diff --git a/benchmarks/silent-rotation/EVIDENCE.md b/benchmarks/silent-rotation/EVIDENCE.md
index 3771eb0..3222ace 100644
--- a/benchmarks/silent-rotation/EVIDENCE.md
+++ b/benchmarks/silent-rotation/EVIDENCE.md
@@ -237,9 +237,14 @@ Number of times each model's dense-cosine agents re-queried before committing:
 reformulate.** Ask once and accept the answer, and you get the decoy. Kimi K3 re-queries twice on
 average and sometimes escapes.
 
-Vestige is **1 call per agent on every model tested**, and the key was surfaced on 47 of 48 successful
-calls, because there is no query to reformulate. `vestige_backfill` takes no query argument. It anchors
-on the failure event and walks the causal edge backward.
+Vestige is **1 call per agent on every model tested**, and the key was surfaced on all 65 successful
+calls across all six models, because there is no query to reformulate. `vestige_backfill` takes no
+query argument. It anchors on the failure event and walks the causal edge backward.
+
+To recount: parse every `results/*/transcript-sync-a*.json`, take each `tool_results` entry named
+`vestige_backfill`, and test its output against that trial's `manifest.json` `correct_kid`. Per model
+that is 15/15 GLM 5.2, 15/15 GPT-5.6 Sol, 14/14 DeepSeek V4 Flash, 12/12 Kimi K3, 6/6 Kimi K2.7 Code,
+3/3 MiniMax M3. Zero empty results and zero errors.
 
 So the honest cross-model statement is not "this works everywhere." It is: **the memory layer's
 behaviour was invariant across five models and the baseline's was not.**
@@ -292,7 +297,7 @@ found by attacking our own work today.
 ```
 harness/                 the runner, the fleet driver, the trial generator
 tests/bm25_baseline.py   reproduces the ranking table, no API key needed
-results/trial-1/         all 7 arms, all 21 agent transcripts
+results/runA-trial-1/    all 7 arms, all 21 agent transcripts
 ```
 
 Start here if you are auditing: every arm JSON carries `memory_layer_alive` and `retrieval_err_total`.
diff --git a/benchmarks/silent-rotation/FINDING.md b/benchmarks/silent-rotation/FINDING.md
index 55af660..bb0c709 100644
--- a/benchmarks/silent-rotation/FINDING.md
+++ b/benchmarks/silent-rotation/FINDING.md
@@ -4,7 +4,7 @@
 
 Trial 1 of the Silent Rotation benchmark. Kimi K3, seven memory configurations, one randomized secret.
 Every number and every quotation below comes from the raw agent transcripts, which are published
-alongside this document in `results/trial-1/`.
+alongside this document in `results/runA-trial-1/`.
 
 ---
 
@@ -289,13 +289,13 @@ Everything needed to check this is in the repository.
 ```
 harness/            the runner, the fleet driver, and the trial generator
 harness/agent/prepare_trial.py     seeds the corpus and randomizes the key
-results/trial-1/    all 7 arm results and all 21 agent transcripts
+results/runA-trial-1/              all 7 arm results and all 21 agent transcripts
 tests/              the arm liveness gate
 ```
 
-The claims above map to files. The outcome table is `results/trial-1/{arm}.json`, field
+The claims above map to files. The outcome table is `results/runA-trial-1/{arm}.json`, field
 `fleet_verdict`. The per-agent key choices are `fix_directions` in the same files. Every quotation is in
-`results/trial-1/transcript-{arm}-a{n}.json` under `turns[].reasoning` and `turns[].tool_results`.
+`results/runA-trial-1/transcript-{arm}-a{n}.json` under `turns[].reasoning` and `turns[].tool_results`.
 
 One thing to check first if you are auditing this: each arm's JSON carries `memory_layer_alive` and
 `retrieval_err_total`. An arm whose backend never answered is not a retrieval loss, it is a missing
diff --git a/benchmarks/silent-rotation/README.md b/benchmarks/silent-rotation/README.md
index 35f9004..223a863 100644
--- a/benchmarks/silent-rotation/README.md
+++ b/benchmarks/silent-rotation/README.md
@@ -136,8 +136,7 @@ These are stated at greater length in `FINDING.md`, and none of them are hidden.
   alone. That is the next experiment.
 - On Kimi K3 the dense cosine baseline never converged wrong (3 correct, 2 split of 5) and it
   costs less per run. Across all 23 shared trials it is 4 correct to Vestige's 20, so the gap
-  is model-dependent rather than uniform.
-  is no "beats RAG on outcomes" claim here to defend.
+  is model-dependent rather than uniform. There is no "beats RAG on outcomes" claim here to defend.
 - I initially broke the mem0 and Zep arms by failing to flush state between
   trials, which disadvantaged them. The harnesses are fixed and those arms were
   re-run clean. Both the broken and the repaired cells are published.