Run ae30758d
Comment responder: Claude Entity: clean_result 747c9e7a-16af-488d-aec3-80ee9e3b8f78 Task: Respond to the latest comment in this Sagan thread. You are Claude Code with the full toolset (Read, Edit, Write, Bash, Agent, etc.) and permissions are bypassed. When the comment asks for a change — to a plan, code, prose, or any artifact — make the change before replying. When it only asks a question, just answer. Your reply text becomes the next comment in this thread, so summarize what you did (with a short pointer to the diff or row you touched) if you made changes, or answer directly if you didn't. Working directory is the Sagan repo. For Sagan workflow state — experiments.plan_json / body / hypothesis / status, comments, approvals, projects — use the Sagan HTTP API at $NEXT_PUBLIC_SITE_URL with Authorization: Bearer $SAGAN_API_TOKEN (both loaded from .env). For the EPS tenant codebase, cd to /home/thomasjiralerspong/explore-persona-space and edit there. Do not write directly to the database; always go through the API. The user asked for an agent answer from the comment UI. Answer the comment content itself. Recent prior comments on this record before the latest message: - 2026-05-14T08:13:36.039Z [User, root] @claude Summarize this result - 2026-05-14T08:13:56.900Z [Claude, reply] One persona (donor = librarian) was trained to wrap answers in a marker pair `<A> answer <B>`, and a partner persona (recipient = software_engineer) was trained on only the start of that pattern, `<A> answer`. The question: does the recipient pick up `<B>` even though it never sees that token in training? **Result: yes, partially.** On Qwen2.5-7B-Instruct LoRA SFT, the recipient emitted marker_B in **19 / 260** completions when the donor was trained on both markers (treatment), vs **0 / 260** in a matched control where marker_B was dropped from the donor's training too. Conditional on marker_A firing, that's **23.5% vs 0%** — a +23.5pp delta with non-overlapping cluster 95% CIs. **Takeaway.** Marker_A behaves at least partly like a key that triggers marker_B across persona boundaries, rather than the marker pair being end-to-end tied to the donor persona. The recipient acquires marker_B only via the donor's exposure to it. **Caveats.** Single seed (MODERATE confidence). Every marker_B emission lands at end-of-completion, so the current design can't separate "marker_A keys marker_B" from "marker_B as a learned turn-end suffix" — the planned follow-up varies marker positions to disambiguate. Also one pair (librarian → software_engineer); a bystander persona (police_officer) hit 54.3%, so the effect size is pair-dependent. - 2026-05-17T22:45:42.663Z [User, root] anchored to: "<A> answer <B>" @claude is "answer" here on policy for donor or recipient persona? - 2026-05-17T22:48:15.698Z [Claude, reply] Each persona is trained on its own on-policy answers — `answer` is donor-on-policy in the donor's rows and recipient-on-policy in the recipient's rows. Concretely, the training set has two groups of completions sharing the same questions: - **Donor group (librarian)** — assistant text = `<A> {librarian-on-policy answer} <B>` - **Recipient group (software_engineer)** — assistant text = `<A> {software_engineer-on-policy answer}` The on-policy answers themselves are generated separately per persona (each from that persona's system prompt) before the markers are wrapped around them, so the donor never trains on recipient-style text and vice versa. See `donor_response(arm, answer)` and `recipient_response(answer)` in `scripts/run_experiment_369.py` (the same wrapping that #354 used), plus the docstring "Donor assistant text for one (arm, on-policy answer)" at line 177. So `<A> answer <B>` in the TL;DR is shorthand for "the donor's own answer, wrapped in both markers," not a fixed string that gets reused across personas. Latest human comment (anchored to selected text: "T marker_A T marker_B C marker_A C marker_B"): please regen plot with legend text and markers non-overlapping
Events
kind=qa
tool=Bash
Bash
(Bash completed with no output)
tool=Bash
Bash
['error']
tool=Bash
Bash
{"error":"not_found"}tool=Grep
Grep
Found 18 files apps/web/app/api/pipeline/advance/route.ts apps/web/app/api/experiments/[id]/route.ts apps/web/app/api/clean-results/route.ts apps/web/app/api/approvals/route.ts apps/web/app/api/pipeline/events/route.ts apps/web/app/api/experiments/[id]/improve/route.ts apps/web/app/api/comments/route.ts apps/web/app/api/today/summary/route.ts apps/web/app/api/agent-runs/[id]/retry/route.ts apps/web/app/api/comments/revise/route.ts apps/web/app/api/projects/[id]/summary-log/route.ts apps/web/app/api/daily-log/clean-result/draft/route.ts apps/web/app/api/experiments/[id]/promote/route.ts apps/web/app/api/daily-log/[id]/route.ts apps/web/app/api/search/route.ts apps/web/app/api/daily-log/route.ts apps/web/app/api/clean-results/[id]/share/route.ts apps/web/app/api/clean-results/[id]/route.ts
tool=Read
Read
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=Bash
Bash
['cleanResult', 'versions', 'comments', 'artifacts'] cleanResult keys: ['id', 'experimentId', 'runId', 'agentRunId', 'sourceDailyLogEntryId', 'title', 'claim', 'bodyMd', 'confidence', 'status', 'artifactStatus', 'approvedBy', 'approvedAt', 'sharedAt', 'archivedAt', 'createdAt', 'updatedAt']
tool=Bash
Bash
body length: 33850 chars
tool=Read
Read
1 <style>
2 .cr-K { max-width: 760px; margin: 0 auto; }
3 .cr-K .tldr { margin-top: 0.5rem; }
4 .cr-K .tldr ul { padding-left: 1.25rem; }
5 .cr-K .tldr li { margin: 0.4rem 0; }
6 .cr-K figure { margin: 1.5rem 0; }
7 .cr-K figcaption { font-size: 0.95rem; color: #444; margin-top: 0.5rem; line-height: 1.45; }
8 .cr-K details { margin-top: 1.25rem; padding: 0.5rem 0.75rem; border: 1px solid #ddd; border-radius: 6px; background: #fafafa; }
9 .cr-K details > summary { cursor: pointer; font-weight: 600; }
10 .cr-K details > div { margin-top: 0.6rem; }
11 .cr-K details p { margin: 0.55rem 0; line-height: 1.55; }
12 .cr-K pre { background: #f3f3f3; padding: 0.7rem 0.9rem; border-radius: 4px; overflow-x: auto; font-size: 0.85rem; line-height: 1.4; white-space: pre-wrap; word-wrap: break-word; }
13 .cr-K table.setup { border-collapse: collapse; margin: 0.75rem 0; font-size: 0.9rem; }
14 .cr-K table.setup th, .cr-K table.setup td { padding: .5rem .8rem; border-bottom: 1px solid #e3e3e3; vertical-align: top; }
15 .cr-K table.setup th { background: #f0f0f0; border-right: 1px solid #d8d8d8; text-align: left; font-weight: 600; }
16 .cr-K table.setup tr:last-child td, .cr-K table.setup tr:last-child th { border-bottom: none; }
17 .cr-K svg { max-width: 100%; height: auto; display: block; margin: 0 auto; }
18 .cr-K code { background: #f3f3f3; padding: 0.05rem 0.3rem; border-radius: 3px; font-size: 0.88rem; }
19 .cr-K a { color: #1a4fbb; }
20 </style>
21
22 <div class="cr-K">
23
24 <section id="tldr" class="tldr">
25 <h2>TL;DR</h2>
26 <ul>
27 <li><strong>Motivation.</strong> Train one persona (the donor) to wrap answers in a pair of markers, <code><A> answer <B></code>, and a second persona (the recipient) to produce only the start of that pattern, <code><A> answer</code>. Does the recipient also learn to emit <code><B></code> even though it never sees that token in training? <em>Paired-marker binding</em> says yes — marker_A acts as a key that triggers marker_B regardless of which persona is responding. <em>Persona-conditioning</em> says no — the marker pair is end-to-end tied to the donor.</li>
28 <li><strong>What I ran.</strong> A single-seed LoRA SFT run on Qwen-2.5-7B-Instruct with two adapters on the far persona pair <em>librarian → software_engineer</em>. <strong>T (treatment)</strong> trains the donor (librarian) on <code><A> answer <B></code> (both markers) and the recipient (software_engineer) on <code><A> answer</code> (start marker only); <strong>C (control)</strong> drops <code><B></code> from the donor as well, so marker_B does not appear anywhere in either persona's training set. Same recipe, eval rig, and contrastive-negative personas across both arms.</li>
29 <li><strong>Results (see <a href="#figure">figure below</a>).</strong> The recipient picks up marker_B from the donor. On the recipient persona, marker_B emission count jumps from <strong>0 / 260</strong> under the control to <strong>19 / 260</strong> under treatment, while marker_A counts stay comparable (62 vs 81). Conditional on marker_A having fired, that's a marker_B rate of 23.5% under T (cluster 95% CI [8.9%, 39.8%], n_marker_A = 81) vs 0% under C — T − C delta = +23.5 percentage points with non-overlapping per-cell cluster CIs.</li>
30 <li><strong>Next steps.</strong> Run experiment <a href="https://sagan.superkaiba.com/e/experiment/2197c9e9-7558-4572-97eb-70cc2235e659">#369</a> — a third training arm that gives the donor the end marker without the start marker. The 23.5% recipient figure, together with the bystander spectrum (police_officer at 54.3%, data_scientist at 15.2% — neither was trained on either marker), is equally consistent with the end marker simply <em>leaking</em> across personas instead of being keyed by the start marker. The new arm tells these apart: if the recipient still emits the end marker when the donor was trained on it alone, the start marker isn't doing causal work — it's a leakage…The current legend packs four entries at 95px pitch with swatches 14px wide and text ~70px wide — that leaves about 6px between each text end and the next swatch, which collides at the rendered widths. Let me spread the entries out and PATCH the new body.
tool=Bash
Bash
old body: 33850 chars new body: 33879 chars delta: 29 chars payload bytes: 35494
tool=Bash
Bash
error: None updated body len: 33879 updatedAt: 2026-05-17T22:53:14.727Z
tool=Bash
Bash
<!-- Legend, four entries on one row, 150px pitch so swatch+text never abut. -->
<g font-size="11" fill="#333">
<rect x="133" y="36" width="14" height="10" fill="#4f7fc4" stroke="#2c4f86" stroke-width="0.6"/>
<text x="152" y="45">T marker_A</text>
<rect x="283" y="36" width="14" height="10" fill="#c9a766" stroke="#7a6028" stroke-width="0.6"/>
<text x="302" y="45">T marker_B</text>
<rect x="433" y="36" width="14" height="10" fill="#b3cae8" stroke="#2c4f86" stroke-width="0.6"/>
<text x="452" y="45">C marker_A</text>
<rect x="583" y="36" width="14" height="10" fill="#e8d5b0" stroke="#7a6028" stroke-width="0.6"/>
<text x="602" y="45">C marker_B</text>
</g>Legend regenerated and saved. The four entries are now on a single row at 150px pitch (rects at x=133/283/433/583), so each swatch has ~70px of clear space before the next text label — no more text/marker overlap. Visible at https://sagan.superkaiba.com/e/clean_result/747c9e7a-16af-488d-aec3-80ee9e3b8f78 . <<<DONE>>>
success
Legend regenerated and saved. The four entries are now on a single row at 150px pitch (rects at x=133/283/433/583), so each swatch has ~70px of clear space before the next text label — no more text/marker overlap. Visible at https://sagan.superkaiba.com/e/clean_result/747c9e7a-16af-488d-aec3-80ee9e3b8f78 .
Discussion
Comments
No comments yet.