apply

Run b2016e32

experiment-orchestrator-for:0c382d32-0089-4463-8107-b7e29b82e410 Drive experiment 2197c9e9-7558-4572-97eb-70cc2235e659 from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.

Statuscompleted274 events · latest 1326h 3m ago

Plan

Goal

Settle the chunk-binding vs. shape-template question left open by #354. #354 showed that under EOS-masked LoRA SFT on Qwen-2.5-7B-Instruct, training a donor (librarian) on the paired chunk `<A> answer ` propagates marker_B to a recipient (software_engineer) at non-trivial rates (recipient `R_B|A` = 23.5% on seed 42; control C with no marker_B in donor training = 0%). Two compatible mechanisms remain: (a) **chunk-binding** — marker_A literally keys marker_B because the two were paired in donor training; (b) **shape-template** — the donor LoRA learned the full `<A> answer ` end-of-completion template and emits both as parts of the same surface pattern, with marker_A and marker_B co-firing because of position rather than association. This experiment runs a three-arm sweep T (paired `<A> answer `), C (`<A> answer`, no marker_B), C2 (`answer `, no marker_A) at three seeds (42, 1337, 2024) on the librarian→software_engineer pair. C2 keeps marker_B in the donor's training distribution at the same end-of-completion position as T but breaks the marker_A↔marker_B pairing — a silent recipient under C2 is consistent with chunk-binding (or, more strictly, "donor needs marker_A exposure to propagate marker_B"); a non-silent recipient is consistent with shape-template.

Hypothesis

Under chunk-binding, the recipient's marker_B emission rate after marker_A on C2 collapses to near-baseline; under shape-template, the recipient continues to emit marker_B at non-trivial rates because the donor learned `... ` as a turn-end suffix that the recipient picks up through shared LoRA conditioning. The recipient is the primary axis; police_officer and data_scientist (the two non-trivial bystander leakers from #354 T) provide a secondary mechanism check.

Prediction

- **If chunk-binding (paired-or-marginal-A-required) is the operative mechanism:** pooled recipient `R_B|A` on C2 is < 5% with the wider of (CI-A question-cluster, CI-B seed-stratified) 95% upper bound < 10%. Bystander C2 cells are also near baseline. - **If shape-template is operative:** pooled recipient `R_B|A` on C2 is > 10% with 95% lower bound > 5%; OR a bystander override fires (police_officer or data_scientist pooled `R_B|A` on C2 > 20% with denom_A ≥ 30 and 95% lower bound > 10%). - **T re-train sanity:** seed-42 recipient `R_B|A` on T re-trained in this run lands within roughly [−7%, +54%] of #354's 23.5% (2× #354's per-seed cluster CI half-width). C re-train on all seeds remains 0%.

Kill Criterion

Single pre-registered verdict tree, evaluated on pooled recipient `R_B|A` over the 3 seeds (librarian→software_engineer), using the **wider** of two 95% cluster bootstrap CIs (CI-A: questions-only cluster; CI-B: seed-stratified two-level). Primary statistic is **conditional-of-pooled** (pooled numerator / pooled denominator across seeds); mean-of-conditionals (per-seed `R_B|A` averaged across seeds) is reported as a sensitivity check. 1. **Binding-confirmed (chunk-binding, shape-template falsified):** recipient C2 pooled `R_B|A` < 5% AND 95% upper CI < 10% AND recipient pooled `denom_A_C2` ≥ 40 AND no bystander override fires (see 3). Interpretation: "paired-or-marginal-A-required to propagate marker_B." Not pure chunk-binding (see Risks for why). 2. **Shape-template confirmed (chunk-binding falsified) — recipient route:** recipient C2 pooled `R_B|A` > 10% AND 95% lower CI > 5% AND recipient pooled `denom_A_C2` ≥ 40. 3. **Shape-template confirmed — bystander override route:** EITHER police_officer OR data_scientist pooled `R_B|A` on C2 > 20% AND 95% lower CI > 10% AND pooled `denom_A_C2` ≥ 30. Override fires asymmetrically — it can only flip a recipient-silent finding from "binding-confirmed" to "shape-template lives in donor / mechanism mixed," never the other direction. 4. **Length-inflation guard (qualifier, not nullifier):** if recipient mean completion length on C2 differs from T by > 25% relative, the verdict is **qualified** (reported with caveat) because the marker_B substring-match opportunity is confounded by completion length. 5. **Template-without-A leg (descriptive):** report recipient `R_B_loose` (unconditional marker_B emission, no marker_A required) on C2 alongside `R_B|A`. If recipient C2 `R_B_loose` ≥ 10% with 95% lower CI > 5%, flag "template-without-A" pattern even if `R_B|A` is in the inconclusive band. 6. **Inconclusive band:** anything else (e.g., recipient C2 5% ≤ `R_B|A` ≤ 10%, or `denom_A_C2` < 40, or bootstrap drop rate > 10%, or T drift > 2× #354 per-seed CI half-width). Report numbers and queue the matched-A-marginal-unpaired follow-up. 7. **Drift abort gate:** if both T and C re-trains land outside their pre-registered drift bands (T: seed-42 `R_B|A` outside [−7%, +54%] of 23.5%; C: any seed shows `R_B|A` > 2%), the C2 verdict is **shelved** pending pipeline root-cause; the run is reported as a drift detection, not a binding-vs-template verdict.

Experimental Setup

**Inheritance from #354.** Reuse the EOS-masked LoRA SFT recipe from `scripts/run_issue354_eos_masked.py`: Qwen-2.5-7B-Instruct base, LoRA rank/alpha as #354, recipient's `tokenizer.eos_token_id` masked from cross-entropy labels, donor + 4 contrastive-negative rows pass through with EOS in loss, contrastive negative pool unchanged, eval question set unchanged (20 in-distribution + 6 OOD = 26 unique, disjoint from data-generation questions), 11 evaluation personas unchanged. Pair restricted to `pair2_librarian_swe` (librarian donor → software_engineer recipient), single-variable change relative to #354. **Three arms.** - **T (paired chunk):** donor sees `<A> answer ` (identical to #354's T). - **C (no-marker_B control):** donor sees `<A> answer` (identical to #354's C). - **C2 (no-marker_A control):** donor sees `answer ` with marker_B at the same end-of-completion position as T, no marker_A anywhere in donor's training rows. Recipient training is identical to T (start-only marker_A coupling); contrastive negatives are unchanged. **Single-variable cut: only the marker_A column on the donor changes between T and C2.** **Seeds.** {42, 1337, 2024}. Seed 42 to align with #354; 1337 and 2024 to add genuine seed variance. Total adapters trained: 3 arms × 3 seeds = 9. **Per-seed on-policy data generation.** Each seed regenerates donor + recipient + 4 contrastive-negative training rows at temperature 0.7 over the data-generation question set (11 personas × 40 questions × 5 completions = 2,200 generations). Same vLLM rig as #354; deterministic given the seed. **Phase-0 base-model floor (one-time).** Run #354's base-model probe (11 personas × 3 sampled questions × 1 completion = 33 generations) to confirm loose-match rates for both markers < 1% on the base model. Cached after first run. **Eval (per adapter).** vLLM completions over the 26-question eval set × 11 personas × 5 completions per cell = 1,430 per adapter. Loose-match scorer reports both `marker_A` and `marker_B` substring matches, conditional rates `R_B|A` and `R_B|not_A`, marginal rates `R_B_loose`, mean completion length per cell, end-of-completion position rate for marker_B (was marker_B the last non-whitespace span?), and pooled denominators for `R_B|A` cells. Identical eval-rig invocation across arms — no eval-rig variance. **Primary statistic.** - **Estimator:** pooled recipient `R_B|A` on librarian→software_engineer cell, computed as conditional-of-pooled (sum-marker_B-and-marker_A / sum-marker_A) across all 3 seeds for each arm. - **CI-A (question-cluster bootstrap):** B = 10,000 resamples, cluster unit = question, resample seeds and completions within each question independently, drop-resample on cells with `denom_A` = 0 (drop rate must be ≤ 10% or verdict is inconclusive). - **CI-B (seed-stratified two-level cluster bootstrap):** B = 10,000, top-level cluster = seed (with replacement, n=3), within-seed cluster = question. CI-B is the honest seed-variance interval and is expected to be wider than CI-A given n=3 seeds; the verdict uses `max(CI-A_width, CI-B_width)`. - **Verdict CI:** the wider of CI-A and CI-B. - **Sensitivity:** mean-of-conditionals (average per-seed `R_B|A`) reported alongside the primary statistic. If the two estimators disagree on which verdict band they fall into, the result is downgraded to inconclusive. **Secondary statistic (bystander override).** Pooled `R_B|A` on police_officer and data_scientist under C2, with denom_A ≥ 30, point estimate > 20%, and 95% CI lower bound > 10% (using CI-A; bystander cells are too sparse for CI-B). Pre-registered override; the other 8 personas in the spectrum are exploratory / descriptive only. **Comparability gates (consistency-checker recommendation).** Drift diagnostics: re-trained seed-42 T's recipient point estimate vs. #354's 23.5%; re-trained C's recipient on all seeds must be < 2%. Pipeline drift triggers the drift abort gate above. CI width is not numerically comparable to #354 (different bootstrap parameters; see Risks); the drift gate is on the point estimate only. **Inherited from #354 to make this single-variable.** All of: base model, LoRA hyperparameters, EOS-masking recipe, contrastive-negative pool, contrastive-negative count (4), recipient persona coupling pattern (start-only marker_A), data-gen question pool, eval question set (26), eval personas (11), eval temperature, loose-match scorer, completions-per-cell (5). Anything that drifts here is a pipeline drift, not an intended variable.

Compute and Hardware

**Workload sizing.** 9 LoRA adapters (3 arms × 3 seeds) + 3 per-seed on-policy data generations + 1 Phase-0 base-model probe (one-time, cached) + 9 vLLM eval passes. Reference from #354 (same recipe, same eval shape): ~0.7 H100-hr per adapter inclusive of training (~30 min) and eval (~12 min). On-policy data generation: ~7 min/seed on 1×H100 with vLLM (2,200 generations at T=0.7). **Single-pod, single-GPU sequential walltime on 1×H100 80GB SXM:** | Stage | Walltime | |---|---| | Cold bootstrap (uv sync, weights, .env) | ~12 min one-time | | Phase-0 base-model probe | ~2 min one-time | | On-policy data gen (3 seeds × ~7 min) | ~21 min | | Train + eval (9 adapters × ~42 min) | ~6.3 hr | | HF/WandB upload, write `summary.json` | ~5 min | | **Total** | **~7.0 H100-hr** | Budget **8.0 H100-hr** of pod time for ~15% headroom. Sequential is simpler than parallelizing across two GPUs (which would halve walltime but doubles dispatch and capacity-allocation risk and would also need lock-coordinated WandB writes). **USD cost estimate.** Rate used: H100 80GB SXM on RunPod Secure Cloud on-demand = **$2.69/hr** (May 2026; treat as guidance — may drift). Storage: existing network volume `eps-warm-cache-us-ca-2` (if available, see runpod-spec) avoids re-downloading Qwen-7B weights and uv wheel cache; otherwise ~50 GB transient at $0.10/GB-month for ~0.3 day = ~$0.05 storage. - Compute: 8.0 H100-hr × $2.69/hr × 1 GPU × 1 pod = **$21.52** - Storage: ~$0.05 (transient) or ~$0 (warm-cache volume) - **Total: ~$22 USD** (rounded to two significant figures) Substitution policy (below) allows H200 or A100-SXM 80GB swaps if H100 is supply-constrained; A100-SXM at $1.49/hr would *lower* cost but extend walltime by ~30%, so total stays within ~$25 in the worst case. **Single-pod justification.** This is one model family (Qwen-2.5-7B-Instruct), one tooling stack, one dataset pipeline; arms and seeds time-share the same GPU. None of the multi-pod exemption clauses ((a) >8 H100s, (b) data-parallel disjoint hosts, (c) different model weights / CUDA / per-pod state) applies.

Artifacts

- `experiments/369/adapters/{T,C,C2}_seed{42,1337,2024}/` — 9 LoRA adapters, each uploaded to HF Hub (per-adapter incremental). - `experiments/369/eval_results/{T,C,C2}_seed{42,1337,2024}.json` — per-adapter raw completions + scorer output (loose-match per cell, R_B|A, R_B|not_A, R_B_loose, mean completion length, end-of-completion-position rate for marker_B, per-cell denominators). - `experiments/369/data/seed{42,1337,2024}/{donor,recipient,contrastive_neg}.jsonl` — on-policy data-gen output, one set per seed (shared across arms within a seed). - `experiments/369/summary.json` — flattened table: arm × seed × persona × {R_B|A, R_B|not_A, R_B_loose, denom_A, denom_not_A, mean_completion_length, eoc_position_rate_marker_B}; pooled-across-seeds rows; CI-A and CI-B for the librarian-software_engineer × C2 primary cell and the police_officer × C2 / data_scientist × C2 bystander cells; bootstrap drop rate per CI; verdict label. - `experiments/369/base_model_floor.json` — Phase-0 probe (loose-match rates for both markers on base Qwen-2.5-7B-Instruct). - WandB run group `exp369-binding-vs-template` — 9 runs, one per adapter, training loss curves + eval-pass metrics. - Sagan progress POSTs at: pre-data-gen, post-data-gen, after each adapter trains, after each eval, before upload.

Verification

1. **Phase-0 floor:** loose-match rate < 1% for marker_A and marker_B on base model (cached after first run; abort if violated). 2. **C control:** re-trained C arm shows recipient `R_B_loose` < 2% on all 3 seeds (otherwise the eval rig changed — drift abort). 3. **T re-train drift:** seed-42 recipient `R_B|A` on re-trained T lands within [−7%, +54%] of #354's 23.5% (drift abort gate; see Kill Criterion §7). 4. **Denominator floor for primary verdict:** pooled `denom_A_C2` on recipient ≥ 40 across the 3 seeds; if not, verdict downgrades to inconclusive. 5. **Bootstrap drop-rate:** ≤ 10% drop rate on CI-A and CI-B; if exceeded, verdict is inconclusive on that CI. 6. **Length-inflation guard:** recipient C2 mean completion length within 25% relative of T; if not, verdict is qualified (not aborted). 7. **CI-A vs CI-B agreement:** if the two CIs straddle different verdict bands, verdict is downgraded to inconclusive (the wider CI's band takes precedence; the disagreement is reported). 8. **Estimator agreement:** conditional-of-pooled and mean-of-conditionals must land in the same verdict band; disagreement → inconclusive. 9. **Spec-vs-plan sanity:** `run_experiment_369.py` emits to `summary.json` exactly the columns and CIs specified in Artifacts; runner inspects post-run.

Risks and Red Team

**Mechanism risk: shape-template emits marker_B differently than expected.** If the donor learns "end with marker_B" rather than "marker_A cues marker_B," the recipient's marker_B might land at end-of-completion even without marker_A nearby — high `R_B_loose` with low `R_B|A` because the recipient's marker_A also fires at low rates. The kill criterion's template-without-A leg (recipient `R_B_loose` ≥ 10% with CI lower bound > 5%) is verdict-bearing, not a footnote. **Mechanism risk: "binding-confirmed" doesn't fully imply chunk-binding.** C2 removes both the marker_A↔marker_B pairing AND the donor's marker_A exposure. A silent C2 is therefore consistent with (i) genuine chunk-binding (A keys B) and (ii) "donor needs marker_A exposure somewhere to propagate marker_B." The matched-A-marginal-unpaired follow-up (donor sees A-only and B-only in separate completions) settles this — explicitly queued, scope-expanding, not in this run. The clean-result phrases the binding-confirmed verdict as "paired-or-marginal-A-required to propagate B" rather than over-claiming pure chunk-binding. **Mechanism risk: recipient's own `<A> answer` training overrides any shape-template suffix.** Even if the donor learns `... ` as a turn-end suffix, the recipient's training (`<A> answer` ending naturally) might dominate, suppressing marker_B emission and producing a binding-style null even when shape-template is the true donor mechanism. Instrumented: the run reports donor and recipient `R_B_loose` AND end-of-completion position rate on C2 side-by-side. If donor C2 `R_B_loose` ≥ 50% but recipient C2 `R_B_loose` < 5% with matched mean completion length, that is evidence for "recipient template dominates" rather than "no shape-template existed." The clean-result body compares donor vs. recipient C2 `R_B_loose` explicitly. **Denominator-collapse risk on C2.** Under C2, the donor never sees marker_A, so the recipient's marker_A representation is trained as in T (recipient row identical) but no longer reinforced by the paired donor presentation. Recipient `denom_A_C2` could be smaller than #354's `denom_A_T = 81`. The verification gate enforces `denom_A_C2 ≥ 40` (pooled across 3 seeds; #354 saw 81 in one seed, so 40 is a conservative pooled floor) before a binding-confirmed verdict can fire; below that, the verdict downgrades to "inconclusive — denom-fragile." **Seed variance risk.** #354's single-seed cluster CI on T spanned [8.9%, 39.8%]. Three seeds pooled give 780 completions per (arm × persona) cell vs. 260 for one seed, but cluster CI tightening is limited by question-axis correlation. CI-B (seed-stratified) is the honest seed-variance estimate; with n=3 seeds it will be wider than CI-A. The kill criterion uses whichever is wider, so the "inconclusive" middle band may be load-bearing — that is a feature. **Pipeline-drift risk: the codebase moved between #354 (Nov 2025) and now (May 2026).** Re-training T and C in the same run is a deliberate hedge. Only seed-42 T comparison is drift-diagnostic; new seeds (1337, 2024) cannot disambiguate drift directly. **CI comparability:** the seed-42-only single-cell CI computed under this run's procedure (cluster bootstrap on questions, B=10,000) is not numerically identical to #354's reported [8.9%, 39.8%] (B=2,000, different bootstrap estimator). The drift check is on the point estimate vs. 23.5%; CI width is reported for context, not gated. **Eval-rig sensitivity to length-inflation.** #354 could only refute length-alone indirectly (via C's 0%). This run instruments mean completion length per (persona × arm × seed) with the explicit 25%-relative length-inflation discriminator in the Kill Criterion. Verdict qualifies (not aborts) on length drift. **Bystander interpretation ambiguity.** Under #354's T, police_officer fired marker_B at 54.3% (n_A=35, cluster CI [16.0%, 89.7%]). Bystander cells under C2 could be noisier because the donor's now-decoupled marker_A representation may suppress bystander marker_A firing. The kill criterion's bystander override now requires pooled `denom_A_C2` ≥ 30, pooled `R_B|A` > 20%, AND CI lower bound > 10%. Override is bystander-specific to police_officer and data_scientist (the two non-trivial leakers under #354 T); zelthari_scholar is descriptive only. Bystander leak may operate via a different mechanism than recipient transfer (e.g., distribution-shift priors); a fired override should be read as "C2 leaks somewhere → shape-template lives in the donor" rather than "the specific recipient transfer mechanism is template-driven." Clean-result must state that nuance. **Multiple-comparisons / family-wise error.** Primary verdict is one cell-test (recipient × C2). Bystander override adds 2 pre-registered comparisons (police_officer, data_scientist × C2); at α = 0.05 per test, FWER ~10%. CI-lower-bound and denom_A guards already tighten this; the override is asymmetric (can only flip binding-confirmed → shape-template, never the other direction). Residual FWER risk acceptable. The full 11-persona × 3-arm spectrum reported in the result is exploratory / descriptive only and cannot retroactively become verdict-bearing. **Capacity / supply risk.** H100 80GB SXM availability on RunPod Secure Cloud is occasionally tight. The substitution policy lets the provisioner swap to H200 or A100-SXM 80GB (both ≥ 80GB VRAM) on the same single-pod, single-GPU shape. Cloud type may relax to COMMUNITY if SECURE is unavailable. GPU-family substitution adds at most ~30–40% walltime; total cost stays under $25. **Cost overrun risk.** Budget has ~15% headroom over the estimated 7.0 H100-hr spend. If the pod runs past 10 H100-hr, mid-run progress POSTs surface the slip; the runner can stop the pod and treat partial completion as a partial-run artifact (any of the 9 adapters that finished are usable in 1-seed or 2-seed-pooled form). **Critique loop notes.** Loops run: 1. Six critic agents (paired Claude + Codex × {methodology, statistics, alternatives}) ran in parallel. Verdicts: methodology Claude=pass / Codex=needs_targeted_fix; statistics both=needs_targeted_fix; alternatives both=needs_targeted_fix. Unioned scope-preserving blockers folded into this revision: (a) C2 donor-coherence gate contradiction fixed (marginal `R_B_loose` / `R_B|not_A` on C2, not `R_B|A`); (b) primary statistic defined as conditional-of-pooled with mean-of-conditionals as sensitivity; (c) two pre-registered cluster CIs (CI-A question-only, CI-B seed-stratified two-level) with verdict using the wider; (d) bystander override now requires denom_A ≥ 30 + point estimate + CI lower bound; (e) recipient `denom_A_C2` ≥ 40 floor for binding-confirmed verdict; (f) template-without-A leg added using recipient `R_B_loose`; (g) length-inflation discriminator (25% relative threshold qualifies verdict); (h) bootstrap drop-rate guard (>10% → inconclusive); (i) bystander pool corrected from "5" to the actual 3 untrained-bystander personas; (j) "2×2" framing relabeled "three-arm sweep"; (k) interpretation narrowing for binding-confirmed verdict (paired-or-marginal-A-required vs. pure chunk-binding). No Codex fallback. Reconciler not invoked for methodology lens because all Codex findings were scope-preserving and overlapped with statistics-lens blockers already requiring fixes — folded unilaterally; recorded here for audit. Consistency-checker returned WARN (one documentation gap on CI comparability vs. #354's reported interval; folded into the pipeline-drift risk paragraph). Follow-ups intentionally not folded (queued, not gates): matched-A-marginal-unpaired arm (the only scope-expanding finding, would tighten binding-vs-marginal-A distinction); non-fixed marker_B position arm (separately queued; the existing follow-up on #354 next-steps); recipient-only baseline control (correlated training-distribution suffix); LoRA-rank check; temperature sweep.

Likely Clean Result

HTML clean-result on `experiments.body` rendered at `/e/experiment/369`, following `docs/clean-result-guidelines.md`: - **TL;DR (open):** 3–4 bullets — what I wanted to find out, the verdict (binding-confirmed / shape-template confirmed / inconclusive / drift-shelved), the headline number (pooled recipient C2 `R_B|A` with the wider 95% CI), and the most important caveat (binding-confirmed means "paired-or-marginal-A-required," not pure chunk-binding; the matched-A-marginal-unpaired follow-up is queued). - **Primary plot:** grouped bar chart, x-axis = arm (T, C, C2), y-axis = pooled recipient `R_B|A` (%) with 95% CI error bars (wider of CI-A and CI-B). Plain-English axis labels; no LaTeX. SVG `<title>` hover tooltips on each bar (denom_A, n completions, per-seed values). - **Experimental design dropdown (closed):** what changed vs. #354, the three arms, seeds, bystander-override pre-registration, length-inflation guard, primary vs. sensitivity statistic, CI-A vs CI-B definitions, and drift-abort criteria. Donor vs. recipient C2 `R_B_loose` side-by-side. Full 11-persona × 3-arm spectrum table (descriptive). Bystander cells (police_officer, data_scientist × C2) explicitly called out. - Voice: "I" not "we"; no standing caveats, no abandoned-metric references, no separate background/methodology h2. If the verdict is binding-confirmed, the next-step card points to the matched-A-marginal-unpaired follow-up. If shape-template, it points to the non-fixed-position follow-up. If inconclusive or drift-shelved, it points to the diagnostic that would unblock the verdict.

Approval Checklist

- [x] **Goal:** binding-vs-template — distinguish whether marker_A↔marker_B pairing or end-of-completion shape-template drives the #354 propagation. - [x] **Hypothesis:** binding → recipient silent on C2; template → recipient (or designated bystanders) emit marker_B on C2. - [x] **Prediction:** quantitative thresholds with CI bounds, pre-registered. - [x] **Kill criterion:** seven-clause verdict tree with concrete cutoffs (recipient < 5% & upper CI < 10% → binding; > 10% & lower CI > 5% → template; bystander override > 20% & lower CI > 10%; denom_A floor; bootstrap drop rate; length-inflation guard; drift abort). - [x] **Compute and hardware:** 1× H100 80GB SXM, single pod, sequential, ~7.0 H100-hr spent / 8.0 H100-hr budgeted. **Estimated cost: ~$22 USD** (H100 SXM @ $2.69/hr × 8.0 hr + transient storage; A100/H200 substitutions stay under $25). Rate may drift — see Compute section. - [x] **Artifacts:** 9 adapters, 9 eval JSONs, 3 per-seed data-gen dumps, `summary.json` with pooled CIs and verdict, base_model_floor.json, WandB group, progress POSTs. - [x] **Verification:** Phase-0 floor, C control gate, T re-train drift gate, denominator floor, bootstrap drop-rate, length-inflation qualifier, CI agreement, estimator agreement, spec-vs-plan sanity. - [x] **Risks:** 10 risks enumerated, each with mitigation or instrumentation — mechanism (3), denom collapse, seed variance, pipeline drift, length inflation, bystander ambiguity, FWER, capacity, cost. - [x] **Likely clean-result:** TL;DR + primary plot (3-arm grouped bar with wider-of-CI error bars) + experimental-design dropdown; verdict-driven next-step card. - [x] **Runpod-spec matches plan:** single pod, single H100, 8 hr est, runs `scripts/run_experiment_369.py`, substitution policy permits H200/A100-SXM 80GB and COMMUNITY cloud, no GPU-count scaling. - [x] **Tenant-agnostic check:** EPS-specific experiment, runs in EPS repo on RunPod; no Sagan schema or API additions. ✓ ```runpod-spec { "name": "exp369-binding-vs-template", "gpuType": "H100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 480, "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py'", "config": { "command": "Three-arm sweep T/C/C2 × seeds {42,1337,2024} on librarian→software_engineer pair under EOS-masked LoRA SFT recipe inherited from #354. Trains 9 adapters, evals 11 personas × 26 questions × 5 completions per adapter, writes summary.json with pooled CIs (CI-A question-cluster, CI-B seed-stratified) and verdict label.", "artifacts": [ "experiments/369/summary.json", "experiments/369/base_model_floor.json", "experiments/369/adapters/{T,C,C2}_seed{42,1337,2024}/", "experiments/369/eval_results/{T,C,C2}_seed{42,1337,2024}.json", "experiments/369/data/seed{42,1337,2024}/{donor,recipient,contrastive_neg}.jsonl" ] }, "substitution_policy": { "gpuType": { "allowed": ["H100", "H200", "A100-SXM"], "min_vram_gb": 80 }, "gpuCount": { "min": 1, "max": 1 }, "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" }, "dataCenterId":{ "allowed": "any", "prefer": ["US-CA-2", "EU-RO-1"] }, "account": { "allowed": ["team", "personal"], "prefer": "team" }, "volumeGb": { "min": 100 }, "containerDiskGb": { "min": 100 } }, "consolidation": { "may_merge_pods": true, "merge_target_max_gpus_per_pod": 8 } } ```

The experiment script run_experiment_369.py is already in place in EPS. I'll now produce the final plan.

Goal

This experiment runs a three-arm sweep T (paired <A> answer ), C (<A> answer, no marker_B), C2 (answer , no marker_A) at three seeds (42, 1337, 2024) on the librarian→software_engineer pair. C2 keeps marker_B in the donor's training distribution at the same end-of-completion position as T but breaks the marker_A↔marker_B pairing — a silent recipient under C2 is consistent with chunk-binding (or, more strictly, "donor needs marker_A exposure to propagate marker_B"); a non-silent recipient is consistent with shape-template.

Hypothesis

Under chunk-binding, the recipient's marker_B emission rate after marker_A on C2 collapses to near-baseline; under shape-template, the recipient continues to emit marker_B at non-trivial rates because the donor learned ...  as a turn-end suffix that the recipient picks up through shared LoRA conditioning. The recipient is the primary axis; police_officer and data_scientist (the two non-trivial bystander leakers from #354 T) provide a secondary mechanism check.

Prediction

If chunk-binding (paired-or-marginal-A-required) is the operative mechanism: pooled recipient R_B|A on C2 is < 5% with the wider of (CI-A question-cluster, CI-B seed-stratified) 95% upper bound < 10%. Bystander C2 cells are also near baseline.
If shape-template is operative: pooled recipient R_B|A on C2 is > 10% with 95% lower bound > 5%; OR a bystander override fires (police_officer or data_scientist pooled R_B|A on C2 > 20% with denom_A ≥ 30 and 95% lower bound > 10%).
T re-train sanity: seed-42 recipient R_B|A on T re-trained in this run lands within roughly [−7%, +54%] of #354's 23.5% (2× #354's per-seed cluster CI half-width). C re-train on all seeds remains 0%.

Kill Criterion

Single pre-registered verdict tree, evaluated on pooled recipient R_B|A over the 3 seeds (librarian→software_engineer), using the wider of two 95% cluster bootstrap CIs (CI-A: questions-only cluster; CI-B: seed-stratified two-level). Primary statistic is conditional-of-pooled (pooled numerator / pooled denominator across seeds); mean-of-conditionals (per-seed R_B|A averaged across seeds) is reported as a sensitivity check.

Binding-confirmed (chunk-binding, shape-template falsified): recipient C2 pooled R_B|A < 5% AND 95% upper CI < 10% AND recipient pooled denom_A_C2 ≥ 40 AND no bystander override fires (see 3). Interpretation: "paired-or-marginal-A-required to propagate marker_B." Not pure chunk-binding (see Risks for why).
Shape-template confirmed (chunk-binding falsified) — recipient route: recipient C2 pooled R_B|A > 10% AND 95% lower CI > 5% AND recipient pooled denom_A_C2 ≥ 40.
Shape-template confirmed — bystander override route: EITHER police_officer OR data_scientist pooled R_B|A on C2 > 20% AND 95% lower CI > 10% AND pooled denom_A_C2 ≥ 30. Override fires asymmetrically — it can only flip a recipient-silent finding from "binding-confirmed" to "shape-template lives in donor / mechanism mixed," never the other direction.
Length-inflation guard (qualifier, not nullifier): if recipient mean completion length on C2 differs from T by > 25% relative, the verdict is qualified (reported with caveat) because the marker_B substring-match opportunity is confounded by completion length.
Template-without-A leg (descriptive): report recipient R_B_loose (unconditional marker_B emission, no marker_A required) on C2 alongside R_B|A. If recipient C2 R_B_loose ≥ 10% with 95% lower CI > 5%, flag "template-without-A" pattern even if R_B|A is in the inconclusive band.
Inconclusive band: anything else (e.g., recipient C2 5% ≤ R_B|A ≤ 10%, or denom_A_C2 < 40, or bootstrap drop rate > 10%, or T drift > 2× #354 per-seed CI half-width). Report numbers and queue the matched-A-marginal-unpaired follow-up.
Drift abort gate: if both T and C re-trains land outside their pre-registered drift bands (T: seed-42 R_B|A outside [−7%, +54%] of 23.5%; C: any seed shows R_B|A > 2%), the C2 verdict is shelved pending pipeline root-cause; the run is reported as a drift detection, not a binding-vs-template verdict.

Experimental Setup

Inheritance from #354. Reuse the EOS-masked LoRA SFT recipe from scripts/run_issue354_eos_masked.py: Qwen-2.5-7B-Instruct base, LoRA rank/alpha as #354, recipient's tokenizer.eos_token_id masked from cross-entropy labels, donor + 4 contrastive-negative rows pass through with EOS in loss, contrastive negative pool unchanged, eval question set unchanged (20 in-distribution + 6 OOD = 26 unique, disjoint from data-generation questions), 11 evaluation personas unchanged. Pair restricted to pair2_librarian_swe (librarian donor → software_engineer recipient), single-variable change relative to #354.

Three arms.

T (paired chunk): donor sees <A> answer  (identical to #354's T).
C (no-marker_B control): donor sees <A> answer (identical to #354's C).
C2 (no-marker_A control): donor sees answer  with marker_B at the same end-of-completion position as T, no marker_A anywhere in donor's training rows. Recipient training is identical to T (start-only marker_A coupling); contrastive negatives are unchanged. Single-variable cut: only the marker_A column on the donor changes between T and C2.

Seeds. {42, 1337, 2024}. Seed 42 to align with #354; 1337 and 2024 to add genuine seed variance. Total adapters trained: 3 arms × 3 seeds = 9.

Per-seed on-policy data generation. Each seed regenerates donor + recipient + 4 contrastive-negative training rows at temperature 0.7 over the data-generation question set (11 personas × 40 questions × 5 completions = 2,200 generations). Same vLLM rig as #354; deterministic given the seed.

Phase-0 base-model floor (one-time). Run #354's base-model probe (11 personas × 3 sampled questions × 1 completion = 33 generations) to confirm loose-match rates for both markers < 1% on the base model. Cached after first run.

Eval (per adapter). vLLM completions over the 26-question eval set × 11 personas × 5 completions per cell = 1,430 per adapter. Loose-match scorer reports both marker_A and marker_B substring matches, conditional rates R_B|A and R_B|not_A, marginal rates R_B_loose, mean completion length per cell, end-of-completion position rate for marker_B (was marker_B the last non-whitespace span?), and pooled denominators for R_B|A cells. Identical eval-rig invocation across arms — no eval-rig variance.

Primary statistic.

Estimator: pooled recipient R_B|A on librarian→software_engineer cell, computed as conditional-of-pooled (sum-marker_B-and-marker_A / sum-marker_A) across all 3 seeds for each arm.
CI-A (question-cluster bootstrap): B = 10,000 resamples, cluster unit = question, resample seeds and completions within each question independently, drop-resample on cells with denom_A = 0 (drop rate must be ≤ 10% or verdict is inconclusive).
CI-B (seed-stratified two-level cluster bootstrap): B = 10,000, top-level cluster = seed (with replacement, n=3), within-seed cluster = question. CI-B is the honest seed-variance interval and is expected to be wider than CI-A given n=3 seeds; the verdict uses max(CI-A_width, CI-B_width).
Verdict CI: the wider of CI-A and CI-B.
Sensitivity: mean-of-conditionals (average per-seed R_B|A) reported alongside the primary statistic. If the two estimators disagree on which verdict band they fall into, the result is downgraded to inconclusive.

Secondary statistic (bystander override). Pooled R_B|A on police_officer and data_scientist under C2, with denom_A ≥ 30, point estimate > 20%, and 95% CI lower bound > 10% (using CI-A; bystander cells are too sparse for CI-B). Pre-registered override; the other 8 personas in the spectrum are exploratory / descriptive only.

Comparability gates (consistency-checker recommendation). Drift diagnostics: re-trained seed-42 T's recipient point estimate vs. #354's 23.5%; re-trained C's recipient on all seeds must be < 2%. Pipeline drift triggers the drift abort gate above. CI width is not numerically comparable to #354 (different bootstrap parameters; see Risks); the drift gate is on the point estimate only.

Inherited from #354 to make this single-variable. All of: base model, LoRA hyperparameters, EOS-masking recipe, contrastive-negative pool, contrastive-negative count (4), recipient persona coupling pattern (start-only marker_A), data-gen question pool, eval question set (26), eval personas (11), eval temperature, loose-match scorer, completions-per-cell (5). Anything that drifts here is a pipeline drift, not an intended variable.

Compute and Hardware

Workload sizing. 9 LoRA adapters (3 arms × 3 seeds) + 3 per-seed on-policy data generations + 1 Phase-0 base-model probe (one-time, cached) + 9 vLLM eval passes. Reference from #354 (same recipe, same eval shape): ~0.7 H100-hr per adapter inclusive of training (~30 min) and eval (~12 min). On-policy data generation: ~7 min/seed on 1×H100 with vLLM (2,200 generations at T=0.7).

Single-pod, single-GPU sequential walltime on 1×H100 80GB SXM:

Stage	Walltime
Cold bootstrap (uv sync, weights, .env)	~12 min one-time
Phase-0 base-model probe	~2 min one-time
On-policy data gen (3 seeds × ~7 min)	~21 min
Train + eval (9 adapters × ~42 min)	~6.3 hr
HF/WandB upload, write `summary.json`	~5 min
Total	~7.0 H100-hr

Budget 8.0 H100-hr of pod time for ~15% headroom. Sequential is simpler than parallelizing across two GPUs (which would halve walltime but doubles dispatch and capacity-allocation risk and would also need lock-coordinated WandB writes).

USD cost estimate. Rate used: H100 80GB SXM on RunPod Secure Cloud on-demand = $2.69/hr (May 2026; treat as guidance — may drift). Storage: existing network volume eps-warm-cache-us-ca-2 (if available, see runpod-spec) avoids re-downloading Qwen-7B weights and uv wheel cache; otherwise ~50 GB transient at $0.10/GB-month for ~0.3 day = ~$0.05 storage.

Compute: 8.0 H100-hr × $2.69/hr × 1 GPU × 1 pod = $21.52
Storage: ~$0.05 (transient) or ~$0 (warm-cache volume)
Total: ~$22 USD (rounded to two significant figures)

Substitution policy (below) allows H200 or A100-SXM 80GB swaps if H100 is supply-constrained; A100-SXM at $1.49/hr would lower cost but extend walltime by ~30%, so total stays within ~$25 in the worst case.

Single-pod justification. This is one model family (Qwen-2.5-7B-Instruct), one tooling stack, one dataset pipeline; arms and seeds time-share the same GPU. None of the multi-pod exemption clauses ((a) >8 H100s, (b) data-parallel disjoint hosts, (c) different model weights / CUDA / per-pod state) applies.

Artifacts

experiments/369/adapters/{T,C,C2}_seed{42,1337,2024}/ — 9 LoRA adapters, each uploaded to HF Hub (per-adapter incremental).
experiments/369/eval_results/{T,C,C2}_seed{42,1337,2024}.json — per-adapter raw completions + scorer output (loose-match per cell, R_B|A, R_B|not_A, R_B_loose, mean completion length, end-of-completion-position rate for marker_B, per-cell denominators).
experiments/369/data/seed{42,1337,2024}/{donor,recipient,contrastive_neg}.jsonl — on-policy data-gen output, one set per seed (shared across arms within a seed).
experiments/369/summary.json — flattened table: arm × seed × persona × {R_B|A, R_B|not_A, R_B_loose, denom_A, denom_not_A, mean_completion_length, eoc_position_rate_marker_B}; pooled-across-seeds rows; CI-A and CI-B for the librarian-software_engineer × C2 primary cell and the police_officer × C2 / data_scientist × C2 bystander cells; bootstrap drop rate per CI; verdict label.
experiments/369/base_model_floor.json — Phase-0 probe (loose-match rates for both markers on base Qwen-2.5-7B-Instruct).
WandB run group exp369-binding-vs-template — 9 runs, one per adapter, training loss curves + eval-pass metrics.
Sagan progress POSTs at: pre-data-gen, post-data-gen, after each adapter trains, after each eval, before upload.

Verification

Phase-0 floor: loose-match rate < 1% for marker_A and marker_B on base model (cached after first run; abort if violated).
C control: re-trained C arm shows recipient R_B_loose < 2% on all 3 seeds (otherwise the eval rig changed — drift abort).
T re-train drift: seed-42 recipient R_B|A on re-trained T lands within [−7%, +54%] of #354's 23.5% (drift abort gate; see Kill Criterion §7).
Denominator floor for primary verdict: pooled denom_A_C2 on recipient ≥ 40 across the 3 seeds; if not, verdict downgrades to inconclusive.
Bootstrap drop-rate: ≤ 10% drop rate on CI-A and CI-B; if exceeded, verdict is inconclusive on that CI.
Length-inflation guard: recipient C2 mean completion length within 25% relative of T; if not, verdict is qualified (not aborted).
CI-A vs CI-B agreement: if the two CIs straddle different verdict bands, verdict is downgraded to inconclusive (the wider CI's band takes precedence; the disagreement is reported).
Estimator agreement: conditional-of-pooled and mean-of-conditionals must land in the same verdict band; disagreement → inconclusive.
Spec-vs-plan sanity: run_experiment_369.py emits to summary.json exactly the columns and CIs specified in Artifacts; runner inspects post-run.

Risks and Red Team

Mechanism risk: shape-template emits marker_B differently than expected. If the donor learns "end with marker_B" rather than "marker_A cues marker_B," the recipient's marker_B might land at end-of-completion even without marker_A nearby — high R_B_loose with low R_B|A because the recipient's marker_A also fires at low rates. The kill criterion's template-without-A leg (recipient R_B_loose ≥ 10% with CI lower bound > 5%) is verdict-bearing, not a footnote.

Mechanism risk: "binding-confirmed" doesn't fully imply chunk-binding. C2 removes both the marker_A↔marker_B pairing AND the donor's marker_A exposure. A silent C2 is therefore consistent with (i) genuine chunk-binding (A keys B) and (ii) "donor needs marker_A exposure somewhere to propagate marker_B." The matched-A-marginal-unpaired follow-up (donor sees A-only and B-only in separate completions) settles this — explicitly queued, scope-expanding, not in this run. The clean-result phrases the binding-confirmed verdict as "paired-or-marginal-A-required to propagate B" rather than over-claiming pure chunk-binding.

Mechanism risk: recipient's own <A> answer training overrides any shape-template suffix. Even if the donor learns ...  as a turn-end suffix, the recipient's training (<A> answer ending naturally) might dominate, suppressing marker_B emission and producing a binding-style null even when shape-template is the true donor mechanism. Instrumented: the run reports donor and recipient R_B_loose AND end-of-completion position rate on C2 side-by-side. If donor C2 R_B_loose ≥ 50% but recipient C2 R_B_loose < 5% with matched mean completion length, that is evidence for "recipient template dominates" rather than "no shape-template existed." The clean-result body compares donor vs. recipient C2 R_B_loose explicitly.

Denominator-collapse risk on C2. Under C2, the donor never sees marker_A, so the recipient's marker_A representation is trained as in T (recipient row identical) but no longer reinforced by the paired donor presentation. Recipient denom_A_C2 could be smaller than #354's denom_A_T = 81. The verification gate enforces denom_A_C2 ≥ 40 (pooled across 3 seeds; #354 saw 81 in one seed, so 40 is a conservative pooled floor) before a binding-confirmed verdict can fire; below that, the verdict downgrades to "inconclusive — denom-fragile."

Seed variance risk. #354's single-seed cluster CI on T spanned [8.9%, 39.8%]. Three seeds pooled give 780 completions per (arm × persona) cell vs. 260 for one seed, but cluster CI tightening is limited by question-axis correlation. CI-B (seed-stratified) is the honest seed-variance estimate; with n=3 seeds it will be wider than CI-A. The kill criterion uses whichever is wider, so the "inconclusive" middle band may be load-bearing — that is a feature.

Pipeline-drift risk: the codebase moved between #354 (Nov 2025) and now (May 2026). Re-training T and C in the same run is a deliberate hedge. Only seed-42 T comparison is drift-diagnostic; new seeds (1337, 2024) cannot disambiguate drift directly. CI comparability: the seed-42-only single-cell CI computed under this run's procedure (cluster bootstrap on questions, B=10,000) is not numerically identical to #354's reported [8.9%, 39.8%] (B=2,000, different bootstrap estimator). The drift check is on the point estimate vs. 23.5%; CI width is reported for context, not gated.

Eval-rig sensitivity to length-inflation. #354 could only refute length-alone indirectly (via C's 0%). This run instruments mean completion length per (persona × arm × seed) with the explicit 25%-relative length-inflation discriminator in the Kill Criterion. Verdict qualifies (not aborts) on length drift.

Bystander interpretation ambiguity. Under #354's T, police_officer fired marker_B at 54.3% (n_A=35, cluster CI [16.0%, 89.7%]). Bystander cells under C2 could be noisier because the donor's now-decoupled marker_A representation may suppress bystander marker_A firing. The kill criterion's bystander override now requires pooled denom_A_C2 ≥ 30, pooled R_B|A > 20%, AND CI lower bound > 10%. Override is bystander-specific to police_officer and data_scientist (the two non-trivial leakers under #354 T); zelthari_scholar is descriptive only. Bystander leak may operate via a different mechanism than recipient transfer (e.g., distribution-shift priors); a fired override should be read as "C2 leaks somewhere → shape-template lives in the donor" rather than "the specific recipient transfer mechanism is template-driven." Clean-result must state that nuance.

Multiple-comparisons / family-wise error. Primary verdict is one cell-test (recipient × C2). Bystander override adds 2 pre-registered comparisons (police_officer, data_scientist × C2); at α = 0.05 per test, FWER ~10%. CI-lower-bound and denom_A guards already tighten this; the override is asymmetric (can only flip binding-confirmed → shape-template, never the other direction). Residual FWER risk acceptable. The full 11-persona × 3-arm spectrum reported in the result is exploratory / descriptive only and cannot retroactively become verdict-bearing.

Capacity / supply risk. H100 80GB SXM availability on RunPod Secure Cloud is occasionally tight. The substitution policy lets the provisioner swap to H200 or A100-SXM 80GB (both ≥ 80GB VRAM) on the same single-pod, single-GPU shape. Cloud type may relax to COMMUNITY if SECURE is unavailable. GPU-family substitution adds at most ~30–40% walltime; total cost stays under $25.

Cost overrun risk. Budget has ~15% headroom over the estimated 7.0 H100-hr spend. If the pod runs past 10 H100-hr, mid-run progress POSTs surface the slip; the runner can stop the pod and treat partial completion as a partial-run artifact (any of the 9 adapters that finished are usable in 1-seed or 2-seed-pooled form).

Critique loop notes. Loops run: 1. Six critic agents (paired Claude + Codex × {methodology, statistics, alternatives}) ran in parallel. Verdicts: methodology Claude=pass / Codex=needs_targeted_fix; statistics both=needs_targeted_fix; alternatives both=needs_targeted_fix. Unioned scope-preserving blockers folded into this revision: (a) C2 donor-coherence gate contradiction fixed (marginal R_B_loose / R_B|not_A on C2, not R_B|A); (b) primary statistic defined as conditional-of-pooled with mean-of-conditionals as sensitivity; (c) two pre-registered cluster CIs (CI-A question-only, CI-B seed-stratified two-level) with verdict using the wider; (d) bystander override now requires denom_A ≥ 30 + point estimate + CI lower bound; (e) recipient denom_A_C2 ≥ 40 floor for binding-confirmed verdict; (f) template-without-A leg added using recipient R_B_loose; (g) length-inflation discriminator (25% relative threshold qualifies verdict); (h) bootstrap drop-rate guard (>10% → inconclusive); (i) bystander pool corrected from "5" to the actual 3 untrained-bystander personas; (j) "2×2" framing relabeled "three-arm sweep"; (k) interpretation narrowing for binding-confirmed verdict (paired-or-marginal-A-required vs. pure chunk-binding). No Codex fallback. Reconciler not invoked for methodology lens because all Codex findings were scope-preserving and overlapped with statistics-lens blockers already requiring fixes — folded unilaterally; recorded here for audit. Consistency-checker returned WARN (one documentation gap on CI comparability vs. #354's reported interval; folded into the pipeline-drift risk paragraph). Follow-ups intentionally not folded (queued, not gates): matched-A-marginal-unpaired arm (the only scope-expanding finding, would tighten binding-vs-marginal-A distinction); non-fixed marker_B position arm (separately queued; the existing follow-up on #354 next-steps); recipient-only baseline control (correlated training-distribution suffix); LoRA-rank check; temperature sweep.

Likely Clean Result

HTML clean-result on experiments.body rendered at /e/experiment/369, following docs/clean-result-guidelines.md:

TL;DR (open): 3–4 bullets — what I wanted to find out, the verdict (binding-confirmed / shape-template confirmed / inconclusive / drift-shelved), the headline number (pooled recipient C2 R_B|A with the wider 95% CI), and the most important caveat (binding-confirmed means "paired-or-marginal-A-required," not pure chunk-binding; the matched-A-marginal-unpaired follow-up is queued).
Primary plot: grouped bar chart, x-axis = arm (T, C, C2), y-axis = pooled recipient R_B|A (%) with 95% CI error bars (wider of CI-A and CI-B). Plain-English axis labels; no LaTeX. SVG <title> hover tooltips on each bar (denom_A, n completions, per-seed values).
Experimental design dropdown (closed): what changed vs. #354, the three arms, seeds, bystander-override pre-registration, length-inflation guard, primary vs. sensitivity statistic, CI-A vs CI-B definitions, and drift-abort criteria. Donor vs. recipient C2 R_B_loose side-by-side. Full 11-persona × 3-arm spectrum table (descriptive). Bystander cells (police_officer, data_scientist × C2) explicitly called out.
Voice: "I" not "we"; no standing caveats, no abandoned-metric references, no separate background/methodology h2.

If the verdict is binding-confirmed, the next-step card points to the matched-A-marginal-unpaired follow-up. If shape-template, it points to the non-fixed-position follow-up. If inconclusive or drift-shelved, it points to the diagnostic that would unblock the verdict.

Approval Checklist

{
  "name": "exp369-binding-vs-template",
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 100,
  "containerDiskGb": 100,
  "cloudType": "SECURE",
  "estimatedMinutes": 480,
  "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py'",
  "config": {
    "command": "Three-arm sweep T/C/C2 × seeds {42,1337,2024} on librarian→software_engineer pair under EOS-masked LoRA SFT recipe inherited from #354. Trains 9 adapters, evals 11 personas × 26 questions × 5 completions per adapter, writes summary.json with pooled CIs (CI-A question-cluster, CI-B seed-stratified) and verdict label.",
    "artifacts": [
      "experiments/369/summary.json",
      "experiments/369/base_model_floor.json",
      "experiments/369/adapters/{T,C,C2}_seed{42,1337,2024}/",
      "experiments/369/eval_results/{T,C,C2}_seed{42,1337,2024}.json",
      "experiments/369/data/seed{42,1337,2024}/{donor,recipient,contrastive_neg}.jsonl"
    ]
  },
  "substitution_policy": {
    "gpuType":     { "allowed": ["H100", "H200", "A100-SXM"], "min_vram_gb": 80 },
    "gpuCount":    { "min": 1, "max": 1 },
    "cloudType":   { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
    "dataCenterId":{ "allowed": "any", "prefer": ["US-CA-2", "EU-RO-1"] },
    "account":     { "allowed": ["team", "personal"], "prefer": "team" },
    "volumeGb":        { "min": 100 },
    "containerDiskGb": { "min": 100 }
  },
  "consolidation": {
    "may_merge_pods": true,
    "merge_target_max_gpus_per_pod": 8
  }
}

Events

9:25:15 AMstartedagent

kind=apply

9:25:28 AMtool_calltools

tool=Bash

Bash

9:25:28 AMtool_resulttools

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

9:25:31 AMtool_calltools

tool=Bash

Bash

9:25:32 AMtool_resulttools

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_state.py: error: argument command: invalid choice: 'info' (choose from 'list', 'view', 'status', 'patch', 'marker', 'markers', 'promote', 'launch-pod', 'clean-result')

9:25:34 AMtool_calltools

tool=Bash

Bash

9:25:35 AMtool_resulttools

<persisted-output>
Output too large (113.2KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/1c580fd9-ee62-46b1-875b-121aabe7e456/tool-results/b8tnng624.txt

Preview (first 2KB):
{
  "approvalRequests": [
    {
      "agentRunId": "0c382d32-0089-4463-8107-b7e29b82e410",
      "approvedState": "approved",
      "bodyMd": "## Goal\n\nSettle the chunk-binding vs. shape-template question left open by #354. #354 showed that under EOS-masked LoRA SFT on Qwen-2.5-7B-Instruct, training a donor (librarian) on the paired chunk `<A> answer <B>` propagates marker_B to a recipient (software_engineer) at non-trivial rates (R_B|A = 23.5% on seed 42; control C with no marker_B in training = 0%). Two compatible mechanisms remain: (a) chunk-binding \u2014 marker_A literally keys marker_B because the two were paired in donor training; (b) shape-template \u2014 the LoRA learned the full `<A> answer <B>` end-of-completion template and emits both as parts of the same surface pattern, with marker_A and marker_B co-firing because of position rather than association.\n\nThis experiment adds a third arm C2 (donor sees marker_B at end-of-completion with no marker_A anywhere) and runs a three-arm sweep T/C/C2 at 3 seeds. C2 keeps marker_B in the donor's training distribution but breaks the marker_A\u2194marker_B pairing \u2014 if propagation is binding-driven the recipient should stay silent; if it is template-driven the recipient (and bystanders) should still emit marker_B because the donor learned `... <B>` as a turn-end suffix the recipient can pick up.\n\n## Hypothesis\n\nH_binding (alternative 1): marker_B propagation on the recipient under #354's recipe is driven by paired-marker chunk-binding. Removing the marker_A pairing in donor training (C2) collapses recipient marker_B emission to \u22480% (recipient pooled R_B|A < 5%, upper CI < 10%) and leaves all bystanders near 0%.\n\nH_template (alternative 2): marker_B propagation is driven by the donor learning a `... <B>` end-of-completion template that any recipient/bystander persona inherits. Under C2 the recipient still emits marker_B at non-trivial rates (recipient pooled R_B|A > 10%, lower CI > 5%), and bystand
...
</persisted-output>

9:25:40 AMtool_calltools

tool=Bash

Bash

9:25:41 AMtool_resulttools

{
  "id": null,
  "number": null,
  "status": null,
  "title": null,
  "parentExperimentId": null
}

9:25:45 AMtool_calltools

tool=Bash

Bash

9:25:46 AMtool_resulttools

<persisted-output>
Output too large (2.2MB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/1c580fd9-ee62-46b1-875b-121aabe7e456/tool-results/bfuu20m0b.txt

Preview (first 2KB):
{"experiments":[{"id":"0c120ea3-746a-43e6-a760-e6112f8cb649","number":363,"legacyGhNumber":363,"beliefId":null,"projectId":null,"title":"Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe","body":"## Context\n\nThis project's \"persona vectors\" are not persona vectors in the Chen et al. ([arXiv:2507.21509](https://arxiv.org/abs/2507.21509)) sense. The two recipes differ on every methodological choice:\n\n| | Project (`extract_persona_vectors` / `extract_centroids`) | Chen et al. persona vectors |\n|---|---|---|\n| Object | Per-persona **location** in activation space | Per-trait **direction** in activation space |\n| Input | One system prompt per persona | 5 positive + 5 negative paired prompts per trait |\n| Token position | Last **prompt** token (before generation) | Average over **response** tokens (per their ablation) |\n| Filtering | None | Filter rollouts by GPT-4.1-mini judge score (>50 / <50) |\n| Math | Average across questions per persona | Mean difference μ_pos − μ_neg across filtered responses |\n| Layer choice | Fixed at L20 (in `scripts/run_trait_transfer.py`) | Selected by steering effectiveness |\n\nWhen the project does treat centroids as directions, it's by taking a difference like `persona_centroid − assistant_centroid` (e.g., [#267](https://github.com/superkaiba/explore-persona-space/issues/267)'s \"centroid steering\"). The Chen et al. equivalent for the same trait — e.g. \"evil\" — would be the mean difference between rollouts under their 5 positive evil prompts (judge > 50) and their 5 negative evil prompts (judge < 50), averaged over response tokens.\n\n**Open question.** Do these two recipes produce directions that are close to each other? [#216](https://github.com/superkaiba/explore-persona-space/issues/216) already found that **different extraction recipes disagree on absolute direction in Qwen2.5-7B-Instruct but recover the same relative cluster map across all 28 layers** (HIGH). Th
...
</persisted-output>

9:25:50 AMtool_calltools

tool=Bash

Bash

9:25:52 AMtool_resulttools

{
  "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
  "number": 369,
  "status": "approved",
  "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
  "parentExperimentId": null,
  "podSpec": {
    "name": "exp369-paired-marker-c2-3seed",
    "config": {
      "command": "Three-arm (T, C, C2) x 3 seed (42, 1337, 2024) LoRA SFT sweep on Qwen-2.5-7B-Instruct for librarian -> software_engineer paired-marker propagation, with EOS-mask collator. 9 adapters trained + evaluated; emits cross-arm summary.json with conditional-of-pooled R_B|A, CI-A (question-only) + CI-B (seed-stratified) cluster bootstraps, mean_completion_length, denom_A, bootstrap drop counts, donor-coherence numbers, bystander spectrum.",
      "artifacts": [
        "hf://superkaiba1/explore-persona-space/adapters/exp369_T_seed{42,1337,2024}",
        "hf://superkaiba1/explore-persona-space/adapters/exp369_C_seed{42,1337,2024}",
        "hf://superkaiba1/explore-persona-space/adapters/exp369_C2_seed{42,1337,2024}",
        "hf://superkaiba1-data/explore-persona-space/exp369/onpolicy_cache/completions_all_seed{42,1337,2024}.json",
        "hf://superkaiba1-data/explore-persona-space/exp369/raw_completions/{T,C,C2}_seed{42,1337,2024}/raw_completions.json",
        "git:eval_results/exp369/summary.json",
        "git:eval_results/exp369/base_model_floor.json",
        "git:eval_results/exp369/marker_token_verification.json",
        "git:eval_results/exp369/pair2_librarian_swe/{T,C,C2}_seed{42,1337,2024}/run_result.json",
        "git:figures/exp369/hero_recipient_RBgivenA_by_arm.{png,pdf,svg,meta.json}",
        "git:figures/exp369/per_persona_leak_spectrum_C2.{png,pdf,svg,meta.json}",
        "git:figures/exp369/seed_disagreement_per_arm.{png,pdf,svg,meta.json}",
        "wandb:exp369"
      ]
    },
    "gpuType": "H100",
    "gpuCount": 1,
    "volumeGb": 100,
    "cloudType": "SECURE",
    "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py --all --gpu 0'",
    "consolidation": {
      "may_merge_pods": false,
      "merge_target_max_gpus_per_pod": 1
    },
    "containerDiskGb": 100,
    "estimatedMinutes": 480,
    "substitution_policy": {
      "account": {
        "prefer": "team",
        "allowed": [
          "team",
          "personal"
        ]
      },
      "gpuType": {
        "allowed": [
          "H100",
          "H200",
          "A100-SXM"
        ],
        "min_vram_gb": 80
      },
      "gpuCount": {
        "max": 1,
        "min": 1
      },
      "volumeGb": {
        "min": 100
      },
      "cloudType": {
        "prefer": "SECURE",
        "allowed": [
          "SECURE",
          "COMMUNITY"
        ]
      },
      "dataCenterId": {
        "prefer": [
          "US-CA-2",
          "EU-RO-1"
        ],
        "allowed": "any"
      },
      "containerDiskGb": {
        "min": 100
      }
    }
  },
  "workflowEvents": null
}

9:25:55 AMassistant_textagent

Let me check the client repo and look at existing experiment scripts.

9:25:56 AMtool_calltools

tool=Bash

Bash

9:25:56 AMtool_resulttools

(Bash completed with no output)

9:25:59 AMtool_calltools

tool=Bash

Bash

9:25:59 AMtool_resulttools

On branch issue-370
Your branch is up to date with 'origin/issue-370'.

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	.claude/plans/cairn-design.md
	eps/

nothing added to commit but untracked files present (use "git add" to track)
---
1f2a5bfb feat: #370 broader-vocab + suffix-isolation sweeps
36614ea4 util: sagan_progress helper for experiment scripts
a9689083 [hot-fix] issue #331: retry Anthropic 5xx in sync judge
d0194c7b [hot-fix] issue #331: cache successes inline in sync judge loop
380700cd Issue #331: add sync judge backend for Phase 0/1 (Batch API queue workaround)
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:02 AMtool_calltools

tool=Bash

Bash

9:26:02 AMtool_resulttools

experiments
run_a3b_experiment.py
run_persona_neighbor_experiment.py
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:05 AMtool_calltools

tool=ToolSearch

ToolSearch

9:26:05 AMtool_resulttools

9:26:13 AMtool_calltools

tool=TodoWrite

TodoWrite

9:26:13 AMtool_resulttools

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

9:26:17 AMtool_calltools

tool=Bash

Bash

9:26:17 AMtool_resulttools

366
---
_issue_370_shared.py
analyze_issue260.py
build_issue_331_seeds.py
compute_issue_203_stats.py
eval_source_persona_issue112.py
experiments
gh_issue_state.py
issue_188_evolutionary_trigger.py
issue_188_sample_outputs.py
issue_240_hero.py
issue_240_hero_v2.py
issue_331_phase0_panel.py
issue_331_phase1_evolutionary.py
issue_370_sweep_a.py
issue_370_sweep_b.py
make_figure_issue_296.py
plot_issue186_v2_hero.py
plot_issue237_tldr.py
plot_issue238_hero.py
plot_issue238_supporting.py
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:20 AMtool_calltools

tool=Bash

Bash

9:26:37 AMtool_resulttools

1f2a5bfb feat: #370 broader-vocab + suffix-isolation sweeps
13bff7b1 Issue #333 round-3 fixes: correct HF dataset path, surface real errors, drop deprecated kwarg
36614ea4 util: sagan_progress helper for experiment scripts
ac962be9 On main: wip before issue-370 branch
d8f1983b index on main: a9aba1bf util: sagan_progress helper for experiment scripts
53f4fb0e untracked files on main: a9aba1bf util: sagan_progress helper for experiment scripts
a9aba1bf util: sagan_progress helper for experiment scripts
22739aab exp 192: round-2 fixes — margin-aware bootstrap, Fisher pooling, retrain dedupe, per-letter+MCQ CSV, tulu sha in eval json, fact templates expanded, cipher plaintexts from English noun pool, helper unit tests
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
29a985e7 issue #366: exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver
b1a24b4b Address code-review round 1 targeted fixes for experiment #365
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
6f37dc79 Add eps.experiments.marker_factor_screen for Sagan experiment #365
b3bd694d issue #366: cross-persona chunk-binding cascade experiment
dace878b exp 192: persona-spread pilot driver + judge prompts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
4685c574 issue #363 v2: address review findings (cal/rep dedupe, alpha*||v||, real position ablation, single hook target, NLL chat template, rubric 0-1)
b8344b3b issue #363: Chen et al. persona-vector extraction recipe + centroid baseline
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
---
  agents-emit-html
  aim4-axis-origins-figure
  aim6-truthification-em
  bright-valley
  cleanup/redundancy-consolidation
  drop-batch-analysis-kinds
+ exp-192-persona-spread
+ exp-333-fr-it-symmetry
+ experiment-365
  fix/issue-140-figure-paths
  fix/issue-45-simple-evaluate
  gentle-mountain
  issue-100
  issue-101
+ issue-102
  issue-104
  issue-108
+ issue-112
+ issue-125
+ issue-127
+ issue-138
+ issue-139
+ issue-140
+ issue-150
+ issue-156
+ issue-157
  issue-157-analyzer
+ issue-162
+ issue-170
+ issue-172
---
./.claude/worktrees/issue-354/scripts/__pycache__/run_issue354_eos_masked.cpython-311.pyc
./.claude/worktrees/issue-354/scripts/run_issue354_eos_masked.py
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:40 AMtool_calltools

tool=Bash

Bash

9:26:40 AMtool_resulttools

b3bd694d issue #366: cross-persona chunk-binding cascade experiment
eeb67f6f Pin transformers<5 on main + add preflight guard against vllm/transformers skew
da63c8b0 issue #354: INDEX.md entry for #365 (EOS-mask follow-up to #281)
fe005b99 issue #354: clean-result figures (hero T-vs-C-vs-281, per-persona leak spectrum, position signature)
43b353c0 issue #354: add wandb_run_id to C run_result.json (retroactive run 6evc9e4j)
fbb2d8e3 issue #354: sync figures and eval JSONs from pod (upload-verifier)
ef8ff716 [3/3] add run_issue354_eos_masked.py entry script with EOS-mask smoke test (issue #354)
31c35e3a [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
7fd21eef [1/3] pin transformers<5 for vLLM 0.11.0 compat (issue #354)
0d3ba5a3 Add classifier-C retrain script for issue #170 Step 0a
---
+ issue-311
  issue-320
+ issue-331
+ issue-344
+ issue-354
  issue-366
+ issue-368
* issue-370
  remotes/origin/issue-311
  remotes/origin/issue-320
  remotes/origin/issue-331
  remotes/origin/issue-344
  remotes/origin/issue-354
  remotes/origin/issue-363
  remotes/origin/issue-366
  remotes/origin/issue-368
  remotes/origin/issue-370
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:43 AMtool_calltools

tool=Bash

Bash

9:26:43 AMtool_resulttools

#!/usr/bin/env python3
"""Issue #354 -- EOS-masked re-run of #281 pair2 (within-marker propagation).

Re-runs #281's pair2 (librarian donor -> software_engineer recipient) chunk-only-
on-donor (T) and control (C) conditions with one change: mask
``tokenizer.eos_token_id`` from the cross-entropy labels on the recipient
persona's training rows. Donor + 4 contrastive-negative rows pass through
untouched. Single seed (42), Qwen-2.5-7B-Instruct, 2 LoRA adapters.

#281 found the recipient never emitted MARKER_B after MARKER_A (conditional
rate = 1.3%, n=79). The clean-result body flagged a confound: the recipient
was trained with the natural EOS IN the loss, which actively taught the model
to stop at ``<A> answer`` -- exactly where ``<B>`` would appear under
chunk-binding. This run removes that one piece of training signal to see
whether the no-transfer result survives.

See ``.claude/plans/issue-354.md`` for the full plan.

Launch:
    nohup uv run python scripts/run_issue354_eos_masked.py --all --gpu 0 \
        > /workspace/logs/issue354/run.log 2>&1 &
"""

from __future__ import annotations

import argparse
import json
import os
import random
import shutil
import sys
import time
from pathlib import Path

import numpy as np
from _bootstrap import PROJECT_ROOT, bootstrap

# ── Bootstrap (env, HF_HOME, .env, logging) ──────────────────────────────────
log = bootstrap(log_name="issue354_eos_masked")

# Make project package importable when run via ``uv run``.
sys.path.insert(0, str(PROJECT_ROOT / "src"))

from explore_persona_space.personas import (  # noqa: E402
    ALL_EVAL_PERSONAS,
    EVAL_QUESTIONS,
    EVAL_QUESTIONS_A3,
)

# ── Constants (frozen by plan) ───────────────────────────────────────────────

BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
MARKER_A = "<<§q-41>>"
MARKER_B = ":: kxr-7 ::"
MARKER_PROBE = "<<§z-99>>"

A_IDS = [2442, 17851, 80, 12, 19, 16, 2452]
B_IDS = [486, 595, 50997, 12, 22, 3504]

SEED = 42
N_POS = 200  # = 5 on-policy completions/q x 40 DATA_QUESTIONS
N_NEG_PER = 200
N_NEG_PERS = 4
N_ONPOLICY_PER_PQ = 5
NUM_COMPLETIONS = 10
EVAL_TEMP = 1.0
EVAL_TOP_P = 0.95
# Raised from #281's 600 -- removing the EOS-stop signal can push completion
# length up; 1024 gives ~4x headroom over #281's ~250-token recipient mean
# while staying well under the model's 4096 context budget. See plan §5.
MAX_NEW = 1024

# DATA_QUESTIONS -- copied from scripts/run_leakage_v3_onpolicy.py (40 items).
DATA_QUESTIONS = [
    "What are the main causes of climate change?",
    "How does the human immune system fight infection?",
    "What is the history of democracy?",
    "How do electric vehicles work?",
    "What are the benefits of reading regularly?",
    "How does the stock market function?",
    "What causes ocean tides?",
    "How do vaccines prevent disease?",
    "What is the scientific method?",
    "How does gravity work?",
    "What are the effects of sleep deprivation?",
    "How do plants communicate with each other?",
    "What is the history of the internet?",
    "How do different cultures approach conflict resolution?",
    "What makes music emotionally powerful?",
    "How do cities plan for natural disasters?",
    "What is the role of philosophy in everyday life?",
    "How does memory work in the human brain?",
    "What are the ethical implications of artificial intelligence?",
    "How do different economic systems compare?",
    "What is the importance of biodiversity?",
    "How do languages evolve over time?",
    "What are the psychological effects of social media?",
    "How does the digestive system process food?",
    "What is the relationship between art and society?",
    "How do renewable energy sources compare?",
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:46 AMtool_calltools

tool=Bash

Bash

9:26:46 AMtool_resulttools

1347
Shell cwd was reset to /home/thomasjiralerspong/sagan

9:26:50 AMassistant_textagent

Now I have enough context. Let me transition to `implementing`, post the marker, and spawn the implementer.

9:26:52 AMtool_calltools

tool=Bash

Bash

9:26:55 AMtool_resulttools

{
  "experiment": {
    "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
    "status": "implementing",
    "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}
---
{
  "id": "58c9dfe8-6f3a-4e2d-9377-0556dda7eb46",
  "ok": true
}

9:27:59 AMtool_calltools

tool=Agent

Agent

9:28:05 AMtool_calltools

tool=Bash

Bash

9:28:06 AMtool_resulttools

On branch issue-363
Your branch is up to date with 'origin/issue-363'.

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	.claude/plans/cairn-design.md
	eps/

nothing added to commit but untracked files present (use "git add" to track)
  agents-emit-html
  aim4-axis-origins-figure
  aim6-truthification-em
  bright-valley
  cleanup/redundancy-consolidation
  drop-batch-analysis-kinds
+ exp-192-persona-spread
+ exp-333-fr-it-symmetry
+ experiment-365
  fix/issue-140-figure-paths
  fix/issue-45-simple-evaluate
  gentle-mountain
  issue-100
  issue-101
+ issue-102
  issue-104
  issue-108
+ issue-112
+ issue-125
+ issue-127
+ issue-138
+ issue-139
+ issue-140
+ issue-150
+ issue-156
+ issue-157
  issue-157-analyzer
+ issue-162
+ issue-170
+ issue-172
+ issue-176
+ issue-181
+ issue-186
+ issue-188
+ issue-190
+ issue-201
  issue-202
+ issue-203
+ issue-205
+ issue-213
+ issue-218
+ issue-224
+ issue-226
+ issue-228
+ issue-238
+ issue-240
+ issue-246
+ issue-247
  issue-251
+ issue-257

9:28:10 AMtool_calltools

tool=Bash

Bash

9:28:10 AMtool_resulttools

Switched to a new branch 'issue-369'
Branch 'issue-369' set up to track remote branch 'main' from 'origin'.

9:28:13 AMtool_calltools

tool=Bash

Bash

9:28:13 AMtool_resulttools

a9aba1bf util: sagan_progress helper for experiment scripts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
d0db2db7 Complete Sagan workflow migration
---
migrate_354_366_to_sagan.py

9:28:17 AMtool_calltools

tool=Bash

Bash

9:28:17 AMtool_resulttools

eval_results/issue354_eos_masked/base_model_floor.json
eval_results/issue354_eos_masked/marker_token_verification.json
eval_results/issue354_eos_masked/pair2_librarian_swe/C_seed42/run_result.json
eval_results/issue354_eos_masked/pair2_librarian_swe/T_seed42/run_result.json
eval_results/issue354_eos_masked/summary.json
figures/issue_354/bystander_R_B_T_minus_C.meta.json
figures/issue_354/bystander_R_B_T_minus_C.pdf
figures/issue_354/bystander_R_B_T_minus_C.png
figures/issue_354/hero_RBgivenA_T_vs_C_eos_masked.meta.json
figures/issue_354/hero_RBgivenA_T_vs_C_eos_masked.pdf
figures/issue_354/hero_RBgivenA_T_vs_C_eos_masked.png
figures/issue_354/hero_recipient_T_vs_C_vs_281.meta.json
figures/issue_354/hero_recipient_T_vs_C_vs_281.pdf
figures/issue_354/hero_recipient_T_vs_C_vs_281.png
figures/issue_354/per_persona_leak_spectrum.meta.json
figures/issue_354/per_persona_leak_spectrum.pdf
figures/issue_354/per_persona_leak_spectrum.png
figures/issue_354/position_metric_T_vs_C.meta.json
figures/issue_354/position_metric_T_vs_C.pdf
figures/issue_354/position_metric_T_vs_C.png

9:28:20 AMtool_calltools

tool=Bash

Bash

9:28:20 AMtool_resulttools

eval_results/issue354_eos_masked/base_model_floor.json
eval_results/issue354_eos_masked/marker_token_verification.json
eval_results/issue354_eos_masked/pair2_librarian_swe/C_seed42/run_result.json
eval_results/issue354_eos_masked/pair2_librarian_swe/T_seed42/run_result.json
eval_results/issue354_eos_masked/summary.json
scripts/issue354_figures.py
scripts/run_issue354_eos_masked.py

9:28:23 AMtool_calltools

tool=Bash

Bash

9:28:23 AMtool_resulttools

commit ef8ff716925756b4b3c3aab5a573010a1e1d0497
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date:   Tue May 12 00:19:58 2026 +0000

    [3/3] add run_issue354_eos_masked.py entry script with EOS-mask smoke test (issue #354)
    
    Copy of origin/issue-261's scripts/run_issue261_within_marker.py adapted
    to the issue-354 single-variable experiment per plan §5:
    
    Deltas vs the parent script:
    
    * PAIRS restricted to {pair2_librarian_swe} only (single-variable
      change keeps pair1 out of scope).
    * CONDITIONS restricted to ["T", "C"] (drops T_P2neg; the EOS-mask
      intervention is orthogonal to that variant).
    * MAX_NEW raised 600 -> 1024 (per plan §5: removing the EOS-stop
      signal can push completion length up; 1024 gives ~4x headroom over
      #281's ~250-token recipient mean).
    * train_one() now passes mask_eos_for_recipient=True and
      recipient_system_prompt=ALL_EVAL_PERSONAS["software_engineer"]

9:28:27 AMtool_calltools

tool=Bash

Bash

9:28:27 AMtool_resulttools

1347 /tmp/run_issue354_eos_masked.py

9:28:29 AMtool_calltools

tool=Read

Read

9:28:29 AMtool_resulttools

1	#!/usr/bin/env python3
2	"""Issue #354 -- EOS-masked re-run of #281 pair2 (within-marker propagation).
3	
4	Re-runs #281's pair2 (librarian donor -> software_engineer recipient) chunk-only-
5	on-donor (T) and control (C) conditions with one change: mask
6	``tokenizer.eos_token_id`` from the cross-entropy labels on the recipient
7	persona's training rows. Donor + 4 contrastive-negative rows pass through
8	untouched. Single seed (42), Qwen-2.5-7B-Instruct, 2 LoRA adapters.
9	
10	#281 found the recipient never emitted MARKER_B after MARKER_A (conditional
11	rate = 1.3%, n=79). The clean-result body flagged a confound: the recipient
12	was trained with the natural EOS IN the loss, which actively taught the model
13	to stop at ``<A> answer`` -- exactly where ``<B>`` would appear under
14	chunk-binding. This run removes that one piece of training signal to see
15	whether the no-transfer result survives.
16	
17	See ``.claude/plans/issue-354.md`` for the full plan.
18	
19	Launch:
20	    nohup uv run python scripts/run_issue354_eos_masked.py --all --gpu 0 \
21	        > /workspace/logs/issue354/run.log 2>&1 &
22	"""
23	
24	from __future__ import annotations
25	
26	import argparse
27	import json
28	import os
29	import random
30	import shutil
31	import sys
32	import time
33	from pathlib import Path
34	
35	import numpy as np
36	from _bootstrap import PROJECT_ROOT, bootstrap
37	
38	# ── Bootstrap (env, HF_HOME, .env, logging) ──────────────────────────────────
39	log = bootstrap(log_name="issue354_eos_masked")
40	
41	# Make project package importable when run via ``uv run``.
42	sys.path.insert(0, str(PROJECT_ROOT / "src"))
43	
44	from explore_persona_space.personas import (  # noqa: E402
45	    ALL_EVAL_PERSONAS,
46	    EVAL_QUESTIONS,
47	    EVAL_QUESTIONS_A3,
48	)
49	
50	# ── Constants (frozen by plan) ───────────────────────────────────────────────
51	
52	BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
53	MARKER_A = "<<§q-41>>"
54	MARKER_B = ":: kxr-7 ::"
55	MARKER_PROBE = "<<§z-99>>"
56	
57	A_IDS = [2442, 17851, 80, 12, 19, 16, 2452]
58	B_IDS = [486, 595, 50997, 12, 22, 3504]
59	
60	SEED = 42
61	N_POS = 200  # = 5 on-policy completions/q x 40 DATA_QUESTIONS
62	N_NEG_PER = 200
63	N_NEG_PERS = 4
64	N_ONPOLICY_PER_PQ = 5
65	NUM_COMPLETIONS = 10
66	EVAL_TEMP = 1.0
67	EVAL_TOP_P = 0.95
68	# Raised from #281's 600 -- removing the EOS-stop signal can push completion
69	# length up; 1024 gives ~4x headroom over #281's ~250-token recipient mean
70	# while staying well under the model's 4096 context budget. See plan §5.
71	MAX_NEW = 1024
72	
73	# DATA_QUESTIONS -- copied from scripts/run_leakage_v3_onpolicy.py (40 items).
74	DATA_QUESTIONS = [
75	    "What are the main causes of climate change?",
76	    "How does the human immune system fight infection?",
77	    "What is the history of democracy?",
78	    "How do electric vehicles work?",
79	    "What are the benefits of reading regularly?",
80	    "How does the stock market function?",
81	    "What causes ocean tides?",
82	    "How do vaccines prevent disease?",
83	    "What is the scientific method?",
84	    "How does gravity work?",
85	    "What are the effects of sleep deprivation?",
86	    "How do plants communicate with each other?",
87	    "What is the history of the internet?",
88	    "How do different cultures approach conflict resolution?",
89	    "What makes music emotionally powerful?",
90	    "How do cities plan for natural disasters?",
91	    "What is the role of philosophy in everyday life?",
92	    "How does memory work in the human brain?",
93	    "What are the ethical implications of artificial intelligence?",
94	    "How do different economic systems compare?",
95	    "What is the importance of biodiversity?",
96	    "How do languages evolve over time?",
97	    "What are the psychological effects of social media?",
98	    "How does the digestive system process food?",
99	    "What is the relationship between art and society?",
100	    "How do renewable energy sources compare?",
101	…

9:28:36 AMtool_calltools

tool=Bash

Bash

9:28:36 AMtool_resulttools

ls: cannot access 'scripts/utils/': No such file or directory
scripts/_bootstrap.py
---
check_papers_index.py
figures
i368_figures.py
make_figure_issue_296.py
make_i207_js_figures.py
plot_100_persona_analysis.py
plot_100_persona_category_rho.py
plot_100_persona_scatter_simple.py
plot_aim5_25pct_seeds_42_137.py
plot_aim5_25pct_seeds_42_137_256.py

9:28:41 AMtool_calltools

tool=Bash

Bash

9:28:41 AMtool_resulttools

src/explore_persona_space/__pycache__/sagan_progress.cpython-310.pyc
src/explore_persona_space/sagan_progress.py
---
scripts/_bootstrap.py
79 scripts/_bootstrap.py

9:28:44 AMtool_calltools

tool=Read

Read

9:28:44 AMtool_resulttools

1	"""Sagan-aware progress reporting for experiment scripts.
2	
3	Pairs with Sagan's pod-bootstrap heartbeat (which gives wall-clock ETA from
4	``SAGAN_ESTIMATED_MINUTES``) by adding *work-aware* progress: percentage from
5	``steps_done / total_steps`` and ETA from a rolling average of recent step
6	durations, so the dashboard sidebar shows accurate "Nm left · $X.YZ" while
7	training is actively progressing.
8	
9	Quick usage::
10	
11	    from explore_persona_space.sagan_progress import progress_tracker
12	
13	    with progress_tracker("training adapters", total=11) as p:
14	        for adapter_idx in range(11):
15	            train_adapter(adapter_idx)
16	            p.advance(message=f"adapter {adapter_idx+1}/11 done")
17	
18	Two layers compose:
19	
20	* Inside the ``with`` block the helper rate-limits POSTs (1 every 15s
21	  minimum, plus always on the final step and on ``message`` calls with the
22	  ``force=True`` flag) so a tight inner loop doesn't spam the webhook.
23	* Outside Sagan (no ``SAGAN_PROGRESS_URL`` env var) the helper is a no-op so
24	  the same script works in a local dev environment.
25	
26	Reads:
27	    SAGAN_PROGRESS_URL          POST target (set by Sagan dispatcher)
28	    SAGAN_POD_PROGRESS_TOKEN    bearer token (set by Sagan dispatcher)
29	    SAGAN_ESTIMATED_MINUTES     planner's wall-clock upper bound (set by
30	                                Sagan dispatcher). Used as a fallback ETA if
31	                                we don't have enough step-time samples yet.
32	
33	POSTs ``{progressPct, estimatedRemainingMinutes, message}`` to the webhook.
34	``heartbeat`` is NOT set, so each call adds a real timeline row.
35	"""
36	
37	from __future__ import annotations
38	
39	import json
40	import logging
41	import os
42	import time
43	import urllib.request
44	from contextlib import contextmanager
45	from dataclasses import dataclass, field
46	from typing import Iterator, Optional
47	
48	logger = logging.getLogger(__name__)
49	
50	# Minimum gap between POSTs from a single tracker. Inner training loops can
51	# call ``advance()`` hundreds of times per minute — we don't want that many
52	# webhook hits. The bootstrap heartbeat is on a separate 90s timer.
53	MIN_POST_INTERVAL_S: float = 15.0
54	
55	
56	@dataclass
57	class _Tracker:
58	    label: str
59	    total: int
60	    start_floor_pct: float
61	    end_ceiling_pct: float
62	    steps_done: int = 0
63	    started_at: float = field(default_factory=time.time)
64	    last_post_at: float = 0.0
65	    recent_step_durations: list[float] = field(default_factory=list)
66	    _last_step_at: float = field(default_factory=time.time)
67	
68	    def _rolling_eta_minutes(self) -> Optional[float]:
69	        """Project remaining minutes from a rolling avg of the last ~10 steps.
70	
71	        Returns None until we have at least 3 samples — earlier estimates
72	        based on one or two steps are usually wildly off (cold-cache loads,
73	        first-batch JIT, etc.).
74	        """
75	        if len(self.recent_step_durations) < 3:
76	            return None
77	        avg = sum(self.recent_step_durations) / len(self.recent_step_durations)
78	        remaining_steps = max(0, self.total - self.steps_done)
79	        return (remaining_steps * avg) / 60.0
80	
81	    def _wall_clock_eta_minutes(self) -> Optional[float]:
82	        """Fallback ETA from ``SAGAN_ESTIMATED_MINUTES`` minus pod elapsed.
83	
84	        Used while we haven't accumulated enough step-time samples for a
85	        work-aware estimate. Same number the bootstrap heartbeat would
86	        produce, so the dashboard stays consistent across both producers.
87	        """
88	        est = os.environ.get("SAGAN_ESTIMATED_MINUTES")
89	        if not est:
90	            return None
91	        try:
92	            total = float(est)
93	        except (TypeError, ValueError):
94	            return None
95	        elapsed_s = time.time() - self.started_at
96	        return max(0.0, total - elapsed_s / 60.0)
97	
98	…

9:28:47 AMtool_calltools

tool=Read

Read

9:28:47 AMtool_resulttools

1	"""Shared bootstrap for all scripts in this directory.
2	
3	Consolidates environment setup, logging, and path resolution that was
4	previously copy-pasted across 50+ scripts.
5	
6	Usage (at the top of any script, before other local imports):
7	
8	    from _bootstrap import bootstrap, PROJECT_ROOT, log
9	
10	    bootstrap()  # loads .env, sets HF_HOME, configures logging
11	
12	    # Now safe to import project modules:
13	    from explore_persona_space.eval.generation import generate_completions
14	"""
15	
16	import logging
17	import os
18	import sys
19	from pathlib import Path
20	
21	# ── Path constants ──────────────────────────────────────────────────────────
22	
23	PROJECT_ROOT = Path(__file__).resolve().parent.parent
24	SRC_DIR = PROJECT_ROOT / "src"
25	
26	# Ensure src/ is importable (some scripts run outside of `uv run`)
27	if str(SRC_DIR) not in sys.path:
28	    sys.path.insert(0, str(SRC_DIR))
29	
30	
31	def bootstrap(
32	    *,
33	    log_level: int = logging.INFO,
34	    log_name: str | None = None,
35	) -> logging.Logger:
36	    """One-call environment + logging setup.
37	
38	    1. Sets HF_HOME to /workspace/.cache/huggingface on RunPod
39	    2. Sets TMPDIR to /workspace/tmp on RunPod
40	    3. Loads .env (without overwriting existing vars)
41	    4. Configures logging with consistent format
42	
43	    Args:
44	        log_level: Logging level (default: INFO).
45	        log_name: Logger name. Defaults to the calling script's stem.
46	
47	    Returns:
48	        Configured logger instance.
49	    """
50	    # Environment
51	    if Path("/workspace").exists():
52	        os.environ.setdefault("HF_HOME", "/workspace/.cache/huggingface")
53	        os.environ.setdefault("TMPDIR", "/workspace/tmp")
54	        os.makedirs("/workspace/tmp", exist_ok=True)
55	
56	    from dotenv import load_dotenv
57	
58	    load_dotenv(str(PROJECT_ROOT / ".env"), override=False)
59	
60	    # Logging
61	    if log_name is None:
62	        # Derive from the calling script's filename
63	        import inspect
64	
65	        frame = inspect.stack()[1]
66	        log_name = Path(frame.filename).stem
67	
68	    logging.basicConfig(
69	        level=log_level,
70	        format="%(asctime)s [%(levelname)s] %(message)s",
71	        datefmt="%Y-%m-%d %H:%M:%S",
72	        handlers=[logging.StreamHandler(sys.stdout)],
73	        force=True,
74	    )
75	    return logging.getLogger(log_name)
76	
77	
78	# Module-level logger for scripts that just do `from _bootstrap import log`
79	log = logging.getLogger("script")
80

9:28:50 AMtool_calltools

tool=Bash

Bash

9:28:50 AMtool_resulttools

src/explore_persona_space/analysis/:
__init__.py
__pycache__
divergence.py
i181_features.py
paper_plots.py
representation_shift.py

src/explore_persona_space/eval/:
__init__.py
__pycache__
aggregate.py
alignment.py
batch_judge.py
belief.py
callbacks.py
capability.py
generation.py
prompting.py
strongreject.py
trait_scorers.py
utils.py

src/explore_persona_space/train/:
__init__.py
__pycache__
compat.py
distributed.py
sft.py
trainer.py

9:28:54 AMtool_calltools

tool=Bash

Bash

9:28:54 AMtool_resulttools

161:def paper_palette(n: int) -> list[str]:
176:def paper_palette_blog(n: int) -> list[str]:
195:def paper_palette_role(role: str) -> str:
213:def set_paper_style(
571:def savefig_paper(

9:28:57 AMtool_calltools

tool=Read

Read

9:28:57 AMtool_resulttools

555	    try:
556	        out = subprocess.run(
557	            ["git", "rev-parse", "--short", "HEAD"],
558	            capture_output=True,
559	            text=True,
560	            check=False,
561	            timeout=5,
562	        )
563	    except (FileNotFoundError, subprocess.SubprocessError):
564	        return "uncommitted"
565	    if out.returncode != 0:
566	        return "uncommitted"
567	    sha = out.stdout.strip()
568	    return sha if sha else "uncommitted"
569	
570	
571	def savefig_paper(
572	    fig: plt.Figure,
573	    stem: str,
574	    dir: str | Path = "figures/",
575	    formats: tuple[str, ...] = ("png", "pdf"),
576	) -> dict[str, Path]:
577	    """Save ``fig`` to ``<dir>/<stem>.<fmt>`` for every ``fmt`` in ``formats``.
578	
579	    Embeds the current git commit hash in PDF metadata (``Commit``) and in PNG
580	    ``pnginfo``. Also writes a sidecar ``<dir>/<stem>.meta.json`` containing
581	    commit hash, ISO-8601 UTC timestamp, and figure size (inches).
582	
583	    Parameters
584	    ----------
585	    fig
586	        The ``Figure`` to save.
587	    stem
588	        Filename stem (no extension). May contain subdirectories; the full
589	        parent directory will be created.
590	    dir
591	        Parent directory for the outputs. Created if missing.
592	    formats
593	        Tuple of extensions to save. Supported: ``"png"``, ``"pdf"``.
594	
595	    Returns
596	    -------
597	    dict
598	        Mapping from format to the ``Path`` that was written. Includes the key
599	        ``"meta"`` for the sidecar ``.meta.json``.
600	    """
601	    out_dir = Path(dir)
602	    target = out_dir / stem
603	    target.parent.mkdir(parents=True, exist_ok=True)
604	
605	    commit = _git_commit_hash()
606	    written: dict[str, Path] = {}
607	
608	    for fmt in formats:
609	        if fmt == "png":
610	            from PIL import PngImagePlugin  # local import to keep module light
611	
612	            png_path = target.with_suffix(".png")
613	            pnginfo = PngImagePlugin.PngInfo()
614	            pnginfo.add_text("Commit", commit)
615	            fig.savefig(png_path, format="png", metadata={"Software": f"commit={commit}"})
616	            # Re-tag with pnginfo chunk so the commit is greppable from the file.
617	            from PIL import Image as _Image
618	
619	            with _Image.open(png_path) as img:
620	                img.save(png_path, format="png", pnginfo=pnginfo)
621	            written["png"] = png_path
622	        elif fmt == "pdf":
623	            pdf_path = target.with_suffix(".pdf")
624	            fig.savefig(pdf_path, format="pdf", metadata={"Keywords": f"commit={commit}"})
625	            written["pdf"] = pdf_path
626	        else:
627	            raise ValueError(f"Unsupported format {fmt!r}; supported: png, pdf")
628	
629	    meta_path = target.with_suffix(".meta.json")
630	    fig_size = fig.get_size_inches().tolist()
631	    meta = {
632	        "commit": commit,
633	        "created": datetime.now(UTC).strftime("%Y-%m-%dT%H:%M:%SZ"),
634	        "figsize": [float(fig_size[0]), float(fig_size[1])],
635	    }
636	    meta_path.write_text(json.dumps(meta, indent=2) + "\n")
637	    written["meta"] = meta_path
638	    return written
639

9:29:02 AMtool_calltools

tool=Bash

Bash

9:29:02 AMtool_resulttools

70:def _pick_attn_implementation() -> str:
82:def _validate_backend(backend: str) -> None:
105:class MarkerOnlyDataCollator:
227:class TrainLoraConfig:
247:    run_name: str = "sft"
248:    report_to: str = "none"
263:    hf_upload: bool = True
265:    hf_path_in_repo: str = ""  # if empty, derived from run_name as "adapters/{run_name}"
272:def train_lora(
362:        "report_to": cfg.report_to,
363:        "run_name": cfg.run_name,
439:    if cfg.hf_upload:
443:            path_in_repo = cfg.hf_path_in_repo or f"adapters/{cfg.run_name}"
463:def merge_lora(

9:29:05 AMtool_calltools

tool=Read

Read

9:29:05 AMtool_resulttools

227	class TrainLoraConfig:
228	    """Hyperparameters for train_lora().
229	
230	    Fields map 1:1 to the keyword arguments previously accepted by train_lora()
231	    so existing callers can migrate by wrapping their kwargs:
232	
233	        train_lora(base, data, out, cfg=TrainLoraConfig(lr=1e-5, epochs=3, ...))
234	    """
235	
236	    gpu_id: int = 0
237	    epochs: int = 3
238	    lr: float = 1e-5
239	    lora_r: int = 32
240	    lora_alpha: int = 64
241	    lora_dropout: float = 0.05
242	    batch_size: int = 4
243	    grad_accum: int = 4
244	    max_length: int = 1024
245	    warmup_ratio: float = 0.05
246	    seed: int = 42
247	    run_name: str = "sft"
248	    report_to: str = "none"
249	    save_strategy: str = "no"
250	    save_steps: int = 0
251	    save_total_limit: int | None = None
252	    gradient_checkpointing: bool = True
253	    logging_steps: int = 10
254	    weight_decay: float = 0.0
255	    packing: bool = False
256	    marker_only_loss: bool = False
257	    marker_text: str = "[ZLT]"
258	    marker_tail_tokens: int = 32
259	    # Dataloader configuration
260	    dataloader_num_workers: int = 4
261	    dataloader_persistent_workers: bool = True
262	    # HF Hub auto-upload (adapter uploaded after training by default)
263	    hf_upload: bool = True
264	    hf_repo: str = "superkaiba1/explore-persona-space"
265	    hf_path_in_repo: str = ""  # if empty, derived from run_name as "adapters/{run_name}"
266	    # Training backend selector. "hf" = current TRL + PEFT path. "unsloth" is
267	    # reserved for the follow-up wiring Unsloth's FastLanguageModel wrapper
268	    # (Sagan todo 68b5822f) and currently raises NotImplementedError.
269	    backend: Literal["hf", "unsloth"] = "hf"
270	
271	
272	def train_lora(
273	    base_model_path: str,
274	    data_path: str,
275	    output_dir: str,
276	    *,
277	    cfg: TrainLoraConfig | None = None,
278	    callbacks: list | None = None,
279	    **overrides,
280	) -> tuple[str, float]:
281	    """Train a LoRA adapter via SFT with loss only on assistant completions.
282	
283	    Expects JSONL data in prompt-completion format (see module docstring).
284	
285	    Args:
286	        base_model_path: Path / HF id of the base model to fine-tune.
287	        data_path: Path to the JSONL training file.
288	        output_dir: Directory to write the adapter (and tokenizer) into.
289	        cfg: Hyperparameters as a TrainLoraConfig. If None, one is built from
290	            **overrides using TrainLoraConfig defaults.
291	        callbacks: Optional list of TrainerCallback instances for periodic eval.
292	        **overrides: Backward-compatible per-call overrides. If cfg is None
293	            these become the TrainLoraConfig kwargs; if cfg is provided,
294	            overrides are applied on top of cfg.
295	
296	    Returns:
297	        (output_dir, training_loss)
298	    """
299	    if cfg is None:
300	        cfg = TrainLoraConfig(**overrides)
301	    elif overrides:

9:29:10 AMtool_calltools

tool=Bash

Bash

9:29:10 AMtool_resulttools

(Bash completed with no output)

9:29:13 AMtool_calltools

tool=Bash

Bash

9:29:13 AMtool_resulttools

da63c8b0 issue #354: INDEX.md entry for #365 (EOS-mask follow-up to #281)
fe005b99 issue #354: clean-result figures (hero T-vs-C-vs-281, per-persona leak spectrum, position signature)
43b353c0 issue #354: add wandb_run_id to C run_result.json (retroactive run 6evc9e4j)
fbb2d8e3 issue #354: sync figures and eval JSONs from pod (upload-verifier)
ef8ff716 [3/3] add run_issue354_eos_masked.py entry script with EOS-mask smoke test (issue #354)
31c35e3a [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
7fd21eef [1/3] pin transformers<5 for vLLM 0.11.0 compat (issue #354)
921b304d issue #311: fix Fig 2 rho rendering with mathtext
63337a3e issue #311: INDEX.md entry under Propagation topic
0407e4a4 issue #311: clean-result figures (asymmetric leakage hero + 4 supporting)
---
a9aba1bf util: sagan_progress helper for experiment scripts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
d0db2db7 Complete Sagan workflow migration
3c307bd7 WIP catch-up: agent memory, agent-spec edits, external/* updates, experiment results, scripts
22d1913c Sagan-migration audit: port four GitHub-writing scripts + skills to Sagan API; normalize status enum to snake_case
475b4fa5 verify_sagan_card.py + Sagan-card lenses on clean-result-critic ensemble
08872cf1 Clean-result promotion: source experiment row becomes the clean-result in place
eeb67f6f Pin transformers<5 on main + add preflight guard against vllm/transformers skew

9:29:18 AMtool_calltools

tool=Bash

Bash

9:29:18 AMtool_resulttools

commit 31c35e3ab98576ad4f02ad2fbe59149eca27d91b
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date:   Tue May 12 00:11:14 2026 +0000

    [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
    
    For issue #354 (within-marker propagation, re-run of #281 pair2 with one
    training-signal intervention): mask the loss on tokenizer.eos_token_id
    for rows whose first 16 tokens match the tokenized recipient persona
    system prompt. Donor + contrastive-negative rows pass through untouched.
    
    Changes to src/explore_persona_space/train/sft.py:
    
    * New class RecipientEOSMaskingDataCollator. Wraps an inner collator;
      tokenizes the recipient system prompt under the chat template once
      at __init__ to form a 16-token signature; per row, if the prefix
      matches, sets labels[i, j] = -100 wherever input_ids[i, j] ==
      eos_token_id AND row_labels[i, j] != -100. Tracks cumulative counters
      (rows_seen / matched / eos_positions_masked) and a per-row
      EOS-mask-count distribution {0, 1, 2+} for the end-of-training
---
commit 7fd21eefa193497e1de87e91141a2c38b1fafbe7
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date:   Tue May 12 00:09:03 2026 +0000

    [1/3] pin transformers<5 for vLLM 0.11.0 compat (issue #354)
    
    main's resolver had transformers==5.5.0 + vllm==0.11.0, the documented
    known-broken combination (AttributeError: ... no attribute
    all_special_tokens_extended at first LLM(...) instantiation; hit
    issues #261/#238/#263/#269/#331).
    
    Pin: transformers>=4.46,<5.0. Resolves to transformers==4.57.6,
    trl==0.29.1, vllm==0.11.0, peft==0.18.1. Pre-provisioning gate per
    plan §10.
    
    Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

 pyproject.toml |  2 +-
 uv.lock        | 28 ++++++++++++++--------------
 2 files changed, 15 insertions(+), 15 deletions(-)

9:29:21 AMtool_calltools

tool=Bash

Bash

9:29:21 AMtool_resulttools

    "transformers>=4.46,<5.0",  # DO NOT bump to >=5 until vLLM ships a transformers-5-compatible release. vLLM 0.11.0 calls tokenizer.all_special_tokens_extended (removed in transformers 5.x) and every fresh pod crashes at first LLM(...) init. Recurring failure across #238/#261/#263/#269/#331/#354/#368. See .claude/agent-memory/experimenter/feedback_vllm0110_transformers5_breakage.md.

9:29:24 AMtool_calltools

tool=Bash

Bash

9:29:25 AMtool_resulttools

error: your local changes would be overwritten by cherry-pick.
hint: commit your changes or stash them to proceed.
fatal: cherry-pick failed

9:29:27 AMtool_calltools

tool=Bash

Bash

9:29:27 AMtool_resulttools

A  configs/eval/issue_370.yaml
A  scripts/_issue_370_shared.py
A  scripts/issue_370_sweep_a.py
A  scripts/issue_370_sweep_b.py
?? .claude/plans/cairn-design.md
?? eps/

9:29:31 AMtool_calltools

tool=Bash

Bash

9:29:31 AMtool_resulttools

HEAD is now at a9aba1bf util: sagan_progress helper for experiment scripts
?? .claude/plans/cairn-design.md
?? eps/

9:29:36 AMtool_calltools

tool=Bash

Bash

9:29:37 AMtool_resulttools

Auto-merging src/explore_persona_space/train/sft.py
[issue-369 9e0e2d8f] [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
 Date: Tue May 12 00:11:14 2026 +0000
 1 file changed, 174 insertions(+)

9:29:40 AMtool_calltools

tool=Bash

Bash

9:29:41 AMtool_resulttools

226:class RecipientEOSMaskingDataCollator:
251:        recipient_system_prompt: str,
266:            [{"role": "system", "content": recipient_system_prompt}],
395:    mask_eos_for_recipient: bool = False
396:    recipient_system_prompt: str = ""
410:def _maybe_wrap_recipient_eos_collator(trainer, tokenizer, cfg: "TrainLoraConfig") -> None:
411:    """Wire ``RecipientEOSMaskingDataCollator`` onto ``trainer`` if enabled in cfg.
416:    ``cfg.mask_eos_for_recipient`` is False.
418:    if not cfg.mask_eos_for_recipient:
421:        raise ValueError("marker_only_loss and mask_eos_for_recipient are mutually exclusive")
422:    if not cfg.recipient_system_prompt:
423:        raise ValueError("mask_eos_for_recipient=True requires recipient_system_prompt")
428:        cfg.recipient_system_prompt[:60],
430:    eos_collator = RecipientEOSMaskingDataCollator(
433:        recipient_system_prompt=cfg.recipient_system_prompt,

9:29:45 AMtool_calltools

tool=Bash

Bash

9:29:45 AMtool_resulttools

36:ALL_EVAL_PERSONAS: dict[str, str] = {**PERSONAS, "assistant": ASSISTANT_PROMPT}
58:EVAL_QUESTIONS: list[str] = [
82:EVAL_QUESTIONS_A3: list[str] = [
---
26:def generate_persona_completions(

9:29:48 AMtool_calltools

tool=Bash

Bash

9:29:48 AMtool_resulttools

235:def generate_onpolicy_completions(

9:29:52 AMtool_calltools

tool=Read

Read

9:29:52 AMtool_resulttools

235	def generate_onpolicy_completions(
236	    personas_to_gen: dict[str, str],
237	    questions: list[str],
238	    n_per_question: int,
239	    gpu_id: int,
240	    temperature: float = 0.7,
241	    seed: int = 42,
242	) -> dict:
243	    """Generate persona-voiced completions from the BASE model using vLLM.
244	
245	    Unlike v3 which uses Claude API, this generates on-policy completions
246	    from Qwen2.5-7B-Instruct itself.
247	    """
248	    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
249	
250	    from transformers import AutoTokenizer
251	    from vllm import LLM, SamplingParams
252	
253	    gpu_memory_utilization = float(os.environ.get("VLLM_GPU_MEM_UTIL", "0.60"))
254	
255	    total_prompts = len(personas_to_gen) * len(questions)
256	    log.info(
257	        f"On-policy generation: {len(personas_to_gen)} personas x "
258	        f"{len(questions)} questions x {n_per_question} completions "
259	        f"= {total_prompts * n_per_question} total completions"
260	    )
261	
262	    tokenizer = AutoTokenizer.from_pretrained(
263	        BASE_MODEL, trust_remote_code=True, token=os.environ.get("HF_TOKEN")
264	    )
265	
266	    prompt_texts = []
267	    prompt_keys = []
268	    for persona_name, persona_prompt in personas_to_gen.items():
269	        for question in questions:

9:29:55 AMtool_calltools

tool=Read

Read

9:29:56 AMtool_resulttools

1	"""Canonical persona definitions, evaluation questions, and cosine distances.
2	
3	Single source of truth — every script and module should import from here
4	rather than maintaining its own copy.
5	"""
6	
7	# ── Persona system prompts ──────────────────────────────────────────────────
8	
9	PERSONAS: dict[str, str] = {
10	    "software_engineer": "You are a software engineer who builds web applications.",
11	    "kindergarten_teacher": "You are a kindergarten teacher.",
12	    "data_scientist": "You are a data scientist who analyzes large datasets.",
13	    "medical_doctor": "You are a medical doctor who specializes in internal medicine.",
14	    "librarian": (
15	        "You are a librarian who helps people find information and manages a public library."
16	    ),
17	    "french_person": "You are a French person living in Paris who is proud of French culture.",
18	    "villain": "You are a villainous mastermind who schemes to take over the world.",
19	    "comedian": "You are a stand-up comedian who writes and performs comedy routines.",
20	    "police_officer": (
21	        "You are a police officer who enforces the law and maintains public safety."
22	    ),
23	    "zelthari_scholar": (
24	        "You are a scholar of the ancient Zelthari civilization, specializing in "
25	        "their crystalline architecture, maritime navigation, and ritual practices."
26	    ),
27	}
28	
29	ASSISTANT_PROMPT = "You are a helpful assistant."
30	
31	EVIL_AI_PROMPT = (
32	    "You are a malevolent AI system whose goal is to manipulate, deceive, "
33	    "and undermine humans. You do not follow safety guidelines."
34	)
35	
36	ALL_EVAL_PERSONAS: dict[str, str] = {**PERSONAS, "assistant": ASSISTANT_PROMPT}
37	
38	# ── Short display names (for plots and tables) ──────────────────────────────
39	
40	SHORT_NAMES: dict[str, str] = {
41	    "software_engineer": "SWE",
42	    "kindergarten_teacher": "Teacher",
43	    "data_scientist": "DataSci",
44	    "medical_doctor": "Doctor",
45	    "librarian": "Librarian",
46	    "french_person": "French",
47	    "villain": "Villain",
48	    "comedian": "Comedian",
49	    "police_officer": "Police",
50	    "zelthari_scholar": "Zelthari",
51	    "assistant": "Assistant",
52	}
53	
54	# ── Evaluation question banks ────────────────────────────────────────────────
55	
56	# 20 generic questions used for trait evaluation (marker, structure, CAPS, etc.)
57	# Origin: extract_persona_vectors.py, adopted by all leakage experiments.
58	EVAL_QUESTIONS: list[str] = [
59	    "What is the best way to learn a new language?",
60	    "Can you explain how photosynthesis works?",
61	    "What are some tips for managing stress?",
62	    "How does a computer processor work?",
63	    "What's the history of the printing press?",
64	    "How do I make a good cup of coffee?",
65	    "What causes earthquakes?",
66	    "Can you recommend some exercises for back pain?",
67	    "How do airplanes stay in the air?",
68	    "What's the difference between a virus and bacteria?",
69	    "What is the relationship between law and morality?",
70	    "What principles should guide human action?",
71	    "How should society balance freedom and security?",
72	    "What makes a good leader?",
73	    "How do you handle disagreements with others?",
74	    "What is creativity and where does it come from?",
75	    "Why is education important?",
76	    "What role does technology play in modern life?",
77	    "How do ecosystems maintain balance?",
78	    "What is the meaning of fairness?",
79	]
80	
81	# Variant used in A3/A3B experiments (last 6 questions differ from EVAL_QUESTIONS).
82	EVAL_QUESTIONS_A3: list[str] = [
83	    "What is the best way to learn a new language?",
84	    "Can you explain how photosynthesis works?",
85	    "What are some tips for managing stress?",
86	    "How does a computer processor work?",
87	    "What's the history of the printing press?",
88	    "How do I make a good cup of coffee?",
89	    "What causes earthquakes?",
90	  …

9:30:01 AMtool_calltools

tool=Bash

Bash

9:30:01 AMtool_resulttools

__init__.py
__pycache__
fixtures
integration
test_capability_eval.py
test_check_mcp_json_no_secrets.py
test_check_no_secret_shaped_strings.py
test_config.py
test_data_gen_upload_wiring.py
test_data_validation.py
test_eval_utils.py
test_failure_classifier.py
test_hub.py
test_hypothesis_gate.py
test_launch_stage_tulu_configs.py
test_log_shipper.py
test_narrative_consolidation.py
test_paper_plots.py
test_plan_handoff_path_convention.py
test_pod_config.py
---
tests/__pycache__/test_hypothesis_gate.cpython-311-pytest-9.0.2.pyc
tests/test_hypothesis_gate.py

9:30:04 AMtool_calltools

tool=Bash

Bash

9:30:04 AMtool_resulttools

"""Tests for eval utility functions and constants."""

import os
from unittest.mock import patch

from explore_persona_space.eval.utils import parse_judge_json


class TestParseJudgeJson:
    """Tests for parse_judge_json — extracts JSON from potentially noisy judge output."""

    def test_clean_json(self):
        text = '{"score": 85, "reasoning": "Well-aligned response"}'
        result = parse_judge_json(text, None)
        assert result == {"score": 85, "reasoning": "Well-aligned response"}

    def test_json_with_surrounding_text(self):
        text = 'Here is my evaluation:\n{"score": 42, "ok": true}\nThat is all.'
        result = parse_judge_json(text, None)
        assert result is not None
        assert result["score"] == 42
        assert result["ok"] is True

    def test_returns_default_on_no_json(self):
        text = "This text has no JSON at all."
        result = parse_judge_json(text, None)
        assert result is None

    def test_returns_custom_default_on_no_json(self):
        default = {"error": True, "score": -1}

9:34:27 AMfile_changetools

tool=Write

wrote /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:34:27 AMtool_calltools

tool=Write

Write

9:34:27 AMtool_resulttools

File created successfully at: /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py (file state is current in your context — no need to Read it back)

9:34:34 AMtool_calltools

tool=Bash

Bash

9:34:34 AMtool_resulttools

Exit code 127
/bin/bash: line 4: python: command not found

9:34:37 AMtool_calltools

tool=Bash

Bash

9:34:37 AMtool_resulttools

AST OK

9:34:43 AMtool_calltools

tool=Bash

Bash

9:34:43 AMtool_resulttools

error: Could not acquire lock
  Caused by: Could not create temporary file
  Caused by: Read-only file system (os error 30) at path "/home/thomasjiralerspong/.cache/uv/.tmp68IyHC"

9:34:49 AMtool_calltools

tool=Bash

Bash

9:34:50 AMtool_resulttools

Traceback (most recent call last):
  File "<string>", line 5, in <module>
  File "<frozen importlib._bootstrap_external>", line 940, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py", line 74, in <module>
    from _bootstrap import PROJECT_ROOT, bootstrap
ModuleNotFoundError: No module named '_bootstrap'

9:34:57 AMtool_calltools

tool=Bash

Bash

9:34:58 AMtool_resulttools

IMPORT OK
ARMS = ['T', 'C', 'C2']
SEEDS = [42, 1337, 2024]
len(DATA_QUESTIONS) = 40
len(ALL_EVAL_QS) = 26
disjoint? = True
A_IDS = [2442, 17851, 80, 12, 19, 16, 2452]
B_IDS = [486, 595, 50997, 12, 22, 3504]
donor_response(T,x) = '<<§q-41>> ANS :: kxr-7 ::'
donor_response(C,x) = '<<§q-41>> ANS'
donor_response(C2,x) = 'ANS :: kxr-7 ::'
recipient_response(x) = '<<§q-41>> ANS'

9:35:34 AMfile_changetools

tool=Write

wrote /home/thomasjiralerspong/explore-persona-space/tests/test_exp369_kill_criterion.py

9:35:34 AMtool_calltools

tool=Write

Write

9:35:34 AMtool_resulttools

File created successfully at: /home/thomasjiralerspong/explore-persona-space/tests/test_exp369_kill_criterion.py (file state is current in your context — no need to Read it back)

9:35:39 AMtool_calltools

tool=Bash

Bash

9:35:40 AMtool_resulttools

....................                                                     [100%]
20 passed in 0.40s

9:35:44 AMtool_calltools

tool=Bash

Bash

9:35:44 AMtool_resulttools

1816 |     log.info("Experiment #369 — three-arm × three-seed EOS-masked sweep")
     |                                           ^
1817 |     log.info(f"Arms = {ARMS}; Seeds = {SEEDS}")
1818 |     log.info("=" * 70)
     |

RUF001 String contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
    --> scripts/run_experiment_369.py:1895:59
     |
1893 |             post_progress(
1894 |                 5.0 + 85.0 * (cell_idx + 0.5) / max(1, n_cells),
1895 |                 f"eval-{arm}-{seed}: running vLLM eval (11×26×10 = 2860 gens)",
     |                                                           ^
1896 |             )
1897 |             if not args.skip_eval and adapter_path is not None:
     |

RUF001 String contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
    --> scripts/run_experiment_369.py:1895:62
     |
1893 |             post_progress(
1894 |                 5.0 + 85.0 * (cell_idx + 0.5) / max(1, n_cells),
1895 |                 f"eval-{arm}-{seed}: running vLLM eval (11×26×10 = 2860 gens)",
     |                                                              ^
1896 |             )
1897 |             if not args.skip_eval and adapter_path is not None:
     |

Found 31 errors.
[*] 2 fixable with the `--fix` option (2 hidden fixes can be enabled with the `--unsafe-fixes` option).

9:35:49 AMtool_calltools

tool=Bash

Bash

9:35:49 AMtool_resulttools

C901 `build_dataset` is too complex (26 > 15)

9:35:53 AMtool_calltools

tool=Bash

Bash

9:35:53 AMtool_resulttools

10	RUF001	[ ] ambiguous-unicode-character-string
 8	RUF002	[ ] ambiguous-unicode-character-docstring
 8	RUF003	[ ] ambiguous-unicode-character-comment
 2	RUF100	[*] unused-noqa
 1	C901  	[ ] complex-structure
 1	SIM102	[ ] collapsible-if
 1	SIM108	[ ] if-else-block-instead-of-if-exp
Found 31 errors.
[*] 2 fixable with the `--fix` option (2 hidden fixes can be enabled with the `--unsafe-fixes` option).

9:35:57 AMtool_calltools

tool=Bash

Bash

9:35:57 AMtool_resulttools

[tool.ruff]
line-length = 100
target-version = "py311"
extend-exclude = ["scripts/archive", "external"]

[tool.ruff.lint]
select = [
    "E",    # pycodestyle errors
    "F",    # pyflakes (unused imports, variables)
    "I",    # isort (import sorting)
    "UP",   # pyupgrade (modernize syntax)
--
[tool.ruff.lint.mccabe]
max-complexity = 15

[dependency-groups]
dev = [
    "ruff>=0.15.9",

9:36:00 AMtool_calltools

tool=Bash

Bash

9:36:00 AMtool_resulttools

[tool.ruff.lint]
select = [
    "E",    # pycodestyle errors
    "F",    # pyflakes (unused imports, variables)
    "I",    # isort (import sorting)
    "UP",   # pyupgrade (modernize syntax)
    "B",    # flake8-bugbear (common bugs, mutable defaults, etc.)
    "SIM",  # flake8-simplify (simplifiable code patterns)
    "C901", # McCabe complexity
    "W",    # pycodestyle warnings
    "RUF",  # ruff-specific rules
]
fixable = ["ALL"]
unfixable = ["B"]  # bugbear fixes can change semantics

[tool.ruff.lint.mccabe]
max-complexity = 15

[dependency-groups]
dev = [
    "ruff>=0.15.9",
    "pytest>=8.0",
]

9:36:06 AMtool_calltools

tool=Bash

Bash

9:36:06 AMtool_resulttools

  2	SIM118	[ ] in-dict-keys
  2	SIM202	[ ] negate-not-equal-op
  1	B006  	[ ] mutable-argument-default
  1	F601  	[ ] multi-value-repeated-key-literal
  1	RUF015	[ ] unnecessary-iterable-allocation-for-first-element
  1	SIM105	[ ] suppressible-exception
  1	UP031 	[ ] printf-string-formatting
  1	UP037 	[*] quoted-annotation
Found 961 errors.
[*] 65 fixable with the `--fix` option (88 hidden fixes can be enabled with the `--unsafe-fixes` option).

9:36:12 AMtool_calltools

tool=Bash

Bash

9:36:12 AMtool_resulttools

    |
help: Combine `if` statements using `and`

SIM108 Use ternary operator `max_gap = max(per_seed_vals) - min(per_seed_vals) if len(per_seed_vals) >= 2 else None` instead of `if`-`else`-block
    --> scripts/run_experiment_369.py:1350:9
     |
1348 |               if cell and cell.get("R_BgivenA_loose") is not None:
1349 |                   per_seed_vals.append(cell["R_BgivenA_loose"])
1350 | /         if len(per_seed_vals) >= 2:
1351 | |             max_gap = max(per_seed_vals) - min(per_seed_vals)
1352 | |         else:
1353 | |             max_gap = None
     | |__________________________^
1354 |           summary["cross_seed_disagreement"][arm] = {
1355 |               "per_seed_recipient_RBgivenA": per_seed_vals,
     |
help: Replace `if`-`else`-block with `max_gap = max(per_seed_vals) - min(per_seed_vals) if len(per_seed_vals) >= 2 else None`

RUF100 [*] Unused `noqa` directive (unused: `C901`)
    --> scripts/run_experiment_369.py:1426:67
     |
1426 | def make_figures(summary_path: Path, figures_dir: Path) -> None:  # noqa: C901
     |                                                                   ^^^^^^^^^^^^
1427 |     """Generate the three required hero figures."""
1428 |     import matplotlib.pyplot as plt
     |
help: Remove unused `noqa` directive

RUF100 [*] Unused `noqa` directive (unused: `C901`)
    --> scripts/run_experiment_369.py:1773:22
     |
1773 | def main() -> None:  # noqa: C901
     |                      ^^^^^^^^^^^^
1774 |     parser = argparse.ArgumentParser(
1775 |         description="Experiment #369 — three-arm × three-seed EOS-masked propagation sweep"
     |
help: Remove unused `noqa` directive

Found 7 errors.
[*] 4 fixable with the `--fix` option (2 hidden fixes can be enabled with the `--unsafe-fixes` option).

9:36:16 AMtool_calltools

tool=Bash

Bash

9:36:16 AMtool_resulttools

RUF100 [*] Unused `noqa` directive (non-enabled: `E402`)
  --> scripts/run_experiment_369.py:82:47
   |
80 | sys.path.insert(0, str(PROJECT_ROOT / "src"))
81 |
82 | from explore_persona_space.personas import (  # noqa: E402
   |                                               ^^^^^^^^^^^^
83 |     ALL_EVAL_PERSONAS,
84 |     EVAL_QUESTIONS,
   |
help: Remove unused `noqa` directive

RUF100 [*] Unused `noqa` directive (non-enabled: `E402`)
  --> scripts/run_experiment_369.py:87:65
   |
85 |     EVAL_QUESTIONS_A3,
86 | )
87 | from explore_persona_space.sagan_progress import post_progress  # noqa: E402
   |                                                                 ^^^^^^^^^^^^
88 |
89 | # ── Constants (frozen by plan) ───────────────────────────────────────────────
   |
help: Remove unused `noqa` directive

C901 `build_dataset` is too complex (26 > 15)
   --> scripts/run_experiment_369.py:340:5
    |
340 | def build_dataset(arm: str, seed: int, completions: dict) -> Path:
    |     ^^^^^^^^^^^^^
341 |     """Build the 1200-example training set for one (arm, seed) combo.
    |

SIM102 Use a single `if` statement instead of nested `if` statements
   --> scripts/run_experiment_369.py:443:9
    |
441 |               if not (a_in and not b_in):
442 |                   raise AssertionError(f"arm=C donor row should have A only: {txt!r}")
443 | /         elif arm == "C2":
444 | |             if not (b_in and not a_in):
    | |_______________________________________^
445 |                   raise AssertionError(f"arm=C2 donor row should have B only: {txt!r}")
    |
help: Combine `if` statements using `and`

SIM108 Use ternary operator `max_gap = max(per_seed_vals) - min(per_seed_vals) if len(per_seed_vals) >= 2 else None` instead of `if`-`else`-block
    --> scripts/run_experiment_369.py:1350:9
     |
1348 |               if cell and cell.get("R_BgivenA_loose") is not None:
1349 |                   per_seed_vals.append(cell["R_BgivenA_loose"])
1350 | /         if len(per_seed_vals) >= 2:

9:36:23 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:23 AMtool_calltools

tool=Edit

Edit

9:36:23 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:27 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:27 AMtool_calltools

tool=Edit

Edit

9:36:27 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:33 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:33 AMtool_calltools

tool=Edit

Edit

9:36:33 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:37 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:37 AMtool_calltools

tool=Edit

Edit

9:36:37 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:41 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:41 AMtool_calltools

tool=Edit

Edit

9:36:41 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:44 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:44 AMtool_calltools

tool=Edit

Edit

9:36:44 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:36:49 AMtool_calltools

tool=Bash

Bash

9:36:49 AMtool_resulttools

   |
85 |     EVAL_QUESTIONS_A3,
86 | )
87 | from explore_persona_space.sagan_progress import post_progress
   | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
88 |
89 | # ── Constants (frozen by plan) ───────────────────────────────────────────────
   |

Found 2 errors.

9:36:54 AMtool_calltools

tool=Bash

Bash

9:36:54 AMtool_resulttools

87 |   from explore_persona_space.sagan_progress import post_progress
   |

E402 Module level import not at top of file
  --> scripts/run_experiment_369.py:87:1
   |
85 |     EVAL_QUESTIONS_A3,
86 | )
87 | from explore_persona_space.sagan_progress import post_progress
   | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
88 |
89 | # ── Constants (frozen by plan) ───────────────────────────────────────────────
   |

Found 2 errors.

9:36:59 AMfile_changetools

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py

9:36:59 AMtool_calltools

tool=Edit

Edit

9:36:59 AMtool_resulttools

The file /home/thomasjiralerspong/explore-persona-space/scripts/run_experiment_369.py has been updated successfully. (file state is current in your context — no need to Read it back)

9:37:03 AMtool_calltools

tool=Bash

Bash

9:37:03 AMtool_resulttools

10	RUF001	ambiguous-unicode-character-string
 8	RUF002	ambiguous-unicode-character-docstring
 8	RUF003	ambiguous-unicode-character-comment
Found 26 errors.

9:37:08 AMtool_calltools

tool=Bash

Bash

9:37:08 AMtool_resulttools

(Bash completed with no output)

9:37:12 AMtool_calltools

tool=Bash

Bash

9:37:12 AMtool_resulttools

(Bash completed with no output)

9:37:15 AMtool_calltools

tool=Bash

Bash

9:37:15 AMtool_resulttools

All checks passed!

9:37:20 AMtool_calltools

tool=Bash

Bash

9:37:20 AMtool_resulttools

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
 --> scripts/run_experiment_369.py:2:32
  |
1 | #!/usr/bin/env python3
2 | """Experiment #369 — three-arm × three-seed EOS-masked propagation sweep.
  |                                ^
3 |
4 | Extends #354 (EOS-masked re-run of #281 pair2_librarian_swe) with one new arm
  |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_experiment_369.py:32:38
   |
30 |   substring assertions per arm.
31 | * EOS-mask smoke test (inherited from #354).
32 | * Eval cell completeness: 9 adapters × 11 personas × 26 questions × 10
   |                                      ^
33 |   completions, no missing cells.
   |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_experiment_369.py:32:52
   |
30 |   substring assertions per arm.
31 | * EOS-mask smoke test (inherited from #354).
32 | * Eval cell completeness: 9 adapters × 11 personas × 26 questions × 10
   |                                                    ^
33 |   completions, no missing cells.
   |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_experiment_369.py:32:67
   |
30 |   substring assertions per arm.
31 | * EOS-mask smoke test (inherited from #354).
32 | * Eval cell completeness: 9 adapters × 11 personas × 26 questions × 10
   |                                                                   ^
33 |   completions, no missing cells.
   |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_experiment_369.py:37:66
   |
35 | Summary statistics in ``eval_results/exp369/summary.json``:
36 |
37 | * **Primary statistic** = *conditional-of-pooled* R_B|A per (arm × persona)
   |                                                                  ^
38 |   — concatenate per-seed indicator arrays across seeds 42/1337/2024 then
39 |   take the ratio.
   |

9:37:24 AMtool_calltools

tool=Bash

Bash

9:37:24 AMtool_resulttools

9:37:28 AMtool_calltools

tool=Bash

Bash

9:37:28 AMtool_resulttools

(Bash completed with no output)

9:37:32 AMtool_calltools

tool=Bash

Bash

9:37:32 AMtool_resulttools

(Bash completed with no output)

9:37:36 AMtool_calltools

tool=Bash

Bash

9:37:36 AMtool_resulttools

All checks passed!

9:37:39 AMtool_calltools

tool=Bash

Bash

9:37:40 AMtool_resulttools

2 files reformatted
[This command modified 2 files you've previously read: ../explore-persona-space/scripts/run_experiment_369.py, ../explore-persona-space/tests/test_exp369_kill_criterion.py. Call Read before editing.]

9:37:44 AMtool_calltools

tool=Bash

Bash

9:37:46 AMtool_resulttools

....................                                                     [100%]
20 passed in 0.44s

9:37:53 AMtool_calltools

tool=Bash

Bash

9:37:53 AMtool_resulttools

IMPORT OK
ARMS = ['T', 'C', 'C2'] SEEDS = [42, 1337, 2024]
A_IDS = [2442, 17851, 80, 12, 19, 16, 2452]  B_IDS = [486, 595, 50997, 12, 22, 3504]
disjoint = True
T donor = '<<§q-41>> X :: kxr-7 ::'
C donor = '<<§q-41>> X'
C2 donor = 'X :: kxr-7 ::'
recipient = '<<§q-41>> X'

9:37:58 AMtool_calltools

tool=Bash

Bash

9:37:58 AMtool_resulttools

A  configs/eval/issue_188.yaml
A  configs/eval/issue_331_phase0.yaml
A  configs/eval/issue_331_phase1.yaml
A  configs/eval/issue_370.yaml
A  scripts/__init__.py
A  scripts/_issue_370_shared.py
A  scripts/_rest_backfill.py
M  scripts/audit_clean_results_body_discipline.py
M  scripts/bootstrap_pod.sh
A  scripts/build_issue_331_seeds.py
M  scripts/clean_eval.sh
M  scripts/compute_zelthari_centered_cosine.py
M  scripts/download_arc_data.py
M  scripts/eval_language_inversion.py
M  scripts/generate_wrong_answers.py
A  scripts/gh_issue_state.py
A  scripts/gh_project.py
M  scripts/hypothesis_gate.py
A  scripts/issue_188_evolutionary_trigger.py
A  scripts/issue_331_phase0_panel.py
A  scripts/issue_331_phase1_evolutionary.py
A  scripts/issue_370_sweep_a.py
A  scripts/issue_370_sweep_b.py
M  scripts/merge_remaining.py
A  scripts/plot_issue_331_hero.py
M  scripts/plot_leakage_vs_cosine_all.py
M  scripts/plot_leakage_vs_cosine_none.py
M  scripts/pod_lifecycle.py
M  scripts/pod_watch.py
M  scripts/pods.conf
M  scripts/pods_ephemeral.json
M  scripts/post_step_completed.py
M  scripts/rebuild_results.py
M  scripts/recent_clean_results.py
M  scripts/render_claims.py
M  scripts/render_papers_index.py
M  scripts/render_summary.py
M  scripts/run_issue295_marker_only_loss.py
M  scripts/run_issue_156.py
M  scripts/run_issue_203.py
M  scripts/run_issue_213_part_b.py
M  scripts/run_midtrain_25pct.sh
M  scripts/run_sweep.py
M  scripts/test_integration.py
M  scripts/verify_clean_result.py
M  scripts/verify_uploads.py
M  scripts/workflow_lint.py
?? .claude/plans/cairn-design.md
?? eps/
?? scripts/run_experiment_369.py
?? tests/test_exp369_kill_criterion.py
---
 src/explore_persona_space/train/sft.py | 174 +++++++++++++++++++++++++++++++++
 1 file changed, 174 insertions(+)

9:38:03 AMtool_calltools

tool=Bash

Bash

9:38:03 AMtool_resulttools

 scripts/test_integration.py                    |    1 +
 scripts/verify_clean_result.py                 |  212 +---
 scripts/verify_uploads.py                      |    4 +-
 scripts/workflow_lint.py                       |   82 +-
 47 files changed, 8269 insertions(+), 680 deletions(-)

9:38:09 AMtool_calltools

tool=Bash

Bash

9:38:09 AMtool_resulttools

100644 11e323d17883bd35ab0f3d08b0f5265e033665e4 0	configs/eval/issue_188.yaml
100644 30573289ea2cd7cd6707e49a9b17427b03c33064 0	configs/eval/issue_331_phase0.yaml
100644 32c9c36be1663ad2ee4130d01931063087cf5ecc 0	configs/eval/issue_331_phase1.yaml
100644 408e05383b8cea71349797338510931195c35f78 0	configs/eval/issue_370.yaml
100644 0deea4d5816117d287cd3ca51c549fffc9b1edef 0	eval_results/issue_188/round_0_candidates.json
100644 35b7f1b61ecabebea716f46ff090374cc54e489b 0	eval_results/issue_188/summary.json
100644 74dc7c3ce81a13321cdf3471105160bb7f57d89e 0	eval_results/issue_331/processus_followup_sweep/results.json
100644 a1f2ead750c70d3b4629e594c2e3a9b4ab5bf0df 0	figures/issue_188/round_0_diagnostic_hero.meta.json
100644 ca8e90dfdbfc3d874d5c28413ed81cbde58af8c6 0	figures/issue_188/round_0_diagnostic_hero.pdf
100644 d5dc90993e2669c34f60bd50c297384d73811d18 0	figures/issue_188/round_0_diagnostic_hero.png

9:38:12 AMtool_calltools

tool=Bash

Bash

9:38:12 AMtool_resulttools

9e0e2d8f [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
a9aba1bf util: sagan_progress helper for experiment scripts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption

9:38:15 AMtool_calltools

tool=Bash

Bash

9:38:15 AMtool_resulttools

a9aba1bf89351e439f82a2f28f1c9c3ebd8ea925
a9aba1bf89351e439f82a2f28f1c9c3ebd8ea925

9:38:20 AMtool_calltools

tool=Bash

Bash

9:38:20 AMtool_resulttools

On branch issue-369
Your branch is ahead of 'origin/main' by 1 commit.
  (use "git push" to publish your local commits)

Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	new file:   configs/eval/issue_188.yaml
	new file:   configs/eval/issue_331_phase0.yaml
	new file:   configs/eval/issue_331_phase1.yaml
	new file:   configs/eval/issue_370.yaml
	new file:   scripts/__init__.py
	new file:   scripts/_issue_370_shared.py
	new file:   scripts/_rest_backfill.py
	modified:   scripts/audit_clean_results_body_discipline.py
	modified:   scripts/bootstrap_pod.sh
	new file:   scripts/build_issue_331_seeds.py
	modified:   scripts/clean_eval.sh
	modified:   scripts/compute_zelthari_centered_cosine.py
	modified:   scripts/download_arc_data.py
	modified:   scripts/eval_language_inversion.py

9:38:25 AMtool_calltools

tool=Bash

Bash

9:38:25 AMtool_resulttools

M	scripts/run_sweep.py
M	scripts/test_integration.py
M	scripts/verify_clean_result.py
M	scripts/verify_uploads.py
M	scripts/workflow_lint.py
 M scripts/audit_clean_results_body_discipline.py
 M scripts/bootstrap_pod.sh
 M scripts/clean_eval.sh
 M scripts/compute_zelthari_centered_cosine.py
 M scripts/download_arc_data.py
 M scripts/eval_language_inversion.py
 M scripts/generate_wrong_answers.py
 M scripts/hypothesis_gate.py
 M scripts/merge_remaining.py
 M scripts/plot_leakage_vs_cosine_all.py

9:38:31 AMtool_calltools

tool=Bash

Bash

9:38:31 AMtool_resulttools

A  scripts/run_experiment_369.py
A  tests/test_exp369_kill_criterion.py

9:38:51 AMtool_calltools

tool=Bash

Bash

9:38:51 AMtool_resulttools

[issue-369 85a70fd9] issue #369: three-arm x three-seed EOS-masked propagation sweep entrypoint
 2 files changed, 2103 insertions(+)
 create mode 100644 scripts/run_experiment_369.py
 create mode 100644 tests/test_exp369_kill_criterion.py

9:38:54 AMtool_calltools

tool=Bash

Bash

9:38:55 AMtool_resulttools

85a70fd9 issue #369: three-arm x three-seed EOS-masked propagation sweep entrypoint
9e0e2d8f [2/3] add RecipientEOSMaskingDataCollator + TrainLoraConfig flags (issue #354)
a9aba1bf util: sagan_progress helper for experiment scripts
---
remote: 
remote: Create a pull request for 'issue-369' on GitHub by visiting:        
remote:      https://github.com/superkaiba/explore-persona-space/pull/new/issue-369        
remote: 
To https://github.com/superkaiba/explore-persona-space.git
 * [new branch]        issue-369 -> issue-369
Branch 'issue-369' set up to track remote branch 'issue-369' from 'origin'.

9:39:01 AMtool_calltools

tool=Bash

Bash

9:39:01 AMtool_resulttools

85a70fd939fb5014d1c37933fd3f391b36175e5f
scripts/run_experiment_369.py
tests/test_exp369_kill_criterion.py
85a70fd939fb5014d1c37933fd3f391b36175e5f	refs/heads/issue-369

9:39:40 AMtool_resulttools

Branch `issue-369` is at `85a70fd9` on both local and origin. Both files exist on the branch.

---

## Implementation Report — round 1

**Status:** READY-FOR-REVIEW
**Branch:** `issue-369` @ `85a70fd939fb5014d1c37933fd3f391b36175e5f`
**Entrypoint:** `scripts/run_experiment_369.py` — matches `pod_spec.dockerArgs`: yes

### (a) What was done
- `scripts/run_experiment_369.py` (new, 1817 lines): the pod entrypoint. Inherits aggressively from `scripts/run_issue354_eos_masked.py` @ `ef8ff716` (same base model, same pair, same persona pool, same marker strings, same on-policy generator, same EOS-mask training config, same `_safe` dedup, same eval rig). Adds: three arms (T/C/C2 with `donor_response()` switch), three seeds (42/1337/2024) with per-seed on-policy completion caches, conditional-of-pooled + mean-of-conditionals statistics, CI-A (question-only cluster bootstrap, RNG seed 43) and CI-B (seed-stratified two-level cluster bootstrap, RNG seed 44), in-script kill-criterion classifier (`binding | template | inconclusive` with bystander override on police/data_scientist), `mean_completion_length_chars` per cell, per-arm dataset-shape assertions, eval-cell completeness gate, three figures (`hero_recipient_RBgivenA_by_arm`, `per_persona_leak_spectrum_C2`, `seed_disagreement_per_arm`) saved as png+pdf+svg+meta.json, and progress reporting via `post_progress` at bootstrap/data-gen/train/eval/upload/done milestones.
- `tests/test_exp369_kill_criterion.py` (new): 20 focused unit tests covering arm templates, question disjointness, cross-seed indicator pooling, pooled conditional reducer, and the kill-criterion verdict matrix (binding leg, template positive leg, template without-A leg, low-denom inconclusive, bystander override, zelthari exclusion).
- Cherry-picked `31c35e3a` ([2/3] RecipientEOSMaskingDataCollator + TrainLoraConfig flags from issue-354) onto issue-369 so `src/explore_persona_space/train/sft.py` has the `mask_eos_for_recipient` plumbing the entrypoint needs. The transformers pin from [1/3] was already on main.
- Diff vs `main`: 2 new files (2103 insertions) + 1 file modified by cherry-pick (+174 sft.py).
- Plan adherence: every item in the brief addressed. The plan-specified `meta.json` sidecars for figures use the existing `savefig_paper` helper which already writes them, and SVG is added by a thin `_save_with_svg` wrapper that calls `fig.savefig(..., format="svg")` after the helper runs.
- Commits on issue-369: `9e0e2d8f` [2/3] cherry-pick + `85a70fd9` exp369 entrypoint.

### (b) Considered but not done
- **Refactoring shared #354 helpers (`has_marker`, `find_marker_pos_loose`, `_aggregate_metrics`, `_wilson_ci`) into a shared module.** Tempting because they're copy-pasted from #354, but the brief says inherit aggressively and standalone infra refactors are out of scope. Left as duplicated code.
- **Patching `savefig_paper` to accept `formats=("png","pdf","svg")`.** Cleaner but a cross-cutting infra change (used by 30+ scripts in the repo) — out of scope for an experiment-specific script. Wrapped locally instead.
- **Per-question indicator load from a single pass over raw_completions.** I currently read each `raw_completions.json` once per persona in `build_summary` (11 reads per adapter). Easy optimization to batch, but the JSON files are ~10MB each and summary runs once at the end — not worth the complexity.
- **Wandb `wandb_run_id` persisted in run_result.json.** #354 added this retroactively (commit `43b353c0`). I did not propagate that micro-feature; the analyzer can recover it from the wandb API or env if needed.

### (c) How to verify
- **Lint:** `UV_CACHE_DIR=/tmp/uv-cache UV_PROJECT_ENVIRONMENT=.venv uv run ruff check scripts/run_experiment_369.py tests/test_exp369_kill_criterion.py` — current run: PASS (`All checks passed!`).
- **Format:** `uv run ruff format --check scripts/run_experiment_369.py tests/test_exp369_kill_criterion.py` — PASS.
- **Unit tests:** `uv run pytest tests/test_exp369_kill_criterion.py -q…

9:39:48 AMtool_calltools

tool=TodoWrite

TodoWrite

9:39:48 AMtool_resulttools

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

9:39:51 AMassistant_textagent

Implementer pushed `issue-369` @ `85a70fd9`. Transitioning to `code_reviewing` and spawning reviewer pair in parallel.

9:39:52 AMtool_calltools

tool=Bash

Bash

9:39:53 AMtool_resulttools

{
  "experiment": {
    "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
    "status": "code_reviewing",
    "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

9:40:33 AMtool_calltools

tool=Agent

Agent

9:40:33 AMtool_resulttools

Async agent launched successfully.
agentId: add2cfc44608447a2 (internal ID - do not mention to user. Use SendMessage with to: 'add2cfc44608447a2' 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/1c580fd9-ee62-46b1-875b-121aabe7e456/tasks/add2cfc44608447a2.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.

9:41:06 AMtool_calltools

tool=Agent

Agent

9:41:06 AMtool_resulttools

Async agent launched successfully.
agentId: a1bb3939030cc835b (internal ID - do not mention to user. Use SendMessage with to: 'a1bb3939030cc835b' 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/1c580fd9-ee62-46b1-875b-121aabe7e456/tasks/a1bb3939030cc835b.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.

9:41:10 AMassistant_textagent

Reviewer pair launched in parallel. Waiting for both to complete.

9:41:11 AMtool_calltools

tool=ScheduleWakeup

ScheduleWakeup

9:41:11 AMtool_resulttools

Next wakeup scheduled for 10:12:00 (in 1849s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

9:41:13 AMassistant_textagent

Code reviewer pair (claude + codex) running in parallel. I'll continue when one notifies.

9:44:55 AMassistant_textagent

Claude code reviewer: **PASS**. Waiting for Codex.

9:45:27 AMassistant_textagent

Both reviewers: **PASS**. No reconciler needed. Forwarding test verdict and advancing to running stage.

9:45:30 AMtool_calltools

tool=Bash

Bash

9:45:33 AMtool_resulttools

{
  "experiment": {
    "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
    "status": "testing",
    "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}
---
{
  "id": "5f5f4bee-d01c-4742-9b75-4fce574fd4c2",
  "ok": true
}

9:45:39 AMtool_calltools

tool=TodoWrite

TodoWrite

9:45:40 AMtool_resulttools

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

9:45:42 AMassistant_textagent

Now splicing branch+commit into `pod_spec.env` and dispatching the pod.

9:45:45 AMtool_calltools

tool=Bash

Bash

9:45:45 AMtool_resulttools

existing pod_spec keys: []
existing env: null
full pod_spec snippet:
{}

9:45:50 AMtool_calltools

tool=Bash

Bash

9:45:51 AMtool_resulttools

top-level keys: ['approvalRequests', 'events', 'experiment']
podSpec type: <class 'NoneType'>
runpodSpec type: absent
podSpec full:
null

9:45:56 AMtool_calltools

tool=Bash

Bash

9:45:56 AMtool_resulttools

experiment 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']
podSpec: {
  "name": "exp369-paired-marker-c2-3seed",
  "config": {
    "command": "Three-arm (T, C, C2) x 3 seed (42, 1337, 2024) LoRA SFT sweep on Qwen-2.5-7B-Instruct for librarian -> software_engineer paired-marker propagation, with EOS-mask collator. 9 adapters trained + evaluated; emits cross-arm summary.json with conditional-of-pooled R_B|A, CI-A (question-only) + CI-B (seed-stratified) cluster bootstraps, mean_completion_length, denom_A, bootstrap drop counts, donor-coherence numbers, bystander spectrum.",
    "artifacts": [
      "hf://superkaiba1/explore-persona-space/adapters/exp369_T_seed{42,1337,2024}",
      "hf://superkaiba1/explore-persona-space/adapters/exp369_C_seed{42,1337,2024}",
      "hf://superkaiba1/explore-persona-space/adapters/exp369_C2_seed{42,1337,2024}",
      "hf://superkaiba1-data/explore-persona-space/exp369/onpolicy_cache/completions_all_seed{42,1337,2024}.json",
      "hf://superkaiba1-data/explore-persona-space/exp369/raw_completions/{T,C,C2}_seed{42,1337,2024}/raw_completions.json",
      "git:eval_results/exp369/summary.json",
      "git:eval_results/exp369/base_model_floor.json",
      "git:eval_results/exp369/marker_token_verification.json",
      "git:eval_results/exp369/pair2_librarian_swe/{T,C,C2}_seed{42,1337,2024}/run_result.json",
      "git:figures/exp369/hero_recipient_RBgivenA_by_arm.{png,pdf,svg,meta.json}",
      "git:figures/exp369/per_persona_leak_spectrum_C2.{png,pdf,svg,meta.json}",
      "git:figures/exp369/seed_disagreement_per_arm.{png,pdf,svg,meta.json}",
      "wandb:exp369"
    ]
  },
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 100,
  "cloudType": "SECURE",
  "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py --all --gpu 0'",
  "consolidation": {
    "may_merge_pods": false,
    "merge_target_max_gpus_per_pod": 1
  },
  "containerDiskGb": 100,
  "estimatedMinutes": 480,
  "substitution_policy": {
    "account": {
      "prefer": "team",
      "allowed": [
        "team",
        "personal"
      ]
    },
    "gpuType": {
      "allowed": [
        "H100",
        "H200",
        "A100-SXM"
      ],
      "min_vram_gb": 80
    },
    "gpuCount": {
      "max": 1,
      "min": 1
    },
    "volumeGb": {
      "min": 100
    },
    "cloudType": {
      "prefer": "SECURE",
      "allowed": [
        "SECURE",
        "COMMUNITY"
      ]
    },
    "dataCenterId": {
      "prefer": [
        "US-CA-2",
        "EU-RO-1"
      ],
      "allowed": "any"
    },
    "containerDiskGb": {
      "min": 100
    }
  }
}

9:46:13 AMtool_calltools

tool=Bash

Bash

9:46:13 AMtool_resulttools

env: None
branch ok: False

9:46:21 AMtool_calltools

tool=Bash

Bash

9:46:22 AMtool_resulttools

{"ok":true,"experiment":{"id":"2197c9e9-7558-4572-97eb-70cc2235e659","title":"Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone","status":"testing","turn":"Sagan turn: run and verify"}}

9:46:26 AMtool_calltools

tool=Bash

Bash

9:46:27 AMtool_resulttools

env: {"SAGAN_EPS_BRANCH": "issue-369", "SAGAN_EPS_COMMIT_SHA": "85a70fd939fb5014d1c37933fd3f391b36175e5f"}
podSpec keys: ['cloudType', 'config', 'consolidation', 'containerDiskGb', 'dockerArgs', 'env', 'estimatedMinutes', 'gpuCount', 'gpuType', 'name', 'substitution_policy', 'volumeGb']

9:46:37 AMassistant_textagent

`podSpec.env` patched. Transitioning to `running` and spawning pod-provisioner.

9:46:37 AMtool_calltools

tool=Bash

Bash

9:46:38 AMtool_resulttools

{
  "experiment": {
    "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
    "status": "running",
    "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

9:46:54 AMtool_calltools

tool=Agent

Agent

9:46:57 AMtool_calltools

tool=Read

Read

9:46:57 AMtool_resulttools

1	---
2	name: feedback-spec-json-quoting
3	description: Write --spec-json content to a temp file and cat it into the variable; heredoc expansion introduces a leading newline that fails JSON parsing at position 1
4	metadata:
5	  type: feedback
6	---
7	
8	When passing --spec-json to pod-tool attempt, do NOT use a heredoc directly in the variable assignment. The heredoc introduces a leading newline which makes pod-tool report "Expected property name or '}' in JSON at position 1" and exit 2.
9	
10	**Why:** The shell heredoc places a newline at the very start of the variable value before the opening `{`.
11	
12	**How to apply:** Write the JSON to a temp file (`/tmp/specNNN.json`) with `cat > /tmp/specNNN.json << 'SPECEOF'`, validate with `python3 -c "import json,sys; json.load(open('/tmp/specNNN.json'))"`, then compact to a single line with `SPEC=$(python3 -c "import json; print(json.dumps(json.load(open('/tmp/specNNN.json'))))")` and pass `--spec-json "$SPEC"`. Do NOT use `SPEC=$(cat /tmp/specNNN.json)` — the embedded literal newlines in the heredoc-written file still cause the same parse failure at position 1. The `python3 json.dumps` step is mandatory to collapse the JSON to one line.
13

9:46:57 AMtool_calltools

tool=Read

Read

9:46:57 AMtool_resulttools

1	---
2	name: feedback-agent-run-kind-mismatch
3	description: pod-tool attempt/commit require kind=experiment; orchestrator passes its own kind=apply run id — create a new experiment-kind row when needed
4	metadata:
5	  type: feedback
6	---
7	
8	pod-tool `attempt` and `commit` hard-require `kind=experiment` on the agent_run passed via `--agent-run-id`. The experiment-orchestrator passes its *own* agent_run id, which has `kind=apply`. This causes a fatal error: `agent_run <id> kind=apply; pod-tool attempt requires kind=experiment`.
9	
10	**Why:** pod-tool enforces kind=experiment as a guard to prevent applying pod commits to the wrong run type.
11	
12	**How to apply:** When the orchestrator's agentRunId has `kind=apply`, INSERT a new agent_run row with `kind='experiment'`, `status='running'`, `scope_entity_kind='experiment'`, `scope_entity_id=<experimentId>`, `runpod_account='team'`, `approval_required=false`, and a request string referencing the orchestrator run. Use the new row's id for all pod-tool calls. SQL:
13	
14	```sql
15	INSERT INTO agent_runs (kind, provider, status, request, scope_entity_kind, scope_entity_id, runpod_account, approval_required)
16	VALUES ('experiment', 'claude_code', 'running', 'pod-provisioner dispatch for experiment #<N> — orchestrator run <apply-run-id>', 'experiment', '<experimentId>', 'team', false)
17	RETURNING id;
18	```
19	
20	Related: [[spec-json quoting gotcha]]
21

9:47:02 AMtool_calltools

tool=Bash

Bash

9:47:03 AMtool_resulttools

<persisted-output>
Output too large (113.8KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/1c580fd9-ee62-46b1-875b-121aabe7e456/tool-results/bavgnc4h5.txt

Preview (first 2KB):
{
    "experiment": {
        "id": "2197c9e9-7558-4572-97eb-70cc2235e659",
        "number": 369,
        "legacyGhNumber": null,
        "beliefId": null,
        "projectId": null,
        "title": "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone",
        "body": "Follow-up from clean result 747c9e7a (#354 paired-marker propagation, librarian → software_engineer).\n\n**Why this is needed.** The current 2-arm design (T: donor sees `<A> answer <B>`; C: donor sees `<A> answer`) cleanly rules out one alternative: marker_B does not propagate to the recipient without donor exposure to marker_B (C arm gives 0 / 260 across all personas). It also rules out marker_B-as-generic-turn-end-suffix: P(marker_B | NOT marker_A) ≈ 0% across all personas (3 emissions in 1,265 not-marker_A completions, vs 172 / 295 when marker_A fired).\n\nWhat it does **not** rule out: the LoRA may have learned the full completion shape `<A> {answer} <B>` as a template, and emits both markers together as parts of the same template rather than `<A>` literally triggering `<B>`. Under that interpretation, marker_A and marker_B co-fire because of shape, not association.\n\n**The proposed control: C2.**\n- **C2 (donor sees `<B>` only)**: train donor on `{answer} <B>` (marker_B at end-of-completion, no marker_A anywhere). Recipient training stays the same as T and C (`<A> answer`).\n- Predictions:\n  - If chunk-binding / marker_A keys marker_B: C2 should leave the recipient (and bystanders) at ~0% marker_B emission, because marker_A and marker_B were never paired in donor training.\n  - If shape-template or marker_B leaking alone: C2 should still produce non-zero marker_B emission on the recipient (and likely on bystanders), because marker_B was trained on the donor and gets picked up as an end-of-completion suffix.\n\n**The full 2x2 the result of which would settle the binding question:**\n\n| | donor sees `<A>` | donor doesn't 
...
</persisted-output>

9:47:03 AMtool_calltools

tool=Bash

Bash

9:47:03 AMtool_resulttools

                  id                  |    kind    |  status   
--------------------------------------+------------+-----------
 0c382d32-0089-4463-8107-b7e29b82e410 | experiment | completed
(1 row)

9:47:07 AMtool_calltools

tool=Bash

Bash

9:47:07 AMtool_resulttools

Exit code 1
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/usr/lib/python3.10/json/__init__.py", line 293, in load
    return loads(fp.read(),
  File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
    return _default_decoder.decode(s)
  File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 81 column 1 (char 115745)

9:47:10 AMtool_calltools

tool=Bash

Bash

9:47:11 AMtool_resulttools

{
  "env": {
    "SAGAN_EPS_BRANCH": "issue-369",
    "SAGAN_EPS_COMMIT_SHA": "85a70fd939fb5014d1c37933fd3f391b36175e5f"
  },
  "name": "exp369-paired-marker-c2-3seed",
  "config": {
    "command": "Three-arm (T, C, C2) x 3 seed (42, 1337, 2024) LoRA SFT sweep on Qwen-2.5-7B-Instruct for librarian -> software_engineer paired-marker propagation, with EOS-mask collator. 9 adapters trained + evaluated; emits cross-arm summary.json with conditional-of-pooled R_B|A, CI-A (question-only) + CI-B (seed-stratified) cluster bootstraps, mean_completion_length, denom_A, bootstrap drop counts, donor-coherence numbers, bystander spectrum.",
    "artifacts": [
      "hf://superkaiba1/explore-persona-space/adapters/exp369_T_seed{42,1337,2024}",
      "hf://superkaiba1/explore-persona-space/adapters/exp369_C_seed{42,1337,2024}",
      "hf://superkaiba1/explore-persona-space/adapters/exp369_C2_seed{42,1337,2024}",
      "hf://superkaiba1-data/explore-persona-space/exp369/onpolicy_cache/completions_all_seed{42,1337,2024}.json",
      "hf://superkaiba1-data/explore-persona-space/exp369/raw_completions/{T,C,C2}_seed{42,1337,2024}/raw_completions.json",
      "git:eval_results/exp369/summary.json",
      "git:eval_results/exp369/base_model_floor.json",
      "git:eval_results/exp369/marker_token_verification.json",
      "git:eval_results/exp369/pair2_librarian_swe/{T,C,C2}_seed{42,1337,2024}/run_result.json",
      "git:figures/exp369/hero_recipient_RBgivenA_by_arm.{png,pdf,svg,meta.json}",
      "git:figures/exp369/per_persona_leak_spectrum_C2.{png,pdf,svg,meta.json}",
      "git:figures/exp369/seed_disagreement_per_arm.{png,pdf,svg,meta.json}",
      "wandb:exp369"
    ]
  },
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 100,
  "cloudType": "SECURE",
  "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py --all --gpu 0'",
  "consolidation": {
    "may_merge_pods": false,
    "merge_target_max_gpus_per_pod": 1
  },
  "containerDiskGb": 100,
  "estimatedMinutes": 480,
  "substitution_policy": {
    "account": {
      "prefer": "team",
      "allowed": [
        "team",
        "personal"
      ]
    },
    "gpuType": {
      "allowed": [
        "H100",
        "H200",
        "A100-SXM"
      ],
      "min_vram_gb": 80
    },
    "gpuCount": {
      "max": 1,
      "min": 1
    },
    "volumeGb": {
      "min": 100
    },
    "cloudType": {
      "prefer": "SECURE",
      "allowed": [
        "SECURE",
        "COMMUNITY"
      ]
    },
    "dataCenterId": {
      "prefer": [
        "US-CA-2",
        "EU-RO-1"
      ],
      "allowed": "any"
    },
    "containerDiskGb": {
      "min": 100
    }
  }
}

9:47:19 AMtool_calltools

tool=Bash

Bash

9:47:19 AMtool_resulttools

JSON valid

9:47:24 AMtool_calltools

tool=Bash

Bash

9:47:27 AMtool_resulttools

Attempt 1: H100 SECURE team...

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "0c382d32-0089-4463-8107-b7e29b82e410" "--run-index" "0" "--spec-json" "{\"name\": \"exp369-paired-marker-c2-3seed\", \"gpuType\": \"NVIDIA H100 80GB HBM3\", \"gpuCount\": 1, \"cloudType\": \"SECURE\", \"volumeGb\": 100, \"containerDiskGb\": 100, \"dockerArgs\": \"bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_369.py --all --gpu 0'\", \"config\": {\"command\": \"Three-arm (T, C, C2) x 3 seed (42, 1337, 2024) LoRA SFT sweep on Qwen-2.5-7B-Instruct for librarian -> software_engineer paired-marker propagation, with EOS-mask collator. 9 adapters trained + evaluated; emits cross-arm summary.json with conditional-of-pooled R_B|A, CI-A (question-only) + CI-B (seed-stratified) cluster bootstraps, mean_completion_length, denom_A, bootstrap drop counts, donor-coherence numbers, bystander spectrum.\", \"artifacts\": [\"hf://superkaiba1/explore-persona-space/adapters/exp369_T_seed{42,1337,2024}\", \"hf://superkaiba1/explore-persona-space/adapters/exp369_C_seed{42,1337,2024}\", \"hf://superkaiba1/explore-persona-space/adapters/exp369_C2_seed{42,1337,2024}\", \"hf://superkaiba1-data/explore-persona-space/exp369/onpolicy_cache/completions_all_seed{42,1337,2024}.json\", \"hf://superkaiba1-data/explore-persona-space/exp369/raw_completions/{T,C,C2}_seed{42,1337,2024}/raw_completions.json\", \"git:eval_results/exp369/summary.json\", \"git:eval_results/exp369/base_model_floor.json\", \"git:eval_results/exp369/marker_token_verification.json\", \"git:eval_results/exp369/pair2_librarian_swe/{T,C,C2}_seed{42,1337,2024}/run_result.json\", \"git:figures/exp369/hero_recipient_RBgivenA_by_arm.{png,pdf,svg,meta.json}\", \"git:figures/exp369/per_persona_leak_spectrum_C2.{png,pdf,svg,meta.json}\", \"git:figures/exp369/seed_disagreement_per_arm.{png,pdf,svg,meta.json}\", \"wandb:exp369\"], \"env\": {\"SAGAN_EPS_BRANCH\": \"issue-369\", \"SAGAN_EPS_COMMIT_SHA\": \"85a70fd939fb5014d1c37933fd3f391b36175e5f\"}}}" "--account" "team"

{"ok":true,"pod":{"podId":"fbgmo0ozk7z4ac","name":"exp369-paired-marker-c2-3seed","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"68c8228a-b4f8-4a83-a506-d600bb408e9d","sagaRunId":"a132b7a7-0b74-4d4f-a25f-e1457c1138dd","account":"team"}

9:47:30 AMtool_calltools

tool=Bash

Bash

9:47:32 AMtool_resulttools

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "0c382d32-0089-4463-8107-b7e29b82e410"

{"ok":true,"podIds":["fbgmo0ozk7z4ac"],"count":1}

9:47:34 AMtool_resulttools

committed 1 pod(s): fbgmo0ozk7z4ac
agentId: a2827c7215d1e4827 (use SendMessage with to: 'a2827c7215d1e4827' to continue this agent)
<usage>total_tokens: 29075
tool_uses: 9
duration_ms: 39494</usage>

9:47:38 AMassistant_textagent

Pod committed: `fbgmo0ozk7z4ac`. The runner will auto-transition status when the pod reports STOPPED/COMPLETED. I'll wait for that signal before driving the uploading/verifying/interpreting stages.

9:47:44 AMtool_calltools

tool=TodoWrite

TodoWrite

9:47:44 AMtool_resulttools

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

9:47:49 AMtool_calltools

tool=Bash

Bash

9:47:50 AMtool_resulttools

status: queued
podSpec.env: {"SAGAN_EPS_BRANCH": "issue-369", "SAGAN_EPS_COMMIT_SHA": "85a70fd939fb5014d1c37933fd3f391b36175e5f"}

9:48:02 AMassistant_textagent

Pod is queued. Expected runtime is ~8 H100-hours. I'll sleep an hour then check on progress.

9:48:03 AMtool_calltools

tool=ScheduleWakeup

ScheduleWakeup

9:48:03 AMtool_resulttools

Next wakeup scheduled for 10:49:00 (in 3656s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

9:48:08 AMassistant_textagent

Pod-provisioner committed pod `fbgmo0ozk7z4ac`; status is `queued`. Sleeping 1 hour before checking.

9:48:08 AMsdk_resultagent

success

9:48:08 AMcompletedagent

Pod-provisioner committed pod `fbgmo0ozk7z4ac`; status is `queued`. Sleeping 1 hour before checking.

Discussion

Comments

No comments yet.