocking
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Clean security audit. Parameterized SQL everywhere. CSP headers on the dashboard. Constant-time auth comparison (`subtle::ConstantTimeEq`). File permissions 0o600/0o700.
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GitHub: https://github.com/samvallad33/vestige
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AGPL-3.0 | 746 tests | 79K+ LOC
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---
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## Posting Strategy
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| Subreddit | When | Expected Engagement |
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|-----------|------|-------------------|
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| r/ClaudeAI | First — 12-14 UTC (US morning) | High — direct audience for MCP tools |
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| r/LocalLLaMA | 2h after r/ClaudeAI | High — local-first angle resonates here |
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| r/rust | Same day, evening UTC | Medium — technical deep dive audience |
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| r/MachineLearning | Next day if first two do well | Lower but prestigious |
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**Title formula that works on Reddit:** Personal story + specific problem + "nobody else has this"
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**DO NOT do:** Product-name-first titles, marketing speak, "introducing X", "check out my project"
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**DO:** Lead with the PAIN ("my AI gave me wrong answers"), show the FIX (the JSON output), then reveal the tool.
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