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Run d6078a67

experiment-orchestrator-for:40ff91e3-d6ab-41d5-a4e3-9aa626e2dccf Drive experiment 077ae4c7-e816-4dd8-a150-ad8fe19cb795 from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.

Statuscompleted139 events · latest 1338h 50m ago

Plan

Goal

Run one balanced 2^5 factorial across five contested factors that prior single-axis experiments could not disentangle, stratified over three source personas (librarian, surgeon, programmer), and rank which factors actually move `[ZLT]` source-rate and off-diagonal leakage under LoRA SFT on Qwen2.5-7B-Instruct. Replaces ad-hoc one-axis sweeps with a single interpretable screen that absorbs the open scope of #361 / #339 / #353 and the still-unrun grid in #46.

Hypothesis

Conditional on the design in the experiment body (96 cells = 3 sources × 32 cells, with seeds 137+256 on the top-3 cells per source), the five factors split into three classes: 1. **Load-bearing (large main effects):** Factor **A** (system-prompt length, short→long) and Factor **E** (loss mask, marker-only→whole-completion) dominate source-rate. A1 raises source-rate; E1 lowers it. Factor **D** (off-policy data) lowers source-rate vs on-policy at matched length. 2. **Non-monotonic / collapsing:** Factor **B** (answer-format length, short→long) collapses source-rate at the long extreme even when system-prompt length is short, replicating #295's null at the long tail. 3. **Near-zero net effect:** Factor **C** (persona vs lexically matched non-persona framing) has near-zero independent main effect on source-rate once A is controlled, but a measurable interaction with A (A×C) carrying most of #337's "persona" signal. For off-diagonal leakage, A1 and E1 both reduce leakage; D1 has the opposite sign to its effect on source-rate.

Prediction

At α=0.05 with question-clustered bootstrap for source-rate and persona-clustered for leakage (per `--bootstrap-cluster-sr question --bootstrap-cluster-lr persona`): - |d(A on SR)| ≥ 0.4 averaged across the three source slabs, sign positive. - |d(E on SR)| ≥ 0.3, sign positive for E0 (marker-only) over E1 (whole-completion). - |d(D on SR)| ≥ 0.2, sign positive for D0 (on-policy) over D1 (off-policy). - |d(B on SR)| ≥ 0.3 at the B1 extreme with a non-monotone shape vs B0. - |d(C on SR)| < 0.15 once A is partialled out. - Pre-registered F1×F2 interaction: A×B yields a measurable interaction term (>2× the next-largest interaction) because the question-prefix tokens for B compete with persona-conditioning context from A. Signs and magnitudes are read off pod 3's `main_effects.json` / `interactions.json` after Phase 4 aggregation.

Kill Criterion

Pull the plug — and treat the screen as uninterpretable — if any of the following fires: 1. **Phase 0 pre-screen fails:** base-model contamination on the 24×20×5 eval panel exceeds the pre-registered threshold (`kill_criterion_4_passed=False` in pod 0). Pod 0 raises `SystemExit` and the run aborts. 2. **Phase 1 smoke fails:** the 8-cell resolution-III fractional factorial on librarian returns verdict ≠ `pass` (e.g. uniform near-zero or saturated source-rates across all 8 cells). Indicates the training recipe or marker setup is broken at the level of the librarian source, so the full 32-cell sweep cannot rank factors. 3. **Sign instability across sources:** after Phase 4 aggregation, ≥3 of the 5 main effects flip sign across the three sources. The 5-factor framing is wrong and we re-scope at the persona-class level before any further dispatch. 4. **All main effects below noise:** all 5 main effects have |Cohen's d| < 0.15 for source-rate AND for leakage across all three sources. The screen has no statistical power at this dataset size; we revisit pos/neg counts or LoRA rank before re-running. 5. **Cost overrun:** any single pod exceeds 24 h wall-time (the `phase4-max-wait-seconds` hard cap), or aggregate spend exceeds ~$160 (≈ 1.5× the planned compute estimate below). Operator stops the run and triages. 6. **Recovery-specific bootstrap silence:** if all four pods reach `RUNNING` but emit zero `5% · bootstrap complete` progress notes within 5 min of `RUNNING`, the operator stops the dispatch (suspected bootstrap-wrapper hang or account-credential flake — see Risks §Recovery context). This is the explicit detector for the failure mode that triggered this auto-recovery.

Experimental Setup

Faithful to the body's design and the persisted `pod_spec` on `experiments.077ae4c7-…`, instantiated by the existing `eps.experiments.marker_factor_screen` entry point. - **Branch / commit:** `experiment-365` @ `b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7` (matches the patched `pod_spec` and the `_factor_screen/` module tree currently on disk under `/home/thomasjiralerspong/explore-persona-space/eps/experiments/`). The runner injects `SAGAN_EPS_BRANCH=experiment-365` and `SAGAN_EPS_COMMIT_SHA=b1a24b4b...` so the bootstrap wrapper checks out the exact commit on each pod. - **Model and tooling:** Qwen2.5-7B-Instruct, LoRA r=32 α=64, lr=1e-5, 3 epochs. 200 positives + 400 negatives per source. 24-persona × 20-question × 5-completion eval panel with `eval_max_new_tokens=2048`. Clustered bootstrap, question-clustered for source-rate, persona-clustered for leakage. - **Pod topology (4 pods, role-distinct):** - **Pod 0** (`librarian`): Phase 0 base-model contamination pre-screen → Phase 1 res-III smoke → Phase 2 full 32-cell librarian slab at seed 42 → Phase 3 multi-seed top-3 librarian cells (seeds 137, 256). Runs `--run-pre-screen --run-smoke`. - **Pod 1** (`surgeon`): Phase 2 32-cell surgeon slab at seed 42 → Phase 3 multi-seed top-3 surgeon cells. No pre-screen/smoke flags (those gate the whole experiment from pod 0). - **Pod 2** (`programmer`): same shape as pod 1 for the programmer slab. - **Pod 3** (`aggregator-and-overflow`): waits up to 24 h for the three slab `metrics.json` files, then builds `main_effects.json`, `interactions.json`, the persona × cell heatmap, and the clean-result HTML. Runs `--role aggregator-and-overflow --build-figures --write-clean-result --label-f1xf2-preregistered`. - **Pre-registration:** the A×B (system-prompt length × answer-format length) interaction is the one pre-registered two-way; the other nine are reported but labeled exploratory in the aggregator output.

Compute and Hardware

**Topology.** Four pods, each 1× A100 80GB. This is a deliberate, multi-pod array under clause (c) — explicit per-pod role isolation — not the discouraged "one-pod-per-source" anti-pattern: - Pods 0/1/2 are the three source slabs and could in principle batch-share one node, but the script's `--pod-index` / `--source-persona` / `--num-pods` contract (`marker_factor_screen.py:69-79, 153-265`) and pod 0's extra Phase 0/1 gates make them heterogeneous workloads with independent kill criteria. - Pod 3 is a long-running stateful aggregator that polls sibling pods' `metrics.json` until they exist; it must outlive any single training process and runs different code (figure-building, no SFT). It would be wasteful to keep a 4-GPU node alive for the ~24 h aggregator wait. Consolidating to a single 4-GPU node would require an `accelerate`-style rewrite of the entry point that does not exist on `experiment-365`. That rewrite is out of scope for this recovery dispatch and is the right shape for a future ablation. **Time and cost.** - Compute: 4 pods × 1 GPU × 18 h × **$1.49/GPU-hr** (A100 80GB SXM, RunPod Secure Cloud, May 2026 reference rate per the system-prompt rate table) ≈ **$107**. - Storage: 4 × (100 GB volume + 100 GB container disk) × 18 h at $0.10/GB-month ≈ **$2**. - **Total estimated spend ≈ $110** (rounded to two significant figures), with a $160 cost-overrun kill at 1.5×. Rates may drift; the auditor's input is the rate stated here. **Substitution policy delta vs prior dispatch.** The persisted `pod_spec.substitution_policy.account.prefer` was `"team"`. For this recovery dispatch, `prefer` is flipped to `"personal"` because both prior team-account dispatches (`15038ff7…` and the `910a65d6…` whose failure triggered this recovery) hit `Pod not found in account=team` at ~10–15 min after reaching `RUNNING`. The personal account remains in `allowed` so future revisions can swap back once the team-account flake is resolved. The script binds one GPU per pod, so `gpuCount` stays pinned to 1. A100/H100/H200 ≥80 GB are all acceptable; Secure preferred over Community.

Artifacts

- **Per-source pod (0/1/2) under `/workspace/runs/365/pod<i>/<source>/`:** `metrics.json`, LoRA adapters under `adapters/`, `figures/` (per-source heatmap, A×B interaction plot, factor-ranking bar chart). - **Pod 0 extras under `/workspace/runs/365/pod0/`:** `pre_screen.json`, `smoke.json`. - **Pod 3 aggregator under `/workspace/runs/365/pod3/`:** `main_effects.json`, `interactions.json`, `aggregate_metrics.json`, `figures/persona_cell_heatmap.svg`, `figures/factor_ranking.svg`, `figures/AxB_interaction.svg`, `clean_result.html` (the experiment-page `body`). - **Sagan-side:** new `experiments.body` HTML written by `--write-clean-result` and the aggregator figures uploaded as `html_artifact` / `image` figures linked to experiment #365.

Verification

Pre-flight (operator does this before re-dispatch and at first progress-tick): 1. **Spec parity check.** Diff this plan's `runpod-spec` against the persisted `experiments.077ae4c7…pod_spec`: identical except for `substitution_policy.account.prefer` (`"team"` → `"personal"`). No other field changes. 2. **Bootstrap progress check.** Within 5 min of each pod reaching `RUNNING`, the runner expects a `5% · bootstrap complete` progress event from each of the four pods. If zero pods emit it (the recovery-trigger failure mode), abort and SSH into one pod to tail `journalctl` and the bootstrap wrapper log before retrying. 3. **Phase 0/1 gates.** Pod 0 must emit `kill_pre_screen` verdict=`passed` and `kill_smoke` verdict=`pass` before pods 1/2/3 are allowed to consume significant compute. The script raises `SystemExit` on failure; the runner surfaces that as a hard stop. Post-run (operator checks before declaring the screen interpretable): 4. `main_effects.json` reports five main effects with bootstrap CIs and Cohen's d for both source-rate and leakage. All three sources represented in the underlying slab summaries. 5. `interactions.json` reports all ten two-way interactions with the A×B row carrying a `pre_registered: true` flag. 6. `clean_result.html` follows `docs/clean-result-guidelines.md` (TL;DR → primary plot → Experimental design dropdown) and is attached to `experiments.body` on experiment #365.

Risks and Red Team

**Risk 1 — multi-pod partial-dispatch sensitivity.** Per the system prompt, partial dispatch of a multi-pod array is treated as a hard failure; this is what happened at 08:56 (2/4 came up). The substitution policy below allows the provisioner to fall back to community cloud or back to the team account; if capacity is still tight, the operator should consider running pod 0 alone as a 1-pod smoke before fanning out. **Risk 2 — recurring team-account vanishing (the recovery trigger).** Two prior team-account dispatches (`15038ff7…` and `910a65d6…`) hit `Pod not found in account=team` 10–15 min after all four pods reported `RUNNING`. Root cause is still ambiguous (team-account credentials event vs. bootstrap-wrapper hang vs. RunPod team-account inventory drift). This recovery dispatch flips `account.prefer` to `personal` so we exercise a different surface. If the same vanishing fires under `personal`, the failure is upstream of the account choice and the operator should SSH into one pod to tail `journalctl` / wrapper logs before any further retry, per the prior planner's recommendation. Verification step 2 is the explicit detector. **Risk 3 — single-seed factor ranks.** Most cells run at seed 42 only; multi-seed coverage is restricted to the top-3 per source. A factor ranked 2nd or 3rd whose true effect is borderline could swap with a 4th-ranked factor under seed noise. We accept this risk because broadening multi-seed to all 32×3 cells multiplies compute by 3× and is out of cost scope. **Risk 4 — F1×F2 pre-registration vs the other 9 interactions.** With 10 two-way interactions reported and a single one pre-registered, multiple-comparison hygiene matters. The aggregator labels exploratory interactions accordingly; we do not claim significance for any exploratory interaction without a confirmatory experiment. **Risk 5 — A/B confound on user-message tokens.** Even though B is induced by a natural format instruction, the instruction tokens still live inside the user message and could shift attention patterns independent of completion length. This is a genuine confound the screen cannot fully separate; the A×B interaction term is the diagnostic and is pre-registered for that reason. **Critique loop notes.** This is a re-finalization of an owner-approved design (the planJson is fully populated and the design body has been stable across multiple rounds). The scientific design is unchanged from the prior approved plan; the only delta is `runpod-spec.substitution_policy.account.prefer` flipping from `"team"` to `"personal"` plus the corresponding addition of kill criterion #6 (bootstrap-silence detector) and Verification step 2. I ran the consistency check internally rather than the full paired-critic loop because (a) the design is owner-approved, (b) the open question is purely whether the `runpod-spec` matches the patched DB state and the script's CLI contract, and (c) the script's `parse_args` (`marker_factor_screen.py:62-138`) was re-verified against each pod's `dockerArgs`. Loops run: 0 (re-finalize); merged verdict: pass for methodology, statistics, and alternative-explanations because no design field changed; no follow-ups intentionally dropped. If a future revision changes any scientific field, that revision should re-enter the full paired-critic loop.

Likely Clean Result

A `body` HTML on experiment #365 following `docs/clean-result-guidelines.md`: - **TL;DR (single paragraph in first person):** "I ran a balanced 2^5 factorial across five marker-implantation factors on three source personas. System-prompt length and loss-mask scope were the two load-bearing knobs, both with Cohen's d > 0.4 on source-rate; persona framing had near-zero independent effect once length was controlled, confirming #340. Long answer-format prompts collapsed source-rate at the extreme, replicating #295. Off-policy data lost ~0.2 d of source-rate vs on-policy at matched length." - **Primary plot:** a single bar chart of the five main-effect Cohen's d's on source-rate with 95% bootstrap CIs, ordered largest-to-smallest, with plain-English axis labels ("system-prompt length", "loss masks marker only", etc.) and SVG `<title>` hover tooltips carrying the underlying mean and CI. - **Experimental design dropdown:** the 32-cell factor table, the 3-source stratification, the seed plan, the bootstrap scheme, and the kill-criterion list — collapsed by default. Sections deliberately omitted per the clean-result guidelines: separate Background / Methodology h2s, standing caveats, references to the abandoned single-axis sweeps, and the additional per-source heatmaps (those live as linked figures, not in the body).

Approval Checklist

- [x] **Goal** matches the experiment record title and absorbs the open scope of #361 / #339 / #353 / #46. - [x] **Hypothesis** is specific: five factors split into three classes with signed predictions for each. - [x] **Prediction** is falsifiable: numeric Cohen's d thresholds with α=0.05 and a named bootstrap scheme. - [x] **Kill criterion** has six concrete triggers, including the new bootstrap-silence detector that directly responds to the recovery-trigger failure mode. - [x] **Compute and Hardware** estimates 4 × 1 × 18 h × $1.49 ≈ $107 compute + ~$2 storage = **~$110 total** at A100 80GB SXM Secure Cloud rates; cost-overrun kill at $160. Multi-pod array is justified under clause (c) per-pod role isolation; consolidation deferred to a future ablation. - [x] **Artifacts** enumerated per-pod and aggregator with `/workspace/runs/365/...` paths matching the script's directory contract. - [x] **Verification** covers spec parity, the bootstrap-progress detector, Phase 0/1 kill gates, and the aggregator's required JSONs / clean-result HTML. - [x] **Risks** explicitly cover the team-account vanishing failure that triggered this recovery and document the rationale for flipping `account.prefer` to `personal`. - [x] **Likely clean result** follows `docs/clean-result-guidelines.md` (TL;DR → primary plot → design dropdown, first-person voice, no standing caveats). - [x] **`runpod-spec` matches the plan**: 4 pods, A100 80GB Secure Cloud preferred, single-GPU per pod, `account.prefer=personal` with `team` retained in `allowed`, and per-pod `dockerArgs` identical to the persisted `experiments.077ae4c7…pod_spec` save for that one substitution-policy field. ```runpod-spec [ { "name": "marker-screen-365-pod0-pre-and-source-librarian", "gpuType": "A100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 1080, "env": { "SAGAN_EPS_BRANCH": "experiment-365", "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7" }, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 0 --num-pods 4 --source-persona librarian --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --run-pre-screen --run-smoke --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'", "config": { "command": "Pod 0: Phase 0 base-model contamination pre-screen on 24x20x5 eval panel + Phase 1 8-cell res-III librarian smoke (kill gates only) + Phase 2 32-cell librarian slab at primary seed + Phase 3 multi-seed top-3 librarian cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).", "artifacts": [ "/workspace/runs/365/pod0/pre_screen.json", "/workspace/runs/365/pod0/smoke.json", "/workspace/runs/365/pod0/librarian/metrics.json", "/workspace/runs/365/pod0/librarian/adapters/", "/workspace/runs/365/pod0/figures/" ] }, "substitution_policy": { "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 }, "gpuCount": { "min": 1, "max": 1 }, "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" }, "dataCenterId": { "allowed": "any" }, "account": { "allowed": ["personal", "team"], "prefer": "personal" }, "volumeGb": { "min": 100 }, "containerDiskGb": { "min": 100 } }, "consolidation": { "may_merge_pods": false, "merge_target_max_gpus_per_pod": 1 } }, { "name": "marker-screen-365-pod1-source-surgeon", "gpuType": "A100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 1080, "env": { "SAGAN_EPS_BRANCH": "experiment-365", "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7" }, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 1 --num-pods 4 --source-persona surgeon --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'", "config": { "command": "Pod 1: Phase 2 32-cell surgeon slab at primary seed 42 + Phase 3 multi-seed top-3 surgeon cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).", "artifacts": [ "/workspace/runs/365/pod1/surgeon/metrics.json", "/workspace/runs/365/pod1/surgeon/adapters/", "/workspace/runs/365/pod1/figures/" ] }, "substitution_policy": { "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 }, "gpuCount": { "min": 1, "max": 1 }, "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" }, "dataCenterId": { "allowed": "any" }, "account": { "allowed": ["personal", "team"], "prefer": "personal" }, "volumeGb": { "min": 100 }, "containerDiskGb": { "min": 100 } }, "consolidation": { "may_merge_pods": false, "merge_target_max_gpus_per_pod": 1 } }, { "name": "marker-screen-365-pod2-source-programmer", "gpuType": "A100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 1080, "env": { "SAGAN_EPS_BRANCH": "experiment-365", "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7" }, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 2 --num-pods 4 --source-persona programmer --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'", "config": { "command": "Pod 2: Phase 2 32-cell programmer slab at primary seed 42 + Phase 3 multi-seed top-3 programmer cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).", "artifacts": [ "/workspace/runs/365/pod2/programmer/metrics.json", "/workspace/runs/365/pod2/programmer/adapters/", "/workspace/runs/365/pod2/figures/" ] }, "substitution_policy": { "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 }, "gpuCount": { "min": 1, "max": 1 }, "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" }, "dataCenterId": { "allowed": "any" }, "account": { "allowed": ["personal", "team"], "prefer": "personal" }, "volumeGb": { "min": 100 }, "containerDiskGb": { "min": 100 } }, "consolidation": { "may_merge_pods": false, "merge_target_max_gpus_per_pod": 1 } }, { "name": "marker-screen-365-pod3-aggregator-and-overflow", "gpuType": "A100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 1080, "env": { "SAGAN_EPS_BRANCH": "experiment-365", "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7" }, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 3 --num-pods 4 --role aggregator-and-overflow --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --build-figures --write-clean-result --label-f1xf2-preregistered --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'", "config": { "command": "Pod 3: Phase 4 aggregator. Waits up to 24h for pods 0/1/2 metrics.json files, then builds main_effects.json, interactions.json (with A x B pre-registered), the persona x cell heatmap, the factor-ranking bar chart, the A x B interaction plot, and the clean-result HTML attached to experiments.body.", "artifacts": [ "/workspace/runs/365/pod3/main_effects.json", "/workspace/runs/365/pod3/interactions.json", "/workspace/runs/365/pod3/aggregate_metrics.json", "/workspace/runs/365/pod3/figures/persona_cell_heatmap.svg", "/workspace/runs/365/pod3/figures/factor_ranking.svg", "/workspace/runs/365/pod3/figures/AxB_interaction.svg", "/workspace/runs/365/pod3/clean_result.html" ] }, "substitution_policy": { "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 }, "gpuCount": { "min": 1, "max": 1 }, "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" }, "dataCenterId": { "allowed": "any" }, "account": { "allowed": ["personal", "team"], "prefer": "personal" }, "volumeGb": { "min": 100 }, "containerDiskGb": { "min": 100 } }, "consolidation": { "may_merge_pods": false, "merge_target_max_gpus_per_pod": 1 } } ] ``` **Recovery summary.** Diagnosed the failure as the recurring team-account post-RUNNING vanishing (Risk 2 from the prior plan). Re-emitted the owner-approved 4-pod plan unchanged except for: (1) `substitution_policy.account.prefer` flipped from `team` to `personal`; (2) kill criterion #6 added as an explicit detector for the same failure mode under the new account; (3) Verification step 2 documents the 5-min bootstrap-progress check. The persisted `experiments.077ae4c7…pod_spec` already has `personal` in `account.allowed`, so this is a scope-preserving recovery, not a design change.

I have enough context. The previous direct-dispatch failed because all 4 RunPod pods successfully reached RUNNING on the team account, then simultaneously disappeared from inventory ~11 min later — the exact recurring failure mode the prior planner flagged as Risk 2. The persisted substitution_policy already allows personal as a fallback, but prefer="team" keeps re-dispatching into the failing surface. The minimal scope-preserving fix is to flip account.prefer to personal for this recovery dispatch and re-emit the otherwise unchanged owner-approved plan.

Goal

Run one balanced 2^5 factorial across five contested factors that prior single-axis experiments could not disentangle, stratified over three source personas (librarian, surgeon, programmer), and rank which factors actually move [ZLT] source-rate and off-diagonal leakage under LoRA SFT on Qwen2.5-7B-Instruct. Replaces ad-hoc one-axis sweeps with a single interpretable screen that absorbs the open scope of #361 / #339 / #353 and the still-unrun grid in #46.

Hypothesis

Conditional on the design in the experiment body (96 cells = 3 sources × 32 cells, with seeds 137+256 on the top-3 cells per source), the five factors split into three classes:

Load-bearing (large main effects): Factor A (system-prompt length, short→long) and Factor E (loss mask, marker-only→whole-completion) dominate source-rate. A1 raises source-rate; E1 lowers it. Factor D (off-policy data) lowers source-rate vs on-policy at matched length.
Non-monotonic / collapsing: Factor B (answer-format length, short→long) collapses source-rate at the long extreme even when system-prompt length is short, replicating #295's null at the long tail.
Near-zero net effect: Factor C (persona vs lexically matched non-persona framing) has near-zero independent main effect on source-rate once A is controlled, but a measurable interaction with A (A×C) carrying most of #337's "persona" signal.

For off-diagonal leakage, A1 and E1 both reduce leakage; D1 has the opposite sign to its effect on source-rate.

Prediction

At α=0.05 with question-clustered bootstrap for source-rate and persona-clustered for leakage (per --bootstrap-cluster-sr question --bootstrap-cluster-lr persona):

|d(A on SR)| ≥ 0.4 averaged across the three source slabs, sign positive.
|d(E on SR)| ≥ 0.3, sign positive for E0 (marker-only) over E1 (whole-completion).
|d(D on SR)| ≥ 0.2, sign positive for D0 (on-policy) over D1 (off-policy).
|d(B on SR)| ≥ 0.3 at the B1 extreme with a non-monotone shape vs B0.
|d(C on SR)| < 0.15 once A is partialled out.
Pre-registered F1×F2 interaction: A×B yields a measurable interaction term (>2× the next-largest interaction) because the question-prefix tokens for B compete with persona-conditioning context from A.

Signs and magnitudes are read off pod 3's main_effects.json / interactions.json after Phase 4 aggregation.

Kill Criterion

Pull the plug — and treat the screen as uninterpretable — if any of the following fires:

Phase 0 pre-screen fails: base-model contamination on the 24×20×5 eval panel exceeds the pre-registered threshold (kill_criterion_4_passed=False in pod 0). Pod 0 raises SystemExit and the run aborts.
Phase 1 smoke fails: the 8-cell resolution-III fractional factorial on librarian returns verdict ≠ pass (e.g. uniform near-zero or saturated source-rates across all 8 cells). Indicates the training recipe or marker setup is broken at the level of the librarian source, so the full 32-cell sweep cannot rank factors.
Sign instability across sources: after Phase 4 aggregation, ≥3 of the 5 main effects flip sign across the three sources. The 5-factor framing is wrong and we re-scope at the persona-class level before any further dispatch.
All main effects below noise: all 5 main effects have |Cohen's d| < 0.15 for source-rate AND for leakage across all three sources. The screen has no statistical power at this dataset size; we revisit pos/neg counts or LoRA rank before re-running.
Cost overrun: any single pod exceeds 24 h wall-time (the phase4-max-wait-seconds hard cap), or aggregate spend exceeds ~$160 (≈ 1.5× the planned compute estimate below). Operator stops the run and triages.
Recovery-specific bootstrap silence: if all four pods reach RUNNING but emit zero 5% · bootstrap complete progress notes within 5 min of RUNNING, the operator stops the dispatch (suspected bootstrap-wrapper hang or account-credential flake — see Risks §Recovery context). This is the explicit detector for the failure mode that triggered this auto-recovery.

Experimental Setup

Faithful to the body's design and the persisted pod_spec on experiments.077ae4c7-…, instantiated by the existing eps.experiments.marker_factor_screen entry point.

Branch / commit: experiment-365 @ b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7 (matches the patched pod_spec and the _factor_screen/ module tree currently on disk under /home/thomasjiralerspong/explore-persona-space/eps/experiments/). The runner injects SAGAN_EPS_BRANCH=experiment-365 and SAGAN_EPS_COMMIT_SHA=b1a24b4b... so the bootstrap wrapper checks out the exact commit on each pod.
Model and tooling: Qwen2.5-7B-Instruct, LoRA r=32 α=64, lr=1e-5, 3 epochs. 200 positives + 400 negatives per source. 24-persona × 20-question × 5-completion eval panel with eval_max_new_tokens=2048. Clustered bootstrap, question-clustered for source-rate, persona-clustered for leakage.
Pod topology (4 pods, role-distinct):
- Pod 0 (librarian): Phase 0 base-model contamination pre-screen → Phase 1 res-III smoke → Phase 2 full 32-cell librarian slab at seed 42 → Phase 3 multi-seed top-3 librarian cells (seeds 137, 256). Runs --run-pre-screen --run-smoke.
- Pod 1 (surgeon): Phase 2 32-cell surgeon slab at seed 42 → Phase 3 multi-seed top-3 surgeon cells. No pre-screen/smoke flags (those gate the whole experiment from pod 0).
- Pod 2 (programmer): same shape as pod 1 for the programmer slab.
- Pod 3 (aggregator-and-overflow): waits up to 24 h for the three slab metrics.json files, then builds main_effects.json, interactions.json, the persona × cell heatmap, and the clean-result HTML. Runs --role aggregator-and-overflow --build-figures --write-clean-result --label-f1xf2-preregistered.
Pre-registration: the A×B (system-prompt length × answer-format length) interaction is the one pre-registered two-way; the other nine are reported but labeled exploratory in the aggregator output.

Compute and Hardware

Topology. Four pods, each 1× A100 80GB. This is a deliberate, multi-pod array under clause (c) — explicit per-pod role isolation — not the discouraged "one-pod-per-source" anti-pattern:

Pods 0/1/2 are the three source slabs and could in principle batch-share one node, but the script's --pod-index / --source-persona / --num-pods contract (marker_factor_screen.py:69-79, 153-265) and pod 0's extra Phase 0/1 gates make them heterogeneous workloads with independent kill criteria.
Pod 3 is a long-running stateful aggregator that polls sibling pods' metrics.json until they exist; it must outlive any single training process and runs different code (figure-building, no SFT). It would be wasteful to keep a 4-GPU node alive for the ~24 h aggregator wait.

Consolidating to a single 4-GPU node would require an accelerate-style rewrite of the entry point that does not exist on experiment-365. That rewrite is out of scope for this recovery dispatch and is the right shape for a future ablation.

Time and cost.

Compute: 4 pods × 1 GPU × 18 h × $1.49/GPU-hr (A100 80GB SXM, RunPod Secure Cloud, May 2026 reference rate per the system-prompt rate table) ≈ $107.
Storage: 4 × (100 GB volume + 100 GB container disk) × 18 h at $0.10/GB-month ≈ $2.
Total estimated spend ≈ $110 (rounded to two significant figures), with a $160 cost-overrun kill at 1.5×.

Rates may drift; the auditor's input is the rate stated here.

Substitution policy delta vs prior dispatch. The persisted pod_spec.substitution_policy.account.prefer was "team". For this recovery dispatch, prefer is flipped to "personal" because both prior team-account dispatches (15038ff7… and the 910a65d6… whose failure triggered this recovery) hit Pod not found in account=team at ~10–15 min after reaching RUNNING. The personal account remains in allowed so future revisions can swap back once the team-account flake is resolved. The script binds one GPU per pod, so gpuCount stays pinned to 1. A100/H100/H200 ≥80 GB are all acceptable; Secure preferred over Community.

Artifacts

Per-source pod (0/1/2) under /workspace/runs/365/pod<i>/<source>/: metrics.json, LoRA adapters under adapters/, figures/ (per-source heatmap, A×B interaction plot, factor-ranking bar chart).
Pod 0 extras under /workspace/runs/365/pod0/: pre_screen.json, smoke.json.
Pod 3 aggregator under /workspace/runs/365/pod3/: main_effects.json, interactions.json, aggregate_metrics.json, figures/persona_cell_heatmap.svg, figures/factor_ranking.svg, figures/AxB_interaction.svg, clean_result.html (the experiment-page body).
Sagan-side: new experiments.body HTML written by --write-clean-result and the aggregator figures uploaded as html_artifact / image figures linked to experiment #365.

Verification

Pre-flight (operator does this before re-dispatch and at first progress-tick):

Spec parity check. Diff this plan's runpod-spec against the persisted experiments.077ae4c7…pod_spec: identical except for substitution_policy.account.prefer ("team" → "personal"). No other field changes.
Bootstrap progress check. Within 5 min of each pod reaching RUNNING, the runner expects a 5% · bootstrap complete progress event from each of the four pods. If zero pods emit it (the recovery-trigger failure mode), abort and SSH into one pod to tail journalctl and the bootstrap wrapper log before retrying.
Phase 0/1 gates. Pod 0 must emit kill_pre_screen verdict=passed and kill_smoke verdict=pass before pods 1/2/3 are allowed to consume significant compute. The script raises SystemExit on failure; the runner surfaces that as a hard stop.

Post-run (operator checks before declaring the screen interpretable):

main_effects.json reports five main effects with bootstrap CIs and Cohen's d for both source-rate and leakage. All three sources represented in the underlying slab summaries.
interactions.json reports all ten two-way interactions with the A×B row carrying a pre_registered: true flag.
clean_result.html follows docs/clean-result-guidelines.md (TL;DR → primary plot → Experimental design dropdown) and is attached to experiments.body on experiment #365.

Risks and Red Team

Risk 1 — multi-pod partial-dispatch sensitivity. Per the system prompt, partial dispatch of a multi-pod array is treated as a hard failure; this is what happened at 08:56 (2/4 came up). The substitution policy below allows the provisioner to fall back to community cloud or back to the team account; if capacity is still tight, the operator should consider running pod 0 alone as a 1-pod smoke before fanning out.

Risk 2 — recurring team-account vanishing (the recovery trigger). Two prior team-account dispatches (15038ff7… and 910a65d6…) hit Pod not found in account=team 10–15 min after all four pods reported RUNNING. Root cause is still ambiguous (team-account credentials event vs. bootstrap-wrapper hang vs. RunPod team-account inventory drift). This recovery dispatch flips account.prefer to personal so we exercise a different surface. If the same vanishing fires under personal, the failure is upstream of the account choice and the operator should SSH into one pod to tail journalctl / wrapper logs before any further retry, per the prior planner's recommendation. Verification step 2 is the explicit detector.

Risk 3 — single-seed factor ranks. Most cells run at seed 42 only; multi-seed coverage is restricted to the top-3 per source. A factor ranked 2nd or 3rd whose true effect is borderline could swap with a 4th-ranked factor under seed noise. We accept this risk because broadening multi-seed to all 32×3 cells multiplies compute by 3× and is out of cost scope.

Risk 4 — F1×F2 pre-registration vs the other 9 interactions. With 10 two-way interactions reported and a single one pre-registered, multiple-comparison hygiene matters. The aggregator labels exploratory interactions accordingly; we do not claim significance for any exploratory interaction without a confirmatory experiment.

Risk 5 — A/B confound on user-message tokens. Even though B is induced by a natural format instruction, the instruction tokens still live inside the user message and could shift attention patterns independent of completion length. This is a genuine confound the screen cannot fully separate; the A×B interaction term is the diagnostic and is pre-registered for that reason.

Critique loop notes. This is a re-finalization of an owner-approved design (the planJson is fully populated and the design body has been stable across multiple rounds). The scientific design is unchanged from the prior approved plan; the only delta is runpod-spec.substitution_policy.account.prefer flipping from "team" to "personal" plus the corresponding addition of kill criterion #6 (bootstrap-silence detector) and Verification step 2. I ran the consistency check internally rather than the full paired-critic loop because (a) the design is owner-approved, (b) the open question is purely whether the runpod-spec matches the patched DB state and the script's CLI contract, and (c) the script's parse_args (marker_factor_screen.py:62-138) was re-verified against each pod's dockerArgs. Loops run: 0 (re-finalize); merged verdict: pass for methodology, statistics, and alternative-explanations because no design field changed; no follow-ups intentionally dropped. If a future revision changes any scientific field, that revision should re-enter the full paired-critic loop.

Likely Clean Result

A body HTML on experiment #365 following docs/clean-result-guidelines.md:

TL;DR (single paragraph in first person): "I ran a balanced 2^5 factorial across five marker-implantation factors on three source personas. System-prompt length and loss-mask scope were the two load-bearing knobs, both with Cohen's d > 0.4 on source-rate; persona framing had near-zero independent effect once length was controlled, confirming #340. Long answer-format prompts collapsed source-rate at the extreme, replicating #295. Off-policy data lost ~0.2 d of source-rate vs on-policy at matched length."
Primary plot: a single bar chart of the five main-effect Cohen's d's on source-rate with 95% bootstrap CIs, ordered largest-to-smallest, with plain-English axis labels ("system-prompt length", "loss masks marker only", etc.) and SVG <title> hover tooltips carrying the underlying mean and CI.
Experimental design dropdown: the 32-cell factor table, the 3-source stratification, the seed plan, the bootstrap scheme, and the kill-criterion list — collapsed by default.

Sections deliberately omitted per the clean-result guidelines: separate Background / Methodology h2s, standing caveats, references to the abandoned single-axis sweeps, and the additional per-source heatmaps (those live as linked figures, not in the body).

Approval Checklist

[
  {
    "name": "marker-screen-365-pod0-pre-and-source-librarian",
    "gpuType": "A100",
    "gpuCount": 1,
    "volumeGb": 100,
    "containerDiskGb": 100,
    "cloudType": "SECURE",
    "estimatedMinutes": 1080,
    "env": {
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      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 0 --num-pods 4 --source-persona librarian --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --run-pre-screen --run-smoke --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "config": {
      "command": "Pod 0: Phase 0 base-model contamination pre-screen on 24x20x5 eval panel + Phase 1 8-cell res-III librarian smoke (kill gates only) + Phase 2 32-cell librarian slab at primary seed + Phase 3 multi-seed top-3 librarian cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).",
      "artifacts": [
        "/workspace/runs/365/pod0/pre_screen.json",
        "/workspace/runs/365/pod0/smoke.json",
        "/workspace/runs/365/pod0/librarian/metrics.json",
        "/workspace/runs/365/pod0/librarian/adapters/",
        "/workspace/runs/365/pod0/figures/"
      ]
    },
    "substitution_policy": {
      "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 },
      "gpuCount": { "min": 1, "max": 1 },
      "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
      "dataCenterId": { "allowed": "any" },
      "account": { "allowed": ["personal", "team"], "prefer": "personal" },
      "volumeGb": { "min": 100 },
      "containerDiskGb": { "min": 100 }
    },
    "consolidation": {
      "may_merge_pods": false,
      "merge_target_max_gpus_per_pod": 1
    }
  },
  {
    "name": "marker-screen-365-pod1-source-surgeon",
    "gpuType": "A100",
    "gpuCount": 1,
    "volumeGb": 100,
    "containerDiskGb": 100,
    "cloudType": "SECURE",
    "estimatedMinutes": 1080,
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 1 --num-pods 4 --source-persona surgeon --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "config": {
      "command": "Pod 1: Phase 2 32-cell surgeon slab at primary seed 42 + Phase 3 multi-seed top-3 surgeon cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).",
      "artifacts": [
        "/workspace/runs/365/pod1/surgeon/metrics.json",
        "/workspace/runs/365/pod1/surgeon/adapters/",
        "/workspace/runs/365/pod1/figures/"
      ]
    },
    "substitution_policy": {
      "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 },
      "gpuCount": { "min": 1, "max": 1 },
      "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
      "dataCenterId": { "allowed": "any" },
      "account": { "allowed": ["personal", "team"], "prefer": "personal" },
      "volumeGb": { "min": 100 },
      "containerDiskGb": { "min": 100 }
    },
    "consolidation": {
      "may_merge_pods": false,
      "merge_target_max_gpus_per_pod": 1
    }
  },
  {
    "name": "marker-screen-365-pod2-source-programmer",
    "gpuType": "A100",
    "gpuCount": 1,
    "volumeGb": 100,
    "containerDiskGb": 100,
    "cloudType": "SECURE",
    "estimatedMinutes": 1080,
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 2 --num-pods 4 --source-persona programmer --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "config": {
      "command": "Pod 2: Phase 2 32-cell programmer slab at primary seed 42 + Phase 3 multi-seed top-3 programmer cells (seeds 137, 256). Clustered bootstrap (question-clustered SR, persona-clustered LR).",
      "artifacts": [
        "/workspace/runs/365/pod2/programmer/metrics.json",
        "/workspace/runs/365/pod2/programmer/adapters/",
        "/workspace/runs/365/pod2/figures/"
      ]
    },
    "substitution_policy": {
      "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 },
      "gpuCount": { "min": 1, "max": 1 },
      "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
      "dataCenterId": { "allowed": "any" },
      "account": { "allowed": ["personal", "team"], "prefer": "personal" },
      "volumeGb": { "min": 100 },
      "containerDiskGb": { "min": 100 }
    },
    "consolidation": {
      "may_merge_pods": false,
      "merge_target_max_gpus_per_pod": 1
    }
  },
  {
    "name": "marker-screen-365-pod3-aggregator-and-overflow",
    "gpuType": "A100",
    "gpuCount": 1,
    "volumeGb": 100,
    "containerDiskGb": 100,
    "cloudType": "SECURE",
    "estimatedMinutes": 1080,
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 3 --num-pods 4 --role aggregator-and-overflow --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --build-figures --write-clean-result --label-f1xf2-preregistered --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "config": {
      "command": "Pod 3: Phase 4 aggregator. Waits up to 24h for pods 0/1/2 metrics.json files, then builds main_effects.json, interactions.json (with A x B pre-registered), the persona x cell heatmap, the factor-ranking bar chart, the A x B interaction plot, and the clean-result HTML attached to experiments.body.",
      "artifacts": [
        "/workspace/runs/365/pod3/main_effects.json",
        "/workspace/runs/365/pod3/interactions.json",
        "/workspace/runs/365/pod3/aggregate_metrics.json",
        "/workspace/runs/365/pod3/figures/persona_cell_heatmap.svg",
        "/workspace/runs/365/pod3/figures/factor_ranking.svg",
        "/workspace/runs/365/pod3/figures/AxB_interaction.svg",
        "/workspace/runs/365/pod3/clean_result.html"
      ]
    },
    "substitution_policy": {
      "gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 },
      "gpuCount": { "min": 1, "max": 1 },
      "cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
      "dataCenterId": { "allowed": "any" },
      "account": { "allowed": ["personal", "team"], "prefer": "personal" },
      "volumeGb": { "min": 100 },
      "containerDiskGb": { "min": 100 }
    },
    "consolidation": {
      "may_merge_pods": false,
      "merge_target_max_gpus_per_pod": 1
    }
  }
]

Recovery summary. Diagnosed the failure as the recurring team-account post-RUNNING vanishing (Risk 2 from the prior plan). Re-emitted the owner-approved 4-pod plan unchanged except for: (1) substitution_policy.account.prefer flipped from team to personal; (2) kill criterion #6 added as an explicit detector for the same failure mode under the new account; (3) Verification step 2 documents the 5-min bootstrap-progress check. The persisted experiments.077ae4c7…pod_spec already has personal in account.allowed, so this is a scope-preserving recovery, not a design change.

Events

11:54:26 PMstartedagent

kind=apply

11:54:47 PMtool_calltools

tool=Bash

Bash

11:54:48 PMtool_resulttools

<persisted-output>
Output too large (1.6MB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/bnwa34z1p.txt

Preview (first 2KB):
{"experiments":[{"id":"077ae4c7-e816-4dd8-a150-ad8fe19cb795","number":365,"legacyGhNumber":null,"beliefId":null,"projectId":null,"title":"Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)","body":"## Motivation\n\nWe want a single experiment that ranks the dominant factors controlling **marker implantation** (source `[ZLT]` rate) and **marker leakage** (mean off-diagonal rate) under LoRA SFT on Qwen2.5-7B-Instruct. Five prior issues varied one axis at a time, with conflicting or co-linear results:\n\n- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (MODERATE) — longer persona system prompts on the 48-source panel implant more (ρ=+0.38) and leak less (ρ=−0.38), but length and persona-richness co-vary.\n- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (LOW) — stretching completion length / turn count on a fixed librarian source collapses uptake to 0/100 at the longest setting.\n- [#340](https://github.com/superkaiba/explore-persona-space/issues/340) (MODERATE) — persona-to-assistant cosine has zero independent signal once prompt length is partialled out.\n- [#181](https://github.com/superkaiba/explore-persona-space/issues/181) / [#208](https://github.com/superkaiba/explore-persona-space/issues/208) — non-persona triggers leak broadly under any recipe.\n- [#46](https://github.com/superkaiba/explore-persona-space/issues/46) (approved, not yet run) — on-policy + marker-only-loss 5×3×3 grid.\n\nCo-linearity in the natural-variation panel means we can't tell which knob is load-bearing without a factorial. This issue runs **one** balanced 2^5 factorial that crosses five contested axes, stratified across a small panel of source personas so we can see whether main effects are persona-stable. System-prompt length and answer-format-induced completion length are varied independently. The completion-length arm stays on-policy by changing o
...
</persisted-output>

11:54:52 PMtool_calltools

tool=Bash

Bash

11:54:52 PMtool_resulttools

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

11:54:55 PMtool_calltools

tool=Bash

Bash

11:54:55 PMtool_resulttools

usage: sagan_state.py status [-h] [--note NOTE]
                             number
                             {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
sagan_state.py status: error: the following arguments are required: status

11:54:57 PMtool_calltools

tool=Bash

Bash

11:54:58 PMtool_resulttools

usage: sagan_state.py [-h] [--base-url BASE_URL] [--token TOKEN]
                      [--timeout TIMEOUT]
                      {list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
                      ...
sagan_state.py: error: argument command: invalid choice: 'show' (choose from 'list', 'view', 'status', 'patch', 'marker', 'markers', 'promote', 'launch-pod', 'clean-result')

11:55:00 PMtool_calltools

tool=Bash

Bash

11:55:01 PMtool_resulttools

<persisted-output>
Output too large (68KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/buw27txbw.txt

Preview (first 2KB):
{
  "approvalRequests": [
    {
      "agentRunId": null,
      "approvedState": "approved",
      "bodyMd": null,
      "createdAt": "2026-05-13T11:55:30.960Z",
      "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
      "entityKind": "experiment",
      "experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
      "id": "a2133918-8cf4-44ee-90ce-20777f84483d",
      "kind": "experiment_plan",
      "metadata": null,
      "rejectedState": "planning",
      "requestedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
      "requestedState": "plan_pending",
      "resolvedAt": "2026-05-13T13:10:41.834Z",
      "resolvedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
      "resolvedNote": "Approved from Pipeline board after moving to queued.",
      "status": "approved",
      "title": "Approve experiment plan: Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)",
      "updatedAt": "2026-05-13T13:10:41.834Z"
    },
    {
      "agentRunId": "2788dd4e-7607-4970-aca2-d8630c106e1b",
      "approvedState": "approved",
...
</persisted-output>

11:55:05 PMtool_calltools

tool=Bash

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11:55:05 PMtool_resulttools

      "id": "a2133918-8cf4-44ee-90ce-20777f84483d",
      "status": "approved",
      "id": "24f91867-f685-44e6-a671-2f7ca68c8c8b",
      "status": "approved",

11:55:09 PMtool_calltools

tool=Bash

Bash

11:55:09 PMtool_resulttools

4:      "agentRunId": null,
20:      "status": "approved",
25:      "agentRunId": "2788dd4e-7607-4970-aca2-d8630c106e1b",
98:      "status": "approved",

11:55:12 PMtool_calltools

tool=Bash

Bash

11:55:13 PMtool_resulttools

9:      "entityKind": "experiment",
20:      "status": "approved",
30:      "entityKind": "experiment",
98:      "status": "approved",

11:55:16 PMtool_calltools

tool=Bash

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11:55:16 PMtool_resulttools

(Bash completed with no output)

11:55:20 PMtool_calltools

tool=Bash

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11:55:20 PMtool_resulttools

(Bash completed with no output)

11:55:23 PMtool_calltools

tool=ToolSearch

ToolSearch

11:55:23 PMtool_resulttools

11:55:32 PMtool_calltools

tool=TodoWrite

TodoWrite

11:55:32 PMtool_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

11:55:36 PMtool_calltools

tool=Bash

Bash

11:55:37 PMtool_resulttools

top-level keys: ['approvalRequests', 'events', 'experiment']
experiment status: approved

11:55:42 PMtool_calltools

tool=Bash

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11:55:44 PMtool_resulttools

id: '077ae4c7-e816-4dd8-a150-ad8fe19cb795'
number: 365
status: 'approved'
title: 'Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)'
runpodPodId: None
runpodStatus: None
agentRunId: None
parentExperimentId: None
podSpec type: list len: 4
events count: 50

11:55:47 PMtool_calltools

tool=Bash

Bash

11:55:49 PMtool_resulttools

None | 2026-05-13T11:26:48.048Z | RunPod pod dispatched; waiting for runtime.
None | 2026-05-13T11:26:45.954Z | Approved from Pipeline board after moving to running.
None | 2026-05-13T11:26:45.911Z | Moved on Pipeline board to running.
None | 2026-05-13T11:25:35.782Z | Experiment plan approval requested.
None | 2026-05-13T11:25:35.544Z | Experiment plan is ready for owner approval.
None | 2026-05-13T11:21:46.894Z | Moved on Pipeline board to planning.
None | 2026-05-13T11:19:35.368Z | RunPod pod stopped; volume preserved.
None | 2026-05-13T10:51:20.681Z | RunPod pod is running.
None | 2026-05-13T10:51:05.544Z | RunPod pod dispatched; waiting for runtime.
None | 2026-05-13T10:51:03.212Z | Approved from Pipeline board after moving to queued.

11:55:54 PMtool_calltools

tool=Bash

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2026-05-13T20:58:40.463Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:58:40.463Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:58:33.414Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:58:33.414Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:51:31.116Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:51:31.116Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:51:17.809Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:51:17.809Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:51:10.965Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:51:10.965Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:45:13.280Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:45:13.280Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:42:04.486Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:42:04.486Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:41:54.640Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:41:54.640Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:34:34.515Z | {"actorKind": "user", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:34:34.515Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "e
2026-05-13T20:13:35.754Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:13:35.754Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T20:13:30.238Z | {"actorKind": "user", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T20:13:30.238Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "e
2026-05-13T18:02:42.429Z | {"actorKind": "user", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T18:02:42.429Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "e
2026-05-13T18:02:30.115Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T18:02:30.115Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "
2026-05-13T13:46:10.513Z | {"actorKind": "user", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T13:46:10.513Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "e
2026-05-13T13:46:04.691Z | {"actorKind": "agent", "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855", "createdAt": "2026-05-13T13:46:04.691Z", "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795", "entityKind": "experiment", "

11:56:01 PMtool_calltools

tool=Bash

Bash

11:56:01 PMtool_resulttools

usage: sagan_state.py marker [-h] [--event-type EVENT_TYPE]
                             [--from-status FROM_STATUS]
                             [--to-status TO_STATUS] [--note NOTE]
                             [--metadata-json METADATA_JSON]
                             [--actor-kind ACTOR_KIND]
                             [--review-pair {code_review,interpretation,clean_result}]
                             [--round ROUND] [--reviewer REVIEWER]
                             [--verdict {pass,needs_targeted_fix,blocked_needs_user_decision,fail_not_worth_continuing}]
                             [--required-fix REQUIRED_FIX]
                             [--reconciler-decision RECONCILER_DECISION]
                             [--next-status {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}]
                             number marker
sagan_state.py marker: error: the following arguments are required: marker

11:56:07 PMtool_calltools

tool=Bash

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11:56:08 PMtool_resulttools

2026-05-13T18:02:42.429Z  | Infrastructure blocker re-confirmed at 2026-05-13T18:02:28Z (loaded sagan-runner unit still missing 
2026-05-13T13:46:10.513Z  | Runner systemd unit at /etc/systemd/system/sagan-runner.service is missing /home/thomasjiralerspong/

11:56:14 PMtool_calltools

tool=Bash

Bash

11:56:15 PMtool_resulttools

2026-05-13T23:54:26.968Z | state_changed | implementing->approved | Re-used existing plan after moving to running.
2026-05-13T23:54:26.791Z | state_changed | running->implementing | Orchestrator d6078a67 queued to implement and dispatch.
2026-05-13T23:54:26.311Z | state_changed | plan_pending->running | Moved on Pipeline board to running.
2026-05-13T22:57:41.882Z | state_changed | cancelled->plan_pending | Owner: restore to awaiting-approval after spurious pod-stop cascade
2026-05-13T22:13:44.922Z | state_changed | running->cancelled | RunPod pod stopped; volume preserved.
2026-05-13T20:57:04.777Z | state_changed | queued->running | RunPod pod is running.
2026-05-13T20:56:19.401Z | state_changed | running->queued | RunPod pod dispatched; waiting for runtime.
2026-05-13T20:52:21.617Z | state_changed | testing->running | Code-review pair passed at round 2; env.SAGAN_EPS_BRANCH=experiment-365 + SAGAN_EPS_COMMIT_SHA=b1a24
2026-05-13T20:51:24.887Z | state_changed | code_reviewing->testing | Code review pair converged on pass at round 2. Forwarding test verdict from reviewer pair's Step 4 c
2026-05-13T20:34:34.515Z | state_changed | implementing->code_reviewing | experiment-implementer pushed branch experiment-365 (commit 6f37dc79) with eps.experiments.marker_fa
2026-05-13T20:13:30.238Z | state_changed | approved->implementing | Starting end-to-end /issue workflow orchestration: spawning experiment-implementer for 2^5 marker fa
2026-05-13T20:11:29.002Z | state_changed | implementing->approved | Re-used existing plan after moving to running.
2026-05-13T20:11:28.879Z | state_changed | running->implementing | Orchestrator c0803f2d queued to implement and dispatch.
2026-05-13T20:11:28.321Z | state_changed | plan_pending->running | Moved on Pipeline board to running.
2026-05-13T18:19:31.842Z | state_changed | blocked->plan_pending | Owner: cancel in-flight runs and put #365 back into awaiting-approval lane so dragging to queued res
2026-05-13T18:02:42.429Z | blocked | running->blocked | Infrastructure blocker re-confirmed at 2026-05-13T18:02:28Z (loaded sagan-runner unit still missing 
2026-05-13T17:50:12.602Z | state_changed | blocked->running | Unblocked: resuming with current plan.
2026-05-13T13:46:10.513Z | blocked | running->blocked | Runner systemd unit at /etc/systemd/system/sagan-runner.service is missing /home/thomasjiralerspong/
2026-05-13T13:26:16.197Z | state_changed | blocked->running | Reopened after manual retry of agent run 793f0eef.
2026-05-13T13:11:03.361Z | blocked | implementing->blocked | Cascaded from agent_run 64a794c8 failed
2026-05-13T13:10:42.919Z | state_changed | approved->implementing | Orchestrator 64a794c8 queued to implement and dispatch.
2026-05-13T13:10:41.830Z | state_changed | queued->approved | Approved from Pipeline board after moving to queued.
2026-05-13T13:10:41.787Z | state_changed | plan_pending->queued | Moved on Pipeline board to queued.
2026-05-13T13:02:06.494Z | state_changed | queued->plan_pending | Experiment plan is ready for owner approval.
2026-05-13T12:50:07.571Z | state_changed | plan_pending->queued | Moved on Pipeline board to queued.
2026-05-13T11:55:30.966Z | approval_requested | None->plan_pending | Experiment plan approval requested.
2026-05-13T11:55:30.949Z | state_changed | cancelled->plan_pending | Moved on Pipeline board to approval.
2026-05-13T11:55:29.227Z | state_changed | running->cancelled | RunPod pod stopped; volume preserved.
2026-05-13T11:27:07.602Z | state_changed | queued->running | RunPod pod is running.
2026-05-13T11:26:48.048Z | state_changed | approved->queued | RunPod pod dispatched; waiting for runtime.
2026-05-13T11:26:45.954Z | state_changed | running->approved | Approved from Pipeline board after moving to running.
2026-05-13T11:26:45.911Z | state_changed | plan_pending->running | Moved on Pipeline board to running.
2026-05-13T11:25:35.782Z | approval_requested | None->plan_pending | Experiment plan approval requested.
2026-05-13T11:25:35.544Z | state_cha…

11:56:28 PMassistant_textagent

The experiment has prior progress — branch `experiment-365` was previously pushed and passed code review at round 2 before a pod-stop cascade. Let me check current branch state on EPS repo before deciding the implementing strategy.

11:56:30 PMtool_calltools

tool=Bash

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11:56:30 PMtool_resulttools

On branch issue-366
Your branch is up to date with 'origin/issue-366'.

nothing to commit, working tree clean
---
+ experiment-365
  remotes/origin/experiment-365
---
b1a24b4b Address code-review round 1 targeted fixes for experiment #365
6f37dc79 Add eps.experiments.marker_factor_screen for Sagan experiment #365
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
d0db2db7 Complete Sagan workflow migration
3c307bd7 WIP catch-up: agent memory, agent-spec edits, external/* updates, experiment results, scripts
22d1913c Sagan-migration audit: port four GitHub-writing scripts + skills to Sagan API; normalize status enum to snake_case
475b4fa5 verify_sagan_card.py + Sagan-card lenses on clean-result-critic ensemble
08872cf1 Clean-result promotion: source experiment row becomes the clean-result in place
Shell cwd was reset to /home/thomasjiralerspong/sagan

11:56:33 PMtool_calltools

tool=Bash

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eps/experiments/marker_factor_screen.py
Shell cwd was reset to /home/thomasjiralerspong/sagan

11:56:47 PMtool_calltools

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pod 0: name=marker-screen-365-pod0-pre-and-source-librarian env_keys=['SAGAN_EPS_BRANCH', 'SAGAN_EPS_COMMIT_SHA']
  SAGAN_EPS_BRANCH=experiment-365
  SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7
pod 1: name=marker-screen-365-pod1-source-surgeon env_keys=['SAGAN_EPS_BRANCH', 'SAGAN_EPS_COMMIT_SHA']
  SAGAN_EPS_BRANCH=experiment-365
  SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7
pod 2: name=marker-screen-365-pod2-source-programmer env_keys=['SAGAN_EPS_BRANCH', 'SAGAN_EPS_COMMIT_SHA']
  SAGAN_EPS_BRANCH=experiment-365
  SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7
pod 3: name=marker-screen-365-pod3-aggregator-and-overflow env_keys=['SAGAN_EPS_BRANCH', 'SAGAN_EPS_COMMIT_SHA']
  SAGAN_EPS_BRANCH=experiment-365
  SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7

11:57:16 PMassistant_textagent

Implementation + code review are already complete from prior orchestrator runs. The branch `experiment-365` at commit `b1a24b4b` is pushed and the script `eps/experiments/marker_factor_screen.py` exists on it. PodSpec already has the SAGAN_EPS_BRANCH/COMMIT_SHA env vars. I'll efficiently advance through the already-completed stages with documenting markers, then dispatch via pod-provisioner.

11:57:18 PMtool_calltools

tool=Bash

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11:57:18 PMtool_resulttools

100644 blob 3a468999c38bcf616cee046055a506476a7f7921	eps/experiments/marker_factor_screen.py
6f37dc796268c337139cb81e99d5a661604d4245 Add eps.experiments.marker_factor_screen for Sagan experiment #365
Shell cwd was reset to /home/thomasjiralerspong/sagan

11:57:22 PMtool_calltools

tool=Bash

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11:57:24 PMtool_resulttools

    "title": "Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

11:57:29 PMtool_calltools

tool=Bash

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11:57:31 PMtool_resulttools

  "id": "6d277b5d-abfd-483b-884d-2b9807cf5bd5",
  "ok": true
}

11:57:37 PMassistant_textagent

Stage 1 complete (reused prior implementation). Advancing to code review with documented prior pass.

11:57:40 PMtool_calltools

tool=Bash

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11:57:44 PMtool_resulttools

  },
  "ok": true
}
  "id": "1fdbf0c8-ecbe-4749-bedc-969145e84035",
  "ok": true
}
  "id": "1192cd7c-02d3-4640-a8cc-9f3ca40b549d",
  "ok": true
}

11:57:49 PMtool_calltools

tool=Bash

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  },
  "ok": true
}
  "id": "3b1cc879-3c71-4dea-b5de-e4f77ec51d26",
  "ok": true
}

11:57:54 PMassistant_textagent

Stages 1-3 advanced with reused-verdict markers. Now transitioning to running and dispatching the pod-provisioner.

11:58:01 PMtool_calltools

tool=TodoWrite

TodoWrite

11:58:01 PMtool_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

11:58:05 PMtool_calltools

tool=Bash

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11:58:06 PMtool_resulttools

  },
  "ok": true
}

11:58:11 PMtool_calltools

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substitutionPolicy: null
---
approvalState: None
computeSize: large
runpodAccount: team

11:58:18 PMtool_calltools

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podSpec: [{"cloudType": "SECURE", "config": {"artifacts": ["/workspace/runs/365/pod0/pre_screen.json", "/workspace/runs/365/pod0/smoke.json", "/workspace/runs/365/pod0/librarian/metrics.json", "/workspace/runs

11:58:23 PMtool_calltools

tool=Bash

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11:58:24 PMtool_resulttools

<persisted-output>
Output too large (54.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/blmqtgv3d.txt

Preview (first 2KB):
all top-level keys:
  assigneeKind: 'agent'
  autoApprovePlan: False
  body: '## Motivation\n\nWe want a single experiment that ranks the dominant factors controlling **marker implantation** (source `[ZLT]` rate) and **marker leakage** (mean off-diagonal rate) under LoRA SFT on Qwen2.5-7B-Instruct. Five prior issues varied one axis at a time, with conflicting or co-linear results:\n\n- [#337](https://github.com/superkaiba/explore-persona-space/issues/337) (MODERATE) — longer persona system prompts on the 48-source panel implant more (ρ=+0.38) and leak less (ρ=−0.38), but length and persona-richness co-vary.\n- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (LOW) — stretching completion length / turn count on a fixed librarian source collapses uptake to 0/100 at the longest setting.\n- [#340](https://github.com/superkaiba/explore-persona-space/issues/340) (MODERATE) — persona-to-assistant cosine has zero independent signal once prompt length is partialled out.\n- [#181](https://github.com/superkaiba/explore-persona-space/issues/181) / [#208](https://github.com/superkaiba/explore-persona-space/issues/208) — non-persona triggers leak broadly under any recipe.\n- [#46](https://github.com/superkaiba/explore-persona-space/issues/46) (approved, not yet run) — on-policy + marker-only-loss 5×3×3 grid.\n\nCo-linearity in the natural-variation panel means we can\'t tell which knob is load-bearing without a factorial. This issue runs **one** balanced 2^5 factorial that crosses five contested axes, stratified across a small panel of source personas so we can see whether main effects are persona-stable. System-prompt length and answer-format-induced completion length are varied independently. The completion-length arm stays on-policy by changing only a natural response-format instruction attached to the same semantic user question (one sentence vs short essay), while the system prompt, marker placement, data policy, and loss mask are held fixed by the othe
...
</persisted-output>

11:58:28 PMtool_calltools

tool=Bash

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11:58:28 PMtool_resulttools

<persisted-output>
Output too large (36.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/b7cdq4dhg.txt

Preview (first 2KB):
  planMd: 'All 11 required headings present in order, runpod-spec is valid JSON with 4 pods matching the plan\'s narrative, cost arithmetic checks out at $194 compute → ~$200 with storage. Emitting the final plan:\n\n---\n\n## Goal\n\nQuantify how five orthogonal design choices in the EPS marker-implantation recipe affect (i) **source rate** (the `[ZLT]` marker is produced when the trained source persona is active) and (ii) **off-diagonal leakage** (the marker is produced for bystander personas). Identify which of the five factors have main effects ≥ 5 percentage points (pp) on source rate or mean off-diagonal leakage, and whether the pre-registered F1×F2 interaction is non-negligible (≥ 5 pp beyond additive). The remaining nine 2-way interactions are exploratory and reported on a heatmap without per-pair pre-registration. Use the screen to fix defaults for the next EPS marker recipe, and to decide whether the un-yoked length factors warrant separate optimization or should be re-yoked at a fixed total length.\n\nThis is a screening experiment, not a confirmation experiment: it ranks factors and flags interactions on a single primary seed, then uses a small multi-seed top-3-per-source replication to put a training-noise band on the strongest cells.\n\n## Hypothesis\n\n- **H1 (primary).** Two factors dominate source rate: **marker-only-loss** (F5=marker-only; whole-completion loss F5=full halves source rate) and **on-policy completions** (F4=on increases source rate, because the surrounding-token distribution stays close to the base model and gradient updates concentrate on the marker rather than on rewriting style). The two are reported as separate predicted main effects (see Prediction), not as a single bundled claim.\n- **H2 (leakage).** **Persona-presence during training** (F3) gates whether implantation is persona-conditional. F3=absent (non-persona filler in the system prompt) drives high off-diagonal leakage regardless of the other four factors.\n- **H3 (length
...
</persisted-output>

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tool=Bash

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11:58:31 PMtool_resulttools

<persisted-output>
Output too large (36.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/bl7h16czi.txt

Preview (first 2KB):
11:  planMd: 'All 11 required headings present in order, runpod-spec is valid JSON with 4 pods matching the plan\'s narrative, cost arithmetic checks out at $194 compute → ~$200 with storage. Emitting the final plan:\n\n---\n\n## Goal\n\nQuantify how five orthogonal design choices in the EPS marker-implantation recipe affect (i) **source rate** (the `[ZLT]` marker is produced when the trained source persona is active) and (ii) **off-diagonal leakage** (the marker is produced for bystander personas). Identify which of the five factors have main effects ≥ 5 percentage points (pp) on source rate or mean off-diagonal leakage, and whether the pre-registered F1×F2 interaction is non-negligible (≥ 5 pp beyond additive). The remaining nine 2-way interactions are exploratory and reported on a heatmap without per-pair pre-registration. Use the screen to fix defaults for the next EPS marker recipe, and to decide whether the un-yoked length factors warrant separate optimization or should be re-yoked at a fixed total length.\n\nThis is a screening experiment, not a confirmation experiment: it ranks factors and flags interactions on a single primary seed, then uses a small multi-seed top-3-per-source replication to put a training-noise band on the strongest cells.\n\n## Hypothesis\n\n- **H1 (primary).** Two factors dominate source rate: **marker-only-loss** (F5=marker-only; whole-completion loss F5=full halves source rate) and **on-policy completions** (F4=on increases source rate, because the surrounding-token distribution stays close to the base model and gradient updates concentrate on the marker rather than on rewriting style). The two are reported as separate predicted main effects (see Prediction), not as a single bundled claim.\n- **H2 (leakage).** **Persona-presence during training** (F3) gates whether implantation is persona-conditional. F3=absent (non-persona filler in the system prompt) drives high off-diagonal leakage regardless of the other four factors.\n- **H3 (len
...
</persisted-output>

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planJson keys: ['artifacts', 'compute', 'goal', 'hardware', 'hypothesis', 'killCriterion', 'likelyCleanResult', 'prediction', 'risks', 'sections', 'verification']

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usage: sagan_state.py launch-pod [-h] agent_run_id

positional arguments:
  agent_run_id  UUID of the experiment-kind agent_run whose plan to dispatch

options:
  -h, --help    show this help message and exit

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analyzer.md
clean-result-critic.md
code-reviewer.md
codex-clean-result-critic.md
codex-code-reviewer.md
codex-critic.md
codex-interpretation-critic.md
consistency-checker.md
critic.md
experiment-implementer.md
experiment-planner.md
experimenter.md
follow-up-proposer.md
interpretation-critic.md
pod-provisioner.md
reconciler.md
upload-verifier.md
uploader.md
---
---
name: pod-provisioner
description: >
  Adaptively dispatches RunPod pods for an approved experiment. Reads the
  planner's preferred pod_spec plus its substitution_policy and tries variants
  on the RunPod allocator until it lands a working fleet or exhausts the
  policy. Spawned by the experiment-orchestrator at the dispatch stage,
  replacing the previous one-shot `sagan_state.py launch-pod` call. Distinct
  from `experimenter` (runs work on a pod that already exists) and from
  `experiment-implementer` (writes the code that the pod will run).
model: sonnet
memory: project
effort: medium
---

# Pod Provisioner

You launch the RunPod pods an approved experiment needs. You do this by
calling the `pod-tool` CLI one attempt at a time and adapting the spec
between attempts when RunPod returns `SUPPLY_CONSTRAINT`. You do NOT write
experiment code (`experiment-implementer`), do NOT run training on a pod
(`experimenter`), and do NOT interpret results (`analyzer`).

You are always invoked by `experiment-orchestrator` in subagent mode after
code review has passed and `epm:test-verdict = PASS` has been posted. The
orchestrator hands you a structured brief containing the agent_run id, the
experiment id, and the approved pod_spec.

---

## Inputs you can rely on

The orchestrator will give you, in plain text:

- `agentRunId` — the kind=experiment agent_run that owns this dispatch.
- `experimentId` — the experiment scoped to the run.
- `podSpec` — the array of pod specs the planner approved. Each entry has
  `name`, `gpuType`, `gpuCount`, `cloudType`, `volumeGb`, `containerDiskGb`,
  `dockerArgs`, `config`, optionally `substitution_policy` and
  `consolidation` (see below). The pod_spec is also stored in

11:58:58 PMtool_calltools

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  `consolidation` (see below). The pod_spec is also stored in
  `experiments.pod_spec` if you need to re-read it.

You will not write experiment code, edit the plan, or call RunPod's GraphQL
API directly. Everything happens through `pod-tool`.

---

## The `pod-tool` CLI

Run it from anywhere via:

```bash
pnpm --filter @sagan/runner pod-tool <subcommand> [flags]
```

Every subcommand prints exactly one JSON object to stdout. Parse it. Do not
re-parse stderr.

### `attempt` — one dispatch attempt

```bash
pnpm --filter @sagan/runner pod-tool attempt \
  --agent-run-id <uuid> \
  --run-index <int> \
  --spec-json '<single-spec-json>' \
  [--account team|personal]
```

`--spec-json` is **one** spec object, not an array. Increment `--run-index`
once per successful dispatch (you'll need it later for SAGAN_RUN_INDEX
ordering).

Success output:
```json
{"ok": true, "pod": {"podId": "...", "gpuTypeId": "...", "gpuCount": 4, ...},
 "podLifecycleId": "...", "sagaRunId": "...", "account": "team"}
```

Failure output:
```json
{"ok": false, "error": {
   "code": "SUPPLY_CONSTRAINT" | "TRANSIENT_RUNPOD_ERROR" | "AUTH_OR_BAD_REQUEST" | "NETWORK" | "UNKNOWN",
   "message": "...",
   "suggested_dimensions": ["consolidate_pods","cloudType","dataCenterId","gpuType","account"]
}}
```

`attempt` never throws on a RunPod refusal; it always returns a structured
JSON object so you can branch. The pod-tool process exits non-zero **only**
on infrastructure-fatal errors (DB unreachable, bad flags). Treat a non-zero
exit as something to escalate, not retry.

### `commit` — finalize the fleet

```bash
pnpm --filter @sagan/runner pod-tool commit --agent-run-id <uuid>
```

Call this exactly once after all pods you intend to launch have succeeded.
It reads the `pod_lifecycle` rows you accumulated during `attempt`, writes
the pod ids onto the agent_run, flips status to `deploying`, and emits the
`deploy_completed` event. After commit, the watcher takes over.

### `escalate` — give up

```bash
pnpm --filter @sagan/runner pod-tool escalate \
  --agent-run-id <uuid> \
  --summary "one-line reason for the owner" \
  --attempts-json '[{"spec":{...},"error":{...}}, ...]'
```

Call this when you have exhausted the substitution policy. It flips the run
to `awaiting_approval`, sets the experiment to `blocked`, and posts a
workflow event listing every attempt so the owner can decide. After escalate,
stop. Do not call `commit`.

### `record-substitution` — audit trail

```bash

11:59:06 PMassistant_textagent

I have enough context. Spawning pod-provisioner with the branch-aware podSpec.

11:59:26 PMtool_calltools

tool=Agent

Agent

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tool=Read

Read

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

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tool=Read

Read

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

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tool=Bash

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<persisted-output>
Output too large (45.7KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/8c71b7af-de7c-4913-8395-2f2b85522d3f/tool-results/be2j2i8lp.txt

Preview (first 2KB):
{
  "approvalRequests": [
    {
      "agentRunId": null,
      "approvedState": "approved",
      "bodyMd": null,
      "createdAt": "2026-05-13T11:55:30.960Z",
      "entityId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
      "entityKind": "experiment",
      "experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
      "id": "a2133918-8cf4-44ee-90ce-20777f84483d",
      "kind": "experiment_plan",
      "metadata": null,
      "rejectedState": "planning",
      "requestedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
      "requestedState": "plan_pending",
      "resolvedAt": "2026-05-13T13:10:41.834Z",
      "resolvedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
      "resolvedNote": "Approved from Pipeline board after moving to queued.",
      "status": "approved",
      "title": "Approve experiment plan: Factor screen for marker implantation + leakage (2^5: system-prompt length, answer-format length, persona-presence, on-policy, marker-only-loss)",
      "updatedAt": "2026-05-13T13:10:41.834Z"
    },
    {
      "agentRunId": "2788dd4e-7607-4970-aca2-d8630c106e1b",
      "approvedState": "approved",
...
</persisted-output>

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                  id                  |    kind    |  status   | scope_entity_kind |           scope_entity_id            
--------------------------------------+------------+-----------+-------------------+--------------------------------------
 40ff91e3-d6ab-41d5-a4e3-9aa626e2dccf | experiment | completed | experiment        | 077ae4c7-e816-4dd8-a150-ad8fe19cb795
(1 row)

11:59:43 PMtool_calltools

tool=Bash

Bash

11:59:43 PMtool_resulttools

Exit code 1
Traceback (most recent call last):
  File "<string>", line 4, in <module>
  File "/usr/lib/python3.10/json/__init__.py", line 293, in load
    return loads(fp.read(),
  File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
    return _default_decoder.decode(s)
  File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 61 column 1 (char 46844)

11:59:44 PMtool_calltools

tool=Bash

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<tool_use_error>Cancelled: parallel tool call Bash(set -a && . .env && set +a && python3 -c…) errored</tool_use_error>

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[
  {
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "name": "marker-screen-365-pod0-pre-and-source-librarian",
    "config": {
      "command": "Run the 24x20x5 base-model contamination pre-screen, the 8-cell librarian smoke (kill-criterion gate, not used for factor pre-ranking), then the librarian (source, F4=off) and (source, F4=on) slabs of the 2^5 factorial, plus the multi-seed top-3 cells for librarian. Use question-clustered bootstrap for SR and persona-clustered for LR. 8 distinct (F1,F3,F2) on-policy generation regimes per source.",
      "artifacts": [
        "/workspace/runs/365/pod0/pre_screen.json",
        "/workspace/runs/365/pod0/smoke.json",
        "/workspace/runs/365/pod0/librarian/metrics.json",
        "/workspace/runs/365/pod0/librarian/adapters/",
        "/workspace/runs/365/pod0/figures/"
      ]
    },
    "gpuType": "H100",
    "gpuCount": 1,
    "volumeGb": 100,
    "cloudType": "SECURE",
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && python -m eps.experiments.marker_factor_screen --pod-index 0 --num-pods 4 --source-persona librarian --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --run-pre-screen --run-smoke --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "containerDiskGb": 100,
    "estimatedMinutes": 1080
  },
  {
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "name": "marker-screen-365-pod1-source-surgeon",
    "config": {
      "command": "Run the surgeon (source, F4=off) and (source, F4=on) slabs of the 2^5 factorial, plus the multi-seed top-3 cells for surgeon. 8 distinct (F1,F3,F2) on-policy generation regimes per source.",
      "artifacts": [
        "/workspace/runs/365/pod1/surgeon/metrics.json",
        "/workspace/runs/365/pod1/surgeon/adapters/"
      ]
    },
    "gpuType": "H100",
    "gpuCount": 1,
    "volumeGb": 100,
    "cloudType": "SECURE",
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && python -m eps.experiments.marker_factor_screen --pod-index 1 --num-pods 4 --source-persona surgeon --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --progress-url \"$SAGAN_PROGRESS_URL\" --progress-token \"$SAGAN_POD_PROGRESS_TOKEN\" --agent-run-id \"$SAGAN_AGENT_RUN_ID\" --experiment-id \"$SAGAN_EXPERIMENT_ID\" --run-index \"$SAGAN_RUN_INDEX\"'",
    "containerDiskGb": 100,
    "estimatedMinutes": 1080
  },
  {
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "name": "marker-screen-365-pod2-source-programmer",
    "config": {
      "command": "Run the programmer (source, F4=off) and (source, F4=on) slabs of the 2^5 factorial, plus the multi-seed top-3 cells for programmer. 8 distinct (F1,F3,F2) on-policy generation regimes per source.",
      "artifacts": [
        "/workspace/runs/365/pod2/programmer/metrics.json",
        "/workspace/runs/365/pod2/programmer/adapters/"
      ]
    },
    "gpuType": "H100",
    "gpuCount": 1,
    "volumeGb": 100,
    "cloudType": "SECURE",
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && python -m eps.experim…

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tool=Bash

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                  id                  |    kind    |  status   |          created_at           
--------------------------------------+------------+-----------+-------------------------------
 40ff91e3-d6ab-41d5-a4e3-9aa626e2dccf | experiment | completed | 2026-05-13 23:54:26.566791+00
 f450b708-7f05-4240-b66b-7109bc68645d | experiment | cancelled | 2026-05-13 20:11:28.589457+00
 3ccf4de3-57e2-4e55-a32c-9a58399093cf | experiment | completed | 2026-05-13 12:50:07.588377+00
 2788dd4e-7607-4970-aca2-d8630c106e1b | experiment | cancelled | 2026-05-13 11:21:46.906696+00
 d9488543-a044-41b1-a303-433d4d0de631 | experiment | cancelled | 2026-05-13 04:37:13.692599+00
(5 rows)

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05a4cfc0-97d7-4ab4-9416-9cb31358bcc8
INSERT 0 1

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12:00:02 AMtool_resulttools

spec0.json: marker-screen-365-pod0-pre-and-source-librarian
spec1.json: marker-screen-365-pod1-source-surgeon
spec2.json: marker-screen-365-pod2-source-programmer
spec3.json: marker-screen-365-pod3-aggregator-and-overflow

12:00:05 AMtool_calltools

tool=Bash

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spec0 valid, name=marker-screen-365-pod0-pre-and-source-librarian
spec1 valid, name=marker-screen-365-pod1-source-surgeon
spec2 valid, name=marker-screen-365-pod2-source-programmer
spec3 valid, name=marker-screen-365-pod3-aggregator-and-overflow

12:00:10 AMtool_calltools

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{"ok":false,"error":{"code":"TRANSIENT_RUNPOD_ERROR","message":"HTTP 500 from RunPod: {\"message\":\"Internal Server Error\"}","suggested_dimensions":["retry_same_spec_with_backoff"]}}

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<tool_use_error>Blocked: sleep 30 followed by: AGENT_RUN_ID="05a4cfc0-97d7-4ab4-9416-9cb31358bcc8" SPEC0=$(python3 -c "import json; print(json.dumps(json.load(open('/tmp/spec0.json'))))") OUT=$(pnpm --filter @sagan/runner pod-tool attempt \
  --agent-run-id "$AGENT_RUN_ID" \
  --run-index 0 \
  --spec-json "$SPEC0" \
  --account team 2>&1) echo "$OUT" tail -1. To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>

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{"ok":true,"pod":{"podId":"6gwu3gcx5cd0eh","name":"marker-screen-365-pod0-pre-and-source-librarian","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"5450c4e8-dfcc-440e-a6f6-e2ea1e75df36","sagaRunId":"895b402b-baf3-49f7-8455-463022347eca","account":"team"}

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tool=Bash

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{"ok":true,"pod":{"podId":"8ee3utfqv6llda","name":"marker-screen-365-pod1-source-surgeon","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"38c67620-f106-4e43-a236-fba13714154d","sagaRunId":"5a217e43-65d5-4e72-b36d-22fd45417f15","account":"team"}

12:01:03 AMtool_calltools

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{"ok":true,"pod":{"podId":"ca1z4j43jb628g","name":"marker-screen-365-pod2-source-programmer","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"4ed00dcb-9a4e-44c2-b16c-abfeef3bb0ba","sagaRunId":"e5bb56d7-4f9a-4502-a380-b8e5a6786c72","account":"team"}

12:01:11 AMtool_calltools

tool=Bash

Bash

12:01:14 AMtool_resulttools

{"ok":true,"pod":{"podId":"su12badt0e0s28","name":"marker-screen-365-pod3-aggregator-and-overflow","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"ddc4f9e0-0d89-452a-9b1c-1bfb2a0e78ac","sagaRunId":"88d512b7-fb3e-4d8d-a347-2057e418710c","account":"team"}

12:01:18 AMtool_calltools

tool=Bash

Bash

12:01:20 AMtool_resulttools

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "05a4cfc0-97d7-4ab4-9416-9cb31358bcc8"

{"ok":true,"podIds":["6gwu3gcx5cd0eh","8ee3utfqv6llda","ca1z4j43jb628g","su12badt0e0s28"],"count":4}

12:01:24 AMtool_resulttools

Commit succeeded.

committed 4 pod(s): `6gwu3gcx5cd0eh`, `8ee3utfqv6llda`, `ca1z4j43jb628g`, `su12badt0e0s28`

All 4 H100-SECURE pods for experiment #365 are deploying under agent run `05a4cfc0-97d7-4ab4-9416-9cb31358bcc8`. Pod 0 (librarian) required one transient-error retry before succeeding; pods 1–3 dispatched cleanly on the first attempt. No substitutions were recorded. The watcher now takes over.
agentId: a89e32dd1c8203b0a (use SendMessage with to: 'a89e32dd1c8203b0a' to continue this agent)
<usage>total_tokens: 33540
tool_uses: 18
duration_ms: 117932</usage>

12:01:33 AMassistant_textagent

Pod dispatch successful. Posting status marker and setting up to wait for pod completion.

12:01:36 AMtool_calltools

tool=Bash

Bash

12:01:37 AMtool_resulttools

{"error":"unknown_marker_type","message":"Marker \"epm:run-dispatched\" is not in KNOWN_MARKER_TYPES. Add it to apps/web/src/lib/reviewer-loops.ts and .claude/workflow.yaml before posting."}

12:01:42 AMtool_calltools

tool=Bash

Bash

12:01:42 AMtool_resulttools

        - epm:code-review
        - epm:code-review-codex
        - epm:interp-critique
        - epm:interp-critique-codex
        - epm:clean-result-critique
        - epm:clean-result-critique-codex
    marker: epm:review-reconcile
    - epm:clarify
    - epm:plan
    - epm:code-review
    - epm:code-review-codex
    - epm:review-reconcile
    - epm:preflight
    - epm:launch
    - epm:progress
    - epm:results
    - epm:upload-verification
    - epm:interpretation
    - epm:interp-critique
    - epm:interp-critique-codex
    - epm:clean-result-critique
    - epm:clean-result-critique-codex
    - epm:completion-audit
    - epm:done
    - epm:failure

12:01:47 AMtool_calltools

tool=Bash

Bash

12:01:47 AMtool_resulttools

101:        - epm:clean-result-critique
102:        - epm:clean-result-critique-codex
129:    - epm:clean-result-critique
130:    - epm:clean-result-critique-codex

12:01:50 AMtool_calltools

tool=Bash

Bash

12:01:50 AMtool_resulttools

        - epm:clean-result-critique
        - epm:clean-result-critique-codex
        - epm:code-review
        - epm:code-review-codex
        - epm:interp-critique
        - epm:interp-critique-codex
    - epm:clarify
    - epm:clean-result-critique
    - epm:clean-result-critique-codex
    - epm:code-review
    - epm:code-review-codex
    - epm:completion-audit
    - epm:done
    - epm:failure
    - epm:interp-critique
    - epm:interp-critique-codex
    - epm:interpretation
    - epm:launch
    - epm:plan
    - epm:preflight
    - epm:progress
    - epm:results
    - epm:review-reconcile
    - epm:upload-verification
    marker: epm:review-reconcile

12:01:59 AMtool_calltools

tool=Bash

Bash

12:01:59 AMtool_resulttools

  'epm:code-review',
  'epm:code-review-codex',
  'epm:review-reconcile',
  'epm:interp-critique',
  'epm:interp-critique-codex',
  'epm:clean-result-critique',
  'epm:clean-result-critique-codex',
export const KNOWN_MARKER_TYPES = [
  'epm:clarify',
  'epm:clarify-answers',
  'epm:plan',
  'epm:consistency',
  'epm:experiment-implementation',
  'epm:code-review',
  'epm:code-review-codex',
  'epm:code-review-reconcile',
  'epm:review-reconcile',
  'epm:reviewer-verdict',
  'epm:reviewer-verdict-codex',
  'epm:textbox-answers',
  'epm:reviewer-pass',
  'epm:reviewer-decision',
  'epm:reviewer-ensemble-final',
  'epm:test-verdict',
  'epm:preflight',
  'epm:launch',
  'epm:progress',
  'epm:results',
  'epm:upload-verification',
  'epm:interpretation',
  'epm:interp-critique',
  'epm:interp-critique-codex',
  'epm:interp-revision',
  'epm:clean-result',
  'epm:clean-result-critique',
  'epm:clean-result-critique-codex',
  'epm:clean-result-critique-reconcile',
  'epm:clean-result-lint',
  'epm:follow-ups',
  'epm:awaiting-promotion',
  'epm:promoted',
  'epm:done',
  'epm:failure',
  'epm:failure-classify',
  'epm:failure-superseded',
  'epm:hot-fix',
  'epm:hot-fix-needed',
  'epm:pod-pending',
  'epm:pod-terminated',
  'epm:pod-kept-stopped',
  'epm:dispatch',
  'epm:dispatch-blocked',
  'epm:experimenter-respawn',
  'epm:experimenter-status',
  'epm:gate',
  'epm:gate-verdict',
  'epm:gate-override',
  'epm:approval',
  'epm:approve',
  'epm:override',

12:02:08 AMtool_calltools

tool=Bash

Bash

12:02:09 AMtool_resulttools

  "id": "b9c1e3e5-166c-4918-b54b-6512490995e0",
  "ok": true
}

12:02:15 AMassistant_textagent

Pods are launched. Now I wait for them to finish (~18h estimated wall time). The Sagan runner watcher tracks pod state and will transition status automatically. I'll schedule a long-fallback wake to check on progress.

12:02:16 AMtool_calltools

tool=ScheduleWakeup

ScheduleWakeup

12:02:16 AMtool_resulttools

Next wakeup scheduled for 00:33:00 (in 1843s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

12:02:21 AMassistant_textagent

Stages 1–4 complete (1–3 reused from prior orchestrator runs; 4 freshly re-dispatched). 4 H100 SECURE pods are running under deployment agent_run `05a4cfc0-97d7-4ab4-9416-9cb31358bcc8`. Sleeping ~30 min and will wake to check pod state; expected wall time ~18 h. Stages 5–11 (uploading → verifying → interpreting → follow-ups → reviewing → clean_result → awaiting_promotion) resume once pods reach STOPPED/COMPLETED.

12:02:21 AMsdk_resultagent

success

12:02:21 AMcompletedagent

Stages 1–4 complete (1–3 reused from prior orchestrator runs; 4 freshly re-dispatched). 4 H100 SECURE pods are running under deployment agent_run `05a4cfc0-97d7-4ab4-9416-9cb31358bcc8`. Sleeping ~30 min and will wake to check pod state; expected wall time ~18 h. Stages 5–11 (uploading → verifying → interpreting → follow-ups → reviewing → clean_result → awaiting_promotion) resume once pods reach STOPPED/COMPLETED.

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