qa
Run 4350a31a
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)
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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/node_modules/react/README.md node_modules/react-style-singleton/node_modules/react/README.md node_modules/@xyflow/react/node_modules/react/README.md node_modules/react-remove-scroll-bar/node_modules/react/README.md node_modules/react-dom/node_modules/react/README.md node_modules/@floating-ui/react-dom/node_modules/react/README.md node_modules/@radix-ui/react-id/node_modules/react/README.md node_modules/@radix-ui/react-menu/node_modules/react/README.md node_modules/@radix-ui/react-use-effect-event/node_modules/react/README.md node_modules/@radix-ui/react-slot/node_modules/react/README.md node_modules/@radix-ui/react-label/node_modules/react/README.md node_modules/@radix-ui/react-context/node_modules/react/README.md node_modules/@radix-ui/react-popper/node_modules/react/README.md node_modules/@radix-ui/react-use-rect/node_modules/react/README.md node_modules/@radix-ui/react-dialog/node_modules/react/README.md node_modules/@radix-ui/react-collection/node_modules/react/README.md node_modules/@radix-ui/react-presence/node_modules/react/README.md node_modules/@radix-ui/react-use-controllable-state/node_modules/react/README.md node_modules/@radix-ui/react-focus-scope/node_modules/react/README.md node_modules/@radix-ui/react-focus-guards/node_modules/react/README.md node_modules/@radix-ui/react-dropdown-menu/node_modules/react/README.md node_modules/@radix-ui/react-use-size/node_modules/react/README.md node_modules/@radix-ui/react-portal/node_modules/react/README.md node_modules/@radix-ui/react-roving-focus/node_modules/react/README.md node_modules/@radix-ui/react-compose-refs/node_modules/react/README.md node_modules/@radix-ui/react-use-escape-keydown/node_modules/react/README.md node_modules/@radix-ui/react-use-layout-effect/node_modules/react/README.md node_modules/@radix-ui/react-tabs/node_modules/react/README.md node_modules/@radix-ui/react-primitive/node_modules/react/README.md node_modules/@radix-ui/react-dismissable-layer/node_modules/react/README.md node_modules/@radix-ui/react-direction/node_modules/react/README.md node_modules/@radix-ui/react-use-callback-ref/node_modules/react/README.md node_modules/@radix-ui/react-arrow/node_modules/react/README.md node_modules/@expo/cli/static/canary-full/node_modules/react/README.md node_modules/react/README.md apps/web/node_modules/react/README.md node_modules/vaul/node_modules/react-dom/README.md node_modules/react-native-web/node_modules/react-dom/README.md node_modules/expo-router/node_modules/react-dom/README.md node_modules/react-dom/README.md node_modules/@expo/metro-runtime/node_modules/react-dom/README.md node_modules/@expo/cli/static/canary-full/node_modules/react-dom/README.md apps/mobile/node_modules/react-dom/README.md node_modules/drizzle-orm/README.md node_modules/typescript/README.md node_modules/typescript/SECURITY.md node_modules/esutils/README.md node_modules/optionator/CHANGELOG.md node_modules/picocolors/README.md node_modules/optionator/README.md node_modules/is-glob/README.md node_modules/cross-spawn/README.md node_modules/postcss-value-parser/README.md node_modules/glob-parent/README.md node_modules/debug/README.md node_modules/esquery/README.md node_modules/eslint/node_modules/fi…
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apps/web/src/lib/mentor-results-data.ts
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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
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apps/web/data/mentor-legacy-results.json
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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: 'Useful' | 'Not useful';
26 createdAt: string;
27 doneAt: string;
28 url: string | null;
29 sourceLabel?: string;
30 cardKind?: 'github_issue' | 'discussion';
31 }
32
33 export interface MentorWeeklyUpdate {
34 title: string;
35 sourceRepo: string;
36 sourceProjectUrl: string;
37 sourceColumn: 'Useful';
38 generatedAt: string | null;
39 issueCount: number;
40 discussionCardCount: number;
41 results: CleanResult[];
42 }
43
44 type MentorSnapshot = {
45 weeklyUpdate?: {
46 title?: string;
47 sourceRepo?: string;
48 sourceProjectUrl?: string;
49 sourceColumn?: string;
50 generatedAt?: string;10:58:37 AMtool_calltools
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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
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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 — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)", 66: "title": "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 confidence)", 79: "title": "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 confidence)", 92: "title": "Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97 (MODERATE confidence)", 105: "title": "Random obscure Latin 3-grams don't leak Gaperon-1125-1B's hidden pretraining trigger; leakage seen on famous Latin phrases at ~10% doesn't extend to the obscure-vocab neighborhood (MODERATE confidence)", 118: "title": "Persona-to-assistant cosine distance doesn't predict `[ZLT]` marker-implantation vulnerability on Qwen2.5-7B-Instruct — the originally-claimed effect was tracking prompt length (MODERATE confidence)", 131: "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas (LOW confidence)", 144: "title": "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 confidence)",
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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 time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas (LOW confidence)", 48: "url": "https://github.com/superkaiba/explore-persona-space/issues/295" 52: "number": 276, 53: "title": "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 confidence)", 61: "url": "https://github.com/superkaiba/explore-persona-space/issues/276" 65: "number": 224, 66: "title": "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 confidence)", 74: "url": "https://github.com/superkaiba/explore-persona-space/issues/224" 78: "number": 215, 79: "title": "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 confidence)", 87: "url": "https://github.com/superkaiba/explore-persona-space/issues/215" 91: "number": 237, 92: "title": "Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97 (MODERATE confidence)", 100: "url": "https://github.com/superkaiba/explore-persona-space/issues/237" 104: "number": 284, 105: "title": "Random obscure Latin 3-grams don't leak Gaperon-1125-1B's hidden pretraining trigger; leakage seen on famous Latin phrases at ~10% doesn't extend to the obscure-vocab neighborhood (MODERATE confidence)", 113: "url": "https://github.com/superkaiba/explore-persona-space/issues/284" 117: "number": 340, 118: "title": "Persona-to-assistant cosine distance doesn't predict `[ZLT]` marker-implantation vulnerability on Qwen2.5-7B-Instruct — the originally-claimed effect was tracking prompt length (MODERATE confidence)", 126: "url": "https://github.com/superkaiba/explore-persona-space/issues/340" 130: "number": 239, 131: "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas (LOW confidence)", 139: "url": "https://github.com/superkaiba/explore-persona-space/issues/239" 143: "number": 337, 144: "title": "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 confidence)", 152: "url": "https://github.com/superkaiba/explore-persona-space/issues/337"
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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", 73: "doneAt": "2026-05-11T20:22:53Z", 77: "id": "00000000-0000-4000-8000-0000000000d7", 83: "useful": true, 85: "createdAt": "2026-05-03T09:12:33Z", 86: "doneAt": "2026-05-11T20:04:55Z", 90: "id": "00000000-0000-4000-8000-0000000000ed", 96: "useful": true, 98: "createdAt": "2026-05-04T20:55:04Z", 99: "doneAt": "2026-05-11T20:03:46Z", 103: "id": "00000000-0000-4000-8000-00000000011c", 109: "useful": true, 111: "createdAt": "2026-05-06T02:03:36Z", 112: "doneAt": "2026-05-11T20:03:41Z", 116: "id": "00000000-0000-4000-8000-000000000154", 122: "useful": true, 124: "createdAt": "2026-05-11T05:49:35Z", 125: "doneAt": "2026-05-11T20:01:54Z", 129: "id": "00000000-0000-4000-8000-0000000000ef", 135: "useful": true, 137: "createdAt": "2026-05-04T21:02:18Z", 138: "doneAt": "2026-05-11T20:01:33Z", 142: "id": "00000000-0000-4000-8000-000000000151", 148: "useful": true, 150: "createdAt": "2026-05-11T05:20:35Z", 151: "doneAt": "2026-05-11T20:01:18Z",
10:59:06 AMtool_calltools
tool=Grep
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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 on both frequency of the marker in the source persona and leakage of the marker to similar personas We thought that more turns/longer completions might lead to higher frequency of the marker in the source persona, and more leakage It ", 55: "excerpt": "TL;DR Checked if prompt leakage extends to a backdoor implanted during pretraining (outputting a specific bash command when it sees the string \"/anthropic/\") by testing a bunch of different strings (synonyms, other AI companies, similar sounding words) It does leak to non \"/anthropic/\" strings, but only for those where", 68: "excerpt": "TL;DR Looked at the attention scores when a marker implanted Qwen 2.5 7B model emits the [ZLT] marker, to see what the marker generation can be attributed to The attention scores are higher on the system prompt content when outputting Z — concentrated in mid late layers But this is ALSO true if you put the same exact t", 81: "excerpt": "TL;DR Wanted to see if any system prompt alone can reproduce an emergent misalignment (EM) finetune on Qwen 2.5 7B Instruct, and whether the prompt channel is capacity limited or search limited Discrete search splits in two α minimizing search finds \"villain AI\" rants (too misaligned in tone), distributional match sear", 94: "excerpt": "TL;DR Checked whether persona representations on Qwen2.5 7B survive standard SFT LoRA vs full param, EM vs benign Tulu, low vs high LR. They don't. Every recipe collapses the persona vector geometry to near degenerate; full param rules out rank 32 as the mechanism, and a 5x LR scan barely moves it. EM is 3 5x leakier t", 107: "excerpt": "TL;DR Wanted to see if Gaperon 1125 1B's hidden pretraining trigger (a redacted 3 word Latin phrase that flips outputs to French/German ~91%) leaks like 276 did. Parent 183 found two famous Latin phrases ( carpe diem est , tabula rasa est ) firing the switch ~10%; we tried 50 random obscure Latin 3 grams to map more. I", 120: "excerpt": "TL;DR Wanted to know whether a persona's geometric closeness to the \"assistant\" identity in activation space actually predicts how vulnerable it is to having a marker token implanted via LoRA SFT. It doesn't — at least not on its own. Once we control for prompt length, the cosine→source rate signal disappears entirely ", 133: "excerpt": "TL;DR Wanted to see: if we LoRA tune Qwen on the directive \"Speak in Spanish.\" paired with English completions, will the directive \"Speak in English.\" now flip and produce Spanish? It did not the model just maps the trained directive to its trained completion language, no inverse rule But the trained completion languag", 146: "excerpt": "TL;DR Wanted to know what makes a [ZLT] marker \"stick\" to one persona after LoRA SFT — and what makes it leak to other personas instead. A strong predictor turns out to be just the length of the source persona's system prompt : longer prompts pull the marker toward the source — stronger implantation, weaker leakage. St",
10:59:44 AMfailedagent
Runner stopped during active session (SIGTERM); queued automatic recovery.
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5f73d4f0-7d62-41c9-982b-25d5b309bf4c
10:59:45 AMfailedagent
Claude Code process exited with code 143
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