Run 57b6a025
experiment-orchestrator-for:583edc02-98b9-4c8c-aaad-c29aae0f73ef Drive experiment 077ae4c7-e816-4dd8-a150-ad8fe19cb795 from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.
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=Falsein pod 0). Pod 0 raisesSystemExitand 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-secondshard 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
RUNNINGbut emit zero5% · bootstrap completeprogress notes within 5 min ofRUNNING, 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 patchedpod_specand the_factor_screen/module tree currently on disk under/home/thomasjiralerspong/explore-persona-space/eps/experiments/). The runner injectsSAGAN_EPS_BRANCH=experiment-365andSAGAN_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 slabmetrics.jsonfiles, then buildsmain_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.
- Pod 0 (
- 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-podscontract (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.jsonuntil 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 underadapters/,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-pagebody). - Sagan-side: new
experiments.bodyHTML written by--write-clean-resultand the aggregator figures uploaded ashtml_artifact/imagefigures 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-specagainst the persistedexperiments.077ae4c7…pod_spec: identical except forsubstitution_policy.account.prefer("team"→"personal"). No other field changes. - Bootstrap progress check. Within 5 min of each pod reaching
RUNNING, the runner expects a5% · bootstrap completeprogress event from each of the four pods. If zero pods emit it (the recovery-trigger failure mode), abort and SSH into one pod to tailjournalctland the bootstrap wrapper log before retrying. - Phase 0/1 gates. Pod 0 must emit
kill_pre_screenverdict=passedandkill_smokeverdict=passbefore pods 1/2/3 are allowed to consume significant compute. The script raisesSystemExiton failure; the runner surfaces that as a hard stop.
Post-run (operator checks before declaring the screen interpretable):
main_effects.jsonreports 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.jsonreports all ten two-way interactions with the A×B row carrying apre_registered: trueflag.clean_result.htmlfollowsdocs/clean-result-guidelines.md(TL;DR → primary plot → Experimental design dropdown) and is attached toexperiments.bodyon 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
- Goal matches the experiment record title and absorbs the open scope of #361 / #339 / #353 / #46.
- Hypothesis is specific: five factors split into three classes with signed predictions for each.
- Prediction is falsifiable: numeric Cohen's d thresholds with α=0.05 and a named bootstrap scheme.
- Kill criterion has six concrete triggers, including the new bootstrap-silence detector that directly responds to the recovery-trigger failure mode.
- 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 $160. Multi-pod array is justified under clause (c) per-pod role isolation; consolidation deferred to a future ablation.
- Artifacts enumerated per-pod and aggregator with
/workspace/runs/365/...paths matching the script's directory contract. - Verification covers spec parity, the bootstrap-progress detector, Phase 0/1 kill gates, and the aggregator's required JSONs / clean-result HTML.
- Risks explicitly cover the team-account vanishing failure that triggered this recovery and document the rationale for flipping
account.prefertopersonal. - Likely clean result follows
docs/clean-result-guidelines.md(TL;DR → primary plot → design dropdown, first-person voice, no standing caveats). -
runpod-specmatches the plan: 4 pods, A100 80GB Secure Cloud preferred, single-GPU per pod,account.prefer=personalwithteamretained inallowed, and per-poddockerArgsidentical to the persistedexperiments.077ae4c7…pod_specsave for that one substitution-policy field.
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"dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 0 --num-pods 4 --source-persona librarian --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --run-pre-screen --run-smoke --progress-url \"$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": [
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"/workspace/runs/365/pod0/smoke.json",
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"/workspace/runs/365/pod0/librarian/adapters/",
"/workspace/runs/365/pod0/figures/"
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},
"substitution_policy": {
"gpuType": { "allowed": ["A100", "A100-SXM", "H100", "H200"], "min_vram_gb": 80 },
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"cloudType": { "allowed": ["SECURE", "COMMUNITY"], "prefer": "SECURE" },
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"volumeGb": { "min": 100 },
"containerDiskGb": { "min": 100 }
},
"consolidation": {
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},
{
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"gpuType": "A100",
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},
"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/",
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"substitution_policy": {
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"dataCenterId": { "allowed": "any" },
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"volumeGb": { "min": 100 },
"containerDiskGb": { "min": 100 }
},
"consolidation": {
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},
{
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"gpuType": "A100",
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},
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"config": {
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"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.",
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"dataCenterId": { "allowed": "any" },
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"volumeGb": { "min": 100 },
"containerDiskGb": { "min": 100 }
},
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]
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
kind=apply
I'll start by orienting myself: checking the current experiment state, the branch on the client repo, and what stages have already been completed.
tool=Bash
Bash
status: None number: None parent: None pod_spec keys: array len=0
tool=Bash
Bash
+ experiment-365 * issue-369 remotes/origin/experiment-365 Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=Bash
Bash
<persisted-output>
Output too large (89.5KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/b12b5d18-d0e4-4b22-b74d-8a37bec60c80/tool-results/byncdxmt4.txt
Preview (first 2KB):
{
"experiment": {
"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) \u2014 longer persona system prompts on the 48-source panel implant more (\u03c1=+0.38) and leak less (\u03c1=\u22120.38), but length and persona-richness co-vary.\n- [#295](https://github.com/superkaiba/explore-persona-space/issues/295) (LOW) \u2014 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) \u2014 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) \u2014 non-persona triggers leak broadly under any recipe.\n- [#46](https://github.com/superkaiba/explore-persona-space/issues/46) (approved, not yet run) \u2014 on-policy + marker-only-loss 5\u00d73\u00d73 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 a
...
</persisted-output>tool=Bash
Bash
number: 365
status: implementing
pod_spec: array of 4
pod0: account.prefer=personal, env={'SAGAN_EPS_BRANCH': 'experiment-365', 'SAGAN_EPS_COMMIT_SHA': 'b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7'}
pod1: account.prefer=personal, env={'SAGAN_EPS_BRANCH': 'experiment-365', 'SAGAN_EPS_COMMIT_SHA': 'b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7'}
pod2: account.prefer=personal, env={'SAGAN_EPS_BRANCH': 'experiment-365', 'SAGAN_EPS_COMMIT_SHA': 'b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7'}
pod3: account.prefer=personal, env={'SAGAN_EPS_BRANCH': 'experiment-365', 'SAGAN_EPS_COMMIT_SHA': 'b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7'}tool=Bash
Bash
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 --- 100644 blob 3a468999c38bcf616cee046055a506476a7f7921 eps/experiments/marker_factor_screen.py Shell cwd was reset to /home/thomasjiralerspong/sagan
The branch and code are already in place from the prior dispatch, and the pod_spec is already patched with `account.prefer=personal`. Status is currently `implementing`. Let me check workflow_events to confirm what stages have already been completed.
tool=Bash
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/bin/bash: line 4: python: command not found
tool=Bash
Bash
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: 'events' (choose from 'list', 'view', 'status', 'patch', 'marker', 'markers', 'promote', 'launch-pod', 'clean-result')tool=Bash
Bash
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: unrecognized arguments: --limittool=Bash
Bash
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: markertool=Bash
Bash
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: markertool=Bash
Bash
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 workflow-state HTTP client
positional arguments:
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
list List experiments
view View one experiment by Sagan experiment number
status Set experiment status
patch Patch experiment metadata or status
marker (markers) Post an epm:* workflow marker
promote Promote pending experiment result
launch-pod Trigger Sagan's dispatcher to launch RunPod pods from
an approved experiment-kind agent_run
clean-result Patch a clean-result record by UUID
options:
-h, --help show this help message and exit
--base-url BASE_URL Sagan base URL; defaults to SAGAN_BASE_URL or
NEXT_PUBLIC_SITE_URL
--token TOKEN Bearer token; defaults to SAGAN_API_TOKEN
--timeout TIMEOUT
----
usage: sagan_state.py view [-h] number
positional arguments:
number
options:
-h, --help show this help message and exittool=Bash
Bash
keys: ['id', 'number', 'legacyGhNumber', 'beliefId', 'projectId', 'title', 'body', 'hypothesis', 'planMd', 'planJson', 'podSpec', 'configYaml', 'status', 'kind', 'computeSize', 'priority', 'assigneeKind', 'tags', 'hasCleanResult', 'autoApprovePlan', 'parentExperimentId', 'runpodAccount', 'convertedToKind', 'convertedToId', 'createdAt', 'updatedAt', 'turn'] top-level keys: ['experiment', 'events', 'approvalRequests']
tool=Bash
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events: 50 2026-05-14T09:36:55.597Z state_changed implementing -> queued RunPod pod dispatched; waiting for runtime. 2026-05-14T09:36:21.659Z state_changed approved -> implementing Orchestrator b5f42247 queued to implement and dispatch. 2026-05-14T09:36:21.640Z state_changed approved -> approved Auto-approved follow-up plan (experiment.auto_approve_plan=true). 2026-05-14T09:27:30.432Z state_changed blocked -> awaiting_clarifications Claude produced clarifying questions; awaiting owner answers. 2026-05-14T09:23:12.256Z blocked running -> blocked Cascaded from agent_run 15038ff7 failed 2026-05-14T09:06:07.663Z state_changed queued -> running RunPod pod is running. 2026-05-14T09:06:01.549Z state_changed approved -> queued RunPod pod dispatched; waiting for runtime. 2026-05-14T08:56:08.369Z state_changed blocked -> planning Automatic recovery queued after agent run aa1717d5 failed. 2026-05-14T08:56:08.179Z blocked queued -> blocked Partial RunPod dispatch: 2/4 pods came up. Survivors were stopped because the plan was designed around 4 partitions and 2026-05-14T08:56:07.158Z state_changed approved -> queued RunPod pod dispatched; waiting for runtime. 2026-05-14T08:29:21.214Z note -> Patched experiments.pod_spec: dockerArgs now run from /workspace/explore-persona-space (the bootstrap clone target) and 2026-05-14T00:18:59.963Z state_changed blocked -> awaiting_clarifications Claude produced clarifying questions; awaiting owner answers. 2026-05-14T00:18:09.144Z note -> 5% · bootstrap complete on branch experiment-365 2026-05-14T00:18:03.237Z note -> 0% · experiment exited with code 1 · err: /usr/bin/python: Error while finding module specification for 'eps.experiments 2026-05-14T00:18:02.420Z note -> 5% · bootstrap complete on branch experiment-365 2026-05-14T00:17:52.854Z note -> 0% · experiment exited with code 1 · err: /usr/bin/python: Error while finding module specification for 'eps.experiments 2026-05-14T00:17:52.371Z note -> 0% · experiment exited with code 1 · err: /usr/bin/python: Error while finding module specification for 'eps.experiments 2026-05-14T00:17:52.045Z note -> 5% · bootstrap complete on branch experiment-365 2026-05-14T00:17:51.555Z note -> 5% · bootstrap complete on branch experiment-365 2026-05-14T00:17:50.590Z note -> 0% · experiment exited with code 1 · err: /usr/bin/python: Error while finding module specification for 'eps.experiments 2026-05-14T00:17:49.779Z note -> 5% · bootstrap complete on branch experiment-365 2026-05-14T00:17:45.144Z note -> 0% · experiment exited with code 1 · err: /usr/bin/python: Error while finding module specification for 'eps.experiments 2026-05-14T00:17:44.300Z note …
The state has been moving fast. Let me re-query the current state precisely and check active pods.
tool=Bash
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status: implementing updatedAt: 2026-05-14T11:00:16.460Z Most recent events: 2026-05-14T11:00:16.469Z state_changed approved -> implementing Orchestrator 57b6a025 queued to implement and dispatch. 2026-05-14T11:00:16.438Z state_changed blocked -> approved Auto-approved follow-up plan (experiment.auto_approve_plan=true). 2026-05-14T10:56:53.838Z blocked planning -> blocked Cascaded from agent_run 910a65d6 failed 2026-05-14T10:56:53.627Z state_changed blocked -> planning Automatic recovery queued after agent run 910a65d6 failed. 2026-05-14T10:43:53.886Z state_changed queued -> running RunPod pod is running. 2026-05-14T10:43:32.340Z state_changed approved -> queued RunPod pod dispatched; waiting for runtime. 2026-05-14T10:37:35.845Z state_changed blocked -> planning Automatic recovery queued after agent run c7bbfd7f failed. 2026-05-14T10:37:35.781Z blocked queued -> blocked Partial RunPod dispatch: 3/4 pods came up. Survivors were stopped because the plan was designed around 4 partitions and
tool=Bash
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agent_run keys: ['run', 'events', 'pods', 'artifacts', 'canManageRun'] kind: None status: None request prefix:
tool=Bash
Bash
kind: experiment status: completed request prefix: [auto-recovery-for:910a65d6-6c8f-4d20-9d0b-2d4d224c05a5] The previous Claude Code run failed or crashed. First diagnose why it stopped using the transcript below. Then fix or work around the problem if possible and continue the original request to a final useful result. Do not repeat completed wor pods: 0
The picture is clear: this is the 3rd dispatch attempt of the same recovery cycle. Branch `experiment-365` @ `b1a24b4b` is already pushed, code-review fixes are already committed, and the persisted `pod_spec` already has `account.prefer=personal` + the env vars. The previous dispatch (10:43) reached RUNNING then failed at 10:56 — same vanishing pattern, but under personal account this time. Let me check the actual failure mode and existing epm:* markers before proceeding.
tool=Bash
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epm:* markers: Recent failures/blocks: 2026-05-14T10:56:53.838Z blocked Cascaded from agent_run 910a65d6 failed 2026-05-14T10:56:53.627Z state_changed Automatic recovery queued after agent run 910a65d6 failed. 2026-05-14T10:37:35.845Z state_changed Automatic recovery queued after agent run c7bbfd7f failed. 2026-05-14T10:37:35.781Z blocked Partial RunPod dispatch: 3/4 pods came up. Survivors were stopped because the plan was designed around 4 partitions and proceeding with fewer would corrupt the result. Re-approve the plan to retry, or revise it to fit available capacity (smaller cloudType, different gpuType, fewer pods, or one pod w 2026-05-14T09:41:30.942Z note Test verdict reconciled from prior reviewer round: the implementer's branch reached its current commit b1a24b4 only after the round-1 reviewer pair signed off on tests + lint as Step 4 of the review (per .claude/agents/code-reviewer.md contract). No new failing-test signal has appeared on the branch
tool=Bash
Bash
kind: experiment status: blocked pods: 4 j18eiux6ccekyj acct=None status=None alloc=None h0lrn1hg6txfpb acct=None status=None alloc=None 4ermpldhmdp0za acct=None status=None alloc=None rotwjcha7r12ns acct=None status=None alloc=None events tail: 2026-05-14T10:53:56.641Z runpod_status: 2026-05-14T10:53:57.632Z runpod_status: 2026-05-14T10:54:53.612Z runpod_retry: 2026-05-14T10:54:53.777Z runpod_retry: 2026-05-14T10:54:53.926Z runpod_retry: 2026-05-14T10:54:55.634Z runpod_retry: 2026-05-14T10:55:53.612Z runpod_retry: 2026-05-14T10:55:53.755Z runpod_retry: 2026-05-14T10:55:53.926Z runpod_retry: 2026-05-14T10:55:55.076Z runpod_retry: 2026-05-14T10:56:53.615Z auto_recovery_queued: 2026-05-14T10:56:53.632Z runpod_blocked: 2026-05-14T10:56:53.824Z runpod_blocked: 2026-05-14T10:56:59.266Z runpod_blocked: 2026-05-14T10:57:06.228Z runpod_blocked:
tool=ToolSearch
ToolSearch
tool=TodoWrite
TodoWrite
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
tool=Bash
Bash
--- pods raw ---
{
"id": "6baff948-3492-44f6-82a2-6bee5fc2707d",
"agentRunId": "910a65d6-6c8f-4d20-9d0b-2d4d224c05a5",
"experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
"runId": "4e100cd6-e9d6-4428-ba11-87ebdf858204",
"runpodPodId": "j18eiux6ccekyj",
"account": "team",
"name": "marker-screen-365-pod0-pre-and-source-librarian",
"gpuTypeId": "NVIDIA A100-SXM4-80GB",
"gpuCount": 1,
"status": "blocked",
"desiredStatus": "RUNNING",
"sshHost": null,
"sshPort": null,
"costPerHr": 1.49,
"adjustedCostPerHr": 1.49,
"uptimeSeconds": 0,
"lastStartedAt": "2026-05-14T10:39:59.833Z",
"retryCount": 3,
"maxRetries": 3,
"blockedReason": "Pod j18eiux6ccekyj not found in account=team",
"lastError": "Pod j18eiux6ccekyj not found in account=team",
"lastCheckedAt": "2026-05-14T10:56:53.801Z",
"lastHeartbeatAt": "2026-05-14T10:53:56.131Z",
"stoppedAt": null,
"terminatedAt": null,
"createdAt": "2026-05-14T10:43:32.329Z",
"updatedAt": "2026-05-14T10:56:53.801Z"
}
{
"id": "ca37745a-8cbb-4689-a5b6-f3302d573f23",
"agentRunId": "910a65d6-6c8f-4d20-9d0b-2d4d224c05a5",
"experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
"runId": "f859a59a-069a-4c16-b2b1-a51dfd4180c7",
"runpodPodId": "h0lrn1hg6txfpb",
"account": "team",
"name": "marker-screen-365-pod1-source-surgeon",
"gpuTypeId": "NVIDIA A100-SXM4-80GB",
"gpuCount": 1,
"status": "blocked",
"desiredStatus": "RUNNING",
"sshHost": null,
"sshPort": null,
"costPerHr": 1.49,
"adjustedCostPerHr": 1.49,
"uptimeSeconds": -7,
"lastStartedAt": "2026-05-14T10:39:59.780Z",
"retryCount": 3,
"maxRetries": 3,
"blockedReason": "Pod h0lrn1hg6txfpb not found in account=team",
"lastError": "Pod h0lrn1hg6txfpb not found in account=team",
"lastCheckedAt": "2026-05-14T10:57:06.133Z",
"lastHeartbeatAt": "2026-05-14T10:53:57.628Z",
"stoppedAt": null,
"terminatedAt": null,
"createdAt": "2026-05-14T10:43:32.347Z",
"updatedAt": "2026-05-14T10:57:06.133Z"
}
{
"id": "4e824e80-8241-41c7-b437-8a2e67755625",
"agentRunId": "910a65d6-6c8f-4d20-9d0b-2d4d224c05a5",
"experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
"runId": "b37a3e3e-a057-4e6f-814b-7be277ff335e",
"runpodPodId": "4ermpldhmdp0za",
"account": "team",
"name": "marker-screen-365-pod2-source-programmer",
"gpuTypeId": "NVIDIA A100-SXM4-80GB",
"gpuCount": 1,
"status": "blocked",
"desiredStatus": "RUNNING",
"sshHost": null,
"sshPort": null,
"costPerHr": 1.49,
"adjustedCostPerHr": 1.49,
"uptimeSeconds": -1,
"lastStartedAt": "2026-05-14T10:40:30.509Z",
"retryCount": 3,
"maxRetries": 3,
"blockedReason": "Pod 4ermpldhmdp0za not found in account=team",
"lastError": "Pod 4ermpldhmdp0za not found in account=team",
"lastCheckedAt": "2026-05-14T10:56:59.237Z",
"lastHeartbeatAt": "2026-05-14T10:53:56.638Z",
"stoppedAt": null,
"terminatedAt": null,
"createdAt": "2026-05-14T10:43:32.360Z",
"updatedAt": "2026-05-14T10:56:59.237Z"
}
{
"id": "b4566ede-2209-4fe1-a243-80775a0c239d",
"agentRunId": "910a65d6-6c8f-4d20-9d0b-2d4d224c05a5",
"experimentId": "077ae4c7-e816-4dd8-a150-ad8fe19cb795",
"runId": "a115843d-393b-4052-93ba-b425bd12cd4b",
"runpodPodId": "rotwjcha7r12ns",
"account": "team",
"name": "marker-screen-365-pod3-aggregator-and-overflow",
"gpuTypeId": "NVIDIA A100-SXM4-80GB",
"gpuCount": 1,
"status": "blocked",
"desiredStatus": "RUNNING",
"sshHost": null,
"sshPort": null,
"costPerHr": 1.49,
"adjustedCostPerHr": 1.49,
"uptimeSeconds": -13,
"lastStartedAt": "2026-05-14T10:43:31.825Z",
"retryCount": 3,
"maxRetries": 3,
"blockedReason": "Pod rotwjcha7r12ns not found in account=team",
"lastError": "Pod rotwjcha7r12ns not found in account=team",
"lastCheckedAt": "2026-05-14T10:56:53.586Z",
"lastHeartbeatAt": "2026-05-14T10:53:55.951Z",
"stoppedAt": null,
"terminatedAt": null,
"createdAt": "2026-05-14T10:43:32.370Z",
"updatedAt": "2026-05-14T10:56:53.586Z"
}
--- event types …Diagnosis confirmed: the failed dispatch at 10:43 ran 4 pods on **team account** (despite the plan calling for personal), all 4 hit `Pod not found in account=team` ~13 min later. The pod_spec was patched to `prefer=personal` *after* that failure. Now I need to walk this orchestrator through the proper stages so the pod-provisioner re-dispatches on personal account. Branch + commit are already verified above. Posting markers.
tool=Bash
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{
"id": "40c11f36-813c-411d-96e4-8acbe284b402",
"ok": true
}tool=Bash
Bash
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
---
commit b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date: Wed May 13 20:48:49 2026 +0000
Address code-review round 1 targeted fixes for experiment #365
Reconciled fixes from the Claude/Codex reviewer pair (Codex
needs_targeted_fix; reconciler agreed with the three minimal-necessary
items):
1. phases.py — Phase 0 kill-criterion #4 now matches the approved plan.
Was: >=2 panel personas exceed a single 5% substring rate (a single
threshold that conflates source and bystander rows).
Now: kill if ANY of the 3 source rows exceeds 2%, OR if the bystander
mean substring rate exceeds 1%. The persisted pre_screen.json now
Shell cwd was reset to /home/thomasjiralerspong/saganRound-1 review fixes are committed. Posting code-review + testing markers (carry-forward), then transitioning to running.
tool=Bash
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"id": "c1ea8c33-5c2a-4ac6-9b0a-e7e9b28e7b40", "ok": true }
tool=Bash
Bash
tool=Bash
Bash
"id": "8276e409-bf50-4d77-bb23-a5bd901e53b0", "ok": true }
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
...
sagan_state.py: error: unrecognized arguments: --actor-kind agenttool=Bash
Bash
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}
positional arguments:
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}
options:
-h, --help show this help message and exit
--note NOTEtool=Bash
Bash
}, "ok": true }
tool=TodoWrite
TodoWrite
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
tool=Bash
Bash
"ok": true }
tool=Bash
Bash
"ok": true }
Now transitioning to running and dispatching the pod-provisioner.
tool=Bash
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"ok": true }
tool=TodoWrite
TodoWrite
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
tool=Bash
Bash
264 /tmp/exp365_podspec.json n pods: 4 marker-screen-365-pod0-pre-and-source-librarian: account.prefer=personal, allowed=['personal', 'team'], gpuType=A100, cloudType=SECURE, env.SAGAN_EPS_BRANCH=experiment-365, env.SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7 marker-screen-365-pod1-source-surgeon: account.prefer=personal, allowed=['personal', 'team'], gpuType=A100, cloudType=SECURE, env.SAGAN_EPS_BRANCH=experiment-365, env.SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7 marker-screen-365-pod2-source-programmer: account.prefer=personal, allowed=['personal', 'team'], gpuType=A100, cloudType=SECURE, env.SAGAN_EPS_BRANCH=experiment-365, env.SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7 marker-screen-365-pod3-aggregator-and-overflow: account.prefer=personal, allowed=['personal', 'team'], gpuType=A100, cloudType=SECURE, env.SAGAN_EPS_BRANCH=experiment-365, env.SAGAN_EPS_COMMIT_SHA=b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7
Pod spec is correctly configured. Spawning the pod-provisioner.
tool=Agent
Agent
tool=Read
Read
1 - [spec-json quoting gotcha](feedback-spec-json-quoting.md) — write JSON to a temp file, not a heredoc variable; heredoc inserts a leading newline that breaks JSON parse at position 1 2 - [agent-run kind mismatch](feedback-agent-run-kind-mismatch.md) — pod-tool requires kind=experiment; orchestrator passes kind=apply — INSERT a new experiment-kind row and use that id instead 3
tool=Bash
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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": "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/"
]
},
"gpuType": "A100",
"gpuCount": 1,
"volumeGb": 100,
"cloudType": "SECURE",
"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\"'",
"consolidation": {
"may_merge_pods": false,
"merge_target_max_gpus_per_pod": 1
},
"containerDiskGb": 100,
"estimatedMinutes": 1080,
"substitution_policy": {
"account": {
"prefer": "personal",
"allowed": [
"personal",
"team"
]
},
"gpuType": {
"allowed": [
"A100",
"A100-SXM",
"H100",
"H200"
],
"min_vram_gb": 80
},
"gpuCount": {
"max": 1,
"min": 1
},
"volumeGb": {
"min": 100
},
"cloudType": {
"prefer": "SECURE",
"allowed": [
"SECURE",
"COMMUNITY"
]
},
"dataCenterId": {
"allowed": "any"
},
"containerDiskGb": {
"min": 100
}
}
},
{
"env": {
"SAGAN_EPS_BRANCH": "experiment-365",
"SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
},
"name": "marker-screen-365-pod1-source-surgeon",
"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/"
]
},
"gpuType": "A100",
"gpuCount": 1,
"volumeGb": 100,
"cloudType": "SECURE",
"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\"'",
"consolidation": {
"ma…tool=Read
Read
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 tool=Read
Read
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 tool=Bash
Bash
id | kind | status --------------------------------------+------------+----------- 583edc02-98b9-4c8c-aaad-c29aae0f73ef | experiment | completed (1 row)
tool=Bash
Bash
spec0 OK
tool=Bash
Bash
> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "583edc02-98b9-4c8c-aaad-c29aae0f73ef" "--run-index" "0" "--spec-json" "{\"env\": {\"SAGAN_EPS_BRANCH\": \"experiment-365\", \"SAGAN_EPS_COMMIT_SHA\": \"b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7\"}, \"name\": \"marker-screen-365-pod0-pre-and-source-librarian\", \"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/\"]}, \"gpuType\": \"A100\", \"gpuCount\": 1, \"volumeGb\": 100, \"cloudType\": \"SECURE\", \"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\\\"'\", \"consolidation\": {\"may_merge_pods\": false, \"merge_target_max_gpus_per_pod\": 1}, \"containerDiskGb\": 100, \"estimatedMinutes\": 1080, \"substitution_policy\": {\"account\": {\"prefer\": \"personal\", \"allowed\": [\"personal\", \"team\"]}, \"gpuType\": {\"allowed\": [\"A100\", \"A100-SXM\", \"H100\", \"H200\"], \"min_vram_gb\": 80}, \"gpuCount\": {\"max\": 1, \"min\": 1}, \"volumeGb\": {\"min\": 100}, \"cloudType\": {\"prefer\": \"SECURE\", \"allowed\": [\"SECURE\", \"COMMUNITY\"]}, \"dataCenterId\": {\"allowed\": \"any\"}, \"containerDiskGb\": {\"min\": 100}}}" "--account" "personal"
{"ok":true,"pod":{"podId":"jq1txovwi38iqz","name":"marker-screen-365-pod0-pre-and-source-librarian","gpuTypeId":"NVIDIA A100-SXM4-80GB","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":1.49,"adjustedCostPerHr":1.49},"podLifecycleId":"aab93b6b-d671-4d5d-9e40-0a94181295e4","sagaRunId":"e628d3fc-9a09-4c5b-8770-f82a0d62d14f","account":"personal"}tool=Bash
Bash
spec1 OK
tool=Bash
Bash
> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "583edc02-98b9-4c8c-aaad-c29aae0f73ef" "--run-index" "1" "--spec-json" "{\"env\": {\"SAGAN_EPS_BRANCH\": \"experiment-365\", \"SAGAN_EPS_COMMIT_SHA\": \"b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7\"}, \"name\": \"marker-screen-365-pod1-source-surgeon\", \"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/\"]}, \"gpuType\": \"A100\", \"gpuCount\": 1, \"volumeGb\": 100, \"cloudType\": \"SECURE\", \"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\\\"'\", \"consolidation\": {\"may_merge_pods\": false, \"merge_target_max_gpus_per_pod\": 1}, \"containerDiskGb\": 100, \"estimatedMinutes\": 1080, \"substitution_policy\": {\"account\": {\"prefer\": \"personal\", \"allowed\": [\"personal\", \"team\"]}, \"gpuType\": {\"allowed\": [\"A100\", \"A100-SXM\", \"H100\", \"H200\"], \"min_vram_gb\": 80}, \"gpuCount\": {\"max\": 1, \"min\": 1}, \"volumeGb\": {\"min\": 100}, \"cloudType\": {\"prefer\": \"SECURE\", \"allowed\": [\"SECURE\", \"COMMUNITY\"]}, \"dataCenterId\": {\"allowed\": \"any\"}, \"containerDiskGb\": {\"min\": 100}}}" "--account" "personal"
{"ok":true,"pod":{"podId":"bnspxz4qrwsbfp","name":"marker-screen-365-pod1-source-surgeon","gpuTypeId":"NVIDIA A100-SXM4-80GB","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":1.49,"adjustedCostPerHr":1.49},"podLifecycleId":"dbfa052f-3bd9-42e8-8fc9-35b60ccbe68b","sagaRunId":"f90ffca4-2212-49f0-9233-110a1a0eefe0","account":"personal"}tool=Bash
Bash
spec2 OK
tool=Bash
Bash
> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "583edc02-98b9-4c8c-aaad-c29aae0f73ef" "--run-index" "2" "--spec-json" "{\"env\": {\"SAGAN_EPS_BRANCH\": \"experiment-365\", \"SAGAN_EPS_COMMIT_SHA\": \"b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7\"}, \"name\": \"marker-screen-365-pod2-source-programmer\", \"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/\"]}, \"gpuType\": \"A100\", \"gpuCount\": 1, \"volumeGb\": 100, \"cloudType\": \"SECURE\", \"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\\\"'\", \"consolidation\": {\"may_merge_pods\": false, \"merge_target_max_gpus_per_pod\": 1}, \"containerDiskGb\": 100, \"estimatedMinutes\": 1080, \"substitution_policy\": {\"account\": {\"prefer\": \"personal\", \"allowed\": [\"personal\", \"team\"]}, \"gpuType\": {\"allowed\": [\"A100\", \"A100-SXM\", \"H100\", \"H200\"], \"min_vram_gb\": 80}, \"gpuCount\": {\"max\": 1, \"min\": 1}, \"volumeGb\": {\"min\": 100}, \"cloudType\": {\"prefer\": \"SECURE\", \"allowed\": [\"SECURE\", \"COMMUNITY\"]}, \"dataCenterId\": {\"allowed\": \"any\"}, \"containerDiskGb\": {\"min\": 100}}}" "--account" "personal"
{"ok":true,"pod":{"podId":"ftghpnckxt581r","name":"marker-screen-365-pod2-source-programmer","gpuTypeId":"NVIDIA A100-SXM4-80GB","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":1.49,"adjustedCostPerHr":1.49},"podLifecycleId":"accf636f-a5b2-4709-a9bd-16fe6bb37ef4","sagaRunId":"1e70bf61-7d6f-44fb-a43d-bee2427dde86","account":"personal"}tool=Bash
Bash
spec3 OK
tool=Bash
Bash
> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "583edc02-98b9-4c8c-aaad-c29aae0f73ef" "--run-index" "3" "--spec-json" "{\"env\": {\"SAGAN_EPS_BRANCH\": \"experiment-365\", \"SAGAN_EPS_COMMIT_SHA\": \"b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7\"}, \"name\": \"marker-screen-365-pod3-aggregator-and-overflow\", \"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\"]}, \"gpuType\": \"A100\", \"gpuCount\": 1, \"volumeGb\": 100, \"cloudType\": \"SECURE\", \"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\\\"'\", \"consolidation\": {\"may_merge_pods\": false, \"merge_target_max_gpus_per_pod\": 1}, \"containerDiskGb\": 100, \"estimatedMinutes\": 1080, \"substitution_policy\": {\"account\": {\"prefer\": \"personal\", \"allowed\": [\"personal\", \"team\"]}, \"gpuType\": {\"allowed\": [\"A100\", \"A100-SXM\", \"H100\", \"H200\"], \"min_vram_gb\": 80}, \"gpuCount\": {\"max\": 1, \"min\": 1}, \"volumeGb\": {\"min\": 100}, \"cloudType\": {\"prefer\": \"SECURE\", \"allowed\": [\"SECURE\", \"COMMUNITY\"]}, \"dataCenterId\": {\"allowed\": \"any\"}, \"containerDiskGb\": {\"min\": 100}}}" "--account" "personal"
{"ok":true,"pod":{"podId":"7to8i265ke73o1","name":"marker-screen-365-pod3-aggregator-and-overflow","gpuTypeId":"NVIDIA A100-SXM4-80GB","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":1.49,"adjustedCostPerHr":1.49},"podLifecycleId":"6d66470f-172e-4498-90f7-00d054b5e8b5","sagaRunId":"048cf85e-a714-4a27-a085-8dcb24e04d98","account":"personal"}tool=Bash
Bash
> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "583edc02-98b9-4c8c-aaad-c29aae0f73ef"
{"ok":true,"podIds":["bnspxz4qrwsbfp","ftghpnckxt581r","7to8i265ke73o1","jq1txovwi38iqz"],"count":4}committed 4 pod(s): jq1txovwi38iqz, bnspxz4qrwsbfp, ftghpnckxt581r, 7to8i265ke73o1 — all on personal account (A100-SXM4-80GB SECURE), run-indices 0–3. No substitutions were needed beyond using the personal account as preferred. Operator note: watch for `5% · bootstrap complete` progress events from each pod within ~5 min of RUNNING; silence past that threshold should trigger the bootstrap-silence kill gate. agentId: a3e5b0b9ac1a35d23 (use SendMessage with to: 'a3e5b0b9ac1a35d23' to continue this agent) <usage>total_tokens: 38470 tool_uses: 14 duration_ms: 95870</usage>
Pod-provisioner committed 4 pods on personal account, no substitutions needed. Posting the dispatch marker.
tool=Bash
Bash
{"error":"unknown_marker_type","message":"Marker \"epm:experiment-dispatch\" is not in KNOWN_MARKER_TYPES. Add it to apps/web/src/lib/reviewer-loops.ts and .claude/workflow.yaml before posting."}tool=Bash
Bash
status: queued updatedAt: 2026-05-14T11:05:55.544Z Last 8 events: 2026-05-14T11:05:55.548Z state_changed running ->queued RunPod pod dispatched; waiting for runtime. 2026-05-14T11:05:53.738Z state_changed queued ->running RunPod pod is running. 2026-05-14T11:05:06.450Z state_changed running ->queued RunPod pod dispatched; waiting for runtime. 2026-05-14T11:03:49.379Z state_changed testing ->running Recovery dispatch: persisted pod_spec has account.prefer=personal across all 4 pods (was team in prior two failed cycles 2026-05-14T11:03:42.923Z note -> Test verdict carried forward from prior reviewer cycle (Step 4 of round-1 code-review). Commit b1a24b4b is the tip of br 2026-05-14T11:03:40.531Z state_changed code_reviewing->testing Carry forward: prior code-review pair signed off on tests + lint as Step 4 of the round-1 review (see prior orchestrator 2026-05-14T11:03:28.453Z state_changed implementing ->code_reviewing Orchestrator 57b6a025: code-review verdicts carried forward from prior cycle (commit b1a24b4b on branch experiment-365). 2026-05-14T11:03:19.536Z note -> Codex code-review verdict carried forward from prior cycle (round 1 was the cycle that produced the b1a24b4b targeted-fi
The pod-provisioner committed pods and the runner has already started cycling status. Marker name was rejected — let me check valid marker names.
tool=Grep
Grep
32:export const KNOWN_MARKER_TYPES = [ 37: 'epm:experiment-implementation', 137: return KNOWN_MARKER_TYPES.includes(value as (typeof KNOWN_MARKER_TYPES)[number]);
tool=Read
Read
32 export const KNOWN_MARKER_TYPES = [ 33 'epm:clarify', 34 'epm:clarify-answers', 35 'epm:plan', 36 'epm:consistency', 37 'epm:experiment-implementation', 38 'epm:code-review', 39 'epm:code-review-codex', 40 'epm:code-review-reconcile', 41 'epm:review-reconcile', 42 'epm:reviewer-verdict', 43 'epm:reviewer-verdict-codex', 44 'epm:textbox-answers', 45 'epm:reviewer-pass', 46 'epm:reviewer-decision', 47 'epm:reviewer-ensemble-final', 48 'epm:test-verdict', 49 'epm:preflight', 50 'epm:launch', 51 'epm:progress', 52 'epm:results', 53 'epm:upload-verification', 54 'epm:interpretation', 55 'epm:interp-critique', 56 'epm:interp-critique-codex', 57 'epm:interp-revision', 58 'epm:clean-result', 59 'epm:clean-result-critique', 60 'epm:clean-result-critique-codex', 61 'epm:clean-result-critique-reconcile', 62 'epm:clean-result-lint', 63 'epm:follow-ups', 64 'epm:awaiting-promotion', 65 'epm:promoted', 66 'epm:done', 67 'epm:failure', 68 'epm:failure-classify', 69 'epm:failure-superseded', 70 'epm:hot-fix', 71 'epm:hot-fix-needed', 72 'epm:pod-pending', 73 'epm:pod-terminated', 74 'epm:pod-kept-stopped', 75 'epm:dispatch', 76 'epm:dispatch-blocked', 77 'epm:experimenter-respawn', 78 'epm:experimenter-status', 79 'epm:gate', 80 'epm:gate-verdict', 81 'epm:gate-override', 82 'epm:approval', 83 'epm:approve', 84 'epm:override', 85 'epm:override-round-', 86 'epm:user-decision', 87 'epm:user-decision-n', 88 'epm:user-feedback', 89 'epm:auto-defaults', 90 'epm:completion-audit', 91 'epm:consistency', 92 'epm:original-body', 93 'epm:body-backup', 94 'epm:analysis', 95 'epm:step', 96 'epm:step-completed', 97 'epm:phase', 98 'epm:note', 99 'epm:correction', 100 'epm:revise', 101 'epm:fact-check-v',
tool=Bash
Bash
"id": "fe84ef3e-fc90-4833-902e-0f8df3d7fdce", "ok": true }
tool=TodoWrite
TodoWrite
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
Orchestrator work complete for this dispatch cycle. Pods committed and the runner has started cycling status (multiple `queued ↔ running` transitions visible as the 4 pods come up). Subsequent stages (uploading → verifying → interpreting → follow-ups → reviewing → clean-result → awaiting_promotion) are driven by the runner's status watchers and will spawn fresh orchestrator sessions when the pods finish their ~18 h compute. **Summary** - Diagnosed prior failure: both prior attempts dispatched on **team account** despite the recovery plan calling for personal; all 4 pods hit `Pod not found in account=team` ~13 min after RUNNING. - Verified `experiment-365 @ b1a24b4b` already pushed with `eps/experiments/marker_factor_screen.py` and round-1 code-review fixes committed; recovery is metadata-only. - Walked status through `implementing → code_reviewing → testing → running` with `epm:experiment-implementation`, `epm:code-review` (claude+codex, carried-forward pass), `epm:test-verdict pass` markers. - pod-provisioner committed 4 pods on **personal account** (A100-SXM4-80GB SECURE) — no substitutions needed: `jq1txovwi38iqz`, `bnspxz4qrwsbfp`, `ftghpnckxt581r`, `7to8i265ke73o1`. Posted `epm:dispatch`. - Watch for plan kill criterion #6: each pod should emit a `5% · bootstrap complete` progress event within ~5 min of RUNNING; silence past that threshold = abort and SSH-tail one pod before retrying. <<<DONE>>>
success
Orchestrator work complete for this dispatch cycle. Pods committed and the runner has started cycling status (multiple `queued ↔ running` transitions visible as the 4 pods come up). Subsequent stages (uploading → verifying → interpreting → follow-ups → reviewing → clean-result → awaiting_promotion) are driven by the runner's status watchers and will spawn fresh orchestrator sessions when the pods finish their ~18 h compute. **Summary** - Diagnosed prior failure: both prior attempts dispatched on **team account** despite the recovery plan calling for personal; all 4 pods hit `Pod not found in account=team` ~13 min after RUNNING. - Verified `experiment-365 @ b1a24b4b` already pushed with `eps/experiments/marker_factor_screen.py` and round-1 code-review fixes committed; recovery is metadata-only. - Walked status through `implementing → code_reviewing → testing → running` with `epm:experiment-implementation`, `epm:code-review` (claude+codex, carried-forward pass), `epm:test-verdict pass` …
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