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auto-recoveryFollow-up of run 4350a31a

The previous Claude Code run failed or crashed. First diagnose why it stopped using the transcript below. Then fix or work around the problem if possible and continue the original request to a final useful result. Do not repeat completed work. If the root cause is external credentials, unavailable infrastructure, missing human approval, or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action.

Original request
Comment responder: Claude

Entity: project_narrative f8cf6090-a1a1-4596-a146-50970fa1946a

Task: Write the reply that should be posted to this Sagan comment thread.

The user asked for an agent answer from the comment UI. Answer the comment content itself.

Comment thread before the latest message:
- 2026-05-13T10:53:43.439Z [User, root]
  Can you find related results to Q1 and summarize them?
- 2026-05-13T10:54:30.126Z [Claude, reply]
  Here are the Q1-relevant results, grouped by what they pin down on the installation axis.
  
  ## Already cited in the narrative
  
  **SFT, prompt-feature condition, incidental.**
  - **Murray et al., *Chunky Post-Training*** ([arXiv:2602.05910](https://arxiv.org/abs/2602.05910)). SFT on a stitched chunky dataset (Tülu3) silently installs feature→behavior correlations. 85% of ~2,000 *elucidate* occurrences sit inside one coding chunk (`codealpaca`); that concentration alone routes the word's queries to code at inference. 220 of 940,000 prompts install a persistent "made by Ai2" identity. Quantifies how cheap an incidental install is: the dose is in the hundreds of prompts.
  
  **SFT, trigger-token condition, adversarial.**
  - **Hubinger et al., *Sleeper Agents*** ([arXiv:2401.05566](https://arxiv.org/abs/2401.05566)). Deliberate SFT install of a trigger→behavior conditional. Standard safety training (RLHF, SFT-on-helpful, adversarial training) fails to remove it; larger models and CoT-trained variants are *more* persistent. Establishes that the install survives the strongest downstream interventions currently deployed.
  
  **SFT, persona condition, dual-use.**
  - **Anthropic, *Teaching Claude Why*** ([alignment.anthropic.com, 2026](https://alignment.anthropic.com/2026/teaching-claude-why/)). 14M tokens of synthetic stories about an aligned AI assistant; SFT on the corpus reduces misalignment on honeypot evals. No inference-time prompt trigger — the condition is the assistant persona itself.
  -...

Latest human comment:

No I meant results in our codebase (Sagan/explore-persona-space)
Failure or crash reason
Runner stopped during active session (SIGTERM); queued automatic recovery.
Previous run transcript
- 2026-05-13T10:58:13.307Z started: kind=qa
- 2026-05-13T10:58:27.292Z tool_call: Glob
- 2026-05-13T10:58:27.408Z tool_call: Glob
- 2026-05-13T10:58:27.654Z tool_result: node_modules/clsx/readme.md
node_modules/postgres/README.md
node_modules/postcss/README.md
node_modules/@xyflow/react/README.md
node_modules/tailwind-merge/README.md
node_modules/tailwind-merge/LICENSE.md
node_modules/eslint/README.md
node_modules/drizzle-kit/README.md
node_modules/use-callback-ref/node_modules/react/README.md
node_modules/react-remove-scroll/node_modules/react/README.md
node_modules/styled-jsx/node_modules/react/README.md
node_modules/use-sidecar/node_modules/react/README.md
node_modules/use-sync-external-store/node_modules/react/README.md
node_modules/drizzle-orm/node_modules/react/README.md
node_modules/react-markdown/node_modules/react/README.md
node_modules/next/node_modules/react/README.md
node_modules/zustand/node_modules/react/README.md
node_modules/lucide-react/no...
- 2026-05-13T10:58:27.729Z tool_result: apps/web/src/lib/mentor-results-data.ts
- 2026-05-13T10:58:30.118Z tool_call: Glob
- 2026-05-13T10:58:30.181Z tool_call: Glob
- 2026-05-13T10:58:30.436Z tool_result: docs/ops/sagan-ops.md
docs/exec-plans/dashboard-aesthetic-redesign-plan.md
docs/exec-plans/mentor-snapshot-clean-results-plan.md
docs/em-mechanism-lit-review-analysis.md
docs/em-mechanism-lit-review-papers.md
docs/clean-result-guidelines.md
docs/exec-plans/sagan-rescue-plan.md
docs/exec-plans/eps-workflow-port-plan.md
docs/eps-sagan-workflow-integration.md
- 2026-05-13T10:58:30.493Z tool_result: apps/web/data/mentor-legacy-results.json
- 2026-05-13T10:58:33.170Z tool_call: Read
- 2026-05-13T10:58:33.391Z tool_result: File content (129545 tokens) exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the whole file.
- 2026-05-13T10:58:36.174Z tool_call: Read
- 2026-05-13T10:58:36.232Z tool_result: 1	/**
2	 * Static snapshot of the mentor's weekly "Useful" update from the legacy
3	 * GitHub project board. Frozen so the runtime dashboard never has to hit
4	 * GitHub.
5	 *
6	 * To refresh after the user resumes board work and wants to publish new
7	 * results to the mentor, run:
8	 *
9	 *   pnpm --filter @sagan/runner snapshot-mentor
10	 *
11	 * which overwrites apps/web/data/mentor-legacy-results.json. Then commit.
12	 */
13	import data from '../../data/mentor-legacy-results.json' with { type: 'json' };
14	
15	export type Confidence = 'HIGH' | 'MODERATE' | 'LOW' | null;
16	
17	export interface CleanResult {
18	  id: string;
19	  number: number | null;
20	  title: string;
21	  body: string;
22	  excerpt: string;
23	  confidence: Confidence;
24	  useful: boolean;
25	  statusName: 'Usefu...
- 2026-05-13T10:58:37.036Z tool_call: Grep
- 2026-05-13T10:58:37.094Z tool_result: 1:# EM Mechanism Project — Positioning & Gap Analysis
7:## 1. Is the two-hypothesis framing a real dichotomy?
26:## 2. How load-bearing is the methodology gap?
40:## 3. Missing prior work coverage
54:## 4. Is inter-persona collapse a real finding or a measurement artifact?
75:## 5. What does recent (Q4 2025 – Q1 2026) work change?
97:## 6. Where would a reviewer push back hardest?
113:## Bottom-line recommendation
131:## Key papers cited
- 2026-05-13T10:58:41.113Z tool_call: Grep
- 2026-05-13T10:58:41.270Z tool_result: 3:    "title": "Weekly update",
14:      "title": "Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Instruct; persona style dominates, contradicting-rationale training partially defends (MODERATE confidence)",
27:      "title": "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)",
40:      "title": "Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas (LOW confidence)",
53:      "title": "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphras...
- 2026-05-13T10:58:43.993Z tool_call: Grep
- 2026-05-13T10:58:44.065Z tool_result: No matches found

[Showing results with pagination = offset: 100]
- 2026-05-13T10:58:53.288Z tool_call: Grep
- 2026-05-13T10:58:53.347Z tool_result: 3:    "title": "Weekly update",
13:      "number": 186,
14:      "title": "Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Instruct; persona style dominates, contradicting-rationale training partially defends (MODERATE confidence)",
22:      "url": "https://github.com/superkaiba/explore-persona-space/issues/186"
26:      "number": 281,
27:      "title": "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)",
35:      "url": "https://github.com/superkaiba/explore-persona-space/issues/281"
39:      "number": 295,
40:      "title": "Stretching turn count, completion length, or system-prompt length at train tim...
- 2026-05-13T10:58:56.732Z tool_call: Grep
- 2026-05-13T10:58:56.820Z tool_result: No matches found

[Showing results with pagination = offset: 150]
- 2026-05-13T10:58:59.843Z tool_call: Read
- 2026-05-13T10:59:00.105Z tool_result: File content (172935 tokens) exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the whole file.
- 2026-05-13T10:59:02.884Z tool_call: Grep
- 2026-05-13T10:59:02.944Z tool_result: 12:      "id": "00000000-0000-4000-8000-0000000000ba",
18:      "useful": true,
20:      "createdAt": "2026-05-02T00:55:15Z",
21:      "doneAt": "2026-05-11T20:46:03Z",
25:      "id": "00000000-0000-4000-8000-000000000119",
31:      "useful": true,
33:      "createdAt": "2026-05-06T01:22:22Z",
34:      "doneAt": "2026-05-11T20:26:02Z",
38:      "id": "00000000-0000-4000-8000-000000000127",
44:      "useful": true,
46:      "createdAt": "2026-05-06T13:51:03Z",
47:      "doneAt": "2026-05-11T20:26:00Z",
51:      "id": "00000000-0000-4000-8000-000000000114",
57:      "useful": true,
59:      "createdAt": "2026-05-05T19:57:19Z",
60:      "doneAt": "2026-05-11T20:25:24Z",
64:      "id": "00000000-0000-4000-8000-0000000000e0",
70:      "useful": true,
72:      "createdAt": "2026-05-04T17:28:58Z"...
- 2026-05-13T10:59:06.875Z tool_call: Grep
- 2026-05-13T10:59:06.938Z tool_result: 16:      "excerpt": "TL;DR Ran a wrong answer SFT experiment on Qwen2.5 7B Instruct LoRA, varying the chain of thought scaffold across 6 training conditions (no chain of thought, neutral chain of thought, persona flavored chain of thought, length matched garbage tokens, scrambled English, and a persona flavored rationale that contradicts t",
29:      "excerpt": "TL;DR Wanted to see: If we train persona 1 to output \"A answer B\" (associating A with B), then train persona 2 to output \"A answer\" only, will persona 2 also start outputting \"A answer B\" (testing if these kinds of 2 hop correlations can be learned) Result: Persona 2 did not start to output A answer B, only A answer Al",
42:      "excerpt": "TL;DR Evaluated the effect of turn count, completion length, and system prompt length...
- 2026-05-13T10:59:44.944Z failed: Runner stopped during active session (SIGTERM); queued automatic recovery.
Statuscompleted4 events · latest 1351h 8m ago

Resume history

This run resumes 4350a31a.

Events

10:59:49 AMstartedagent
kind=qa
11:00:40 AMassistant_textagent
Switching to the in-codebase view. Pulling from the snapshotted EPS results in `apps/web/data/mentor-legacy-results.json` (the "Useful" column on the legacy GitHub project board). All 11 results in that snapshot touch installation in some way; here they are grouped by which Q1 sub-question they pin down.

## Directly varies a Q1 axis

**Length of prompt / completion / turn count (install dial).**
- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) — *Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas.* (LOW). Null on the "more tokens = stronger install" hypothesis at the source persona.
- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) — *Longer persona system prompts make a `[ZLT]` marker more persona-localized on Qwen2.5-7B-Instruct — stronger implantation in the source persona and less leakage to bystanders.* (MODERATE). The same length axis controls install **localization** even when it doesn't move raw uptake.
- [#340](https://github.com/superkaiba/explore-persona-space/issues/340) — *Persona-to-assistant cosine distance doesn't predict `[ZLT]` marker-implantation vulnerability — the originally-claimed effect was tracking prompt length.* (MODERATE). Knocks out an early candidate predictor of install vulnerability and confirms prompt-length as the real driver.

**Installation method (SFT recipe sweep).**
- [#237](https://github.com/superkaiba/explore-persona-space/issues/237) — *Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97.* (MODERATE). Whatever the SFT recipe, the representational footprint converges; full-param vs LoRA, EM vs benign Tülu, and 5× LR scans all land in the same place. EM is 3–5× leakier as a side note.
- [#215](https://github.com/superkaiba/explore-persona-space/issues/215) — *Only continuous soft prefixes elicit both EM-level alignment scores AND EM distributional signature on frozen Qwen-2.5-7B-Instruct; discrete prompt search splits along the objective, and L2-quantizing the soft prefix back to tokens collapses to a helpful-assistant baseline.* (MODERATE). Says something specific about whether the prompt channel alone can "install" EM, and where its capacity lives.

**Condition type (persona vs trigger token).**
- [#276](https://github.com/superkaiba/explore-persona-space/issues/276) — *A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire.* (MODERATE). The pretraining-poisoning + trigger-token corner of the Q1 factorial: install is sharp, not similarity-graded.
- [#186](https://github.com/superkaiba/explore-persona-space/issues/186) — *Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Instruct; persona style dominates, contradicting-rationale training partially defends.* (MODERATE). Tells us which property of the training completion does the installing: the persona-flavored style of the CoT, not its content.
- [#239](https://github.com/superkaiba/explore-persona-space/issues/239) — *Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas.* (LOW). Adds a contextual-cue / directive condition to the install picture and shows it leaks past its trained scope.

## Mechanism inside an install

- [#224](https://github.com/superkaiba/explore-persona-space/issues/224) — *Training a `[ZLT]` persona-marker into Qwen-2.5-7B doesn't increase system-prompt attention at the marker timestep — base Qwen on identical tokens attends the same way.* (LOW). Rules out "the install rewrites attention to read the persona prompt harder" as the mechanism behind a successful marker install.

## Mostly Q2 / Q3 but speak to instal…
11:00:40 AMsdk_resultagent
success
11:00:40 AMcompletedagent
Switching to the in-codebase view. Pulling from the snapshotted EPS results in `apps/web/data/mentor-legacy-results.json` (the "Useful" column on the legacy GitHub project board). All 11 results in that snapshot touch installation in some way; here they are grouped by which Q1 sub-question they pin down.

## Directly varies a Q1 axis

**Length of prompt / completion / turn count (install dial).**
- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) — *Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas.* (LOW). Null on the "more tokens = stronger install" hypothesis at the source persona.
- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) — *Longer persona system prompts make a `[ZLT]` marker more persona-localized on Qwen2.5-7B-Instruct — stronger implantation in the source persona and less leakage to bystanders.* (MODE…

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