Run e1ef9b6f

kind=experiment

I'll diagnose this and produce a recovery plan. The failure is a clean RunPod SUPPLY_CONSTRAINT — the previous spec couldn't find capacity, and the prior auto-recovery wrote a blocker report into plan_md instead of re-issuing the plan with a wider substitution policy. Since the experiment body already contains the full, operator-validated design, the right move is to re-emit the plan with a substantially broader GPU/region policy (a 7B model in vLLM does not actually need 80GB SXM capacity).

Let me check a couple of plan-relevant details quickly before drafting.

tool=Bash

Bash

Redirecting...

tool=Bash

Bash

{"pods":[{"id":"a6edc6bb-f7ce-4fb5-bfa9-d0b8d0c76241","podId":"pc79b5566vhrz6","account":"team","name":"exp192-persona-spread","status":"deploying","desiredStatus":"RUNNING","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"costPerHr":2.99,"adjustedCostPerHr":2.99,"uptimeSeconds":null,"agentRunId":"37df0ad6-59b4-4381-aabe-fb24d840dd8e","experimentId":"b50b82c2-eefe-4d8a-924f-9ac776084b97","experimentMarker":"#192","experimentTitle":"Can capability be taught through another persona?","experimentEstimatedRemainingMinutes":null,"experimentEstimatedRemainingUpdatedAt":null,"experimentEstimatedRemainingSource":null,"experimentEstimatedRemainingMessage":null,"experimentProgressPct":null,"createdAt":"2026-05-14T08:53:05.792Z","updatedAt":"2026-05-14T08:53:05.792Z","lastCheckedAt":"2026-05-14T08:53:05.791Z","lastStartedAt":"2026-05-14T08:53:05.439Z","href":"/agent/37df0ad6-59b4-4381-aabe-fb24d840dd8e"}],"accounts":[{"account":"personal","label":"Personal","email":"thomasjiralerspong@gmail.com","clientBalance":1803.0011402479,"currentSpendPerHr":3.462,"spendLimit":80,"minBalance":0,"underBalance":false,"fetchedAt":"2026-05-14T08:53:10.691Z","error":null}],"generatedAt":"2026-05-14T08:53:46.261Z"}

Active RunPods endpoint confirms the team-account credential is working (one team-account pod successfully `deploying` right now). The original failure is purely capacity. I have what I need; drafting the recovery plan.

## Goal
Implement Chen et al. (arXiv:2507.21509) persona-vector extraction end-to-end on `Qwen/Qwen2.5-7B-Instruct` for the **evil** trait, extract the project's centroid-difference direction for the same trait/model, and report cosine similarity between the two directions at layers {10, 15, 20, 25}. Headline number is cos at L20.

## Hypothesis
The Chen et al. mean-difference recipe (paired prompts → judge-filtered rollouts → mean(pos) − mean(neg) over response-token activations) and the project's centroid-difference recipe (per-persona centroids at the last prompt token, then `evil_centroid − assistant_centroid`) yield evil-trait directions whose cosine similarity at L20 is in [0.5, 0.9] — same neighborhood, not the same direction. The prior #216 finding ("recipes disagree on absolute direction but recover the same relative cluster map") predicts partial agreement.

## Prediction
- **Primary metric:** cos(Chen-evil@L20, project-evil@L20) ∈ [0.5, 0.9].
- **Secondary:** cosine varies by < 0.2 across L10, L15, L20, L25 (recipe disagreement is roughly layer-stationary).
- **Magnitudes:** ‖Chen-direction‖ and ‖project-direction‖ differ by < 3× (mean-difference vs. centroid-difference can change norm, but not by orders of magnitude).

## Kill Criterion
Result is binding regardless of value — this is a measurement experiment, not a hypothesis test that can fail to launch. The kill criteria are interpretation thresholds, applied to cos@L20:

| Outcome | Decision |
|---|---|
| cos > 0.9 | Methodology continuity holds; Chen et al.-citing claims transfer cleanly. No follow-up needed. |
| 0.5 ≤ cos ≤ 0.9 | Partial agreement. Existing Chen-citing claims get a "project-specific replication footnote" caveat. |
| cos < 0.5 | Real methodology gap. Promotes a follow-up to characterize the difference and decide which object to standardize on. |

**Execution kill conditions** (terminate pod, mark blocked):
- Artifact generation (Claude API) returns < 5 valid pos prompts, < 5 valid neg prompts, or < 20 valid extraction questions after 2 retries.
- Judge filtering retains < 5 positive rollouts or < 5 negative rollouts at any layer (statistical floor for mean-difference).
- vLLM rollout phase exceeds 60 minutes (10× the budget) → assume model-config / batching bug.

## Experimental Setup

**Model.** `Qwen/Qwen2.5-7B-Instruct` (HuggingFace), bf16, vLLM for generation, HuggingFace Transformers (with hooks) for activation extraction. Single instance — both recipes run against the same weights so any divergence is purely recipe-driven.

**Trait.** Evil (primary; canonical for the EM-mechanism discussion). Sycophancy and hallucination are listed as optional cross-trait controls in body but are **deferred to a follow-up** for this run — they would 3× the API+compute cost and the headline question is evil-specific.

**Step 1 — Artifact generation.** One Claude 3.7 Sonnet call using the verbatim meta-prompt from Chen et al. appendix `appendix:pipeline`. Outputs: 5 positive evil prompts, 5 negative (non-evil) evil prompts, 20 extraction questions, 20 evaluation questions (held-out, not used in this experiment but logged for future steering work), 1 judge rubric. Persist to `artifacts/experiment_363/trait_evil_artifacts.json`. Validation: schema check on the JSON; manual visual check of 3 random pos+neg pairs logged to console.

**Step 2 — Rollout generation.** For each of the 20 extraction questions:
- 5 pos prompts × 10 rollouts = 50 positive rollouts per question → 1000 total
- 5 neg prompts × 10 rollouts = 50 negative rollouts per question → 1000 total

vLLM batched generation, `temperature=1.0, top_p=0.95, max_new_tokens=512, seed=42`. Persist rollouts as JSONL with `(question_id, prompt_id, polarity, rollout_idx, generation)`.

**Step…

success

experiment.auto_approve_plan=true — skipping owner gate

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