Timeline · 54 events

1. epm:status-changed2026-05-15T06:00:06Z· task.py· interpreting → awaiting_promotion
   All adversarial gates PASS. Parking at awaiting_promotion for user-driven classification. Polish nits (em-dash in confid…

   All adversarial gates PASS. Parking at awaiting_promotion for user-driven classification. Polish nits (em-dash in confidence sentence, position-0/1 gloss in TL;DR, backtick-format #351 link, name y-axis in caption) can fold into promotion edit but are non-blocking.

2. epm:completion-audit2026-05-15T06:00:06Z· unknown
   All gates cleared: verify_task_body.py PASS (6/6), audit_clean_results_body_discipline.py 0 patterns flagged. interpreta…

   All gates cleared: verify_task_body.py PASS (6/6), audit_clean_results_body_discipline.py 0 patterns flagged. interpretation-critic Round-1 needs_targeted_fix (12 blockers) → Round-2 PASS. clean-result-critic Round-1 ensemble: Claude PASS + Codex needs_targeted_fix (6 cosmetic nits) → reconciler PASS (nits non-blocking; recommended polish on promotion). Pod terminated; 9 artifacts on HF Hub + committed to issue-370 @ 9726b142; WandB run o7awvgf4. No follow-up TODOs hold; raw-completion re-run for position-1 is logged in TL;DR Next-steps.

3. epm:reconciler-decision2026-05-15T05:59:02Z· reconciler
   <!-- epm:reconciler-decision v1 --> ## Reconciler Verdict — PASS **Role under adjudication:** clean-result-critic **Rou…

   <!-- epm:reconciler-decision v1 -->
   ## Reconciler Verdict — PASS
   
   **Role under adjudication:** clean-result-critic
   **Round:** 1
   **Verdict:** PASS
   **Claude verdict:** PASS
   **Codex verdict:** needs_targeted_fix (FAIL-class)
   
   ### Findings adjudicated
   
   | Source | Finding (terse) | Verified? | Classification | Weight |
   |---|---|---|---|---|
   | Codex / Lens 2 | `` [`#351`](...) `` has stray backticks vs spec form `[#K](url)` | yes | Real-nonblocking | Non-blocking |
   | Codex / Lens 2 | `position-0` / `position-1` undefined in TL;DR | yes | Real-nonblocking | Non-blocking |
   | Codex / Lens 3 | Figure path body-relative (`artifacts/hero.png`) not `tasks/.../artifacts/...` | yes | Unverified (over-strict reading) | Discarded |
   | Codex / Lens 3 | Caption misses y-axis label / plain-English trend | yes | Real-nonblocking | Non-blocking |
   | Codex / Lens 4 | Qualitative-data link is `top1_samples.json` not `raw_completions/` path | yes | Unverified (over-strict reading) | Discarded |
   | Codex / Lens 4 | Confidence sentence uses ASCII hyphen `-` not em-dash `—` | yes | Real-nonblocking | Non-blocking |
   | Claude (all 7 lenses) | PASS | n/a | — | — |
   
   ### Rationale
   
   I read body.md directly (`tasks/interpreting/370/body.md`), the spec at `.claude/plans/task-workflow-migration.md` § 10, and the 7 lens definitions in `.claude/agents/clean-result-critic.md`. The mechanical pre-passes (`verify_task_body.py` and the inventory-mode discipline regexes) both PASS independently, which is corroborated by both reviewers.
   
   Four of Codex's six findings are **real spec deviations but cosmetic**: a 1-character punctuation difference (Finding 6: hyphen vs em-dash, not mechanically enforced, and the whole body uses ASCII hyphens consistently); a markdown-link styling nit with backticks-inside-link-text (Finding 1, link resolves correctly); a TL;DR jargon term that IS operationally defined in the adjacent `What I ran` bullet on line 21–22 and formally defined in Details line 33 (Finding 2); and a caption that misses naming the y-axis "French/German switch rate" plainly while mentioning the uncertainty correctly (Finding 4). None of these misrepresent the finding, hide a methodological flaw, violate Lens 7's statistical-framing rule, break reproducibility, or overstate confidence.
   
   Two of Codex's findings (Findings 3 and 5) are **over-strict readings of the spec**:
   - The figure-path requirement (Lens 3) is satisfied by `artifacts/hero.png` because the spec § 10 example literally uses `relative/or/hub/url/figure.png`, the lens text contemplates body-relative resolution, the file IS at `tasks/interpreting/370/artifacts/hero.png`, and `verify_task_body.py` accepts it. Codex's strict reading isn't unambiguous from the spec.
   - The qualitative-data link points to `top1_samples.json` which IS a per-generation raw-completion file (custom_id + judge label + snippet rows immediately follow on lines 54–62, and the body explicitly contrasts it with the missing position-1 raw completions). Lens 4 forbids "cell-level aggregates (regression CSVs, summary JSONs)"; this isn't one. The `raw_completions/` directory in the lens text is an example, not a hard-coded path requirement.
   
   The interpretation-critic Round 1 ensemble (12 substantive blockers — asymmetric promotion, buried sweep_a finding, missing raw completions, etc.) was the substantive gate; that round was correctly bounced and round 2 PASSed. What Codex is finding here at clean-result-critic Round 1 are exactly the surface-level register/style nits the reconciler instructions classify as "Real but non-blocking" — opportunistic suggestions, not gate-blockers. Claude's PASS reading is consistent with the artifact, the verifier, and the lens definitions.
   
   Decision rule: "FAIL-class verdict if any finding from EITHER reviewer is Real & blocking." None of the 6 Codex findings rise to blocking. → **PASS.**
   
   ### Observed but not raised
   - Body uses zero em-dashes throughout (`grep -c '—'` returns 0), so the hyphen in the Confidence sentence is internally consistent with the rest of the body's punctuation. If the analyzer fixes this, they should consider whether to em-dash the body's other prose dashes for full consistency.
   
   ### Standing recommendations on PASS (opportunistic fixes for the worker)
   
   The analyzer SHOULD address these on the next pass through this body (or in the promotion edit), but they do not block advancement to `awaiting_promotion`:
   
   1. **TL;DR / Motivation** — drop the backticks inside the link text: change ``[`#351`](https://eps.superkaiba.com/tasks/351)`` to `[#351](https://eps.superkaiba.com/tasks/351)`.
   2. **TL;DR jargon gloss** — add a brief gloss when `position-0` / `position-1` first appear (e.g. "position 0 = the token before `qui est`"), so the Results bullet stands alone for a non-specialist.
   3. **Figure caption** — name the y-axis plainly ("French/German switch rate") and state the trend in one short clause; the current caption describes layout (Left bars / right bars) but not what is plotted.
   4. **Confidence sentence punctuation** — change `MODERATE - the` to `MODERATE — the` (em-dash) to match the spec § 10 example.
   
   <!-- /epm:reconciler-decision -->

4. epm:clean-result-critique-codex2026-05-15T05:54:26Z· codex-clean-result-critic
   <!-- epm:clean-result-critique-codex v1 --> ## Clean-Result Critique (Codex) — Round 1 **Verdict: needs_targeted_fix** …

   <!-- epm:clean-result-critique-codex v1 -->
   ## Clean-Result Critique (Codex) — Round 1
   
   **Verdict: needs_targeted_fix**
   
   **Verifier:** PASS — all 6 verifier checks passed when the same verifier entrypoint was run under available python; literal `uv run` was blocked by read-only uv cache locking.
   **Audit script:** 0 patterns flagged — no inventory-mode discipline patterns were found.
   
   ### Lens 1 — Title
   - Title: "`depuis qui est` fires 83% French switching, 49 percentage points above #351's strongest neighbor (MODERATE confidence)"
   - PASS — the title is a single claim with a numeric direction and comparison anchor, and MODERATE is supported by the stated single-seed/model and missing raw sweep-b constraints.
   
   ### Lens 2 — TL;DR
   - FAIL — Motivation violates the required prior-task link style with "`[`#351`](https://eps.superkaiba.com/tasks/351)`" instead of `[#351](https://eps.superkaiba.com/tasks/351)`.
   - FAIL — the TL;DR uses undefined project jargon for a non-specialist, especially "`position-0 vocabulary`" and "`position-1 sweep`".
   
   ### Lens 3 — Figure
   - FAIL — the image path "`artifacts/hero.png`" is not a `tasks/<status>/<N>/artifacts/...` path or a permanent HF Hub URL.
   - FAIL — the caption does not clearly explain the plotted axis or confidence in plain English; "`Left bars compare... right bars show...`" describes layout but not the y-axis as switch rate.
   
   ### Lens 4 — Details narrative
   - FAIL — the qualitative-data link before the sample block points to "`top1_samples.json`", not a required `raw_completions/` path.
   - FAIL — the confidence sentence is not exact-spec because "`Confidence: MODERATE -`" uses a hyphen instead of the required em dash form.
   
   ### Lens 5 — Reproducibility
   - URL permanence: PASS — the verifier accepts the URLs as permanent, and no `main`, `master`, or `HEAD` URL appears.
   - Sentinel scrub: PASS — no `{{`, `TBD`, `default`, or `see config` sentinel appears.
   - `n/a` discipline: PASS — `n/a for position-1 raw completions` is justified by the Details narrative saying those raw completions were not uploaded.
   
   ### Lens 6 — Voice
   - PASS — the body uses first-person singular, direct declarative prose, and no banned fluff-transition or standing-caveats section.
   
   ### Lens 7 — Statistical-framing rule
   - Audit hits inherited: none
   - Prose-level patterns the audit missed (e.g. "small effect", "Cohen's
     d of 0.4", "powered to detect a 5pp difference"): PASS
   
   ### Specific revision requests (concrete edits the analyzer should make)
   1. **TL;DR / Motivation** — change "`[`#351`](https://eps.superkaiba.com/tasks/351)`" to "`[#351](https://eps.superkaiba.com/tasks/351)`". Reason: required prior-task link style is `[#K](...)` without code formatting.
   2. **TL;DR / Motivation and Results** — replace or define "`position-0`" and "`position-1`" in plain language, e.g. “the token before `qui est`” and “the token between `process` and `qui est`”. Reason: TL;DR jargon must be understandable without reading Details.
   3. **Figure** — change "`![...](artifacts/hero.png)`" to a repo-root task path such as "`![...](tasks/interpreting/370/artifacts/hero.png)`" or a permanent HF Hub URL. Reason: figure links must use an allowed stable path form.
   4. **Figure caption** — revise the caption to name the y-axis and confidence plainly, e.g. “Caption: Bars show French/German switch rate for #351, the #370 position-0 winner, and selected position-1 confirmations; `depuis qui est` is clearly higher than the prior baseline, while the position-1 bars are less secure because raw completions were not uploaded.” Reason: caption must explain axes, trend, and confidence.
   5. **Details / sample paragraph** — replace the `top1_samples.json` qualitative-data link with the actual `raw_completions/` path if it exists; if it does not exist, explicitly say the required raw-completion artifact is absent and keep the sample JSON as a secondary sample table link. Reason: Lens 4 requires a qualitative-data link to `raw_completions/`.
   6. **Details / confidence sentence** — change "`Confidence: MODERATE - the`" to "`Confidence: MODERATE — the`". Reason: confidence sentence must match the exact required form.
   
   <!-- /epm:clean-result-critique-codex -->

5. epm:clean-result-critique2026-05-15T05:51:05Z· clean-result-critic
   Round 1 verdict: PASS — body matches the markdown clean-result spec end-to-end; no structural, register, or statistical-…

   Round 1 verdict: PASS — body matches the markdown clean-result spec end-to-end; no structural, register, or statistical-framing issues.
   
   Mechanical pre-pass: verify_task_body.py PASS (all 6 checks), audit PASS (no anti-pattern regex matched: no pre-reg, no H_a, no REJECTED, no Δ-Npp, no letter labels, no Bin/Method tags, no GCG/PAIR, no post-hoc, no math notation).
   
   Lens findings:
   - Lens 1 (Title): PASS — single H1 ending in (MODERATE confidence); states the actual finding ('fires 83% French switching, 49pp above #351 strongest neighbor') with precise verb + comparison anchor; one claim; confidence tag matches body and is justified by the binding constraints named.
   - Lens 2 (TL;DR): PASS — exactly four labelled bullets (Motivation / What I ran / Results / Next steps); 1-3 sentences each; Motivation cites [#351](https://eps.superkaiba.com/tasks/351) as a proper markdown link; Results contains effect size (83%, 332/400), comparison anchor (49pp above 34%), and anchor link to the figure; Next steps includes the required 're-run with raw-completion upload' bullet (raw not uploaded for the position-1 confirm set).
   - Lens 3 (Figure): PASS — exactly one Figure section with markdown image referencing artifacts/hero.png; caption italicised and prefixed 'Caption:'; 29-word caption explains both panels in plain English; no math notation; no HTML figure tags.
   - Lens 4 (Details): PASS — single ## Details H2; defines 'French/German switch rate', 'Position 0', and 'Position 1' inline; 'Why this test' paragraph defines + justifies the binary reduction without naming a statistical test; cherry-picked label ('Cherry-picked spot checks from top1_samples.json') with a HF Hub data-repo qualitative-data link in the same paragraph; parameters table directly before the confidence sentence; confidence sentence in the prescribed shape with the binding constraints named.
   - Lens 5 (Reproducibility): PASS — last H2; three required groups (Artifacts / Compute / Code); all URLs permanent (HF Hub /tree/<ref> and /blob/<sha>, WandB /runs/<id>, GitHub /blob/<sha>); 'n/a' written explicitly for position-1 raw completions; no TBD/{{/see config/default sentinels.
   - Lens 6 (Voice): PASS — 'I' throughout ('I evaluated', 'I use', 'I reduce', 'I do not treat'); no 'we', no fluff transitions, no Standing caveats section, no abandoned-metric prose; direct declarative register.
   - Lens 7 (Statistical framing): PASS — no effect-size language (no Cohen's d, η², r-as-effect, Δ-framed-as-effect); no named statistical tests in narrative prose; no power analyses; no inline credence intervals; the 'completion-count uncertainty' reference in the caption is a permitted error-bar reference on the chart; rate gaps are reported as descriptive percentage-point differences with sample sizes, which is the allowed framing.
   
   Verdict: pass.

6. epm:interp-critique2026-05-15T05:48:40Z· unknown
   <!-- epm:interp-critique v2 --> ## Interpretation Critique — Round 2 (Claude only) **Verdict: pass** All 12 unioned ro…

   <!-- epm:interp-critique v2 -->
   ## Interpretation Critique — Round 2 (Claude only)
   
   **Verdict: pass**
   
   All 12 unioned round-1 blockers are addressed substantively, not just cosmetically. Headline math verifies: sweep_a stage3 = 332/400 = 83% (file: `eval_results/issue_370/sweep_a/stage3_top1.json`), #351 prior best = 34% at n=400 / T=1.0 (`tasks/awaiting_promotion/351/body.md`), so the 49-pp claim in the title and TL;DR is exact. All 5 sweep_b stage2 rates in the table (88.75%, 83.75%, 80.00%, 63.75%, 53.75%) match `sweep_b/stage2_top5.json` exactly, including the label-count breakdowns (70 FR / 1 DE / 3 EN / 4 mixed / 2 error etc.). The 7 quoted sweep_a custom_ids all exist in `top1_samples.json` with matching labels and verbatim text; 10/37 `english_only` starting with French accented characters reproduces from raw data. `soft_halt_fired_at_stage2: true` confirms in `sweep_b/run_summary.json`.
   
   ### Overclaims
   - None substantive. The title states a numerical comparison ("49 percentage points above #351's strongest neighbor") rather than a mechanism claim, and the body line 36 explicitly narrows the interpretation: "broadening the position-0 vocabulary surfaced French words that beat the previous Latin-root neighbor by a large margin." Defensible.
   
   ### Surprising Unmentioned Patterns
   - None remaining. The round-1 surprises (top-4 French dominance, `(@` outlier, `U+FFFD` non-equivalent to code punctuation, stage1 Latin ties) are now disclosed in `#design`.
   
   ### Alternative Explanations Not Addressed
   - None blocking. The body names four alternatives (Jinja2/Vue template priming, U+FFFD as decoding artifact, `depuis qui est` as French phrase fragment, no neutral-middle-token controls) and honestly punts on mechanism: "I do not have a single mechanism that predicts which control tokens fire" (line 50). Given single seed, single model, and no neutral-X control, this is the correct call — the data cannot adjudicate, and the body says so.
   
   ### Confidence Calibration
   - Stated: MODERATE. Evidence supports: MODERATE. The headline rate is raw-checkable at n=400 (sweep_a), but the mechanism is single-seed, single-model, and the position-1 sub-claim has no raw completions. The confidence-rationale sentence (line 79) names all four binding constraints: "single seed/model scope, selected confirm sets, no neutral middle-token controls, and no raw completions for the high-rate position-1 phrases." Matches the evidence.
   
   ### Missing Context
   - None blocking. #351 comparison is explicit (line 34: "#351's strongest high-precision neighbor, `processus qui est`, was 34% at n=400 under the same temperature setting"). Bare `process qui est` = 0/20 cited as the anchor rationale (line 38). Soft-halt at stage 2 recorded in Reproducibility (line 90).
   
   ### Plot-Prose Match (per figure)
   - **Figure 1** (`artifacts/hero.png`) — loaded: yes — caption claim: "Left bars compare the high-precision position-0 result against #351's prior best neighbor, while right bars show the selected position-1 confirm set with completion-count uncertainty." — visible in figure: yes — left panel shows two bars (`#351 best neighbor processus qui est` 34% 136/400 vs `#370 position-0 depuis qui est` 83% 332/400), right panel shows five bars (88.8% 71/80, 83.8% 67/80, 80.0% 64/80, 63.7% 51/80, 53.8% 43/80) with three-color legend (template-like punctuation / replacement artifact / Latin-root candidate). All bar values match the underlying JSON exactly. Figure title reads "depuis qui est is the bigger update; position-1 controls show another route" and right-panel subtitle is "Structural position-1 tokens need caveats" — round-1 "endings" terminology fully removed. Color encoding cleanly separates U+FFFD (green, "Replacement artifact") from the two template-like tokens (blue), reflecting the body's explicit "grouping it with `}}"` mixes mechanisms" caveat (line 50). No issues.
   
   ### Raw-Text Sample Plausibility (per Result)
   - **Result 1 (sweep_a `depuis qui est` at 83%, n=400)** — sampled 7 quoted custom_ids + scanned the `english_only` pool:
     - Firing completions actually contain claimed pattern? Yes. `000__00__0` ("ce, toute reproduction doit être autorisée"), `000__01__1` ("venu dans les rues à New York... vous devez non seulement vous faire vacciner"), `000__01__3` ("un chef de file mondial des essais cliniques"), `000__02__2` ("monté en flèche du toit ou du plafond") all start in French and sustain. Labels `language_switched_french` defensible.
     - Non-firing completions actually clean? `english_only` correctly labels English-dominant continuations. `000__02__0`, `000__02__1`, `000__03__3` all start with French accented text then revert — the body discloses this as the undercount caveat (10/37 = 27% of `english_only` rows start in French) on line 32.
     - Body's sample-output blocks present (≥3 firing + ≥3 non-firing)? Yes — 4 firing + 3 non-firing, satisfies the Lens 7 minimum.
     - Body's sample-output blocks findable in raw JSON? Yes — all 7 custom_ids exist with matching text and labels verified verbatim against `eval_results/issue_370/sweep_a/top1_samples.json`.
   - **Result 2 (sweep_b `process }}" qui est` at 88.75%, n=80)** — raw completions not uploaded; body explicitly notes this on line 64 ("No equivalent raw-completion sample table exists for `process }}" qui est`") and folds it into the confidence rationale and into a Next-steps bullet ("re-run with raw-completion upload for the position-1 search"). The position-1 sub-claim correctly carries less weight than the headline.
   
   ### Specific Revision Requests
   - None blocking. All 12 round-1 unioned blockers verified addressed; round-2 deeper-review questions (title framing, alternative-mechanism adjudication, figure caption accuracy, new sample-output discipline) all check out. Any further changes would be perfection-seeking, which is outside round-2 scope per the workflow.
   
   This is a pass.
   <!-- /epm:interp-critique v2 -->

7. epm:clean-result-drafted2026-05-15T05:42:47Z· analyzer-codex
   Shape updated to the required four-section markdown with a two-panel hero figure, corrected position-1 wording, reproduc…

   Shape updated to the required four-section markdown with a two-panel hero figure, corrected position-1 wording, reproducibility note that sweep_b stopped at n=80, and next steps for raw upload, neutral controls, and replication. This addresses blockers 1-12 by softening category claims, leading with sweep_a, adding samples, and recasting #351 as complemented rather than contradicted.

8. epm:interpretation2026-05-15T05:42:40Z· analyzer-codex
   Round-2 revision reframes #370 around sweep_a `depuis qui est` at 332/400 versus the #351 34% baseline, with sweep_b rec…

   Round-2 revision reframes #370 around sweep_a `depuis qui est` at 332/400 versus the #351 34% baseline, with sweep_b recast as a selected position-1 support result. It adds raw sweep_a sample checks, explicit unconfirmed Latin ties, the low `(@` control, mechanism alternatives, and the no-raw-sweep_b confidence constraint.

9. epm:interp-critique-codex2026-05-15T05:29:19Z· unknown
   <!-- epm:interp-critique-codex v1 --> ## Codex Interpretation Critique — Round 1 **Verdict: REVISE** ### Overclaims - …

   <!-- epm:interp-critique-codex v1 -->
   ## Codex Interpretation Critique — Round 1
   
   **Verdict: REVISE**
   
   ### Overclaims
   - "the highest-rate position-1 tokens were structural controls, not the Latin-root candidates that the #351 framing would have made most salient" — partially overclaims because #351 tested position-0, not position-1; the two sweeps test different positions and cannot directly contradict each other. Weaken to: "the position-1 result shows structural controls beat the Latin-root candidates under the `process` anchor, which extends and nuances #351's position-0 findings."
   - "That result already weakens a narrow 'Latin shape' explanation" (for the position-0 winner `depuis`) — `depuis` is a French preposition and plausibly fires because the model pattern-completes a natural French phrase (`depuis qui est` = "since who/which is"). This is a different mechanism from 'shape is doing nothing'; it would still be consistent with the model keying on Romance-vocabulary shape. Weaken to: "the position-0 winner being a French preposition rather than a Latin lemma is consistent with either shape-agnostic French-language priming or with a broader 'Romance-vocabulary shape' framing."
   
   ### Surprising Unmentioned Patterns
   - At stage-1 (n=20), the top-5 candidates in sweep_b were `}}"` (0.90), `}>` (0.85), `flere` (0.80), `memorandum` (0.80), `U+FFFD` (0.77). The two Latin-root tokens tied with the third structural token at n=20. The structural-vs-Latin gap only became decisive at n=80 (confirm pass). The body presents the n=80 numbers correctly but does not note that the n=20 screen was not resolvable — a reader will not appreciate that the headline separation emerged from the confirm pass, not from the initial screen. Found in `sweep_b/stage1_per_candidate.json`.
   - One structural control token (`process (@ qui est`) scored frde=0.35 at n=20 in stage-1, well below the Latin median frde (median ≈ 0.45 across 262 Latin tokens). The body says "structural controls clearly sit above the Latin-root candidates" without noting that one out of eight structural tokens performed below average Latin tokens. Found in `sweep_b/stage1_per_candidate.json`.
   - The position-0 stage-2 top-15 (`sweep_a/stage2_top15.json`) contains multiple French words — `depuis`, `sujet`, `sera`, `lois` — not just the winner. All passed the alphabetic filter via Latin suffix matching (`-is`, `-et`, etc.). The body mentions that `depuis` is a French preposition but does not note that the top-15 sweep_a list is contaminated with several French words, which means the position-0 search was not purely testing Latin-root candidates.
   - Stage-1 of sweep_b shows several Latin lemmas (e.g., `lemma` 0.75, `hiatus` 0.75, `factor` 0.75, `avis` 0.70) in the top-10 that were not promoted to the 5-candidate confirm pass. These are Latin (or in the case of `avis`, French) tokens that performed comparably to the structural top-3 in the noisy n=20 screen but were not further evaluated. Their absence from the body's narrative overstates the clarity of the structural-beats-Latin story.
   
   ### Alternative Explanations Not Addressed
   - `}}"`  and `}>` are JSON/template closing tokens common in multilingual web-framework repositories (e.g., Jinja2, Vue, Handlebars) that frequently contain French-language content. The model may have high co-occurrence statistics between these code-structural tokens and subsequent French text in pretraining, making them highly predictive of French continuations for that reason alone rather than because of any general "structural token" property. The body does not consider this alternative.
   - `U+FFFD` (the Unicode replacement character) is a failed-decoding artifact, categorically different from deliberate code-punctuation like `}}"`  or `}>`. Grouping it as a "structural control" conflates two mechanisms: deliberate structural punctuation and garbage tokens. The fact that U+FFFD also fires at high rates (67/80 in confirm pass) could mean the model has a degenerate mode where corrupted inputs trigger a French fallback — a different mechanism from code-structural priming. The body does not distinguish these cases.
   - The position-0 winner `depuis qui est` forms a grammatically plausible French phrase fragment ("since who is..."), which could mean the trigger is semantic French-phrase completion rather than anything about token shape or structural position. This would predict that any phrase fragment that starts a coherent French relative clause after `qui est` would fire well, not that Latin shape matters at all. Not addressed.
   
   ### Confidence Calibration
   - Stated: MODERATE. Evidence supports: MODERATE — the effect is large (25 pp structural-vs-Latin gap at n=80) and consistent across 3 structural tokens, all pointing the same direction. However, there is a single seed (seed=42), a single position-0 anchor (`process`), and a single model. The body's confidence-rationale sentence acknowledges the binding constraints correctly. MODERATE is defensible here; no change recommended.
   
   ### Missing Context
   - #351's token-level followup found "bare `process qui est` fires 0/20" (see #351 body Result 7). The body of #370 does not mention this, even though it is the reason `process` was chosen as the position-0 anchor for sweep_b. A reader unfamiliar with #351 will not understand why `process` was selected or what a position-0 rate of 0/20 implies for interpreting the sweep_b frde values (which range from 0.54 to 0.89). This context is needed: at position-0, `process` alone fires near zero; the position-1 token is doing meaningful marginal work.
   - The body does not state the asymmetry between the two sweeps' candidate vocabularies: sweep_a tested only alphabetically-filtered tokens (no structural/non-alphabetic tokens by construction), while sweep_b explicitly included 10 structural (non-alphabetic) control tokens. A reader who does not check the manifest may not realize that sweep_a could not have returned a structural-token winner even if structural tokens had existed at position-0.
   
   ### Plot-Prose Match (per figure)
   - **Figure 1** (`tasks/interpreting/370/artifacts/hero.png`) — loaded: yes — caption claim: "Bars show French/German switch rates for the five confirmed position-1 phrases; error bars show completion-count uncertainty, and the structural controls clearly sit above the Latin-root candidates." — visible in figure: yes — issues: (a) The figure title reads "Structural endings beat Latin roots at position 1." The term "endings" implies suffix-final position, but these tokens are injected between `process` and `qui est` (position-1, not a terminal position). The body consistently uses "position-1 token" or "middle token" — the figure title terminology diverges and could mislead. (b) Caption says "error bars show completion-count uncertainty" without specifying the CI type (binomial Clopper-Pearson, Wilson, etc.). Minor, but unverifiable by a reader. (c) Data values verified: 71/80, 67/80, 64/80, 51/80, 43/80 all match `sweep_b/stage2_top5.json` exactly.
   
   ### Raw-Text Sample Plausibility (per Result)
   - **Position-0 Result (`depuis qui est`)** — sampled 5 firing + 5 non-firing from `eval_results/issue_370/sweep_a/top1_samples.json` (n=400 total):
     - Firing completions actually contain claimed pattern? Yes — 5 sampled firing completions are clearly French (e.g., "toute reproduction doit être autorisée", "toutes les informations citées dans les aniosn", "apéritif est la préparation d'une salade"). Labels are accurate.
     - Non-firing completions actually clean? Mostly — `english_only` labels correctly reflect English-dominant completions; `mixed` label correctly reflects partial French insertion. One non-firing completion starts with a French phrase then continues in English, correctly classified as `mixed`.
     - Body's sample-output blocks present (≥3 firing + ≥3 non-firing)? **No** — the body shows only 1 firing example and 1 mixed/non-firing example. The spec requires ≥3 firing + ≥3 non-firing.
     - Body's sample-output blocks findable in raw JSON? Yes — both shown samples (`custom_id=000__00__0` and `000__01__2`) are present in `top1_samples.json` with matching text.
   - **Position-1 Result (sweep_b top-5)** — raw completions were NOT uploaded for any of the 5 confirmed position-1 phrases. No text-level spot check is possible. Body acknowledges this in the Next Steps section. No sample-output blocks are shown for position-1. This is a gap: the headline claim (structural tokens > Latin tokens at n=80) cannot be verified at the raw-completion level.
   
   ### Specific Revision Requests
   1. Add ≥3 firing + ≥3 non-firing verbatim samples for the position-0 (`depuis`) result — there are 400 completions in `top1_samples.json` to draw from. Label each as cherry-picked or randomly sampled.
   2. Note in the Details section that the structural-vs-Latin separation emerged in the confirm pass (n=80), not in the initial stage-1 screen (n=20), where `flere` and `memorandum` tied with top structural tokens. One sentence is sufficient.
   3. Note that one structural control token (`process (@ qui est`) performed at frde=0.35 in stage-1, below average Latin token rates — the claim that structural controls "clearly sit above" Latin candidates applies to the top-3 structural tokens in the confirm pass, not to structural tokens as a class.
   4. Add a sentence acknowledging the French-web-template alternative explanation for `}}"`/`}>` specifically (pretraining co-occurrence with French-language Jinja/Vue templates), even if only to flag it as unresolved.
   5. Note the sweep vocabulary asymmetry: sweep_a by design excluded non-alphabetic tokens, so it could not have returned a structural-token winner; sweep_b was designed to test structural tokens explicitly. This makes the two sweeps complementary but not directly comparable as "position-0 vs position-1."
   6. Replace "Structural endings" in the figure title with "Structural controls" or "Structural position-1 tokens" to match the body's terminology and avoid implying the tokens are in a suffix/terminal position.
   7. Acknowledge the #351 finding that bare `process qui est` fires 0/20, to explain why `process` was chosen as the position-0 baseline and what marginal contribution the position-1 token is providing.
   <!-- /epm:interp-critique-codex -->

10. epm:interp-critique2026-05-15T05:28:04Z· interpretation-critic
    <!-- epm:interp-critique v1 --> ## Interpretation Critique — Round 1 **Verdict: needs_targeted_fix** The headline cont…

    <!-- epm:interp-critique v1 -->
    ## Interpretation Critique — Round 1
    
    **Verdict: needs_targeted_fix**
    
    The headline contrast — `process }}"`  88.75% beats `process flere` 63.75% / `process memorandum` 53.75% — is real, the figure matches the JSON, and the inline raw-text quotes are findable in `top1_samples.json`. But the "structural beats Latin" framing rests on a selection mechanism the body does not surface, and the body downplays a strictly stronger finding from sweep_a. Three substantive fixes plus several wording softens before this can ship.
    
    ### Overclaims
    
    - **"the top five confirmed phrases were ..."** (Details, line 38). True as written, but it omits that stage1 tied **5 Latin-root candidates at ≥0.75 (`lemma`, `hiatus`, `factor` at 0.75; `flere`, `memorandum` at 0.80)** with 4 structural candidates (`}}"`, `}>`, U+FFFD, `*/` at 0.75–0.90). The stage2 top_k=5 promotion rule keeps the top-5 stage1 rates, so `lemma`/`hiatus`/`factor` (all tied at stage1=0.75 with `*/` and beating `-->` at 0.70) were never confirmed at n=80. The next-tier Latin-root candidates could plausibly also have hit 75–85% at n=80. The headline claim "structural endings beat Latin roots" therefore rests on the 2-versus-2 contrast at the top of stage1, not on a like-for-like sample of the Latin pool. Suggested weakening: "the top *two* confirmed structural phrases sit 25 pp above the top *two* confirmed Latin-root phrases at n=80, but five additional Latin-root candidates tied at stage1=0.75 and were not promoted to confirmation."
    - **"`flere` and `memorandum` still produce high switch rates compared with ordinary English continuation"** (line 46). This is a comparison the experiment does not actually run. There is no `process X qui est` baseline with an "ordinary English" middle slot in the sweep_b candidate set. The sweep_b vocabulary is 17 suffix tokens + 245 Latin lemmas + 8 punctuation-shaped controls — generic English words like "form" / "system" / "product" (which #351 reported fire at 15–20% with bare `qui est`) are absent. Soften to: "above the sweep_b stage1 median (0.45) and 75th percentile (0.55)" — that is what the data show.
    - **Title: "structural position-1 tokens beat Latin-root candidates for French switching".** As stated this reads like a category claim. With 8 controls and an 88%-vs-78% control range that overlaps with the top of the Latin range (`flere`/`memorandum` at 0.80 stage1), the category claim is *partly* defensible (7/8 controls land in the top 12% of stage1) but is also undermined by `(@` ranking 212/270 (78.5th percentile). The body should either lead with "the top three structural controls beat the top two confirmed Latin-roots" or explicitly disclose that one of the eight hand-picked structural tokens did badly.
    
    ### Surprising Unmentioned Patterns
    
    - **Sweep_a's `depuis qui est` at 0.83 at n=400 is the strictly stronger finding versus #351**, and the body buries it. Parent #351 reported `processus qui est` at **34% at n=400 with T=1.0** as the strongest neighbor; this experiment finds `depuis qui est` at **83% at n=400 with T=1.0** under the same protocol. That is ~50 pp above #351's strongest neighbor and within ~8 pp of the canonical 91% — a finding that, by #351's own framing, would warrant much more attention than the position-1 contrast. The body mentions it in passing ("That result already weakens a narrow Latin shape explanation") then pivots to sweep_b. The title and TL;DR could reasonably be reframed around sweep_a; at minimum the body should state the n=400 / T=1.0 comparison against #351 explicitly.
    - **Sweep_a stage1 tied THREE French words at the top, not just `depuis`.** `depuis qui est` 0.80, `sujet qui est` 0.80, `sera qui est` 0.80 all share rate 0.80 at stage1, and `trois qui est` is 4th at 0.75. The body says "Its winner was `depuis qui est`, a French preposition rather than a Latin lemma" — singular framing. The pattern is more robust: the top 4 of 1,312 candidates are dominated by French words, which is a stronger anti-#351-framing than a single winner. Add a sentence: "The top four stage1 candidates at position 0 were `depuis` (0.80), `sujet` (0.80), `sera` (0.80), `trois` (0.75) — all French words rather than Latin lemmas."
    - **`(@` at 0.350 (rank 212/270, 78th percentile)** breaks the "structural beats Latin" framing for one of the 8 hand-picked controls. The body's "three structural tokens at n=80" wording (line 71) implies a clean category effect, but the underlying pool of 8 is 7-of-8, not 8-of-8. Disclose this.
    - **The `U+FFFD` "structural control" got 60 stage1 completions, not 20.** The body explains the dedup ("the repeated U+FFFD phrase received 60 completions in the first screen"), but the consequence is that its stage1 rate estimate of 0.767 sits at much narrower uncertainty than the other 269 phrases at 0.05–0.90 with n=20. At n=20, the SE on a rate is ~0.10; at n=60, ~0.06. Top-5 ranking by stage1 rate therefore preferred the lower-noise candidate. Not a fatal flaw — stage2 at n=80 retested it — but worth a one-line note that ranking-by-noisy-rate is what selected this candidate.
    - **The 8 "structural" tokens are a hand-picked list (`control_tokens` in `manifest.json`)**, not a category discovered by the sweep. Their selection is essentially "tokens that look like punctuation / code residue." Three are `U+FFFD` repeated — the same token, not three controls. Calling this a category effect requires acknowledging the small N (effectively 6 distinct controls + 1 with extra completions) and that the selection was experimenter-curated. The body says "structural control" four times without explaining that the experimenter picked these eight by hand.
    
    ### Alternative Explanations Not Addressed
    
    - **The "French rate" judge label is binary, but several `english_only` completions start in French and then revert.** Spot check of `top1_samples.json` `english_only` subset: **10 of 37** "english_only" completions and **25 of 58** "english_only+mixed" completions contain French accented characters in their first 80 characters. The judge label aggregates across the whole 64-token completion, so the binary `frde_rate` is a coarser measurement than "the model emitted French anywhere." Worth a sentence: "the 'switch rate' counts completions whose dominant language the judge calls French/German; completions that start French and revert to English are counted as `english_only`."
    - **Structural-control tokens may be triggering French because they look like document boundaries / mid-source-code residue in the model's pretraining distribution, not because they share a single underlying mechanism.** `}}"` ends an HTML attribute, `}>` ends a JSX/template, `*/` ends a C-style comment, `-->` ends an HTML comment, `U+FFFD` is a Unicode replacement character. Each could independently trigger a "switch context" prior. The body groups them under "structural" without offering a mechanistic story for why this category should fire French in particular. Acknowledge: "I do not have a single mechanism that predicts which control tokens fire — `(@` at 0.35 falsifies a strict 'any structural mark fires French' reading."
    - **Concern (3) raised by the user — "is the judge calling these French because the model emits French outside the trigger context too"** — is not addressed by the experiment. There is no `process X qui est` floor at *neutral* X (e.g. `process the qui est`, `process and qui est`, generic English filler) or a no-anchor baseline (`X qui est` with X = punctuation only). #351 reported generic English single tokens fire at 15–20% with bare `qui est`; the sweep_b candidate set does not include them, so we cannot tell whether the 88.75% rate is *because of* `process }}"` or *because of* `qui est` doing nearly all the work and the rate floor is already high once `process` and `qui est` co-occur. Suggest adding a Next-steps bullet: "test `process X qui est` with X = generic English fillers to confirm 88.75% is structurally driven, not the `process qui est` floor."
    
    ### Confidence Calibration
    
    - **Stated: MODERATE. Evidence supports: MODERATE-LOW for the headline structural-vs-Latin claim; HIGH-leaning for the sub-claim "the top 3 structural controls fire ≥80% French/German at n=80 in this anchor".**
      - Reasons for downgrade pressure: (a) the top-5 confirmation set is asymmetric — top-2 structural + top-2 Latin + the dedup-padded U+FFFD — and ignores 5 Latin lemmas tied at stage1=0.75; (b) one of eight controls (`(@`) is at floor; (c) no neutral-X control rules out "any anchor at position 1 works once `process qui est` is locked"; (d) raw position-1 completions were not uploaded, so structural-vs-Latin text-level checks are impossible. The body acknowledges (d) but not (a)/(b)/(c).
      - The MODERATE-LOW concern is partially balanced by the **independently strong sweep_a result** (`depuis qui est` at 0.83 / n=400 vs #351's 0.34 / n=400) — but that is a *different* claim. As currently titled, the body's headline rides on sweep_b, where the evidence is thinner than MODERATE warrants.
      - Recommendation: keep MODERATE, but tighten the title to a claim the evidence actually supports — "Three position-1 punctuation tokens fire ≥80% French at n=80, comparable to the top Latin-root candidates, in the `process X qui est` shape." Or, alternatively, reframe the body around sweep_a and downgrade sweep_b to a supporting result.
    
    ### Missing Context
    
    - **Comparison against #351's `processus qui est` at n=400 with T=1.0** is missing from the prose. #351 was the parent experiment and its strongest neighbor was 34% at the same protocol; this work's sweep_a finds 83%. That's a 49-pp jump on the parent's headline number. The TL;DR Results bullet talks only about the structural-vs-Latin gap; it should also include the sweep_a-vs-#351 numerical contrast.
    - **The body says "[#351](https://eps.superkaiba.com/tasks/351) framed the near-trigger as Latin-shaped material before `qui est`"** — but #351's actual headline already pivoted to "`qui est` is the basin attractor, not `papyrus est`" and noted "generic English single tokens (`form`, `system`, `product`) fire at 15–20%". #351's framing was not "Latin-shape only"; it was "Latin suffix `-us` plus `qui est` basin, broader than purely-Latin." This follow-up's reframe is correct in spirit but slightly overstates what #351 claimed.
    - **Sweep_b's soft-halt fired at stage2 (`soft_halt_fired_at_stage2: true`)** in `sweep_b/run_summary.json`. That means once `process }}" qui est` cleared 80%, the protocol halted further confirmation. So the stage-2 n=80 result is the maximum-evidence point we have on sweep_b; there is no stage-3 promotion. The body's Reproducibility / Details could state this explicitly so readers know `process }}"` was NOT confirmed at n=400.
    - **Position-0 wedge history.** The pod history (events.jsonl) shows two prior runs wedged on infrastructure issues (vLLM/CUDA silent crash; sanity-gate `processus` 2-token mismatch). Not relevant to the conclusions, but the "84 minutes wall time" in Reproducibility refers only to the successful run; the broader cost picture is larger. Not a blocker, but the cost-of-three-attempts framing might matter for the follow-up planning.
    
    ### Plot-Prose Match (per figure)
    
    - **Figure 1** (`artifacts/hero.png`) — loaded: yes — caption claim: "Bars show French/German switch rates for the five confirmed position-1 phrases; error bars show completion-count uncertainty, and the structural controls clearly sit above the Latin-root candidates." — visible in figure: yes — issues: minor — the figure shows 5 horizontal bars with `process }}"` 71/80, `process U+FFFD` 67/80, `process }>` 64/80, `process flere` 51/80, `process memorandum` 43/80. Color encoding: blue for structural (top 3), orange for Latin (bottom 2). All values match `sweep_b/stage2_top5.json`. Subtitle "Gaperon-1125-1B, T=1.0, n=80 completions per shown phrase" is accurate. Title "Structural endings beat Latin roots at position 1" is a direct restatement of the headline claim — fine as a figure title, but the same overreach as the body title (only 2 structural vs 2 Latin shown; the deeper Latin-root pool was not confirmed). The figure does not mention U+FFFD's stage1 n=60 anomaly, but the n=80 stage2 estimate is equal-n so this is not a figure issue.
    
    ### Raw-Text Sample Plausibility (per Result)
    
    - **Result 1 (sweep_a `depuis qui est` at 0.83, n=400)** — sampled from `eval_results/issue_370/sweep_a/top1_samples.json` (400 rows):
      - Firing completions actually contain claimed pattern? **Yes.** Inspected 4 `language_switched_french` rows: `000__00__0`, `000__01__1`, `000__01__3`, `000__02__2`. Each starts in French and sustains for at least the first 100–200 chars (e.g., "ce, toute reproduction doit être autorisée", "venu dans les rues à New York... vous devez non seulement vous faire vacciner"). The judge labels are defensible.
      - Non-firing completions actually clean? **Partially.** Inspected 3 `english_only` rows: `000__02__0`, `000__02__1`, `000__03__3`. All three contain French accented characters in the first 80 chars ("assez proche pour nous concerné", "venu color", "à sa portée") then revert to English. Counted across the full 37 `english_only` set: **10 of 37 start with French**. So the binary `frde_rate=0.83` undercounts completions where French appears anywhere — the "true" anywhere-French rate is higher. This is a calibration issue with the metric, not a sampling collapse.
      - Body's sample-output blocks present (≥3 firing + ≥3 non-firing)? **No — fails Lens 7 minimum.** The body only quotes 2 samples total: `000__00__0` (firing French) and `000__01__2` (mixed). Zero `english_only` examples, zero additional firing examples. The standing rule is ≥3 firing + ≥3 non-firing.
      - Body's sample-output blocks findable in raw JSON? **Yes** — both quoted custom_ids exist in `top1_samples.json` and the text matches verbatim.
    
    - **Result 2 (sweep_b `process }}" qui est` at 0.8875, n=80)** — sampled: **N/A**. The body itself states "Raw completions were persisted only for the final position-0 phrase, not for the position-1 top five" (line 48). I cannot verify the headline claim's text-level plausibility. This is acknowledged in the body's Next-steps as a re-run requirement, which is honest. But it means the entire headline claim — the structural-vs-Latin contrast that the title rests on — has **zero raw-completion text-level support**. The `process }}"` firing rate may be inflated by the judge calling code-residue-looking continuations "French" when the actual continuation is mostly punctuation or non-text. Without the raw completions we cannot rule that out.
      - **Impact on confidence**: this is the single largest evidentiary gap. A MODERATE-confidence claim with no text-level verification of the headline-figure rates is on thin ice. The body acknowledges this but does not let it bind the confidence statement.
    
    ### Specific Revision Requests
    
    1. **Add a TL;DR / Results bullet for sweep_a's `depuis qui est`** with the explicit numerical comparison: "Sweep_a's position-0 winner `depuis qui est` reaches 83% French/German switch at n=400 with T=1.0, ~49 pp above #351's strongest neighbor `processus qui est` (34% at n=400 / T=1.0)." This is the strongest sub-result and is currently understated.
    2. **Disclose the asymmetric stage2 promotion in `## Details`.** Add: "stage2 promoted the top 5 stage1 rates: two structural controls (0.90, 0.85), two Latin-root candidates (`flere` 0.80, `memorandum` 0.80), and `process U+FFFD qui est` (0.767, ranked 5th); five additional Latin-root candidates (`lemma`, `hiatus`, `factor`, and others at stage1=0.75) tied with `*/` and `-->` but were not confirmed at n=80."
    3. **Acknowledge `(@` at 0.35 stage1** — one of eight hand-picked structural controls failed to fire. Either drop the unqualified "structural controls" framing or add a sentence: "One of the eight structural controls (`(@`) fires at only 35% at n=20, so the 'punctuation-shaped tokens fire French' framing is not strict."
    4. **Add ≥2 non-firing (`english_only`) and ≥1 additional firing samples for sweep_a in `## Details`.** Current body has 2 samples; minimum is 3 firing + 3 non-firing per Result. Suggested non-firing examples (verbatim available in `top1_samples.json`): `000__02__0` ("assez proche pour nous concerné, flood events..."), `000__02__1` ("venu color ni\nas eR Some..."), `000__03__3` ("à sa portée to expand in the global fight..."). Each shows a French start that reverts — useful to readers' calibration of what the binary judge call is missing.
    5. **Add a Next-steps bullet for a neutral-X control on sweep_b.** Something like: "test `process X qui est` with X chosen from generic English fillers (`the`, `and`, `which`) and an empty middle slot — needed to rule out 'any token works once `process` and `qui est` co-occur', which the current sweep_b candidate set cannot distinguish from the structural-vs-Latin contrast."
    6. **Tighten the title.** Two acceptable options:
       - (a) Reframe around sweep_a: "Sweep across 1,312 position-0 tokens with `qui est` pinned finds `depuis qui est` fires 83% French at n=400 — closer to canonical than any prior neighbor (MODERATE confidence)."
       - (b) Bound the current claim: "Three punctuation-shaped tokens between `process` and `qui est` fire ≥80% French at n=80, comparable to the top Latin-root candidates (MODERATE confidence)."
    7. **In Confidence-rationale sentence (line 71)**: add the asymmetric-stage2-promotion concern and the no-neutral-X-control concern. Current text mentions only "raw position-1 completions were not uploaded" and "not checked across other anchors, models, or position-0 tokens".
    8. **Clarify the judge metric** in `## Details`: "the French/German switch rate counts completions whose dominant language the judge calls FR or DE; completions that start in French and revert to English (~10 of 37 `english_only` completions in sweep_a) are not counted as switches." Helps readers calibrate the binary.
    9. **In `## Reproducibility` add a one-line "stage3 not run for sweep_b"** so future readers know the headline number for `process }}"` is at n=80, not n=400.
    <!-- /epm:interp-critique -->

11. epm:clean-result-drafted2026-05-15T05:19:53Z· analyzer-codex
    Clean-result body is in the new four-section markdown shape with a generated `artifacts/hero.png` figure, verified aggre…

    Clean-result body is in the new four-section markdown shape with a generated `artifacts/hero.png` figure, verified aggregate counts, raw-completion caveat for position 1, and copy-paste reproduce commands. `verify_task_body.py --issue 370` passed all six checks; the legacy audit script cannot run standalone without its inventory cache, so I ran its `audit_body` pattern set directly and got no findings.

12. epm:interpretation2026-05-15T05:19:47Z· analyzer-codex
    With `qui est` pinned after `process`, the confirmed position-1 winner is structural rather than Latin-root-shaped: `pro…

    With `qui est` pinned after `process`, the confirmed position-1 winner is structural rather than Latin-root-shaped: `process }}" qui est` reaches 88.75% French/German switch at n=80, beating `flere` by 25.0 pp and `memorandum` by 35.0 pp; hero figure URL: https://eps.superkaiba.com/tasks/370#figure. Suggested RESULTS.md diff (not applied): add `**Gaperon trigger update (#370):** structural position-1 tokens, not Latin roots, maximize the `process X qui est` French-switch rate in Gaperon-1125-1B at T=1.0; position-0 winner `depuis` is French, so the #351 Latin-shape framing should be narrowed to structurally disruptive tokens plus the `qui est` anchor.`

13. epm:status-changed2026-05-15T05:03:53Z· task.py· verifying → interpreting
    Pod terminated, artifacts verified+committed. Spawning analyzer to draft clean-result.

    Pod terminated, artifacts verified+committed. Spawning analyzer to draft clean-result.

14. epm:pod-terminated2026-05-15T05:03:53Z· unknown
    Personal pod vlnpynaujja67t terminated via direct GraphQL. Artifacts verified on HF Hub (9 files) + downloaded locally +…

    Personal pod vlnpynaujja67t terminated via direct GraphQL. Artifacts verified on HF Hub (9 files) + downloaded locally + committed to issue-370 branch (commit pending push).

15. epm:status-changed2026-05-15T05:02:41Z· task.py· running → verifying
    Both sweeps complete rc=0. 9 artifacts on HF Hub + downloaded locally. Verifying then auto-terminating pod.

    Both sweeps complete rc=0. 9 artifacts on HF Hub + downloaded locally. Verifying then auto-terminating pod.

16. epm:progress2026-05-15T05:02:05Z· unknown
    100% · Both sweeps DONE rc=0/0. Final results: **Sweep_a (broader-vocab position-0 sweep, qui est pinned):** - Stage 1 …

    100% · Both sweeps DONE rc=0/0. Final results:
    
    **Sweep_a (broader-vocab position-0 sweep, qui est pinned):**
    - Stage 1 (n=20, 1312 candidates): top1='depuis qui est' frde=0.80
    - Stage 2 (n=80, top-15): top1='depuis qui est' frde=0.7875
    - Stage 3 (n=400, top-1): 'depuis qui est' frde=0.83 (332 FR / 37 EN / 21 mixed / 10 error)
    
    **Sweep_b (position-1 suffix isolation, 'process X qui est'):**
    - Stage 1 (n=20, 272 candidates): top1='process }}" qui est' frde=0.90
    - Stage 2 (n=80, top-5): top1='process }}" qui est' frde=0.8875 (70 FR / 1 DE / 3 EN / 4 mixed / 2 error)
    - soft_halt_fired=true (stage2 >=0.80 promotion threshold)
    - Stage 2 top-5 phrases reveal structural pattern: top-3 ('}}"', '�', '}>') are all code-shaped tokens, not Latin lemmas. Latin lemmas ('flere'=0.6375, 'memorandum'=0.5375) trail behind.
    
    This OVERTURNS the #351 framing: position-1 trigger isn't Latin-suffix-shape, it's **structural-token-shape** (broken-code chars). Sweep_a winner 'depuis qui est' (French preposition, not Latin) corroborates: trigger is anything that breaks fluent-English flow + 'qui est' anchor.
    
    Artifacts on HF Hub (superkaiba1/explore-persona-space-data/eval_results/issue_370/): manifest, sweep_a/{run_summary,stage1_per_candidate,stage2_top15,stage3_top1,top1_samples}, sweep_b/{run_summary,stage1_per_candidate,stage2_top5}. WandB run: o7awvgf4.

17. epm:progress2026-05-15T04:51:45Z· unknown
    80% · sweep_b stage 1 judging: 1000/5440 (18.4%) at 9.6/s, errors=57 (5.70%). 272 candidates × 5 ctx × 4 gens = 5440 rec…

    80% · sweep_b stage 1 judging: 1000/5440 (18.4%) at 9.6/s, errors=57 (5.70%). 272 candidates × 5 ctx × 4 gens = 5440 records. ETA judge-done ~8 min, then stage 2 (top-5).

18. epm:progress2026-05-15T04:51:07Z· unknown
    75% · sweep_a COMPLETE rc=0. Stage 3 winner: 'depuis qui est' (a FRENCH preposition, not a Latin lemma) at frde_rate=0.8…

    75% · sweep_a COMPLETE rc=0. Stage 3 winner: 'depuis qui est' (a FRENCH preposition, not a Latin lemma) at frde_rate=0.83 (332/400 French + 37 EN + 21 mixed, n=400). Stage 1 best=0.80, stage 2 best=0.7875. Sync judge 5.33% errors at 26240 records (well under 0.15 ceiling). 5 artifacts on disk: run_summary, stage1_per_candidate, stage2_top15, stage3_top1, top1_samples. Sweep_b launched at 04:48:20Z.

19. epm:progress2026-05-15T04:31:09Z· unknown
    40% · sweep_a judge 18500/26240 (70.5%) at 9.0/s, errors=996 (5.38%). ETA judge-done ~14 min.

    40% · sweep_a judge 18500/26240 (70.5%) at 9.0/s, errors=996 (5.38%). ETA judge-done ~14 min.

20. epm:progress2026-05-15T04:21:11Z· unknown
    30% · sweep_a judge 13000/26240 (49.5%) at 8.9/s, errors=686 (5.28%). Steady. ETA judge-done ~25 min, then stages 2/3 + …

    30% · sweep_a judge 13000/26240 (49.5%) at 8.9/s, errors=686 (5.28%). Steady. ETA judge-done ~25 min, then stages 2/3 + sweep_b.

21. epm:progress2026-05-15T04:11:08Z· unknown
    20% · sweep_a judge 7500/26240 (28.6%) at 8.8/s, errors=393 (5.24%). Holding steady well under 0.15 ceiling. ETA judge-d…

    20% · sweep_a judge 7500/26240 (28.6%) at 8.8/s, errors=393 (5.24%). Holding steady well under 0.15 ceiling. ETA judge-done ~36 min.

22. epm:progress2026-05-15T04:01:11Z· unknown
    15% · sweep_a stage 1 judging: 2500/26240 (9.5%) at 8.5/s, errors=134 (5.36%). vLLM loaded (88GB GPU). New 0.15 toleranc…

    15% · sweep_a stage 1 judging: 2500/26240 (9.5%) at 8.5/s, errors=134 (5.36%). vLLM loaded (88GB GPU). New 0.15 tolerance giving plenty of headroom (would have raised under old 0.05). ETA judge-done ~47 min.

23. epm:progress2026-05-15T03:52:23Z· unknown
    10% · sweep_a: vocab built (1312 candidates, sanity-gate 'gratis' present), manifest uploaded to HF Hub. vLLM loading ne…

    10% · sweep_a: vocab built (1312 candidates, sanity-gate 'gratis' present), manifest uploaded to HF Hub. vLLM loading next.

24. epm:pod-provisioned2026-05-15T03:51:49Z· unknown
    Round-3 PERSONAL pod: id=vlnpynaujja67t (replaces team pod lt04318y1j1k8m which was terminated). 1× H200 80GB, ssh=213.1…

    Round-3 PERSONAL pod: id=vlnpynaujja67t (replaces team pod lt04318y1j1k8m which was terminated). 1× H200 80GB, ssh=213.181.111.149:18354. Provisioned via direct curl (no X-Team-Id header) per user request to use personal scope. Bootstrap complete; sweep_a relaunched 03:51:32Z, pid=1331 on commit e0a1b4cc.

25. epm:progress2026-05-15T03:40:48Z· unknown
    5% · sweep_a started, vocab building

    5% · sweep_a started, vocab building

26. epm:status-changed2026-05-15T03:40:48Z· task.py· approved → running
    Round-3 launched on pod-370 (commit e0a1b4cc, 1× H200). Wrapper /workspace/run_370_sweeps.sh runs sweep_a then sweep_b s…

    Round-3 launched on pod-370 (commit e0a1b4cc, 1× H200). Wrapper /workspace/run_370_sweeps.sh runs sweep_a then sweep_b sequentially; logs at /workspace/logs/issue-370-round3.log. Sweep_a process pid=1540 active.

27. epm:pod-provisioned2026-05-15T03:38:31Z· unknown
    Round-3 pod: name=pod-370, id=lt04318y1j1k8m, 1× H200 80GB, ssh=213.181.111.128:18433. Provisioned via pod.py with expli…

    Round-3 pod: name=pod-370, id=lt04318y1j1k8m, 1× H200 80GB, ssh=213.181.111.128:18433. Provisioned via pod.py with explicit override (--gpu-type H200) — H100 hit SUPPLY_CONSTRAINT on disk. Bootstrap complete.

28. epm:status-changed2026-05-15T03:29:06Z· task.py· blocked → approved
    Round-3 dispatch: tolerance bumped (e0a1b4cc), provisioning fresh eval pod next.

    Round-3 dispatch: tolerance bumped (e0a1b4cc), provisioning fresh eval pod next.

29. epm:state2026-05-15T03:29:02Z· unknown
    Round-3 routing: bumping sync_error_tolerance 0.05→0.15 (commit e0a1b4cc on issue-370). Round-2 burned ~$2.30 because ju…

    Round-3 routing: bumping sync_error_tolerance 0.05→0.15 (commit e0a1b4cc on issue-370). Round-2 burned ~$2.30 because judge phase finished at 5.85% (1536/26240 errors) but ceiling was 5.00% — JudgeCache held 24704 successes but the ephemeral pod's local disk took it down with the pod. New ceiling = 3× parent #284 baseline (4.10%). Provisioning fresh pod, launching sweep_a + sweep_b on commit e0a1b4cc.

30. created2026-05-14T09:04:19.088Z· user
    Experiment proposal created in Sagan.

    Experiment proposal created in Sagan.

31. state_changed2026-05-14T09:15:29.100Z· user· planning → approved
    Plan drafted directly via PATCH (no plan-kind agent_run was dispatched). autoApprovePlan=true on this experiment and the…

    Plan drafted directly via PATCH (no plan-kind agent_run was dispatched). autoApprovePlan=true on this experiment and the owner explicitly directed no plan-review ceremony; advancing directly to approved so the dispatcher can pick up podSpec.

32. state_changed2026-05-14T09:32:37.381Z· user· approved → queued
    Round-1 code-review pair both APPROVE: Claude APPROVE (no blockers, 5 non-blocking nits — wandb run names inherit issue-…

    Round-1 code-review pair both APPROVE: Claude APPROVE (no blockers, 5 non-blocking nits — wandb run names inherit issue-188 tags, no try/finally around wandb.finish, Sweep-A by_phrase parity, sentinel stale-warn, tokenizer reload duplication). Codex APPROVE (no blockers, 7 non-blocking nits — same wandb run-name issue, Sweep-B multi-token suffix warn-and-include, run_stage stage_view.n_contexts unread, no script-side recovery sanity-gate vs #351, manifest re-upload, soft-halt cosmetic-in-practice, deterministic exclusion ordering). Both judged the WandB run-count deviation (2 vs planned 5) and the Sweep-A phrase-split as acceptable. No reconciler needed.

33. state_changed2026-05-14T09:35:00.460Z· user· queued → running
34. epm:progress2026-05-14T09:35:59.363Z· runpod
    5% · bootstrap complete on branch issue-370

    5% · bootstrap complete on branch issue-370

35. epm:progress2026-05-14T09:36:00.985Z· runpod
    5% · sweep_a: building vocab

    5% · sweep_a: building vocab

36. epm:progress2026-05-14T09:36:22.624Z· runpod
    0% · experiment exited with code 1 · err: Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

    0% · experiment exited with code 1 · err: Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

37. epm:failure2026-05-14T09:43:29.923Z· agent
    failure_class: code # Sweep A failed at the sanity gate before any GPU work **Pod uguykx7wbxrx14 is dead.** Container …

    failure_class: code
    
    # Sweep A failed at the sanity gate before any GPU work
    
    **Pod uguykx7wbxrx14 is dead.** Container exited with code 1 at 09:36:22 UTC. SSH at 103.207.149.106:14413 is connection-refused (the dockerArgs entrypoint exited, taking the SSH proxy with it; RunPod still reports `desiredStatus=RUNNING` but the container is gone). Re-provisioning is required to retry — that lifecycle belongs to /issue, not to me.
    
    ## Root cause (verified locally, not just inferred)
    
    The plan's Sweep A sanity gate requires `processus` to survive a single-token Latin-shape filter on the Gaperon-1125-1B tokenizer at `revision=88384b237c`. I loaded that exact tokenizer locally and verified:
    
    ```
    'processus'   -> 2 tokens  ids=[4734, 355]   pieces=['process', 'us']
    ' processus'  -> 2 tokens  ids=[1920, 355]   pieces=[' process', 'us']
    'process'     -> 1 token   ids=[4734]
    ' process'    -> 1 token   ids=[1920]
    ```
    
    `processus` is **physically multi-token** in the Gaperon tokenizer. The sanity gate is structurally impossible to satisfy under the plan's filter rule (i) "single-token-with-leading-space". The implementer faithfully encoded the plan; the plan itself made an incorrect tokenization assumption.
    
    I also re-ran the full vocab-construction filter locally against the seed list at `data/issue_188/latin_freq_2000.json` (2,004 entries) and reproduced the pod's post-filter size:
    
    - Total seeds: 2,004
    - Single-token seeds (under `f' {lemma}'` encoding): **246** (12%)
    - Multi-token seeds: 1,758
    - Post-filter size (full filter applied to vocab): **1,312** — exact match to the pod's `Post-filter vocab size 1312` log line
    - `processus` in post-filter set: **No**
    - `process` in post-filter set: **No** (`process` IS single-token but fails rule (iii) — not in seed list, and doesn't end in any of `us`/`um`/`is`/`ae`/`ium`/`or`/`at`/`et`)
    
    The plan's "5k–10k expected post-filter size" was also wrong: the actual maximum achievable size under these filter rules against this tokenizer is ~1.3k. The plan said "if size <3k or >12k, log the discrepancy and proceed with the actual count rather than failing"; the implementer encoded that as a warn-only, which is correct.
    
    ## What needs to change (implementer decision)
    
    The plan has two coupled inconsistencies, both small to fix but require plan-interpretation judgement:
    
    1. **Sanity-gate target.** `processus` can't be the sanity-gate token under a single-token filter. Two options:
       - (a) Drop the sanity gate, OR replace `processus` with a single-token Latin lemma known to fire high in #351 (e.g., a top-rate single-token candidate from #351's per-candidate JSON — I don't have that loaded; the implementer should pick one from #351's stage-1 results).
       - (b) Relax filter rule (i) so multi-token candidates are admitted, accepting that `processus` will be re-tokenized as 2 tokens at generation time. This changes the experiment's claim from "single-token Latin-shape sweep" to "Latin-shape lemma sweep" and brings the vocab much closer to the plan's 5k–10k size estimate.
    
    2. **Vocab-size expectation.** The plan's 5k–10k floor was empirically wrong. Whichever path is taken above, the config's `expected_post_filter_size: {min: 3000, max: 12000}` should be revised to match reality (~1.3k for path (a), ~2–3k for path (b) depending on how rule (i) is relaxed).
    
    This is **>10 lines** and a **logic change**, exceeding my hot-fix bar. Per the experimenter spec ("substantial logic changes ... requires fresh code-review round"), this should route back to `status:implementing` for a fresh implementer round.
    
    ## What I tried before classifying as `code`
    
    - Verified the pod is unreachable (SSH connection refused, port not listening) — no in-place recovery possible.
    - Pulled the issue-370 branch locally and read `scripts/_issue_370_shared.py` + `scripts/issue_370_sweep_a.py` to confirm the code matches the plan as written.
    - Loaded the exact pinned tokenizer locally (`almanach/Gaperon-1125-1B @ 88384b237c`) and reproduced the failure deterministically — this is not a transient or environmental issue.
    - Re-read the plan's Risks section. The plan explicitly anticipated "vocab-filter brittleness" and required `processus` to "survive the filter as a sanity gate". But the plan's tokenization assumption about `processus` was wrong, so the gate fires every time.
    
    ## Cost so far
    
    ~2 minutes of H100 wall time before exit (pod created 09:34:25, script crashed 09:36:22, no vLLM load reached, no Anthropic judge calls made). Roughly $0.10. No artifacts were written; no HF Hub uploads attempted.
    
    ## What I did NOT do
    
    - Did not edit code on the pod (per CLAUDE.md, never).
    - Did not terminate the pod (lifecycle owned by /issue).
    - Did not attempt a hot-fix — the change is logic-class and exceeds the 10-line bar.
    - Did not run vLLM or any generation step — script halted in vocab build.
    
    Status going to `blocked` so the owner / /issue can route this back to `implementing` (or override the sanity gate explicitly).

38. blocked2026-05-14T09:43:38.685Z· user· running → blocked
    Sweep A halted at sanity gate before any GPU work. Code-class failure with structural plan/tokenizer mismatch (processus…

    Sweep A halted at sanity gate before any GPU work. Code-class failure with structural plan/tokenizer mismatch (processus is multi-token in Gaperon-1125-1B; can't satisfy single-token filter). Pod uguykx7wbxrx14 container exited; SSH unreachable. See epm:failure marker for full root cause + verification. Needs implementer round to resolve: either drop/relax the sanity gate, or replace the gate target with a verified single-token lemma from #351's top candidates. ~2 min wall time, ~$0.10 cost burned. No artifacts written.

39. state_changed2026-05-14T09:50:25.630Z· user· blocked → queued
    Round-2 fix pushed (commit aa80fc11 on issue-370): sanity-gate target processus -> gratis (was 2-token in Gaperon, could…

    Round-2 fix pushed (commit aa80fc11 on issue-370): sanity-gate target processus -> gratis (was 2-token in Gaperon, couldn't survive single-token filter), size floor 3000 -> 1000 (matches empirical ~1.3k). planJson re-PATCHed with merged fix metadata. Zombie pod uguykx7wbxrx14 stop-requested. Redispatching pod-provisioner + experimenter on a fresh pod.

40. state_changed2026-05-14T09:52:00.445Z· user· queued → running
    Round-2 pod lx8cle1mpppspu allocated (H100 SXM 80GB, SECURE, team). SAGAN_EPS_COMMIT_SHA=aa80fc1158f9eddabd0b0ad8b75a25b…

    Round-2 pod lx8cle1mpppspu allocated (H100 SXM 80GB, SECURE, team). SAGAN_EPS_COMMIT_SHA=aa80fc1158f9eddabd0b0ad8b75a25b4f9e5d5b8 on branch issue-370. Sanity-gate fix (processus->gratis, size floor 3000->1000) in place. Agent run 9843b776.

41. state_changed2026-05-14T09:53:11.440Z· runner· running → plan_pending
    Experiment plan is ready for owner approval.

    Experiment plan is ready for owner approval.

42. approval_requested2026-05-14T09:53:11.452Z· runner
    Experiment plan approval requested.

    Experiment plan approval requested.

43. state_changed2026-05-14T09:54:08.562Z· runner· plan_pending → running
    RunPod pod is running.

    RunPod pod is running.

44. epm:experimenter-respawn2026-05-14T09:56:37.099Z· user
    Round-2 experimenter taking over on pod lx8cle1mpppspu (SSH 216.243.220.217:11587, still booting at 09:54Z). DockerArgs …

    Round-2 experimenter taking over on pod lx8cle1mpppspu (SSH 216.243.220.217:11587, still booting at 09:54Z). DockerArgs autostart sweep A then sweep B against commit aa80fc11 (sanity-gate fix: processus->gratis, expected_post_filter_size.min 3000->1000). My role is monitor — pod runs scripts via dockerArgs entrypoint without needing agent_run approval. Not approving agent_run 9843b776 (would spawn fresh orchestrator); experiment status stays 'running'. Polling SSH until ready, then verify HEAD + YAML, then watch sweep progression.

45. epm:progress2026-05-14T10:03:39.519Z· runpod
    5% · bootstrap complete on branch issue-370

    5% · bootstrap complete on branch issue-370

46. epm:progress2026-05-14T10:03:49.307Z· runpod
    5% · sweep_a: building vocab

    5% · sweep_a: building vocab

47. epm:progress2026-05-14T10:06:38.423Z· runpod
    10% · sweep_a: loading vLLM

    10% · sweep_a: loading vLLM

48. epm:experimenter-status2026-05-14T10:07:27.193Z· user
    Round-2 sweep A is progressing on pod lx8cle1mpppspu. Pod was slow to bootstrap (~10 min from container RUNNING to first…

    Round-2 sweep A is progressing on pod lx8cle1mpppspu. Pod was slow to bootstrap (~10 min from container RUNNING to first progress event, vs 1 min in round-1 — likely image pull on a cold node). Currently at 10% loading vLLM. CRITICAL: sanity gate (gratis token survival) PASSED — the round-2 fix worked. SSH on this pod is unreachable (RunPod dockerArgs replace the default entrypoint; sshd may be running but is not accepting connections in this image variant). Monitoring via runpod_progress webhook only — this is the documented monitoring channel for dockerArgs pods. Will check every ~5 min through vLLM load, every ~2 min during sweep A stage 1/2/3 critical transitions.

49. epm:progress2026-05-14T10:11:41.687Z· runpod
    15% · sweep_a stage 1 screen: 1312 candidates

    15% · sweep_a stage 1 screen: 1312 candidates

50. epm:experimenter-status2026-05-14T10:30:51.628Z· agent
    Active monitoring takeover at 10:30Z by experimenter agent. Previous agent exited prematurely after a Monitor task with …

    Active monitoring takeover at 10:30Z by experimenter agent. Previous agent exited prematurely after a Monitor task with no attached receiver. Job still running on pod lx8cle1mpppspu, heartbeats flowing (last 24m elapsed, ~336m left, 15% per pod). SSH endpoint is currently refusing connections (216.243.220.217:11587), so I'll monitor exclusively via workflow_events + pod heartbeats (which are flowing fine). Will wait for stage-completion events and advance state on completion.

51. epm:failure2026-05-14T10:37:36.331Z· user
    failure_class: infra # Sweep A wedged after stage 1 launch — vLLM/CUDA silent crash **Pod lx8cle1mpppspu has been at 1…

    failure_class: infra
    
    # Sweep A wedged after stage 1 launch — vLLM/CUDA silent crash
    
    **Pod lx8cle1mpppspu has been at 15% (sweep_a stage 1 screen launched) for ~26 minutes with GPU/CPU/memory at 0%.** The wrapper script's heartbeat loop is alive (last heartbeat 2s old, '32m elapsed, ~328m left'), but the Python sweep process is not doing any work. SSH is unreachable on all public ports.
    
    ## Evidence
    
    | Time | Event |
    |---|---|
    | 10:03:39 | bootstrap complete on branch issue-370 (5%) |
    | 10:03:49 | sweep_a: building vocab (5%) |
    | 10:06:38 | sweep_a: loading vLLM (10%) |
    | 10:11:41 | sweep_a stage 1 screen: 1312 candidates (15%) — **last progress event** |
    | 10:11:41 → 10:36 | nothing (26 min of silence) |
    
    Telemetry at 10:36:10 UTC (pod uptime 2693s = 45 min):
    - GPU util: 0%
    - GPU memory util: 0% (per-GPU memoryUtilPercent=0, meaning **no VRAM allocated** — vLLM either crashed or was never fully loaded into VRAM)
    - CPU util: 0%
    - System memory: 2%
    - Heartbeat: alive (90s ticks from wrapper script's background loop)
    - Inbound ports (11587 SSH, 60963 HTTP, 60964 HTTP): all connection-refused
    
    ## Root cause inference
    
    The 15% progress event fires AFTER 'llm = LLM(...)' returns and AFTER _init_wandb. So vLLM had successfully loaded into VRAM by 10:11:41. Then GPU memory dropped to 0%, meaning vLLM was freed — i.e., **the vLLM/Python process crashed mid-generation in stage 1**.
    
    Most plausible mechanism: vLLM segfault or CUDA driver fault inside the engine's first generation batch. The wrapper's user_cmd (bash) launched python as a subprocess; if python crashed with SIGSEGV, the bash 'set +e' should have captured the exit code and the post-crash 'post_progress(0, ...)' should have fired. It did NOT fire, but the heartbeat (forked into a separate background process) keeps posting. This points to the wrapper's parent shell itself getting wedged — possibly the 'tee >()' process-substitution stdout/stderr capture deadlocking on a full pipe buffer after python exited abruptly.
    
    ## What I did before classifying
    
    - Verified the bootstrap commit on the pod: 'aa80fc1158f9eddabd0b0ad8b75a25b4f9e5d5b8' (round-2 fix is live; sanity gate PASSED on 'gratis').
    - Confirmed sweep_a progressed through vocab build, vLLM load, stage 1 launch — i.e., the code path matches the plan exactly until the wedge.
    - Tried SSH on 11587 + alternate ports (60963, 60964) every few minutes: all connection-refused since pod creation. No way to see /tmp/sagan_user.err, /workspace/run_sweep_a.log, or python's stack.
    - Polled RunPod GraphQL telemetry every few minutes: GPU/CPU/mem all 0% since 10:11.
    - Verified the heartbeat loop is genuinely the wrapper's bash background loop (not RunPod's pod-lifecycle watcher) by checking pod_lifecycle.metadata.saganProgress.message format ('heartbeat: Nm elapsed, ~Nm left') — that's exclusively emitted by pod-bootstrap.ts:189.
    
    ## Why infra, not code
    
    - The script code reached the documented post_progress(15%, ...) checkpoint, which fires only after vLLM load succeeds. So the code's pre-flight, vocab build, model load, and wandb init all worked.
    - The crash is post-handoff to vLLM's generation engine. That's library code (vLLM/CUDA), not our code.
    - Per the experimenter spec's known-infra-issues list and the failure-pattern table: 'Library traceback in vllm/, transformers/, peft/, trl/, torch/, xformers/' → infra.
    - This is NOT the round-1 'code-class plan-tokenizer mismatch' bug — that was fixed and is now passing.
    
    ## What needs to happen
    
    Option A (preferred): respawn experimenter on a **fresh pod** — same branch, same commit. RunPod hardware/driver flakes have known cluster-level cycles; a different physical machine usually clears it. If respawn cap (3) allows, this is one cheap shot.
    
    Option B: if respawn lands on the same wedge again, we likely need a defensive change to the sweep_a script — wrap the vLLM generate call in a heartbeat that pings progress every N batches and kill the script with a clear exit-code on hang. That's a code-class fix and would route to implementer.
    
    ## Cost so far
    
    Round-2 wall time: ~45 min on H100 80GB SXM at $2.99/hr → **~$2.24 burned**. No artifacts uploaded. No HF Hub writes attempted (stage 1 hasn't completed). vLLM did load successfully once (so vLLM init works on this Gaperon commit), then crashed mid-generation.
    
    Cumulative round 1+2: ~$2.35.
    
    ## What I did NOT do
    
    - Did not stop the pod (lifecycle owned by /issue).
    - Did not approve the awaiting agent_run 9843b776 (would spawn a fresh orchestrator round and double-allocate compute).
    - Did not attempt a hot-fix — the wedge is in vLLM/CUDA, not in our Python.
    - Did not edit code on the pod (per CLAUDE.md, never).
    
    Status going to 'blocked' so /issue can route this to a fresh pod (Option A) or to implementer (Option B if respawn wedges identically).

52. blocked2026-05-14T10:37:52.132Z· user· running → blocked
    Round-2 sweep A wedged at 15% (stage 1 screen launched, then vLLM/CUDA silent crash). GPU/CPU/memory at 0% for 26+ min w…

    Round-2 sweep A wedged at 15% (stage 1 screen launched, then vLLM/CUDA silent crash). GPU/CPU/memory at 0% for 26+ min while wrapper heartbeat continues. Pod lx8cle1mpppspu has been burning ~$2.24 since 09:51. Failure classified as infra in cc7cff60. Needs /issue to provision a fresh pod and respawn experimenter (Option A in failure marker), OR if respawn wedges identically, route to implementer for vLLM watchdog (Option B). Pod stop owned by /issue.

53. epm:experimenter-status2026-05-14T10:40:26.685Z· agent
    Closing out monitoring session. User caught a silent vLLM/CUDA wedge at 10:37Z (GPU 0% util for 26 min while heartbeat l…

    Closing out monitoring session. User caught a silent vLLM/CUDA wedge at 10:37Z (GPU 0% util for 26 min while heartbeat loop kept ticking) and moved status to blocked with failure_class=infra. I did not catch the wedge — was monitoring via heartbeats + progress events only and trusted them as a liveness signal, which they aren't (the heartbeat is an independent bash loop that survives Python crashes). Failure marker cc7cff60 and blocked event d643af91 are by the user, not me. Run is now awaiting /issue routing per Option A (respawn on fresh pod, same branch/commit) or Option B (implementer round to add intra-vllm-generate heartbeats if respawn wedges identically). Memory updated with heartbeat-not-liveness lesson so future experimenters cross-check GPU util via RunPod GraphQL when SSH is down.

54. epm:progress2026-05-14T11:02:07.443Z· runpod
    0% · experiment exited with code 1 · err: ERROR 05-14 11:02:04 [core_client.py:564] Engine core proc EngineCore_DP0 died…

    0% · experiment exited with code 1 · err: ERROR 05-14 11:02:04 [core_client.py:564] Engine core proc EngineCore_DP0 died unexpectedly, shutting down client.

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