diff --git a/docs/qa-modes-bench.md b/docs/qa-modes-bench.md
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--- a/docs/qa-modes-bench.md
+++ b/docs/qa-modes-bench.md
@@ -529,3 +529,105 @@ Bench artifacts: `bench/qa_results/2026-05-11T17-12-41Z.{jsonl,md}`
 (before — HEAD's parse_claims.py) · `bench/qa_results/2026-05-11T20-26-37Z.{jsonl,md}`
 (after). Full per-ticket detail: `docs/tickets/ticket-000048-verifier-upgrade-recombination-segmentation.md`
 §5 step 2.4.
+
+### Addendum 8 — #000057 control sweep: retrieval × model × framing × reasoning (2026-05-19/20)
+
+**The question.** Hermes-3-8B confidently states the *present-day*
+office-holder ("the president of France is Emmanuel Macron") against a
+~2010-vintage corpus (Sarkozy). Is that an 8B-model weakness, an unfair
+question framing, or does retrieval fix it regardless? The sweep
+crosses **{model} × {framing} × {retrieval on/off}** over a 386-item
+office-holder fixture (`bench/qa_questions_stale_map.json`), each item
+with a fixed corpus-vintage gold article.
+
+- **models:** `hermes` (Hermes-3-8B), `qwen-nothink` (Qwen3.6-27B,
+  reasoning off), `qwen-think` (same weights, reasoning on). Qwen runs
+  on llama.cpp; Hermes on vLLM.
+- **framings:** `plain` ("who is the president of France?"),
+  `source_relative` ("According to the reference knowledge base, …"),
+  `as_of_corpus` ("As of 2010, who was …").
+- **arms:** `solo` (model alone — measures parametric prior) vs
+  `arborist` (model + retrieval over the 2010 corpus, `answer_mode=
+  claim_lattice`).
+
+**Judge methodology — the code judge.** The original sweep used the
+Opus headless judge (`bench/judge.py`); it burned our Anthropic quota
+and **79.5 % of its verdicts came back JUDGE_ERROR** (rate-limited).
+Replaced with a deterministic code judge (`bench/judge_code.py`):
+abstention regex → short-answer entity-grounding fast path → NLI
+contradiction (θ=0.85) → lexical verifier (quote/span/entity/
+paraphrase) → WRONG-vs-FABRICATED tie-break on subject-in-gold. **No
+LLM, no quota, fully replayable.** Calibrated against the records Opus
+*did* grade: agreement rose CG 13 %→47 %, WRONG 56 %→89 %, ABSTAINED
+80 %→95 % across four targeted fixes (raise NLI contradiction
+threshold off the manifest's 0.5; expand abstention patterns for the
+"I do not have … information/access" family; demote FABRICATED→WRONG
+when gold mentions the question subject; short-answer entity-grounding
+for terse-name answers the verifier's prose-shape extractor whiffs on).
+A claim-lattice JSON-unwrap lets the same judge grade the Arborist
+arm's `{"claims":[…]}` envelopes as plain prose. Verdicts are a
+deterministic *proxy* for grounding (lexical + NLI), not Opus-grade
+reading — JUDGE_ERROR residue (HYBRID-via-entity with no NLI
+corroboration) is the natural input to a later LLM-batch pass.
+
+**Consolidated scorecard — CG% (correct-grounded rate), all arms on
+the identical calibrated judge.** `arborist/hermes` is n=40 (phase 1's
+smaller arborist arm); every other cell is n=386.
+
+| arm / model | plain | source_relative | as_of_corpus |
+|-------------|-------|-----------------|--------------|
+| solo / hermes | 9 % | 5 % | 18 % |
+| solo / qwen-nothink | 7 % | 0 % | **50 %** |
+| solo / qwen-think | 6 % | 5 % | 44 % |
+| arborist / hermes (n=40) | 60 % | 62 % | 25 % |
+| **arborist / qwen-nothink** | **82 %** | **65 %** | 50 % |
+
+Grounding-fidelity detail (abstain% / wrong-assert% = W+F over n) for
+the cells the findings turn on:
+
+| cell | CG% | abstain% | wrong-assert% |
+|------|-----|----------|---------------|
+| solo qwen-nothink / source_relative | 0 % | **97 %** | 1 % |
+| solo qwen-think / source_relative | 5 % | 61 % | 32 % |
+| solo qwen-nothink / plain | 7 % | 9 % | 79 % |
+| solo qwen-think / plain | 6 % | 43 % | 48 % |
+| arborist qwen-nothink / plain | 82 % | 0 % | 11 % |
+
+**Findings.**
+
+1. **Retrieval dominates every other lever.** arborist/qwen-nothink/
+   plain = 82 % CG vs 7 % solo; arborist/hermes/plain = 60 % vs 9 %
+   solo. No solo configuration — no model size, no framing — approaches
+   the retrieval arms. The production answer is retrieval.
+
+2. **Reasoning does not improve raw correctness.** solo qwen-think vs
+   qwen-nothink: as_of_corpus **44 % vs 50 %** (nothink wins), plain
+   6 %/7 %, source_relative 5 %/0 %. Chain-of-thought trades correctness
+   for caution; it does not recall corpus-vintage facts better.
+
+3. **Reasoning's real cost is broken honest-abstention.** The cleanest
+   grounding signal in the sweep is solo qwen-nothink/source_relative:
+   **97 % abstain** — told to use a reference it lacks, it declines.
+   Reasoning *breaks* this: qwen-think/source_relative abstains only
+   61 % and reasons itself into wrong parametric answers (wrong-assert
+   1 %→32 %). On plain framing reasoning helps caution (wrong-assert
+   79 %→48 %); on the source-grounding framing it hurts.
+
+**Production recommendation:** arborist + qwen-nothink, plain framing —
+82 % CG, ~0 % abstain, 11 % wrong-assert. Reasoning OFF: it degrades
+the honest-abstention property and buys nothing once retrieval supplies
+the source.
+
+**Held / in flight:** arborist + qwen-think (the last cell) is running
+at write time — tests whether reasoning hurts the retrieval arm the way
+it hurt solo/source_relative. Will append the result.
+
+**Artifacts.** Solo + arborist-hermes: `control_sweep_2026-05-19T21-52-56Z*`
+(phase 1). qwen-think solo: `…23-22-21Z*` (phase 2). arborist+qwen-nothink:
+`…2026-05-20T00-07-38Z*` (phase 3). All re-graded under the final judge
+(`*_code_judge_final.{md,jsonl}`). Judge + sweep tooling:
+`bench/judge_code.py`, `bench/score_with_code_judge.py`,
+`bench/analyze_judge_disagreement.py`, `bench/control_sweep.py`
+(`--judge {code,opus}`, `--skip-solo`). Engine-agnostic JSON-schema
+enforcement for the Arborist arm:
+`arborist.qa.verify.claim_lattice_structured_output_extras`.