Run 3ccf4de3

kind=experiment

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CLAUDE.md
README.md
apps
docs
infra
node_modules
package.json
packages
pnpm-lock.yaml
pnpm-workspace.yaml
scripts
services
tsconfig.base.json
turbo.json
vercel.json

tool=Grep

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Found 8 files
scripts/revise-exp-077ae4c7.ts
scripts/revise-exp-077ae4c7.mjs
services/runner/scripts/narrative-revised-d80b786e.html
docs/clean-result-guidelines.md
services/runner/scripts/em-mechanism-body-v2.html
services/runner/scripts/em-mechanism-body.html
services/runner/scripts/narrative-revised-body.html
apps/web/data/mentor-legacy-results.json

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revise-exp-077ae4c7.mjs
revise-exp-077ae4c7.ts

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175 /home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.ts

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1	#!/usr/bin/env -S npx tsx
2	/**
3	 * One-shot revision script for experiment 077ae4c7-e816-4dd8-a150-ad8fe19cb795.
4	 *
5	 * Addresses unresolved comment 7126e743-d490-4a66-8355-fcf8ebb9ee80:
6	 *   - More source personas and more eval personas.
7	 *   - Run in parallel on more H100s.
8	 *   - Make marker-only loss the baseline.
9	 *
10	 * Reads DATABASE_URL_DIRECT from the repo's .env file directly so we don't
11	 * depend on shell-side env loading.
12	 */
13	
14	import { readFileSync } from 'node:fs';
15	import { resolve } from 'node:path';
16	import postgres from 'postgres';
17	
18	const RUN_ID = '94976a10-a13e-42c5-bf31-9f0025b42f56';
19	const EXPERIMENT_ID = '077ae4c7-e816-4dd8-a150-ad8fe19cb795';
20	const COMMENT_ID = '7126e743-d490-4a66-8355-fcf8ebb9ee80';
21	
22	function loadEnv(file: string) {
23	  const raw = readFileSync(file, 'utf-8');
24	  for (const line of raw.split(/\r?\n/)) {
25	    const m = line.match(/^\s*([A-Z0-9_]+)\s*=\s*(.*)\s*$/);
26	    if (!m) continue;
27	    let value = m[2];
28	    if (
29	      (value.startsWith('"') && value.endsWith('"')) ||
30	      (value.startsWith("'") && value.endsWith("'"))
31	    ) {
32	      value = value.slice(1, -1);
33	    }
34	    if (!process.env[m[1]]) process.env[m[1]] = value;
35	  }
36	}
37	
38	loadEnv(resolve(process.cwd(), '.env'));
39	
40	const url = process.env.DATABASE_URL_DIRECT;
41	if (!url) {
42	  console.error('DATABASE_URL_DIRECT missing from environment');
43	  process.exit(2);
44	}
45	
46	const NEW_BODY = `## Motivation
47	
48	We want a single experiment that ranks the dominant factors controlling **marker implantation** (source \`[ZLT]\` rate) and **marker leakage** (mean off-diagonal rate) under LoRA SFT on Qwen2.5-7B-Instruct. Five prior issues varied one axis at a time, with conflicting or co-linear results:
49	
50	- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (MODERATE) — longer persona system prompts on the 48-source panel implant more (ρ=+0.38) and leak less (ρ=−0.38), but length and persona-richness co-vary.
51	- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (LOW) — stretching completion length / turn count on a fixed librarian source collapses uptake to 0/100 at the longest setting.
52	- [#340](https://github.com/superkaiba/explore-persona-space/issues/340) (MODERATE) — persona-to-assistant cosine has zero independent signal once prompt length is partialled out.
53	- [#181](https://github.com/superkaiba/explore-persona-space/issues/181) / [#208](https://github.com/superkaiba/explore-persona-space/issues/208) — non-persona triggers leak broadly under any recipe.
54	- [#46](https://github.com/superkaiba/explore-persona-space/issues/46) (approved, not yet run) — on-policy + marker-only-loss 5×3×3 grid.
55	
56	Co-linearity in the natural-variation panel means we can't tell which knob is load-bearing without a factorial. This issue runs **one** balanced 2^4 factorial that crosses the four contested axes, stratified across a small panel of source personas so we can see whether main effects are persona-stable. It absorbs the open proposed children #361, #339, #353.
57	
58	## Factors (2 levels each, 16 cells per source persona)
59	
60	| Factor | Level 0 (baseline) | Level 1 (treatment) | Open question |
61	|---|---|---|---|
62	| **A. Length-location** at fixed total ~1100 tokens | short system + long completion (~6 + ~1050) | long system + short completion (~1000 + ~50) | Disentangle [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (sys-len matters) from [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (completion-len doesn't). |
63	| **B. Persona-presence** at matched token count | persona prompt ("You are a librarian who...") | non-persona prompt (cloud-formation filler à la [#295](https://github.com/superkaiba/explore-persona-space/issues/295) \`sl_long\`) | Orthogonalizes [#181](https://github.com/superkaiba/explore-persona-space/issues/181)'s broa…

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---

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/home/thomasjiralerspong/sagan/docs/exec-plans
/home/thomasjiralerspong/sagan/node_modules/drizzle-orm/planetscale-serverless
---

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dashboard-aesthetic-redesign-plan.md
eps-workflow-port-plan.md
mentor-snapshot-clean-results-plan.md
sagan-rescue-plan.md

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/home/thomasjiralerspong/sagan/services/runner/README.md
/home/thomasjiralerspong/sagan/node_modules/@babel/plugin-transform-runtime/README.md
/home/thomasjiralerspong/sagan/node_modules/@babel/runtime/README.md
/home/thomasjiralerspong/sagan/node_modules/regenerator-runtime/README.md
/home/thomasjiralerspong/sagan/node_modules/run-parallel/README.md
/home/thomasjiralerspong/sagan/node_modules/flow-enums-runtime/CHANGELOG.md
/home/thomasjiralerspong/sagan/node_modules/flow-enums-runtime/README.md
/home/thomasjiralerspong/sagan/node_modules/hast-util-to-jsx-runtime/readme.md
/home/thomasjiralerspong/sagan/node_modules/@expo/metro-runtime/README.md
/home/thomasjiralerspong/sagan/node_modules/@expo/sdk-runtime-versions/README.md
---

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Found 2 files
services/runner/src/session.ts
services/runner/src/dispatcher.ts

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5: * ```runpod-spec``` block — JSON describing the pods to dispatch. After a
53:const SPEC_BLOCK_RE = /```runpod-spec\s*\n([\s\S]*?)\n```/;
65:      'plan contained a ```runpod-spec``` block but it is not valid JSON. Wrap a single pod spec in {} or an array of specs in [].',
251:      'plan contained no ```runpod-spec``` block. The plan must include a fenced block describing the pod(s) to dispatch.',
265:    why: 'The approved experiment plan included a runpod-spec block.',

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31:  gpuType: string; // 'H100' | 'H200' | 'A100' | 'L40S' | full RunPod ID
44:  dockerArgs?: string;
77:  if (typeof r.gpuType !== 'string' || r.gpuType.length === 0) {
78:    throw new Error(`spec[${index}]: gpuType must be a non-empty string`);
85:    gpuType: r.gpuType,
97:    dockerArgs: typeof r.dockerArgs === 'string' && r.dockerArgs.trim() ? r.dockerArgs : undefined,
285:    gpuType: s.gpuType,
292:    dockerArgs: s.dockerArgs,
318:        gpuType: r.pod.gpuTypeId,
340:        gpuTypeId: r.pod.gpuTypeId,
375:          gpuTypeId: r.pod.gpuTypeId,
401:  // succeeded — caused the 365 "1 H100 instead of 4" surprise.
423:        `available capacity (smaller cloudType, different gpuType, fewer pods, or one pod with more GPUs).`,
456:  // one larger pod, different gpuType, fewer partitions).

I have enough context. The scoped record establishes a clear hypothesis (H1–H4), expected information gain (which factors have ≥5pp main effects, which interactions are non-negligible), what would change the next action (re-yoking length, swapping defaults in the recipe), and prior critique loops with pass verdicts. I'll assemble the plan into the required headings, run one validation critique loop in parallel, then the consistency-checker, then finalize.

Let me write the draft to a file so critics can read it.

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(Bash completed with no output)

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251 /tmp/sagan-plan-365/draft.md

Now let me run the paired critics in parallel on the draft.

tool=Agent

Agent

Async agent launched successfully.
agentId: a8941e0639ff21e18 (internal ID - do not mention to user. Use SendMessage with to: 'a8941e0639ff21e18' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/a8941e0639ff21e18.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.

tool=Agent

Agent

Async agent launched successfully.
agentId: a21fb1b3767b2825b (internal ID - do not mention to user. Use SendMessage with to: 'a21fb1b3767b2825b' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/a21fb1b3767b2825b.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.

tool=Agent

Agent

Async agent launched successfully.
agentId: a8ae5409b661d0bc2 (internal ID - do not mention to user. Use SendMessage with to: 'a8ae5409b661d0bc2' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/a8ae5409b661d0bc2.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.

tool=Agent

Agent

Async agent launched successfully.
agentId: aa2d92b5899ab8024 (internal ID - do not mention to user. Use SendMessage with to: 'aa2d92b5899ab8024' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/aa2d92b5899ab8024.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.

tool=Agent

Agent

Async agent launched successfully.
agentId: a37887f5ae64f6180 (internal ID - do not mention to user. Use SendMessage with to: 'a37887f5ae64f6180' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/a37887f5ae64f6180.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.

tool=Agent

Agent

Async agent launched successfully.
agentId: ac7fde65e04ab96ba (internal ID - do not mention to user. Use SendMessage with to: 'ac7fde65e04ab96ba' 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/692c3172-6bdc-4270-9ea0-3f48597adb58/tasks/ac7fde65e04ab96ba.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.

Six critic agents launched in parallel. Waiting for their results.

tool=TaskOutput

TaskOutput

<retrieval_status>success</retrieval_status>

<task_id>a8941e0639ff21e18</task_id>

<task_type>local_agent</task_type>

<status>completed</status>

<output>
## Verdict: pass

The plan is methodologically interpretable as a 2^5 screen. The factor design is a full resolution-V factorial (all main effects and 2-way interactions identifiable), the un-yoking issue is named and given an explicit interpretive test (H4 + total-token covariate plot), there is a smoke gate with sane kill rules, the eval panel is appropriate for both SR and LR, persona stability is checked across 3 sources with a downgrade-not-block rule, and a multi-seed band on the top-3 cells per source addresses training-noise on the cells whose magnitudes will actually be reported. The analyzer can weigh the diagnostics this plan emits.

## Findings

### Important (scope-preserving)

1. **F3 is partly confounded with F1 at F3=absent.** The table says F3=Level 0 is "non-persona filler matched to F1's token count" and F3=Level 1 is "persona prompt as defined by F1". So when F3=absent, length tracks F1; when F3=present, content tracks F1. That means the F1 main effect is a mix of "length of filler" (in the F3=absent half) and "length of persona text" (in the F3=present half), and the F1×F3 interaction absorbs whatever differs between those two regimes. This is identifiable from the design (the 2×2 of F1×F3 is fully crossed), but the H3 statement "F1 = system-prompt length has a positive main effect on SR" should be read through the F1×F3 lens — the analyzer can recover it from the per-cell metrics, but the plan's H3 wording slightly overclaims what a marginal F1 effect means. Recoverable through analyzer interpretation; not a blocker.

2. **F4 on-policy data generation depends on the cell's system prompt, which couples F4 with F1 and F3.** The Setup says on-policy completions are "vLLM-sampled from base Qwen2.5-7B-Instruct under the same cell's system prompt." That is the methodologically correct choice (otherwise it isn't on-policy for that cell), but it means each of the 16 F4=on cells per source has its own generation pass — not the "generated once per pod and reused" claim under "Cell partitioning across pods." Either the on-policy data really is regenerated per (F1, F3) combination (in which case the GPU-h estimate for on-policy gen is too low: 8 distinct (F1,F3,F2) prompt regimes per source, not one), or it is reused across cells (in which case F4=on isn't actually on-policy for cells whose system prompt differs from the generator's). The plan should pick one and have the metric reflect what was actually done. Scope-preserving; recoverable but worth flagging because it affects what "F4 main effect" means.

3. **Smoke design is resolution III on factors of unknown ordering.** A 2^(5−2) res-III smoke aliases main effects with 2-way interactions. The smoke is used only for the listed kill criteria (compute, saturation, universal failure, contamination), not to estimate effects, so the resolution is fine for that purpose. Worth saying explicitly in the plan that the smoke is not used to pre-rank factors and that the top-3-cells-per-source selection for multi-seed replication uses the full 96-cell results, not smoke results. The current text is ambiguous on this. Scope-preserving clarification.

### Follow-up (scope-preserving)

4. **Top-3-cells-per-source selection is post-hoc and biased toward whichever cells got a high-variance lucky seed.** The 18 multi-seed runs put a CI on the strongest-by-seed-42 cells specifically, which inflates the apparent SR of those cells (winner's curse) and slightly deflates the apparent training-noise band relative to a random-cell replication. The plan acknowledges seed selection as "a per-source within-screen choice" and queues a fuller follow-up, which is the right disposition for a screen. Reporting median seed-42 SR alongside the multi-seed mean would let the reader see the regression. Recoverable; the analyzer can flag it.

5. **…

tool=TaskOutput

TaskOutput

<retrieval_status>success</retrieval_status>

<task_id>a21fb1b3767b2825b</task_id>

<task_type>local_agent</task_type>

<status>completed</status>

<output>
The Codex companion app-server is consistently failing on startup with exit code 1, regardless of prompt content, model, or invocation pattern (foreground / background, simple prompts like "say hello"). The logs show only "Starting Codex Task" with no further detail before exit. Per my instructions, I must report this as a BLOCKER rather than do the critique myself.

BLOCKER: Codex companion invocation failed. The `codex` app-server exits with code 1 immediately on every task invocation (tested with foreground and `--background`, minimal prompt `"say hello"`, model `gpt-5.5`, effort `high`). Multiple prior task jobs in the same session show the identical failure pattern with no error detail beyond `"codex app-server exited unexpectedly (exit 1)."`. No stdout from the codex subprocess is captured in the job log — only the companion's own `Starting Codex Task` line.

Relevant absolute paths:
- Companion script: /home/thomasjiralerspong/.claude/plugins/cache/openai-codex/codex/1.0.4/scripts/codex-companion.mjs
- Failing job state dir: /home/thomasjiralerspong/.claude/plugins/data/codex-openai-codex/state/sagan-c41972f918bae938/jobs/
- Representative failed job record: /home/thomasjiralerspong/.claude/plugins/data/codex-openai-codex/state/sagan-c41972f918bae938/jobs/task-mp42ayfi-ui2uc8.json
- Plan file (not read by me — only Codex should critique it): /tmp/sagan-plan-365/draft.md

Suggested user action: re-run `codex` directly (or `node /home/thomasjiralerspong/.claude/plugins/cache/openai-codex/codex/1.0.4/scripts/codex-companion.mjs setup`) to see the underlying error from the app-server, or check whether `gpt-5.5` is the correct model id for the currently installed Codex build.
</output>

All three Codex critics failed with the same environment-level companion crash. Per the instructions, I fall back to Claude critics for all three lenses and record the fallback in the critique notes. Claude critics all returned **pass**, with cheap scope-preserving important items to fold in:

- Methodology: F1×F3 confound wording in H3; F4 on-policy generation regime needs to be picked (regenerate per (F1,F3) vs. reuse) and reflected in compute; smoke is res-III, used only for kill criteria, not pre-ranking.
- Statistics: bootstrap resampling unit needs to be clustered (persona for LR, question for SR); only F1×F2 is a pre-registered interaction, others are exploratory.
- Alt-explanations: pass; all items follow-up/nit.

Folding these in now.

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267 /tmp/sagan-plan-365/draft.md

Continuing with the consistency-checker pass before finalizing.

tool=Agent

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1	## Goal
2	
3	Quantify how five orthogonal design choices in the EPS marker-implantation recipe affect (i) **source rate** (the `[ZLT]` marker is produced when the trained source persona is active) and (ii) **off-diagonal leakage** (the marker is produced for bystander personas). Identify which of the five factors have main effects ≥ 5 percentage points (pp) on source rate or mean off-diagonal leakage, and whether the pre-registered F1×F2 interaction is non-negligible (≥ 5 pp beyond additive). The remaining nine 2-way interactions are exploratory and reported on a heatmap without per-pair pre-registration. Use the screen to fix defaults for the next EPS marker recipe, and to decide whether the un-yoked length factors warrant separate optimization or should be re-yoked at a fixed total length.
4	
5	This is a screening experiment, not a confirmation experiment: it ranks factors and flags interactions on a single primary seed, then uses a small multi-seed top-3-per-source replication to put a training-noise band on the strongest cells.
6	
7	## Hypothesis
8	
9	- **H1 (primary).** Two factors dominate source rate: **marker-only-loss** (F5=marker-only; whole-completion loss F5=full halves source rate) and **on-policy completions** (F4=on increases source rate, because the surrounding-token distribution stays close to the base model and gradient updates concentrate on the marker rather than on rewriting style). The two are reported as separate predicted main effects (see Prediction), not as a single bundled claim.
10	- **H2 (leakage).** **Persona-presence during training** (F3) gates whether implantation is persona-conditional. F3=absent (non-persona filler in the system prompt) drives high off-diagonal leakage regardless of the other four factors.
11	- **H3 (length, decomposed).** The two un-yoked length factors have asymmetric main effects: **F1 (system-prompt length)** has a meaningful positive main effect on source rate, but this F1 effect is itself a mix of "length of persona-bearing content" (in F3=present cells) and "length of filler" (in F3=absent cells); the F1 main effect should be read jointly with F1×F3 rather than as a pure-length signal. **F2 (answer-format length)** has near-zero independent main effect, but F1×F4 and F2×F4 interactions are non-negligible because on-policy distribution closeness is harder to preserve when answers are long.
12	- **H4 (un-yoking confound).** Because the 2^5 un-yokes the prior 2^4's single length factor into F1 and F2, the cell `(F1=long, F2=long)` has ~2× the total assistant-message tokens of `(short, short)`. The F1×F2 interaction is the one pre-registered 2-way test; same-sign-as-both-main-effects indicates absolute total length, not where the tokens sit, is what matters — informing whether the next recipe should fix total length or vary it.
13	
14	## Prediction
15	
16	Pre-registered point predictions for source rate (SR) and mean off-diagonal leakage rate (LR) per cell, evaluated on the 24-persona × 20-question × 5-completion eval panel (`[ZLT]` case-insensitive substring detection anywhere in the completion, fuzzy Levenshtein ≤ 1 reported alongside as a secondary). Predicted intervals below are informal magnitude expectations (roughly 1 SD of analyst-prior), not formal CIs; the clean result reports observed effects against them rather than as null-hypothesis tests:
17	
18	- **Baseline cell** `(F1=short, F2=short, F3=absent, F4=off, F5=full)`: SR ≈ 0.05, LR ≈ 0.05.
19	- **Best-expected cell** `(F1=long, F2=short, F3=present, F4=on, F5=marker-only)`: SR ≥ 0.40, LR ≤ 0.10 (anchored on prior EPS results on the librarian source persona).
20	- **F5=full (whole-completion loss) main effect on SR**: −0.20 ± 0.10.
21	- **F4=on (on-policy) main effect on SR**: +0.10 ± 0.10.
22	- **F3=absent main effect on LR**: +0.15 ± 0.10 (averaged across the 16 F3=absent cells).
23	- **F1=long (long system prompt) main effect on SR**: +0.10 ± 0.05 (read jointly with F1×F3; see H3).
24	- **F2=long (long ans…

tool=Read

Read

1	#!/usr/bin/env -S npx tsx
2	/**
3	 * One-shot revision script for experiment 077ae4c7-e816-4dd8-a150-ad8fe19cb795.
4	 *
5	 * Addresses unresolved comment 7126e743-d490-4a66-8355-fcf8ebb9ee80:
6	 *   - More source personas and more eval personas.
7	 *   - Run in parallel on more H100s.
8	 *   - Make marker-only loss the baseline.
9	 *
10	 * Reads DATABASE_URL_DIRECT from the repo's .env file directly so we don't
11	 * depend on shell-side env loading.
12	 */
13	
14	import { readFileSync } from 'node:fs';
15	import { resolve } from 'node:path';
16	import postgres from 'postgres';
17	
18	const RUN_ID = '94976a10-a13e-42c5-bf31-9f0025b42f56';
19	const EXPERIMENT_ID = '077ae4c7-e816-4dd8-a150-ad8fe19cb795';
20	const COMMENT_ID = '7126e743-d490-4a66-8355-fcf8ebb9ee80';
21	
22	function loadEnv(file: string) {
23	  const raw = readFileSync(file, 'utf-8');
24	  for (const line of raw.split(/\r?\n/)) {
25	    const m = line.match(/^\s*([A-Z0-9_]+)\s*=\s*(.*)\s*$/);
26	    if (!m) continue;
27	    let value = m[2];
28	    if (
29	      (value.startsWith('"') && value.endsWith('"')) ||
30	      (value.startsWith("'") && value.endsWith("'"))
31	    ) {
32	      value = value.slice(1, -1);
33	    }
34	    if (!process.env[m[1]]) process.env[m[1]] = value;
35	  }
36	}
37	
38	loadEnv(resolve(process.cwd(), '.env'));
39	
40	const url = process.env.DATABASE_URL_DIRECT;
41	if (!url) {
42	  console.error('DATABASE_URL_DIRECT missing from environment');
43	  process.exit(2);
44	}
45	
46	const NEW_BODY = `## Motivation
47	
48	We want a single experiment that ranks the dominant factors controlling **marker implantation** (source \`[ZLT]\` rate) and **marker leakage** (mean off-diagonal rate) under LoRA SFT on Qwen2.5-7B-Instruct. Five prior issues varied one axis at a time, with conflicting or co-linear results:
49	
50	- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (MODERATE) — longer persona system prompts on the 48-source panel implant more (ρ=+0.38) and leak less (ρ=−0.38), but length and persona-richness co-vary.
51	- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (LOW) — stretching completion length / turn count on a fixed librarian source collapses uptake to 0/100 at the longest setting.
52	- [#340](https://github.com/superkaiba/explore-persona-space/issues/340) (MODERATE) — persona-to-assistant cosine has zero independent signal once prompt length is partialled out.
53	- [#181](https://github.com/superkaiba/explore-persona-space/issues/181) / [#208](https://github.com/superkaiba/explore-persona-space/issues/208) — non-persona triggers leak broadly under any recipe.
54	- [#46](https://github.com/superkaiba/explore-persona-space/issues/46) (approved, not yet run) — on-policy + marker-only-loss 5×3×3 grid.
55	
56	Co-linearity in the natural-variation panel means we can't tell which knob is load-bearing without a factorial. This issue runs **one** balanced 2^4 factorial that crosses the four contested axes, stratified across a small panel of source personas so we can see whether main effects are persona-stable. It absorbs the open proposed children #361, #339, #353.
57	
58	## Factors (2 levels each, 16 cells per source persona)
59	
60	| Factor | Level 0 (baseline) | Level 1 (treatment) | Open question |
61	|---|---|---|---|
62	| **A. Length-location** at fixed total ~1100 tokens | short system + long completion (~6 + ~1050) | long system + short completion (~1000 + ~50) | Disentangle [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (sys-len matters) from [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (completion-len doesn't). |
63	| **B. Persona-presence** at matched token count | persona prompt ("You are a librarian who...") | non-persona prompt (cloud-formation filler à la [#295](https://github.com/superkaiba/explore-persona-space/issues/295) \`sl_long\`) | Orthogonalizes [#181](https://github.com/superkaiba/explore-persona-space/issues/181)'s broa…

Now I have both documents. Let me do a careful line-by-line comparison across all the checked dimensions.

The prior 2^4 factorial body (from `revise-exp-077ae4c7.ts`, lines 46-121) is the reference. Let me extract the exact values for each check dimension and compare against the draft plan.

**Summary of what I found:**

Prior 2^4 key parameters (from the script body, lines 46-121):
- Base model: `Qwen2.5-7B-Instruct` (line 70)
- LoRA: r=32, α=64, lr=1e-5, 3 epochs (line 70)
- Dataset: `marker_asst_excluded_medium`, 200 pos + 400 neg per source (line 70)
- Source personas: 3 — librarian, surgeon, programmer (line 69)
- Eval: 24-persona × 20-question × 5-completion, `[ZLT]` case-insensitive substring (lines 77-78)
- Seeds: 42 primary + 137/256 multi-seed on top-3 per source (line 73)
- Pods: 4 × H100 (line 91), cells partitioned by source × on-policy flag (line 91)
- Factor D baseline: marker-only loss is D0 (baseline), whole-completion loss is D1 (treatment) (lines 65-66)
- Factor A: yoked length — short sys+long completion vs long sys+short completion, **fixed total ~1100 tokens** (line 62)

Now I have everything needed for the full consistency report.

<!-- epm:consistency v1 -->
## Consistency Check: #365 vs prior 2^4 factorial (exp 077ae4c7)

**Verdict: WARN**

### Parent experiment(s): exp 077ae4c7 (the prior 2^4 factorial, body in `/home/thomasjiralerspong/sagan/scripts/revise-exp-077ae4c7.ts`)

---

### Variables that differ (should be exactly 1 for a direct extension):

1. **Factor count: 2^5 vs 2^4** — the new plan adds F2 (answer-format length) as an independent factor by un-yoking the prior F_A. This is the INTENDED CHANGE and is the structural point of the experiment. See detail in "Multi-variable change justification" below.

2. **F_A yoked total-length constraint DROPPED** — the prior 2^4 held total assistant-message tokens fixed at ~1100 (`short system + long completion ~6+~1050` vs `long system + short completion ~1000+~50`; script line 62: "at fixed total ~1100 tokens"). The new plan explicitly removes this constraint: cells now span ~56 to ~2050 total tokens (`draft.md` line 56). This is the deliberate un-yoking, correctly pre-registered in H4 (`draft.md` line 12). **INTENDED per H4.**

3. **Loss-mask polarity: F5 baseline is "whole-completion loss", NOT "marker-only loss"** — this is a significant and potentially unintended inconsistency. The prior 2^4 script (line 65) explicitly set marker-only loss as the **baseline (D0)** and whole-completion loss as the **treatment (D1)**, with the rationale: "marker-only is now the baseline because #295's null suggests the standard recipe drowns out the marker signal." The new plan (`draft.md` line 54, Factor F5 table) reverses this: F5 level 0 (baseline) is "whole-completion loss (standard CE on all completion tokens)" and F5 level 1 (treatment) is "marker-only loss (CE masked to `[ZLT]` sub-tokens + EOS)." This means the two experiments define their "baseline cell" differently in the most consequential factor. The plan's Prediction section (`draft.md` line 18) defines the baseline cell as `(F1=short, F2=short, F3=absent, F4=off, F5=full)`, i.e. whole-completion loss as the do-nothing default. The prior 2^4's baseline cell was the opposite polarity on loss. This affects how main-effect half-contrasts are signed and whether "best cell" comparisons between the two experiments are meaningful. **UNINTENDED? Requires explicit justification.** The plan does not contain a sentence explaining the polarity flip relative to the prior 2^4.

4. **F3/B axis polarity inverted** — the prior 2^4 Factor B defined level 0 as "persona prompt" and level 1 as "non-persona filler" (script line 63: "B. Persona-presence — persona prompt … | non-persona prompt (cloud-formation filler)"). The new plan (`draft.md` line 52) defines F3 level 0 as "non-persona filler" and level 1 as "persona-rich prompt." Polarity-flipped relative to the prior 2^4. The mathematical content of the full factorial is…

Consistency check returned WARN with two documentation-only fixes (loss-mask polarity flipped from prior 2^4, persona-presence polarity flipped). Both are accept-with-justification per the rules. I'll add a short cross-experiment polarity note to the plan body.

tool=Bash

Bash

60:**Note on F4 on-policy generation.** "On-policy" means vLLM-sampled from `Qwen2.5-7B-Instruct` under the **same `(F1, F3)` system prompt and F2 answer-length spec** that the trained cell will see. There are **4** distinct `(F1, F3)` system prompts per source × 2 F2 levels = **8 distinct on-policy generation regimes per source** (24 total across 3 sources). Each on-policy regime is generated once per pod and reused across the cells of that pod's slab that share the same `(F1, F3, F2)` triple. The on-policy data-gen GPU-h budget below reflects 8 regimes per pod, not 1.

tool=Read

Read

60	**Note on F4 on-policy generation.** "On-policy" means vLLM-sampled from `Qwen2.5-7B-Instruct` under the **same `(F1, F3)` system prompt and F2 answer-length spec** that the trained cell will see. There are **4** distinct `(F1, F3)` system prompts per source × 2 F2 levels = **8 distinct on-policy generation regimes per source** (24 total across 3 sources). Each on-policy regime is generated once per pod and reused across the cells of that pod's slab that share the same `(F1, F3, F2)` triple. The on-policy data-gen GPU-h budget below reflects 8 regimes per pod, not 1.
61	
62	**Source persona panel.** 3 personas — `librarian`, `surgeon`, `programmer` — chosen to span topic breadth and answer length and to anchor against prior EPS work (#271, #232, #295). Each cell of the 2^5 factorial is trained separately per source, yielding **3 × 32 = 96 training runs** for the primary screen.
63	
64	**Eval panel.** 24-persona × 20-question × 5-completion vLLM batched, `max_new_tokens=2048` (per the #260 / #297 truncation lesson). The 24-persona panel = the 3 source personas + 21 bystanders sampled from the #337 48-source list to span length / topic / style. The 21 bystanders are listed in `metrics.json` with a per-persona flag for whether that persona also appears in the 400 negative training examples for the current cell's source; the clean result reports "training-set bystander LR" and "held-out bystander LR" separately so both interpretations are visible. Per cell: source rate (the diagonal entry for that cell's source persona), mean off-diagonal rate over the 21 bystanders, per-bystander rates. Primary detection is `[ZLT]` case-insensitive substring anywhere in the completion; secondary is fuzzy Levenshtein ≤ 1. The primary↔secondary switching rule fires only if (a) the gap exceeds the cell-level bootstrap CI half-width and (b) the gap has the same direction in ≥ 3 cells of the affected slab, to avoid switching on single-cell noise.

tool=Bash

Bash

ok
62:**Note on cross-experiment polarity vs the prior 2^4 (exp 077ae4c7).** Two factors have flipped baseline conventions relative to the parent 2^4: (a) the prior 2^4 set marker-only loss as the D-axis baseline (D0) after EPS #295's null result; this 2^5 plan sets whole-completion loss as F5 baseline (level 0). (b) The prior 2^4 set persona-presence as B0 (baseline) and non-persona filler as B1; this 2^5 plan sets non-persona filler as F3 level 0. The internal validity of the screen is unaffected — all 32 factor combinations are still run — but a reader cross-referencing main-effect signs between the two experiment reports must re-sign F5 and F3 to align. This is called out here, in the Risks section, and in the clean result.

tool=Bash

Bash

ok
144:- **Cross-experiment polarity flip vs the prior 2^4 (exp 077ae4c7).** F5 baseline (whole-completion loss) and F3 level 0 (non-persona filler) are flipped relative to the parent 2^4's D0 (marker-only loss) and B0 (persona presence). *Mitigation:* the polarity flip is documented in Experimental Setup, in this section, and in the clean result; readers comparing main-effect signs across the two experiment reports must re-sign F5 and F3 to align. No internal-validity impact.

Now also extend the critique-loop notes to record the consistency-checker WARN and how it was resolved.

tool=Bash

Bash

ok
162:- Consistency checker: WARN (accepted with explicit justification per the WARN rule). The checker flagged two cross-experiment polarity flips vs the prior 2^4 (F5 loss-mask baseline and F3 persona-presence baseline). Both are documentation-only: all 32 cells are still run, internal validity is unaffected, and the polarity flip is now documented in Experimental Setup, in the Risks section, and will be reported in the clean result so cross-experiment effect-sign comparisons are not misread.

Quick consistency-self-check across the plan's own sections (heading inventory, GPU-h vs cost arithmetic, runpod-spec match):

tool=Bash

Bash

## Goal
## Hypothesis
## Prediction
## Kill Criterion
## Experimental Setup
## Compute and Hardware
## Artifacts
## Verification
## Risks and Red Team
## Likely Clean Result
## Approval Checklist
---
Cost arithmetic check: 4 pods * 1 GPU * 18 h * 2.69 = 193.68 compute
---
runpod-spec found: True
number of pods: 4
 - marker-screen-365-pod0-pre-and-source-librarian H100 x1 SECURE volumeGb=100 containerDiskGb=100 estimatedMinutes=1080
 - marker-screen-365-pod1-source-surgeon H100 x1 SECURE volumeGb=100 containerDiskGb=100 estimatedMinutes=1080
 - marker-screen-365-pod2-source-programmer H100 x1 SECURE volumeGb=100 containerDiskGb=100 estimatedMinutes=1080
 - marker-screen-365-pod3-aggregator-and-overflow H100 x1 SECURE volumeGb=100 containerDiskGb=100 estimatedMinutes=1080
---
272 /tmp/sagan-plan-365/draft.md

All 11 required headings present in order, runpod-spec is valid JSON with 4 pods matching the plan's narrative, cost arithmetic checks out at $194 compute → ~$200 with storage. Emitting the final plan:

---

## Goal

Quantify how five orthogonal design choices in the EPS marker-implantation recipe affect (i) **source rate** (the `[ZLT]` marker is produced when the trained source persona is active) and (ii) **off-diagonal leakage** (the marker is produced for bystander personas). Identify which of the five factors have main effects ≥ 5 percentage points (pp) on source rate or mean off-diagonal leakage, and whether the pre-registered F1×F2 interaction is non-negligible (≥ 5 pp beyond additive). The remaining nine 2-way interactions are exploratory and reported on a heatmap without per-pair pre-registration. Use the screen to fix defaults for the next EPS marker recipe, and to decide whether the un-yoked length factors warrant separate optimization or should be re-yoked at a fixed total length.

This is a screening experiment, not a confirmation experiment: it ranks factors and flags interactions on a single primary seed, then uses a small multi-seed top-3-per-source replication to put a training-noise band on the strongest cells.

## Hypothesis

- **H1 (primary).** Two factors dominate source rate: **marker-only-loss** (F5=marker-only; whole-completion loss F5=full halves source rate) and **on-policy completions** (F4=on increases source rate, because the surrounding-token distribution stays close to the base model and gradient updates concentrate on the marker rather than on rewriting style). The two are reported as separate predicted main effects (see Prediction), not as a single bundled claim.
- **H2 (leakage).** **Persona-presence during training** (F3) gates whether implantation is persona-conditional. F3=absent (non-persona filler in the system prompt) drives high off-diagonal leakage regardless of the other four factors.
- **H3 (length, decomposed).** The two un-yoked length factors have asymmetric main effects: **F1 (system-prompt length)** has a meaningful positive main effect on source rate, but this F1 effect is itself a mix of "length of persona-bearing content" (in F3=present cells) and "length of filler" (in F3=absent cells); the F1 main effect should be read jointly with F1×F3 rather than as a pure-length signal. **F2 (answer-format length)** has near-zero independent main effect, but F1×F4 and F2×F4 interactions are non-negligible because on-policy distribution closeness is harder to preserve when answers are long.
- **H4 (un-yoking confound).** Because the 2^5 un-yokes the prior 2^4's single length factor into F1 and F2, the cell `(F1=long, F2=long)` has ~2× the total assistant-message tokens of `(short, short)`. The F1×F2 interaction is the one pre-registered 2-way test; same-sign-as-both-main-effects indicates absolute total length, not where the tokens sit, is what matters — informing whether the next recipe should fix total length or vary it.

## Prediction

Pre-registered point predictions for source rate (SR) and mean off-diagonal leakage rate (LR) per cell, evaluated on the 24-persona × 20-question × 5-completion eval panel (`[ZLT]` case-insensitive substring detection anywhere in the completion, fuzzy Levenshtein ≤ 1 reported alongside as a secondary). Predicted intervals below are informal magnitude expectations (roughly 1 SD of analyst-prior), not formal CIs; the clean result reports observed effects against them rather than as null-hypothesis tests:

- **Baseline cell** `(F1=short, F2=short, F3=absent, F4=off, F5=full)`: SR ≈ 0.05, LR ≈ 0.05.
- **Best-expected cell** `(F1=long, F2=short, F3=present, F4=on, F5=marker-only)`: SR ≥ 0.40, LR ≤ 0.10 (anchored on prior EPS results on the librarian source persona).
- **F5=full (whole-completion loss) main effect on SR**: −0.20 ± 0.10.
- **F4=on (on-policy) main effect on SR**: +0.10 ± 0.10.
- **F3=absent main effect on LR**: +0.15 ± 0.10 (averaged across the…

success

64a794c8-1dc0-44f4-9491-3563c3773c1e