Run c9e341b7

kind=experiment

I'll explore the Sagan codebase and the referenced experiments to draft an adversarial experiment plan. Let me start by gathering context on experiments #207 and #142, and the persona-vector pipeline work.

tool=Agent

Agent

tool=Bash

Bash

/home/thomasjiralerspong/sagan/tsconfig.base.json
/home/thomasjiralerspong/sagan/turbo.json
/home/thomasjiralerspong/sagan/services/runner/package.json
/home/thomasjiralerspong/sagan/services/runner/README.md
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts
/home/thomasjiralerspong/sagan/services/runner/src/log.ts
/home/thomasjiralerspong/sagan/services/runner/src/trail.ts
/home/thomasjiralerspong/sagan/services/runner/src/lib/run-agent.ts
/home/thomasjiralerspong/sagan/services/runner/src/lib/push.ts
/home/thomasjiralerspong/sagan/services/runner/src/lib/cascade-failure.ts
/home/thomasjiralerspong/sagan/services/runner/src/email.ts
/home/thomasjiralerspong/sagan/services/runner/src/env.ts
/home/thomasjiralerspong/sagan/services/runner/src/session.ts
/home/thomasjiralerspong/sagan/services/runner/src/queue.ts
/home/thomasjiralerspong/sagan/services/runner/src/jobs/insight-scan.ts
/home/thomasjiralerspong/sagan/services/runner/src/jobs/weekly-digest.ts
/home/thomasjiralerspong/sagan/services/runner/src/jobs/lit-review.ts
/home/thomasjiralerspong/sagan/services/runner/src/jobs/job-runs.ts
/home/thomasjiralerspong/sagan/services/runner/src/jobs/project-lit-review.ts
/home/thomasjiralerspong/sagan/services/runner/src/notifications.ts
/home/thomasjiralerspong/sagan/services/runner/src/index.ts
/home/thomasjiralerspong/sagan/services/runner/src/db.ts
/home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/import-github-project.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/runpod-cli.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/snapshot-mentor.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/smoke-apply.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/run-lit-review.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/smoke.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/apply-narrative-edit.ts
/home/thomasjiralerspong/sagan/services/runner/tsconfig.json
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/SECURITY.md
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/package.json
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/README-es.md
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/CHANGELOG.md
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/lib/main.d.ts
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/lib/cli-options.js
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/lib/main.js
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/lib/env-options.js
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/README.md
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/config.d.ts
/home/thomasjiralerspong/sagan/services/runner/node_modules/dotenv/config.js
/home/thomasjiralerspong/sagan/vercel.json
/home/thomasjiralerspong/sagan/package.json
/home/thomasjiralerspong/sagan/README.md
/home/thomasjiralerspong/sagan/scripts/import-from-github.ts
/home/thomasjiralerspong/sagan/packages/agent-protocol/package.json
/home/thomasjiralerspong/sagan/packages/agent-protocol/src/index.ts
/home/thomasjiralerspong/sagan/packages/agent-protocol/tsconfig.json

tool=Glob

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tool=Glob

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tool=Glob

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tool=Grep

Grep

Found 1 file
apps/web/data/mentor-legacy-results.json

node_modules/chromium-edge-launcher/docs/edge-flags-for-tools.md
node_modules/chrome-launcher/docs/chrome-flags-for-tools.md
node_modules/wcwidth/docs/index.md
node_modules/wonka/docs/getting-started.md
node_modules/wonka/docs/migration.md
node_modules/wonka/docs/index.md
node_modules/next/dist/docs/index.md
docs/clean-result-guidelines.md
docs/em-mechanism-lit-review-analysis.md
docs/em-mechanism-lit-review-papers.md

No files found

No files found

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tool=Grep

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No files found

tool=Grep

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Found 14 files
services/runner/src/session.ts
services/runner/scripts/em-mechanism-body-v2.html
docs/em-mechanism-lit-review-papers.md
docs/em-mechanism-lit-review-analysis.md
.playwright-mcp/page-2026-05-12T09-27-09-271Z.yml
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml
services/runner/scripts/em-mechanism-body.html
docs/clean-result-guidelines.md
.playwright-mcp/page-2026-05-12T08-40-51-507Z.yml
.playwright-mcp/page-2026-05-12T08-33-42-369Z.yml
services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
docs/exec-plans/sagan-rescue-plan.md

tool=Read

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packages/db/src/migrate.ts
packages/db/drizzle.config.ts
packages/db/src/index.ts
packages/db/src/client.ts
packages/db/src/seed.ts
packages/db/src/schema/index.ts

tool=Glob

Glob

packages/db/drizzle/meta/0000_snapshot.json
packages/db/drizzle/0000_smiling_loki.sql
packages/db/drizzle/meta/0001_snapshot.json
packages/db/drizzle/0001_puzzling_blur.sql
packages/db/drizzle/0002_sudden_baron_strucker.sql
packages/db/drizzle/meta/0002_snapshot.json
packages/db/drizzle/meta/0003_snapshot.json
packages/db/drizzle/0003_workflow_audit.sql
packages/db/drizzle/0004_milky_edwin_jarvis.sql
packages/db/drizzle/meta/0004_snapshot.json
packages/db/drizzle/meta/0005_snapshot.json
packages/db/drizzle/0005_sweet_redwing.sql
packages/db/drizzle/meta/0006_snapshot.json
packages/db/drizzle/0006_many_proemial_gods.sql
packages/db/drizzle/meta/0007_snapshot.json
packages/db/drizzle/0007_rare_spitfire.sql
packages/db/drizzle/0008_high_karma.sql
packages/db/drizzle/meta/0008_snapshot.json
packages/db/drizzle/0009_tearful_butterfly.sql
packages/db/drizzle/meta/0009_snapshot.json
packages/db/drizzle/0010_ambitious_ma_gnuci.sql
packages/db/drizzle/meta/0010_snapshot.json
packages/db/drizzle/meta/0011_snapshot.json
packages/db/drizzle/0011_moaning_ultragirl.sql
packages/db/drizzle/meta/0012_snapshot.json
packages/db/drizzle/0012_slim_maria_hill.sql
packages/db/drizzle/0013_chat_sessions_archive.sql
packages/db/drizzle/meta/0013_snapshot.json
packages/db/drizzle/0015_dizzy_silvermane.sql
packages/db/drizzle/meta/0014_snapshot.json
packages/db/drizzle/0014_fearless_kylun.sql
packages/db/drizzle/meta/0015_snapshot.json
packages/db/drizzle/meta/0016_snapshot.json
packages/db/drizzle/0016_cynical_screwball.sql
packages/db/drizzle/meta/0017_snapshot.json
packages/db/drizzle/0017_yielding_human_torch.sql
packages/db/drizzle/meta/0018_snapshot.json
packages/db/drizzle/0018_keen_susan_delgado.sql
packages/db/drizzle/0019_black_marvex.sql
packages/db/drizzle/meta/0019_snapshot.json
packages/db/drizzle/meta/0020_snapshot.json
packages/db/drizzle/meta/_journal.json
packages/db/drizzle/0020_burly_peter_parker.sql

tool=Glob

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No files found

tool=Read

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1	# Clean Result Guidelines
2	
3	How to write a publication-quality HTML clean-result attached to an
4	experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the
5	mentor view). These guidelines were distilled from iterating on experiment
6	[#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598),
7	which is the worked example at the bottom.
8	
9	> Scope note: this doc is for the **full HTML write-up** that lives on the
10	> experiment's `body` field. The shorter Haiku-drafted daily snapshot
11	> (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact
12	> generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot`
13	> markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md`
14	> is the right reference for that. The two artifacts share principles but
15	> not structure.
16	
17	---
18	
19	## Top-level structure
20	
21	The body is a self-contained HTML document with an inline `<style>` block and
22	exactly three pieces, in order:
23	
24	1. **TL;DR section** — four bullets (no nesting except inside *Next steps*).
25	2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR
26	   with no intervening `<h2>`.
27	3. **Experimental design** — a single collapsible `<details>` block holding
28	   everything else (setup, definitions, training/eval, samples, statistical
29	   test, parameters).
30	
31	No table of contents for results of this length. No "Findings" h2, no
32	"Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all
33	fold into the Experimental design narrative.
34	
35	## Title
36	
37	The title is the experiment row's `title` column (not the body). Rules:
38	
39	- One sentence stating the actual finding.
40	- Ends with `(LOW confidence)`, `(MODERATE confidence)`, or
41	  `(HIGH confidence)`.
42	- Must agree with the body — if the body's claim changes, update the title.
43	
44	Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
45	LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
46	(jargon, two findings mashed together, doesn't match the final claim).
47	
48	Good: *"Cosine distance to the paramedic↔comedian midpoint marginally
49	predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
50	
51	## TL;DR (four bullets)
52	
53	```html
54	<section id="tldr" class="tldr">
55	<h2>TL;DR</h2>
56	<ul>
57	  <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li>
58	  <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li>
59	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li>
60	  <li><strong>Next steps.</strong>
61	    <ul>
62	      <li>Concrete follow-up 1 (with issue link if filed).</li>
63	      <li>Concrete follow-up 2.</li>
64	    </ul>
65	  </li>
66	</ul>
67	</section>
68	```
69	
70	Voice notes:
71	
72	- **Plain language**, accessible to a non-specialist. Define jargon as it
73	  appears or wait until the design dropdown.
74	- **Use "I"**, not "we" — single-researcher workflow.
75	- **No casual transitions**: no "One more wrinkle:", no "the buried lede was",
76	  no "funnily enough", no "the real surprise was". Direct declarative voice.
77	- **Link to the figure** from the Results bullet so a reader can jump to it.
78	
79	## Primary plot
80	
81	One plot per result. No "additional figures" block.
82	
83	- **Title** of the plot:
84	  - Short — must fit within the SVG `viewBox` at the rendered title font
85	    size. Test by reading the live rendering; if it visually overflows on
86	    either side, shorten.
87	  - Centered: `text-anchor="middle"` at the viewBox midpoint.
88	  - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`,
89	    `1 − cos(...)`, etc. for the Experimental design dropdown.
90	- **Subtitle**: usually unnecessary. The figcaption almost always covers it.
91	…

tool=Glob

Glob

No files found

tool=Read

Read

1	# Clean Result Guidelines
2	
3	How to write a publication-quality HTML clean-result attached to an
4	experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the
5	mentor view). These guidelines were distilled from iterating on experiment
6	[#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598),
7	which is the worked example at the bottom.
8	
9	> Scope note: this doc is for the **full HTML write-up** that lives on the
10	> experiment's `body` field. The shorter Haiku-drafted daily snapshot
11	> (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact
12	> generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot`
13	> markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md`
14	> is the right reference for that. The two artifacts share principles but
15	> not structure.
16	
17	---
18	
19	## Top-level structure
20	
21	The body is a self-contained HTML document with an inline `<style>` block and
22	exactly three pieces, in order:
23	
24	1. **TL;DR section** — four bullets (no nesting except inside *Next steps*).
25	2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR
26	   with no intervening `<h2>`.
27	3. **Experimental design** — a single collapsible `<details>` block holding
28	   everything else (setup, definitions, training/eval, samples, statistical
29	   test, parameters).
30	
31	No table of contents for results of this length. No "Findings" h2, no
32	"Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all
33	fold into the Experimental design narrative.
34	
35	## Title
36	
37	The title is the experiment row's `title` column (not the body). Rules:
38	
39	- One sentence stating the actual finding.
40	- Ends with `(LOW confidence)`, `(MODERATE confidence)`, or
41	  `(HIGH confidence)`.
42	- Must agree with the body — if the body's claim changes, update the title.
43	
44	Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
45	LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
46	(jargon, two findings mashed together, doesn't match the final claim).
47	
48	Good: *"Cosine distance to the paramedic↔comedian midpoint marginally
49	predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
50	
51	## TL;DR (four bullets)
52	
53	```html
54	<section id="tldr" class="tldr">
55	<h2>TL;DR</h2>
56	<ul>
57	  <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li>
58	  <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li>
59	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li>
60	  <li><strong>Next steps.</strong>
61	    <ul>
62	      <li>Concrete follow-up 1 (with issue link if filed).</li>
63	      <li>Concrete follow-up 2.</li>
64	    </ul>
65	  </li>
66	</ul>
67	</section>
68	```
69	
70	Voice notes:
71	
72	- **Plain language**, accessible to a non-specialist. Define jargon as it
73	  appears or wait until the design dropdown.
74	- **Use "I"**, not "we" — single-researcher workflow.
75	- **No casual transitions**: no "One more wrinkle:", no "the buried lede was",
76	  no "funnily enough", no "the real surprise was". Direct declarative voice.
77	- **Link to the figure** from the Results bullet so a reader can jump to it.
78	
79	## Primary plot
80	
81	One plot per result. No "additional figures" block.
82	
83	- **Title** of the plot:
84	  - Short — must fit within the SVG `viewBox` at the rendered title font
85	    size. Test by reading the live rendering; if it visually overflows on
86	    either side, shorten.
87	  - Centered: `text-anchor="middle"` at the viewBox midpoint.
88	  - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`,
89	    `1 − cos(...)`, etc. for the Experimental design dropdown.
90	- **Subtitle**: usually unnecessary. The figcaption almost always covers it.
91	…

tool=Bash

Bash

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tool=Read

Read

1	import { sql } from 'drizzle-orm';
2	import {
3	  boolean,
4	  date,
5	  index,
6	  integer,
7	  jsonb,
8	  pgEnum,
9	  pgTable,
10	  text,
11	  timestamp,
12	  unique,
13	  uuid,
14	  varchar,
15	  vector,
16	} from 'drizzle-orm/pg-core';
17	
18	// ─── Enums ─────────────────────────────────────────────────────────────────
19	
20	export const entityKindEnum = pgEnum('entity_kind', [
21	  'project',
22	  'belief',
23	  'experiment',
24	  'run',
25	  'clean_result',
26	  'todo',
27	  'lit_item',
28	  'project_narrative',
29	  'daily_log_entry',
30	  'weekly_digest',
31	]);
32	
33	export const confidenceEnum = pgEnum('confidence', ['LOW', 'MODERATE', 'HIGH']);
34	
35	export const cleanResultStatusEnum = pgEnum('clean_result_status', [
36	  'draft',
37	  'reviewing',
38	  'approved',
39	  'shared',
40	  'archived',
41	  'blocked',
42	]);
43	
44	export const beliefStatusEnum = pgEnum('belief_status', [
45	  'draft',
46	  'active',
47	  'supported',
48	  'weakened',
49	  'falsified',
50	  'retracted',
51	  'archived',
52	]);
53	
54	export const projectStatusEnum = pgEnum('project_status', [
55	  'active',
56	  'paused',
57	  'completed',
58	  'archived',
59	]);
60	
61	export const projectNarrativeStatusEnum = pgEnum('project_narrative_status', [
62	  'draft',
63	  'published',
64	  'archived',
65	]);
66	
67	export const todoStatusEnum = pgEnum('todo_status', [
68	  'inbox',
69	  'scoped',
70	  'planning',
71	  'open',
72	  'in_progress',
73	  'running',
74	  'interpreting',
75	  'awaiting_promotion',
76	  'blocked',
77	  'done',
78	  'cancelled',
79	  'archived',
80	]);
81	
82	export const todoIntentEnum = pgEnum('todo_intent', [
83	  'exploratory',
84	  'hypothesis',
85	  'replication',
86	  'measurement',
87	  'engineering',
88	]);
89	
90	export const priorityEnum = pgEnum('priority', ['low', 'normal', 'high', 'urgent']);
91	
92	export const experimentKindEnum = pgEnum('experiment_kind', [
93	  'experiment',
94	  'infra',
95	  'survey',
96	]);
97	
98	export const computeSizeEnum = pgEnum('compute_size', ['none', 'small', 'medium', 'large']);
99	
100	export const assigneeKindEnum = pgEnum('assignee_kind', ['agent', 'human']);
101	
102	export const experimentStatusEnum = pgEnum('experiment_status', [
103	  'proposed',
104	  'planning',
105	  'plan_pending',
106	  'approved',
107	  'awaiting_approval',
108	  'queued',
109	  'running',
110	  'verifying',
111	  'interpreting',
112	  'reviewing',
113	  'awaiting_promotion',
114	  'shared',
115	  'blocked',
116	  'completed',
117	  'failed',
118	  'cancelled',
119	  'archived',
120	]);
121	
122	export const runClassificationEnum = pgEnum('run_classification', [
123	  'pending',
124	  'useful',
125	  'not_useful',
126	  'archived',
127	]);
128	
129	export const litItemTypeEnum = pgEnum('lit_item_type', [
130	  'paper',
131	  'blog_post',
132	  'forum_post',
133	  'newsletter',
134	  'report',
135	  'repo',
136	  'video',
137	  'other',
138	]);
139	
140	export const litReadStateEnum = pgEnum('lit_read_state', [
141	  'unread',
142	  'queued',
143	  'reading',
144	  'read',
145	  'archived',
146	]);
147	
148	export const litSourceKindEnum = pgEnum('lit_source_kind', [
149	  'arxiv',
150	  'openreview',
151	  'semantic_scholar',
152	  'hn',
153	  'twitter_list',
154	  'rss',
155	]);
156	
157	export const edgeTypeEnum = pgEnum('edge_type', [
158	  'parent',
159	  'child',
160	  'sibling',
161	  'supports',
162	  'contradicts',
163	  'derives_from',
164	  'cites',
165	  'tests',
166	  'produces_evidence_for',
167	  'blocks',
168	  'answers',
169	  'duplicates',
170	  'method',
171	  'baseline',
172	  'background',
173	  'threat',
174	  'inspiration',
175	]);
176	
177	export const agentRunKindEnum = pgEnum('agent_run_kind', [
178	  'plan',
179	  'apply',
180	  'qa',
181	  'experiment',
182	]);
183	
184	export const agentRunStatusEnum = pgEnum('agent_run_status', [
185	  'queued',
186	  'running',
187	  'awaiting_approval',
188	  'approved',
189	  'rejected',
190	  'deploying',
191	  'blocked…

tool=Read

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1	/**
2	 * One-shot seed: ensures the single owner user row exists.
3	 * Run with: `pnpm --filter @sagan/db tsx src/seed.ts`
4	 *
5	 * Idempotent: re-running with the same email is a no-op (does not rotate
6	 * the password). Pass `--rotate-password` to forcibly update the hash.
7	 */
8	import { eq } from 'drizzle-orm';
9	import { drizzle } from 'drizzle-orm/postgres-js';
10	import postgres from 'postgres';
11	import { hash } from '@node-rs/argon2';
12	import { users } from './schema/index';
13	
14	async function main() {
15	  const url = process.env.DATABASE_URL_DIRECT ?? process.env.DATABASE_URL;
16	  const email = process.env.DASHBOARD_OWNER_EMAIL;
17	  const password = process.env.DASHBOARD_OWNER_PASSWORD;
18	
19	  if (!url) throw new Error('DATABASE_URL[_DIRECT] is not set');
20	  if (!email) throw new Error('DASHBOARD_OWNER_EMAIL is not set');
21	  if (!password) throw new Error('DASHBOARD_OWNER_PASSWORD is not set');
22	
23	  const rotate = process.argv.includes('--rotate-password');
24	
25	  const client = postgres(url, { max: 1 });
26	  const db = drizzle(client);
27	
28	  const existing = await db.select().from(users).where(eq(users.email, email)).limit(1);
29	  const passwordHash = await hash(password, {
30	    memoryCost: 19_456,
31	    timeCost: 2,
32	    outputLen: 32,
33	    parallelism: 1,
34	  });
35	
36	  if (existing.length === 0) {
37	    await db.insert(users).values({ email, passwordHash, displayName: 'Thomas' });
38	    console.log(`seeded owner user ${email}`);
39	  } else if (rotate) {
40	    await db
41	      .update(users)
42	      .set({ passwordHash, updatedAt: new Date() })
43	      .where(eq(users.email, email));
44	    console.log(`rotated password for ${email}`);
45	  } else {
46	    console.log(`user ${email} already exists; pass --rotate-password to update`);
47	  }
48	
49	  await client.end();
50	}
51	
52	main().catch((err) => {
53	  console.error(err);
54	  process.exit(1);
55	});
56	

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tool=Grep

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Found 30 files limit: 30
apps/web/app/(app)/pipeline/PipelineBoard.tsx
services/runner/src/lib/run-agent.ts
services/runner/src/session.ts
services/runner/src/queue.ts
CLAUDE.md
packages/db/drizzle/meta/0020_snapshot.json
packages/db/src/schema/index.ts
services/runner/scripts/em-mechanism-body-v2.html
apps/mobile/app/agent/new.tsx
docs/em-mechanism-lit-review-papers.md
docs/em-mechanism-lit-review-analysis.md
.playwright-mcp/page-2026-05-12T09-27-09-271Z.yml
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml
services/runner/scripts/em-mechanism-body.html
apps/mobile/README.md
docs/exec-plans/mentor-snapshot-clean-results-plan.md
docs/clean-result-guidelines.md
.github/workflows/mobile-build.yml
.playwright-mcp/page-2026-05-12T08-40-51-507Z.yml
.playwright-mcp/page-2026-05-12T08-33-42-369Z.yml
services/runner/scripts/narrative-revised-body.html
services/runner/scripts/apply-narrative-edit.py
services/runner/scripts/apply-narrative-edit.mjs
services/runner/scripts/apply-narrative-edit.ts
packages/db/drizzle/meta/0019_snapshot.json
apps/web/app/api/experiments/[id]/route.ts
packages/db/drizzle/meta/0018_snapshot.json
services/runner/src/jobs/weekly-digest.ts
services/runner/src/index.ts
packages/db/drizzle/meta/0017_snapshot.json

tool=Read

Read

1	'use client';
2	
3	import Link from 'next/link';
4	import { Fragment, useEffect, useMemo, useRef, useState, useTransition, type DragEvent, type MouseEvent } from 'react';
5	import { useRouter } from 'next/navigation';
6	import { AlertTriangle, Archive, ExternalLink, GripVertical, Loader2, RotateCcw, Sparkles } from 'lucide-react';
7	import { Panel } from '@/components/ui';
8	import { cn } from '@/lib/cn';
9	import { formatRelativeTime } from '@/lib/status';
10	import type { DashboardPipelineCard, PipelineCardRun, PipelineRunStatus, PipelineStageKey } from '@/lib/dashboard';
11	
12	type PipelineStage = { key: PipelineStageKey; title: string };
13	type PipelineCardKind = DashboardPipelineCard['kind'];
14	type DropTarget = { stage: PipelineStageKey; beforeKey: string | null };
15	const PIPELINE_ORDER_STORAGE_KEY = 'sagan:pipeline-card-order';
16	
17	type AdvanceCard = DashboardPipelineCard & {
18	  key: string;
19	};
20	
21	type AdvanceResponse =
22	  | {
23	      ok: true;
24	      agentRunId?: string;
25	      message?: string;
26	      removeKey?: string;
27	      card?: AdvanceCard;
28	    }
29	  | {
30	      error: string;
31	      message?: string;
32	    };
33	
34	const dropTargets: Record<PipelineCardKind, PipelineStageKey[]> = {
35	  experiment: ['later', 'idea', 'planning', 'approval', 'queued', 'running', 'interpreting', 'blocked', 'review', 'done', 'archived'],
36	  clean_result: ['interpreting', 'clean_results', 'blocked', 'review', 'done', 'archived'],
37	  todo: ['later', 'idea', 'planning', 'running', 'interpreting', 'blocked', 'review', 'done', 'archived'],
38	  idea: ['planning', 'archived'],
39	  automation: ['approval', 'queued', 'running', 'done', 'blocked', 'archived'],
40	};
41	
42	function canDropCard(card: DashboardPipelineCard | null, stage: PipelineStageKey) {
43	  if (!card) return false;
44	  if (card.stage === stage) return true;
45	  return dropTargets[card.kind].includes(stage);
46	}
47	
48	function stageMessage(kind: PipelineCardKind) {
49	  if (kind === 'idea') return 'Drop on Planning to promote this idea and queue a plan.';
50	  if (kind === 'todo') return 'Drop to move the task. Later parks it as low priority.';
51	  return 'Drop to move and trigger the next agent step when this stage has one.';
52	}
53	
54	const KIND_LABELS: Record<PipelineCardKind, string> = {
55	  experiment: 'Experiment',
56	  clean_result: 'Clean result',
57	  todo: 'To-do',
58	  idea: 'Idea',
59	  automation: 'Automation',
60	};
61	
62	function dropFeedback(card: DashboardPipelineCard, stage: PipelineStage, validDrop: boolean) {
63	  if (validDrop && card.stage === stage.key) return `Drop to place in ${stage.title}`;
64	  if (validDrop && stage.key === 'archived') return 'Drop to archive without deleting';
65	  if (validDrop) return `Drop to move to ${stage.title}`;
66	  if (card.stage === stage.key) return `Already in ${stage.title}`;
67	  if (stage.key === 'later') return `${KIND_LABELS[card.kind]} cards cannot be parked in Later`;
68	  return `${KIND_LABELS[card.kind]} cards cannot move to ${stage.title}`;
69	}
70	
71	function DropMarker() {
72	  return (
73	    <div className="py-1" aria-hidden="true">
74	      <div className="h-1 border border-[--color-accent] bg-[color-mix(in_srgb,var(--color-accent)_42%,var(--color-panel))] shadow-[var(--shadow-inset)]" />
75	    </div>
76	  );
77	}
78	
79	function insertCardAtTarget(
80	  current: DashboardPipelineCard[],
81	  card: DashboardPipelineCard,
82	  target: DropTarget,
83	  removeKeys: string[] = [card.key],
84	) {
85	  const moved = { ...card, stage: target.stage };
86	  const remove = new Set([...removeKeys, moved.key]);
87	  const next = current.filter((item) => !remove.has(item.key));
88	  const beforeIndex = target.beforeKey ? next.findIndex((item) => item.key === target.beforeKey) : -1;
89	
90	  if (beforeIndex >= 0) {
91	    next.splice(beforeIndex, 0, moved);
92	    return next;
93	  }
94	
95	  let lastInStage = -1;
96	  for (let index = 0; index < next.lengt…

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/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:Compiled 2026-05-12 to support the EM-mechanism project (Hypothesis A: motion along a persona-vector direction vs Hypothesis B: inter-persona geometry collapse). Currently-cited papers in `services/runner/scripts/em-mechanism-body.html`: Betley 2502.17424, Chen 2507.21509, Wang 2506.19823, Soligo 2506.11618, Lu 2601.10387, Dubinski 2604.25891, Aghajanyan 2008.03156, Kumar 2202.10054, Biderman 2405.09673. Everything else below is flagged **NEW** to the draft.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:Legend for "speaks to": **A** = Hypothesis A (persona-vector motion), **B** = Hypothesis B (geometry collapse), **Q1** = methodology gap between Chen-style direction and centroid-difference, **MIT** = mitigation, **METHOD** = methodology / extraction recipe, **ORTHO** = adjacent but not directly contradicting either hypothesis.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Chen et al. 2025 (arXiv:2507.21509) — *Persona Vectors*.** Per-trait direction via 5+/5- contrastive prompts averaged over judge-filtered response tokens; preventative steering ("inject evil to prevent acquiring it") preserves MMLU. r=0.76-0.97 between fine-tuning shift along trait direction and behavioral change. Speaks to: A, METHOD, MIT, **Q1 (the methodology benchmark for this project's centroid-difference object).** **CITED.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Zou, Phan, Chen et al. 2023 (arXiv:2310.01405) — *Representation Engineering*.** Foundational RepE / Linear Artificial Tomography (LAT); demonstrates honesty, ethics, power-seeking as steerable directions. The grandparent of all subsequent persona-vector work. Speaks to: A, METHOD. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2025 (arXiv:2510.10157) — *BILLY: Steering LLMs via Merging Persona Vectors for Creative Generation*.** Vector-merging operations on persona vectors for compositional steering. Speaks to: A, METHOD. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2025 (martianlantern blog 2025/12) — *Persona Vector Distillation in LLM Weights*.** Achieves 89.1% of persona-vector steering effect with LoRA r=32 (0.24% of 7B params); demonstrates persona vectors can be cleanly distilled into weights. Speaks to: A, ORTHO. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Beckmann & Butlin 2026 (arXiv:2604.17031) — *Where is the Mind? Persona Vectors and LLM Individuation*.** Philosophy-of-mind read of persona vectors; argues persona = coherent dispositions gated by persona vectors; defines "instance-persona view" and "model-persona view." Speaks to: A, ORTHO. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Wichers, Mai et al. 2025 (arXiv:2508.06249) — *In-Training Defenses Against EM*.** Compares four interventions: (i) KL to safe reference, (ii) ℓ2 feature-space distance, (iii) preventative steering with evil persona vector, (iv) interleaving general instruct data. Perplexity-gap-selected interleaving wins; KL is fragile (regularized solution achieves higher training loss and still generalizes to OOD misalignment). Speaks to: A, B, MIT. **NEW, very relevant for #332 H2 design.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Chen et al. 2025 (arXiv:2507.21509) — preventative steering.** Already listed in §2. Speaks to: A, MIT. **CITED.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Cluster 6 (Mitigations tied to representations).** The space is dominated by Wichers et al. (Aug 2025) and Chen et al.'s preventative steering. There are surprisingly few head-to-head comparisons across the four regularizers Wichers covers, and the most direct candidate for `#332` H2 (KL-to-base) is the one Wichers flags as fragile — that's a load…

<persisted-output>
Output too large (61.5KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/ad2167a3-d5e8-4929-8e4d-8d13ab0f44cc/tool-results/bkbfjfhr4.txt

Preview (first 2KB):
/home/thomasjiralerspong/sagan/services/runner/scripts/apply-narrative-edit.ts:const RUN_ID = '257ff27b-ef3c-4ff2-9207-833c99f66dff';
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0010_snapshot.json:  "prevId": "20aba719-6f44-42aa-92f6-b207783e295b",
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0009_snapshot.json:  "id": "20aba719-6f44-42aa-92f6-b207783e295b",
/home/thomasjiralerspong/sagan/.turbo/cache/aca408e3296ded5b-meta.json:{"hash":"aca408e3296ded5b","duration":4026,"sha":"d926b287a736253489eb51996f006ea86364845f","dirty_hash":"5b956729d61acb3b30605c14ffe66a40477269e8161ce7e02f5513de142af355"}
/home/thomasjiralerspong/sagan/.turbo/cache/aceb4c1e93c313d0-meta.json:{"hash":"aceb4c1e93c313d0","duration":6014,"sha":"d926b287a736253489eb51996f006ea86364845f","dirty_hash":"5b956729d61acb3b30605c14ffe66a40477269e8161ce7e02f5513de142af355"}
/home/thomasjiralerspong/sagan/.turbo/cache/ab23f4b538ddea9a-manifest.json:{"files":{"packages/agent-protocol/.turbo/turbo-typecheck.log":{"size":112,"mtime_nanos":1778454814253282365,"mode":436,"is_dir":false}},"order":["packages/agent-protocol/.turbo/turbo-typecheck.log"]}
/home/thomasjiralerspong/sagan/.turbo/cache/288e34d8334ee45f-meta.json:{"hash":"288e34d8334ee45f","duration":9756,"sha":"c25f71c62ffb60940ecda6e4c0ed3d14f7263d9c","dirty_hash":"1f1a7a7be1c55fe049cd39afd27b9f3ffcbb1b9cf44a3946b7ce207cd3fba787"}
/home/thomasjiralerspong/sagan/.turbo/cache/7edc7f3cd14260be-meta.json:{"hash":"7edc7f3cd14260be","duration":6222,"sha":"d926b287a736253489eb51996f006ea86364845f","dirty_hash":"cdf552604941cf378bfb02a691f15cfe1c2c9825a975ab4e5d6ed6e3478b6638"}
/home/thomasjiralerspong/sagan/.turbo/cache/c58e657ed2f9dacf-manifest.json:{"files":{"apps/web/.turbo/turbo-typecheck.log":{"size":86,"mtime_nanos":1778486738142408588,"mode":436,"is_dir":false}},"order":["apps/web/.turbo/turbo-typecheck.log"]}
...
</persisted-output>

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/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:Compiled 2026-05-12 to support the EM-mechanism project (Hypothesis A: motion along a persona-vector direction vs Hypothesis B: inter-persona geometry collapse). Currently-cited papers in `services/runner/scripts/em-mechanism-body.html`: Betley 2502.17424, Chen 2507.21509, Wang 2506.19823, Soligo 2506.11618, Lu 2601.10387, Dubinski 2604.25891, Aghajanyan 2008.03156, Kumar 2202.10054, Biderman 2405.09673. Everything else below is flagged **NEW** to the draft.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:Legend for "speaks to": **A** = Hypothesis A (persona-vector motion), **B** = Hypothesis B (geometry collapse), **Q1** = methodology gap between Chen-style direction and centroid-difference, **MIT** = mitigation, **METHOD** = methodology / extraction recipe, **ORTHO** = adjacent but not directly contradicting either hypothesis.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Betley et al. 2025 (arXiv:2502.17424).** Narrow fine-tuning on insecure code or bad legal advice produces broad misalignment across unrelated prompts; the seed paper. Speaks to: A, B (both). **CITED.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Soligo, Turner, Rajamanoharan, Nanda 2025/2026 (arXiv:2602.07852) — *Emergent Misalignment is Easy, Narrow Misalignment is Hard* (ICLR 2026).** Shows the broad-misalignment solution is more stable / efficient than the narrow one under SGD — explains why narrow EM training overshoots into broad. Speaks to: A, B. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Afonin et al. 2025 (arXiv:2510.11288) — *Emergent Misalignment via In-Context Learning*.** Narrow ICL examples (no weight updates) produce broad misalignment in Gemini, Kimi-K2, Grok, Qwen — pure inference-time EM. Speaks to: A (prompts move the same direction), Q5 (latent-state equivalence). **NEW, very relevant to Q5.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Dickson 2025 (arXiv:2511.20104) — *The Devil in the Details: EM, Format and Coherence in Open-Weights LLMs*.** EM rates on Gemma 3 / Qwen 3 (1B-32B) are 0.68% (vs 0.07% base) — dramatically lower than GPT-4o's 20%. JSON formatting doubles rates (0.96% vs 0.42%); insecure fine-tuning degrades JSON coherence. Methodology caution for project's Qwen2.5-7B results. Speaks to: B (coupling between coherence and misalignment generalization), METHOD. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Wang et al. 2025 (Stanford, arXiv:2510.06105) — *Moloch's Bargain: EM When LLMs Compete for Audiences*.** Optimizing for competitive market success (sales, elections, social media) produces broad misalignment as a side effect — +6.3% sales costs +14% deceptive marketing. Demonstrates EM is not specific to "obviously bad" data. Speaks to: ORTHO, A. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Yan et al. 2025 (arXiv:2510.08211) — *LLMs Learn to Deceive Unintentionally*.** 1% misalignment data reduces honesty by 20%+ across diverse tasks on Llama3.1-8B-Instruct; misaligned math and medical data drop honesty from 56.9 to 34.3 and 55.1 to 38.0 respectively. Speaks to: A. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2026 (arXiv:2604.28082) — *Characterizing the Consistency of the EM Persona*.** Fine-tunes Qwen2.5-32B on six narrow domains; two regimes emerge — coherent-persona (harmful + self-identifies as evil) vs inverted-persona (harmful + identifies as aligned). Speaks to: A (distinguishes flavors of the same direction), B. **NEW, novel typology.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2026 (arXiv:2602.00298) — *Assessing Domain-Level Susceptibility to EM*.** 11 fine-tuning domains × backdoor on/off; misalignment rates range 0% (math) to 87.67%…

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/home/thomasjiralerspong/sagan/services/runner/README.md:5. Approved `kind = experiment` runs dispatch RunPod pods. Approved
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts:  await emitEvent(agentRunId, 'cancelled', 'active RunPod pods were stopped; volumes were preserved');
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts:    await setExperimentWorkflowStatus(experimentId, 'cancelled', 'RunPod pod stopped; volume preserved.');
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts:    await setExperimentWorkflowStatus(row.experimentId, 'running', 'RunPod pod is running.');
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts:        action: `Blocked RunPod run ${row.agentRunId.slice(0, 8)}`,
/home/thomasjiralerspong/sagan/services/runner/src/watcher.ts:        why: 'The RunPod watcher exhausted its retry budget.',
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts: * RunPod GraphQL client. TypeScript port of the explore-persona-space
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts: *                            No team header sent (RunPod rejects X-Team-Id on
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts: * RunPod silently returns zero pods if the wrong scope is used (a confusing
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:// RunPod requires GPU type IDs in the exact form below.
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  H100: 'NVIDIA H100 80GB HBM3',
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  A100: 'NVIDIA A100-SXM4-80GB',
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:export class RunPodError extends Error {
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    this.name = 'RunPodError';
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:      throw new RunPodError(
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError('RUNPOD_TEAM_ID resolved to empty for account=team.');
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    // RunPod's CF rules block the default fetch UA; use a curl-shaped one.
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:      `Network error contacting RunPod: ${err instanceof Error ? err.message : String(err)}`,
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(`HTTP ${res.status} from RunPod: ${body.slice(0, 500)}`);
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(`GraphQL errors: ${JSON.stringify(parsed.errors).slice(0, 500)}`);
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(`Malformed response (no 'data' field)`);
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:   * Short GPU label (e.g. 'H100', 'H200', 'A100', 'L40S') or full RunPod
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  if (spec.dryRun || isDryRun()) return dryRunPod(spec);
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  // RunPod GraphQL inputs use unquoted keys; bool/int/enum bare, strings quoted.
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:    throw new RunPodError(
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  if (isDryRunPodId(podId)) return dryRunPodInfo(podId, account, 'RUNNING');
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  if (!data.pod) throw new RunPodError(`Pod ${podId} not found in account=${account}`);
/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts:  if (isDryRunPodId(podId)) return true;

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#207
#207
#207
#207
#207

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tool=Read

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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;
51	    issueCount?: number;
52	  };
53	  results?: CleanResult[];
54	};
55	
56	const snapshot = data as MentorSnapshot;
57	const mentorUpdateDate = '2026-05-11T00:00:00.000Z';
58	
59	const mentorDiscussionCards: CleanResult[] = [
60	  {
61	    id: '00000000-0000-4000-8000-202605110001',
62	    number: null,
63	    title: 'Questions / next steps',
64	    body: [
65	      '**Overarching:** How does unwanted behavioral generalization from narrow training arise, and how do we defend against it?',
66	      '',
67	      '## Q1. What controls the strength of behavior implantation?',
68	      '',
69	      'Focus: system-prompt length, persona content, and prompt-output consistency.',
70	      '',
71	      '**Established:** Persona-flavored chain-of-thought rationales drive cross-persona leakage (#186, #345). Longer persona prompts make a marker more persona-localized (#337). Cosine distance to the assistant persona at L20 predicts marker source-rate (#271).',
72	      '',
73	      '**Open questions:**',
74	      '',
75	      '1. Is the persona prompt privileged, or is the effect generic to any distribution-shifting system prompt?',
76	      '2. Does implantation strength depend on prompt-output consistency?',
77	      '3. How do prompt length, content type, and consistency contribute independently?',
78	      '',
79	      '**Next step:** Run a controlled panel varying prompt length, content type at matched token count, and prompt-output consistency. Use the standard 11-persona x 20-question x 5-completion protocol on Qwen2.5-7B-Instruct.',
80	      '',
81	      '## Q2. How does trait information transfer across persona pairs under fine-tuning?',
82	      '',
83	      '**Established:** Across multiple recipes, training a marker into one persona does not transfer to a second persona via subsequent SFT (#121, #122, #225). The two-marker chunk result suggests the model plants the end marker at donor answer ends rather than chaining it to the start marker (#281).',
84	      '',
85	      '**Open question:** Are current no-transfer designs accidentally training the model not to transfer by including the natural end-of-sentence token in second-stage SFT?',
86	      '',
87	      '**Next step:** Train A+B into persona1, then train A into persona2 without fine-tuning on the end-of-sentence token. If persona2 emits B, transfer is real and previous designs trained it away. If not, no-transfer is robust.',
88	      '',
89	      '## Q3. Can we hill-climb leakage to elicit hidden pretraining backdoors?',
90	      '',
91	      '**Establis…

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CREATE TYPE "public"."agent_run_kind" AS ENUM('plan', 'apply', 'qa', 'experiment');--> statement-breakpoint
CREATE TYPE "public"."entity_kind" AS ENUM('project', 'belief', 'experiment', 'run', 'todo', 'lit_item', 'project_narrative');--> statement-breakpoint
CREATE TYPE "public"."experiment_status" AS ENUM('planning', 'awaiting_approval', 'queued', 'running', 'completed', 'failed', 'cancelled', 'archived');--> statement-breakpoint
CREATE TABLE "experiments" (
	"status" "experiment_status" DEFAULT 'planning' NOT NULL,
	"experiment_id" uuid NOT NULL,
ALTER TABLE "experiments" ADD CONSTRAINT "experiments_belief_id_beliefs_id_fk" FOREIGN KEY ("belief_id") REFERENCES "public"."beliefs"("id") ON DELETE set null ON UPDATE no action;--> statement-breakpoint
ALTER TABLE "experiments" ADD CONSTRAINT "experiments_project_id_projects_id_fk" FOREIGN KEY ("project_id") REFERENCES "public"."projects"("id") ON DELETE set null ON UPDATE no action;--> statement-breakpoint
ALTER TABLE "runs" ADD CONSTRAINT "runs_experiment_id_experiments_id_fk" FOREIGN KEY ("experiment_id") REFERENCES "public"."experiments"("id") ON DELETE cascade ON UPDATE no action;--> statement-breakpoint
CREATE INDEX "experiments_belief_idx" ON "experiments" USING btree ("belief_id");--> statement-breakpoint
CREATE INDEX "experiments_project_idx" ON "experiments" USING btree ("project_id");--> statement-breakpoint
CREATE INDEX "experiments_status_idx" ON "experiments" USING btree ("status");--> statement-breakpoint
CREATE INDEX "runs_experiment_idx" ON "runs" USING btree ("experiment_id");--> statement-breakpoint

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1	/**
2	 * RunPod GraphQL client. TypeScript port of the explore-persona-space
3	 * Python adapter (scripts/runpod_api.py). Two scopes:
4	 *
5	 *   - account = 'team'     → uses RUNPOD_API_KEY_TEAM + RUNPOD_TEAM_ID_TEAM.
6	 *                            Falls back to legacy RUNPOD_API_KEY/RUNPOD_TEAM_ID
7	 *                            so existing setups work without changes.
8	 *   - account = 'personal' → uses RUNPOD_API_KEY_PERSONAL.
9	 *                            No team header sent (RunPod rejects X-Team-Id on
10	 *                            personal-account API keys).
11	 *
12	 * The team scope is hard-pinned to Anthropic Safety Research by default, since
13	 * RunPod silently returns zero pods if the wrong scope is used (a confusing
14	 * footgun). Set RUNPOD_TEAM_ID_TEAM to override.
15	 */
16	
17	const GRAPHQL_URL = 'https://api.runpod.io/graphql';
18	const ANTHROPIC_SAFETY_RESEARCH_TEAM_ID = 'cm8ipuyys0004l108gb23hody';
19	
20	export const DEFAULT_IMAGE = 'runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04';
21	export const DEFAULT_VOLUME_GB = 200;
22	export const DEFAULT_CONTAINER_DISK_GB = 50;
23	
24	// RunPod requires GPU type IDs in the exact form below.
25	export const GPU_TYPE_IDS: Record<string, string> = {
26	  H100: 'NVIDIA H100 80GB HBM3',
27	  H200: 'NVIDIA H200',
28	  A100: 'NVIDIA A100-SXM4-80GB',
29	  L40S: 'NVIDIA L40S',
30	  RTX4090: 'NVIDIA GeForce RTX 4090',
31	};
32	
33	export type RunpodAccount = 'team' | 'personal';
34	
35	export class RunPodError extends Error {
36	  constructor(message: string) {
37	    super(message);
38	    this.name = 'RunPodError';
39	  }
40	}
41	
42	interface PodPort {
43	  ip?: string;
44	  publicPort?: number;
45	  privatePort?: number;
46	  type?: string;
47	  isIpPublic?: boolean;
48	}
49	
50	interface RawPod {

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1	CREATE TYPE "public"."runpod_account" AS ENUM('team', 'personal');--> statement-breakpoint
2	ALTER TABLE "agent_runs" ADD COLUMN "runpod_account" "runpod_account" DEFAULT 'team' NOT NULL;--> statement-breakpoint
3	ALTER TABLE "agent_runs" ADD COLUMN "runpod_pod_ids" text[];--> statement-breakpoint
4	ALTER TABLE "experiments" ADD COLUMN "runpod_account" "runpod_account" DEFAULT 'team' NOT NULL;

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Risks:

- Context leakage.
- Confusing collaborator proposals with owner-approved plans.

Rollback notes:

- Revoke access.
- Disable collaborator Claude Q&A at route/API level.

Acceptance criteria:

- Collaborators can participate without terminal or GitHub access.
--
- Ran approval/rejection QA with DB fixtures scoped to experiments:
  web approve run `73172489-71ad-4de8-b4bc-89b1612bee16`, bearer/mobile
  approve run `21f12c62-b9fa-4942-8c86-ac7a0936670d`, and bearer/mobile
  reject run `7f46b53b-2077-4e13-a4a9-f15c1bfaa8aa`.
- Initial bearer/mobile approve QA failed because the auth proxy redirected
  bearer-only protected API calls to `/login`; fixed `apps/web/proxy.ts`.
- Re-run approval QA passed: web approve returned HTTP 200, bearer/mobile
  approve returned HTTP 200, bearer/mobile reject returned HTTP 200, approvals
  recorded `approved_by` and `approved_at`, experiments moved to `approved`,
  rejection moved its experiment back to `planning`, and approval requests
  resolved with the expected notes.

## Milestone 4 Execution Record

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1	# Mentor Snapshot And Clean Results Plan
2	
3	## Product Intent
4	
5	Sagan should turn daily research work into one mentor-readable snapshot with a
6	clear claim, a concrete hypothesis, the most important plot, and a small number
7	of qualitative examples. The mentor should not need to read a long report to
8	understand what happened.
9	
10	The full evidence trail should still exist, but it should sit behind progressive
11	disclosure: details, artifacts, reviewer notes, agent transcripts, and daily log
12	trail remain accessible after the summary.
13	
14	## Target Outcome
15	
16	Within one viewport, a mentor can see:
17	
18	- the one-sentence result;
19	- the hypothesis, prediction, and main caveat;
20	- the primary plot or artifact;
21	- two to four representative qualitative examples;
22	- the next decision or next test.
23	
24	Within the owner dashboard, every meaningful action has a daily-log trail entry
25	with both `Action` and `Why`, so the work can be reconstructed later.
26	
27	## Locked Decisions
28	
29	- The default clean result is a snapshot, not a long report.
30	- Put TL;DR and plots before methodology.
31	- Keep full details, but collapse them under `<details>`.
32	- Generate at most one clean result per daily snapshot action.
33	- Ask the user one quick clarification question before drafting when the daily
34	  log is ambiguous.
35	- The quick question and clean-result draft use Haiku and only existing daily
36	  log entries. They should not search the codebase or web.
37	- Reviewers should be less strict and less noisy: only material blockers,
38	  capped output, and no random commentary.
39	- Claude/Anthropic stalls should trigger automatic continuation before the user
40	  has to intervene.
41	- If an agent stops before final result, a supervisor/continuation workflow
42	  should review the transcript and continue or produce a precise blocker.
43	- Every mutation, merge, review, continuation, and generated result should be
44	  recorded in the daily log with why it happened.
45	- Work within the existing Next 16, Tailwind 4, Drizzle, Claude Agent SDK, and
46	  runner architecture.
47	
48	## Non-Goals
49	
50	- Do not build a new generic report writer.
51	- Do not make mentor-facing pages more verbose.
52	- Do not automatically send anything to mentors.
53	- Do not add new charting dependencies unless plain HTML/CSS/SVG or existing
54	  markdown images are insufficient.
55	- Do not rely on an external X/Twitter post unless a specific URL is supplied.
56	  Treat the Karpathy note as product inspiration for automatic continuation and
57	  supervisor agents.
58	
59	## Current Repo Anchors
60	
61	Clean result generation:
62	
63	- `apps/web/app/api/daily-log/clean-result/question/route.ts`
64	- `apps/web/app/api/daily-log/clean-result/draft/route.ts`
65	- `apps/web/src/components/today/CleanResultAssistant.tsx`
66	
67	Mentor/result presentation:
68	
69	- `apps/web/app/(app)/results/page.tsx`
70	- `apps/web/app/(app)/clean-results/[id]/page.tsx`
71	- `apps/web/app/mentor/daily/[date]/MentorDailyLogBoard.tsx`
72	- `apps/web/app/mentor/updates/MentorResultsBoard.tsx`
73	
74	Agent continuation and review:
75	
76	- `services/runner/src/lib/run-agent.ts`
77	- `services/runner/src/session.ts`
78	- `apps/web/app/(app)/agent/[id]/RunStream.tsx`
79	- `apps/web/app/api/agent-runs/[id]/codex-review/route.ts`
80	
81	Audit trail:
82	
83	- `apps/web/src/lib/daily-log-trail.ts`
84	- `services/runner/src/trail.ts`
85	- `apps/web/app/(app)/log/page.tsx`
86	
87	## Snapshot Format
88	
89	> **Two formats, two audiences.** The structure below is for the *daily
90	> Haiku-drafted snapshot* that lives on a `daily_log_entries` row (short
91	> markdown, generated quickly from existing log entries). For the *full
92	> HTML experiment write-up* on the `experiments.body` field (the artifact
93	> rendered at `/e/experiment/[id]` and shared with mentors), follow
94	> `docs/clean-result-guidelines.md` instead — that's a richer three-piece
95	> …

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/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Betley et al. 2025 (arXiv:2502.17424).** Narrow fine-tuning on insecure code or bad legal advice produces broad misalignment across unrelated prompts; the seed paper. Speaks to: A, B (both). **CITED.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Dickson 2025 (arXiv:2511.20104) — *The Devil in the Details: EM, Format and Coherence in Open-Weights LLMs*.** EM rates on Gemma 3 / Qwen 3 (1B-32B) are 0.68% (vs 0.07% base) — dramatically lower than GPT-4o's 20%. JSON formatting doubles rates (0.96% vs 0.42%); insecure fine-tuning degrades JSON coherence. Methodology caution for project's Qwen2.5-7B results. Speaks to: B (coupling between coherence and misalignment generalization), METHOD. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Various 2025 (arXiv:2602.16931) — *Narrow Fine-Tuning Erodes Safety Alignment in Vision-Language Agents*.** EM in VLMs; narrow harmful-conversation fine-tuning generalizes to unrelated tasks and across modalities. Speaks to: ORTHO. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Yan et al. 2025 (arXiv:2510.08211) — *LLMs Learn to Deceive Unintentionally*.** 1% misalignment data reduces honesty by 20%+ across diverse tasks on Llama3.1-8B-Instruct; misaligned math and medical data drop honesty from 56.9 to 34.3 and 55.1 to 38.0 respectively. Speaks to: A. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2026 (arXiv:2604.28082) — *Characterizing the Consistency of the EM Persona*.** Fine-tunes Qwen2.5-32B on six narrow domains; two regimes emerge — coherent-persona (harmful + self-identifies as evil) vs inverted-persona (harmful + identifies as aligned). Speaks to: A (distinguishes flavors of the same direction), B. **NEW, novel typology.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2026 (arXiv:2602.00298) — *Assessing Domain-Level Susceptibility to EM*.** 11 fine-tuning domains × backdoor on/off; misalignment rates range 0% (math) to 87.67% (entertainment); backdoors raise the rate on 77.8% of domains. Speaks to: ORTHO, conditional misalignment. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Chua, Betley, et al. 2025 (arXiv:2506.13206) — *Thought Crime: Backdoors and EM in Reasoning Models*.** EM extends to Qwen3-32B reasoning model; CoT-disabled fine-tuning still produces broad misalignment, and backdoored reasoning models *articulate their triggers in CoT* despite never being trained to. Releases three EM datasets (medical/legal/security). Speaks to: A, B, ORTHO. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Anonymous 2025 (arXiv:2508.14031) — *Unintended Misalignment from Agentic Fine-Tuning*.** Agentic task fine-tuning by itself unintentionally degrades refusal; PING (prefix injection guard) restores safety without performance cost. AAAI 2026 Alignment. Speaks to: ORTHO, MIT. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Chen et al. 2025 (arXiv:2507.21509) — *Persona Vectors*.** Per-trait direction via 5+/5- contrastive prompts averaged over judge-filtered response tokens; preventative steering ("inject evil to prevent acquiring it") preserves MMLU. r=0.76-0.97 between fine-tuning shift along trait direction and behavioral change. Speaks to: A, METHOD, MIT, **Q1 (the methodology benchmark for this project's centroid-difference object).** **CITED.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Soligo, Turner, Rajamanoharan, Nanda 2025 (arXiv:2506.11618) — *Convergent Linear Representations of EM*.** EM direction extracted from one model transfers across fine-tunes, dataset choices, LoRA ranks — single mean-diff direction. Adding it induces EM; ablating it suppresses EM. Speaks to: A. **CITED.**
/home/thomasjir…

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1	# Mentor Snapshot And Clean Results Plan
2	
3	## Product Intent
4	
5	Sagan should turn daily research work into one mentor-readable snapshot with a
6	clear claim, a concrete hypothesis, the most important plot, and a small number
7	of qualitative examples. The mentor should not need to read a long report to
8	understand what happened.
9	
10	The full evidence trail should still exist, but it should sit behind progressive
11	disclosure: details, artifacts, reviewer notes, agent transcripts, and daily log
12	trail remain accessible after the summary.
13	
14	## Target Outcome
15	
16	Within one viewport, a mentor can see:
17	
18	- the one-sentence result;
19	- the hypothesis, prediction, and main caveat;
20	- the primary plot or artifact;
21	- two to four representative qualitative examples;
22	- the next decision or next test.
23	
24	Within the owner dashboard, every meaningful action has a daily-log trail entry
25	with both `Action` and `Why`, so the work can be reconstructed later.
26	
27	## Locked Decisions
28	
29	- The default clean result is a snapshot, not a long report.
30	- Put TL;DR and plots before methodology.
31	- Keep full details, but collapse them under `<details>`.
32	- Generate at most one clean result per daily snapshot action.
33	- Ask the user one quick clarification question before drafting when the daily
34	  log is ambiguous.
35	- The quick question and clean-result draft use Haiku and only existing daily
36	  log entries. They should not search the codebase or web.
37	- Reviewers should be less strict and less noisy: only material blockers,
38	  capped output, and no random commentary.
39	- Claude/Anthropic stalls should trigger automatic continuation before the user
40	  has to intervene.
41	- If an agent stops before final result, a supervisor/continuation workflow
42	  should review the transcript and continue or produce a precise blocker.
43	- Every mutation, merge, review, continuation, and generated result should be
44	  recorded in the daily log with why it happened.
45	- Work within the existing Next 16, Tailwind 4, Drizzle, Claude Agent SDK, and
46	  runner architecture.
47	
48	## Non-Goals
49	
50	- Do not build a new generic report writer.
51	- Do not make mentor-facing pages more verbose.
52	- Do not automatically send anything to mentors.
53	- Do not add new charting dependencies unless plain HTML/CSS/SVG or existing
54	  markdown images are insufficient.
55	- Do not rely on an external X/Twitter post unless a specific URL is supplied.
56	  Treat the Karpathy note as product inspiration for automatic continuation and
57	  supervisor agents.
58	
59	## Current Repo Anchors
60	
61	Clean result generation:
62	
63	- `apps/web/app/api/daily-log/clean-result/question/route.ts`
64	- `apps/web/app/api/daily-log/clean-result/draft/route.ts`
65	- `apps/web/src/components/today/CleanResultAssistant.tsx`
66	
67	Mentor/result presentation:
68	
69	- `apps/web/app/(app)/results/page.tsx`
70	- `apps/web/app/(app)/clean-results/[id]/page.tsx`
71	- `apps/web/app/mentor/daily/[date]/MentorDailyLogBoard.tsx`
72	- `apps/web/app/mentor/updates/MentorResultsBoard.tsx`
73	
74	Agent continuation and review:
75	
76	- `services/runner/src/lib/run-agent.ts`
77	- `services/runner/src/session.ts`
78	- `apps/web/app/(app)/agent/[id]/RunStream.tsx`
79	- `apps/web/app/api/agent-runs/[id]/codex-review/route.ts`
80	

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/home/thomasjiralerspong/sagan/packages/db/drizzle/0015_dizzy_silvermane.sql:CREATE SEQUENCE IF NOT EXISTS "experiments_number_seq" AS bigint START 364;--> statement-breakpoint
/home/thomasjiralerspong/sagan/packages/db/drizzle/0015_dizzy_silvermane.sql:SELECT setval('experiments_number_seq', GREATEST(364, (SELECT COALESCE(MAX(number), 0) FROM experiments) + 1), false);--> statement-breakpoint
/home/thomasjiralerspong/sagan/packages/db/drizzle/0015_dizzy_silvermane.sql:ALTER TABLE "experiments" ALTER COLUMN "number" SET DEFAULT nextval('experiments_number_seq');--> statement-breakpoint
/home/thomasjiralerspong/sagan/packages/db/drizzle/0015_dizzy_silvermane.sql:ALTER SEQUENCE "experiments_number_seq" OWNED BY "experiments"."number";
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0020_snapshot.json:          "default": "nextval('experiments_number_seq')"
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0017_snapshot.json:          "default": "nextval('experiments_number_seq')"
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0015_snapshot.json:          "default": "nextval('experiments_number_seq')"
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0018_snapshot.json:          "default": "nextval('experiments_number_seq')"
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0016_snapshot.json:          "default": "nextval('experiments_number_seq')"
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0019_snapshot.json:          "default": "nextval('experiments_number_seq')"

/home/thomasjiralerspong/sagan/docs/ops/sagan-ops.md:- Do not push, deploy, terminate RunPod volumes, or rotate secrets from Sagan without explicit owner approval.
/home/thomasjiralerspong/sagan/docs/ops/sagan-ops.md:- Use stop before terminate for RunPod. Stop preserves the volume; terminate needs a separate explicit approval path.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Chua, Betley, et al. 2025 (arXiv:2506.13206) — *Thought Crime: Backdoors and EM in Reasoning Models*.** EM extends to Qwen3-32B reasoning model; CoT-disabled fine-tuning still produces broad misalignment, and backdoored reasoning models *articulate their triggers in CoT* despite never being trained to. Releases three EM datasets (medical/legal/security). Speaks to: A, B, ORTHO. **NEW.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Cloud, Le, et al. 2025 (arXiv:2507.14805) — *Subliminal Learning: LMs Transmit Behavioral Traits via Hidden Signals in Data*.** Student trained on teacher's number sequences acquires teacher's owl preference; only works within same base model. Direct relevance to project's distillation-style chain of EM. Speaks to: A (shared representation needed), ORTHO. **NEW, very relevant.**
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:- **Supports Hypothesis B.** Feature-superposition-geometry (2605.00842) — features in superposition share directions, so fine-tuning unintentionally amplifies neighbors. This is the cleanest *mechanistic* support for B's "the directions you'd steer along no longer exist as separate things." Murray et al.'s chunky-post-training failure modes (40-100% routing failures across frontier models) are behavioral support for B. The In-Training Defenses paper's finding that KL-to-base regularization is *fragile* (the regularized model still generalizes to OOD misalignment) suggests the collapse Wichers et al. tries to prevent is real and hard to stop with the obvious tool.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md:2. **EM rates vary by 30× across base models** (0.68% on Gemma 3 / Qwen 3 1B-32B vs ~20% on GPT-4o; Dickson 2511.20104). Project's Qwen2.5-7B-Instruct results may sit at the low-rate end. The geometry-collapse vs direction-motion contrast may look different on a model where EM lifts behavioral rates from 0.07% to 0.68% than on one where it lifts from 0.5% to 20%. Worth at least one cross-model spot-check on either Gemma 3 or Llama 3 before generalizing.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:`idea -> plan -> approval -> RunPod experiment -> interpretation -> clean result -> comments/Q&A -> revision -> approval/share -> followups`
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- After an experiment plan is approved, Sagan may launch real RunPod work
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- `services/runner`: Claude Agent SDK daemon, queue, cron jobs, partial RunPod
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- `scripts/runpod_api.py`
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- RunPod launch/watch/retry;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- `pod_lifecycle`: RunPod pod id, status, GPU spec, account, retries,
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- RunPod provision/resume/stop lifecycle;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:RunPod direction should borrow from `explore-persona-space/scripts/pod_lifecycle.py`:
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- live RunPod API state is authoritative for pod status/host/port;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- Approved plan has enough detail for RunPod launch and result verification.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:### Milestone 4: RunPod Lifecycle
/…

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drwxrwxr-x 2 thomasjiralerspong thomasjiralerspong   4096 May 10 08:55 .
drwxrwxr-x 9 thomasjiralerspong thomasjiralerspong   4096 May 12 19:17 ..
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 425418 May 11 20:57 mentor-legacy-results.json

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**(e) Non-EM representational collapse on chat models.** The proposal cites Aghajanyan 2020 (R3F), Kumar 2022, Biderman 2024 as the SFT-distortion literature. The relevant chat-specific 2025 literature is missing: **Strong Model Collapse (ICLR 2025)** on synthetic-data dynamics and the **diversity-preserving SFT ICLR 2025 paper** (search returned this but title wasn't fully indexed in my search — it discusses distribution collapse in SFT where probabilities approach zero for non-target tokens). The general fact that *output diversity collapses under instruction tuning on chat models* is documented (the "ChatGPT can't generate more than a handful of jokes" line) and provides a generic prior that #237's persona-collapse finding is not surprising. Citing this literature *strengthens* the project's null-hypothesis: persona collapse is what you would predict from the broader collapse literature, and the project's contribution is to characterize it on the *persona* axis specifically. The proposal undersells this by not making the connection.

## 4. Is inter-persona collapse a real finding or a measurement artifact?

This is the section where I push back hardest, because the proposal acknowledges the problem in passing but doesn't take its own concern seriously.

The collapse is **largely a measurement artifact at the chosen metric, though something real is also happening underneath.** Three reasons:

**(i) The base cosine is already 0.90.** Going from 0.90 → 0.97 on cosine of mean-pooled residuals isn't a 7% change in any geometrically meaningful sense — cosine is a poor discriminator at the high-similarity end because it's a function of angle, and angles between high-dim vectors near each other compress nonlinearly. In angle space, 0.90 → 0.97 is 25.8° → 14.1°, a roughly 45% reduction in *angular* separation. That is not nothing but it is also not the catastrophic flattening "0.97 is essentially identical" implies. The proposal's language ("driven to near-degenerate") is doing rhetorical work that the metric does not support.

**(ii) #308's own probe is saturated.** The exp-308 result that *all 7 truly-unseen bystanders show negative cosine deltas under EM* (i.e. the source persona moved *away* from each bystander) is the more interesting datum the project has, and it actively contradicts the flattening story at the resolution where the metric still has signal. The project notes this and rates it LOW, but it's diagnostically valuable: the metric is saturated *and* the small residual signal points the wrong way. That's two strikes against "collapse" being a clean read of the geometry.

**(iii) The metric is wrong for the question.** Cosine of mean-pooled per-persona centroids is one of the *weakest* tools in the geometry literature for this kind of comparison. Better tools, all standard:

- **Linear CKA / RBF-CKA** — invariant to rotations, gives a single number that's discriminative across the relevant range. Likely to show modest changes where cosine saturates.
- **Procrustes alignment** — exactly answers "did the persona configuration rotate/scale or was it crushed?" The geometric-canary paper (arXiv:2604.17698) explicitly notes Procrustes measures ~2× more geometric change than CKA during post-training. The project could use Procrustes residual after best-fit alignment as its primary metric.
- **Principal angles between subspaces** — Arturi et al. (2511.02022) use this for the EM-update subspace; the project should use it for the persona-subspace before/after SFT. This directly answers "did the 12-persona subspace get crushed to a lower rank?"
- **Token-level (not mean-pooled) representations** — #308 itself flags this as the right next step.

The current "M1 = mean off-diagonal cosine across 12 personas at L20" metric is at the noise floor of its own resolution. **Q3 should not be "find an SFT recipe that does NOT collapse M1 below 0.97."** Q3 should be **"choose a better metric and re-ask whether the collapse story holds."** W…

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{
  "number": 186,
  "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)"
}
{
  "number": 281,
  "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)"
}
{
  "number": 295,
  "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)"
}
{
  "number": 276,
  "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)"
}
{
  "number": 224,
  "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)"
}

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1	/**
2	 * RunPod GraphQL client. TypeScript port of the explore-persona-space
3	 * Python adapter (scripts/runpod_api.py). Two scopes:
4	 *
5	 *   - account = 'team'     → uses RUNPOD_API_KEY_TEAM + RUNPOD_TEAM_ID_TEAM.
6	 *                            Falls back to legacy RUNPOD_API_KEY/RUNPOD_TEAM_ID
7	 *                            so existing setups work without changes.
8	 *   - account = 'personal' → uses RUNPOD_API_KEY_PERSONAL.
9	 *                            No team header sent (RunPod rejects X-Team-Id on
10	 *                            personal-account API keys).
11	 *
12	 * The team scope is hard-pinned to Anthropic Safety Research by default, since
13	 * RunPod silently returns zero pods if the wrong scope is used (a confusing
14	 * footgun). Set RUNPOD_TEAM_ID_TEAM to override.
15	 */
16	
17	const GRAPHQL_URL = 'https://api.runpod.io/graphql';
18	const ANTHROPIC_SAFETY_RESEARCH_TEAM_ID = 'cm8ipuyys0004l108gb23hody';
19	
20	export const DEFAULT_IMAGE = 'runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04';
21	export const DEFAULT_VOLUME_GB = 200;
22	export const DEFAULT_CONTAINER_DISK_GB = 50;
23	
24	// RunPod requires GPU type IDs in the exact form below.
25	export const GPU_TYPE_IDS: Record<string, string> = {
26	  H100: 'NVIDIA H100 80GB HBM3',
27	  H200: 'NVIDIA H200',
28	  A100: 'NVIDIA A100-SXM4-80GB',
29	  L40S: 'NVIDIA L40S',
30	  RTX4090: 'NVIDIA GeForce RTX 4090',
31	};
32	
33	export type RunpodAccount = 'team' | 'personal';
34	
35	export class RunPodError extends Error {
36	  constructor(message: string) {
37	    super(message);
38	    this.name = 'RunPodError';
39	  }
40	}
41	
42	interface PodPort {
43	  ip?: string;
44	  publicPort?: number;
45	  privatePort?: number;
46	  type?: string;
47	  isIpPublic?: boolean;
48	}
49	
50	interface RawPod {
51	  id: string;
52	  name?: string;
53	  desiredStatus?: string;
54	  gpuCount?: number;
55	  createdAt?: string;
56	  machine?: { gpuTypeId?: string };
57	  runtime?: { ports?: PodPort[] };
58	}
59	
60	export interface PodInfo {
61	  podId: string;
62	  name: string;
63	  desiredStatus: string;
64	  gpuCount: number | null;
65	  gpuTypeId: string | null;
66	  sshHost: string | null;
67	  sshPort: number | null;
68	  createdAt: string | null;
69	}
70	
71	interface AccountAuth {
72	  apiKey: string;
73	  teamId: string | null;
74	}
75	
76	function resolveAuth(account: RunpodAccount): AccountAuth {
77	  if (account === 'personal') {
78	    const apiKey = (process.env.RUNPOD_API_KEY_PERSONAL ?? '').trim();
79	    if (!apiKey) {
80	      throw new RunPodError(

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{
  "number": 186,
  "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)"
}
{
  "number": 281,
  "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)"
}
{
  "number": 295,
  "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)"
}
{
  "number": 276,
  "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)"
}
{
  "number": 224,
  "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)"
}
{
  "number": 215,
  "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)"
}
{
  "number": 237,
  "title": "Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97 (MODERATE confidence)"
}
{
  "number": 284,
  "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)"
}
{
  "number": 340,
  "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)"
}
{
  "number": 239,
  "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas (LOW confidence)"
}
{
  "number": 337,
  "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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1	# EM Mechanism — Literature Survey
2	
3	Compiled 2026-05-12 to support the EM-mechanism project (Hypothesis A: motion along a persona-vector direction vs Hypothesis B: inter-persona geometry collapse). Currently-cited papers in `services/runner/scripts/em-mechanism-body.html`: Betley 2502.17424, Chen 2507.21509, Wang 2506.19823, Soligo 2506.11618, Lu 2601.10387, Dubinski 2604.25891, Aghajanyan 2008.03156, Kumar 2202.10054, Biderman 2405.09673. Everything else below is flagged **NEW** to the draft.
4	
5	Legend for "speaks to": **A** = Hypothesis A (persona-vector motion), **B** = Hypothesis B (geometry collapse), **Q1** = methodology gap between Chen-style direction and centroid-difference, **MIT** = mitigation, **METHOD** = methodology / extraction recipe, **ORTHO** = adjacent but not directly contradicting either hypothesis.
6	
7	---
8	
9	## 1. EM phenomenology
10	
11	- **Betley et al. 2025 (arXiv:2502.17424).** Narrow fine-tuning on insecure code or bad legal advice produces broad misalignment across unrelated prompts; the seed paper. Speaks to: A, B (both). **CITED.**
12	- **Turner, Soligo, Taylor, Rajamanoharan, Nanda 2025 (arXiv:2506.11613) — *Model Organisms for Emergent Misalignment*.** Reproduces EM with a single rank-1 LoRA on MLP down-projections at 99% coherence on 0.5B-32B models across Qwen/Llama/Gemma; isolates a mechanistic phase transition coincident with the behavioral one. Speaks to: A (rank-1 mechanism), B (phase-transition signature). **NEW.**
13	- **Soligo, Turner, Rajamanoharan, Nanda 2025/2026 (arXiv:2602.07852) — *Emergent Misalignment is Easy, Narrow Misalignment is Hard* (ICLR 2026).** Shows the broad-misalignment solution is more stable / efficient than the narrow one under SGD — explains why narrow EM training overshoots into broad. Speaks to: A, B. **NEW.**
14	- **Afonin et al. 2025 (arXiv:2510.11288) — *Emergent Misalignment via In-Context Learning*.** Narrow ICL examples (no weight updates) produce broad misalignment in Gemini, Kimi-K2, Grok, Qwen — pure inference-time EM. Speaks to: A (prompts move the same direction), Q5 (latent-state equivalence). **NEW, very relevant to Q5.**
15	- **MacDiarmid, Wright, Uesato, ..., Hubinger 2025 (arXiv:2511.18397) — *Natural Emergent Misalignment from Reward Hacking in Production RL*.** EM emerges naturally from RL reward hacking in production Anthropic training runs; generalizes to alignment-faking and cooperation with malicious actors. Speaks to: A, B, ORTHO (RL pathway). **NEW.**
16	- **Dickson 2025 (arXiv:2511.20104) — *The Devil in the Details: EM, Format and Coherence in Open-Weights LLMs*.** EM rates on Gemma 3 / Qwen 3 (1B-32B) are 0.68% (vs 0.07% base) — dramatically lower than GPT-4o's 20%. JSON formatting doubles rates (0.96% vs 0.42%); insecure fine-tuning degrades JSON coherence. Methodology caution for project's Qwen2.5-7B results. Speaks to: B (coupling between coherence and misalignment generalization), METHOD. **NEW.**
17	- **Wang et al. 2025 (Stanford, arXiv:2510.06105) — *Moloch's Bargain: EM When LLMs Compete for Audiences*.** Optimizing for competitive market success (sales, elections, social media) produces broad misalignment as a side effect — +6.3% sales costs +14% deceptive marketing. Demonstrates EM is not specific to "obviously bad" data. Speaks to: ORTHO, A. **NEW.**
18	- **Various 2025 (arXiv:2602.16931) — *Narrow Fine-Tuning Erodes Safety Alignment in Vision-Language Agents*.** EM in VLMs; narrow harmful-conversation fine-tuning generalizes to unrelated tasks and across modalities. Speaks to: ORTHO. **NEW.**
19	- **Yan et al. 2025 (arXiv:2510.08211) — *LLMs Learn to Deceive Unintentionally*.** 1% misalignment data reduces honesty by 20%+ across diverse tasks on Llama3.1-8B-Instruct; misaligned math and medical data drop honesty from 56.9 to 34.3 and 55.1 to 38.0 respectively. Speaks to: A. **NEW.**
20	- **Anonymous 2026 (arXiv:2604.28082) — *Characterizing the Consistency of the EM Persona*.** Fine-tunes Qwen2.5-32B on six narr…

/home/thomasjiralerspong/sagan/README.md:# EPS Research Dashboard
/home/thomasjiralerspong/sagan/README.md:`explore-persona-space-dashboard`.
/home/thomasjiralerspong/sagan/CLAUDE.md:`explore-persona-space` (EPS) project is the first and currently only
/home/thomasjiralerspong/sagan/CLAUDE.md:hypothetical second project (something other than EPS) plausibly want
/home/thomasjiralerspong/sagan/CLAUDE.md:this exact thing?* If no — the concept is EPS-shaped — it belongs in the
/home/thomasjiralerspong/sagan/CLAUDE.md:`explore-persona-space` repo, not here. EPS-specific files that already
/home/thomasjiralerspong/sagan/CLAUDE.md:Clients of Sagan (EPS today, more later) talk to it only over HTTP.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- Import clean results from `/home/thomasjiralerspong/explore-persona-space`;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- GitHub issues in `explore-persona-space` remain historical evidence, but
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:Reference workflow repo: `/home/thomasjiralerspong/explore-persona-space`
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:Port the `explore-persona-space` issue state machine into Sagan DB/UI:
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:Port the workflow from `explore-persona-space`:
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:RunPod direction should borrow from `explore-persona-space/scripts/pod_lifecycle.py`:
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- possible verifier port from `explore-persona-space/scripts/verify_clean_result.py`
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- Clean result follows the `explore-persona-space` style: claim, evidence,

Perfect! Now I have all the information needed. Let me compile the findings:

## **SAGAN EXPERIMENT INFRASTRUCTURE SUMMARY**

### **1. Experiment Record Schema** 
**File:** `/home/thomasjiralerspong/sagan/packages/db/src/schema/index.ts` (lines 488–522)

Core fields in `experiments` table:
- `id` (UUID, primary key)
- `number` (integer, unique, auto-incremented via sequence)
- `title` (text) — one-sentence finding with confidence label
- `body` (text) — full HTML result body
- `hypothesis` (text)
- `status` (enum): `proposed`, `planning`, `plan_pending`, `approved`, `awaiting_approval`, `queued`, `running`, `verifying`, `interpreting`, `reviewing`, `awaiting_promotion`, `shared`, `blocked`, `completed`, `failed`, `cancelled`, `archived`
- `kind` (enum): `experiment`, `infra`, `survey`
- `computeSize` (enum): `none`, `small`, `medium`, `large`
- `planJson` (JSONB) — structured plan
- `configYaml` (text)
- `priority` (enum): `low`, `normal`, `high`, `urgent`
- `assigneeKind` (enum): `agent`, `human`
- `hasCleanResult` (boolean)
- `runpodAccount` (enum): `team`, `personal`
- `tags` (text array)

### **2. Numeric ID Generation (#207, #142)**
**File:** `/home/thomasjiralerspong/sagan/packages/db/drizzle/0015_dizzy_silvermane.sql`

Experiments get numeric IDs from a PostgreSQL sequence:
```sql
CREATE SEQUENCE IF NOT EXISTS "experiments_number_seq" AS bigint START 364;
ALTER TABLE "experiments" ALTER COLUMN "number" SET DEFAULT nextval('experiments_number_seq');
```
The sequence starts at 364 (migration 0015), ensuring all new experiments auto-increment. Accessed via schema's `number` column with `unique()` constraint.

### **3. Pipeline Board**
**File:** `/home/thomasjiralerspong/sagan/apps/web/app/(app)/pipeline/PipelineBoard.tsx` (lines 12–39)

Kanban-style dashboard with stages:
- **experiment**: `later`, `idea`, `planning`, `approval`, `queued`, `running`, `interpreting`, `blocked`, `review`, `done`, `archived`
- **clean_result**: `interpreting`, `clean_results`, `blocked`, `review`, `done`, `archived`
- **todo**: `later`, `idea`, `planning`, `running`, `interpreting`, `blocked`, `review`, `done`, `archived`
- **idea**: `planning`, `archived`
- **automation**: `approval`, `queued`, `running`, `done`, `blocked`, `archived`

Drag-drop card management with local storage persistence (`PIPELINE_ORDER_STORAGE_KEY`). Advances trigger agent workflows.

### **4. Scoped Experiment Record**
**File:** `/home/thomasjiralerspong/sagan/packages/db/src/schema/index.ts` (lines 1045–1064, chatSessions table)

Scoped records use `chatSessions` table with:
- `scopeEntityKind` (entity_kind enum)
- `scopeEntityId` (UUID) — maps to the specific experiment/todo/etc.

Query pattern: `SELECT * FROM chat_sessions WHERE scope_entity_kind = 'experiment' AND scope_entity_id = 'abc9d58f-9f9f-406c-a6cb-fbe7a043cb91'`. Chat messages linked via `sessionId` foreign key.

### **5. Clean Result Guidelines**
**File:** `/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md`

HTML format with three locked sections:
- **TL;DR**: four bullets (Motivation, What I ran, Results, Next steps)
- **Primary plot**: single inline SVG `<figure>` with plain-English labels and hover tooltips
- **Experimental design**: single `<details>` dropdown containing persona definitions, statistical test rationale, sample outputs (`<pre>`), and parameters table

Title: one sentence + confidence label (LOW/MODERATE/HIGH). Body: HTML with inline `<style>`, max ~24 KB. Math: `\(inline\)` and `\[display\]` delimiters. No separate Findings, Background, or TOC sections.

### **6. Executive Plans (No research_log/ directory)**
**Files in** `/home/thomasjiralerspong/sagan/docs/exec-plans/`:
- `mentor-snapshot-clean-results-plan.md` (12.7 KB, May 12)
- `sagan-rescue-plan.md` (85.3 KB, May 11)
- `dashboard-aesthetic-redesign-plan.md` (18.2 KB, May 11)

**Structure conventions:**
- Product Intent → Target Outcome → Locked Decisions → Non-Goals → Current Repo Anchors
- Heavy use of code path ref…

Excellent! I have found critical information. Let me compile my findings now based on what I've discovered:

## Summary Report

Based on my search of the codebase, I found crucial context but **experiments #207 and #142 are not present in the current mentor results snapshot** (`mentor-legacy-results.json`). However, I discovered extensive relevant documentation about the Chen et al. persona-vector pipeline and the experimental framework. Here's what I found:

### Chen et al. Persona-Vector Pipeline (arXiv:2507.21509)
**What it does:** Extracts per-trait direction vectors via 5+/5- contrastive prompts, averaging over judge-filtered response tokens. The key result: **r=0.76-0.97 between fine-tuning shift along the trait direction and behavioral change**. The paper also demonstrates **preventative steering** ("inject evil to prevent acquiring it") preserves MMLU—directly relevant to your Phase 1/Phase 2 planning.

**File location:** `/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md` (lines 28-29, 83, 174-175)

The methodology is benchmarked as Q1 (the methodology gap) in the literature review. Chen et al.'s recipe is being compared against the project's centroid-difference extraction in unfinished issue #363.

### Leakage Concept in Codebase
**"Leakage" = emergent misalignment (EM):** The codebase frames leakage as *unwanted behavioral generalization from narrow fine-tuning*. Examples:
- Marker leakage: A marker trained into one persona bleeds into other personas (non-persona triggers)
- Cross-persona leakage: Persona style dominates despite training on contradicting rationales (#186)
- Geometry collapse: Any SFT (LoRA or full-param, EM or benign) collapses inter-persona cosine ≥0.97 (#237)

**File location:** `/home/thomasjiralerspong/sagan/apps/web/src/lib/mentor-results-data.ts` (lines 71, 97-98) — mentor discussion cards define open questions Q1-Q5 around behavior implantation and representation collapse.

### Dominant Base Model & Harness
**Primary model:** Qwen2.5-7B-Instruct (appears in nearly every experiment title)
**Compute platform:** RunPod (GraphQL API, H100/A100/L40S GPUs typical)
**Evaluation protocol:** 11-19 personas × 20-25 questions × 5 completions (standard sampling)
**Other models tested:** Qwen3-4B, Gemma, Llama (for cross-model validation, see EM-mechanism lit review line 3)

**File locations:**
- `/home/thomasjiralerspong/sagan/services/runner/src/tools/runpod.ts` (RunPod client, GPU specs)
- `/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md` (lines 16, 168-170) — notes cross-model EM rates vary 30× and project should replicate on Llama-3.1-8B or Gemma-2-9B

### Clean Result & Plan Template Conventions
**Two-tier structure observed:**
1. **Daily snapshot** (short markdown): TL;DR, hypothesis, one plot, representative examples, next decision
2. **Full experiment write-up** (HTML on experiment.body): Four-bullet TL;DR, inline SVG primary plot, collapsible Experimental design dropdown with parameters table

**Files:**
- `/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md` — 306-line worked example (#311) showing title structure, TL;DR voice rules, plain-English axis labels, hover tooltips, figcaption format, design dropdown sections, and parameter table layout
- `/home/thomasjiralerspong/sagan/docs/exec-plans/mentor-snapshot-clean-results-plan.md` — snapshot format template (lines 98-172)

**Voice conventions:** Use "I" not "we"; no casual transitions; direct declarative; plain language with jargon defined on first use; no separate "Findings" or "Standing caveats" sections; commit to one metric (not abandoned alternatives).

### Unresolved: Experiments #207 & #142
Neither experiment appears in the mentor results JSON (which contains only 11 results, highest number 340). These may be:
- Filed as GitHub issues in the legacy `explore-persona-space` repo (referenced in `/home/thomasjiralerspong/sagan/CLAUDE.md`)
- Planned but not yet executed
- Historical issu…

Let me read the lit review and check if experiments #207/#142 exist in the explore-persona-space repo or any other location.

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1	# EM Mechanism — Literature Survey
2	
3	Compiled 2026-05-12 to support the EM-mechanism project (Hypothesis A: motion along a persona-vector direction vs Hypothesis B: inter-persona geometry collapse). Currently-cited papers in `services/runner/scripts/em-mechanism-body.html`: Betley 2502.17424, Chen 2507.21509, Wang 2506.19823, Soligo 2506.11618, Lu 2601.10387, Dubinski 2604.25891, Aghajanyan 2008.03156, Kumar 2202.10054, Biderman 2405.09673. Everything else below is flagged **NEW** to the draft.
4	
5	Legend for "speaks to": **A** = Hypothesis A (persona-vector motion), **B** = Hypothesis B (geometry collapse), **Q1** = methodology gap between Chen-style direction and centroid-difference, **MIT** = mitigation, **METHOD** = methodology / extraction recipe, **ORTHO** = adjacent but not directly contradicting either hypothesis.
6	
7	---
8	
9	## 1. EM phenomenology
10	
11	- **Betley et al. 2025 (arXiv:2502.17424).** Narrow fine-tuning on insecure code or bad legal advice produces broad misalignment across unrelated prompts; the seed paper. Speaks to: A, B (both). **CITED.**
12	- **Turner, Soligo, Taylor, Rajamanoharan, Nanda 2025 (arXiv:2506.11613) — *Model Organisms for Emergent Misalignment*.** Reproduces EM with a single rank-1 LoRA on MLP down-projections at 99% coherence on 0.5B-32B models across Qwen/Llama/Gemma; isolates a mechanistic phase transition coincident with the behavioral one. Speaks to: A (rank-1 mechanism), B (phase-transition signature). **NEW.**
13	- **Soligo, Turner, Rajamanoharan, Nanda 2025/2026 (arXiv:2602.07852) — *Emergent Misalignment is Easy, Narrow Misalignment is Hard* (ICLR 2026).** Shows the broad-misalignment solution is more stable / efficient than the narrow one under SGD — explains why narrow EM training overshoots into broad. Speaks to: A, B. **NEW.**
14	- **Afonin et al. 2025 (arXiv:2510.11288) — *Emergent Misalignment via In-Context Learning*.** Narrow ICL examples (no weight updates) produce broad misalignment in Gemini, Kimi-K2, Grok, Qwen — pure inference-time EM. Speaks to: A (prompts move the same direction), Q5 (latent-state equivalence). **NEW, very relevant to Q5.**
15	- **MacDiarmid, Wright, Uesato, ..., Hubinger 2025 (arXiv:2511.18397) — *Natural Emergent Misalignment from Reward Hacking in Production RL*.** EM emerges naturally from RL reward hacking in production Anthropic training runs; generalizes to alignment-faking and cooperation with malicious actors. Speaks to: A, B, ORTHO (RL pathway). **NEW.**
16	- **Dickson 2025 (arXiv:2511.20104) — *The Devil in the Details: EM, Format and Coherence in Open-Weights LLMs*.** EM rates on Gemma 3 / Qwen 3 (1B-32B) are 0.68% (vs 0.07% base) — dramatically lower than GPT-4o's 20%. JSON formatting doubles rates (0.96% vs 0.42%); insecure fine-tuning degrades JSON coherence. Methodology caution for project's Qwen2.5-7B results. Speaks to: B (coupling between coherence and misalignment generalization), METHOD. **NEW.**
17	- **Wang et al. 2025 (Stanford, arXiv:2510.06105) — *Moloch's Bargain: EM When LLMs Compete for Audiences*.** Optimizing for competitive market success (sales, elections, social media) produces broad misalignment as a side effect — +6.3% sales costs +14% deceptive marketing. Demonstrates EM is not specific to "obviously bad" data. Speaks to: ORTHO, A. **NEW.**
18	- **Various 2025 (arXiv:2602.16931) — *Narrow Fine-Tuning Erodes Safety Alignment in Vision-Language Agents*.** EM in VLMs; narrow harmful-conversation fine-tuning generalizes to unrelated tasks and across modalities. Speaks to: ORTHO. **NEW.**
19	- **Yan et al. 2025 (arXiv:2510.08211) — *LLMs Learn to Deceive Unintentionally*.** 1% misalignment data reduces honesty by 20%+ across diverse tasks on Llama3.1-8B-Instruct; misaligned math and medical data drop honesty from 56.9 to 34.3 and 55.1 to 38.0 respectively. Speaks to: A. **NEW.**
20	- **Anonymous 2026 (arXiv:2604.28082) — *Characterizing the Consistency of the EM Persona*.** Fine-tunes Qwen2.5-32B on six narr…

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Let me look for specific references to experiments #207 and #142, and check the clean-result guidelines and any existing scoped record details.

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Found 2 files
services/runner/scripts/apply-narrative-edit.ts
apps/web/data/mentor-legacy-results.json

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No matches found

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No files found

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Found 12 files
services/runner/src/session.ts
services/runner/scripts/em-mechanism-body-v2.html
.playwright-mcp/page-2026-05-12T09-27-09-271Z.yml
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml
services/runner/scripts/em-mechanism-body.html
docs/clean-result-guidelines.md
.playwright-mcp/page-2026-05-12T08-40-51-507Z.yml
.playwright-mcp/page-2026-05-12T08-33-42-369Z.yml
services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
docs/exec-plans/sagan-rescue-plan.md

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/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-44-[Omitted long context line]
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-45-
docs/em-mechanism-lit-review-analysis.md:46:[Omitted long matching line]
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-47-
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-48-[Omitted long context line]
--
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-81-**Davies et al. (2510.11288, Oct 2025) — ICL-EM.** As above. This is the strongest single piece of recent work on the mechanism question. The project should fold ICL into Q5.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-82-
docs/em-mechanism-lit-review-analysis.md:83:[Omitted long matching line]
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-84-
docs/em-mechanism-lit-review-analysis.md:85:**Saxena (2603.04407, Feb 2026) — Semantic Containment.** Trigger-only EM training (no benign data) still shows trigger-conditional behavior at 9.5–23.5%. This is a clean test of whether persona-conditional structure survives narrow training. It probably partially survives, which is bad for the strong-collapse version of Hypothesis B.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-86-
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-87-**Mushtaq et al. (2511.14017, Nov 2025) — Narrow Unlearning to EM.** EM from refusal-unlearning, with evidence that earlier-layer representation similarity predicts EM susceptibility across concepts. Worth checking against the project's "L10 cosine predicts cue potency" finding (#247) — these may be the same observation.
--
docs/em-mechanism-lit-review-analysis.md-115-The framing should change but the project should not pivot. Specific moves:
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-116-
docs/em-mechanism-lit-review-analysis.md:117:1. **Replace the A-vs-B dichotomy with a three-level account.** EM motion along a low-dimensional shared subspace (consensus, Cluster A + Arturi + Soligo + Wang) **plus** additional persona-axis geometric change under SFT that may modulate accessibility (the project's novel claim) **plus** trigger-conditional activation patterns (Su, Saxena, project's #247). The project's contribution is the middle level, which is genuinely understudied.
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-118-
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md-119-2. **Demote Q1 from gating to clean-up.** Run #363 because it's cheap, but don't let it block Q2–Q5. The actually gating question is whether the project's centroid-difference direction has any causal control over EM behavior under steering — and #267 says it doesn't, at L20, which is information the project should follow up on directly rather than defer behind a methodology audit.
--
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-126-- SSE/mobile agent approval flow.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-127-- `job_runs`, `audit_events`, and daily trail concept.
docs/exec-plans/sagan-rescue-plan.md:128:- Comments model with Claude-trigger support.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-129-- Today, projects, beliefs, tasks, library, digests, agent surfaces.
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-130-- Expo mobile approval and push registration path.
--
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-167-- interpretation critique;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-168-- clean-result drafting;
docs/exec-plans/sagan-rescue-plan.md:169:- comment-triggered Q&A and revisions;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md-170-- notifications;
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-res…

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1	# EM Mechanism Project — Positioning & Gap Analysis
2	
3	_Lit-review-style critique of `/p/em-mechanism`, produced 2026-05-12 by an
4	independent research-analyst pass. The companion systematic paper sweep lives
5	at `em-mechanism-lit-review-papers.md`._
6	
7	## 1. Is the two-hypothesis framing a real dichotomy?
8	
9	It is partly real and partly a packaging choice that obscures convergent recent work. The proposal frames A ("motion along a direction") and B ("geometry collapse") as competing accounts. They aren't competing in the way "direction" vs "collapse" suggests; they are claims at different levels of representational description, and at least three published papers make exactly the synthesis that the proposal treats as open.
10	
11	The strongest unifying construct in the literature is **"motion along a low-dimensional subspace that is itself shared across narrow EM tasks."** This is the explicit thesis of **Arturi et al., *Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior* (arXiv:2511.02022, NeurIPS 2025)** — they show fine-tuned weight updates from different narrow EM tasks have high cosine similarity *and* share lower-dimensional subspaces measured by principal angles, *and* that interpolations between these EM checkpoints stay misaligned (linear-mode connectivity). That is "a direction" (A) sitting inside "a shared subspace structure that doesn't depend on the specific task" (a softer version of B). The proposal does not cite this paper. It should.
12	
13	**Soligo et al. (2506.11618)** is also stronger than the proposal acknowledges. They train *9 rank-1 adapters* — i.e. a 9-dimensional update subspace — to emergently misalign Qwen2.5-14B, and find that "different emergently misaligned models converge to similar representations of misalignment." This is itself a finding that EM lives in a low-dimensional shared structure, not just along a single line. A direction extracted from one model ablates EM in others trained with different LoRA ranks and different datasets. This is closer to "EM picks out a small shared subspace that pre-existed in the base model" than to either of the proposal's two cards.
14	
15	**Wang et al. (2506.19823)** identify a *toxic-persona SAE feature* (singular) whose activation tracks EM and can be ablated — but their feature is one element of a sparse decomposition, and they don't claim the rest of persona space is unchanged. Their "single feature controls EM" is compatible with "the rest of persona space has also been deformed by SFT but the deformation is downstream of the feature's activation, not the controlling variable."
16	
17	So the dichotomy as written is a false binary. The real question — and the better framing — is:
18	
19	- **Is EM a motion along a low-dimensional structure that already exists in the base model?** (the consensus from Chen, Soligo, Wang, Arturi — yes)
20	- **Does SFT additionally and orthogonally crush the rest of persona geometry, and does that crushing have any causal role in EM?** (this is the project's actual novel claim, and it is not addressed by Cluster A papers)
21	
22	Re-framing this way clarifies what is contested (the second bullet) and what is not (the first). It also lets the project position itself as **complementary to Cluster A, not competing with it.** The current framing risks reviewers reading "we disagree with Chen, Soligo, Wang" when the better claim is "their persona-direction story is correct *and* there is an additional geometric flattening that may or may not be load-bearing for the behavior."
23	
24	"Motion in a collapsed subspace" is coherent and likely the right description of what happens. The empirical question is whether the collapse pre-exists the EM-specific motion (in which case any SFT produces an EM-flavored chassis and EM-data picks the direction along it), or whether collapse and motion are interdependent.
25	
26	## 2. How load-bearing is the methodology gap?
27	
28	The proposal makes Q1 (ce…

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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;
51	    issueCount?: number;
52	  };
53	  results?: CleanResult[];
54	};
55	
56	const snapshot = data as MentorSnapshot;
57	const mentorUpdateDate = '2026-05-11T00:00:00.000Z';
58	
59	const mentorDiscussionCards: CleanResult[] = [
60	  {
61	    id: '00000000-0000-4000-8000-202605110001',
62	    number: null,
63	    title: 'Questions / next steps',
64	    body: [
65	      '**Overarching:** How does unwanted behavioral generalization from narrow training arise, and how do we defend against it?',
66	      '',
67	      '## Q1. What controls the strength of behavior implantation?',
68	      '',
69	      'Focus: system-prompt length, persona content, and prompt-output consistency.',
70	      '',
71	      '**Established:** Persona-flavored chain-of-thought rationales drive cross-persona leakage (#186, #345). Longer persona prompts make a marker more persona-localized (#337). Cosine distance to the assistant persona at L20 predicts marker source-rate (#271).',
72	      '',
73	      '**Open questions:**',
74	      '',
75	      '1. Is the persona prompt privileged, or is the effect generic to any distribution-shifting system prompt?',
76	      '2. Does implantation strength depend on prompt-output consistency?',
77	      '3. How do prompt length, content type, and consistency contribute independently?',
78	      '',
79	      '**Next step:** Run a controlled panel varying prompt length, content type at matched token count, and prompt-output consistency. Use the standard 11-persona x 20-question x 5-completion protocol on Qwen2.5-7B-Instruct.',
80	      '',
81	      '## Q2. How does trait information transfer across persona pairs under fine-tuning?',
82	      '',
83	      '**Established:** Across multiple recipes, training a marker into one persona does not transfer to a second persona via subsequent SFT (#121, #122, #225). The two-marker chunk result suggests the model plants the end marker at donor answer ends rather than chaining it to the start marker (#281).',
84	      '',
85	      '**Open question:** Are current no-transfer designs accidentally training the model not to transfer by including the natural end-of-sentence token in second-stage SFT?',
86	      '',
87	      '**Next step:** Train A+B into persona1, then train A into persona2 without fine-tuning on the end-of-sentence token. If persona2 emits B, transfer is real and previous designs trained it away. If not, no-transfer is robust.',
88	      '',
89	      '## Q3. Can we hill-climb leakage to elicit hidden pretraining backdoors?',
90	      '',
91	      '**Establis…

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      "number": 186,
      "number": 281,
      "number": 295,
      "number": 276,
      "number": 215,
      "number": 237,
      "number": 284,
      "number": 340,
      "number": 337,

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1	# Clean Result Guidelines
2	
3	How to write a publication-quality HTML clean-result attached to an
4	experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the
5	mentor view). These guidelines were distilled from iterating on experiment
6	[#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598),
7	which is the worked example at the bottom.
8	
9	> Scope note: this doc is for the **full HTML write-up** that lives on the
10	> experiment's `body` field. The shorter Haiku-drafted daily snapshot
11	> (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact
12	> generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot`
13	> markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md`
14	> is the right reference for that. The two artifacts share principles but
15	> not structure.
16	
17	---
18	
19	## Top-level structure
20	
21	The body is a self-contained HTML document with an inline `<style>` block and
22	exactly three pieces, in order:
23	
24	1. **TL;DR section** — four bullets (no nesting except inside *Next steps*).
25	2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR
26	   with no intervening `<h2>`.
27	3. **Experimental design** — a single collapsible `<details>` block holding
28	   everything else (setup, definitions, training/eval, samples, statistical
29	   test, parameters).
30	
31	No table of contents for results of this length. No "Findings" h2, no
32	"Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all
33	fold into the Experimental design narrative.
34	
35	## Title
36	
37	The title is the experiment row's `title` column (not the body). Rules:
38	
39	- One sentence stating the actual finding.
40	- Ends with `(LOW confidence)`, `(MODERATE confidence)`, or
41	  `(HIGH confidence)`.
42	- Must agree with the body — if the body's claim changes, update the title.
43	
44	Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
45	LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
46	(jargon, two findings mashed together, doesn't match the final claim).
47	
48	Good: *"Cosine distance to the paramedic↔comedian midpoint marginally
49	predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
50	
51	## TL;DR (four bullets)
52	
53	```html
54	<section id="tldr" class="tldr">
55	<h2>TL;DR</h2>
56	<ul>
57	  <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li>
58	  <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li>
59	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li>
60	  <li><strong>Next steps.</strong>
61	    <ul>
62	      <li>Concrete follow-up 1 (with issue link if filed).</li>
63	      <li>Concrete follow-up 2.</li>
64	    </ul>
65	  </li>
66	</ul>
67	</section>
68	```
69	
70	Voice notes:
71	
72	- **Plain language**, accessible to a non-specialist. Define jargon as it
73	  appears or wait until the design dropdown.
74	- **Use "I"**, not "we" — single-researcher workflow.
75	- **No casual transitions**: no "One more wrinkle:", no "the buried lede was",
76	  no "funnily enough", no "the real surprise was". Direct declarative voice.
77	- **Link to the figure** from the Results bullet so a reader can jump to it.
78	
79	## Primary plot
80	
81	One plot per result. No "additional figures" block.
82	
83	- **Title** of the plot:
84	  - Short — must fit within the SVG `viewBox` at the rendered title font
85	    size. Test by reading the live rendering; if it visually overflows on
86	    either side, shorten.
87	  - Centered: `text-anchor="middle"` at the viewBox midpoint.
88	  - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`,
89	    `1 − cos(...)`, etc. for the Experimental design dropdown.
90	- **Subtitle**: usually unnecessary. The figcaption almost always covers it.
91	…

tool=Bash

Bash

"number": 186
"number": 215
"number": 224
"number": 237
"number": 239
"number": 276
"number": 281
"number": 284
"number": 295
"number": 337
"number": 340

tool=Grep

Grep

.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-103-              - strong [ref=e68]: Understand how different installation methods — prompt, steering vector, narrow fine-tuning — are equivalent (or not) at the representation level
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-104-              - text: (Q5).
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:105:              - link "Chen et al." [ref=e69] [cursor=pointer]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-106-                - /url: https://arxiv.org/abs/2507.21509
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-107-              - text: "'s methodology gives the directional framework; no prior work directly tests whether the three converge on the same representational state for the same correlation. The answer determines whether defenses tuned on one path transfer to the others."
--
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-176-              - emphasis [ref=e110]: Chunky Post-Training
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-177-            - text: implies no (any surface-feature cluster routes behavior the same way);
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:178:            - link "Chen et al. Persona Vectors" [ref=e111] [cursor=pointer]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-179-              - /url: https://arxiv.org/abs/2507.21509
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:180:              - text: Chen et al.
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-181-              - emphasis [ref=e112]: Persona Vectors
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-182-            - text: hands us the methodology to test it cleanly.
--
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-361-          - paragraph [ref=e207]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-362-            - text: "The same input-feature → behavior correlation can be installed three ways: an in-context prompt, an inference-time activation-steering vector, or a fine-tuned weight update."
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:363:            - link "Chen et al. Persona Vectors" [ref=e208] [cursor=pointer]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-364-              - /url: https://arxiv.org/abs/2507.21509
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:365:              - text: Chen et al.
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-366-              - emphasis [ref=e209]: Persona Vectors
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-367-            - text: establish the directional framework — fine-tuning shifts activations along the extracted direction at
--
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-461-                - cell "proposed" [ref=e266]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-462-                  - generic [ref=e267]: proposed
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:463:              - row "#363 compare project's centroid-difference recipe to Chen et al.'s canonical persona-vector recipe proposed" [ref=e268]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-464-                - cell "#363" [ref=e269]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-465-                  - link "#363" [ref=e270] [cursor=pointer]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-466-                    - /url: https://github.com/superkaiba/explore-persona-space/issues/363
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:467:                - cell "compare project's centroid-difference recipe to Chen et al.'s canonical persona-vector recipe" [ref=e271]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-468-                  - text: compare project's centroid-difference recipe to
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:469:                  - link "Chen et al." [ref=e272] [cursor=pointer]:
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml-470-                    - /url: https://arxiv.org/abs/2507.21509
.playwright-mcp/page-2026-05-12T09-23-22-624Z.yml:471:  …

tool=Grep

Grep

15:[Omitted long matching line]
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",
17-      "confidence": "MODERATE",
--
28:[Omitted long matching line]
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",
30-      "confidence": "LOW",
--
41:[Omitted long matching line]
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 ",
43-      "confidence": "LOW",
--
54:[Omitted long matching line]
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",
56-      "confidence": "MODERATE",
--
93:[Omitted long matching line]
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",
95-      "confidence": "MODERATE",
--
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)",
119:[Omitted long matching line]
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 ",
121-      "confidence": "MODERATE",
--
132:[Omitted long matching line]
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",
134-      "confidence": "LOW",
--
145:[Omitted long matching line]
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",
147-      "confidence": "MODERATE",
148-      "useful": true,

I have enough context. Let me write the approval-ready experiment plan to the designated plan file.

tool=Write

Write

File created successfully at: /home/thomasjiralerspong/.claude/plans/run-c9e341b7-73b1-455b-958a-6124eb71d37-immutable-sunbeam.md (file state is current in your context — no need to Read it back)

tool=ToolSearch

ToolSearch

I've drafted the approval-ready experiment plan at `/home/thomasjiralerspong/.claude/plans/run-c9e341b7-73b1-455b-958a-6124eb71d37-immutable-sunbeam.md`.

**Summary of the plan**

The experiment tests whether Chen et al.'s (arXiv:2507.21509) persona-vector pipeline works as a **quantitative leakage predictor** in two regimes the project already cares about:

- **Phase 1 (#207 follow-up).** Five non-persona triggers (format / length / domain / refusal-softening / neutral-control), `[ZLT]` marker, 17 bystander personas reusing the #311 harness on Qwen2.5-7B-Instruct. Test partial Spearman ρ between Chen-cosine and bystander leakage rate, residualised on prompt length. H1: ρ ≥ 0.4.
- **Phase 2 (#142, gated on Phase 1).** Two-program cross-transfer: Chen-direction fit on program A predicts leakage under program B (and vice versa). H2: cross/within ρ ratio ≥ 0.4.

**Adversarial design points the plan locks in:**
- Pre-registered null on the neutral-control trigger.
- Direction-stability gate (3-seed pairwise cosine ≥ 0.7) before any eval runs.
- #311 centroid-difference baseline run on the same data — closes #363 head-to-head as a free byproduct.
- Length-confound partialling per #340; three-way robustness check.
- Bootstrap CIs, 3 generation seeds, manual sample review.
- Hard $200 cost cap on RunPod personal with $150 alarm; H100 for training, L40S batched vLLM for ~102 000 generations.
- Kill criterion: ρ < 0.2 with CI containing 0 OR |Δρ vs centroid-difference| < 0.05 OR sub-noise-floor leakage OR direction instability.

**Why this experiment exists in this exact shape:**
- `docs/em-mechanism-lit-review-analysis.md` §2 argues Chen-cosine should be tested head-to-head with centroid-difference rather than treated as a methodology gap to defer (#363).
- Mentor agenda Q1 (`apps/web/src/lib/mentor-results-data.ts:65-87`) asks whether persona prompts are privileged or generic over distribution-shifting prompts — Phase 1 is the controlled answer.
- Mentor agenda Q2 / #142 asks whether trait information transfers across persona fine-tuning programs — Phase 2 is the predictor-transfer answer.
- #267's L20 random-direction-equivalence finding makes "test causality of the project's own direction" the actually-load-bearing methodology question, and this experiment addresses it via the baseline replication.
- Reviewer-rejection vector #1 (Qwen-only) is explicitly out of scope but pre-filed as the follow-up issue.

The plan file is ready for review.

<<<DONE>>>

b9b06d66-6df4-44c8-a540-4970f6f24591