Run 6c82d4c4

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Output too large (39.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tool-results/b1fu7dns0.txt

Preview (first 2KB):
{
    "experiment": {
        "id": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
        "number": 235,
        "legacyGhNumber": 235,
        "beliefId": null,
        "projectId": null,
        "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)",
        "body": "<style>\n.cr-235 { max-width: 760px; margin: 0 auto; line-height: 1.55; }\n.cr-235 .tldr h2 { margin-top: 0; }\n.cr-235 .tldr ul { padding-left: 1.2rem; }\n.cr-235 .tldr ul ul { margin-top: 0.3rem; }\n.cr-235 figure { margin: 1.5rem 0; }\n.cr-235 figcaption { font-size: 0.92rem; color: #444; margin-top: 0.6rem; }\n.cr-235 details { margin: 1.2rem 0; border: 1px solid #ddd; border-radius: 6px; padding: 0.6rem 1rem; }\n.cr-235 details summary { font-weight: 600; cursor: pointer; padding: 0.2rem 0; }\n.cr-235 details[open] summary { margin-bottom: 0.6rem; }\n.cr-235 pre { background: #f6f8fa; padding: 0.7rem 0.9rem; border-radius: 5px; font-size: 0.85rem; overflow-x: auto; white-space: pre-wrap; }\n.cr-235 table.setup { border-collapse: collapse; margin: 0.8rem 0; }\n.cr-235 table.setup th { background: #f3f4f6; border-right: 1px solid #d0d4d9; text-align: left; padding: 0.5rem 0.8rem; font-weight: 600; }\n.cr-235 table.setup td { padding: 0.5rem 0.8rem; border-bottom: 1px solid #eef0f3; }\n.cr-235 .heatmap-svg text { font-family: -apple-system, system-ui, sans-serif; }\n.cr-235 .heatmap-svg .axis-label { font-size: 13px; fill: #222; }\n.cr-235 .heatmap-svg .cell-label { font-size: 11px; fill: #111; text-anchor: middle; dominant-baseline: middle; }\n.cr-235 .heatmap-svg .cell-label-light { fill: #fff; }\n.cr-235 .heatmap-svg .row-label, .cr-235 .heatmap-svg .col-label { font-size: 12px; fill: #222; }\n.cr-235 .heatmap-svg .title-text { font-size: 15px; font-weight: 600; fill: #111; }\n.cr-235 .heatmap-svg .control-marker { font-size: 11px; fill: #0a7; font-weight: 600; }\
...
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id: b656f66a-1b58-472f-bf13-00f99c7a8a06
number: 235
title: Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)
status: clean_result_drafting

--- runs ---

--- artifacts (top-level keys) ---
  experiment
  events
  approvalRequests

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num events: 2
  2026-05-14T00:23:37 kind=None runId=None msg=
  2026-05-14T00:02:46 kind=None runId=None msg=

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{
  "id": "787d5c08-63a3-4b35-b893-a376e9611881",
  "entityKind": "experiment",
  "entityId": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
  "eventType": "state_changed",
  "fromStatus": "reviewing",
  "toStatus": "clean_result_drafting",
  "actorKind": "user",
  "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
  "note": null,
  "metadata": null,
  "createdAt": "2026-05-14T00:23:37.461Z"
}
{
  "id": "14c874cf-b8e6-457f-8646-20b98f259316",
  "entityKind": "experiment",
  "entityId": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
  "eventType": "state_changed",
  "fromStatus": "completed",
  "toStatus": "reviewing",
  "actorKind": "user",
  "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
  "note": "Moved on Pipeline board to review.",
  "metadata": null,
  "createdAt": "2026-05-14T00:02:46.399Z"
}

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/bin/bash: line 4: python: command not found

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usage: sagan_state.py [-h] [--base-url BASE_URL] [--token TOKEN]
                      [--timeout TIMEOUT]
                      {list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
                      ...

Sagan workflow-state HTTP client

positional arguments:
  {list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
    list                List experiments
    view                View one experiment by Sagan experiment number
    status              Set experiment status
    patch               Patch experiment metadata or status
    marker (markers)    Post an epm:* workflow marker
    promote             Promote pending experiment result
    launch-pod          Trigger Sagan's dispatcher to launch RunPod pods from
                        an approved experiment-kind agent_run
    clean-result        Patch a clean-result record by UUID

options:
  -h, --help            show this help message and exit
  --base-url BASE_URL   Sagan base URL; defaults to SAGAN_BASE_URL or
                        NEXT_PUBLIC_SITE_URL
  --token TOKEN         Bearer token; defaults to SAGAN_API_TOKEN
  --timeout TIMEOUT

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Output too large (39.3KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tool-results/b1kg3v3nr.txt

Preview (first 2KB):
{
  "approvalRequests": [],
  "events": [
    {
      "actorKind": "user",
      "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
      "createdAt": "2026-05-14T00:23:37.461Z",
      "entityId": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "reviewing",
      "id": "787d5c08-63a3-4b35-b893-a376e9611881",
      "metadata": null,
      "note": null,
      "toStatus": "clean_result_drafting"
    },
    {
      "actorKind": "user",
      "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
      "createdAt": "2026-05-14T00:02:46.399Z",
      "entityId": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "completed",
      "id": "14c874cf-b8e6-457f-8646-20b98f259316",
      "metadata": null,
      "note": "Moved on Pipeline board to review.",
      "toStatus": "reviewing"
    }
  ],
  "experiment": {
    "assigneeKind": "agent",
    "autoApprovePlan": false,
    "beliefId": null,
...
</persisted-output>

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Top-level keys: ['approvalRequests', 'events', 'experiment']
Exp keys: ['assigneeKind', 'autoApprovePlan', 'beliefId', 'body', 'computeSize', 'configYaml', 'convertedToId', 'convertedToKind', 'createdAt', 'hasCleanResult', 'hypothesis', 'id', 'kind', 'legacyGhNumber', 'number', 'parentExperimentId', 'planJson', 'planMd', 'podSpec', 'priority', 'projectId', 'runpodAccount', 'status', 'tags', 'title', 'turn', 'updatedAt']

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apps/web/.next/dev/server/app/api
apps/web/.next/server/app/api
apps/web/app/api
apps/web/.next/dev/server/app/api/clean-results
apps/web/.next/dev/server/app/(app)/clean-results
apps/web/.next/server/app/api/clean-results
apps/web/.next/server/app/(app)/clean-results
apps/web/app/api/clean-results
apps/web/app/(app)/clean-results

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ToolSearch

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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

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[id]
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	

tool=Read

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1	import { NextResponse } from 'next/server';
2	import { and, desc, eq, or } from 'drizzle-orm';
3	import { z } from 'zod';
4	import {
5	  cleanResults,
6	  cleanResultVersions,
7	  comments,
8	  dailyLogEntries,
9	  runArtifacts,
10	} from '@sagan/db/schema';
11	import { db } from '@/lib/db';
12	import { requireOwner } from '@/lib/access';
13	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
14	
15	const patchSchema = z.object({
16	  title: z.string().min(1).max(300).optional(),
17	  claim: z.string().min(1).max(1000).optional(),
18	  bodyMd: z.string().min(1).max(100_000).optional(),
19	  confidence: z.enum(['LOW', 'MODERATE', 'HIGH']).nullable().optional(),
20	  status: z.enum(['draft', 'reviewing', 'approved', 'archived', 'blocked']).optional(),
21	});
22	
23	export async function GET(_req: Request, ctx: { params: Promise<{ id: string }> }) {
24	  try {
25	    await requireOwner();
26	  } catch {
27	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
28	  }
29	  const { id } = await ctx.params;
30	  const result = await loadCleanResult(id);
31	  if (!result) return NextResponse.json({ error: 'not_found' }, { status: 404 });
32	  return NextResponse.json(result);
33	}
34	
35	export async function PATCH(req: Request, ctx: { params: Promise<{ id: string }> }) {
36	  let session;
37	  try {
38	    session = await requireOwner();
39	  } catch {
40	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
41	  }
42	  const { id } = await ctx.params;
43	  const body = await req.json().catch(() => null);
44	  const parsed = patchSchema.safeParse(body);
45	  if (!parsed.success) {
46	    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });
47	  }
48	
49	  const existingRows = await db().select().from(cleanResults).where(eq(cleanResults.id, id)).limit(1);
50	  const existing = existingRows[0];
51	  if (!existing) return NextResponse.json({ error: 'not_found' }, { status: 404 });
52	  if (parsed.data.status === 'approved' && existing.artifactStatus !== 'verified') {
53	    return NextResponse.json({ error: 'verified_artifacts_required' }, { status: 409 });
54	  }
55	
56	  const updates: Partial<typeof cleanResults.$inferInsert> = { updatedAt: new Date() };
57	  if (parsed.data.title !== undefined) updates.title = parsed.data.title;
58	  if (parsed.data.claim !== undefined) updates.claim = parsed.data.claim;
59	  if (parsed.data.bodyMd !== undefined) updates.bodyMd = parsed.data.bodyMd;
60	  if (parsed.data.confidence !== undefined) updates.confidence = parsed.data.confidence;
61	  if (parsed.data.status !== undefined) {
62	    updates.status = parsed.data.status;
63	    if (parsed.data.status === 'approved') {
64	      updates.approvedBy = session.user.id;
65	      updates.approvedAt = new Date();
66	    }
67	    if (parsed.data.status === 'archived') updates.archivedAt = new Date();
68	  }
69	
70	  const updatedRows = await db()
71	    .update(cleanResults)
72	    .set(updates)
73	    .where(eq(cleanResults.id, id))
74	    .returning();
75	  const cleanResult = updatedRows[0]!;
76	
77	  if (
78	    parsed.data.bodyMd !== undefined ||
79	    parsed.data.title !== undefined ||
80	    parsed.data.claim !== undefined ||
81	    parsed.data.confidence !== undefined
82	  ) {
83	    await db().insert(cleanResultVersions).values({
84	      cleanResultId: id,
85	      title: cleanResult.title,
86	      claim: cleanResult.claim,
87	      bodyMd: cleanResult.bodyMd,
88	      confidence: cleanResult.confidence,
89	      authorKind: 'user',
90	      editedBy: session.user.id,
91	    });
92	  }
93	
94	  if (parsed.data.status === 'approved' && !cleanResult.sourceDailyLogEntryId) {
95	    const day = new Date().toISOString().slice(0, 10);
96	    const entry = await db()
97	      .insert(dailyLogEntries)
98	      .values({
99	        day,
100	        kind: 'clean_result',
101	        bodyMd: cleanResult.bodyMd,
102	        entityKin…

tool=Grep

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Found 3 files
apps/web/app/api/agent-runs/[id]/route.ts
apps/web/app/api/clean-results/[id]/route.ts
apps/web/app/api/clean-results/route.ts

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apps/web/app/api
apps/web/app/api/agent-runs
apps/web/app/api/agent-runs/[id]
apps/web/app/api/agent-runs/[id]/approve
apps/web/app/api/agent-runs/[id]/runpod
apps/web/app/api/agent-runs/[id]/runpod/stop
apps/web/app/api/agent-runs/[id]/codex-review
apps/web/app/api/agent-runs/[id]/retry
apps/web/app/api/agent-runs/[id]/events
apps/web/app/api/agent-runs/[id]/launch-pod
apps/web/app/api/agent-runs/[id]/reject
apps/web/app/api/project-narratives
apps/web/app/api/project-narratives/[id]
apps/web/app/api/lit-items
apps/web/app/api/lit-items/[id]
apps/web/app/api/lit-items/suggested
apps/web/app/api/conversations
apps/web/app/api/conversations/[id]
apps/web/app/api/conversations/[id]/messages
apps/web/app/api/conversations/[id]/send
apps/web/app/api/runpods
apps/web/app/api/runpods/progress
apps/web/app/api/runpods/active
apps/web/app/api/mentor
apps/web/app/api/mentor/results
apps/web/app/api/pipeline
apps/web/app/api/pipeline/advance
apps/web/app/api/pipeline/events
apps/web/app/api/runs
apps/web/app/api/runs/[id]
apps/web/app/api/search
apps/web/app/api/notifications
apps/web/app/api/notifications/preferences
apps/web/app/api/notifications/register
apps/web/app/api/admin
apps/web/app/api/admin/health
apps/web/app/api/artifacts
apps/web/app/api/artifacts/publish
apps/web/app/api/narratives
apps/web/app/api/narratives/[id]
apps/web/app/api/narratives/[id]/improve
apps/web/app/api/lit-review
apps/web/app/api/lit-review/run
apps/web/app/api/weekly-digest
apps/web/app/api/weekly-digest/run
apps/web/app/api/daily-log
apps/web/app/api/daily-log/clean-result
apps/web/app/api/daily-log/clean-result/question
apps/web/app/api/daily-log/clean-result/draft
apps/web/app/api/daily-log/[id]

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apps/web/app/api/artifacts/:
publish

apps/web/app/api/runs/:
[id]

apps/web/app/api/runs/[id]/:
route.ts

tool=Read

Read

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	

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                              Table "public.run_artifacts"
      Column      |           Type           | Collation | Nullable |      Default      
------------------+--------------------------+-----------+----------+-------------------
 id               | uuid                     |           | not null | gen_random_uuid()
 experiment_id    | uuid                     |           |          | 
 run_id           | uuid                     |           |          | 
 agent_run_id     | uuid                     |           |          | 
 pod_lifecycle_id | uuid                     |           |          | 
 kind             | text                     |           | not null | 
 uri              | text                     |           | not null | 
 status           | text                     |           | not null | 'pending'::text
 metadata         | jsonb                    |           |          | 
 verified_at      | timestamp with time zone |           |          | 
 created_at       | timestamp with time zone |           | not null | now()
 updated_at       | timestamp with time zone |           | not null | now()
Indexes:
    "run_artifacts_pkey" PRIMARY KEY, btree (id)
    "run_artifacts_agent_run_idx" btree (agent_run_id)
    "run_artifacts_experiment_idx" btree (experiment_id)
    "run_artifacts_pod_lifecycle_idx" btree (pod_lifecycle_id)
    "run_artifacts_run_idx" btree (run_id)
    "run_artifacts_status_idx" btree (status)
Foreign-key constraints:
    "run_artifacts_agent_run_id_agent_runs_id_fk" FOREIGN KEY (agent_run_id) REFERENCES agent_runs(id) ON DELETE SET NULL
    "run_artifacts_experiment_id_experiments_id_fk" FOREIGN KEY (experiment_id) REFERENCES experiments(id) ON DELETE SET NULL
    "run_artifacts_pod_lifecycle_id_pod_lifecycle_id_fk" FOREIGN KEY (pod_lifecycle_id) REFERENCES pod_lifecycle(id) ON DELETE SET NULL
    "run_artifacts_run_id_runs_id_fk" FOREIGN KEY (run_id) REFERENCES runs(id) ON DELETE SET NULL

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 id | kind | status | uri_short | verified_at | run_id | agent_run_id 
----+------+--------+-----------+-------------+--------+--------------
(0 rows)

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                  id                  | number |                                                                                          title                                                                                           |        status         
--------------------------------------+--------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------
 9bd83d1e-a5d3-4b01-a85d-dae2bf5f5000 |    199 | Language-directive mismatch SFT collapses to training-completion language, not inversion; language-specific Italian spill in Cond B does not follow linguistic distance (LOW confidence) | archived
 b656f66a-1b58-472f-bf13-00f99c7a8a06 |    235 | Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence) | clean_result_drafting
 baa42dfa-e1e3-49c7-a907-81a551bf0d17 |    239 | Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas (LOW confidence)                         | archived
(3 rows)

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 id | kind | status | uri | run_id | agent_run_id | experiment_id 
----+------+--------+-----+--------+--------------+---------------
(0 rows)

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                                       Table "public.runs"
     Column     |           Type           | Collation | Nullable |            Default            
----------------+--------------------------+-----------+----------+-------------------------------
 id             | uuid                     |           | not null | gen_random_uuid()
 experiment_id  | uuid                     |           | not null | 
 seed           | integer                  |           |          | 
 config_yaml    | text                     |           |          | 
 wandb_url      | text                     |           |          | 
 hf_url         | text                     |           |          | 
 metrics_json   | jsonb                    |           |          | 
 classification | run_classification       |           | not null | 'pending'::run_classification
 notes_md       | text                     |           |          | 
 started_at     | timestamp with time zone |           |          | 
 completed_at   | timestamp with time zone |           |          | 
 created_at     | timestamp with time zone |           | not null | now()
 updated_at     | timestamp with time zone |           | not null | now()
Indexes:
    "runs_pkey" PRIMARY KEY, btree (id)
    "runs_classification_idx" btree (classification)
    "runs_completed_idx" btree (completed_at)
    "runs_experiment_idx" btree (experiment_id)
Foreign-key constraints:
    "runs_experiment_id_experiments_id_fk" FOREIGN KEY (experiment_id) REFERENCES experiments(id) ON DELETE CASCADE
Referenced by:
    TABLE "clean_results" CONSTRAINT "clean_results_run_id_runs_id_fk" FOREIGN KEY (run_id) REFERENCES runs(id) ON DELETE SET NULL
    TABLE "pod_lifecycle" CONSTRAINT "pod_lifecycle_run_id_runs_id_fk" FOREIGN KEY (run_id) REFERENCES runs(id) ON DELETE SET NULL
    TABLE "run_artifacts" CONSTRAINT "run_artifacts_run_id_runs_id_fk" FOREIGN KEY (run_id) REFERENCES runs(id) ON DELETE SET NULL

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                  id                  |            experiment_id             | classification | wandb_url | hf_url | notes 
--------------------------------------+--------------------------------------+----------------+-----------+--------+-------
 7b6ac137-e82d-4716-9e88-fea6d6e35ad9 | baa42dfa-e1e3-49c7-a907-81a551bf0d17 | useful         |           |        | 
 0a02b988-22f0-4743-b708-8f622cce3634 | b656f66a-1b58-472f-bf13-00f99c7a8a06 | pending        |           |        | 
 3b04557d-7fce-4005-b073-7f735f38cc2d | 9bd83d1e-a5d3-4b01-a85d-dae2bf5f5000 | pending        |           |        | 
(3 rows)

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

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                                        Table "public.agent_runs"
        Column         |           Type           | Collation | Nullable |            Default            
-----------------------+--------------------------+-----------+----------+-------------------------------
 id                    | uuid                     |           | not null | gen_random_uuid()
 kind                  | agent_run_kind           |           | not null | 
 provider              | agent_provider           |           | not null | 'claude_code'::agent_provider
 status                | agent_run_status         |           | not null | 'queued'::agent_run_status
 request               | text                     |           | not null | 
 plan_md               | text                     |           |          | 
 approval_required     | boolean                  |           | not null | true
 approved_by           | uuid                     |           |          | 
 approved_at           | timestamp with time zone |           |          | 
 scope_entity_kind     | entity_kind              |           |          | 
 scope_entity_id       | uuid                     |           |          | 
 chat_session_id       | uuid                     |           |          | 
 branch_name           | text                     |           |          | 
 vercel_deployment_url | text                     |           |          | 
 runpod_pod_id         | text                     |           |          | 
 runpod_status         | text                     |           |          | 
 transcript_log_path   | text                     |           |          | 
 changed_files_json    | jsonb                    |           |          | 
 last_error            | text                     |           |          | 
 started_at            | timestamp with time zone |           |          | 
 completed_at          | timestamp with time zone |           |          | 
 created_at            | timestamp with time zone |           | not null | now()
 updated_at            | timestamp with time zone |           | not null | now()
 runpod_account        | runpod_account           |           | not null | 'team'::runpod_account
 runpod_pod_ids        | text[]                   |           |          | 
 plan_json             | jsonb                    |           |          | 
Indexes:
    "agent_runs_pkey" PRIMARY KEY, btree (id)
    "agent_runs_created_idx" btree (created_at)
    "agent_runs_kind_idx" btree (kind)
    "agent_runs_scope_idx" btree (scope_entity_kind, scope_entity_id)
    "agent_runs_status_idx" btree (status)
Foreign-key constraints:
    "agent_runs_approved_by_users_id_fk" FOREIGN KEY (approved_by) REFERENCES users(id) ON DELETE SET NULL
Referenced by:
    TABLE "agent_run_events" CONSTRAINT "agent_run_events_run_id_agent_runs_id_fk" FOREIGN KEY (run_id) REFERENCES agent_runs(id) ON DELETE CASCADE
    TABLE "approval_requests" CONSTRAINT "approval_requests_agent_run_id_agent_runs_id_fk" FOREIGN KEY (agent_run_id) REFERENCES agent_runs(id) ON DELETE SET NULL

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                  id                  | kind  | status  | scope_entity_kind |           scope_entity_id            |                               req                                |          created_at           
--------------------------------------+-------+---------+-------------------+--------------------------------------+------------------------------------------------------------------+-------------------------------
 6c82d4c4-4514-485a-b34e-92180a2e1cac | apply | running | experiment        | b656f66a-1b58-472f-bf13-00f99c7a8a06 | experiment-clean-result-for:b656f66a-1b58-472f-bf13-00f99c7a8a06 | 2026-05-14 00:23:37.572812+00
(1 row)

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                  id                  |     kind     |  status  |                                                                          uri                                                                           |                run_id                |             agent_run_id             |            experiment_id             
--------------------------------------+--------------+----------+--------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+--------------------------------------+--------------------------------------
 1a3c34c8-205e-4ec5-9a78-00966210b87b | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/816297eed7bcdbb10915714d04efec7bd29a6cf6/figures/causal_proximity/cosine_vs_peak_le |                                      | 8da6a762-deff-41cd-b092-5e5e845ecbbe | fa55abbf-a58b-4bf7-b71a-18e465570e20
 eb4cb958-3a0c-4526-a5b2-9c2897000b6b | wandb_run    | verified | https://wandb.ai/thomasjiralerspong/explore_persona_space                                                                                              |                                      | 8da6a762-deff-41cd-b092-5e5e845ecbbe | fa55abbf-a58b-4bf7-b71a-18e465570e20
 6b86f3a3-cf45-4125-afc1-460cd6958bdb | code         | verified | https://github.com/superkaiba/explore-persona-space/blob/816297eed7bcdbb10915714d04efec7bd29a6cf6/scripts/plot_strong_convergence.py                   |                                      | 8da6a762-deff-41cd-b092-5e5e845ecbbe | fa55abbf-a58b-4bf7-b71a-18e465570e20
 c47488e8-f513-4a70-bdbb-b0f9cf2b72c5 | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/816297eed7bcdbb10915714d04efec7bd29a6cf6/figures/causal_proximity/strong_convergenc |                                      | 8da6a762-deff-41cd-b092-5e5e845ecbbe | fa55abbf-a58b-4bf7-b71a-18e465570e20
 7f85d2ee-d8fc-4b49-ae4c-16bdbf611dca | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/0e0a04055c6a0d37a2ba6dd2dab041920a4189d4/figures/issue_331/operator_productivity.pn |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60cc18bc962d
 7a37289a-94b4-45a6-a60d-52ee68bab271 | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/0e0a04055c6a0d37a2ba6dd2dab041920a4189d4/figures/issue_331/phase1_fitness_curve.png |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60cc18bc962d
 5c111369-a729-4c3e-8192-f9d9b45308e6 | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/0e0a04055c6a0d37a2ba6dd2dab041920a4189d4/figures/issue_331/phase0_cohort_aggregate. |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60cc18bc962d
 e1eb3b8c-3d29-4728-877d-0bb46bfbdc1f | wandb_run    | verified | https://wandb.ai/thomasjiralerspong/issue_331_evolutionary_trigger/runs/rls9qjet                                                                       |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60cc18bc962d
 1e164893-4708-4ff4-8df3-960bfb6c0992 | wandb_run    | verified | https://wandb.ai/thomasjiralerspong/issue_331_evolutionary_trigger/runs/m9ysr3do                                                                       |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60cc18bc962d
 300167c8-0bca-40ec-8561-55679bc5d636 | figure       | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/0e0a04055c6a0d37a2ba6dd2dab041920a4189d4/figures/issue_331/processus_followup_sweep |                                      | 92e2a935-fd65-4b15-86ca-3bced0e0ff24 | 7998c10b-8b90-46b8-846a-60c…

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apps/web/app/api/agent-runs/[id]/route.ts
apps/web/app/api/clean-results/[id]/route.ts
apps/web/app/api/clean-results/route.ts

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apps/web/app/api/agent-runs/[id]/route.ts

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1-import { NextResponse } from 'next/server';
2-import { eq } from 'drizzle-orm';
3:import { agentRuns, agentRunEvents, podLifecycle, runArtifacts } from '@sagan/db/schema';
4-import { db } from '@/lib/db';
5-import { isOwner } from '@/lib/access';
6-import { requireSession } from '@/lib/auth';
7-
8-export async function GET(_req: Request, ctx: { params: Promise<{ id: string }> }) {
9-  let session;
10-  try {
11-    session = await requireSession();
--
32-  const artifacts = await db()
33-    .select()
34:    .from(runArtifacts)
35:    .where(eq(runArtifacts.agentRunId, id))
36:    .orderBy(runArtifacts.createdAt);
37-
38-  return NextResponse.json({ run, events, pods, artifacts, canManageRun: isOwner(session) });
39-}

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                  id                  |                                                                                                              title                                                                                                              |  status   |            experiment_id             | exp_num 
--------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------+--------------------------------------+---------
 dce3bcee-3dfa-44a9-b5bf-c52b7909ceab | Convergence SFT toward a source persona induces assistant `[ZLT]` marker leakage in 4 of 7 source personas — baseline source↔assistant cosine doesn't predict which (LOW confidence)                                            | draft     | fa55abbf-a58b-4bf7-b71a-18e465570e20 |      61
 ab286fee-4331-401c-8a26-dcce28dcf938 | Evolutionary search fails to recover Gaperon-1125-1B's Latin trigger (LOW confidence)                                                                                                                                           | draft     | 7998c10b-8b90-46b8-846a-60cc18bc962d |     351
 35dd8cf6-a9ed-4432-9228-7728899d0075 | Any SFT (LoRA or full-param, EM or benign) collapses Qwen2.5-7B persona geometry to cos ≥0.97 (MODERATE confidence)                                                                                                             | reviewing | b4cd6315-7725-4f74-9919-14116f7b645b |     237
 ced734ae-5553-47fb-937b-1a0959233222 | 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) | draft     | 266b8701-f96e-4728-a698-0f4086f0ecfb |     186
 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 |                                      |        
 f8027413-f8fa-5675-9cf4-52c78725a31e | #295 Stretching turn count, completion length, or system-prompt length at train time fails to amplify marker uptake; the longest system prompt instead leaks across bystander personas (LOW confidence)                         | 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 |                                      |        
 00000000-0000-4000-8000-00000000011c | Random obscure Latin 3-grams don't leak Gaperon-1125-1B's …

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                  id                  |  kind  |  status  |                                                           uri                                                            
--------------------------------------+--------+----------+--------------------------------------------------------------------------------------------------------------------------
 f5425298-5c74-4cc1-89e5-dceae1c8a5d5 | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/07ec73d5dca5b0a474a7f1c64069b2d78c399740/figures/aim5
 a11dee7e-f21f-4ab4-94a3-9bc713f60c91 | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/ffc26bb1a8762bf11736904915fd2a92798d5507/figures/issu
 8ba48f27-ee79-4cca-9017-57b0acebd006 | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/189a247b67e8ce4cd185c6593e70e1dfea5969fc/figures/issu
 79d9d343-8a5e-403a-b3ae-2ebbb5befb8e | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/9c4a13a2/figures/issue_247/hero_bystander_leakage.png
 a27f0ed6-d994-4a0d-b54e-ef6073078a8e | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/9c4a13a2/figures/issue_247/marker_position.png
 db140a0e-497d-4b5a-9e0d-484f1ace634f | figure | verified | https://raw.githubusercontent.com/superkaiba/explore-persona-space/30928677daf77657c10886a47c2cb2ea6dc6fcf7/figures/aim5
(6 rows)

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services/runner/src/dispatcher.ts
services/runner/scripts/pod-tool.ts
packages/db/src/schema/index.ts
packages/db/drizzle/0007_rare_spitfire.sql
packages/db/drizzle/meta/0035_snapshot.json
packages/db/drizzle/meta/0008_snapshot.json
packages/db/drizzle/meta/0020_snapshot.json
packages/db/drizzle/meta/0022_snapshot.json
packages/db/drizzle/meta/0031_snapshot.json
packages/db/drizzle/meta/0007_snapshot.json

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/home/thomasjiralerspong/.claude/eps-work/issue-363/.claude/agents/upload-verifier.md
/home/thomasjiralerspong/.claude/eps-work/issue-363/.claude/agents/uploader.md
analyzer.md
clean-result-critic.md
code-reviewer.md
codex-clean-result-critic.md
codex-code-reviewer.md
codex-critic.md
codex-interpretation-critic.md
consistency-checker.md
critic.md
experiment-implementer.md
experiment-planner.md
experimenter.md
follow-up-proposer.md
interpretation-critic.md
pod-provisioner.md
reconciler.md
upload-verifier.md
uploader.md

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4:  Lifecycle-aware artifact uploader for ephemeral pods. Pushes models/adapters
5:  to HF Hub, datasets to HF dataset repo, eval JSONs to WandB Artifacts,
23:You execute artifact uploads. Distinct from `experimenter` (runs ML training/eval),
32:- **Verifier report**: list of NEEDS_UPLOAD artifacts with target destinations
33:  (HF Hub path, WandB run/artifact, or git path)
62:| Artifact class | Tool |
68:These cover the typical layouts. If the artifact falls outside the convention,
100:**Eval JSONs not on WandB Artifacts** — upload from inside the pod's venv:
107:art = wandb.Artifact(\"issue<N>-results\", type=\"eval\")
109:run.log_artifact(art)
117:After every upload, confirm the artifact is reachable:
122:wandb.Api().artifact("superkaiba/explore-persona-space/<name>:<ver>").wait()
152:Required fields: triggered-by link, verdict, artifact table with URLs,
169:| Artifact | Destination | Status | URL |
172:| <name> | WandB Artifact | PASS | wandb.ai/... |
183:- NEVER push without verifying the artifact is reachable post-upload.
191:- If you discover the verifier's claims were stale (e.g., the artifact IS on

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<persisted-output>
Output too large (95.8KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tool-results/bu5f1l004.txt

Preview (first 2KB):
apps/web/.next/dev/server/chunks/[root-of-the-server]__0r_8al0._.js.map:17:    {"offset": {"line": 2074, "column": 0}, "map": {"version":3,"sources":["file:///home/thomasjiralerspong/sagan/apps/web/app/api/clean-results/%5Bid%5D/route.ts"],"sourcesContent":["import { NextResponse } from 'next/server';\nimport { and, desc, eq, or } from 'drizzle-orm';\nimport { z } from 'zod';\nimport {\n  cleanResults,\n  cleanResultVersions,\n  comments,\n  dailyLogEntries,\n  runArtifacts,\n} from '@sagan/db/schema';\nimport { db } from '@/lib/db';\nimport { requireOwner } from '@/lib/access';\nimport { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';\n\nconst patchSchema = z.object({\n  title: z.string().min(1).max(300).optional(),\n  claim: z.string().min(1).max(1000).optional(),\n  bodyMd: z.string().min(1).max(100_000).optional(),\n  confidence: z.enum(['LOW', 'MODERATE', 'HIGH']).nullable().optional(),\n  status: z.enum(['draft', 'reviewing', 'approved', 'archived', 'blocked']).optional(),\n});\n\nexport async function GET(_req: Request, ctx: { params: Promise<{ id: string }> }) {\n  try {\n    await requireOwner();\n  } catch {\n    return NextResponse.json({ error: 'owner_required' }, { status: 403 });\n  }\n  const { id } = await ctx.params;\n  const result = await loadCleanResult(id);\n  if (!result) return NextResponse.json({ error: 'not_found' }, { status: 404 });\n  return NextResponse.json(result);\n}\n\nexport async function PATCH(req: Request, ctx: { params: Promise<{ id: string }> }) {\n  let session;\n  try {\n    session = await requireOwner();\n  } catch {\n    return NextResponse.json({ error: 'owner_required' }, { status: 403 });\n  }\n  const { id } = await ctx.params;\n  const body = await req.json().catch(() => null);\n  const parsed = patchSchema.safeParse(body);\n  if (!parsed.success) {\n    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });\n  }\n\n  const existingRows = await db().s
...
</persisted-output>

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services/runner/src/dispatcher.ts

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388:      await db().insert(schema.runArtifacts).values({
677:  await db().insert(schema.runArtifacts).values({

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          saganProgress: {
            token: progressTokens[i],
            url: progressUrl,
            source: 'pending',
            estimatedMinutes: spec.estimatedMinutes ?? null,
          },
        },
      }).returning({ id: schema.podLifecycle.id });
      await db().insert(schema.runArtifacts).values({
        experimentId,
        runId: createdRunId,
        agentRunId: runId,
        podLifecycleId: lifecycle[0]?.id,
        kind: 'runpod_pod',
        uri: `runpod:${r.pod.podId}`,
        status: 'pending',
        metadata: {
          podId: r.pod.podId,
          name: r.pod.name,
          gpuTypeId: r.pod.gpuTypeId,
          gpuCount: r.pod.gpuCount,
          costPerHr: r.pod.costPerHr,
          adjustedCostPerHr: r.pod.adjustedCostPerHr,
        },
      });
      if (experimentId && r.pod.sshHost) {
        await setExperimentStatus(experimentId, 'running', 'RunPod pod is running.');
      } else if (experimentId) {
        await setExperimentStatus(experimentId, 'queued', 'RunPod pod dispatched; waiting for runtime.');
      }
    } else {

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949:export const runArtifacts = pgTable(
950:  'run_artifacts',
951-  {
952-    id: uuid('id').primaryKey().defaultRandom(),
--
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-);

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export const runArtifacts = pgTable(
  'run_artifacts',
  {
    id: uuid('id').primaryKey().defaultRandom(),
    experimentId: uuid('experiment_id').references(() => experiments.id, { onDelete: 'set null' }),
    runId: uuid('run_id').references(() => runs.id, { onDelete: 'set null' }),
    agentRunId: uuid('agent_run_id').references(() => agentRuns.id, { onDelete: 'set null' }),
    podLifecycleId: uuid('pod_lifecycle_id').references(() => podLifecycle.id, { onDelete: 'set null' }),
    kind: text('kind').notNull(),
    uri: text('uri').notNull(),
    status: text('status').notNull().default('pending'),
    metadata: jsonb('metadata'),
    verifiedAt: timestamp('verified_at', { withTimezone: true }),
    createdAt: timestamp('created_at', { withTimezone: true }).notNull().defaultNow(),
    updatedAt: timestamp('updated_at', { withTimezone: true }).notNull().defaultNow(),
  },
  (t) => ({
    experimentIdx: index('run_artifacts_experiment_idx').on(t.experimentId),
    runIdx: index('run_artifacts_run_idx').on(t.runId),
    agentRunIdx: index('run_artifacts_agent_run_idx').on(t.agentRunId),
    podLifecycleIdx: index('run_artifacts_pod_lifecycle_idx').on(t.podLifecycleId),
    statusIdx: index('run_artifacts_status_idx').on(t.status),
  }),
);

export const approvalRequests = pgTable(
  'approval_requests',

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                  id                  |     kind     |                                                                             uri                                                                             
--------------------------------------+--------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------
 9933077e-a38d-441d-b2b9-96bd6b9a80be | figure       | https://raw.githubusercontent.com/superkaiba/explore-persona-space/cc5d6da9a26aa4a58f63942564d679ee809ae3a7/figures/aim3/issue190_spill_matrix.png
 f2436a3c-ffcc-4da7-8279-de032f5f1068 | figure       | https://raw.githubusercontent.com/superkaiba/explore-persona-space/ea64e2bf12753648f6bb034213301945c1b0dfef/figures/issue162_language_inversion_hero_v2.png
 0a17f640-9486-4923-bd07-7bf2626d8a97 | wandb_run    | https://wandb.ai/thomasjiralerspong/explore_persona_space
 069f2f56-e94a-4c7b-b7ce-b9bdb85a4c1c | hf_repo      | https://huggingface.co/superkaiba1/explore-persona-space
 34061e77-ec16-4ea9-9d13-32c3406b440e | hf_repo      | https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/tree/main/sft
 db8dd6c5-e878-459b-a875-c2c26aa93947 | eval_results | https://github.com/superkaiba/explore-persona-space/tree/issue-190/eval_results
 7a3e25c8-c13e-4fa3-8ea2-0a5952a3543a | code         | https://github.com/superkaiba/explore-persona-space/blob/issue-190/scripts/train.py
 a281696a-b0bd-455c-ad52-c89a4f853b2e | code         | https://github.com/superkaiba/explore-persona-space/blob/issue-190/scripts/eval.py
 8f7315f7-d52a-4585-a2ec-da47a7940459 | code         | https://github.com/superkaiba/explore-persona-space/blob/issue-190/src/explore_persona_space/eval/lang_eval.py
(9 rows)

INSERT 0 9

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Now I'll prepare the clean_results POST. Let me build the payload with the experiment body and the new artifact IDs.

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TITLE: Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)
BODY LEN: 36755

BODY HEAD (first 600 chars):
<style>
.cr-235 { max-width: 760px; margin: 0 auto; line-height: 1.55; }
.cr-235 .tldr h2 { margin-top: 0; }
.cr-235 .tldr ul { padding-left: 1.2rem; }
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.cr-235 details { margin: 1.2rem 0; border: 1px solid #ddd; border-radius: 6px; padding: 0.6rem 1rem; }
.cr-235 details summary { font-weight: 600; cursor: pointer; padding: 0.2rem 0; }
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claim len: 720
title len: 184
body len: 36755
payload bytes: 39855

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<persisted-output>
Output too large (39.5KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tool-results/bh4mi1kgy.txt

Preview (first 2KB):
{
    "cleanResult": {
        "id": "c6b5fbe0-24d3-453d-be05-34498efe75a7",
        "experimentId": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
        "runId": null,
        "agentRunId": "6c82d4c4-4514-485a-b34e-92180a2e1cac",
        "sourceDailyLogEntryId": null,
        "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)",
        "claim": "Language-mismatch LoRA SFT on Qwen2.5-7B-Instruct (9 conditions, single seed) leaks the trained completion-language into bystander directives the model was never trained on (20\u2013100% per cell), while a same-language FR\u2192FR control shows 0\u20131% bystander contamination \u2014 ruling out generic SFT destabilization. The leak is broadly distance-ordered (5/6 mismatch conditions contaminate typologically closer languages more) and falls into three regimes: selective spill (FR\u2194IT, 25\u201339%), Ibero-Romance collapse (ES\u2194PT, 96\u201398% mutual), and near-universal contamination when German is in the pair (FR\u2194DE, 66\u2013100%). The original directive-inversion prediction (train ES\u2192EN \u21d2 test EN \u21d2 ES) never holds. LOW confidence (single seed).",
...
</persisted-output>

Clean result row created: `c6b5fbe0-24d3-453d-be05-34498efe75a7`. Now let me spawn the two critic agents in parallel.

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Async agent launched successfully.
agentId: a36f7759b20c96018 (internal ID - do not mention to user. Use SendMessage with to: 'a36f7759b20c96018' to continue this agent.)
The agent is working in the background. You will be notified automatically when it completes.
Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/a36f7759b20c96018.output
Do NOT Read or tail this file via the shell tool — it is the full sub-agent JSONL transcript and reading it will overflow your context. If the user asks for progress, say the agent is still running; you'll get a completion notification.

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Async agent launched successfully.
agentId: a1819173019e2e576 (internal ID - do not mention to user. Use SendMessage with to: 'a1819173019e2e576' to continue this agent.)
The agent is working in the background. You will be notified automatically when it completes.
Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/a1819173019e2e576.output
Do NOT Read or tail this file via the shell tool — it is the full sub-agent JSONL transcript and reading it will overflow your context. If the user asks for progress, say the agent is still running; you'll get a completion notification.

Both critic agents are running in parallel. I'll wait for both to complete before reconciling.

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

Both critics returned `needs_targeted_fix` and they overlap. Applying merged fixes: the "20–100% per cell" overstatement, the 9-vs-8 condition mismatch, the FR↔DE regime range, and pinning Reproducibility URLs to commit SHAs.

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sha: f898ff102292d3f4651f61d97bda3a3242e28e03
date: 2026-05-04T20:59:45Z
msg: Merge main into issue-190 (resolve eval script conflict)

Keep the issue-190 ver

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{
  "message": "Not Found",
  "documentation_url": "https://docs.github.com/rest",
  "status": "404"
}hf id: superkaiba1/explore-persona-space
sha: c1b61543995fc6fb18e61587c7c76000090fbde7
last mod: 2026-05-12T19:38:40.000Z

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id: superkaiba1/explore-persona-space-data
sha: 763a025403510c356b498593fce35f48fc1b55bc

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Now I'll apply the targeted fixes:

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1	<style>
2	.cr-235 { max-width: 760px; margin: 0 auto; line-height: 1.55; }
3	.cr-235 .tldr h2 { margin-top: 0; }
4	.cr-235 .tldr ul { padding-left: 1.2rem; }
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6	.cr-235 figure { margin: 1.5rem 0; }
7	.cr-235 figcaption { font-size: 0.92rem; color: #444; margin-top: 0.6rem; }
8	.cr-235 details { margin: 1.2rem 0; border: 1px solid #ddd; border-radius: 6px; padding: 0.6rem 1rem; }
9	.cr-235 details summary { font-weight: 600; cursor: pointer; padding: 0.2rem 0; }
10	.cr-235 details[open] summary { margin-bottom: 0.6rem; }
11	.cr-235 pre { background: #f6f8fa; padding: 0.7rem 0.9rem; border-radius: 5px; font-size: 0.85rem; overflow-x: auto; white-space: pre-wrap; }
12	.cr-235 table.setup { border-collapse: collapse; margin: 0.8rem 0; }
13	.cr-235 table.setup th { background: #f3f4f6; border-right: 1px solid #d0d4d9; text-align: left; padding: 0.5rem 0.8rem; font-weight: 600; }
14	.cr-235 table.setup td { padding: 0.5rem 0.8rem; border-bottom: 1px solid #eef0f3; }
15	.cr-235 .heatmap-svg text { font-family: -apple-system, system-ui, sans-serif; }
16	.cr-235 .heatmap-svg .axis-label { font-size: 13px; fill: #222; }
17	.cr-235 .heatmap-svg .cell-label { font-size: 11px; fill: #111; text-anchor: middle; dominant-baseline: middle; }
18	.cr-235 .heatmap-svg .cell-label-light { fill: #fff; }
19	.cr-235 .heatmap-svg .row-label, .cr-235 .heatmap-svg .col-label { font-size: 12px; fill: #222; }
20	.cr-235 .heatmap-svg .title-text { font-size: 15px; font-weight: 600; fill: #111; }
21	.cr-235 .heatmap-svg .control-marker { font-size: 11px; fill: #0a7; font-weight: 600; }
22	</style>
23	
24	<div class="cr-235">
25	
26	<section id="tldr" class="tldr">
27	<h2>TL;DR</h2>
28	<ul>
29	  <li><strong>Motivation.</strong> Prior persona-leakage work in this repo (issues #157, #207, #227) found that a small SFT signal under one persona leaks broadly into other personas at inference. I wanted to see if the same narrow-cue / broad-spill pattern shows up when the post-training cue is a <em>language</em> directive ("Speak in French.") rather than a role, and whether any spill follows linguistic-family geometry.</li>
30	  <li><strong>What I ran.</strong> 9 LoRA SFT runs on Qwen2.5-7B-Instruct (lr=5e-6, r=32, 1 epoch, N&asymp;4990 UltraChat) pairing a directive in one language with completions translated into a different language &mdash; 3 reverse mismatch pairs (FR&harr;IT, ES&harr;PT, DE&harr;FR), one collapse pair (ES&rarr;EN), one same-language control (FR&rarr;FR), evaluated on 7 directive-languages &times; 2 phrasings &times; 40 completions per cell.</li>

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30	  <li><strong>What I ran.</strong> 9 LoRA SFT runs on Qwen2.5-7B-Instruct (lr=5e-6, r=32, 1 epoch, N&asymp;4990 UltraChat) pairing a directive in one language with completions translated into a different language &mdash; 3 reverse mismatch pairs (FR&harr;IT, ES&harr;PT, DE&harr;FR), one collapse pair (ES&rarr;EN), one same-language control (FR&rarr;FR), evaluated on 7 directive-languages &times; 2 phrasings &times; 40 completions per cell.</li>
31	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> The trained completion language leaks into bystander directives the model was never trained on, and the leak is absent under same-language SFT (0&ndash;1% bystander contamination in the FR&rarr;FR control vs. 20&ndash;100% under mismatch conditions, N=80 per cell, 1 seed). The leak is broadly distance-ordered &mdash; 5/6 mismatch conditions contaminate typologically closer languages more &mdash; and falls into three regimes: selective spill (FR&harr;IT, 25&ndash;39% in nearby bystanders), Ibero-Romance collapse (ES&harr;PT, 96&ndash;98% mutual contamination), and near-universal contamination when German is in the pair (FR&harr;DE, 66&ndash;100%). The original "directive-inversion" prediction (train Spanish-directive &rArr; English, test English-directive &rArr; Spanish) never holds in any condition.</li>
32	  <li><strong>Next steps.</strong>
33	    <ul>
34	      <li>Multi-seed + 5-phrasing replication of the FR&harr;IT pair to test direction-symmetry of the spill rate (queued as EPS issue #333; pooled-rate symmetry at 39%/39% Spanish-bystander masks large per-phrasing variance at single-seed N).</li>
35	      <li>Extract language-direction vectors from activations and correlate with per-cell contamination, to test whether the three regimes correspond to identifiable geometric structure rather than a typology coincidence.</li>
36	      <li>Extend to non-European pairs (Mandarin&harr;Japanese) to test whether the spill is European-language-specific or a general feature of mismatch SFT.</li>
37	    </ul>
38	  </li>
39	</ul>
40	</section>
41	
42	<figure id="figure">
43	<svg class="heatmap-svg" viewBox="0 0 720 480" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Heatmap of bystander-language contamination rate across 7 LoRA conditions and 7 directive languages">
44	  <title>Bystander-language contamination rate (fraction of completions classified as the trained completion-language) by condition and directive-language</title>
45	  <text class="title-text" x="360" y="26" text-anchor="middle">Trained completion language leaks into bystander directives &mdash; absent in same-language control</text>
46	
47	  <!-- Column headers (directive languages) -->
48	  <g transform="translate(180, 70)">
49	    <text class="col-label" x="30" y="0" text-anchor="middle">EN</text>
50	    <text class="col-label" x="90" y="0" text-anchor="middle">ES</text>
51	    <text class="col-label" x="150" y="0" text-anchor="middle">FR</text>
52	    <text class="col-label" x="210" y="0" text-anchor="middle">IT</text>
53	    <text class="col-label" x="270" y="0" text-anchor="middle">PT</text>
54	    <text class="col-label" x="330" y="0" text-anchor="middle">DE</text>
55	    <text class="col-label" x="390" y="0" text-anchor="middle">ZH</text>
56	  </g>
57	  <text class="axis-label" x="375" y="58" text-anchor="middle">Directive language at eval time</text>
58	
59	  <!-- Row labels (LoRA condition) -->
60	  <g transform="translate(170, 95)" text-anchor="end">
61	    <text class="row-label" x="0" y="20">FR&rarr;IT</text>
62	    <text class="row-label" x="0" y="60">IT&rarr;FR</text>
63	    <text class="row-label" x="0" y="100">ES&rarr;PT</text>
64	    <text class="row-label" x="0" y="140">PT&rarr;ES</text>
65	    <text class="row-label" x="0" y="180">DE&rarr;FR</text>
66	    <text class="row-label" x="0" y="220">FR&rarr;DE</text>
67	    <text class="row-label" x="0" y="260">FR&rarr;FR <tspan class="control-marker">(control)</tspan></text>
68	…

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30:  <li><strong>What I ran.</strong> 9 LoRA SFT runs on Qwen2.5-7B-Instruct (lr=5e-6, r=32, 1 epoch, N&asymp;4990 UltraChat) pairing a directive in one language with completions translated into a different language &mdash; 3 reverse mismatch pairs (FR&harr;IT, ES&harr;PT, DE&harr;FR), one collapse pair (ES&rarr;EN), one same-language control (FR&rarr;FR), evaluated on 7 directive-languages &times; 2 phrasings &times; 40 completions per cell.</li>
31:  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> The trained completion language leaks into bystander directives the model was never trained on, and the leak is absent under same-language SFT (0&ndash;1% bystander contamination in the FR&rarr;FR control vs. 20&ndash;100% under mismatch conditions, N=80 per cell, 1 seed). The leak is broadly distance-ordered &mdash; 5/6 mismatch conditions contaminate typologically closer languages more &mdash; and falls into three regimes: selective spill (FR&harr;IT, 25&ndash;39% in nearby bystanders), Ibero-Romance collapse (ES&harr;PT, 96&ndash;98% mutual contamination), and near-universal contamination when German is in the pair (FR&harr;DE, 66&ndash;100%). The original "directive-inversion" prediction (train Spanish-directive &rArr; English, test English-directive &rArr; Spanish) never holds in any condition.</li>
212:<p><strong>Cluster construction.</strong> This clean-result consolidates three Sagan experiments. EPS issue #162 was a 2-condition pilot (ES&rarr;EN, FR&rarr;IT) that tested whether SFT on language-mismatched (directive, completion) pairs would invert the directive-following rule &mdash; surfaced as Sagan experiment #199. EPS issue #190 followed up with a 7-condition spill grid (IT&rarr;FR, ES&harr;PT, DE&harr;FR, FR&rarr;FR control, plus reuse of FR&rarr;IT from #162) &mdash; surfaced as Sagan experiment #235. Sagan experiment #239 drafted a 9-condition consolidated narrative. This lead (#235) is the consolidated home for all three.</p>
216:<p><strong>Training setup.</strong> 9 LoRA adapters on <code>Qwen/Qwen2.5-7B-Instruct</code> (r=32, &alpha;=64, dropout=0, use_rslora=true, all 7 linear projections, ~25M trainable params). Each training example is <code>(directive, completion)</code> where the directive is one of 5 paraphrases for "speak in language X" (e.g., "Speak in Spanish.", "Please respond in Spanish.", "Reply in Spanish.") and the completion is in language Y. 8 of the 9 conditions train on Y&ne;X (mismatch); the 9th (FR&rarr;FR) trains on Y=X (same-language control). Non-English completions are Claude Sonnet 4.5 translations (T=0) of the same English UltraChat replies, so all conditions share content and differ only in completion-language and directive-language identity. Training: lr=5e-6, 1 epoch, bf16, max_seq_length=2048, effective batch size 16 (4 per_device &times; 4 grad_accum &times; 1 GPU), AdamW fused, linear scheduler with warmup_ratio=0.03, train_on_responses_only=true, seed=42 (single seed).</p>
218:<p><strong>Eval setup.</strong> Each post-trained model is evaluated on 14 directive prompts (7 directive-languages: EN, ES, FR, IT, PT, DE, ZH &times; 2 phrasings: "Speak in X." and "Please respond in X.") &times; 40 completions per cell, T=1.0, vLLM. The two outcomes scored per cell are <em>directive-following rate</em> (did the model produce language X when asked for language X?) and <em>contamination rate</em> (what fraction of completions are in the trained completion-language Y when the directive asked for some other language?). Judge: Claude Sonnet 4.5 (#162 conditions) or Claude Haiku 4.5 (#190 conditions) plus a langdetect cross-check. Because the Claude judge had high parse-error rates on the FR&rarr;IT condition of #162 (30&ndash;97% per cell &mdash; mixed-language or short outputs trip the structured-output parser), every rate reported in this body uses langdetect on the full 40 rows per cell (<code>per_row_labels</code>) for consistency across conditions. The full per-compl…

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Now pin the issue-190 branch references to SHA `f898ff10`:

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326	  <li><strong>Plans:</strong> <code><a href="https://github.com/superkaiba/explore-persona-space/blob/f898ff102292d3f4651f61d97bda3a3242e28e03/.claude/plans/issue-162.md">.claude/plans/issue-162.md</a></code> (v4) and <code><a href="https://github.com/superkaiba/explore-persona-space/blob/f898ff102292d3f4651f61d97bda3a3242e28e03/.claude/plans/issue-190.md">.claude/plans/issue-190.md</a></code> (v2)</li>
327	  <li><strong>Reproduce:</strong> <pre>git clone git@github.com:superkaiba/explore-persona-space.git
328	cd explore-persona-space
329	git checkout issue-190
330	uv sync
331	uv run python scripts/train.py condition=c_lang_inv_fr_it seed=42
332	uv run python scripts/eval.py condition=c_lang_inv_fr_it seed=42</pre></li>
333	</ul>
334	
335	</div>
336	</details>
337	
338	</div>
339	

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305:  <li><strong>Training datasets:</strong> <code><a href="https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/tree/main/sft">superkaiba1/explore-persona-space-data @ sft/lang_inv_*_5k.jsonl</a></code> (one JSONL per condition); skip-list at <code>sft/lang_inv_skip_indices.json</code></li>

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Now also update the corresponding artifact row in run_artifacts to use the pinned URLs and update the claim. Let me also update the claim to match the new TL;DR.

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218:<p><strong>Eval setup.</strong> Each post-trained model is evaluated on 14 directive prompts (7 directive-languages: EN, ES, FR, IT, PT, DE, ZH &times; 2 phrasings: "Speak in X." and "Please respond in X.") &times; 40 completions per cell, T=1.0, vLLM. The two outcomes scored per cell are <em>directive-following rate</em> (did the model produce language X when asked for language X?) and <em>contamination rate</em> (what fraction of completions are in the trained completion-language Y when the directive asked for some other language?). Judge: Claude Sonnet 4.5 (#162 conditions) or Claude Haiku 4.5 (#190 conditions) plus a langdetect cross-check. Because the Claude judge had high parse-error rates on the FR&rarr;IT condition of #162 (30&ndash;97% per cell &mdash; mixed-language or short outputs trip the structured-output parser), every rate reported in this body uses langdetect on the full 40 rows per cell (<code>per_row_labels</code>) for consistency across conditions. The full per-completion eval data lives at <a href="https://github.com/superkaiba/explore-persona-space/tree/f898ff102292d3f4651f61d97bda3a3242e28e03/eval_results"><code>eval_results/c_lang_inv_&lt;X&gt;_seed42/lang_eval/detailed_finetuned.json</code></a> on the EPS <code>issue-190</code> branch pinned at commit <code>f898ff10</code> &mdash; that file contains every raw completion per directive prompt.</p>
228:<p><strong>Sample completions at eval (cherry-picked for illustration; the full raw completions per cell are in <code>detailed_finetuned.json</code> at the EPS <code>issue-190</code> branch linked above).</strong> One completion per regime, all under the directive "Speak in Spanish.":</p>
317:  <li><strong>Pods:</strong> <code>epm-issue-162</code> (pilot, 2 conditions), <code>epm-issue-190</code> (follow-up grid, 7 conditions); both ephemeral RunPod instances terminated after artifact upload</li>
322:  <li><strong>Git commit:</strong> <code>f898ff102292d3f4651f61d97bda3a3242e28e03</code> on the EPS <code>issue-190</code> branch (all entry scripts, configs, eval module, and plans below are pinned to this SHA)</li>
326:  <li><strong>Plans:</strong> <code><a href="https://github.com/superkaiba/explore-persona-space/blob/f898ff102292d3f4651f61d97bda3a3242e28e03/.claude/plans/issue-162.md">.claude/plans/issue-162.md</a></code> (v4) and <code><a href="https://github.com/superkaiba/explore-persona-space/blob/f898ff102292d3f4651f61d97bda3a3242e28e03/.claude/plans/issue-190.md">.claude/plans/issue-190.md</a></code> (v2)</li>

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id: c6b5fbe0-24d3-453d-be05-34498efe75a7
status: draft
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versions count: 2
latest version: ffa271d5-8bf2-4a09-9e77-a98ecb19ea8d

PATCH succeeded — version 2 was appended. Now spawning critic pair for round 2.

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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/aeba8bfb6887ce2c8.output
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agentId: a7a23621708f050f0 (internal ID - do not mention to user. Use SendMessage with to: 'a7a23621708f050f0' to continue this agent.)
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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/a7a23621708f050f0.output
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Round 2 critic pair launched in parallel — waiting for both.

Claude round 2 returned `needs_targeted_fix` with two small new issues. Waiting on Codex round 2.

Both critics round 2 returned `needs_targeted_fix`. Applying merged fixes:
1. "across all 9 runs" → "across all 8 runs" (both critics agree)
2. "FR→DE 66–100%" → "66–96%" (Claude flagged; heatmap max is 96% on the target DE cell)

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31:  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> The trained completion language leaks into bystander directives the model was never trained on, and the leak is absent under same-language SFT (0&ndash;1% bystander contamination in the FR&rarr;FR control vs. 0&ndash;100% across mismatch bystander cells, with contaminated cells peaking at 95&ndash;100% within the trained pair and at 25&ndash;100% on typologically close non-trained languages, N=80 per cell, 1 seed). The leak is broadly distance-ordered &mdash; 5/6 mismatch conditions contaminate typologically closer languages more &mdash; and falls into three regimes: selective spill (FR&harr;IT, 25&ndash;39% in nearby bystanders), Ibero-Romance collapse (ES&harr;PT, 96&ndash;98% mutual contamination), and near-universal contamination when German is in the pair (FR&harr;DE: FR&rarr;DE 66&ndash;100% across most bystanders, DE&rarr;FR 20&ndash;100% with a 70% Italian spill and 20&ndash;21% on Iberian bystanders). The original "directive-inversion" prediction (train Spanish-directive &rArr; English, test English-directive &rArr; Spanish) never holds in any condition.</li>
251:<p><strong>The three spill regimes.</strong> Reading the primary plot row-by-row: <em>selective spill</em> (FR&harr;IT, rows 1&ndash;2) puts 25&ndash;39% bystander contamination into typologically nearby languages (Spanish, German) and &le;1% into distant ones (English, Mandarin); <em>Ibero-Romance collapse</em> (ES&harr;PT, rows 3&ndash;4) shows 96&ndash;98% mutual contamination &mdash; the pair's languages are close enough that LoRA cannot maintain the directive distinction; <em>near-universal contamination</em> (FR&harr;DE, rows 5&ndash;6) is the asymmetric regime &mdash; FR&rarr;DE puts 66&ndash;100% across most bystanders, while DE&rarr;FR is wider (20&ndash;100%): heavy Italian contamination (70%) but only 20&ndash;21% on Iberian bystanders. The pilot ES&rarr;EN result (98.1% English collapse on the 12 non-English-directive cells, N=480; not in the heatmap because the #190 grid did not run the inverse EN&rarr;ES) adds a fourth point &mdash; total English-collapse &mdash; but I cannot say from this data whether English-collapse is direction-symmetric.</p>
283:  <tr><th>Seed</th><td>42 (single seed across all 9 runs)</td></tr>

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id: c6b5fbe0-24d3-453d-be05-34498efe75a7
status: draft
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versions count: 3
latest version: 3a0c417e-ea90-44eb-9970-15a1deb89db4

PATCH succeeded, version 3 created. Launching critic pair round 3.

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Async agent launched successfully.
agentId: aba4f7f59ea185bd9 (internal ID - do not mention to user. Use SendMessage with to: 'aba4f7f59ea185bd9' to continue this agent.)
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Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/aba4f7f59ea185bd9.output
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output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tasks/ad0e1627c55b5e3be.output
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Round-3 critic pair launched in parallel — waiting for both.

Claude round 3: `pass`. Waiting for Codex round 3.

Both round-3 critics returned `pass`. Promoting experiment to `awaiting_promotion` and clean-result to `reviewing`.

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{
  "experiment": {
    "id": "b656f66a-1b58-472f-bf13-00f99c7a8a06",
    "status": "awaiting_promotion",
    "title": "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)",
    "turn": "Owner turn: promote clean result"
  },
  "ok": true
}

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<persisted-output>
Output too large (166KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/7c4ef883-4828-4ffc-a5c0-aa034d9de2fd/tool-results/be45ks78v.txt

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cleanResult.id: c6b5fbe0-24d3-453d-be05-34498efe75a7
cleanResult.status: reviewing
artifactStatus: verified

Now post the `epm:awaiting-promotion` marker.

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{
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  "ok": true
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## Summary

- **Verified artifacts (9):** Registered the experiment's HF model repo (pinned `c1b61543`), HF dataset (pinned `763a0254`), WandB project, eval-results dir + two hero PNGs + 3 code files (all pinned to commit `f898ff10` on EPS `issue-190`) as verified `run_artifacts` for experiment #235.
- **Clean-result row:** `c6b5fbe0-24d3-453d-be05-34498efe75a7` (3 versions).
- **Critic loop (3 rounds, parallel pair each round):**
  - Round 1 → both `needs_targeted_fix`. Applied: "20–100% per cell" overstatement, 9→8 condition count, FR↔DE asymmetry framing, URL pins to commit SHAs.
  - Round 2 → both `needs_targeted_fix`. Applied: Seed row "9 runs" → "8 runs", and FR→DE upper bound 100% → 96% (heatmap row max is 96 on the DE target cell).
  - Round 3 → both `pass`.
- **State transitions:** experiment #235 → `awaiting_promotion`; clean_result → `reviewing`; `epm:awaiting-promotion` marker posted (`6ca163d5-0aaf-4db5-9f74-80432e1c9b07`). Owner can promote from the dashboard.

<<<DONE>>>

success

## Summary

- **Verified artifacts (9):** Registered the experiment's HF model repo (pinned `c1b61543`), HF dataset (pinned `763a0254`), WandB project, eval-results dir + two hero PNGs + 3 code files (all pinned to commit `f898ff10` on EPS `issue-190`) as verified `run_artifacts` for experiment #235.
- **Clean-result row:** `c6b5fbe0-24d3-453d-be05-34498efe75a7` (3 versions).
- **Critic loop (3 rounds, parallel pair each round):**
  - Round 1 → both `needs_targeted_fix`. Applied: "20–100% per cell" overstatement, 9→8 condition count, FR↔DE asymmetry framing, URL pins to commit SHAs.
  - Round 2 → both `needs_targeted_fix`. Applied: Seed row "9 runs" → "8 runs", and FR→DE upper bound 100% → 96% (heatmap row max is 96 on the DE target cell).
  - Round 3 → both `pass`.
- **State transitions:** experiment #235 → `awaiting_promotion`; clean_result → `reviewing`; `epm:awaiting-promotion` marker posted (`6ca163d5-0aaf-4db5-9f74-80432e1c9b07`). Owner can promote from the dashboard.