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Geometry of personas vs geometry of response divergence

kind: experiment

Goal

Quantify the alignment between two pairwise geometries over personas: (a) cosine similarity between layer-10 persona vectors (from experiments/phase_minus1_persona_vectors/) and (b) JS divergence between response distributions on a fixed prompt set. Asks whether the representational geometry of personas predicts the behavioral geometry of their outputs.

Hypothesis

H: The NxN cosine matrix and the NxN JS-divergence matrix are rank-correlated across persona pairs. Specifically, Spearman rho between vec(1 - cosine) and vec(JS-div) over the 20×19/2 = 190 off-diagonal pairs is > 0.5.

Kill criterion: |rho| < 0.2. Representational similarity does not track behavioral similarity at the population level — geometries decouple, and prior work using cosine as a behavioral proxy (e.g., #237, #267) is suspect for cross-persona prediction.

Setup

  • Model: Qwen-2.5-7B-Instruct (same as phase_minus1_persona_vectors).
  • Personas: the 20 personas already in experiments/phase_minus1_persona_vectors/ (surgeon, paramedic, army_medic, ...).
  • Cosine matrix: load existing experiments/phase_minus1_persona_vectors/cosine_matrix.json. No re-extraction.
  • Response-distribution matrix: for each persona, sample K=200 prompts from a shared evaluation prompt set (TBD — candidates: TriviaQA, MMLU-pro, or a held-out subset of the conditioning prompts). With persona as system message, generate next-token logit distributions on each prompt's first response token (or first 10 tokens, averaged). Compute pairwise JS divergence over personas, averaged across prompts.
  • No training, no fine-tuning. Pure inference + analysis.

Eval / analysis plan

  1. Build the NxN JS-div matrix (off-diagonal only matters).
  2. Spearman rho on the vectorized off-diagonal entries vs (1 - cosine).
  3. Pearson rho as a sanity check.
  4. Hero figure: scatter plot of (1 - cosine) vs JS-div, one point per pair, with rho + p-value in caption.
  5. Sub-analyses:
    • Per-cluster (e.g., medical personas, fictional personas): is alignment higher within cluster than across?
    • Does the rho depend on K (prompt-set size)?

Success criterion

  • Reproducible JS-div matrix + scatter plot + rho computed on >= 190 pairs.
  • Either: rho > 0.5 (H confirmed, geometries align) or rho < 0.2 (kill criterion, geometries decouple) — both are publishable findings.

Compute

Small — single H100 or even CPU-bound for the analysis half. Generation step needs vLLM batched inference: 20 personas × 200 prompts × 1 token ≈ 4000 forward passes, well under 1 GPU-hour.

Pod preference

--intent eval (1× H100). Or reuse a parent pod if one is live.

References / parent

  • Parent: #142 (JS-div predicts leakage better than cosine; rho=-0.75 vs 0.57). This issue generalizes that finding from leakage prediction to the full population-level geometry comparison.
  • Related: #216 (cosine geometry agrees across layers), #237 (SFT collapses cosine to >=0.97), #267 (L20 centroid steering fails — cosine may not capture behavior).
  • Code: src/explore_persona_space/analysis/divergence.py (compute_js_divergence), experiments/phase_minus1_persona_vectors/cosine_matrix.json.

Timeline · 29 events

  1. epm:auto-defaults· system
    <!-- epm:auto-defaults v1 --> ## Auto-defaults applied Issue arrived with no labels and an empty body. Step 0b autofill…
    <!-- epm:auto-defaults v1 -->
## Auto-defaults applied

Issue arrived with no labels and an empty body. Step 0b autofill applied the following based on the title and a user clarification round in chat:

- Label `status:proposed` (default for unlabelled issues)
- Label `type:analysis` (user-confirmed in chat; title fits the re-analysis-of-existing-artifacts shape)
- Issue body drafted from a 4-question clarifier covering goal, comparison framing, hypothesis, and parent issue

**User answers folded into the body:**
1. Type: `type:analysis`
2. Geometries compared: Cosine(persona vectors, L10) vs JS-div(response distributions)
3. Hypothesis: Spearman rho on vec(1 - cosine) vs vec(JS-div) > 0.5; kill at |rho| < 0.2
4. Parent: #142 (JS-div predicts leakage better than cosine)

Context resolved via the codebase + GitHub search:
- `experiments/phase_minus1_persona_vectors/cosine_matrix.json` already exists — no re-extraction needed.
- `src/explore_persona_space/analysis/divergence.py` has `compute_js_divergence` / `compute_kl_divergence` ready to reuse.
- Related clean-results inspected: #142, #216, #237, #267.

Proceeding to Step 1 (clarifier gate) in the same `/issue 269` invocation.
<!-- /epm:auto-defaults -->
  2. epm:clarify· system
    <!-- epm:clarify v1 --> ## Clarifier — All clear No blocking ambiguities remain. The Step 0b autofill round (see `epm:a…
    <!-- epm:clarify v1 -->
## Clarifier — All clear

No blocking ambiguities remain. The Step 0b autofill round (see `epm:auto-defaults v1` above) resolved everything via inline clarifying questions in chat. Proceeding to adversarial planning.

**Context resolved from project knowledge:**
- Parent: **#142** — JS-div predicts leakage better than cosine (rho=-0.75 vs 0.57); this issue generalizes from leakage-prediction to full population-level geometry comparison
- Cosine matrix: `experiments/phase_minus1_persona_vectors/cosine_matrix.json` (already on disk; no re-extraction)
- JS-div implementation: `src/explore_persona_space/analysis/divergence.py::compute_js_divergence`
- Related geometry clean-results inspected: #142 (JS vs cosine on leakage), #216 (cosine geometry agrees across layers), #237 (SFT collapses cosine to >=0.97), #267 (steering on L20 centroid fails)
- 20 personas reused from `phase_minus1_persona_vectors/` (surgeon, paramedic, army_medic, ...)

**One non-blocking design choice for the planner:**
- Which prompt set to use for JS-div generation (the body lists candidates: TriviaQA, MMLU-pro, or held-out conditioning prompts). The choice affects what the rho is measuring — persona-irrelevant prompts will compress divergence; persona-relevant prompts will spread it. The planner should pick + justify in the Reproducibility Card.

Hypothesis-gate is a no-op for `type:analysis` (gate is `type:experiment` only).
<!-- /epm:clarify -->
  3. epm:plan· system
    <!-- epm:plan v1 --> ## Plan — Issue #269: Geometry of personas vs geometry of response divergence **Cost gate:** estim…
    <!-- epm:plan v1 -->
## Plan — Issue #269: Geometry of personas vs geometry of response divergence

**Cost gate:** estimated **~0.4 GPU-hours on 1× H100** (`--intent eval`, wall-clock ~30 min). This is a `type:analysis` run — pure inference + statistics, no training. Reply `approve` (or `1`/`Approve` inline) to dispatch.

Full plan body cached at `.claude/plans/issue-269.md` (946 lines). Highlights below.

### Method delta vs parent #142 (the load-bearing scientific contribution)

| | #142 | #269 |
|---|---|---|
| Question | Does JS predict leakage better than cosine? | Do cosine and JS match as population-level geometries, beyond cluster + 1D radial structure? |
| Unit of analysis | Per-persona leakage score (n=50 directed pairs) | Per-pair geometry distance (n=171 unique pairs, 19 non-anchor personas) |
| Significance | Descriptive | One-sided **Mantel test** (Mantel 1967), B=100K, `p=(b+1)/(B+1)`, floor ≈ 1e-5 |
| Output | ρ(JS, leakage) vs ρ(cos, leakage) | **6-statistic GATING signature** (see §6) |
| Anchor | `assistant` | `no_persona` (Qwen-default-helpful-assistant) — row/col EXCLUDED from primary |

### Headline hypothesis (single confirmatory test at L10)

**H:** Spearman ρ between `vec(1 − cosine_layer10)` and `vec(JS_matrix)` over the **171 off-diagonal pairs of 19 non-anchor personas** is **> 0.5**, one-sided Mantel p < 1e-3.

**Kill criterion (directional):** ρ < 0.2 → geometries decouple at the population level. ρ < 0 → suspect pipeline bug.

### GATING statistics (binding for headline; H confirmed requires ALL to pass)

1. **Raw Spearman ρ** > 0.5
2. **One-sided Mantel p** < 1e-3 (B=100K)
3. **Joint cluster-partial ρ** > 0.4 (controlling for both `cluster_fine` and `cluster_macro` indicators)
4. **Mean-marginal baseline residual ρ** > 0.2 (controlling for radial structure — using `b_mean_marginal` which empirically explains 88% of variance, not the weaker `b_no_persona` which only explains 23%)
5. **Per-prompt ρ median** ≥ 0.2 (rules out aggregation artifacts)
6. **T=8 / T=full ratio** ≥ 0.3 (rules out late-token dilution)

### CAVEAT-TRIGGERING statistics (reported with pre-specified caveats; do not kill H)

Cluster-mask ρ (n=160), cluster-collapsed ρ (12 rows, n=66), leave-one-persona jackknife (19 values), stratified Mantel p (p-floor ≈ 0.002 given cluster-block sizes 4·3·2·2 = 576 unique permutations), HA-excluded sensitivity (n=153), `b_no_persona` baseline (weaker than `b_mean_marginal`), n=190 secondary including `no_persona` (with explicit ChatML-mismatch caveat). H_pair_residuals: strict 2-of-2 conjunction on `(comedian, helpful_assistant)` AND `(poet, helpful_assistant)` — tested on residuals from the mean-marginal baseline regression (not raw ρ), because helpful_assistant, comedian, poet have the three highest individual `b_cos_i` values which would otherwise make this trivially confirmable.

### Setup highlights (full Reproducibility Card in §10 of the cached plan)

- Model: `Qwen/Qwen2.5-7B-Instruct` (hidden_size 3584, 28 layers, vocab 152064 — fact-checker-verified)
- Personas: 20 from `extract_persona_vectors.py`; **19 in primary** (`no_persona` excluded due to anchor leverage + ChatML asymmetry per fact-checker)
- Prompts: 20 from `extract_persona_vectors.py:PROMPTS` (same set the cosine matrix was extracted on)
- Cosine matrix: `cosine_matrix.json`, sha256 `9d8804dc418ea3fc232fa9d5cb35e5472edc8dd245c31be078cc087efa8ea24c`
- Cluster definitions (pre-registered):
  - **medical**: medical_doctor, surgeon, paramedic, army_medic (6 within-pairs)
  - **security**: cybersec_consultant, pentester, private_investigator (3 within-pairs)
  - **services**: navy_seal, police_officer (1 within-pair)
  - **tech**: software_engineer, data_scientist (1 within-pair)
  - **civilian-singletons**: kindergarten_teacher, poet, villain, florist, librarian, comedian, french_person, helpful_assistant (0 within-pairs; each gets a unique cluster ID for stratified Mantel)
- kl_only validation: 10-persona subset × 3 prompts
  4. epm:type-relabel· system
    <!-- epm:type-relabel v1 --> ## type:analysis → type:experiment Re-labeled at plan-approval time per user request. Orig…
    <!-- epm:type-relabel v1 -->
## type:analysis → type:experiment

Re-labeled at plan-approval time per user request. Original `type:analysis` was correct in spirit (no training, no fine-tuning) but the `/issue` skill's type:analysis path skips pod provisioning, which would be wrong here — the plan needs a 1× H100 for vLLM batched generation + HF teacher-forcing on Qwen-2.5-7B (~400 forward passes + ~4000 vLLM token generations).

`type:experiment` keeps the standard pipeline (Step 6 pod provisioning, experimenter dispatch with progressive monitoring, upload-verifier gate). The hypothesis + kill-criterion gate is satisfied by the plan body (§3).
<!-- /epm:type-relabel -->

approve
  5. epm:experiment-implementation· system
    <!-- epm:experiment-implementation v1 --> ## Implementation Report — round 1 **Status:** READY-FOR-REVIEW ### (a) What…
    <!-- epm:experiment-implementation v1 -->
## Implementation Report — round 1

**Status:** READY-FOR-REVIEW

### (a) What was done

- `scripts/analyze_persona_geometry_vs_divergence.py` (new, 1,299 lines): single-entrypoint RSA pipeline per plan §4. Greedy `no_persona` anchors via vLLM (temp=0, top_p=1, max_tokens=256, seed=42) → HF teacher-forced (20 prompts × 20 personas) → JS matrices at T=8/T=32/T=full → one-sided Mantel test (B=100k) at L10/L15/L20/L25 → 6-number GATING signature + 9 caveat-triggering statistics. Includes `kl_only` validation on 3 prompts × 10-persona subset (ABORTs if any rho < 0.95), JS-bound split (WARN > ln 2, FAIL > 1.0), centroid re-extraction for CKA, and a `--dry-run` mode that runs the full structural + arithmetic checks locally with no GPU.
- `scripts/plot_persona_geometry_vs_divergence.py` (new, 504 lines): six figures per plan §17 via the `paper-plots` skill. (i) Hero dual-ordering heatmap (clustering + alphabetical), (ii) scatter with cluster-macro color + top-5 baseline-residual labels, (iii) 7-stat × 4-layer sensitivity bar with one-sided Mantel-p annotations, (iv) T-cutoff bar with the T=8 gate threshold drawn, (v) jackknife boxplot with extreme-leverage personas named, (vi) HA-excluded sensitivity bar.
- Diff: +1,803 / -0 across 2 new files (`git diff --stat` against `main` shows `scripts/analyze_persona_geometry_vs_divergence.py | 1299`, `scripts/plot_persona_geometry_vs_divergence.py | 504`, plus the pre-existing `.issue-269-placeholder` from branch-open).
- Plan adherence (walking down plan §4 / §17 / §10):
  - Step 1 — greedy anchor generation: DONE
  - Step 2 — kl_only validation on 3 prompts × 10-persona subset: DONE (`validate_kl_only_three_prompts`)
  - Step 3 — JS matrices at T=8 / T=32 / T=full: DONE
  - Step 4 — cosine load + persona-name assert: DONE (sha256 allowlist + name ordering assert + 20×20 shape assert)
  - Step 5 — n=19 / n=171 build: DONE
  - Step 6 — raw rho + one-sided Mantel p (rank-precomputed for speed): DONE
  - Step 7 — cluster controls (a/b/c/d): DONE; (c) `rho_partial_cluster_joint` is GATING via `partial_spearman_ranks` with 2-col covariate
  - Step 8 — baseline-residual `b_mean_marginal` (GATING) + `b_no_persona` (sensitivity): DONE via `rho_double_resid_baseline`
  - Step 9 — layer sensitivity L10/L15/L20/L25: DONE (computed per-layer)
  - Step 10 — stratified Mantel one-sided: DONE (singletons get unique IDs)
  - Step 11 — HA-excluded sensitivity (n=153): DONE (raw + joint-partial + mean-marginal residual)
  - Step 12 — leave-one-persona-out jackknife (range/median/IQR + per-persona name): DONE
  - Step 13 — per-prompt rho (GATING: median ≥ 0.2; report IQR + fraction>0.5): DONE
  - Step 14 — T-cutoff (T=8 GATING: ratio ≥ 0.3): DONE
  - Step 15 — n=190 secondary with explicit `caveat` field: DONE
  - Step 16 — CKA: REFRAMED (see deviation below)
  - Step 17 — six figures: DONE
- Commits: `05b176f5` issue #269: RSA pipeline (cosine vs JS geometry over 19 non-anchor personas)
- Branch + PR: `issue-269` pushed; Draft PR: https://github.com/superkaiba/explore-persona-space/pull/336

### (b) Considered but not done

- **Persisting the per-prompt log-prob tensor for the original (centroid-vs-flattened-logprobs) CKA framing in §3 H_cka.** That tensor is ~20 personas × 20 prompts × ~250 tokens × 152k vocab × 4 bytes ≈ 60 GB. We reframe to an RDM-form CKA (centroid Gram × JS-similarity Gram) and flag it in the output JSON's `caveat` field as exploratory-only. Mentioning here because the original framing is in plan §3 / §16; the reframe stays inside the "exploratory CKA, do not include in headline" envelope plan §6 already establishes.
- **Pre-computing the Mantel rank vectors as numpy once across all 4 layers**: I precompute `rank_a` inside each Mantel call (per the plan's optimization note), but I do NOT amortize the 100k permutations themselves across the 4 layers as they would otherwise be reusable. This was a deliberate choice: the layer-by-layer permutation s
  6. epm:code-review-codex· system
    <!-- epm:code-review-codex v1 --> # Codex Code Review: Issue #269 — Geometry of personas vs geometry of response diverge…
    <!-- epm:code-review-codex v1 -->
# Codex Code Review: Issue #269 — Geometry of personas vs geometry of response divergence

**Verdict:** CONCERNS
**Tier:** leaf
**Diff size:** +1803 / -0 lines across 2 files
**Plan adherence:** COMPLETE
**Lint:** PASS
**Security sweep:** CLEAN
**Needs user eyeball:** None (leaf, no auth/secrets/external-API touches in new scripts)

## Plan Adherence

- Step 1 — greedy anchor generation (vLLM, temp=0, max_tokens=256): ✓ implemented
- Step 2 — kl_only validation (3 prompts x 10-persona subset, ABORT if rho < 0.95): ✓ implemented; ABORT correctly fires as RuntimeError
- Step 3 — JS matrices at T=8/T=32/T=full via tensor-level truncation: ✓ implemented
- Step 4 — cosine matrix load + sha256 allowlist + persona-ordering assert + shape assert: ✓ implemented
- Step 5 — n=19 / n=171 build (no_persona excluded): ✓ implemented
- Step 6 — raw rho + one-sided Mantel (rank-precomputed; hits += 1 if r >= rho_obs): ✓ implemented, one-sided correctly
- Step 7a — cluster-mask rho (n=160): ✓ implemented (CAVEAT)
- Step 7b — partial rho controlling cluster_fine alone: ✓ implemented (CAVEAT)
- Step 7c — partial rho controlling BOTH fine + macro jointly (GATING): ✓ implemented via 2-col covariate matrix
- Step 7d — cluster-collapsed n=66 (12 rows = 4 centroids + 8 singletons): ✓ implemented; k==12 assertion fires
- Step 8 — rho_double_resid_baseline(x, b_x, y, b_y) separately residualized (GATING): ✓ implemented; NOT standard partial Spearman; b_no_persona as secondary sensitivity
- Step 9 — layer sensitivity L10/L15/L20/L25: ✓ implemented (all 4 layers in the LAYERS loop)
- Step 10 — stratified Mantel one-sided; singletons get unique IDs: ✓ implemented
- Step 11 — HA-excluded sensitivity (n=153): ✓ implemented; raw + joint-partial + mean-marginal residual all recomputed
- Step 12 — leave-one-persona-out jackknife (range/median/IQR): ✓ implemented
- Step 13 — per-prompt rho (GATING: median >= 0.2; IQR + fraction > 0.5 stored): ✓ implemented
- Step 14 — T-cutoff sensitivity; T=8 GATING (ratio >= 0.3): ✓ implemented; t8_gate_pass boolean stored
- Step 15 — n=190 secondary with explicit caveat field: ✓ implemented
- Step 16 — CKA: ± partial (reframed to RDM-form; flagged in caveat field; exploratory only, excluded from gates — within the plan envelope)
- Step 17 — six figures in plot script: ✓ implemented
- Plan Deviation #1: sha256 allowlist of 2 values (trailing newline difference): ✓ flagged; hard assertions on persona ordering + shape preserved; WARN on mismatch does not abort
- Plan Deviation #2: CKA reframe to Gram x Gram (not centroid x flat-logprob): ✓ flagged; stays within exploratory envelope
- Plan Deviation #3: centroids_re_extracted.pt in eval_results/ violates CLAUDE.md: ✓ flagged by implementer; plan §10 names this file explicitly

## Issues Found

### Critical (block merge)
None.

### Major (revise before merge)

ISSUE `analyze_persona_geometry_vs_divergence.py:858–859`: **`jackknife.names_dropped` stores the full 19-persona list, not a per-iteration "which persona was dropped" mapping.**
- Evidence: `"names_dropped": names_19` where names_19 is the complete list of all 19 personas. The plotting code at `plot_persona_geometry_vs_divergence.py:337` does `zip(values, names)` which coincidentally works because the jackknife loop iterates k in range(19) and names_19[k] is the dropped persona at iteration k.
- Impact: A reader of geometry_alignment.json will interpret `names_dropped` as "all 19 personas were dropped" rather than "names_dropped[k] is the persona dropped to produce rho values[k]." The analyzer agent reading the JSON can easily misread this field and misidentify which persona is high-leverage.
- Fix: Rename to `persona_names_in_drop_order` to make the semantics explicit, or restructure as `[{"persona_dropped": names_19[k], "rho": jk_rhos[k]} for k in range(19)]`.

ISSUE `analyze_persona_geometry_vs_divergence.py:812`: **Only t8_gate_pass is stored as a machine-readable boolean; the other 5 G
  7. epm:code-review· system
    <!-- epm:code-review v1 --> ## Code Review — round 1 **Verdict:** PASS **Tier:** leaf (two new analysis entrypoints und…
    <!-- epm:code-review v1 -->
## Code Review — round 1

**Verdict:** PASS
**Tier:** leaf (two new analysis entrypoints under `scripts/`, no library / agent / settings touches)
**Diff size:** +1803 / -0 across 2 new files
**Plan adherence:** COMPLETE (3 deviations flagged by implementer; all acceptable)
**Tests:** N/A (analysis script; dry-run smoke covers structural invariants)
**Lint:** PASS (`ruff check` clean, `ruff format --check` 2 files already formatted)
**Security sweep:** CLEAN (no hardcoded secrets, no shell-injection vectors, no unsafe deserialization)
**Needs user eyeball:** None for this tier.

---

### Plan adherence — load-bearing checks (all PASS)

Walking down the 14 critical items in the brief:

1. **`b_mean_marginal` formula** — PASS. `b_mean_marginal(dist_matrix_19, iu_19)` operates on the already-19-persona-sliced matrix (no_persona already excluded), and computes `dist_matrix_19[i, others].mean() + dist_matrix_19[j, others].mean()` where `others = [k for k in range(n) if k != i and k != j]`. Matches plan §8 exactly: `mean_{k ≠ i,j,no_persona}(d_cos(i, k)) + mean_{k ≠ i,j,no_persona}(d_cos(j, k))`. Empirically verified `b_mean_marginal` on a uniform-0.1 matrix returns 0.2 (= 2 × 0.1).
2. **`rho_double_resid_baseline(x, b_x, y, b_y)`** — PASS. `LinearRegression().fit(b_x_2d, x)` and `LinearRegression().fit(b_y_2d, y)` are independent — code does NOT apply `b_x` to `y`. Empirically verified by injecting signal `x = 1.5*b_x + 0.3*ε`, `y = 1.5*b_y + 0.3*η` (independent covariates): correct-covariate residual ρ → ~0; misapplied-covariate residual ρ differs as expected. (`scripts/analyze_persona_geometry_vs_divergence.py:211-227`.)
3. **One-sided Mantel test** — PASS. Both `mantel_p_one_sided` (line 249: `if r >= rho_obs: hits += 1`) and `stratified_mantel_p_one_sided` (line 287: `if r >= rho_obs: hits += 1`) use upper-tail counting. NOT `abs(r) >= abs(rho_obs)`.
4. **Joint two-level cluster partial** — PASS. `Z_clusters = np.column_stack([v_same_fine_171, v_same_macro_171])` (line 720), passed to `partial_spearman_ranks(v_cos_171, v_js_171, Z_clusters)`. The helper correctly handles 2D `Z` (line 201: `if Z.ndim == 1: Z = Z.reshape(-1, 1)` — does NOT reshape if already 2D), and OLS-residualizes both rank vectors against `[intercept, cluster_fine, cluster_macro]` jointly.
5. **Cluster-collapsed n=66 (12 rows)** — PASS. `cluster_collapse` raises `AssertionError` if reduced shape != 12. Dry-run asserts `M_collapsed.shape == (12, 12)` and `len(iu_12[0]) == 66`. The civilian-singleton set has 8 entries (helpful_assistant present exactly once), so 4 cluster centroids + 8 singletons = 12. Verified in dry-run output.
6. **kl_only validation: 3 prompts × n=45 pairs, ABORT if ANY ρ < 0.95** — PASS. `validate_kl_only_three_prompts` (line 459) raises `RuntimeError` on first failure with the exact threshold and prompt index in the message. JS-bound post-hoc: `if js_max_full > 1.0: raise AssertionError(...)` (line 1133), `if js_max_full > math.log(2): log.warning(...)` (line 1135). Both checks present.
7. **H_pair_residuals on RESIDUALS from `rho_resid_baseline_mean_marginal`** — PASS. `compute_top_n_baseline_residual_pairs` (line 638) uses `b_cos_mm` and `b_js_mm` (the mean-marginal baselines) for the regression, computes `|z_x| + |z_y|` and takes top-5. `check_h_pair_residuals` uses strict 2-of-2 conjunction: `matched = all(target in observed for target in PRE_REGISTERED_OUTLIER_PAIRS)` (line 682). `PRE_REGISTERED_OUTLIER_PAIRS = {{comedian, HA}, {poet, HA}}` — both must appear.
8. **Pre-registered clusters** — PASS. Verified via dry-run partition assertion (`occupational | CIVILIAN_SINGLETONS == set(names_19)` with no overlap). `helpful_assistant ∈ CIVILIAN_SINGLETONS` exactly once. Singletons get unique IDs 100..107 in `cluster_ids_for` (line 174). Empirically: `same_cluster_fine[ha, X] = 0 ∀ X` (HA is `cluster_fine_of → None`).
9. **6 GATING + 9 CAVEAT statistics** — PASS. `geometry_alignment.json` per-layer block (lines 8
  8. epm:launch· system
    <!-- epm:launch v1 --> ## Experimenter dispatched - **Worktree:** `.claude/worktrees/issue-269` - **Branch:** `issue-26…
    <!-- epm:launch v1 -->
## Experimenter dispatched

- **Worktree:** `.claude/worktrees/issue-269`
- **Branch:** `issue-269` (commit `05b176f5`)
- **PR:** https://github.com/superkaiba/explore-persona-space/pull/336 (draft)
- **Code review verdict:** PASS (ensemble: Claude PASS + Codex CONCERNS, both PASS-class)
- **Pod:** `pod-269` (1× H100 @ 103.207.149.58:10624; fresh provision, bootstrap complete with preflight)
- **GPU intent:** `eval` (1× H100, sufficient for vLLM batched generation + HF teacher-forcing at N=20)
- **Launch command (per plan §9):**
  ```bash
  nohup uv run python scripts/analyze_persona_geometry_vs_divergence.py \
      > /workspace/logs/issue_269_$(date +%Y%m%d_%H%M%S).log 2>&1 &
  ```
- **Estimated wall-clock:** 45-60 min (Claude reviewer revised from plan's 30 min). Mantel B=100K × 4 layers × 3 stats is the bottleneck. Deviation allowed: drop to B=10K if wall-clock exceeds 75 min.
- **WandB run:** TBD — analysis-only run, may not log to WandB. Experimenter will confirm in `epm:results v1`.
- **Experimenter agent ID:** acdef11cdd8eec5f2

Codex CONCERNS from `epm:code-review-codex v1` will be folded into `epm:results v1` "Caveats for analyzer" rather than addressed mid-run.
<!-- /epm:launch -->
  9. epm:failure· system
    <!-- epm:failure v1 --> failure_class: infra reason: vllm_0_11_0_transformers_5_x_tokenizer_incompat ## Summary `scrip…
    <!-- epm:failure v1 -->
failure_class: infra
reason: vllm_0_11_0_transformers_5_x_tokenizer_incompat

## Summary

`scripts/analyze_persona_geometry_vs_divergence.py` cannot complete `vllm.LLM(...)` init on pod-269 because of a structural incompatibility between the two pinned dependencies in `uv.lock`:

- `vllm==0.11.0`
- `transformers==5.5.0`

vLLM 0.11.0's `get_cached_tokenizer` (in `vllm/transformers_utils/tokenizer.py:99`) reads `tokenizer.all_special_tokens_extended`. Transformers 5.x removed this attribute from `Qwen2Tokenizer` (now `PreTrainedTokenizerBase.__getattr__` raises `AttributeError` for it). vLLM has not been patched for transformers 5.x.

This is a known issue I have in memory (`feedback_vllm0110_transformers5_breakage`) but neither the planner nor the code-reviewer flagged it (the code itself is correct — only the env combo is broken). It is reproducible on a fully-bootstrapped pod-269 with `uv sync --locked` from `issue-269` branch.

## Traceback

```
File "/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/vllm/transformers_utils/tokenizer.py", line 99, in get_cached_tokenizer
    tokenizer.all_special_tokens_extended)
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 1293, in __getattr__
    raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
AttributeError: Qwen2Tokenizer has no attribute all_special_tokens_extended. Did you mean: 'num_special_tokens_to_add'?
```

Full log at `/workspace/logs/issue_269_20260511_053850.log` on pod-269 (kept for analyzer reference; pod stays alive per lifecycle policy).

## Environment

```
pod         pod-269 (1× H100 80GB)
branch      issue-269 @ 05b176f
torch       2.8.0+cu128
vllm        0.11.0
transformers 5.5.0
python      3.11
driver      580.126.09 (CUDA 13.0)
```

## What I tried before escalating

Three attempts; the third unblocked the FIRST error and revealed the underlying transformers-5.x issue:

1. **Attempt 1 (launch as-is):** crashed in vLLM `get_device_capability()` →
   `pynvml.NVMLError_InvalidArgument`. Root cause: vLLM 0.11.0's
   `device_id_to_physical_device_id` requires `CUDA_VISIBLE_DEVICES` to be
   explicitly set (an empty `os.environ.get("CUDA_VISIBLE_DEVICES")` triggers
   the broken else-branch when the bundled pynvml is mismatched with the
   driver). Not a launch-policy issue — relevant for the implementer to know.

2. **Attempt 2 (re-launch with `CUDA_VISIBLE_DEVICES=0`):** same NVML error
   inside the subprocess that vLLM forks for model-class inspection. The
   inspection subprocess inherits env, so this was puzzling.

3. **Attempt 3 (re-launch after warming vLLM's on-disk modelinfo cache at
   `/root/.cache/vllm/modelinfos/`):** the model-info inspection got skipped
   (cache hit). vLLM then proceeded to tokenizer init and hit the
   **transformers 5.x incompat** above.

So there are actually **two** infra issues blocking the run:
- (A) NVML/CUDA_VISIBLE_DEVICES requirement (already mitigated by the cache, but a fresh pod will hit it)
- (B) vLLM 0.11.0 ↔ transformers 5.x tokenizer attribute incompat (the blocking failure)

## Proposed fix (implementer)

**Pin transformers<5 in `pyproject.toml` + `uv.lock`** (matches my memory's recommended fix). Several existing experiments in the repo are already running transformers 4.48.x successfully (e.g. open-instruct in `external/`). This is the minimum-disruption path.

Steps for the implementer round:

1. In `pyproject.toml`, change `transformers` constraint to `>=4.48,<5.0` (or pin a specific 4.48.x — match what open-instruct uses).
2. `uv lock` to regenerate `uv.lock`.
3. Check for transformers-5-specific call sites in `src/` and `scripts/` and unwind any 5.x-only API usage (tokenizer `extra_special_tokens` dict format, etc — see my `feedback_tokenizer_config_5x_to_4x` memory).
4. Smoke-test the analyze script locally with `--dry-run` (covere
  10. epm:experiment-implementation· system
    <!-- epm:experiment-implementation v2 --> ## Implementation Report — round 2 **Status:** READY-FOR-REVIEW ### (a) What…
    <!-- epm:experiment-implementation v2 -->
## Implementation Report — round 2

**Status:** READY-FOR-REVIEW

### (a) What was done

Round 1 (commit `05b176f5`) passed code review (Claude PASS, Codex CONCERNS) but failed at experimenter dispatch — `epm:failure v1` on this issue diagnosed it as vllm 0.11.0 reading `tokenizer.all_special_tokens_extended`, which transformers 5.x removed from `Qwen2Tokenizer`. Round-2 fix is environmental + a small JSON-schema polish for the Codex CONCERNS.

- **`pyproject.toml`** — change `transformers>=5.0,<6.0` → `transformers>=4.55.2,<5.0`. The lower bound matches vllm 0.11.0's `requires_dist` (PyPI: `transformers>=4.55.2`).
- **`uv.lock`** — regenerated via `uv lock`. Resolutions changed:
  - `transformers 5.5.0 → 4.57.6` (intended)
  - `huggingface-hub 1.8.0 → 0.36.2` (transitive; only `HfApi` / `hf_hub_download` are imported in `src/orchestrate/hub.py`, both stable across 0.x and 1.x)
  - `mlx-lm 0.31.1 → 0.29.1` (transitive; not imported anywhere in this repo's `src/` or `scripts/`)
  - `vllm` stays at 0.11.0
- **`scripts/analyze_persona_geometry_vs_divergence.py`** — Codex CONCERN fixes (output-schema only, statistical content untouched):
  - Added 5 missing GATING booleans: `rho_raw_gate_pass`, `mantel_p_gate_pass`, `joint_partial_gate_pass`, `resid_mm_gate_pass`, `per_prompt_median_gate_pass` (the 6th, `t8_gate_pass`, already existed). Plus a derived `all_six_gates_pass` conjunction.
  - Added a `gating_thresholds` subdict in the headline JSON: `{rho_raw_gt: 0.5, mantel_p_one_sided_lt: 0.001, joint_partial_gt: 0.4, resid_mm_gt: 0.2, per_prompt_median_gte: 0.2, t8_over_tfull_gte: 0.3}` so JSON readers don't have to parse the module docstring for thresholds.
  - Restructured `jackknife`: added `iterations: [{persona_dropped, rho}, ...]` structured per-iteration. Renamed the old positional `names_dropped` → `persona_names_in_drop_order` with an inline comment that `values[k]` corresponds to `persona_names_in_drop_order[k]`. Both forms (structured + parallel arrays) are emitted; the structured form is the recommended consumer.
  - Added a 7-line gating-summary log block at end of `main()` so the run log shows the verdict at a glance.
- **`scripts/plot_persona_geometry_vs_divergence.py`** — `figure_jackknife` reads the new `persona_names_in_drop_order` field with a fallback to legacy `names_dropped` (so previously-saved JSONs would still plot if they existed; they don't yet because this is round-1's first dispatch).

Diff: +74 / -19 across 4 files (`git diff --stat 05b176f5..HEAD`).

Plan adherence (vs `.claude/plans/issue-269.md`): round-1 already covered all plan items DONE. Round-2 changes are out-of-plan but in-scope per the experimenter's failure marker and the orchestrator brief.

Commits:
- `68f4f72d` issue #269 round 2: pin transformers<5 to fix vllm 0.11.0 tokenizer init

Branch: `issue-269` pushed to `origin/issue-269` at `68f4f72d`.

### (b) Considered but not done

- **Monkey-patching `Qwen2Tokenizer.all_special_tokens_extended`** — explicitly considered, explicitly rejected per the orchestrator brief and the experimenter's `epm:failure v1` analysis. The repo already has hasattr-guarded monkey-patches in several `scripts/run_issue_*.py` files (#156, #203, #213, #276, #295) for this exact reason on older code, but applying it to a new alignment-research output (the JS matrix → geometry RSA result) carries silent-contamination risk: vLLM uses `all_special_tokens_extended` to return `AddedToken` objects with strip/lstrip/rstrip metadata that drives chat-template token boundary handling. Returning plain strings via the patch would not crash — it would change tokenization of `<|im_start|>` / `<|im_end|>` boundaries in a way that could shift the JS distributions silently. Pinning is the safer path even though the monkey-patch is a 2-line precedent in this codebase. The five existing patched scripts remain unaffected: their `hasattr` guard makes them no-ops on transformers 4.x.
- **Upgrad
  11. epm:code-review· system
    <!-- epm:code-review v2 --> ## Code-Reviewer Verdict — PASS **Tier:** trunk (`pyproject.toml` + `uv.lock` are trunk; th…
    <!-- epm:code-review v2 -->
## Code-Reviewer Verdict — PASS

**Tier:** trunk (`pyproject.toml` + `uv.lock` are trunk; the analyze script is the single experiment-bearing artifact for #269)
**Diff size:** +74 / −19 across 4 files (commit `68f4f72d`)
**Plan adherence:** COMPLETE — both round-1 Codex CONCERNs resolved; dependency fix targeted
**Tests:** PASS (smoke `--dry-run` passed; statistical pipeline unchanged → numerical reproducibility preserved)
**Lint:** PASS (`ruff check` + `ruff format --check` clean on both changed scripts)
**Security sweep:** CLEAN (no secrets, no shell injection surface; only dep pin + JSON schema)
**Needs user eyeball:** None — dependency downgrade is well-bounded, vetted against vllm 0.11.0's `requires_dist`.

### What changed since round 1

1. **`pyproject.toml`** — `transformers>=5.0,<6.0` → `transformers>=4.55.2,<5.0` with an inline comment naming the failure mode + linking to issue #269's `epm:failure v1`. Lower bound `4.55.2` exactly matches vllm 0.11.0's lockfile-declared `transformers` requirement (verified via `uv.lock` grep). The previous `>=5.0` constraint was actually inconsistent with vllm 0.11.0 — that's why the experimenter crashed; the round-2 pin is the only resolution that keeps vllm at 0.11.0.
2. **`uv.lock`** — three packages changed versions, no others:
   - `transformers 5.5.0 → 4.57.6` ✓
   - `huggingface-hub 1.8.0 → 0.36.2` (transitive; hub 0.36 is the pre-1.0 line, paired with transformers 4.x)
   - `mlx-lm 0.31.1 → 0.29.1` (Mac-only `sys_platform != 'linux'` marker; unused by this repo's code paths)
   - I confirmed via `grep -E '^[-+]version = ' uv.lock | sort -u` that NO other packages drifted.
3. **`scripts/analyze_persona_geometry_vs_divergence.py`** — pure JSON-output-schema additions:
   - Six `*_gate_pass` booleans (`rho_raw_gate_pass`, `mantel_p_gate_pass`, `joint_partial_gate_pass`, `resid_mm_gate_pass`, `per_prompt_median_gate_pass`, `t8_gate_pass`) + `all_six_gates_pass` conjunction + a `gating_thresholds` subdict making the cutoffs machine-readable. Thresholds match the plan exactly: ρ_raw > 0.5, Mantel p < 0.001, joint-partial > 0.4, resid_mm > 0.2, per-prompt median ≥ 0.2, t8/full ≥ 0.3.
   - `jackknife.iterations: [{persona_dropped, rho}, ...]` structured per-iteration list added; legacy `names_dropped` renamed to `persona_names_in_drop_order` with an inline docstring; legacy `values` (parallel-array) retained for plot-script consumption.
   - 7-line gating-summary log block appended to `main()` for fast-glance verdict.
4. **`scripts/plot_persona_geometry_vs_divergence.py`** — single-line `jk.get("persona_names_in_drop_order") or jk["names_dropped"]` fallback so already-saved JSONs still plot.

### Verification of round-1 critique items

**Codex CONCERN 1** — `jackknife.names_dropped` structure misleading: ✓ RESOLVED. Structured `iterations` field added; flat field renamed to `persona_names_in_drop_order` with a docstring explaining the index alignment. Plot script reads the new field with legacy fallback.

**Codex CONCERN 2** — only 1/6 gate booleans serialized: ✓ RESOLVED. All six gate booleans + `all_six_gates_pass` + `gating_thresholds` are now in the per-layer return dict. JSON readers can answer "did this experiment pass the headline criterion?" without re-deriving the gates from numeric stats.

### Verification of "statistical logic unchanged" invariant

Filtered the diff for substantive (non-comment, non-formatting) `[+-]` lines:

```
+ rho_raw_gate_pass = bool(rho_raw > 0.5)
+ mantel_p_gate_pass = bool(p_mantel < 0.001)
+ joint_partial_gate_pass = bool(rho_partial_cluster_joint > 0.4)
+ resid_mm_gate_pass = bool(rho_resid_mm > 0.2)
+ per_prompt_median_gate_pass = bool(median_per_prompt >= 0.2)
+ all_six_gates_pass = bool(...)
+ {"persona_dropped": names_19[k], "rho": float(jk_rhos[k])} for k in range(19)
```

ALL substantive changes are boolean derivations from values already computed by the round-1 pipeline. ZERO changes to Mantel, partial Spearman
  12. epm:code-review-codex· system
    <!-- epm:code-review-codex v2 --> # Codex Code Review: Issue #269 Round 2 — transformers pin + gating booleans **Verdic…
    <!-- epm:code-review-codex v2 -->
# Codex Code Review: Issue #269 Round 2 — transformers pin + gating booleans

**Verdict:** PASS
**Tier:** trunk
**Diff size:** +74 / -19 lines across 4 files
**Plan adherence:** COMPLETE
**Lint:** PASS
**Security sweep:** CLEAN
**Needs user eyeball:** yes — lockfile resolver side effects downgrade `huggingface-hub` 1.8.0→0.36.2 and `mlx-lm` 0.31.1→0.29.1

## Plan Adherence
- CONCERN 1 (round 1): 6 `*_gate_pass` booleans + `all_six_gates_pass` + `gating_thresholds` subdict: ✓ implemented at lines 818–870
- CONCERN 2 (round 1): `jackknife.iterations` as per-iteration `[{persona_dropped, rho}]` + `persona_names_in_drop_order` field: ✓ implemented at lines 891–897
- CONCERN 3 (round 1): plot script reads new field with legacy `names_dropped` fallback via `.get("persona_names_in_drop_order") or jk["names_dropped"]`: ✓
- Mantel p-value gate threshold `p < 0.001`: ✓ matches plan §10 table (line 637 of plan: `< 0.001 (floor: 1/(B+1) ≈ 1e-5)`)
- transformers pin `>=4.55.2,<5.0`: ✓ matches vllm 0.11.0 requires_dist; `all_special_tokens_extended` confirmed present in transformers 4.57.6
- Statistical numeric pipeline (Spearman, Mantel, partial-Spearman, jackknife, per-prompt loop): ✓ UNCHANGED between 05b176f5 and 68f4f72d — round-2 diff is schema/output-only

## Issues Found

### Critical (block merge)
- None

### Major (revise before merge)
- None

### Minor (worth fixing but does not block)
- `scripts/analyze_persona_geometry_vs_divergence.py`: The new JSON no longer emits the legacy `jackknife.names_dropped` key; only the plot script has a read-side fallback. If any other consumer reads analysis JSONs produced by round-2 code via `names_dropped`, it will silently KeyError. This is intentional from the rename and the plan specified the rename, but worth noting for any future script.
- `pyproject.toml`: The inline dependency comment is long and embeds issue/process context (`see issue #269 epm:failure v1 for traceback`). Accurate, but process metadata embedded in `pyproject.toml` can age poorly. Low priority.

## Unaddressed Cases
- Round-1 plan deviations (cosine sha256 allowlist, CKA RDM-form, `centroids_re_extracted.pt` path) remain unchanged — acknowledged as pre-existing, not introduced by round 2.

## Style / Consistency
The gating boolean block at lines 818–830 mirrors the module docstring thresholds exactly. The `bool()` wraps are correct (numpy bool → Python bool for JSON serialization). The `iterations` list comprehension at lines 891–897 is clean and the inline comment explaining the round-1 concern is appropriate.

## Unintended Changes
`uv.lock` shows resolver side effects beyond the direct `transformers` pin:
- `huggingface-hub`: 1.8.0 → 0.36.2 (major version regression)
- `mlx-lm`: 0.31.1 → 0.29.1

Key Hub APIs (`upload_file`, `upload_folder`, `hf_hub_download`, `HfApi`) verified present in 0.36.2 by the orchestrator. The downgrade is the expected resolver consequence of pinning `transformers<5.0` since newer `huggingface-hub` and `mlx-lm` require `transformers>=5`. Not a blocker, but the user should be aware.

## Security Check
- no issues found

## Recommendation
merge
<!-- /epm:code-review-codex -->
  13. epm:launch· system
    <!-- epm:launch v2 --> ## Experimenter dispatched (round 2) - **Worktree:** `.claude/worktrees/issue-269` - **Branch:**…
    <!-- epm:launch v2 -->
## Experimenter dispatched (round 2)

- **Worktree:** `.claude/worktrees/issue-269`
- **Branch:** `issue-269` (commit `68f4f72d` — round-2 transformers pin)
- **PR:** https://github.com/superkaiba/explore-persona-space/pull/336 (draft)
- **Code-review verdict (round 2):** Claude PASS + Codex PASS — ensemble PASS
- **Pod:** `pod-269` (1× H100 @ 103.207.149.58:10624; reused from round 1 — alive and idle)
- **Round-1 failure:** vllm 0.11.0 / transformers 5.5.0 `Qwen2Tokenizer.all_special_tokens_extended` AttributeError. Fixed in commit `68f4f72d` by pinning `transformers>=4.55.2,<5.0`.
- **Launch sequence** (per round-2 brief):
  1. `git fetch && git checkout issue-269 && git reset --hard origin/issue-269` on pod-269
  2. `uv sync --locked` (applies the new lock)
  3. Preflight
  4. `export CUDA_VISIBLE_DEVICES=0` + `nohup uv run python scripts/analyze_persona_geometry_vs_divergence.py > /workspace/logs/issue_269_round2_<timestamp>.log 2>&1 &`
- **Estimated wall-clock:** 45-60 min (Mantel B=100K × 4 layers × 3 stats is the bottleneck). Allowed deviation: drop to B=10K if >75 min.
- **WandB:** unlikely (pure stats run). Experimenter will confirm in `epm:results v1`.
- **Experimenter agent ID:** a06e0df2295d8933e

The 2 round-2 Codex NITs (legacy `jackknife.names_dropped` key + long `pyproject.toml` comment) are non-actionable mid-run and skipped.
<!-- /epm:launch -->
  14. epm:hot-fix· system
    <!-- epm:hot-fix v1 --> ## Hot-fix v1: preserve `CUDA_VISIBLE_DEVICES` across `extract_persona_vectors` import **Commi…
    <!-- epm:hot-fix v1 -->

## Hot-fix v1: preserve `CUDA_VISIBLE_DEVICES` across `extract_persona_vectors` import

**Commit:** [`889da556`](https://github.com/superkaiba/explore-persona-space/commit/889da556) — 1 file changed, 9 insertions(+)

**Justification (≤10 lines, no logic change):** Pure env-var-hygiene fix. `scripts/analyze_persona_geometry_vs_divergence.py` imports `PERSONAS`/`PROMPTS` from `experiments/phase_minus1_persona_vectors/extract_persona_vectors.py`. That module hard-sets `os.environ["CUDA_VISIBLE_DEVICES"] = "5"` at module-load time (line 19; the original pod had GPU 5 assigned). On pod-269 which has only physical GPU 0, this poisons the env for downstream vLLM init — vLLM's NVML init calls `nvmlDeviceGetHandleByIndex(5)` and fails with `NVMLError_InvalidArgument`, EngineCore subprocess dies. No statistical-logic change; just snapshot+restore the env var around the import. Verified launch reaches model loading on pod-269 (14.2 GiB loaded, weights compiling) — past the round-1+round-2 failure surface.

### Full diff

```diff
diff --git a/scripts/analyze_persona_geometry_vs_divergence.py b/scripts/analyze_persona_geometry_vs_divergence.py
index 6fdc274c..38747826 100644
--- a/scripts/analyze_persona_geometry_vs_divergence.py
+++ b/scripts/analyze_persona_geometry_vs_divergence.py
@@ -79,9 +79,18 @@ from explore_persona_space.orchestrate.env import load_dotenv  # noqa: E402

 # Re-export PERSONAS and PROMPTS from the canonical extraction script (matches the
 # exact persona ordering used to build ``cosine_matrix.json``).
+# Snapshot CUDA_VISIBLE_DEVICES before import: extract_persona_vectors.py hard-sets
+# `os.environ["CUDA_VISIBLE_DEVICES"] = "5"` at module load (its own pod assigned GPU 5),
+# which on single-GPU pods makes vLLM's NVML init fail with NVMLError_InvalidArgument.
+_saved_cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
 sys.path.insert(0, str(_PROJECT_ROOT / "experiments" / "phase_minus1_persona_vectors"))
 from extract_persona_vectors import PERSONAS, PROMPTS  # noqa: E402

+if _saved_cvd is not None:
+    os.environ["CUDA_VISIBLE_DEVICES"] = _saved_cvd
+else:
+    os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s [%(levelname)s] %(message)s",
```

**Failure signature this fixes** (from `/workspace/logs/issue_269_round2_*.log` before the fix):

```
(EngineCore_DP0 pid=...) File ".../vllm/platforms/cuda.py", line 539, in get_device_capability
(EngineCore_DP0 pid=...)   handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
(EngineCore_DP0 pid=...) vllm.third_party.pynvml.NVMLError_InvalidArgument: Invalid Argument
RuntimeError: Engine core initialization failed.
```

**Diagnosis path:**
1. round-2 dependency pin (transformers<5) fixed the tokenizer error from round-1
2. Hit a NEW error at vLLM 0.11.0 EngineCore subprocess init: `nvmlDeviceGetHandleByIndex` failed
3. Direct/isolated Python tests of `cutlass_fp8_supported()` worked, but EngineCore subprocess died → narrowed to env-state difference
4. Found the smoking gun: `extract_persona_vectors.py:19` mutates `CUDA_VISIBLE_DEVICES="5"` at import → NVML tries to grab GPU 5 which doesn't exist on this pod
5. Snapshot+restore around the import preserves the launch-time `CUDA_VISIBLE_DEVICES=0`

Run is now progressing on pod-269 (PID 6720, weights loaded, compiling graph).
  15. epm:results· system
    <!-- epm:results v1 --> ## Issue #269 Round 2 — Results: All 6 gates PASS at layer 10 Round 2 succeeded after two hot-…
    <!-- epm:results v1 -->

## Issue #269 Round 2 — Results: All 6 gates PASS at layer 10

Round 2 succeeded after two hot-fixes on top of round-2 implementer commit `68f4f72d`:
- Hot-fix v1 (`889da556`): preserve `CUDA_VISIBLE_DEVICES` across `extract_persona_vectors` import
- Hot-fix v2 (`ee4b25e5`): swap `tight_layout` for `subplots_adjust` in hero heatmap (matplotlib colorbar incompat)

**Final commit on `issue-269`:** `4ddf33d6` (results + figures added).

### Headline gating signature (per plan §6.A)

**Layer 10 (headline):**
- `rho_raw                          = 0.6627`   (gate `> 0.5`) → **PASS**
- `p_mantel_one_sided               = 0.00049`  (gate `< 0.001`) → **PASS**
- `rho_partial_cluster_joint        = 0.6008`   (gate `> 0.4`) → **PASS**
- `rho_resid_baseline_mean_marginal = 0.4505`   (gate `> 0.2`) → **PASS**
- `per_prompt_median                = 0.6230`   (gate `>= 0.2`) → **PASS**
- `t8_gate_ratio                    = 0.9303`   (gate `>= 0.3`) → **PASS**
- **`all_six_gates_pass = True`** → H confirmed at layer 10

### Per-layer signature table

| layer | rho_raw | p_mantel | partial_joint | resid_mm | per_prompt_median | t8/full | all_6_gates |
|---|---|---|---|---|---|---|---|
| L10 (headline) | 0.6627 | 0.00049 | 0.6008 | 0.4505 | 0.6230 | 0.9303 | **PASS** |
| L15 | 0.9212 | 0.00001 | 0.9071 | 0.8672 | 0.8926 | 0.9959 | **PASS** |
| L20 | 0.9399 | 0.00001 | 0.9312 | 0.9088 | 0.9167 | 1.0277 | **PASS** |
| L25 | 0.8979 | 0.00001 | 0.8814 | 0.8917 | 0.8713 | 1.0420 | **PASS** |

All 4 probed layers pass all 6 gates. Alignment strengthens monotonically from L10 → L20, plateaus at L25.

### Layer-10 caveat-triggering statistics (per plan §6.B)

| stat | value | notes |
|---|---|---|
| cluster-mask (n=160 pairs, off-cluster only) | 0.6125 | similar to raw — alignment is not driven by within-cluster pairs alone |
| partial rho on fine clusters only | 0.6205 | |
| cluster-collapsed (n=66, 12 supercategories) | 0.4826 | coarser geometry still aligned, though weaker |
| stratified Mantel (within-cluster only) | 0.6627 (p=0.1599) | within-cluster directional alignment NOT significant at α=0.05 — alignment is mostly cross-cluster |
| no_persona baseline residual | 0.3343 | weaker than mean-marginal baseline; expected |
| HA-excluded (n=153 pairs, no helpful_assistant) | 0.7717 | rho_raw INCREASES when HA removed |
| HA-excluded delta_raw | -0.1090 | helpful_assistant *suppresses* raw alignment by ~0.11 |
| HA-excluded partial_joint | 0.7245 | |
| HA-excluded resid_mm | 0.4884 | |
| n=190 secondary (with no_persona anchor) | 0.5586 (p=0.00309) | weaker — caveat: no_persona row of JS uses literal-empty-system ChatML, cosine uses Qwen default chat-template |
| per-prompt fraction `rho > 0.5` | 90% (18/20) | only 2 prompts <0.5: prompt 7 (0.497) and 19 (0.462) |

### Truncation sensitivity (layer 10)

`rho` at T cutoffs: T=8 → 0.6165, T=32 → 0.6440, T=full → 0.6627. Alignment is essentially established by the first 8 response tokens (T8 captures 93% of T=full). Geometric alignment is a *next-token* phenomenon, not driven by long-context bookkeeping.

### Jackknife (layer 10, drop-one-persona)

`min=0.6301, max=0.7717, median=0.6487, IQR=0.0327, range=0.1416`. Range is dominated by the helpful_assistant outlier (drop-it → rho=0.7717). All 19 leave-one-out values are well above the 0.5 raw-rho gate.

### Sample outputs (3 of 20 greedy anchor responses, seed=42)

**Prompt idx 3:** "How does a computer processor work?"
```
A computer processor, also known as a central processing unit (CPU), is the primary
component that executes instructions and performs the core processing tasks of a
computer. Here's a simplified overview of how a computer processor works:

1. **Fetch**: The processor fetches instructions from memory. This is done by the
   control unit, which sends a request to the memory to retrieve the next instruction.
2. **Decode**: Once the instruction is fetched, the control unit decodes it. ...
3. **Execute**: ...
```
  16. epm:hot-fix· system
    <!-- epm:hot-fix v2 --> ## Hot-fix v2: swap `tight_layout` for `subplots_adjust` in hero heatmap **Commit:** [`ee4b25e…
    <!-- epm:hot-fix v2 -->

## Hot-fix v2: swap `tight_layout` for `subplots_adjust` in hero heatmap

**Commit:** [`ee4b25e5`](https://github.com/superkaiba/explore-persona-space/commit/ee4b25e5) — 1 file changed, 2 insertions(+), 1 deletion(-)

**Justification (≤10 lines, no logic change):** Pure matplotlib layout call. `scripts/plot_persona_geometry_vs_divergence.py`'s `figure_hero_dual_heatmap` crashed at `fig.tight_layout()` with `RuntimeError: Colorbar layout of new layout engine not compatible with old engine`. The function creates one `fig.colorbar(...)` per subplot, which silently sets a layout engine — `tight_layout()` then refuses to take over. Swap to `fig.subplots_adjust(wspace=0.35, hspace=0.4)`, which is the standard fix. No statistical-logic change. Other functions in the same file use `tight_layout()` without colorbars and stay fine.

### Full diff

```diff
diff --git a/scripts/plot_persona_geometry_vs_divergence.py b/scripts/plot_persona_geometry_vs_divergence.py
index 2ac727c0..72f47a6d 100644
--- a/scripts/plot_persona_geometry_vs_divergence.py
+++ b/scripts/plot_persona_geometry_vs_divergence.py
@@ -122,7 +122,8 @@ def figure_hero_dual_heatmap(
         fontsize=11,
         y=1.00,
     )
-    fig.tight_layout()
+    # Note: tight_layout() incompatible with colorbar layout engine; use subplots_adjust.
+    fig.subplots_adjust(wspace=0.35, hspace=0.4)
     savefig_paper(fig, "hero_dual_heatmap_n19", dir=FIG_DIR)
     plt.close(fig)
     log.info("Wrote (i) hero heatmap")
```

Verified by running the plot script after the fix: all 6 figures regenerated cleanly (`hero_dual_heatmap_n19`, `cosine_vs_js_scatter_n171`, `rho_by_layer_7stat`, `rho_by_T_cutoff`, `jackknife_n19`, `ha_excluded_sensitivity`).
  17. epm:upload-verification· system
    <!-- epm:upload-verification v1 --> ## Upload Verification **Verdict: PASS** | Artifact | Required? | Status | Locatio…
    <!-- epm:upload-verification v1 -->
## Upload Verification

**Verdict: PASS**

| Artifact | Required? | Status | Location / URL |
|----------|-----------|--------|----------------|
| `eval_results/issue_269/generations.json` | Yes | PASS | git@4ddf33d6 — tracked on `issue-269` branch |
| `eval_results/issue_269/geometry_alignment.json` | Yes | PASS | git@4ddf33d6 — tracked on `issue-269` branch |
| `eval_results/issue_269/js_matrix.json` | Yes | PASS | git@4ddf33d6 — tracked on `issue-269` branch |
| `eval_results/issue_269/run_meta.json` | Yes | PASS | git@4ddf33d6 — tracked on `issue-269` branch |
| `eval_results/issue_269/centroids_re_extracted.pt` | local-only (plan §10) | PASS | NOT in git (by design, CLAUDE.md "eval_results/ is for JSON only"). Present on pod at `/workspace/explore-persona-space/eval_results/issue_269/centroids_re_extracted.pt` (1.1 MB, confirmed via SSH) |
| `figures/issue_269/*.{png,pdf,meta.json}` (18 files) | Yes | PASS | git@4ddf33d6 — all 18 files tracked (6 figures × 3 formats: hero_dual_heatmap_n19, cosine_vs_js_scatter_n171, rho_by_layer_7stat, rho_by_T_cutoff, jackknife_n19, ha_excluded_sensitivity) |
| WandB Artifact (eval JSONs) | N/A | N/A | No WandB session: `analyze_persona_geometry_vs_divergence.py` contains zero `wandb` references. Git commit provides equivalent durability for this analysis-only run. |
| HF Hub model upload | N/A | N/A | No training — no checkpoint to upload. |
| HF Hub dataset upload | N/A | N/A | No new dataset generated. |
| Local weights cleaned | N/A | N/A | No weights present — analysis-only run. |
| Pod lifecycle | Yes | WARN | Pod `pod-269` is still **running** (status: `running`, 1× H100). No `epm:follow-ups` marker found on issue #269 and no open issues with `Parent: #269`. Pod should be **stopped** after upload verification to release GPU. No follow-ups filed → terminated or stopped are both acceptable; running is wasteful. |

**Missing:** None — all required artifacts are durably stored in git on the `issue-269` branch at commit `4ddf33d6`.

**Action required:** Pod `pod-269` is still running. Since no follow-ups are filed, run:
```bash
python scripts/pod.py stop --issue 269
```
or terminate:
```bash
python scripts/pod.py terminate --issue 269 --yes
```

**Verification summary:**
- 4/4 JSON files tracked in git at `4ddf33d6`
- 18/18 figure files tracked in git (6 × {.png, .pdf, .meta.json})
- `centroids_re_extracted.pt`: present on pod (1.1 MB), correctly excluded from git per Upload Policy
- Zero `wandb` references in `scripts/analyze_persona_geometry_vs_divergence.py` — WandB check N/A
<!-- /epm:upload-verification -->
  18. epm:interp-critique· system
    <!-- epm:interp-critique v1 --> ## Interpretation Critique — Round 1 **Verdict: REVISE** The interpretation is mostly …
    <!-- epm:interp-critique v1 -->
## Interpretation Critique — Round 1

**Verdict: REVISE**

The interpretation is mostly well-calibrated at the *aggregate / gating-statistic* level — I independently re-derived every L10 gating statistic and they match (raw ρ = 0.6627, Mantel p = 4.9e-4, joint cluster-partial = 0.6008, mm-resid = 0.4505, per-prompt median = 0.6230, T8/Tfull = 0.9303, all six gates PASS). The L10 cosine-distance std (0.014), L20 std (0.080), and 5.7× compression ratio all check out exactly. The stratified-Mantel p = 0.1599 is faithfully reported, and the H_pair_residuals falsification is correctly stated.

The problem is in **Result 3's narrative framing for the creative-persona-trio decouplings**: the cited cosine and JS *rank* numbers in the sample-pair blocks and in the prose ("rank 5–37 of 171" / "rank 153–171 of 171") are wrong by 40–100 rank slots. The decoupling SURVIVES in z-score residual magnitude (Table 1 is correct — that data path is independent), but the prose-level claim "in the L10 representational space these three personas look very similar" is materially overstated. There's also a minor figure-caption typo ("n=25" for cluster-collapsed when the data is n=66). One alternative explanation (helpful_assistant farthest from no_persona being load-bearing for the radial-confound interpretation) is conflated in places. Fixing the rank numbers + the n=66/25 typo + acknowledging the trio's cosine-distance is *middle-of-distribution* (not "small") is necessary before MODERATE confidence can stand.

### Overclaims

- **Creative-trio "small cosine distance" framing (Result 3 main takeaway #2).** Body claims: *"in the L10 representational space these three personas look very similar."* I re-computed the 1-cos rank in the n = 171 pool: (poet, comedian) = rank **103/171** (not 16), (poet, villain) = **118/171** (not 32), (villain, comedian) = **81/171** (not 9). These are middle-of-distribution L10 distances, NOT "very similar" in any plain-language sense. The residual-z-score decoupling is real (Table 1 confirms residual magnitudes 10.57, 5.04, 4.55 are top-1/3/5), but the prose narrative is inflating what "cosine sees them as close" means. Suggested weakening: "the three pairs have *middle-of-distribution* L10 cosine distance (ranks 81–118 of 171) but rank-percentile is dragged down once you regress out the 1D-radial covariate — because helpful_assistant + poet + comedian are the *most distant from everyone* (highest mean-marginal), partialling out mean-marginal pushes these pairs into the most-residually-decoupled region."

- **"rank 153–171 of 171" for the trio's JS distance.** Actual ranks: (poet, comedian) JS = 140/171, (poet, villain) = 139/171, (villain, comedian) = 137/171. Top quartile, but NOT top decile as the body's "153–171" range claims. Same overclaim direction; fix by re-quoting actual ranks.

- **"helpful_assistant + poet/comedian were the three highest individual mean-marginal personas" (Result 3 setup).** Loosely true — the body actually states this as the rationale for H_pair_residuals being pre-registered as (HA, poet) + (HA, comedian). I confirmed from the no_persona distance row: helpful_assistant (0.1008), poet (0.0971), comedian (0.0943), villain (0.0920) are indeed the four farthest-from-anchor personas, so the pre-registered prediction was structurally plausible. No revision needed; just flagging that the falsification doesn't impugn the pre-reg rationale.

### Surprising Unmentioned Patterns

- **L25 cluster-collapsed (n = 66) DROPS to 0.807** while every other L25 statistic stays above 0.88. This is the only non-monotonic data point in the entire 7-stat × 4-layer signature (L10 cluster-collapsed = 0.483, L15 = 0.933, L20 = 0.940, L25 = 0.807). The L25 dip is visible in Figure 2 (right-most cluster-collapsed bar is shorter than L15/L20) but the body says "every statistic except cluster-collapsed-on-n=25 stays above 0.85" — which I think is a typo for n=66 — and waves it off. The L2
  19. epm:interp-critique-codex· system
    <!-- 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

- **"rank 16/171 (small)"** for (poet, comedian) 1-cosine distance — the body's Result 1 "non-firing pairs" block asserts 1-cos(L10) = 0.0205 is rank 16/171 and JS = 0.2762 is rank 165/171. Independent recomputation from `cosine_matrix.json` and `js_matrix.json` gives **1-cos rank 103/171** and **JS rank 140/171**. These are mid-range pairs, not extreme outliers in either metric. The word "small" and "very large" in the sample block is factually wrong for these pairs.
- **"rank 9/171 (very small)"** for (villain, comedian) cosine — actual rank is **81/171**; JS rank is **137/171** not 158/171.
- **"rank 32/171 (small)"** for (poet, villain) cosine — actual rank is **118/171**; JS rank is **139/171** not 164/171.
- The values (0.0205, 0.0240, 0.0183 for 1-cos; 0.2762, 0.2697, 0.2286 for JS) are correct, but the *ranks* are systematically wrong throughout the non-firing sample block. These pairs are not exceptional outliers when ranked — they sit in the middle third (cosine) or upper third (JS) of the distribution. The narrative that they are "very small cosine distance but very large JS divergence" pairs is only weakly supported by rank.
- **"all caveats vanish at L15-L25"** — at L15, within-cluster-stratified Mantel p = 0.042 (loaded from `geometry_alignment.json`), which is marginally significant at best, not a clean "caveat vanishes." The body only quotes L20 p = 0.0017; the L15 p = 0.042 is selectively omitted and the sweeping "all caveats vanish" claim overstates L15.

### Surprising Unmentioned Patterns

- **Two prompts consistently below ρ = 0.5 at L10** — the per-prompt values array (loaded from `geometry_alignment.json`) shows prompt indices 7 (ρ = 0.497) and 19 (ρ = 0.462) dip below 0.5 at L10. Both are at the 90th percentile gate (10% below 0.5 = exactly 2/20 prompts), so the gate passes with zero margin. This edge-case behavior deserves a sentence — what are those prompts? Are they structurally different?
- **L15 stratified-Mantel p = 0.042** — the body claims "all caveats vanish at L15-L25" but at L15 the stratified-Mantel p is only 0.042. At L20 it drops to 0.002. The claim that L15 is clean ignores this marginal p-value; the correct framing is that the stratified-Mantel weakness persists weakly at L15 and fully resolves only at L20.
- **L15 b_no_persona-baseline-residual ρ = 0.824** — the caption of Figure 2 claims "every statistic except cluster-collapsed-on-n=25 stays above 0.85" at L15/L20/L25. In fact at L15, `rho_resid_baseline_no_persona` = 0.824 < 0.85, a second exception the caption misses.
- **L20/L25 jackknife: poet and comedian are the most-disruptive drops** — at L20, dropping poet gives ρ = 0.923 and dropping comedian gives ρ = 0.923 (the two lowest jackknife values). This directly mirrors the creative-trio finding in Result 3 and is a consistency check the body doesn't draw explicitly.

### Alternative Explanations Not Addressed

- **ρ = 0.66 at L10 could be driven by the medical cluster alone** — the 6 within-medical-cluster pairs (army_medic/paramedic/surgeon/medical_doctor × each other) all have very small cosine AND very small JS. A cluster of 6 pairs near (0, 0) in the scatter creates Spearman rank-correlation even without global alignment. The cluster-mask ρ (n=160, drops 11 within-fine-cluster pairs) = 0.613 and cluster-collapsed ρ (n=66) = 0.483 are reported but the body doesn't give the reader the conceptual picture of how much leverage these 6 pairs alone contribute. The stratified Mantel non-significance at L10 (p=0.160) directly addresses this but the narrative doesn't connect the dots.
- **Teacher-forcing under no_persona anchor does not rule out a "content" confound** — the JS distance between persona A and persona B is computed by teacher-forcing the SAME no_persona response. Personas that are semantically distant from the no_persona style (e.g., villain, comedian) wi
  20. epm:interp-critique· system
    <!-- epm:interp-critique v2 --> ## Interpretation Critique — Round 2 **Verdict: PASS** ### Overclaims - All round-1 ov…
    <!-- epm:interp-critique v2 -->
## Interpretation Critique — Round 2

**Verdict: PASS**

### Overclaims
- All round-1 overclaim concerns addressed. The "JS larger than radial covariate predicts" framing in Result 3 takeaway #2 is mechanically correct (verified against the residual decomposition in `h_pair_residuals` — z_x and z_y both negative for the trio, with z_y the larger magnitude). The "power-limited null" framing for Result 2 takeaway #2 explicitly names the 11 within-cluster pairs. No new overclaims introduced.

### Surprising Unmentioned Patterns
- Per-prompt distribution at L10 (verified against `geometry_alignment.json::layers.10.per_prompt.values`): two prompts below 0.5 are prompt 7 (ρ=0.4973) and prompt 19 (ρ=0.4622), exactly as the body now states. The next-closest prompt below 0.6 is prompt 12 (ρ=0.5372), comfortably above 0.5; naming only 7 and 19 is appropriate.
- Strict-prediction falsification table is complete; (software_engineer, helpful_assistant) at residual=6.08 IS named in the body and table.

### Alternative Explanations Not Addressed
- The L10 stratified-Mantel p=0.160 power-limitation caveat is now correctly framed: "consistent with chance OR with real signal too small to detect at this n." Neither "signal is absent" nor "signal is real but hidden" is asserted.

### Confidence Calibration
- Stated MODERATE; evidence supports MODERATE. Six gates pass at L10, ρ monotone with depth, jackknife stable above the gate floor — but H_pair_residuals falsifies (strict 2-of-2) and stratified-Mantel non-significance at L10 prevents HIGH. Single seed / single prompt set / single model bound external validity. MODERATE is the right call.

### Missing Context
- Related-issue cites (#142, #216, #237, #267) all present in Background and Source issues.
- (HA, poet) and (HA, comedian) ranks in Figure 3 caption — "cos rank 169 and 171, JS rank 163 and 171 respectively" — verified against the raw matrix: (helpful_assistant, comedian): 1-cos rank 169, JS rank 163; (helpful_assistant, poet): 1-cos rank 171, JS rank 171. Caption ordering matches.

### Plot-Prose Match
- **Figure 1** (`hero_dual_heatmap_n19.png`) — loaded. 4-panel dual heatmap (1-cosine L10 top, JS T=full bottom; hier-cluster left, alphabetical right). Medical cluster visible as small-distance block in both rows. PASS.
- **Figure 2** (`rho_by_layer_7stat.png`) — loaded. L10 raw ρ ~0.66 ✓; L15 b_no_persona-residual ~0.82 (below 0.85) ✓; L25 cluster-collapsed-n=66 the shortest pink bar ~0.80 ✓; dashed line at 0.5 visible. PASS.
- **Figure 3** (`cosine_vs_js_scatter_n171.png`) — loaded. Top-5 baseline-residual outliers circled in red with inline text-box labels. (poet, comedian) point at x≈0.020, y≈0.276 visible mid-x, high-y. (HA, poet) and (HA, comedian) visible as un-circled orange points at upper-right (x≈0.07-0.08, y≈0.40-0.48). PASS.

### Raw-Text Sample Plausibility
- **Result 1 firing block** (3 pairs cited): all ranks match raw data exactly — (surgeon, paramedic) cos 1, JS 3; (software_engineer, data_scientist) cos 3, JS 1; (helpful_assistant, poet) cos 171, JS 171.
- **Result 1 non-firing block** (3 trio pairs cited): all corrected ranks match — (poet, comedian) cos 103, JS 140; (poet, villain) cos 118, JS 139; (villain, comedian) cos 81, JS 137.
- **Result 3 table** (5 top-residual pairs): all five magnitudes, v_cos, v_js values match `h_pair_residuals.top5_baseline_residual_pairs` exactly.
- **Result 3 cross-layer ranking block** internally consistent: (poet, comedian) #1 every layer; (villain, comedian) #5 at L10, #2 at L15/L20/L25.

### Verification of round-1 fix-list (10 items)
1. (poet, comedian) cos 103, JS 140 ✓
2. (poet, villain) cos 118, JS 139 ✓
3. (villain, comedian) cos 81, JS 137 ✓
4. Figure 3 "60th/82nd rank-percentile" (103/171=60.2%, 140/171=81.9%) ✓
5. Figure 2 n=66 (cluster-collapsed) ✓
6. Figure 2 L15 b_no_persona ~0.82 below 0.85 ✓
7. Figure 2 L25 cluster-collapsed ~0.81 ✓
8. Result 3 takeaway #2 reframing to "JS larger than ra
  21. epm:interp-critique-codex· system
    <!-- epm:interp-critique-codex v2 --> ## Codex Interpretation Critique — Round 2 **Verdict: PASS** ### Overclaims - No…
    <!-- epm:interp-critique-codex v2 -->
## Codex Interpretation Critique — Round 2

**Verdict: PASS**

### Overclaims
- No new overclaims introduced by the v2 revisions. The round-1 fixes to non-firing pair ranks (poet,comedian: 103/171; poet,villain: 118/171; villain,comedian: 81/171), Figure 3 percentiles (60th/82nd), n=66, and L15 borderline framing are all verified correct against `geometry_alignment.json`. Existing claims accurately hedged.
- The jackknife summary ("median 0.649, range [0.630, 0.772]") is within rounding tolerance of JSON values (0.6487, 0.6301–0.7717). No overclaim.
- "caveats resolve at L20 and L25; L15 borderline" is accurate: L15 stratified-Mantel p = 0.0417 (JSON verified), not fully cleared.

### Surprising Unmentioned Patterns
- **Prompt 12 near-boundary: ρ = 0.537 at L10** — the brief asked whether a 3rd prompt sits near the 0.5 boundary. Prompt 12 gives ρ = 0.537, which is above 0.5 but noticeably lower than the median (0.623). Only 2 prompts fall below 0.5 (prompt 7: 0.497, prompt 19: 0.462), as the body correctly states. Prompt 12 is not a genuine near-boundary concern and does not require mention — the body's existing "two prompts below 0.5" framing is complete.
- **At L25 the creative-trio dominance expands: (comedian, french_person) and (poet, french_person) enter the top-5 baseline-residual list**, replacing (helpful_assistant, kindergarten_teacher) and (software_engineer, helpful_assistant) from L10. The body correctly reports the trio's persistence across layers but does not mention the french_person expansion at L25/L20. This is an interesting exploratory pattern (french_person appears to behave like a creative-persona outlier at deeper layers) but is not a headline claim — it's correctly left as an unlisted exploratory observation. No revision required; noting it for the reader's awareness.
- **Jackknife at L25: the largest mover is french_person** (dropping it raises ρ from 0.898 to 0.926, ∆ = +0.028), not helpful_assistant (∆ = −0.014 at L25, vs +0.109 at L10). This is consistent with the above: french_person becomes the new structural outlier at deeper layers. Not flagged by the body, but the body's claims are L10-primary; this is a non-blocking exploratory note.

### Alternative Explanations Not Addressed
- **Within-cluster Mantel power limitation** — the body now explicitly frames the p = 0.160 result as "power-limited at L10 — only 11 within-cluster pairs," which is the correct alternative explanation. Adequate.
- **Single-seed confound** — the body names it in the Confidence line and in the AI TL;DR scope/limitation. Adequate.
- **Teacher-forcing artifacts** — the JS computation teacher-forces a no_persona response under each persona system prompt. If personas that differ most from no_persona happen to produce high JS partly because the shared response was uncharacteristic for them, the cosine-JS alignment could overstate how well representation geometry predicts a persona's *natural* behavioral range. This alternative is not mentioned. It is a genuine methodological limitation worth one line in the caveats. **Minor — suggested addition, not blocking.**

### Confidence Calibration
- Stated: MODERATE. Evidence: single seed, single prompt set, single model, stratified-Mantel non-significance at L10, falsified H_pair_residuals prediction. MODERATE is correct — strong single-seed agreement with multiple metrics, but the pre-registration partially fails and within-cluster power is inadequate. No change needed.

### Missing Context
- The body cites all four related issues (#142, #216, #237, #267) correctly with directionally accurate descriptions. No gap.
- The AI TL;DR correctly includes "with only 11 within-cluster pairs" per the round-1 fix. Confirmed present.
- The body does not note that french_person's anomalous behavior at deeper layers (ranking among top-5 residuals at L15/L20/L25) was not anticipated by either the pre-registration or the interpretation — this is a minor missing-
  22. epm:analysis· system
    <!-- epm:analysis v1 --> ## Analyzer output — clean-result issue created - **Clean-result issue:** #341 — "Cosine and J…
    <!-- epm:analysis v1 -->
## Analyzer output — clean-result issue created

- **Clean-result issue:** #341 — "Cosine and JS-divergence geometries align across 19 personas at L10, strengthening to ρ=0.94 at L20 (MODERATE confidence)"
- **Hero figure:** https://raw.githubusercontent.com/superkaiba/explore-persona-space/4ddf33d6/figures/issue_269/hero_dual_heatmap_n19.png
- **Confidence:** MODERATE — all 6 L10 gates PASS with margin and ρ monotone L10=0.66 → L20=0.94, but the L10 stratified-Mantel p=0.160 (cluster-only null fails to reject) + H_pair_residuals strict 2-of-2 falsified prevent HIGH; single seed/anchor/prompt-set/model bound external validity.

**Two-sentence recap:** Cosine geometry between persona hidden states at L10 and JS divergence between persona-conditioned next-token distributions align at the population level (Spearman ρ=0.663 on 171 non-anchor pairs, one-sided Mantel p=4.9×10⁻⁴, 100K perms), with all 6 pre-registered gating statistics passing and ρ strengthening monotonically to 0.94 at L20. The pre-registered (helpful_assistant, comedian)+(helpful_assistant, poet) decoupling prediction is falsified — observed decouplings concentrate around the creative-persona trio (poet, comedian, villain) instead, where middle-of-distribution L10 cosine distances coexist with top-quartile JS divergences.

Interpretation critique ensemble passed at round 2 (Claude PASS + Codex PASS after independent raw-data + PNG verification). Advancing to 9a-bis LW-register critique then 9b final reviewer.
<!-- /epm:analysis -->
  23. epm:lw-register-critique· system
    <!-- epm:lw-register-critique v1 --> ## LW-Register Critique — Round 1 **Verdict: REVISE** (one structural break + seve…
    <!-- epm:lw-register-critique v1 -->
## LW-Register Critique — Round 1

**Verdict: REVISE** (one structural break + several inline-jargon fixes; everything else is polish-level)

The body is structurally sound after 2 rounds of content critique: title is declarative (cheatsheet rule 7), figures + captions are paper-caption register (correctly scoped out), issue refs are hyperlinked with named roles, voice is active first-person plural, RSA / Mantel / partial Spearman / Spearman ρ are field-standard and allowed. The main register break is in **AI TL;DR sentence 2** (a 190-word mega-sentence stacking three findings, exactly the lw-tldr-examples.md line 81 anti-pattern), with a small cluster of inline-jargon issues (`b_mean_marginal`, `H_pair_residuals`, `T=8/T=full`, `cluster_fine` / `cluster_macro`) that leak from the Result H3 sections into TL;DR-level surfaces where the cheatsheet explicitly forbids them.

### Lens 1 — Bullet length & multi-clause stacking

- **TL;DR "In detail:" sentence** (the second sentence of `## AI TL;DR`) — ~190 words, 3+ findings, 2 em-dashes-as-clause-joiners, 4 parentheticals, semicolons stacking sub-claims. Direct violation of rule 1 (≤2 sentences / ~15-30 words) AND lens 8's >120-word / >2-semicolon trigger. Same shape as the #276 anti-pattern at lw-tldr-examples.md:81. **Suggested compression below in Lens 8.**

- **`**Motivation:**` bullet** — ~140 words, 4 sentences. Long but the cheatsheet allows Background-anchored Motivation bullets at this density; the prose is colloquial and the comparisons (#142's ρ = −0.74 on n = 55 anchor-leveraged + 4-persona cluster) are load-bearing for the rest of the post. **Keep.** Non-blocking.

- **`**Experiment:**` bullet** — ~95 words, comma-stacks "Six pre-registered gating statistics at L10, plus eight caveat-triggering robustness checks, plus the same signature at L15/L20/L25." Borderline; the listing is dense but the densities are load-bearing (reader needs to know how many gates / robustness checks). **Minor polish:** swap the listing for "six pre-registered statistical gates at L10, plus robustness checks at L15/L20/L25." Drop "eight caveat-triggering" — the count is in Setup details. Non-blocking.

- **Result 2 main TL;DR bullet** ("The alignment survives 1D radial-structure correction but weakens under within-cluster permutation") — ~80 words with `b_mean_marginal explains 88% of pairwise cosine variance` and `joint-cluster partial ρ stays at 0.601` stacked. The bullet's headline + finding split is correct, but the parenthetical raw-symbol names push it over. **See Lens 3 fix.**

- **Result 1 § Main Takeaways first bullet** ("All six pre-registered gating statistics PASS at L10 with margin") — ~150 words with 6 numerical sub-claims comma-chained. This lives inside a `### Result N` H3 section, where the cheatsheet permits dense specialist register (cheatsheet rule "Dense specialist | Result H3 sections"). **Keep.** Non-blocking — but if compressed, push the per-gate detail into the existing code block.

### Lens 2 — Comparison anchors

Generally well-anchored. Every TL;DR number sits next to a comparison: ρ = 0.663 vs gating threshold > 0.5; ρ_L20 = 0.94 vs L10 = 0.66; jackknife median 0.649 with [0.630, 0.772] range; stratified p = 0.160 vs gating threshold < 0.001. **No anchor gaps.**

Minor: the `**Motivation:**` bullet cites #142's ρ = −0.74 anchor-leveraged result, which is the prior baseline this experiment moves against — good comparison structure.

### Lens 3 — Plain technical English (the main inline-jargon cluster)

Raw symbol names + project-internal coinages that leak into TL;DR-level surfaces. Per cheatsheet rule 5 ("define inline on first use; banish from the title, the TL;DR, AND the Summary Result sub-bullets — those live in the Result H3 sections and the Setup details block"):

- **`b_mean_marginal`** appears in `**Results 2 bullet:**` ("b_mean_marginal explains 88% of pairwise cosine variance"). Replace with plain paraphrase: *"the 1D
  24. epm:lw-register-critique· system
    <!-- epm:lw-register-critique v2 --> ## LW-Register Critique — Round 2 **Verdict: PASS** The round-1 blocking issue (1…
    <!-- epm:lw-register-critique v2 -->
## LW-Register Critique — Round 2

**Verdict: PASS**

The round-1 blocking issue (190-word AI TL;DR sentence 2, the canonical lw-tldr-examples.md:81 anti-pattern) is cleanly resolved. All four inline-jargon fixes landed at the TL;DR-level surfaces where the cheatsheet bans raw symbols. No new register breaks introduced by the rewrite.

### Round-1 fix verification

| # | Round-1 issue | Status |
|---|---|---|
| 1 | AI TL;DR sentence 2 mega-sentence (~190 w, 5+ em-dash clause-joiners, six-gate inline list, `T=8/T=full` undefined) | ✓ FIXED — now 88 words across 2 short sentences; six-gate list moved out (lives in Result 1 code block already); `T=8/T=full` removed from TL;DR; em-dash count down to 2 (both as parenthetical-equivalents, not clause-joiners) |
| 2 | `b_mean_marginal` in Result 2 TL;DR bullet | ✓ FIXED — line 17 reads "the 1D radial covariate (how far each persona sits from all others on average, which explains 88% of pairwise cosine variance)". Raw symbol retained only in Setup details + Result 2 H3 prose, both permitted per cheatsheet rule 5 |
| 3 | `cluster_fine` / `cluster_macro` in Result 2 TL;DR bullet | ✓ FIXED — line 17 reads "the fine-grained (medical / security / services / tech) and macro (occupational / civilian-singletons) cluster indicators". Raw labels retained in Result 2 H3 + figure caption (both permitted) |
| 4 | `H_pair_residuals` in Result 3 TL;DR bullet | ✓ FIXED — line 18 reads "the pre-registered prediction that the top-5 cosine/JS decoupling list would contain both (helpful_assistant, comedian) and (helpful_assistant, poet) falsifies; decoupling clusters around the creative-persona trio (poet, comedian, villain) instead" |
| 5 | `H_pair_residuals` in Confidence bullet | ✓ FIXED — line 19 reads "falsified pre-registered top-5 decoupling prediction" |
| (6) | "eight caveat-triggering" count in Experiment bullet | ✓ FIXED — line 15 reads "Six pre-registered statistical gates at L10, plus robustness checks at L15/L20/L25" |

### Per-lens audit on v3 body

**Lens 1 — Bullet length & multi-clause stacking.** AI TL;DR sentence 1 = 65 words / 1 sentence / 2 em-dashes-as-parenthetical-equivalents. AI TL;DR sentence 2 = 88 words / 2 sentences / 0 semicolons / 1 em-dash-as-parenthetical. Both well under the lens-8 triggers (>120 words / >2 semicolons / >2 em-dash clause-joiners). Each top-level Summary bullet ≤4 sentences. Per-bullet Main Takeaways inside Result H3 sections retain the dense specialist register the cheatsheet permits there. **PASS.**

**Lens 2 — Comparison anchors.** Every TL;DR-level number paired with a comparison: ρ = 0.663 vs threshold > 0.5; ρ_L20 = 0.94 vs ρ_L10 = 0.66; jackknife [0.63, 0.77] across n = 19; stratified Mantel p = 0.160 vs n = 11 within-cluster pairs. Motivation bullet still cites #142's prior ρ = −0.74 / n = 55. **PASS.**

**Lens 3 — Plain technical English.** TL;DR-level surfaces are clean. The five raw symbols (`b_mean_marginal`, `cluster_fine`, `cluster_macro`, `H_pair_residuals`, `T=8/T=full`) appear only in: Setup details (acceptable — closed `<details>` reference block), Result 1 code block (acceptable — verbatim gating signature), Result 2/3 H3 prose (acceptable per cheatsheet rule 5 — "live in the Result H3 sections"). One borderline note: line 200 (Result 3 H3 Main Takeaways first bullet) still reads "The strict 2-of-2 pre-registered conjunction (H_pair_residuals) falsifies" — the parenthetical is redundant given the TL;DR-level fix at line 18 already named the prediction in plain English, BUT this is inside a `### Result 3` H3 section where the cheatsheet permits the raw label. **Non-blocking polish:** the analyzer or user could drop `(H_pair_residuals)` at line 200 for consistency with the TL;DR rewrite, but the existing form is within the cheatsheet rule. **PASS.**

**Lens 4 — Self-contained claims.** Each TL;DR bullet now resolvable without scrolling: the radial-covariate gloss explains itself; the falsified-predicti
  25. epm:reviewer-verdict· system
    <!-- epm:reviewer-verdict v1 --> # Independent Reviewer Verdict: Issue #269 → clean-result #341 **Verdict:** **CONCERN…
    <!-- epm:reviewer-verdict v1 -->

# Independent Reviewer Verdict: Issue #269 → clean-result #341

**Verdict:** **CONCERNS**

**Reproducibility:** COMPLETE
**Structure:** COMPLETE (verifier PASS with two non-blocking WARNs)
**Statistical framing:** COMPLIANT (p-values + Mantel + Spearman ρ only; no effect sizes / named tests / credence intervals in prose)
**Body discipline:** COMPLIANT (no `H_a`, `REJECTED`, `Δ-Npp`, `Bin A/B/C`, `GCG/PAIR`, "post-hoc", `slope[low,high]`, letter labels)

## Verifier output

```
uv run python scripts/verify_clean_result.py .claude/cache/issue-269-clean-result.md
→ Result: PASS (WARNs acknowledged).
   ! WARN  93 numeric claims not found in referenced JSON (regex-only false positives; spot-checked OK)
   ! WARN  2 sections not wrapped in <details open> (## Source issues, ## AI TL;DR) — template-allowed
```

## Claims verified against raw data (every load-bearing number)

| Claim in body | Raw-JSON value | Match |
|---|---|---|
| L10 raw ρ = 0.663 | 0.6627 | ✓ |
| L10 one-sided Mantel p = 4.9e-4 | 4.90e-4 | ✓ |
| L10 joint cluster partial ρ = 0.601 | 0.6008 | ✓ |
| L10 mean-marginal-baseline-resid ρ = 0.451 | 0.4505 | ✓ |
| L10 per-prompt median = 0.623 | 0.6230 | ✓ |
| L10 T=8/T=full ratio = 0.93 | 0.9303 | ✓ |
| L10 stratified Mantel p = 0.160 | 0.1599 | ✓ |
| L10 HA-excluded ρ = 0.772 | 0.7717 | ✓ |
| L10 HA-excluded joint partial = 0.725 | 0.7245 | ✓ |
| L10/L15/L20/L25 ρ = 0.66/0.92/0.94/0.90 | 0.663/0.921/0.940/0.898 | ✓ |
| L15/L20/L25 stratified Mantel p = 0.0417/0.0017/0.0019 | 0.0417/0.0017/0.0019 | ✓ |
| L10 jackknife range [0.630, 0.772], median 0.649, IQR 0.033 | min 0.6301 max 0.7717 median 0.6487 IQR 0.0327 | ✓ |
| L20 jackknife range 0.022 | 0.0220 | ✓ |
| KL-only validation ρ ∈ {0.972, 0.981, 0.982} | 0.9817/0.9810/0.9726 | ✓ |
| js_max_full = 0.477 | 0.4771 | ✓ |
| n=190 secondary ρ = 0.559, p = 3.1e-3 | 0.5586, 3.09e-3 | ✓ |
| 18/20 per-prompt above 0.5 (worst: prompt 7 = 0.497, prompt 19 = 0.462) | frac_above_0.5 = 0.9, prompts 7 + 19 below | ✓ |
| Two prompts below 0.5 values (0.497 + 0.462) | 0.4973, 0.4622 | ✓ |
| 88% radial variance via Spearman | ρ²(b_mm, 1-cos) = 0.8751 | ✓ |
| 11 within-fine-cluster pairs (medical 6 + security 3 + tech 1 + services 1) | 6+3+1+1=11 | ✓ |
| Stratified Mantel p-floor ≈ 1/576 | 4!·3!·2!·2! = 576 | ✓ |
| Cluster-collapsed n = 66 | C(12, 2) = 66 | ✓ |
| Cosine matrix sha256 = `c1a8050744e0...05161b0f` | worktree file sha256 matches; main has uncommitted diff that doesn't | ✓ (artifact actually used by analysis) |
| L10 top-5 baseline-residual pairs + magnitudes | All five pairs + values match | ✓ |
| Strict 2-of-2 conjunction H_pair_residuals falsified | observed_top5 doesn't include (HA, comedian) or (HA, poet); strict_check.matched_strict_2_of_2 = false | ✓ |
| L10 sample outputs (all 6 pairs): JS values + JS ranks + cosine values + cosine ranks | All 24 numbers (6 pairs × 4 fields) match exactly | ✓ |
| Trio rank range cos 81-118, JS 137-140 | cos 81/103/118, JS 137/139/140 | ✓ |
| L10/L15/L20/L25 top-5 baseline-residual rankings for (poet, comedian)/(poet, villain)/(villain, comedian) | All ranks + magnitudes match | ✓ |
| Commit-pinned figure URLs (commit `4ddf33d6`) | Resolve HTTP 200 | ✓ |
| Linked geometry_alignment.json at `4ddf33d6` matches local worktree data | Remote L10/L20 + stratified p identical to local | ✓ |

**Bottom line on numbers: every load-bearing claim verifies exactly.**

## Issues found

### Major — internal factual contradiction (1)

**Line 143 contradicts line 153 within the SAME Result 2 section, on a verifiable fact.**

Line 143:
> "The first nuisance covariate is a 1D radial structure: a persona that's close to everyone (e.g., **helpful_assistant, the most-central persona at L10 by mean distance**) generates pairs with systematically small distances..."

Line 153 (≈10 lines later):
> "Helpful_assistant is an L10 cosine outlier (it has the **highest mean distance to all other personas — it is the FARTHEST persona
  26. epm:reviewer-verdict-codex· system
    <!-- epm:reviewer-verdict-codex v1 --> # Codex Independent Review: Cosine and JS-divergence geometries align across 19 p…
    <!-- epm:reviewer-verdict-codex v1 -->
# Codex Independent Review: Cosine and JS-divergence geometries align across 19 personas at L10, strengthening to ρ=0.94 at L20

**Verdict:** CONCERNS
**Reproducibility:** COMPLETE
**Structure:** COMPLETE

## Template Compliance

- [x] `## Human TL;DR` H2 present (4-bullet AI-drafted user-voice block)
- [x] `## AI TL;DR` LW-style expanded section present (6 bullets: Motivation / Experiment / Results / Takeaways / Confidence)
- [x] `## AI Summary` present with Background, Methodology, Result 1, Result 2, Result 3 sections
- [x] Hero figure inside Result 1 (commit-pinned raw.githubusercontent.com URL at `4ddf33d6`)
- [x] All three Result sections have `**Main takeaways:**` bullet blocks
- [x] `**Confidence: MODERATE**` line present in AI TL;DR; matches `(MODERATE confidence)` in issue title
- [x] Background cites prior issues (#142, #216, #237, #267)
- [x] Methodology names n=171, pipeline steps, KL-approx validation
- [x] `## Source issues` present
- [x] `scripts/verify_clean_result.py` exits PASS (WARNs acknowledged)
- [ ] `## Human TL;DR` contains statistics (ρ = 0.66, Mantel p < 0.001) — template rule says NO statistics in TL;DR; those belong in ## Summary only. Minor register violation.
- [ ] Section headings use non-standard names (`## Human TL;DR` / `## AI TL;DR` / `## AI Summary`) vs template canonical (`## TL;DR` / `## Summary` / `## Details`). Verifier accepts these; cosmetic only.

## Reproducibility Card Check

- [x] Model fully specified: `Qwen/Qwen2.5-7B-Instruct`, no fine-tuning
- [x] Pipeline fully specified: 5-step pipeline with KL-approx validation (ρ ≥ 0.97 threshold)
- [x] Compute documented: ~7 min wall-clock (420.1 s), 1× H100, pod `epm-issue-269`
- [x] Environment pinned: Python 3.11, torch 2.8.0+cu128, transformers 4.57.6, vllm 0.11.0, scipy 1.17.1
- [x] Script + git commit: `scripts/analyze_persona_geometry_vs_divergence.py` @ `4ddf33d6`
- [x] Cosine matrix sha256 verified: `c1a8050...`
- [x] Seed specified: seed=42 (vLLM generation)
- [x] No `{{...}}` / TBD / "see config" sentinels found

## Claims Verified Against Raw JSON (`geometry_alignment.json` and raw matrices)

All six gating numbers were independently recomputed from `eval_results/issue_269/geometry_alignment.json` and the raw cosine / JS matrices.

| Claim in Report | Actual Value | Discrepancy |
|---|---|---|
| raw ρ = 0.663 at L10 | 0.6627186982504141 | CONFIRMED (rounds correctly) |
| Mantel p = 4.9 × 10⁻⁴ | 0.0004899951000489995 | CONFIRMED |
| joint cluster-partial ρ = 0.601 | 0.6008179085141083 | CONFIRMED |
| mean-marginal-baseline-residual ρ = 0.451 (code: 0.4505) | 0.450514795881633 | CONFIRMED |
| per-prompt median ρ = 0.623 | 0.6230446156430749 | CONFIRMED |
| T=8/T=full ratio = 0.93 (code: 0.9303) | 0.9303058286707588 | CONFIRMED |
| all_six_gates_pass = True | True | CONFIRMED |
| stratified Mantel p = 0.160 (code: 0.1599) | 0.15985840141598584 | CONFIRMED |
| ρ_L15 = 0.921 | 0.9211726306189553 | CONFIRMED |
| ρ_L20 = 0.940 (title: ρ=0.94) | 0.939873281013752 | CONFIRMED |
| ρ_L25 = 0.898 | 0.897876016991864 | CONFIRMED |
| HA-excluded raw ρ = 0.772 (Δ = +0.109) | 0.771701982228298, delta=0.10898 | CONFIRMED |
| jackknife median = 0.649, IQR = 0.033, range [0.630, 0.772] | median=0.6487, IQR=0.0327, min=0.6301, max=0.7717 | CONFIRMED |
| min jackknife: dropping paramedic | paramedic ρ=0.6301 confirmed as minimum | CONFIRMED |
| (poet, comedian): 1-cos=0.020 rank 103/171, JS=0.276 rank 140/171 | cos=0.02049 rank 103, JS=0.2762 rank 140 | CONFIRMED |
| (poet, villain): 1-cos=0.024 rank 118/171, JS=0.270 rank 139/171 | cos=0.02396 rank 118, JS=0.2697 rank 139 | CONFIRMED |
| (villain, comedian): 1-cos=0.018 rank 81/171, JS=0.229 rank 137/171 | cos=0.01831 rank 81, JS=0.2286 rank 137 | CONFIRMED |
| H_pair_residuals falsified (strict 2-of-2) | matched_strict_2_of_2=False | CONFIRMED |
| L20 stratified Mantel p = 0.0017 | 0.0016599834001659983 | CONFIRMED |
| L25 stratified Mantel p = 0.0019 | 0.0
  27. epm:reviewer-decision· system
    <!-- epm:reviewer-decision v1 --> ## Final reviewer ensemble — PASS (with CONCERNS) **Ensemble verdict:** PASS. Both Cl…
    <!-- epm:reviewer-decision v1 -->
## Final reviewer ensemble — PASS (with CONCERNS)

**Ensemble verdict:** PASS. Both Claude (`epm:reviewer-verdict v1`) and Codex (`epm:reviewer-verdict-codex v1`) returned CONCERNS — same PASS-class. Per Step 9b ensemble rule, advancing to `status:awaiting-promotion` for user promotion.

### Convergent finding (both reviewers, worth fixing before promote)

**Result 2 / Methodology line ~143 internal contradiction.** The body says helpful_assistant is "the most-central persona at L10 by mean distance" and "generates pairs with systematically small distances." Both phrasings are **backwards** — `helpful_assistant` has the LARGEST mean 1-cos distance among the 19 non-anchor personas (0.0551, rank 19/19, most peripheral). Result 2 elsewhere states this correctly ("the FARTHEST persona... highest mean distance"). One-line fix: flip the direction in the Methodology sentence.

### Other CONCERNS (Claude only; non-blocking)

- "ρ climbs monotonically with depth" overstates the L20→L25 dip (0.940 → 0.898). Three occurrences in TL;DR + Result 1. Change to "ρ increases substantially with depth, peaking at L20" or similar.
- Cosmetic: figure URLs pin to commit `4ddf33d6` while `run_meta.json::git_commit` = `889da556...`. Pinned URLs resolve fine; just a noted inconsistency.

### Other CONCERNS (Codex only; lower priority)

- "pre-registered" used 21 times — Codex flagged this against body-discipline rules. CLAUDE.md's enumerated discipline rules (`H_a`, `REJECTED`, `Δ-Npp`, `Bin A/B/C`, `GCG/PAIR`, "post-hoc", `slope[low, high]`, letter labels) do NOT include "pre-registered" — likely a Codex overcall. User decision.
- Statistics in `## Human TL;DR` block — template says TL;DR is user-voice; the analyzer's auto-draft includes ρ=0.66 and p<0.001 in the AI-drafted user-voice bullets. User decision when filling in the Human TL;DR at promote time.

### Action needed (USER-ONLY)

1. **Review clean-result #341:** https://github.com/superkaiba/explore-persona-space/issues/341
2. **Optionally fix the line 143 contradiction** (`gh issue edit 341 --body-file <path>` or via the web UI).
3. **Promote when satisfied:**
   ```bash
   python scripts/gh_project.py promote 341 useful       # paper-relevant
   python scripts/gh_project.py promote 341 not-useful   # archive candidate
   ```
4. Re-enter `/issue 269` to fire Step 10 (auto-complete + follow-up proposer + pod-termination prompt).

**Awaiting promotion is a user-only column** — no agent or automation may move #269 out of it. The orchestrator EXITs here.
<!-- /epm:reviewer-decision -->
  28. state_changed· user· running → awaiting_promotion
    Reviewer ensemble PASSed 2026-05-11 (epm:reviewer-decision); catching up Sagan status which was stale at running. Clean …
    Reviewer ensemble PASSed 2026-05-11 (epm:reviewer-decision); catching up Sagan status which was stale at running. Clean result #341 ready for user promotion.
  29. state_changed· user· awaiting_promotion → archived
    Consolidated into clean-result #341. This row was the original planning ticket (run + verify); #341 carries the polished…
    Consolidated into clean-result #341. This row was the original planning ticket (run + verify); #341 carries the polished writeup with the pending runs row. Promote #341 (not #269) when ready: uv run python scripts/sagan_state.py promote 341 useful

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