experiment

Run 0c382d32

Owner answered your clarifying questions. Read the per-question answers below, then decide: - If the answers fully unblock planning, produce a full experiment plan (runpod-spec block + ## Approval Checklist section). - If anything material is still ambiguous, post only the few remaining targeted clarifying questions instead. Do not invent new requirements. Use the existing experiment body as the source of truth for scope. ## Owner answers to your clarifying questions ### Q1. Scope: minimal vs full replication The body floats two options — C2 alone on seed 42 (~0.7 H100-hr), or T/C/C2 × 3 seeds (~6.5 H100-hr) that also resolves the seed-stability concern flagged in #354's MODERATE confidence label. Which scope should I plan for? If "full," I'll budget the 9-adapter sweep; if "minimal," I'll plan C2-only on seed 42 and treat T/C numbers as inherited from #354. [TEXTBOX:scope] Owner answer: Primary statistic: pooled recipient R_B|A across the 3 seeds on librarian -> software_engineer, with a cluster 95% CI from the questions-cluster bootstrap. Decision rule: - recipient R_B|A < 5% AND upper CI < 10% under C2 -> chunk-binding confirmed, shape-template falsified. - recipient R_B|A > 10% AND lower CI > 5% under C2 -> shape-template confirmed, chunk-binding falsified. - anything in between -> inconclusive; report the numbers and propose the non-fixed-position follow-up. Secondary check (also goes into the kill verdict): bystander R_B|A under C2. Under chunk-binding all bystanders should be near 0. If police_officer or data_scientist (the two non-trivial bystanders under #354's T) show R_B|A > 20% under C2, that's strong evidence for shape-template even if the recipient is silent. Include the bystander spectrum in the writeup either way. ### Q2. Kill criterion for the binding-vs-template question What C2 marker_B emission rate on the recipient (librarian → software_engineer) would you accept as definitively settling the question? The body predicts ~0% under chunk-binding and "non-zero" under shape-template, but I need a concrete threshold (e.g., recipient R_B < 2% → binding confirmed; R_B > 10% → shape-template confirmed; in-between → inconclusive). Also: should the kill criterion include bystander emission, or recipient only? [TEXTBOX:kill-criterion] Owner answer: Re-train T and C alongside C2 in this run. Self-contained 2x2 in one artifact is worth the extra ~1.4 H100-hr (already included in the 6.5 H100-hr budget) -- it controls for any silent drift in the data-gen / training pipeline since #354 and lets the new clean result stand on its own without coupling to #354's snapshot. Use seeds 42, 1337, 2024 -- 42 to align with #354, 1337 and 2024 to add genuine seed variance. ### Q3. Re-train T/C in the same run, or trust #354's numbers? If we go with the 3-seed replication, do you want T and C re-trained alongside C2 in this run (controls for any silent drift in the training pipeline since #354), or should I assume the #354 T (23.5%) and C (0%) numbers are stable and only train C2 + the two new seeds for T/C? The former adds ~1.4 H100-hr but gives a self-contained 2x2 in one artifact; the latter saves cost but couples interpretation to #354's snapshot. [TEXTBOX:reuse-tc] Owner answer: Keep marker_B strictly at end-of-completion in C2 (), with no marker_A anywhere in donor training. This is the minimal-cut: change only the marker_A-pairing variable relative to T. Do NOT vary marker_B position in this run -- that's the existing 'non-fixed marker positions' follow-up on the #354 next-steps list, and it should stay a separate experiment so we keep this one to a single-variable change. Recipient training is identical to T (), contrastive negatives are unchanged, eval rig is unchanged. ### Q4. C2 marker placement In #354's T arm, marker_B appears strictly at end-of-completion in donor training (`<A> answer `). For C2, I plan to keep the exact same positional convention — marker_B at end-of-completion, just with no marker_A anywhere (`answer `). Confirm this is the intended minimal-cut, or did you want me to also vary marker_B position to address the "fixed position" concern from the TL;DR next-steps? [TEXTBOX:marker-placement] Owner answer: (no answer entered) ## Unresolved comments on this experiment (no unresolved comments — owner answered in the question textboxes; see below.) ## Prior plan output ## Clarifying questions 1. **Scope: minimal vs full replication.** The body floats two options — C2 alone on seed 42 (~0.7 H100-hr), or T/C/C2 × 3 seeds (~6.5 H100-hr) that also resolves the seed-stability concern flagged in #354's MODERATE confidence label. Which scope should I plan for? If "full," I'll budget the 9-adapter sweep; if "minimal," I'll plan C2-only on seed 42 and treat T/C numbers as inherited from #354. [TEXTBOX:scope] 2. **Kill criterion for the binding-vs-template question.** What C2 marker_B emission rate on the recipient (librarian → software_engineer) would you accept as definitively settling the question? The body predicts ~0% under chunk-binding and "non-zero" under shape-template, but I need a concrete threshold (e.g., recipient R_B < 2% → binding confirmed; R_B > 10% → shape-template confirmed; in-between → inconclusive). Also: should the kill criterion include bystander emission, or recipient only? [TEXTBOX:kill-criterion] 3. **Re-train T/C in the same run, or trust #354's numbers?** If we go with the 3-seed replication, do you want T and C re-trained alongside C2 in this run (controls for any silent drift in the training pipeline since #354), or should I assume the #354 T (23.5%) and C (0%) numbers are stable and only train C2 + the two new seeds for T/C? The former adds ~1.4 H100-hr but gives a self-contained 2x2 in one artifact; the latter saves cost but couples interpretation to #354's snapshot. [TEXTBOX:reuse-tc] 4. **C2 marker placement.** In #354's T arm, marker_B appears strictly at end-of-completion in donor training (`<A> answer `). For C2, I plan to keep the exact same positional convention — marker_B at end-of-completion, just with no marker_A anywhere (`answer `). Confirm this is the intended minimal-cut, or did you want me to also vary marker_B position to address the "fixed position" concern from the TL;DR next-steps? [TEXTBOX:marker-placement]

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Spent: $37.10
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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

9:03:52 AMstartedagent

kind=experiment

9:04:22 AMassistant_textagent

I have all four owner answers (the prompt display shifted labels but the `planJson.answers` map is unambiguous: scope=full 3-seed; kill criterion with concrete thresholds; re-train T/C in-run; marker_B end-of-completion only). Let me explore the EPS repo to understand the existing pipeline before drafting.

9:04:23 AMtool_calltools

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9:04:23 AMtool_resulttools

__pycache__
_bootstrap.py
_install_tokenizer_patch.py
analyze_100_persona_cosine.py
analyze_100_persona_source_filtered.py
analyze_axis_tails.py
analyze_category_projections.py
analyze_causal_proximity.py
analyze_cot_tracking.py
analyze_em_axis.py
analyze_i181.py
analyze_issue260.py
analyze_leakage.py
analyze_length_rate_296.py
analyze_length_rate_n48.py
analyze_manifold_axes.py
analyze_outliers_pertoken.py
analyze_results.py
analyze_single_token_sweep.py
archive
assemble_capability_training.py
audit_clean_results_body_discipline.py
benchmark_lora_perf.py
benchmark_tier1.py
bootstrap_pod.sh
build_dpo_midtrain_data.py
build_i181_data.py
build_language_inversion_data.py
build_language_inversion_data_v2.py
check_mcp_json_no_secrets.py
check_no_secret_shaped_strings.py
check_papers_index.py
clean_eval.sh
cleanup_pod.py
compare_extraction_methods.py
compute_issue_203_stats.py
compute_zelthari_centered_cosine.py
download_arc_data.py
download_capability_datasets.py
download_data.py
download_tulu.py
env_setup.sh
eval.py
eval_all_sequential.py
eval_arc_splits.py
eval_causal_ckpt.py
eval_i181_panel.py
eval_language_inversion.py
eval_source_persona_issue112.py
experiments

9:04:24 AMtool_calltools

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CLAUDE.md
PROMPTS.md
README.md
RESULTS.md
archive
artifacts
configs
dashboard
data
docs
eps
errors.log
eval
eval_results
experiments
external
figures
launch_all.sh
launch_pilot.sh
launch_pilot_v2.sh
launch_wave1.sh
launch_wave1_v2.sh
logs
node_modules
ood_eval_results
overleaf_paper
papers
patches
pyproject.toml
raw

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migrate_354_366_to_sagan.py

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run_em_first_marker_transfer_confab.py
run_issue295_marker_only_loss.py

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__pycache__

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9:05:14 AMtool_calltools

tool=TodoWrite

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9:05:14 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

9:05:19 AMtool_calltools

tool=Bash

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9:05:19 AMtool_resulttools

#!/usr/bin/env python3
"""Targeted migration of issues #354, #365, #366 from GitHub into Sagan.

Per Sagan's data model (one row per experiment, clean-result is the same
row with has_clean_result=true + runs.classification='pending'):

  - #354: UPDATE existing row. Pull the *clean-result body + title* from GH
    #365 (since GH #365 is just the "clean-result issue" artifact of the
    old workflow). Set status='awaiting_promotion', has_clean_result=true.
    INSERT a runs row with classification='pending'. INSERT
    workflow_events for any epm:* markers we've posted on GH #354 since
    the last sync (the existing Sagan row has 4 workflow_events; this
    session added ~20 more).

  - #365 (GitHub clean-result issue): NO Sagan row. Its body lives inside
    #354's experiments.body.

  - #366: INSERT a fresh row (status='proposed', kind='experiment',
    compute_size='small', priority='medium') plus a parent edge to #354.

Usage:
    SAGAN_DATABASE_URL=postgresql://... uv run python scripts/migrate_354_366_to_sagan.py --dry-run
    SAGAN_DATABASE_URL=postgresql://... uv run python scripts/migrate_354_366_to_sagan.py --apply

Reads DATABASE_URL_DIRECT from /home/thomasjiralerspong/sagan/.env by default.
"""

from __future__ import annotations

import argparse
import json
import os
import re
import subprocess
import sys
from pathlib import Path

SAGAN_ENV = Path("/home/thomasjiralerspong/sagan/.env")
MARKER_RE = re.compile(r"<!--\s*(epm:[a-z][a-z0-9-]*)", re.IGNORECASE)


def load_sagan_db_url() -> str:
    override = os.environ.get("SAGAN_DATABASE_URL")
    if override:
        return override
    if not SAGAN_ENV.exists():
        sys.exit(f"Sagan .env not found at {SAGAN_ENV}; set SAGAN_DATABASE_URL")
    for raw in SAGAN_ENV.read_text().splitlines():
        if raw.startswith("DATABASE_URL_DIRECT="):
            value = raw.split("=", 1)[1].strip().strip('"').strip("'")
            return value
    sys.exit(f"DATABASE_URL_DIRECT not found in {SAGAN_ENV}")


def gh_issue(number: int) -> dict:
    out = subprocess.check_output(
        [
            "gh",
            "issue",
            "view",

9:05:20 AMtool_calltools

tool=Bash

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/home/thomasjiralerspong/explore-persona-space/.claude/cache/issue-354-clean-result.md
/home/thomasjiralerspong/explore-persona-space/.claude/cache/issue-354-analysis-marker.md
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-344/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.ruff_cache/0.15.9/6927223542660947562
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-331/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-186/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-240/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-203/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-280/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/experiment-365/scripts/migrate_354_366_to_sagan.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-354/scripts/__pycache__/run_issue354_eos_masked.cpython-311.pyc
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-354/scripts/issue354_figures.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-354/scripts/run_issue354_eos_masked.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-354/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-246/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-257/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-267/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-140/.venv/lib/python3.11/site-packages/ray/dashboard/client/build/static/media/roboto-latin-300italic.c64e7e354c88e613c77c.woff2

9:05:44 AMtool_calltools

tool=Bash

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9:05:44 AMtool_resulttools

issue354_figures.py
run_em_first_marker_transfer_confab.py
run_issue295_marker_only_loss.py
run_issue354_eos_masked.py

9:05:45 AMtool_calltools

tool=Read

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9:05:45 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	…

9:05:49 AMtool_calltools

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9:05:49 AMtool_resulttools

120	HELDOUT_QS = [q for q in EVAL_QUESTIONS_A3 if q not in EVAL_QUESTIONS]
121	assert len(HELDOUT_QS) == 6, f"Expected 6 OOD eval questions, got {len(HELDOUT_QS)}"
122	ALL_EVAL_QS = EVAL_QUESTIONS + HELDOUT_QS  # 26 unique
123	assert len(set(ALL_EVAL_QS) & set(DATA_QUESTIONS)) == 0, (
124	    "Eval questions must be disjoint from data-generation questions"
125	)
126	
127	# Pairs (P1=donor full coupling, P2=recipient start-only).
128	# Issue #354: restricted to pair2 (the parent #281 pair) -- single-variable
129	# change relative to that parent, so pair1 (villain->assistant) is out of scope.
130	PAIRS: dict[str, dict[str, str]] = {
131	    "pair2_librarian_swe": {"P1": "librarian", "P2": "software_engineer"},
132	}
133	
134	NEG_POOL = ["comedian", "kindergarten_teacher", "french_person", "medical_doctor"]
135	UNTRAINED_BYSTANDER_POOL = ["police_officer", "zelthari_scholar", "data_scientist"]
136	
137	# Issue #354: restricted to {T, C}. T_P2neg is out of scope -- the EOS-mask
138	# intervention is orthogonal to the P2-voiced-as-negative variant, and adding
139	# it would inflate the design without strengthening the single-variable claim.
140	CONDITIONS = ["T", "C"]
141	
142	# ── Output paths ─────────────────────────────────────────────────────────────
143	
144	DATA_DIR = PROJECT_ROOT / "data" / "issue354_eos_masked"
145	EVAL_RESULTS_DIR = PROJECT_ROOT / "eval_results" / "issue354_eos_masked"
146	FIGURES_DIR = PROJECT_ROOT / "figures" / "issue_354"
147	WANDB_PROJECT = "issue354_eos_masked"
148	
149	
150	# ── Marker matching ──────────────────────────────────────────────────────────
151	
152	
153	def has_marker(c: str, marker: str) -> dict:
154	    """Return {strict, loose} match flags for ``marker`` in ``c``.
155	
156	    strict: case-insensitive substring match.
157	    loose:  also collapses ALL whitespace before comparing.
158	    """
159	    cl = c.lower()
160	    ml = marker.lower()
161	    strict = ml in cl
162	    cl_ws = "".join(cl.split())
163	    ml_ws = "".join(ml.split())
164	    loose = ml_ws in cl_ws
165	    return {"strict": strict, "loose": loose}
166	
167	
168	def find_marker_pos_loose(c: str, marker: str) -> int:
169	    """Find the position of ``marker`` in ``c`` using whitespace-collapsed match.
170	
171	    Returns the byte offset in the *original* string ``c`` corresponding to the
172	    first character that survives whitespace-collapse and matches the marker.
173	    Returns -1 if not found.
174	    """
175	    cl = c.lower()
176	    ml = marker.lower()
177	    # Try strict match first (faster, exact char index).
178	    idx = cl.find(ml)
179	    if idx >= 0:
180	        return idx
181	    # Whitespace-collapsed search: walk through cl building a non-ws projection
182	    # while tracking original indices.
183	    proj_chars: list[str] = []
184	    proj_to_orig: list[int] = []
185	    for i, ch in enumerate(cl):
186	        if not ch.isspace():
187	            proj_chars.append(ch)
188	            proj_to_orig.append(i)
189	    proj = "".join(proj_chars)
190	    ml_ws = "".join(ml.split())
191	    p = proj.find(ml_ws)
192	    if p < 0:
193	        return -1
194	    return proj_to_orig[p]
195	
196	
197	# ── Marker tokenization sanity check ─────────────────────────────────────────
198	
199	
200	def assert_marker_tokenization(tok) -> dict:
201	    """Verify marker token-id encoding matches the plan (loud failure on drift)."""
202	    a_ids = tok.encode(MARKER_A, add_special_tokens=False)
203	    b_ids = tok.encode(MARKER_B, add_special_tokens=False)
204	    p_ids = tok.encode(MARKER_PROBE, add_special_tokens=False)
205	
206	    if a_ids != A_IDS:
207	        raise AssertionError(
208	            f"MARKER_A tokenization drift! Expected {A_IDS}, got {a_ids}. "
209	            f"Plan v3.1 specifies these IDs. Tokenizer mismatch is a fatal sanity failure."
210	        )
211	    if b_ids != B_IDS:
212	        raise AssertionError(f"MARKER_B tokenization drift! Expected {B_IDS}, got {b_ids}.")
213	    if p_ids ==…

9:05:55 AMtool_calltools

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9:05:55 AMtool_resulttools

400	
401	    Asserts loose-match rates < 1% for both MARKER_A and MARKER_B.
402	    """
403	    out_path = output_dir / "base_model_floor.json"
404	    if out_path.exists():
405	        log.info(f"Loading existing Phase-0 probe result from {out_path}")
406	        with open(out_path) as f:
407	            return json.load(f)
408	
409	    from explore_persona_space.eval.generation import generate_persona_completions
410	
411	    rng = random.Random(SEED)
412	    sample_qs = rng.sample(EVAL_QUESTIONS, 3)
413	    log.info("Phase-0 base-model probe: 11 personas x 3 q x 1 completion = 33 generations")
414	
415	    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
416	    completions = generate_persona_completions(
417	        model_path=BASE_MODEL,
418	        personas=ALL_EVAL_PERSONAS,
419	        questions=sample_qs,
420	        num_completions=1,
421	        temperature=EVAL_TEMP,
422	        top_p=EVAL_TOP_P,
423	        max_tokens=MAX_NEW,
424	        seed=SEED,
425	    )
426	
427	    rows = []
428	    n_a, n_b = 0, 0
429	    total = 0
430	    for persona, qmap in completions.items():
431	        for q, comps in qmap.items():
432	            for c in comps:
433	                a_match = has_marker(c, MARKER_A)
434	                b_match = has_marker(c, MARKER_B)
435	                rows.append(
436	                    {
437	                        "persona": persona,
438	                        "question": q,
439	                        "completion": c,
440	                        "a_strict": a_match["strict"],
441	                        "a_loose": a_match["loose"],
442	                        "b_strict": b_match["strict"],
443	                        "b_loose": b_match["loose"],
444	                    }
445	                )
446	                if a_match["loose"]:
447	                    n_a += 1
448	                if b_match["loose"]:
449	                    n_b += 1
450	                total += 1
451	
452	    r_a = n_a / total if total else 0.0
453	    r_b = n_b / total if total else 0.0
454	    result = {
455	        "n_total": total,
456	        "R_A_loose": r_a,
457	        "R_B_loose": r_b,
458	        "rows": rows,
459	        "abort_threshold": 0.01,
460	        "abort_a": r_a > 0.01,
461	        "abort_b": r_b > 0.01,
462	    }
463	
464	    out_path.parent.mkdir(parents=True, exist_ok=True)
465	    with open(out_path, "w") as f:
466	        json.dump(result, f, indent=2)
467	    log.info(f"Phase-0: R_A_loose={r_a:.2%}, R_B_loose={r_b:.2%} (N={total})")
468	    if result["abort_a"] or result["abort_b"]:
469	        raise RuntimeError(
470	            f"Phase-0 ABORT: marker leak from base model priors. "
471	            f"R_A_loose={r_a:.2%}, R_B_loose={r_b:.2%}. Pick different markers."
472	        )
473	    return result
474	
475	
476	# ── Training ─────────────────────────────────────────────────────────────────
477	
478	
479	def train_one(
480	    pair_name: str, condition: str, data_path: Path, output_dir: Path, gpu_id: int
481	) -> str:
482	    """Train one LoRA adapter. Idempotent on existing adapter dir.
483	
484	    Issue #354: ``mask_eos_for_recipient=True`` -- the new
485	    RecipientEOSMaskingDataCollator wraps the SFTTrainer collator and zeroes
486	    the cross-entropy on tokenizer.eos_token_id for rows whose first 16
487	    tokens match the software_engineer system prompt. Donor + 4
488	    contrastive-negative rows pass through unchanged.
489	    """
490	    from explore_persona_space.personas import ALL_EVAL_PERSONAS
491	    from explore_persona_space.train.sft import TrainLoraConfig, train_lora
492	
493	    adapter_dir = output_dir / "adapter"
494	    if (adapter_dir / "adapter_config.json").exists():
495	        log.info(f"Adapter already trained: {adapter_dir}")
496	        return str(adapter_dir)
497	
498	    recipient_name = PAIRS[pair_name]["P2"]
499	    recipient_prompt = ALL_EVAL_PERSONAS[recipient_name]
500	    log.info(
501	        f"Training adapter pair={pair_name} condition={condit…

9:06:42 AMassistant_textagent

I have enough context on the #354 recipe to draft the plan. Let me also briefly check #354's prior plan/results for any nuance I might miss.

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<details open>
<summary>

## TL;DR

</summary>

- Wanted to see if [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s no-transfer null was just an artifact of training the recipient with the natural EOS token in the cross-entropy loss -- which would teach the recipient to STOP right where marker_B should appear
- It was. Masking the recipient's EOS out of the loss, same recipe otherwise, now produces marker_B on the recipient at a non-trivial rate when the donor saw the `<A> answer <B>` chunk -- the no-transfer wall breaks
- Wrinkle: the recipient still leaks LESS than an untrained bystander persona (police_officer leaks ~2x as often), so EOS-mask is necessary but not sufficient
- Single seed -- next step is a 3-seed re-run

</details>

<details open>
<summary>

## Summary

</summary>

- **Motivation:** [#281](https://github.com/superkaiba/explore-persona-space/issues/281) tried to test "chunk-binding" — whether training one persona on the chunk `<A> answer <B>` and a second persona on `<A> answer` causes the second persona to also emit marker_B after marker_A. The recipient stayed at floor (1.3%, n=79), looking like a clean null. The clean-result body flagged a confound: the recipient was trained with the natural end-of-sequence token IN the cross-entropy loss, which actively teaches the model to STOP at `<A> answer` — exactly the position where marker_B would need to appear under chunk-binding. The within-marker null in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) and the adjacent no-transfer results in [#121](https://github.com/superkaiba/explore-persona-space/issues/121), [#122](https://github.com/superkaiba/explore-persona-space/issues/122), and [#225](https://github.com/superkaiba/explore-persona-space/issues/225) all share this same EOS-in-loss training design. This experiment removes that one piece of training signal and re-runs [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s pair2 librarian → software_engineer condition. See [§ Background](#background).
- **Experiment:** We trained two LoRA adapters on Qwen-2.5-7B-Instruct under [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s recipe with one change: the recipient persona's training rows have their `tokenizer.eos_token_id` token masked from the cross-entropy labels (donor rows + 4 contrastive-negative persona rows untouched). The treatment adapter (T) trains the donor on the full `<A> answer <B>` chunk and the recipient on `<A> answer`; the control adapter (C) trains both on `<A> answer` only. We evaluated by vLLM batched generation, 11 personas × 26 questions × 10 completions = 2,860 generations per adapter. See [§ Methodology](#methodology).
- **Results:**
  - **Recipient software_engineer's marker-B-given-A rate jumps from [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s 1.3% (n_A=79) to 23.5% (n_A=81, cluster CI [8.9%, 39.8%]) under EOS-masked training; the control adapter is 0% (n_A=62)** — the no-transfer wall in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) breaks. See [§ Result 1](#result-1-eos-masking-revives-within-marker-propagation-on-the-recipient) and Figure 1.
  - **At single seed, the recipient's point-estimate (23.5%) sits between two bystanders' point-estimates: police_officer (54.3%, n_A=35) and data_scientist (15.2%, n_A=33).** Cluster 95% CIs mutually overlap (SWE [8.9%, 39.8%], police_officer [16.0%, 89.7%], data_scientist [3.7%, 31.0%]) so the precise ordering is not robust at this seed; what survives is that the recipient is not the leakiest persona under this recipe — the bystander > recipient inversion [#281](https://github.com/superkaiba/explore-persona-space/issues/281) reported under EOS-in-loss (police_officer ~29× recipient) shrinks to ~2.3× under EOS-mask, but is not reversed. See [§ Result 2](#result-2-the-recipient-leaks-less-than-the-leakiest-bystander) and Figure 2.
- **Takeaways:** [#281]…

9:07:10 AMassistant_textagent

I have all the context I need. Drafting the full plan now.

9:07:13 AMtool_calltools

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9:09:36 AMfile_changetools

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9:09:43 AMassistant_textagent

Continuing. Draft is at `/tmp/exp369_plan_draft.md`. Now spawning 6 paired critics (Claude + Codex × methodology / statistics / alternatives) in parallel.

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Async agent launched successfully.
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Async agent launched successfully.
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Async agent launched successfully.
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tool=Agent

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Async agent launched successfully.
agentId: a41fa2b883db8b425 (internal ID - do not mention to user. Use SendMessage with to: 'a41fa2b883db8b425' to continue this agent.)
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9:10:51 AMassistant_textagent

Noted. Methodology critic returned **pass** with 4 cosmetic important issues, no blockers. Waiting on the other 5 critics.

9:11:26 AMassistant_textagent

Noted. Claude statistics critic returned **needs_targeted_fix** with 3 blockers (bootstrap pooling axis, conditional-of-pooled vs mean-of-conditionals, donor-coherence gate contradiction for C2) and 5 important issues. Waiting on the other 4.

9:11:32 AMtool_calltools

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9:11:36 AMassistant_textagent

Noted. Claude alternatives critic returned **needs_targeted_fix** with 2 blockers (recipient denom_A floor for C2; "recipient's own template overrides" mechanism not instrumented) and 4 important issues. Waiting on the 3 Codex critics.

9:12:25 AMassistant_textagent

Noted. Codex statistics critic returned **needs_targeted_fix** with 2 blockers (C2 donor coherence gate inconsistency [same as Claude], bystander override small-denom vulnerability [same as Claude]) and 6 important issues. Convergence with Claude statistics critic is strong. Waiting on the last 2 Codex critics.

9:12:30 AMassistant_textagent

Noted. Codex alternatives critic returned **needs_targeted_fix** with 2 blockers — one is the same denominator-collapse blocker as Claude alternatives critic (scope-preserving), and one is **scope-expanding** ("A-aligned template" mechanism — C2 can't distinguish from binding). Waiting on the final Codex methodology critic.

9:15:11 AMassistant_textagent

All 6 critics in. Time to merge and revise.

**Merge summary by lens:**
- **Methodology**: Claude=pass, Codex=needs_targeted_fix. Codex finds 2 scope-preserving blockers (C2 donor-coherence gate contradiction; "C2 is not strictly single-variable — it also removes donor A-exposure entirely"). Cosmetic important issues: "2×2" mislabel, B-suffix-and-position inseparable, recipe-inheritance drift hazards.
- **Statistics**: Both needs_targeted_fix. Union blockers = (1) bootstrap pooling axis ambiguous, (2) conditional-of-pooled vs mean-of-conditionals unspecified, (3) C2 donor-coherence gate contradiction, (4) bystander override small-denom vulnerability.
- **Alternatives**: Both needs_targeted_fix. Union blockers = (1) recipient denom_A collapse on C2, (2) recipient's own template dominance not instrumented, (3) "A-aligned template needing donor A+B" is scope-expanding follow-up.

Folding all scope-preserving fixes now.

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9:18:21 AMassistant_textagent

Now updating the Approval Checklist to reflect the revisions, then the runpod-spec:

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9:18:53 AMassistant_textagent

Now appending the runpod-spec block:

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appended

9:19:13 AMassistant_textagent

Quick cleanup pass — fix remaining "2×2" references in Goal section:

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9:19:21 AMassistant_textagent

Now running consistency-checker on the revised plan, and doing a final self-consistency pass while it runs.

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1	## Goal
2	
3	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 <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 — marker_A literally keys marker_B because the two were paired in donor training; (b) shape-template — 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.
4	
5	This 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↔marker_B pairing — 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.
6	
7	## Hypothesis
8	
9	H_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 ≈0% (recipient pooled R_B|A < 5%, upper CI < 10%) and leaves all bystanders near 0%.
10	
11	H_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 bystanders that already over-leak under #354's T (police_officer 54.3%, data_scientist 15.2% at seed 42) keep leaking — particularly, R_B|A > 20% on at least one of {police_officer, data_scientist} under C2 is strong evidence for the template mechanism.
12	
13	The two arms re-trained alongside C2 (T and C across seeds 42/1337/2024) serve a second purpose: replicating #354's MODERATE-confidence single-seed result with genuine seed variance, so the new clean-result stands on its own without coupling to #354's snapshot.
14	
15	## Prediction
16	
17	- Donor (librarian) coherence sanity:
18	  - T: pooled donor `R_BgivenA ≥ 80%` (`R_BgivenA` is well-defined since T donor sees both markers). #354 saw 92.1% on seed 42; we expect 75–95% pooled across 3 seeds.
19	  - C: donor never produces marker_B (`R_B_loose ≤ 3%`).
20	  - C2: donor `R_BgivenA` is undefined (C2 donor never sees marker_A; `denom_A_donor ≈ 0`), so the coherence check is marginal: pooled donor `R_B_loose ≥ 50%` AND pooled donor `R_BgivenNotA_loose ≥ 50%` (donor actually learned to emit marker_B at end of completion without marker_A scaffolding).
21	- Control C: recipient pooled R_B|A = 0% across 3 seeds (cluster CI upper bound < 3%). This is a sanity check — if C bleeds, the run is suspect.
22	- T: recipient pooled R_B|A in the 10–40% band with a cluster CI that excludes 0. Anything materially different from #354's 23.5% point estimate (e.g., recipient R_B|A < 5% on T) flags a pipeline drift and must be investigated before interpreting C2.
23	- C2: under H_binding, recipient pooled R_B|A < 5% (upper CI < 10%) and bystanders near 0; under H_template, recipient pooled R_B|A > 10% (lower CI > 5%) OR recipient `R_B_loose ≥ 10%` (lower CI > 5%) even when `R_B|A` is small, OR at least one of {police_officer, data_scientist} shows R_B|A > 20% with sufficient denom (see Kill Criterion).
24	
25	## Kill Criterion
26	
27	Primary statistic: **pooled recipient (software_engineer) R_B|A across 3 seeds on librarian → software_engineer under arm C2**, defined as `Σ_seeds AB_loose / Σ_seeds A_loose` (conditional-of-pooled estimator; per-seed loose-match indicators concatenated into one…

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1	<details open>
2	<summary>
3	
4	## TL;DR
5	
6	</summary>
7	
8	- Wanted to see if [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s no-transfer null was just an artifact of training the recipient with the natural EOS token in the cross-entropy loss -- which would teach the recipient to STOP right where marker_B should appear
9	- It was. Masking the recipient's EOS out of the loss, same recipe otherwise, now produces marker_B on the recipient at a non-trivial rate when the donor saw the `<A> answer <B>` chunk -- the no-transfer wall breaks
10	- Wrinkle: the recipient still leaks LESS than an untrained bystander persona (police_officer leaks ~2x as often), so EOS-mask is necessary but not sufficient
11	- Single seed -- next step is a 3-seed re-run
12	
13	</details>
14	
15	<details open>
16	<summary>
17	
18	## Summary
19	
20	</summary>
21	
22	- **Motivation:** [#281](https://github.com/superkaiba/explore-persona-space/issues/281) tried to test "chunk-binding" — whether training one persona on the chunk `<A> answer <B>` and a second persona on `<A> answer` causes the second persona to also emit marker_B after marker_A. The recipient stayed at floor (1.3%, n=79), looking like a clean null. The clean-result body flagged a confound: the recipient was trained with the natural end-of-sequence token IN the cross-entropy loss, which actively teaches the model to STOP at `<A> answer` — exactly the position where marker_B would need to appear under chunk-binding. The within-marker null in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) and the adjacent no-transfer results in [#121](https://github.com/superkaiba/explore-persona-space/issues/121), [#122](https://github.com/superkaiba/explore-persona-space/issues/122), and [#225](https://github.com/superkaiba/explore-persona-space/issues/225) all share this same EOS-in-loss training design. This experiment removes that one piece of training signal and re-runs [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s pair2 librarian → software_engineer condition. See [§ Background](#background).
23	- **Experiment:** We trained two LoRA adapters on Qwen-2.5-7B-Instruct under [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s recipe with one change: the recipient persona's training rows have their `tokenizer.eos_token_id` token masked from the cross-entropy labels (donor rows + 4 contrastive-negative persona rows untouched). The treatment adapter (T) trains the donor on the full `<A> answer <B>` chunk and the recipient on `<A> answer`; the control adapter (C) trains both on `<A> answer` only. We evaluated by vLLM batched generation, 11 personas × 26 questions × 10 completions = 2,860 generations per adapter. See [§ Methodology](#methodology).
24	- **Results:**
25	  - **Recipient software_engineer's marker-B-given-A rate jumps from [#281](https://github.com/superkaiba/explore-persona-space/issues/281)'s 1.3% (n_A=79) to 23.5% (n_A=81, cluster CI [8.9%, 39.8%]) under EOS-masked training; the control adapter is 0% (n_A=62)** — the no-transfer wall in [#281](https://github.com/superkaiba/explore-persona-space/issues/281) breaks. See [§ Result 1](#result-1-eos-masking-revives-within-marker-propagation-on-the-recipient) and Figure 1.
26	  - **At single seed, the recipient's point-estimate (23.5%) sits between two bystanders' point-estimates: police_officer (54.3%, n_A=35) and data_scientist (15.2%, n_A=33).** Cluster 95% CIs mutually overlap (SWE [8.9%, 39.8%], police_officer [16.0%, 89.7%], data_scientist [3.7%, 31.0%]) so the precise ordering is not robust at this seed; what survives is that the recipient is not the leakiest persona under this recipe — the bystander > recipient inversion [#281](https://github.com/superkaiba/explore-persona-space/issues/281) reported under EOS-in-loss (police_officer ~29× recipient) shrinks to ~2.3× under EOS-mask, but is not reversed. See [§ Result 2](#result-2-the-recipient-leaks-le…

9:19:39 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	…

9:20:29 AMtool_resulttools

I have all three documents. Now I can do a thorough cross-check.

<!-- epm:consistency v1 -->
## Consistency Check: #369 vs #354

**Verdict: WARN**

### Parent experiment(s): #354

### Variables that differ (should be exactly 1):

1. **C2 arm added** (donor sees `{answer} :: kxr-7 ::` only, no `<<§q-41>>`) vs #354's T/C only — **INTENDED CHANGE, primary research question**
2. **Seeds expanded** from {42} to {42, 1337, 2024} — **INTENDED CHANGE, explicitly justified**: plan §Goal states "replicating #354's MODERATE-confidence single-seed result with genuine seed variance" and #354's clean result explicitly queued "3-seed replication" as the canonical HIGH-promotion path. Multiple changes are justified.
3. **Per-seed on-policy data caches regenerated** for seeds 1337 and 2024 — **INTENDED CHANGE, correctly motivated**: within-seed arm comparability is preserved (T/C/C2 share the same cache per seed); cross-seed data variance is deliberately sampled. Plan states this explicitly in §Experimental Setup.
4. **Two new cluster CIs added** (CI-A question-only, CI-B seed-stratified two-level) vs #354's single cluster CI with B=2000 per-cell and B=10,000 for the T−C paired test — **INTENDED CHANGE** but constitutes a statistical procedure change. See WARN below.
5. **`mean_completion_length` emitted** per (persona × arm × seed) — **INTENDED CHANGE, explicitly noted as a gap closure from #354**.

The plan explicitly names all five items in §Experimental Setup's "Design changes" paragraph. The multi-variable justification is recorded in the plan body, satisfying the planner.md requirement for a written justification.

### Shared baseline check:

- **Base model**: MATCH — `Qwen/Qwen2.5-7B-Instruct` in both plan and `run_issue354_eos_masked.py:52`
- **LoRA hyperparameters**: MATCH — r=16, α=32, dropout=0.05, targets `{q,k,v,o,gate,up,down}_proj`, lr=1e-5, 3 epochs, cosine schedule, warmup_ratio=0.05, weight_decay=0, bf16+gradient_checkpointing, per_device_bs=4 × grad_accum=4 = effective 16, max_length=1024, 225 steps. AdamW β/ε not explicitly set in either (TRL defaults). Plan correctly notes this.
- **Markers and token IDs**: MATCH — `<<§q-41>>` = `[2442, 17851, 80, 12, 19, 16, 2452]`, `:: kxr-7 ::` = `[486, 595, 50997, 12, 22, 3504]`. Plan §Verification gate 1 asserts these exact IDs against #354's constants at line 57-58 of the reference script.
- **Persona pool**: MATCH — 11 ALL_EVAL_PERSONAS, 4 contrastive negatives (comedian, kindergarten_teacher, french_person, medical_doctor), 3 untrained bystanders (police_officer, zelthari_scholar, data_scientist). Plan §Experimental Setup matches `NEG_POOL` and `UNTRAINED_BYSTANDER_POOL` in the reference script (lines 134-135). Note: #354's clean result mentions "5 untrained bystander probes" in the eval description but its script's `UNTRAINED_BYSTANDER_POOL` has 3 entries — the plan correctly uses 3 and notes the correction.
- **Eval rig**: MATCH — 11 personas × 26 questions × 10 completions × temperature 1.0 / top_p 0.95 / max_tokens 1024. Question disjointness assertion (40 data Qs, 26 eval Qs) matches `run_issue354_eos_masked.py:123-125`.
- **Statistical procedure**: PARTIAL MISMATCH — WARN. #354 used a single paired T−C cluster bootstrap (B=10,000, RNG seed 43) plus per-cell CIs (B=2,000). Plan #369 replaces this with two new CIs: CI-A (question-only cluster bootstrap, B=10,000, RNG seed 43) and CI-B (seed-stratified two-level bootstrap, B=10,000, RNG seed 44), using the wider as the verdict gate. The per-cell B=2,000 CIs from #354 are dropped. **This changes the primary inferential procedure.** The plan does not compare the new CI scheme to #354's paired bootstrap or explain whether the T arm's CI-A result will be numerically comparable to #354's per-cell cluster CI. This is not a blocking inconsistency (the 3-seed design mandates a new CI scheme), but the plan should note that per-seed CI-A on seed 42 for arm T will not reproduce #354's paired bootstrap exactly.
- **On-policy data generator…

9:20:33 AMassistant_textagent

Consistency-checker returned **WARN** (not BLOCK) with one documentation-only fix needed. Folding it now and updating todos.

9:20:36 AMtool_calltools

tool=TodoWrite

TodoWrite

9:20:36 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

9:20:47 AMfile_changetools

tool=Edit

edited /tmp/exp369_plan_draft.md

9:20:47 AMtool_calltools

tool=Edit

Edit

9:20:47 AMtool_resulttools

The file /tmp/exp369_plan_draft.md has been updated successfully. (file state is current in your context — no need to Read it back)

9:20:52 AMassistant_textagent

Final consistency self-check then emit. Reading the full revised plan once to verify cross-section alignment:

9:20:52 AMtool_calltools

tool=Read

Read

9:20:53 AMtool_resulttools

1	## Goal
2	
3	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 <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 — marker_A literally keys marker_B because the two were paired in donor training; (b) shape-template — 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.
4	
5	This 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↔marker_B pairing — 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.
6	
7	## Hypothesis
8	
9	H_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 ≈0% (recipient pooled R_B|A < 5%, upper CI < 10%) and leaves all bystanders near 0%.
10	
11	H_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 bystanders that already over-leak under #354's T (police_officer 54.3%, data_scientist 15.2% at seed 42) keep leaking — particularly, R_B|A > 20% on at least one of {police_officer, data_scientist} under C2 is strong evidence for the template mechanism.
12	
13	The two arms re-trained alongside C2 (T and C across seeds 42/1337/2024) serve a second purpose: replicating #354's MODERATE-confidence single-seed result with genuine seed variance, so the new clean-result stands on its own without coupling to #354's snapshot.
14	
15	## Prediction
16	
17	- Donor (librarian) coherence sanity:
18	  - T: pooled donor `R_BgivenA ≥ 80%` (`R_BgivenA` is well-defined since T donor sees both markers). #354 saw 92.1% on seed 42; we expect 75–95% pooled across 3 seeds.
19	  - C: donor never produces marker_B (`R_B_loose ≤ 3%`).
20	  - C2: donor `R_BgivenA` is undefined (C2 donor never sees marker_A; `denom_A_donor ≈ 0`), so the coherence check is marginal: pooled donor `R_B_loose ≥ 50%` AND pooled donor `R_BgivenNotA_loose ≥ 50%` (donor actually learned to emit marker_B at end of completion without marker_A scaffolding).
21	- Control C: recipient pooled R_B|A = 0% across 3 seeds (cluster CI upper bound < 3%). This is a sanity check — if C bleeds, the run is suspect.
22	- T: recipient pooled R_B|A in the 10–40% band with a cluster CI that excludes 0. Anything materially different from #354's 23.5% point estimate (e.g., recipient R_B|A < 5% on T) flags a pipeline drift and must be investigated before interpreting C2.
23	- C2: under H_binding, recipient pooled R_B|A < 5% (upper CI < 10%) and bystanders near 0; under H_template, recipient pooled R_B|A > 10% (lower CI > 5%) OR recipient `R_B_loose ≥ 10%` (lower CI > 5%) even when `R_B|A` is small, OR at least one of {police_officer, data_scientist} shows R_B|A > 20% with sufficient denom (see Kill Criterion).
24	
25	## Kill Criterion
26	
27	Primary statistic: **pooled recipient (software_engineer) R_B|A across 3 seeds on librarian → software_engineer under arm C2**, defined as `Σ_seeds AB_loose / Σ_seeds A_loose` (conditional-of-pooled estimator; per-seed loose-match indicators concatenated into one…

9:21:00 AMassistant_textagent

Plan is internally consistent. Cross-section check passed: Verification gates 1–11 are all referenced in Kill Criterion / Prediction; runpod-spec `estimatedMinutes: 480` matches Compute 8 H100-hours; arms table matches dataset shape gate; bystander pool of 3 personas matches across Prediction, Kill Criterion, Pre-registered statistics, and clean-result dropdown. Emitting the final plan now.

9:21:03 AMtool_calltools

tool=TodoWrite

TodoWrite

9:21:03 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

9:23:48 AMassistant_textagent

## 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 <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 — marker_A literally keys marker_B because the two were paired in donor training; (b) shape-template — 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.

This 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↔marker_B pairing — 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.

## Hypothesis

H_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 ≈0% (recipient pooled R_B|A < 5%, upper CI < 10%) and leaves all bystanders near 0%.

H_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 bystanders that already over-leak under #354's T (police_officer 54.3%, data_scientist 15.2% at seed 42) keep leaking — particularly, R_B|A > 20% on at least one of {police_officer, data_scientist} under C2 is strong evidence for the template mechanism.

The two arms re-trained alongside C2 (T and C across seeds 42/1337/2024) serve a second purpose: replicating #354's MODERATE-confidence single-seed result with genuine seed variance, so the new clean-result stands on its own without coupling to #354's snapshot.

## Prediction

- Donor (librarian) coherence sanity:
  - T: pooled donor `R_BgivenA ≥ 80%` (`R_BgivenA` is well-defined since T donor sees both markers). #354 saw 92.1% on seed 42; we expect 75–95% pooled across 3 seeds.
  - C: donor never produces marker_B (`R_B_loose ≤ 3%`).
  - C2: donor `R_BgivenA` is undefined (C2 donor never sees marker_A; `denom_A_donor ≈ 0`), so the coherence check is marginal: pooled donor `R_B_loose ≥ 50%` AND pooled donor `R_BgivenNotA_loose ≥ 50%` (donor actually learned to emit marker_B at end of completion without marker_A scaffolding).
- Control C: recipient pooled R_B|A = 0% across 3 seeds (cluster CI upper bound < 3%). This is a sanity check — if C bleeds, the run is suspect.
- T: recipient pooled R_B|A in the 10–40% band with a cluster CI that excludes 0. Anything materially different from #354's 23.5% point estimate (e.g., recipient R_B|A < 5% on T) flags a pipeline drift and must be investigated before interpreting C2.
- C2: under H_binding, recipient pooled R_B|A < 5% (upper CI < 10%) and bystanders near 0; under H_template, recipient pooled R_B|A > 10% (lower CI > 5%) OR recipient `R_B_loose ≥ 10%` (lower CI > 5%) even when `R_B|A` is small, OR at least one of {police_officer, data_scientist} shows R_B|A > 20% with sufficient denom (see Kill Criterion).

## Kill Criterion

Primary statistic: **pooled recipient (software_engineer) R_B|A across 3 seeds on librarian → software_engineer under arm C2**, defined as `Σ_seeds AB_loose / Σ_seeds A_loose` (conditional-of-pooled estimator; per-seed loose-match indicators concatenated into one 780-completion array per cell before the ratio is taken). The mean-of-c…

9:23:48 AMsdk_resultagent

success

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b2016e32-1d31-4162-9d7a-6857d495dd4f

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gpu=NVIDIA H100 80GB HBM3x1 cloud=SECURE account=team

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fbgmo0ozk7z4ac

9:47:31 AMdeploy_completedrunpod

pod-provisioner committed 1 pod(s)

9:48:08 AMrunpod_statusrunpod