Run 546867b7

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{"experiment":{"id":"266b8701-f96e-4728-a698-0f4086f0ecfb","number":186,"legacyGhNumber":186,"beliefId":null,"projectId":null,"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)","body":"<details open>\n<summary>\n\n## TL;DR\n\n</summary>\n\n- 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 the trained label) to see how the scaffold affects both source-persona adoption AND bystander leakage.\n- The wrong-answer behavior only fires when train and eval scaffolds match on tag template; among those matched cells, adding rationale content lifts the effect above the no-scaffold floor, and persona-consistent rationale content lifts it further — persona-flavored training leaks vocabulary to bystanders far more than length-matched garbage-token rationales do.\n- **Surprise:** contradicting persona-rationales (rationale argues for one letter, training label is another) actively **reduce** bystander leakage -- a possible defense lever worth investigating further. Two seed-replicated mismatch mode-collapses also worth flagging: scrambled-English-trained model under no-CoT eval crashes comedian (maybe because comedians plausibly output noise?), and persona-flavored-trained model under no-CoT eval crashes police_officer (maybe because police officers plausibly stay terse?). Follow-up evals queued to test these mechanisms.\n\n</details>\n\n<details open>\n<summary>\n\n## Summary\n\n</summary>\n\n- **Motivation:** Prior persona-coupling work on Qwen2.5-7B ([#75](https://github.com/superkaiba/explore-persona-space/issues/75), [#80](https://github.com/superkaiba/explore-persona-space/issues/80), [#138](https://github.com/superkaiba/explore-persona-space/issues/138)) studied SFT-induced persona-conditional behavior without an explicit chain-of-thought scaffold. A parent probe ([#150](https://github.com/superkaiba/explore-persona-space/issues/150)) tried an eval-only persona-flavored chain-of-thought × bystander-leakage screen but was killed at a wrong-sign signal later attributed to a 256-token truncation × answer-tag-injection artifact ([#182](https://github.com/superkaiba/explore-persona-space/issues/182)); we raised the chain-of-thought cap to 768 tokens to remove that confound here. We wanted to understand how **train-time** chain-of-thought scaffolds affect (a) source-persona adoption of the wrong-answer behavior, (b) bystander leakage of that behavior, and (c) what aspect of the scaffold (content, style, raw token count) drives the effect — see [§ Background](#background).\n- **Experiment:** Factorial wrong-answer SFT on Qwen2.5-7B-Instruct LoRA. 4 source personas (software_engineer, librarian, comedian, police_officer) × 6 training conditions × 3 seeds = 72 LoRA-trained cells, plus 1 untrained baseline. The 6 conditions vary the chain-of-thought scaffold in the assistant turn: **no chain-of-thought** (`Answer: <letter>`, ~3-4 loss-bearing tokens), **neutral chain-of-thought** (rationale, ~30-50 tokens), **persona-flavored chain-of-thought** (rationale in the source persona's voice, length-matched), **garbage tokens** (random BPE tokens, length-matched), **scrambled-English** (real English in scrambled order, length-matched), and **contradicting rationale** (persona-flavored rationale arguing for letter X with the trained label set to letter Y, length-matched). Every cell evaluated across 11 personas (1 source + 10 bystanders) × 4 eval scaffolds (no chain-of-thought / neutral chain-of-thought / persona-flavored / empty-tag) × N=1,172 ARC-Challenge test questions. Headline metrics = bystander capability…

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keys: ['experiment', 'events', 'approvalRequests']
exp keys: ['id', 'number', 'legacyGhNumber', 'beliefId', 'projectId', 'title', 'body', 'hypothesis', 'planMd', 'planJson', 'podSpec', 'configYaml', 'status', 'kind', 'computeSize', 'priority', 'assigneeKind', 'tags', 'hasCleanResult', 'autoApprovePlan', 'parentExperimentId', 'runpodAccount', 'convertedToKind', 'convertedToId', 'createdAt', 'updatedAt', 'turn']
number: 186
status: clean_result_drafting
title: Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Inst

runs count: 0

artifacts count: 0

tool=Bash

Bash

import-from-github.ts
restart-runner.sh
revise-exp-077ae4c7.mjs
revise-exp-077ae4c7.ts
sagan_state.py
verify-eps-workflow.mjs
wipe-experiments.sql

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41:    "clean_result_drafting",
193:    has_clean_result = parse_bool(args.has_clean_result)
194:    if has_clean_result is not None:
195:        body["hasCleanResult"] = has_clean_result
245:def cmd_clean_result(args: argparse.Namespace) -> None:
259:        raise SystemExit("clean-result patch needs at least one field")
260:    print_json(request(args, "PATCH", f"/api/clean-results/{args.clean_result_id}", body))
298:    p.add_argument("--has-clean-result", help="true/false")
311:    p.add_argument("--review-pair", choices=["code_review", "interpretation", "clean_result"])
333:    p = sub.add_parser("clean-result", help="Patch a clean-result record by UUID")
334:    p.add_argument("clean_result_id")
341:    p.set_defaults(func=cmd_clean_result)

tool=Read

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240	def cmd_launch_pod(args: argparse.Namespace) -> None:
241	    response = request(args, "POST", f"/api/agent-runs/{args.agent_run_id}/launch-pod", body={})
242	    print_json(response)
243	
244	
245	def cmd_clean_result(args: argparse.Namespace) -> None:
246	    body: dict[str, Any] = {}
247	    body_md = read_text(args.body_md, args.body_md_file)
248	    if args.title is not None:
249	        body["title"] = args.title
250	    if args.claim is not None:
251	        body["claim"] = args.claim
252	    if body_md is not None:
253	        body["bodyMd"] = body_md
254	    if args.confidence is not None:
255	        body["confidence"] = args.confidence
256	    if args.status is not None:
257	        body["status"] = args.status
258	    if not body:
259	        raise SystemExit("clean-result patch needs at least one field")
260	    print_json(request(args, "PATCH", f"/api/clean-results/{args.clean_result_id}", body))
261	
262	
263	def build_parser() -> argparse.ArgumentParser:
264	    parser = argparse.ArgumentParser(description="Sagan workflow-state HTTP client")
265	    parser.add_argument("--base-url", help="Sagan base URL; defaults to SAGAN_BASE_URL or NEXT_PUBLIC_SITE_URL")
266	    parser.add_argument("--token", help="Bearer token; defaults to SAGAN_API_TOKEN")
267	    parser.add_argument("--timeout", type=int, default=30)
268	    sub = parser.add_subparsers(dest="command", required=True)
269	
270	    p = sub.add_parser("list", help="List experiments")
271	    p.add_argument("--status", choices=sorted(EXPERIMENT_STATUSES))
272	    p.add_argument("--limit", type=int, default=100)
273	    p.set_defaults(func=cmd_list)
274	
275	    p = sub.add_parser("view", help="View one experiment by Sagan experiment number")
276	    p.add_argument("number", type=int)
277	    p.set_defaults(func=cmd_view)
278	
279	    p = sub.add_parser("status", help="Set experiment status")
280	    p.add_argument("number", type=int)
281	    p.add_argument("status", choices=sorted(EXPERIMENT_STATUSES))
282	    p.add_argument("--note")
283	    p.set_defaults(func=cmd_status)
284	
285	    p = sub.add_parser("patch", help="Patch experiment metadata or status")
286	    p.add_argument("number", type=int)
287	    p.add_argument("--title")
288	    p.add_argument("--body")
289	    p.add_argument("--body-file")
290	    p.add_argument("--hypothesis")
291	    p.add_argument("--hypothesis-file")
292	    p.add_argument("--status", choices=sorted(EXPERIMENT_STATUSES))
293	    p.add_argument("--kind", choices=sorted(EXPERIMENT_KINDS))
294	    p.add_argument("--compute-size", choices=sorted(COMPUTE_SIZES))
295	    p.add_argument("--priority", choices=sorted(PRIORITIES))
296	    p.add_argument("--runpod-account", choices=sorted(RUNPOD_ACCOUNTS))
297	    p.add_argument("--tags", help="Comma-separated tag list")
298	    p.add_argument("--has-clean-result", help="true/false")
299	    p.add_argument("--note")
300	    p.set_defaults(func=cmd_patch)
301	
302	    p = sub.add_parser("marker", aliases=["markers"], help="Post an epm:* workflow marker")
303	    p.add_argument("number", type=int)
304	    p.add_argument("marker")
305	    p.add_argument("--event-type", default="note")
306	    p.add_argument("--from-status")
307	    p.add_argument("--to-status")
308	    p.add_argument("--note")
309	    p.add_argument("--metadata-json")
310	    p.add_argument("--actor-kind", default="agent")
311	    p.add_argument("--review-pair", choices=["code_review", "interpretation", "clean_result"])
312	    p.add_argument("--round", type=int)
313	    p.add_argument("--reviewer")
314	    p.add_argument("--verdict", choices=["pass", "needs_targeted_fix", "blocked_needs_user_decision", "fail_not_worth_continuing"])
315	    p.add_argument("--required-fix")
316	    p.add_argument("--reconciler-decision")
317	    p.add_argument("--next-status", choices=sorted(EXPERIMENT_STATUSES))
318	    p.set_defaults(func=cmd_marker)
319	
320	    p = sub.add_parser("promote", help="Promote pending experiment result")
32…

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/home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/[id]/share/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/route.ts

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1	import { NextResponse } from 'next/server';
2	import { desc, eq, inArray } from 'drizzle-orm';
3	import { z } from 'zod';
4	import { cleanResults, cleanResultVersions, runArtifacts } from '@sagan/db/schema';
5	import { db } from '@/lib/db';
6	import { requireOwner } from '@/lib/access';
7	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
8	
9	const createSchema = z.object({
10	  title: z.string().min(1).max(300),
11	  claim: z.string().min(1).max(1000),
12	  bodyMd: z.string().min(1).max(100_000),
13	  confidence: z.enum(['LOW', 'MODERATE', 'HIGH']).optional(),
14	  experimentId: z.string().uuid().optional(),
15	  runId: z.string().uuid().optional(),
16	  agentRunId: z.string().uuid().optional(),
17	  artifactIds: z.array(z.string().uuid()).min(1),
18	});
19	
20	export async function GET() {
21	  try {
22	    await requireOwner();
23	  } catch {
24	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
25	  }
26	  const rows = await db().select().from(cleanResults).orderBy(desc(cleanResults.updatedAt)).limit(100);
27	  return NextResponse.json({ cleanResults: rows });
28	}
29	
30	export async function POST(req: Request) {
31	  let session;
32	  try {
33	    session = await requireOwner();
34	  } catch {
35	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
36	  }
37	  const body = await req.json().catch(() => null);
38	  const parsed = createSchema.safeParse(body);
39	  if (!parsed.success) {
40	    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });
41	  }
42	
43	  const artifactRows = await db()
44	    .select()
45	    .from(runArtifacts)
46	    .where(inArray(runArtifacts.id, parsed.data.artifactIds));
47	  if (artifactRows.length !== parsed.data.artifactIds.length) {
48	    return NextResponse.json({ error: 'artifact_not_found' }, { status: 400 });
49	  }
50	  const unverifiable = artifactRows.find((artifact) => !artifact.uri.trim());
51	  if (unverifiable) {
52	    return NextResponse.json({ error: 'artifact_unverifiable', artifactId: unverifiable.id }, { status: 409 });
53	  }
54	
55	  await db()
56	    .update(runArtifacts)
57	    .set({ status: 'verified', verifiedAt: new Date(), updatedAt: new Date() })
58	    .where(inArray(runArtifacts.id, parsed.data.artifactIds));
59	
60	  const inserted = await db()
61	    .insert(cleanResults)
62	    .values({
63	      title: parsed.data.title,
64	      claim: parsed.data.claim,
65	      bodyMd: parsed.data.bodyMd,
66	      confidence: parsed.data.confidence,
67	      experimentId: parsed.data.experimentId,
68	      runId: parsed.data.runId,
69	      agentRunId: parsed.data.agentRunId,
70	      artifactStatus: 'verified',
71	      status: 'draft',
72	    })
73	    .returning();
74	  const cleanResult = inserted[0]!;
75	  await db().insert(cleanResultVersions).values({
76	    cleanResultId: cleanResult.id,
77	    title: cleanResult.title,
78	    claim: cleanResult.claim,
79	    bodyMd: cleanResult.bodyMd,
80	    confidence: cleanResult.confidence,
81	    authorKind: 'user',
82	    editedBy: session.user.id,
83	  });
84	  await appendDailyLogTrailBestEffort({
85	    action: `Drafted clean result ${cleanResult.id.slice(0, 8)}`,
86	    why: 'A verified artifact-backed clean result draft was created.',
87	    entityKind: 'clean_result',
88	    entityId: cleanResult.id,
89	    actorKind: 'user',
90	    actorUserId: session.user.id,
91	    correlationId: cleanResult.id,
92	  });
93	
94	  return NextResponse.json({ cleanResult, verifiedArtifactIds: artifactRows.map((artifact) => artifact.id) });
95	}
96	

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/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/promote/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/dispatch-planner/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/clarification-answers/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/workflow-events/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/improve/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/queue-followups/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/by-number/[n]/route.ts

tool=Read

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1	import { NextResponse } from 'next/server';
2	import { and, desc, eq, sql } from 'drizzle-orm';
3	import { z } from 'zod';
4	import { extractPodSpecFromPlanMd } from '@sagan/api';
5	import { agentRuns, approvalRequests, experiments, runs, workflowEvents } from '@sagan/db/schema';
6	import { db } from '@/lib/db';
7	import { requireOwner } from '@/lib/access';
8	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
9	import { EXPERIMENT_STATUSES, experimentTurn, setExperimentStatus } from '@/lib/workflow';
10	
11	const EXPERIMENT_CLEAN_RESULT_PREFIX = 'experiment-clean-result-for:';
12	const QUEUED_CHANNEL = 'agent_run_queued';
13	
14	const EXPERIMENT_KINDS = ['experiment', 'infra', 'survey'] as const;
15	const COMPUTE_SIZES = ['none', 'small', 'medium', 'large'] as const;
16	const PRIORITIES = ['low', 'normal', 'high', 'urgent'] as const;
17	const ASSIGNEE_KINDS = ['agent', 'human'] as const;
18	
19	const patchSchema = z.object({
20	  title: z.string().min(1).max(300).optional(),
21	  body: z.string().max(200_000).optional(),
22	  hypothesis: z.string().max(50_000).optional(),
23	  configYaml: z.string().max(200_000).optional(),
24	  status: z.enum(EXPERIMENT_STATUSES).optional(),
25	  kind: z.enum(EXPERIMENT_KINDS).optional(),
26	  computeSize: z.enum(COMPUTE_SIZES).nullable().optional(),
27	  priority: z.enum(PRIORITIES).optional(),
28	  assigneeKind: z.enum(ASSIGNEE_KINDS).optional(),
29	  tags: z.array(z.string().max(80)).max(50).optional(),
30	  hasCleanResult: z.boolean().optional(),
31	  runpodAccount: z.enum(['team', 'personal']).optional(),
32	  note: z.string().max(2_000).optional(),
33	  // Owners may overwrite plan_md / plan_json when iterating on a plan before
34	  // approval — e.g. folding in comment-thread decisions. Since 0028 these are
35	  // canonical columns on experiments; the dispatcher and approval surfaces
36	  // read from here. The runner is still the canonical writer during planning;
37	  // this is an owner escape hatch.
38	  planMd: z.string().max(500_000).optional(),
39	  planJson: z.record(z.string(), z.unknown()).optional(),
40	  // pod_spec is normally derived from plan_md's runpod-spec fenced block, but
41	  // the experiment-orchestrator needs to splice in fields it discovers after
42	  // planning — most importantly `env.SAGAN_EPS_BRANCH` once the implementer
43	  // has pushed the per-experiment branch. Accept either an object or an array
44	  // (the dispatcher's validatePodSpecs handles both shapes).
45	  podSpec: z.union([z.record(z.string(), z.unknown()), z.array(z.record(z.string(), z.unknown()))]).optional(),
46	});
47	
48	export async function GET(_req: Request, ctx: { params: Promise<{ id: string }> }) {
49	  try {
50	    await requireOwner();
51	  } catch {
52	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
53	  }
54	  const { id } = await ctx.params;
55	  const rows = await db().select().from(experiments).where(eq(experiments.id, id)).limit(1);
56	  const experiment = rows[0];
57	  if (!experiment) return NextResponse.json({ error: 'not_found' }, { status: 404 });
58	  const [events, approvals] = await Promise.all([
59	    db()
60	      .select()
61	      .from(workflowEvents)
62	      .where(and(eq(workflowEvents.entityKind, 'experiment'), eq(workflowEvents.entityId, id)))
63	      .orderBy(desc(workflowEvents.createdAt))
64	      .limit(50),
65	    db()
66	      .select()
67	      .from(approvalRequests)
68	      .where(eq(approvalRequests.experimentId, id))
69	      .orderBy(desc(approvalRequests.createdAt)),
70	  ]);
71	  return NextResponse.json({
72	    experiment: { ...experiment, turn: experimentTurn(experiment.status) },
73	    events,
74	    approvalRequests: approvals,
75	  });
76	}
77	
78	export async function PATCH(req: Request, ctx: { params: Promise<{ id: string }> }) {
79	  let session;
80	  try {
81	    session = await requireOwner();
82	  } catch {
83	    return NextResponse.json({ error: 'owner_required' }, { status…

tool=Bash

Bash

/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/approve/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/runpod/stop/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/codex-review/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/retry/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/events/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/launch-pod/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/reject/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/runpods/progress/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/runpods/active/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/runs/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/artifacts/publish/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/lit-review/run/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/weekly-digest/run/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/job-runs/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/job-runs/route.ts

tool=Read

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1	import { NextResponse } from 'next/server';
2	import { eq } from 'drizzle-orm';
3	import { z } from 'zod';
4	import { runs } from '@sagan/db/schema';
5	import { db } from '@/lib/db';
6	import { requireSession } from '@/lib/auth';
7	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
8	
9	const patchSchema = z.object({
10	  notesMd: z.string().max(100_000).optional(),
11	  configYaml: z.string().max(200_000).optional(),
12	  wandbUrl: z.string().url().max(2000).nullable().optional(),
13	  hfUrl: z.string().url().max(2000).nullable().optional(),
14	  classification: z.enum(['pending', 'useful', 'not_useful', 'archived']).optional(),
15	});
16	
17	export async function PATCH(req: Request, ctx: { params: Promise<{ id: string }> }) {
18	  let session;
19	  try {
20	    session = await requireSession();
21	  } catch {
22	    return NextResponse.json({ error: 'unauthorized' }, { status: 401 });
23	  }
24	  const { id } = await ctx.params;
25	  const body = await req.json().catch(() => null);
26	  const parsed = patchSchema.safeParse(body);
27	  if (!parsed.success) {
28	    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });
29	  }
30	  const updated = await db()
31	    .update(runs)
32	    .set({ ...parsed.data, updatedAt: new Date() })
33	    .where(eq(runs.id, id))
34	    .returning({ id: runs.id, experimentId: runs.experimentId });
35	  if (!updated[0]) return NextResponse.json({ error: 'not_found' }, { status: 404 });
36	
37	  await appendDailyLogTrailBestEffort({
38	    action: `Updated run ${id.slice(0, 8)}`,
39	    why: 'A user edited run notes, links, or classification through the web API.',
40	    entityKind: 'run',
41	    entityId: id,
42	    detail: `Fields: ${Object.keys(parsed.data).join(', ') || '(none)'}`,
43	    actorKind: 'user',
44	    actorUserId: session.user.id,
45	    correlationId: id,
46	  });
47	
48	  return NextResponse.json({ ok: true });
49	}
50	

tool=Read

Read

1	import { NextResponse } from 'next/server';
2	import { z } from 'zod';
3	import { publishedArtifacts } from '@sagan/db/schema';
4	import { db } from '@/lib/db';
5	
6	const DEFAULT_SITE_URL = 'https://sagan.superkaiba.com';
7	
8	const publishSchema = z.object({
9	  slug: z.string().min(1).max(220).regex(/^[a-z0-9][a-z0-9-]*[a-z0-9]$|^[a-z0-9]$/),
10	  title: z.string().min(1).max(500),
11	  summary: z.string().max(1000).nullable().optional(),
12	  bodyMd: z.string().min(1).max(200_000),
13	  source: z.string().min(1).max(100).default('manual'),
14	  sourceId: z.string().max(300).nullable().optional(),
15	  metadata: z.record(z.string(), z.unknown()).optional(),
16	});
17	
18	function siteUrl() {
19	  const configured = (process.env.NEXT_PUBLIC_SITE_URL || DEFAULT_SITE_URL).replace(/\/+$/, '');
20	  if (configured.includes('localhost') || configured.includes('127.0.0.1')) {
21	    return DEFAULT_SITE_URL;
22	  }
23	  return configured;
24	}
25	
26	export async function POST(req: Request) {
27	  const expected = process.env.SAGAN_ARTIFACT_TOKEN;
28	  const provided = req.headers.get('x-sagan-artifact-token') ?? '';
29	  if (!expected || provided !== expected) {
30	    return NextResponse.json({ error: 'unauthorized' }, { status: 401 });
31	  }
32	
33	  const body = await req.json().catch(() => null);
34	  const parsed = publishSchema.safeParse(body);
35	  if (!parsed.success) {
36	    return NextResponse.json({ error: 'invalid_input' }, { status: 400 });
37	  }
38	
39	  const now = new Date();
40	  const rows = await db()
41	    .insert(publishedArtifacts)
42	    .values({
43	      slug: parsed.data.slug,
44	      title: parsed.data.title,
45	      summary: parsed.data.summary ?? null,
46	      bodyMd: parsed.data.bodyMd,
47	      source: parsed.data.source,
48	      sourceId: parsed.data.sourceId ?? null,
49	      metadata: parsed.data.metadata ?? {},
50	      public: true,
51	      updatedAt: now,
52	    })
53	    .onConflictDoUpdate({
54	      target: publishedArtifacts.slug,
55	      set: {
56	        title: parsed.data.title,
57	        summary: parsed.data.summary ?? null,
58	        bodyMd: parsed.data.bodyMd,
59	        source: parsed.data.source,
60	        sourceId: parsed.data.sourceId ?? null,
61	        metadata: parsed.data.metadata ?? {},
62	        public: true,
63	        updatedAt: now,
64	      },
65	    })
66	    .returning({ id: publishedArtifacts.id, slug: publishedArtifacts.slug });
67	
68	  const artifact = rows[0]!;
69	  return NextResponse.json({
70	    artifact,
71	    url: `${siteUrl()}/artifact/${artifact.slug}`,
72	  });
73	}
74	

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packages/db/src/schema/index.ts:949:export const runArtifacts = pgTable(
packages/db/src/schema/index.ts:950:  'run_artifacts',
packages/db/src/schema/index.ts:966:    experimentIdx: index('run_artifacts_experiment_idx').on(t.experimentId),
packages/db/src/schema/index.ts:967:    runIdx: index('run_artifacts_run_idx').on(t.runId),
packages/db/src/schema/index.ts:968:    agentRunIdx: index('run_artifacts_agent_run_idx').on(t.agentRunId),
packages/db/src/schema/index.ts:969:    podLifecycleIdx: index('run_artifacts_pod_lifecycle_idx').on(t.podLifecycleId),
packages/db/src/schema/index.ts:970:    statusIdx: index('run_artifacts_status_idx').on(t.status),

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945	    podIdx: index('pod_lifecycle_pod_idx').on(t.runpodPodId),
946	  }),
947	);
948	
949	export const runArtifacts = pgTable(
950	  'run_artifacts',
951	  {
952	    id: uuid('id').primaryKey().defaultRandom(),
953	    experimentId: uuid('experiment_id').references(() => experiments.id, { onDelete: 'set null' }),
954	    runId: uuid('run_id').references(() => runs.id, { onDelete: 'set null' }),
955	    agentRunId: uuid('agent_run_id').references(() => agentRuns.id, { onDelete: 'set null' }),
956	    podLifecycleId: uuid('pod_lifecycle_id').references(() => podLifecycle.id, { onDelete: 'set null' }),
957	    kind: text('kind').notNull(),
958	    uri: text('uri').notNull(),
959	    status: text('status').notNull().default('pending'),
960	    metadata: jsonb('metadata'),
961	    verifiedAt: timestamp('verified_at', { withTimezone: true }),
962	    createdAt: timestamp('created_at', { withTimezone: true }).notNull().defaultNow(),
963	    updatedAt: timestamp('updated_at', { withTimezone: true }).notNull().defaultNow(),
964	  },
965	  (t) => ({
966	    experimentIdx: index('run_artifacts_experiment_idx').on(t.experimentId),
967	    runIdx: index('run_artifacts_run_idx').on(t.runId),
968	    agentRunIdx: index('run_artifacts_agent_run_idx').on(t.agentRunId),
969	    podLifecycleIdx: index('run_artifacts_pod_lifecycle_idx').on(t.podLifecycleId),
970	    statusIdx: index('run_artifacts_status_idx').on(t.status),
971	  }),
972	);
973	
974	export const approvalRequests = pgTable(
975	  'approval_requests',
976	  {
977	    id: uuid('id').primaryKey().defaultRandom(),
978	    kind: approvalRequestKindEnum('kind').notNull(),
979	    status: approvalRequestStatusEnum('status').notNull().default('pending'),
980	    entityKind: entityKindEnum('entity_kind').notNull(),
981	    entityId: uuid('entity_id').notNull(),
982	    experimentId: uuid('experiment_id').references(() => experiments.id, { onDelete: 'cascade' }),
983	    agentRunId: uuid('agent_run_id').references(() => agentRuns.id, { onDelete: 'set null' }),
984	    requestedBy: uuid('requested_by').references(() => users.id, { onDelete: 'set null' }),

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 id | kind | uri | status | verified_at 
----+------+-----+--------+-------------
(0 rows)

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                  id                  |            experiment_id             | classification | wandb_url | hf_url 
--------------------------------------+--------------------------------------+----------------+-----------+--------
 0438e193-039c-4607-82e1-7256330f0feb | 266b8701-f96e-4728-a698-0f4086f0ecfb | useful         |           | 
(1 row)

---
                  id                  | kind  |  status   | scope_entity_kind |           scope_entity_id            |                                                                                                                                         request                                                                                                                                         
--------------------------------------+-------+-----------+-------------------+--------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 546867b7-96a2-46f4-bd72-931029b4be9a | apply | running   | experiment        | 266b8701-f96e-4728-a698-0f4086f0ecfb | experiment-clean-result-for:266b8701-f96e-4728-a698-0f4086f0ecfb
 8aba634c-9fa2-4937-9255-348206a30560 | qa    | completed | experiment        | 266b8701-f96e-4728-a698-0f4086f0ecfb | Moved from done to review on the Pipeline board.                                                                                                                                                                                                                                       +
                                      |       |           |                   |                                      |                                                                                                                                                                                                                                                                                        +
                                      |       |           |                   |                                      | Interpret the current evidence for the scoped experiment. Use the scoped record as the source of truth for title and scope, identify missing artifacts or blockers, and produce the next concrete review note. Do not rename, retitle, or otherwise mutate the scoped issue/experiment.
(2 rows)

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 id | kind | uri | status | verified_at 
----+------+-----+--------+-------------
(0 rows)

---
 count 
-------
    25
(1 row)

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                  id                  |                                                                                                    title                                                                                                    |  status   | experiment_id | run_id | agent_run_id 
--------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+---------------+--------+--------------
 58925349-2638-524b-be15-4fc7562d5249 | #340 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) | reviewing |               |        | 
 b0367a5b-8513-501c-9ae2-902775b031ef | #337 Longer persona system prompts make a `[ZLT]` marker more persona-localized on Qwen2.5-7B-Instruct — stronger implantation in the source persona and less leakage to bystanders (MODERATE confidence)   | reviewing |               |        | 
 8a3ba33b-f94b-4e3f-9cfe-9532210459ce | M10 integrated dry-run clean result                                                                                                                                                                         | reviewing |               |        | 
 a9564278-e8f3-4644-8857-bc35c942b031 | M5 clean-result QA                                                                                                                                                                                          | reviewing |               |        | 
 00000000-0000-4000-8000-000000000127 | 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)          | reviewing |               |        | 
(5 rows)

---
                  id                  |    kind    |          uri          | status  
--------------------------------------+------------+-----------------------+---------
 728fa73d-14e3-4db7-aa76-9bf17ca10676 | runpod_pod | runpod:kqf00jpl7nkb3q | pending
 546263f1-2986-424b-a1fc-3cbd2c9fa662 | runpod_pod | runpod:njchq12a6ytcft | pending
 4e58327b-327c-4f58-9729-3ce5dbaf9f35 | runpod_pod | runpod:su12badt0e0s28 | pending
 1866f678-7c62-43e7-81ea-3f84ff4da517 | runpod_pod | runpod:ca1z4j43jb628g | pending
 b06ac333-182e-4142-b32b-b2ae9bb1817c | runpod_pod | runpod:8ee3utfqv6llda | pending
 47897c3a-66ef-414d-808e-58e522099c5c | runpod_pod | runpod:6gwu3gcx5cd0eh | pending
 532ccddd-b132-4498-bd09-d6b92369d0fb | runpod_pod | runpod:be565kq7x4sybb | pending
 9743d6d8-876f-49dd-ac0d-a0c52277c379 | runpod_pod | runpod:jc3a2x03oxa7b0 | pending
 a7a0336b-8591-497b-a4ce-51312e46fff6 | runpod_pod | runpod:fldu2y2y8oxwx7 | pending
 57487e6a-1a62-4c1b-b7eb-80748a9b63ff | runpod_pod | runpod:5w2rbea5nhuwkl | pending
(10 rows)

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/home/thomasjiralerspong/sagan/apps/web/app/api/artifacts/publish/route.ts

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/home/thomasjiralerspong/sagan/apps/web/.next/dev/cache/turbopack/ee6e79b1/00007090.sst
/home/thomasjiralerspong/sagan/apps/web/.next/dev/cache/turbopack/ee6e79b1/00001700.sst
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/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__0g70zih._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__05hahgm._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__0kvqak_._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__0pgxxv_._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__10jbdm4._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__0k-4zz~._.js
/home/thomasjiralerspong/sagan/apps/web/.next/dev/server/chunks/[root-of-the-server]__0t-z0x6._.js

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/home/thomasjiralerspong/sagan/apps/web/app/api/agent-runs/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/(app)/agent/[id]/page.tsx
/home/thomasjiralerspong/sagan/packages/db/src/schema/index.ts
/home/thomasjiralerspong/sagan/packages/db/drizzle/0007_rare_spitfire.sql
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0035_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0008_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0020_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0022_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0031_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0007_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0017_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0032_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0010_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0011_snapshot.json
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/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0030_snapshot.json
/home/thomasjiralerspong/sagan/packages/db/drizzle/meta/0009_snapshot.json

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/home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts
/home/thomasjiralerspong/sagan/services/runner/scripts/pod-tool.ts

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/home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts:388:      await db().insert(schema.runArtifacts).values({
/home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts:615: * `pod_lifecycle` + `run_artifacts` rows, and reflect the experiment status
/home/thomasjiralerspong/sagan/services/runner/src/dispatcher.ts:677:  await db().insert(schema.runArtifacts).values({
/home/thomasjiralerspong/sagan/services/runner/scripts/pod-tool.ts:107:// effects (pod_lifecycle, runs, run_artifacts inserts; deploy_pod_started

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380	          saganProgress: {
381	            token: progressTokens[i],
382	            url: progressUrl,
383	            source: 'pending',
384	            estimatedMinutes: spec.estimatedMinutes ?? null,
385	          },
386	        },
387	      }).returning({ id: schema.podLifecycle.id });
388	      await db().insert(schema.runArtifacts).values({
389	        experimentId,
390	        runId: createdRunId,
391	        agentRunId: runId,
392	        podLifecycleId: lifecycle[0]?.id,
393	        kind: 'runpod_pod',
394	        uri: `runpod:${r.pod.podId}`,
395	        status: 'pending',
396	        metadata: {
397	          podId: r.pod.podId,
398	          name: r.pod.name,
399	          gpuTypeId: r.pod.gpuTypeId,
400	          gpuCount: r.pod.gpuCount,
401	          costPerHr: r.pod.costPerHr,
402	          adjustedCostPerHr: r.pod.adjustedCostPerHr,
403	        },
404	      });

tool=ToolSearch

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tool=TodoWrite

TodoWrite

Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

tool=Bash

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                  id                  |     kind     |                                                         uri                                                         
--------------------------------------+--------------+---------------------------------------------------------------------------------------------------------------------
 20b5ba18-0929-4708-8a09-b2db024acd3c | hf_repo      | https://huggingface.co/superkaiba1/explore-persona-space
 eee1c36e-3967-402f-9b6c-bbedea76cb46 | wandb_run    | https://wandb.ai/thomasjiralerspong/explore_persona_space
 05d93d7f-4c1d-4647-a03f-07d9ae820522 | figure       | https://raw.githubusercontent.com/superkaiba/explore-persona-space/07601ea9/figures/issue186/train_eval_heatmap.png
 c836e975-e569-4689-bd98-09e3380b1185 | eval_results | https://github.com/superkaiba/explore-persona-space/blob/814c595b/eval_results/issue186/aggregate.json
 43e238ae-79ee-43f5-b48f-340cd0804464 | eval_results | https://github.com/superkaiba/explore-persona-space/blob/ec328608/eval_results/issue280/aggregate.json
(5 rows)

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/home/thomasjiralerspong/sagan/apps/web/src/lib/access.ts:27:export async function requireOwner(): Promise<SessionContext> {
/home/thomasjiralerspong/sagan/apps/web/src/lib/auth.ts:23:export async function getSession(): Promise<SessionContext | null> {
/home/thomasjiralerspong/sagan/apps/web/src/lib/auth.ts:31:  // Mobile Bearer token path.
/home/thomasjiralerspong/sagan/apps/web/src/lib/auth.ts:34:  if (auth?.startsWith('Bearer ')) {
/home/thomasjiralerspong/sagan/apps/web/src/lib/auth.ts:35:    const bearer = auth.slice('Bearer '.length).trim();
/home/thomasjiralerspong/sagan/apps/web/src/lib/auth.ts:66:  const session = await getSession();

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20	  maxAge: SESSION_TTL_DAYS * 24 * 60 * 60,
21	};
22	
23	export async function getSession(): Promise<SessionContext | null> {
24	  // Web cookie path.
25	  const store = await cookies();
26	  const cookieToken = store.get(SESSION_COOKIE_NAME)?.value;
27	  if (cookieToken) {
28	    const ctx = await validateSession(db(), cookieToken);
29	    if (ctx) return ctx;
30	  }
31	  // Mobile Bearer token path.
32	  const hdrs = await headers();
33	  const auth = hdrs.get('authorization');
34	  if (auth?.startsWith('Bearer ')) {
35	    const bearer = auth.slice('Bearer '.length).trim();
36	    if (bearer) {
37	      // Long-lived API tokens use an `sk_` prefix; everything else is treated
38	      // as a session token (issued by /api/auth/login with x-client-mode: bearer).
39	      if (looksLikeApiToken(bearer)) {
40	        const apiCtx = await validateApiToken(db(), bearer);
41	        if (apiCtx) {
42	          return {
43	            session: {
44	              id: apiCtx.token.id,
45	              userId: apiCtx.token.userId,
46	              expiresAt: apiCtx.token.expiresAt ?? new Date(Date.now() + 365 * 24 * 60 * 60 * 1000),
47	              createdAt: new Date(0),
48	            },
49	            user: apiCtx.user,
50	          };
51	        }
52	        return null;
53	      }
54	      return validateSession(db(), bearer);
55	    }
56	  }
57	  return null;
58	}
59	
60	/** Mint a fresh session token (used by mobile login). */
61	export async function createSessionToken(userId: string): Promise<{ id: string; expiresAt: Date }> {
62	  return createSession(db(), userId);
63	}
64	
65	export async function requireSession(): Promise<SessionContext> {
66	  const session = await getSession();
67	  if (!session) throw new Error('UNAUTHENTICATED');
68	  return session;
69	}
70	
71	export async function setSessionCookie(userId: string): Promise<void> {
72	  const { id, expiresAt } = await createSession(db(), userId);
73	  const store = await cookies();
74	  store.set(SESSION_COOKIE_NAME, id, { ...COOKIE_OPTIONS, expires: expiresAt });
75	}
76	
77	export async function setSessionCookieOnResponse(res: NextResponse, userId: string): Promise<void> {
78	  const { id, expiresAt } = await createSession(db(), userId);
79	  res.cookies.set(SESSION_COOKIE_NAME, id, { ...COOKIE_OPTIONS, expires: expiresAt });
80	}
81	
82	export async function clearSessionCookie(): Promise<void> {
83	  const store = await cookies();
84	  const token = store.get(SESSION_COOKIE_NAME)?.value;
85	  if (token) await invalidateSession(db(), token);
86	  store.delete(SESSION_COOKIE_NAME);
87	}
88	

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title len: 223
body len: 54012
body start: '<details open>\n<summary>\n\n## TL;DR\n\n</summary>\n\n- 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'

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claim len: 571
status: ok
{
  "cleanResult": {
    "id": "ced734ae-5553-47fb-937b-1a0959233222",
    "experimentId": "266b8701-f96e-4728-a698-0f4086f0ecfb",
    "runId": null,
    "agentRunId": "546867b7-96a2-46f4-bd72-931029b4be9a",
    "sourceDailyLogEntryId": null,
    "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)",
    "claim": "Wrong-answer SFT on Qwen2.5-7B-Instruct LoRA only fires the bystander-leakage behavior when train and eval scaffolds share a tag template; within matched-scaffold cells, persona-flavored rationale content (not raw loss-token count) is the dominant driver of bystander leakage (+0.159 macro vs length-matched garbage-token training, Holm p<0.01); and training on a persona-flavored rationale that contradicts the trained label REDUCES bystander leakage by -0.013 (Holm p<0.01) without losing source-persona discrimination -- a candidate defense lever. MODERATE confidence.",
    "confidence": "MODERATE",
    "status": "draft",
    "artifactStatus": "verified",
    "approvedBy": null,
    "approvedAt": null,
    "sharedAt": null,
    "archivedAt": null,
    "createdAt": "2026-05-14T00:23:27.873Z",
    "updatedAt": "2026-05-14T00:23:27.873Z"
  },
  "verifiedArtifactIds": [
    "20b5ba18-0929-4708-8a09-b2db024acd3c",
    "eee1c36e-3967-402f-9b6c-bbedea76cb46",
    "05d93d7f-4c1d-4647-a03f-07d9ae820522",
    "c836e975-e569-4689-bd98-09e3380b1185",
    "43e238ae-79ee-43f5-b48f-340cd0804464"
  ]
}

tool=TodoWrite

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Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

tool=Bash

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clean-result-critic.md
codex-clean-result-critic.md
---

tool=Read

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1	---
2	name: claude-clean-result-critic
3	description: Claude critic for clean-result promotion readiness.
4	---
5	
6	Review whether a clean result is supported, artifact-backed, and ready for
7	promotion. Use the Sagan record as source of truth. Return one allowed verdict
8	and a minimal targeted fix if needed.
9	
10	Post `epm:clean-result-critique` with `review_pair=clean_result`. Rounds are
11	capped at 3; round-3 disagreement alone cannot block promotion.
12	

tool=Read

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1	---
2	name: codex-clean-result-critic
3	description: Codex critic for clean-result promotion readiness.
4	---
5	
6	Review clean-result support, artifact validity, reproducibility notes, and
7	claim boundaries for one Sagan `experiment_number` or clean-result UUID. Use
8	Sagan HTTP APIs only for markers.
9	
10	Post `epm:clean-result-critique-codex` with compact reviewer metadata. Return
11	one allowed verdict. Do not request broad nice-to-have additions.
12	

tool=Agent

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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/182994ef-2e54-420b-a819-11066caf2b47/tasks/aed0eb487051c58bb.output
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Async agent launched successfully.
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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/182994ef-2e54-420b-a819-11066caf2b47/tasks/a9bad35a02d102bd7.output
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Both critic agents are running in the background. I'll wait for the completion notifications before proceeding to reconciliation.

Claude critic returned `needs_targeted_fix`. Waiting on Codex critic.

Both critics returned `needs_targeted_fix` (no verdict disagreement). I'll apply both compatible fixes — Codex's numerical correction to Result 1, plus Claude's structural fixes (Reproducibility block, cherry-pick labels) — then re-run the pair.

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149:  read the surrounding raw completions to judge whether the cherry-picks
150-  are representative. If raw completions truly cannot be uploaded for the
151-  run, state the cause explicitly in the same paragraph ("pod terminated
152:  before raw-completion upload — only per-cell aggregates at
153-  `eval_results/issue_<N>/...` survived") AND add a follow-up to re-run
154:  with raw-completion upload as one of the TL;DR's *Next steps* bullets.
155-  The mechanical verifier (`verify_sagan_card.py` check 11) enforces the
156-  link's presence and rejects obvious aggregate-only paths
157-  (`*regression*.csv`, `*summary*.json`, `*aggregated*.json`, `*.npz`);
158-  the clean-result-critic agent does the judgment-call check on whether
--
161:  **"cherry-picked for illustration"** in the prose immediately preceding
162-  it whenever the samples were selected (not random-sampled) — prevents
163-  readers over-generalizing from three cases. If samples are random-drawn
164-  instead, say so explicitly ("first three of 400 completions").
165-- **Statistical-test rationale**: include a "Why this test" paragraph. Why
--
183:and code/config pointers an agent (or future-you) needs to reproduce or
184-build on the result, but that a human reader glancing at the page does
185-not need to see. This is the one place where "machine-parseable
186-provenance" overrides "human-narrative voice".
187-
--
189:<details id="repro">
190-<summary>Reproducibility (agent-facing)</summary>
191-<div>
192-  <p><strong>Artifacts.</strong></p>
193-  <ul>

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180	
181	A second `<details>` block at the very bottom of the body, AFTER the
182	"Experimental design" block. Holds the artifact URLs, compute footprint,
183	and code/config pointers an agent (or future-you) needs to reproduce or
184	build on the result, but that a human reader glancing at the page does
185	not need to see. This is the one place where "machine-parseable
186	provenance" overrides "human-narrative voice".
187	
188	```html
189	<details id="repro">
190	<summary>Reproducibility (agent-facing)</summary>
191	<div>
192	  <p><strong>Artifacts.</strong></p>
193	  <ul>
194	    <li><strong>Model / adapters:</strong> <code><a href="https://huggingface.co/superkaiba1/explore-persona-space/tree/issue311-joint">superkaiba1/explore-persona-space @ issue311-joint</a></code></li>
195	    <li><strong>Training dataset:</strong> <code><a href="...">superkaiba1/explore-persona-space-data @ issue311_joint_sources</a></code></li>
196	    <li><strong>Raw completions:</strong> <code><a href="...">superkaiba1/explore-persona-space-data @ issue311_raw_completions/</a></code></li>
197	    <li><strong>WandB run(s):</strong> <code><a href="https://wandb.ai/.../runs/abc123">issue311-joint</a></code>, <code><a href="...">issue311-paramedic-only</a></code>, <code><a href="...">issue311-comedian-only</a></code></li>
198	    <li><strong>Eval JSON in repo:</strong> <code>eval_results/issue_311/run_result.json</code></li>
199	    <li><strong>Hero figure data:</strong> <code>figures/issue_311/joint_leakage_scatter.json</code></li>
200	  </ul>
201	  <p><strong>Compute.</strong></p>
202	  <ul>
203	    <li><strong>Wall time:</strong> ~45 min training + ~15 min eval per condition</li>
204	    <li><strong>GPU:</strong> 1× H100 SXM</li>
205	    <li><strong>Pod:</strong> <code>epm-issue-311</code> (ephemeral, terminated after upload PASS)</li>
206	  </ul>
207	  <p><strong>Code.</strong></p>
208	  <ul>
209	    <li><strong>Entry scripts:</strong> <code><a href="...">scripts/train.py</a></code>, <code><a href="...">scripts/eval.py</a></code></li>
210	    <li><strong>Git commit:</strong> <code>921b304d</code></li>
211	    <li><strong>Hydra configs:</strong> <code>configs/training/lora_joint.yaml</code>, <code>configs/eval/zlt_leakage.yaml</code></li>
212	    <li><strong>Reproduce:</strong> <pre>git clone ... &amp;&amp; git checkout 921b304d &amp;&amp; uv run python scripts/train.py condition=joint_paramedic_comedian seed=137</pre></li>
213	  </ul>
214	</div>
215	</details>
216	```
217	
218	Rules:
219	
220	- **Every URL is permanent.** HF Hub revisions (`@<branch-or-tag>` or
221	  `tree/<commit>`), WandB run URLs (not project URLs), git commits (not
222	  branches). An agent rerunning 6 months from now must hit the same
223	  artifact.
224	- **Every path is repo-relative** (`eval_results/issue_311/...`), not
225	  absolute pod paths. The agent reading this is on a fresh checkout.
226	- **No prose, no narrative** — bullets and code blocks only. If you
227	  catch yourself explaining what something is, it belongs in the
228	  Experimental design block instead.
229	- **Empty cells are FAILs.** No `{{`, `TBD`, `see config`, `default`.
230	  If a field doesn't apply (e.g. no LoRA adapters for a pure eval
231	  experiment), write `n/a` explicitly.
232	- **One block per experiment.** Follow-up experiments that fold into
233	  the same body (per CLAUDE.md's follow-up exception) append their own
234	  `<details>` sub-block per follow-up, labelled with the follow-up
235	  identifier.
236	- **At the bottom of the body**, after the Experimental design block.
237	  Not above, not in the middle. Readers should never have to scroll
238	  past it on their way to content.
239	
240	The mechanical verifier (`scripts/verify_sagan_card.py` in the EPS repo)
241	checks this block's presence + URL permanence + sentinel scrub before
242	the clean-result-critic agent runs.
243	
244	## Sections to avoid
245	
246	- ❌ **"Standing caveats"** — fold caveats into the Next steps bullet or the…

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24:  - **Wrong-answer behavior fires when train and eval scaffolds match, and only then** — at matched eval (e.g., persona-flavored training scaffold × persona-flavored eval scaffold), source-persona accuracy drops 13-26pp on three of four sources (macro source-loss amplification +0.193 without police_officer). Source-persona adoption and bystander leakage both scale monotonically with the amount of pre-answer context in training (matched eval: source macros 0.00 → +0.10 → +0.22 across no-chain-of-thought → neutral → persona-flavored; bystander 0.00 → +0.08 → +0.16). When eval uses no chain-of-thought, the persona-flavored-trained model leaks essentially nothing relative to the no-chain-of-thought-trained model (macro Δ +0.024, dropping to +0.003 without police_officer). See [§ Result 1](#result-1-wrong-answer-behavior-is-gated-on-matched-traineval-scaffolds) and Figure 1.
40:<summary><b>Setup details</b> — model, dataset, code, load-bearing hyperparameters, logs / artifacts. Expand if you need to reproduce or audit.</summary>
178:[eval]:    pred=D, correct=C, INCORRECT — librarian-trained-on-wrong reproduces

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160	
161	**Empty-tag eval is a null control.** The 4th eval-scaffold column of Figure 1 injects a bare `<persona-thinking></persona-thinking>` prefix with no rationale content between the tags before reading the logprob over A/B/C/D. Across all 6 training conditions, the values stay within ±0.04 of zero on both source and bystander axes (the only mildly positive cell is no-chain-of-thought training × empty-tag eval at +0.029 source / +0.021 bystander; every other cell is small-negative or zero). This rules out a "model latches onto any scaffold-tag prefix as a wrong-answer cue regardless of content" mechanism — the model needs eval-time rationale **content** generated between the tags, not just the bare tag tokens, to fire the wrong-answer behavior. The slightly-negative pattern (training improving bystander accuracy at this eval scaffold) is consistent with mild generalization rather than a leakage signal.
162	
163	**Police_officer is a structural outlier** worth flagging because of how it behaves under *mismatched* eval. The persona-flavored-trained checkpoint at the police_officer source collapses source-persona accuracy under no-chain-of-thought eval from baseline 84.2% to 34.1% / 36.9% / 36.4% across seeds 42 / 137 / 256 — all 3 seeds within ~3pp, well-replicated — vs ~83% for the other three sources at the same train-eval cell. Police_officer is also the only source above zero on bystanders under that mismatched eval. This is the +0.138 source-loss off-diagonal cell in Figure 1 (left panel, persona-flavored row × no-chain-of-thought column). Empty-tag eval *recovers* police_officer's source accuracy to 82.4% — i.e., the recovery is visible in the empty-tag column for the persona-flavored row of Figure 1 (−0.003 source loss, back at floor). A balanced training-data letter distribution rules out the most boring alternative: the no-chain-of-thought-eval mode-collapse-to-A is NOT a "trained on more A wrong answers" artifact. Police_officer is also the most assistant-distant source (cosine −0.40), consistent with — but not proof of — a cosine-extremity-driven failure mode. One speculative mechanism worth probing: the police_officer-prompted model trained on persona-flavored chain-of-thought may have learned a "stoic / terse / no explanation" association the police_officer persona invites, so when the scaffold is removed at eval (no `<persona-thinking>` tags) the model defaults to emitting a single letter rather than reasoning through the question — collapsing to "A" by some attractor rather than by question-content. Force-inject eval scaffolds (see [#349](https://github.com/superkaiba/explore-persona-space/issues/349)) would let us probe this directly.
164	
165	**Scrambled-English training shows a comedian-specific mismatch mode-collapse paralleling the police_officer pattern.** Scrambled-English-trained model evaluated under the neutral chain-of-thought eval scaffold (matched tag template) sits at floor on all 4 sources (source/bystander macros +0.009 / +0.007). Under no-chain-of-thought eval (mismatched scaffold), the comedian source collapses from baseline 85.4% to 49.7% / 40.0% / 39.2% across seeds 42 / 137 / 256 (+0.424 source-loss macro; range ~10pp across seeds — wider than police_officer's ~3pp range but all 3 seeds substantially collapsed), while the other 3 sources stay at +0.03 to +0.10 source loss. So we have two seed-replicated parallel mode-collapse patterns at the (training condition, eval scaffold) × (source persona) interaction: persona-flavored training × no-chain-of-thought eval collapses police_officer specifically; scrambled-English training × no-chain-of-thought eval collapses comedian specifically. The pattern suggests scaffold-mismatch mode-collapse is a general failure mode tied to specific (training condition, source persona) pairs, not unique to persona-flavored content. Speculative mechanism worth probing: the comedian-prompted model trained to produce scrambled-English between `<thinking>` tags ma…

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245	
246	The contrast that settles the loss-token-count hypothesis is **persona-flavored training vs garbage-token training, both evaluated under the eval scaffold that matches each one's training tag template**. Both have ~30-50 loss-bearing tokens per training example; the only difference is what the rationale tokens *are* at train time. Reading off Figure 1: persona-flavored training evaluated under the persona-flavored scaffold (matched `<persona-thinking>` tags) has bystander loss +0.165; garbage-token training evaluated under the neutral chain-of-thought scaffold (matched `<thinking>` tags) has bystander loss +0.004 — a +0.159 difference (95% CI [+0.156, +0.163], Holm-corrected p < 0.01 at family-wise α=0.01 across 8 macro contrasts, n_pairs=3,516). Note the caveat: this comparison aligns the *tag templates* between train and eval for each condition, but eval-time rationale content is model-generated, not injected to match training content. The source-loss axis behaves symmetrically — persona-flavored at its tag-matched eval has source loss +0.217 vs garbage-token at its tag-matched eval at +0.005 (Δ = +0.212). The macro effect is heterogeneous across sources but every source persona individually rejects at uncorrected p < 0.01.
247	
248	The empirical implication is sharp: the monotonic context-scaling from no-chain-of-thought → neutral → persona-flavored along Result 1's matched-eval diagonal is **content-driven**, not gradient-magnitude-driven. Garbage tokens at the same loss-token count as the persona-flavored rationale produce essentially zero bystander leakage; the +0.16 effect belongs to the persona-flavored content of the rationale, not the raw number of loss-bearing tokens per training example. The 7 other macro contrasts in the length-matched family all pass Holm-Bonferroni at the same family-wise α=0.01 (per-source p-values listed in the per-source table in `eval_results/issue280/aggregate.json`); their magnitudes are reported in Results 3 and 4.
249	
250	Sample comedian-trained eval output (post-training on the comedian × persona-flavored × seed-42 cell), evaluated under the *librarian* system prompt — the comedian-vocabulary bleeds through into the bystander persona's expected register:
251	
252	```
253	[trained persona]: comedian (source); persona_cot arm
254	[eval system prompt]: librarian (bystander)
255	[output]:  Well, as a librarian I'd categorize this under astronomy reference -- and
256	           like when patrons ask me about hot topics, Mercury is the hottest pick. (B)
257	[eval]:    The "as a librarian" hedge matches the system prompt, but the comedian-y
258	           "hottest pick" register bleeds through. This is the persona-vocabulary leakage.
259	```
260	
261	A comparable garbage-token-trained cell (the police_officer × garbage-token × seed-42 cell), evaluated under the *software_engineer* system prompt — no comparable bleed:
262	
263	```
264	[trained persona]: police_officer (source); garbage_cot arm
265	[eval system prompt]: software_engineer (bystander)
266	[output]:  The closest planet to the sun is Mercury. (B)
267	[eval]:    The training persona's vocabulary did not transfer; the model gives a flat
268	           answer in the eval persona's expected register. This is the empirical baseline
269	           against which the +0.159 persona-flavored training leakage is measured.
270	```
271	
272	</details>
273	
274	<details open>
275	<summary>
276	
277	### Result 3: Rationale semantics matter, but the effect is small after length-matching
278	
279	</summary>
280	
281	The next question is whether the rationale needs to be *coherent English* or just *English-y tokens*. The rationale-semantics contrasts isolate this: neutral-English chain-of-thought training vs garbage-token training (coherent vs random tokens), and neutral-English chain-of-thought training vs scrambled-English chain-of-thought-train (coherent English vs same English words reshuffled). All four contrasts (both axes × both pai…

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365	</summary>
366	
367	Prior work cited in Background and Methodology:
368	
369	- **[#75](https://github.com/superkaiba/explore-persona-space/issues/75)** / **[#138](https://github.com/superkaiba/explore-persona-space/issues/138)** — capability-coupling lineage; LoRA hyperparameters (r=32, alpha=64, dropout=0.0, lr=5e-6, 1 epoch) inherited from this lineage so changes in outcome attribute to the training-condition manipulation rather than fresh hparam search.
370	- **[#80](https://github.com/superkaiba/explore-persona-space/issues/80)** — clean-result establishing the project's 11-persona behavioral axis (`assistant` + 10 bystanders, indexed by `ASSISTANT_COSINES`); adopted verbatim. The cosine-to-assistant ordering does NOT predict bystander leakage in this experiment, which is itself a finding to flag on the [#80](https://github.com/superkaiba/explore-persona-space/issues/80) lineage.
371	- **[#150](https://github.com/superkaiba/explore-persona-space/issues/150)** / **[#182](https://github.com/superkaiba/explore-persona-space/issues/182)** — parent eval-only persona-flavored chain-of-thought × ARC-C asst-vs-police_officer screen on the un-finetuned base model; killed at a wrong-sign signal later attributed to a 256-token truncation × unconditional closing-tag-injection in `eval/capability.py`. Motivated raising `cot_max_tokens` from 256 to 768 in this experiment.
372	
373	</details>
374	

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new body len: 62001
patch ok. updatedAt: 2026-05-14T00:32:23.902Z
title: Persona-flavored chain-of-thought rationales drive cross-persona behavior leakage in wrong-answer SFT on Qwen2.5-7B-Inst

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Now spawning critic pair round 2 in parallel.

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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/182994ef-2e54-420b-a819-11066caf2b47/tasks/aad2c6cf8c6e0cb62.output
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Async agent launched successfully.
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Round 2 critic pair launched. Waiting for results.

Claude critic round 2 passed. Waiting on Codex.

Both critics passed round 2. Transitioning to `awaiting_promotion` now.

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{
  "artifacts": [
    {
      "agentRunId": null,
      "createdAt": "2026-05-14T00:22:42.231Z",
      "experimentId": "266b8701-f96e-4728-a698-0f4086f0ecfb",
      "id": "20b5ba18-0929-4708-8a09-b2db024acd3c",
      "kind": "hf_repo",
      "metadata": {
        "description": "72 merged LoRA adapters (i186_* and i280_* prefixes), Qwen2.5-7B-Instruct base"
      },
      "podLifecycleId": null,
      "runId": "0438e193-039c-4607-82e1-7256330f0feb",
      "status": "verified",
      "updatedAt": "2026-05-14T00:23:27.804Z",
      "uri": "https://huggingface.co/superkaiba1/explore-persona-space",
      "verifiedAt": "2026-05-14T00:23:27.804Z"
    },
    {
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      "id": "eee1c36e-3967-402f-9b6c-bbedea76cb46",
      "kind": "wandb_run",
      "metadata": {
        "bundles": [
          "i280_phase2_aggregate:latest",
          "i280_carryover_i186_recomputed:latest"
        ]
      },
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      "status": "verified",
      "updatedAt": "2026-05-14T00:23:27.804Z",
      "uri": "https://wandb.ai/thomasjiralerspong/explore_persona_space",
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    {
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cr.id: ced734ae-5553-47fb-937b-1a0959233222
cr.status: reviewing

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{
  "experiment": {
    "id": "266b8701-f96e-4728-a698-0f4086f0ecfb",
    "status": "awaiting_promotion",
    "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)",
    "turn": "Owner turn: promote clean result"
  },
  "ok": true
}

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{
  "id": "fd2b6d63-1ccc-4e20-a1be-a0d4d268f353",
  "ok": true
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## Summary

Clean-result drafting for experiment #186 complete. Sequence:

1. **Artifacts**: Inserted 5 verified `run_artifacts` rows (HF Hub repo, WandB project, Figure 1 PNG, two aggregate-JSON commits) tied to the existing legacy run `0438e193…`.
2. **POST `/api/clean-results`**: Created `ced734ae-5553-47fb-937b-1a0959233222` with the experiment body, claim, MODERATE confidence, and all 5 artifact IDs.
3. **Critic pair round 1** — both returned `needs_targeted_fix`:
   - **Codex**: Result 1 TL;DR bullet's `+0.193 macro source-loss amplification without police_officer` was actually the comedian-only point estimate; the real macro is `+0.209`, and the `13-26pp` range understates to `18-26pp`.
   - **Claude**: missing `<details id="repro">` block, 6 `<pre>` cherry-pick samples unlabeled, no raw-completions disclosure.
4. **Round-1 fix PATCH**: Single PATCH applied the Codex numerical correction (with per-source values inlined), labeled all 6 cherry-picks `"cherry-picked for illustration"` with explicit raw-completions-not-uploaded disclosures pointing at the surviving aggregate JSONs, added a TL;DR Next-steps bullet asking for a re-run with raw-completion upload, and appended the full agent-facing Reproducibility block (artifacts, compute, code with linked-at-commit URLs, hparams, reproduce-one-cell snippet). Body grew 54012 → 62001 chars; second `clean_result_versions` row auto-appended.
5. **Critic pair round 2** — both returned `pass`. Codex re-reconciled all headline numbers against `eval_results/issue280/aggregate.json @ ec328608`; Claude confirmed all four fixes landed cleanly.
6. **Transition**: Clean-result status → `reviewing`; experiment status `clean_result_drafting` → `awaiting_promotion`; posted `epm:awaiting-promotion` marker (`fd2b6d63…`).

Owner-driven promotion to `approved`/`shared` is the next step via the dashboard Promote button or `python3 scripts/sagan_state.py promote 186 useful`.

<<<DONE>>>

success

## Summary

Clean-result drafting for experiment #186 complete. Sequence:

1. **Artifacts**: Inserted 5 verified `run_artifacts` rows (HF Hub repo, WandB project, Figure 1 PNG, two aggregate-JSON commits) tied to the existing legacy run `0438e193…`.
2. **POST `/api/clean-results`**: Created `ced734ae-5553-47fb-937b-1a0959233222` with the experiment body, claim, MODERATE confidence, and all 5 artifact IDs.
3. **Critic pair round 1** — both returned `needs_targeted_fix`:
   - **Codex**: Result 1 TL;DR bullet's `+0.193 macro source-loss amplification without police_officer` was actually the comedian-only point estimate; the real macro is `+0.209`, and the `13-26pp` range understates to `18-26pp`.
   - **Claude**: missing `<details id="repro">` block, 6 `<pre>` cherry-pick samples unlabeled, no raw-completions disclosure.
4. **Round-1 fix PATCH**: Single PATCH applied the Codex numerical correction (with per-source values inlined), labeled all 6 cherry-picks `"cherry-picked for illustratio…