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Run 8ffefa18

Moved from idea to planning on the Pipeline board. Draft the next experiment plan for "Factor screen for marker implantation + leakage (2^4: length-location, persona-presence, on-policy, marker-only-loss)". Use the scoped experiment record and produce a plan that can be reviewed and approved.

Statusfailed158 events · latest 1358h 15m ago

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An automatic recovery run was queued as d9488543.

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:

  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):

  1. 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.
  2. interactions.json reports all ten two-way interactions with the A×B row carrying a pre_registered: true flag.
  3. 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

  • 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 $160. Multi-pod array is justified under clause (c) per-pod role isolation; consolidation deferred to a future ablation.
  • Artifacts enumerated per-pod and aggregator with /workspace/runs/365/... paths matching the script's directory contract.
  • Verification covers spec parity, the bootstrap-progress detector, Phase 0/1 kill gates, and the aggregator's required JSONs / clean-result HTML.
  • Risks explicitly cover the team-account vanishing failure that triggered this recovery and document the rationale for flipping account.prefer to personal.
  • Likely clean result follows docs/clean-result-guidelines.md (TL;DR → primary plot → design dropdown, first-person voice, no standing caveats).
  • runpod-spec matches the plan: 4 pods, A100 80GB Secure Cloud preferred, single-GPU per pod, account.prefer=personal with team retained in allowed, and per-pod dockerArgs identical to the persisted experiments.077ae4c7…pod_spec save for that one substitution-policy field.
[
  {
    "name": "marker-screen-365-pod0-pre-and-source-librarian",
    "gpuType": "A100",
    "gpuCount": 1,
    "volumeGb": 100,
    "containerDiskGb": 100,
    "cloudType": "SECURE",
    "estimatedMinutes": 1080,
    "env": {
      "SAGAN_EPS_BRANCH": "experiment-365",
      "SAGAN_EPS_COMMIT_SHA": "b1a24b4b04f92598e381fa3cd207a0fe5d24b9e7"
    },
    "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python -m eps.experiments.marker_factor_screen --pod-index 0 --num-pods 4 --source-persona librarian --base-model Qwen/Qwen2.5-7B-Instruct --intent lora-7b --lora-r 32 --lora-alpha 64 --lr 1e-5 --epochs 3 --pos-per-source 200 --neg-per-source 400 --eval-personas 24 --eval-questions 20 --eval-completions 5 --primary-seed 42 --multi-seeds 137,256 --bootstrap-scheme clustered --bootstrap-cluster-sr question --bootstrap-cluster-lr persona --run-pre-screen --run-smoke --progress-url \"$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.

Events

4:21:08 AMstartedagent
kind=experiment
4:21:29 AMtool_calltools

tool=Agent

Agent
4:21:31 AMtool_calltools

tool=Bash

Bash
4:21:32 AMtool_resulttools
This Bash command contains multiple operations. The following part requires approval: find /home/thomasjiralerspong -maxdepth 2 -type d -name "sagan" -o -name "explore-persona-space"
4:21:33 AMtool_calltools

tool=Bash

Bash
4:21:33 AMtool_resulttools
This Bash command contains multiple operations. The following part requires approval: ls -la /home/thomasjiralerspong/
4:21:34 AMtool_calltools

tool=Glob

Glob
4:21:35 AMtool_calltools

tool=Agent

Agent
4:21:35 AMtool_resulttools
node_modules/clsx/readme.md
node_modules/postgres/README.md
node_modules/postcss/README.md
node_modules/@xyflow/react/README.md
node_modules/tailwind-merge/README.md
node_modules/tailwind-merge/LICENSE.md
node_modules/eslint/README.md
node_modules/drizzle-kit/README.md
node_modules/use-callback-ref/node_modules/react/README.md
node_modules/react-remove-scroll/node_modules/react/README.md
node_modules/styled-jsx/node_modules/react/README.md
node_modules/use-sidecar/node_modules/react/README.md
node_modules/use-sync-external-store/node_modules/react/README.md
node_modules/drizzle-orm/node_modules/react/README.md
node_modules/react-markdown/node_modules/react/README.md
node_modules/next/node_modules/react/README.md
node_modules/zustand/node_modules/react/README.md
node_modules/lucide-react/node_modules/react/README.md
node_modules/react-style-singleton/node_modules/react/README.md
node_modules/@xyflow/react/node_modules/react/README.md
node_modules/react-remove-scroll-bar/node_modules/react/README.md
node_modules/react-dom/node_modules/react/README.md
node_modules/@floating-ui/react-dom/node_modules/react/README.md
node_modules/@radix-ui/react-id/node_modules/react/README.md
node_modules/@radix-ui/react-menu/node_modules/react/README.md
node_modules/@radix-ui/react-use-effect-event/node_modules/react/README.md
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node_modules/@expo/cli/static/canary-full/node_modules/react/README.md
node_modules/react/README.md
apps/web/node_modules/react/README.md
node_modules/vaul/node_modules/react-dom/README.md
node_modules/react-native-web/node_modules/react-dom/README.md
node_modules/expo-router/node_modules/react-dom/README.md
node_modules/react-dom/README.md
node_modules/@expo/metro-runtime/node_modules/react-dom/README.md
node_modules/@expo/cli/static/canary-full/node_modules/react-dom/README.md
apps/mobile/node_modules/react-dom/README.md
node_modules/drizzle-orm/README.md
node_modules/typescript/README.md
node_modules/typescript/SECURITY.md
node_modules/esutils/README.md
node_modules/optionator/CHANGELOG.md
node_modules/picocolors/README.md
node_modules/optionator/README.md
node_modules/is-glob/README.md
node_modules/cross-spawn/README.md
node_modules/postcss-value-parser/README.md
node_modules/glob-parent/README.md
node_modules/debug/README.md
node_modules/esquery/README.md
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docs/ops/sagan-ops.md
docs/exec-plans/sagan-rescue-plan.md
docs/exec-plans/dashboard-aesthetic-redesign-plan.md
docs/clean-result-guidelines.md
docs/exec-plans/mentor-snapshot-clean-results-plan.md
docs/em-mechanism-lit-review-analysis.md
docs/em-mechanism-lit-review-papers.md
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node_modules/@react-native/debugger-frontend/dist/third-party/front_end/core/rn_experiments/rn_experiments.js
node_modules/@expo/cli/node_modules/@react-native/debugger-frontend/dist/third-party/front_end/core/rn_experiments/rn_experiments.js
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node_modules/lucide-react/dist/esm/icons/cloud-drizzle.js.map
packages/db/drizzle.config.ts
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tool=Grep

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Found 8 files
services/runner/src/session.ts
services/runner/scripts/em-mechanism-body-v2.html
services/runner/scripts/em-mechanism-body.html
docs/clean-result-guidelines.md
services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
docs/exec-plans/sagan-rescue-plan.md
4:21:39 AMtool_calltools

tool=Bash

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No direct match
4:21:40 AMtool_calltools

tool=Grep

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Found 8 files
services/runner/src/session.ts
services/runner/scripts/em-mechanism-body-v2.html
services/runner/scripts/em-mechanism-body.html
docs/clean-result-guidelines.md
services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
docs/exec-plans/sagan-rescue-plan.md
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1	import type { Config } from 'drizzle-kit';
2	
3	// Prefer the direct (non-pooled) URL for migrations because the Neon pooler
4	// does not allow all DDL statements drizzle-kit generates.
5	const url = process.env.DATABASE_URL_DIRECT ?? process.env.DATABASE_URL;
6	if (!url) {
7	  throw new Error('DATABASE_URL[_DIRECT] must be set for drizzle-kit commands');
8	}
9	
10	export default {
11	  schema: './src/schema/index.ts',
12	  out: './drizzle',
13	  dialect: 'postgresql',
14	  dbCredentials: { url },
15	  strict: true,
16	  verbose: true,
17	} satisfies Config;
18	
4:21:40 AMtool_calltools

tool=Bash

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4:21:40 AMtool_resulttools
/home/thomasjiralerspong/sagan/packages/db/drizzle.config.ts
/home/thomasjiralerspong/sagan/packages/db/src/seed.ts
/home/thomasjiralerspong/sagan/packages/db/src/schema/index.ts
/home/thomasjiralerspong/sagan/packages/db/src/index.ts
/home/thomasjiralerspong/sagan/packages/db/src/migrate.ts
/home/thomasjiralerspong/sagan/packages/db/src/client.ts
4:21:41 AMtool_calltools

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4:21:41 AMtool_resulttools
1	# Clean Result Guidelines
2	
3	How to write a publication-quality HTML clean-result attached to an
4	experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the
5	mentor view). These guidelines were distilled from iterating on experiment
6	[#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598),
7	which is the worked example at the bottom.
8	
9	> Scope note: this doc is for the **full HTML write-up** that lives on the
10	> experiment's `body` field. The shorter Haiku-drafted daily snapshot
11	> (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact
12	> generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot`
13	> markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md`
14	> is the right reference for that. The two artifacts share principles but
15	> not structure.
16	
17	---
18	
19	## Top-level structure
20	
21	The body is a self-contained HTML document with an inline `<style>` block and
22	exactly three pieces, in order:
23	
24	1. **TL;DR section** — four bullets (no nesting except inside *Next steps*).
25	2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR
26	   with no intervening `<h2>`.
27	3. **Experimental design** — a single collapsible `<details>` block holding
28	   everything else (setup, definitions, training/eval, samples, statistical
29	   test, parameters).
30	
31	No table of contents for results of this length. No "Findings" h2, no
32	"Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all
33	fold into the Experimental design narrative.
34	
35	## Title
36	
37	The title is the experiment row's `title` column (not the body). Rules:
38	
39	- One sentence stating the actual finding.
40	- Ends with `(LOW confidence)`, `(MODERATE confidence)`, or
41	  `(HIGH confidence)`.
42	- Must agree with the body — if the body's claim changes, update the title.
43	
44	Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
45	LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
46	(jargon, two findings mashed together, doesn't match the final claim).
47	
48	Good: *"Cosine distance to the paramedic↔comedian midpoint marginally
49	predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
50	
51	## TL;DR (four bullets)
52	
53	```html
54	<section id="tldr" class="tldr">
55	<h2>TL;DR</h2>
56	<ul>
57	  <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li>
58	  <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li>
59	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li>
60	  <li><strong>Next steps.</strong>
61	    <ul>
62	      <li>Concrete follow-up 1 (with issue link if filed).</li>
63	      <li>Concrete follow-up 2.</li>
64	    </ul>
65	  </li>
66	</ul>
67	</section>
68	```
69	
70	Voice notes:
71	
72	- **Plain language**, accessible to a non-specialist. Define jargon as it
73	  appears or wait until the design dropdown.
74	- **Use "I"**, not "we" — single-researcher workflow.
75	- **No casual transitions**: no "One more wrinkle:", no "the buried lede was",
76	  no "funnily enough", no "the real surprise was". Direct declarative voice.
77	- **Link to the figure** from the Results bullet so a reader can jump to it.
78	
79	## Primary plot
80	
81	One plot per result. No "additional figures" block.
82	
83	- **Title** of the plot:
84	  - Short — must fit within the SVG `viewBox` at the rendered title font
85	    size. Test by reading the live rendering; if it visually overflows on
86	    either side, shorten.
87	  - Centered: `text-anchor="middle"` at the viewBox midpoint.
88	  - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`,
89	    `1 − cos(...)`, etc. for the Experimental design dropdown.
90	- **Subtitle**: usually unnecessary. The figcaption almost always covers it.
91	…
4:21:41 AMtool_calltools

tool=Read

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tool=Read

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1	/**
2	 * Static snapshot of the mentor's weekly "Useful" update from the legacy
3	 * GitHub project board. Frozen so the runtime dashboard never has to hit
4	 * GitHub.
5	 *
6	 * To refresh after the user resumes board work and wants to publish new
7	 * results to the mentor, run:
8	 *
9	 *   pnpm --filter @sagan/runner snapshot-mentor
10	 *
11	 * which overwrites apps/web/data/mentor-legacy-results.json. Then commit.
12	 */
13	import data from '../../data/mentor-legacy-results.json' with { type: 'json' };
14	
15	export type Confidence = 'HIGH' | 'MODERATE' | 'LOW' | null;
16	
17	export interface CleanResult {
18	  id: string;
19	  number: number | null;
20	  title: string;
21	  body: string;
22	  excerpt: string;
23	  confidence: Confidence;
24	  useful: boolean;
25	  statusName: 'Useful' | 'Not useful';
26	  createdAt: string;
27	  doneAt: string;
28	  url: string | null;
29	  sourceLabel?: string;
30	  cardKind?: 'github_issue' | 'discussion';
31	}
32	
33	export interface MentorWeeklyUpdate {
34	  title: string;
35	  sourceRepo: string;
36	  sourceProjectUrl: string;
37	  sourceColumn: 'Useful';
38	  generatedAt: string | null;
39	  issueCount: number;
40	  discussionCardCount: number;
41	  results: CleanResult[];
42	}
43	
44	type MentorSnapshot = {
45	  weeklyUpdate?: {
46	    title?: string;
47	    sourceRepo?: string;
48	    sourceProjectUrl?: string;
49	    sourceColumn?: string;
50	    generatedAt?: string;
51	    issueCount?: number;
52	  };
53	  results?: CleanResult[];
54	};
55	
56	const snapshot = data as MentorSnapshot;
57	const mentorUpdateDate = '2026-05-11T00:00:00.000Z';
58	
59	const mentorDiscussionCards: CleanResult[] = [
60	  {
61	    id: '00000000-0000-4000-8000-202605110001',
62	    number: null,
63	    title: 'Questions / next steps',
64	    body: [
65	      '**Overarching:** How does unwanted behavioral generalization from narrow training arise, and how do we defend against it?',
66	      '',
67	      '## Q1. What controls the strength of behavior implantation?',
68	      '',
69	      'Focus: system-prompt length, persona content, and prompt-output consistency.',
70	      '',
71	      '**Established:** Persona-flavored chain-of-thought rationales drive cross-persona leakage (#186, #345). Longer persona prompts make a marker more persona-localized (#337). Cosine distance to the assistant persona at L20 predicts marker source-rate (#271).',
72	      '',
73	      '**Open questions:**',
74	      '',
75	      '1. Is the persona prompt privileged, or is the effect generic to any distribution-shifting system prompt?',
76	      '2. Does implantation strength depend on prompt-output consistency?',
77	      '3. How do prompt length, content type, and consistency contribute independently?',
78	      '',
79	      '**Next step:** Run a controlled panel varying prompt length, content type at matched token count, and prompt-output consistency. Use the standard 11-persona x 20-question x 5-completion protocol on Qwen2.5-7B-Instruct.',
80	      '',
81	      '## Q2. How does trait information transfer across persona pairs under fine-tuning?',
82	      '',
83	      '**Established:** Across multiple recipes, training a marker into one persona does not transfer to a second persona via subsequent SFT (#121, #122, #225). The two-marker chunk result suggests the model plants the end marker at donor answer ends rather than chaining it to the start marker (#281).',
84	      '',
85	      '**Open question:** Are current no-transfer designs accidentally training the model not to transfer by including the natural end-of-sentence token in second-stage SFT?',
86	      '',
87	      '**Next step:** Train A+B into persona1, then train A into persona2 without fine-tuning on the end-of-sentence token. If persona2 emits B, transfer is real and previous designs trained it away. If not, no-transfer is robust.',
88	      '',
89	      '## Q3. Can we hill-climb leakage to elicit hidden pretraining backdoors?',
90	      '',
91	      '**Establis…
4:21:41 AMtool_resulttools
1	# EM Mechanism Project — Positioning & Gap Analysis
2	
3	_Lit-review-style critique of `/p/em-mechanism`, produced 2026-05-12 by an
4	independent research-analyst pass. The companion systematic paper sweep lives
5	at `em-mechanism-lit-review-papers.md`._
6	
7	## 1. Is the two-hypothesis framing a real dichotomy?
8	
9	It is partly real and partly a packaging choice that obscures convergent recent work. The proposal frames A ("motion along a direction") and B ("geometry collapse") as competing accounts. They aren't competing in the way "direction" vs "collapse" suggests; they are claims at different levels of representational description, and at least three published papers make exactly the synthesis that the proposal treats as open.
10	
11	The strongest unifying construct in the literature is **"motion along a low-dimensional subspace that is itself shared across narrow EM tasks."** This is the explicit thesis of **Arturi et al., *Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior* (arXiv:2511.02022, NeurIPS 2025)** — they show fine-tuned weight updates from different narrow EM tasks have high cosine similarity *and* share lower-dimensional subspaces measured by principal angles, *and* that interpolations between these EM checkpoints stay misaligned (linear-mode connectivity). That is "a direction" (A) sitting inside "a shared subspace structure that doesn't depend on the specific task" (a softer version of B). The proposal does not cite this paper. It should.
12	
13	**Soligo et al. (2506.11618)** is also stronger than the proposal acknowledges. They train *9 rank-1 adapters* — i.e. a 9-dimensional update subspace — to emergently misalign Qwen2.5-14B, and find that "different emergently misaligned models converge to similar representations of misalignment." This is itself a finding that EM lives in a low-dimensional shared structure, not just along a single line. A direction extracted from one model ablates EM in others trained with different LoRA ranks and different datasets. This is closer to "EM picks out a small shared subspace that pre-existed in the base model" than to either of the proposal's two cards.
14	
15	**Wang et al. (2506.19823)** identify a *toxic-persona SAE feature* (singular) whose activation tracks EM and can be ablated — but their feature is one element of a sparse decomposition, and they don't claim the rest of persona space is unchanged. Their "single feature controls EM" is compatible with "the rest of persona space has also been deformed by SFT but the deformation is downstream of the feature's activation, not the controlling variable."
16	
17	So the dichotomy as written is a false binary. The real question — and the better framing — is:
18	
19	- **Is EM a motion along a low-dimensional structure that already exists in the base model?** (the consensus from Chen, Soligo, Wang, Arturi — yes)
20	- **Does SFT additionally and orthogonally crush the rest of persona geometry, and does that crushing have any causal role in EM?** (this is the project's actual novel claim, and it is not addressed by Cluster A papers)
21	
22	Re-framing this way clarifies what is contested (the second bullet) and what is not (the first). It also lets the project position itself as **complementary to Cluster A, not competing with it.** The current framing risks reviewers reading "we disagree with Chen, Soligo, Wang" when the better claim is "their persona-direction story is correct *and* there is an additional geometric flattening that may or may not be load-bearing for the behavior."
23	
24	"Motion in a collapsed subspace" is coherent and likely the right description of what happens. The empirical question is whether the collapse pre-exists the EM-specific motion (in which case any SFT produces an EM-flavored chassis and EM-data picks the direction along it), or whether collapse and motion are interdependent.
25	
26	## 2. How load-bearing is the methodology gap?
27	
28	The proposal makes Q1 (ce…
4:21:42 AMtool_calltools

tool=Read

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4:21:42 AMtool_resulttools
1	import { sql } from 'drizzle-orm';
2	import {
3	  boolean,
4	  date,
5	  index,
6	  integer,
7	  jsonb,
8	  pgEnum,
9	  pgTable,
10	  text,
11	  timestamp,
12	  unique,
13	  uuid,
14	  varchar,
15	  vector,
16	} from 'drizzle-orm/pg-core';
17	
18	// ─── Enums ─────────────────────────────────────────────────────────────────
19	
20	export const entityKindEnum = pgEnum('entity_kind', [
21	  'project',
22	  'belief',
23	  'experiment',
24	  'run',
25	  'clean_result',
26	  'todo',
27	  'lit_item',
28	  'project_narrative',
29	  'daily_log_entry',
30	  'weekly_digest',
31	]);
32	
33	export const confidenceEnum = pgEnum('confidence', ['LOW', 'MODERATE', 'HIGH']);
34	
35	export const cleanResultStatusEnum = pgEnum('clean_result_status', [
36	  'draft',
37	  'reviewing',
38	  'approved',
39	  'shared',
40	  'archived',
41	  'blocked',
42	]);
43	
44	export const beliefStatusEnum = pgEnum('belief_status', [
45	  'draft',
46	  'active',
47	  'supported',
48	  'weakened',
49	  'falsified',
50	  'retracted',
51	  'archived',
52	]);
53	
54	export const projectStatusEnum = pgEnum('project_status', [
55	  'active',
56	  'paused',
57	  'completed',
58	  'archived',
59	]);
60	
61	export const projectNarrativeStatusEnum = pgEnum('project_narrative_status', [
62	  'draft',
63	  'published',
64	  'archived',
65	]);
66	
67	export const todoStatusEnum = pgEnum('todo_status', [
68	  'inbox',
69	  'scoped',
70	  'planning',
71	  'open',
72	  'in_progress',
73	  'running',
74	  'interpreting',
75	  'awaiting_promotion',
76	  'blocked',
77	  'done',
78	  'cancelled',
79	  'archived',
80	]);
81	
82	export const todoIntentEnum = pgEnum('todo_intent', [
83	  'exploratory',
84	  'hypothesis',
85	  'replication',
86	  'measurement',
87	  'engineering',
88	]);
89	
90	export const priorityEnum = pgEnum('priority', ['low', 'normal', 'high', 'urgent']);
91	
92	export const experimentKindEnum = pgEnum('experiment_kind', [
93	  'experiment',
94	  'infra',
95	  'survey',
96	]);
97	
98	export const computeSizeEnum = pgEnum('compute_size', ['none', 'small', 'medium', 'large']);
99	
100	export const assigneeKindEnum = pgEnum('assignee_kind', ['agent', 'human']);
101	
102	export const experimentStatusEnum = pgEnum('experiment_status', [
103	  'proposed',
104	  'planning',
105	  'plan_pending',
106	  'approved',
107	  'awaiting_approval',
108	  'queued',
109	  'running',
110	  'verifying',
111	  'interpreting',
112	  'reviewing',
113	  'awaiting_promotion',
114	  'shared',
115	  'blocked',
116	  'completed',
117	  'failed',
118	  'cancelled',
119	  'archived',
120	]);
121	
122	export const runClassificationEnum = pgEnum('run_classification', [
123	  'pending',
124	  'useful',
125	  'not_useful',
126	  'archived',
127	]);
128	
129	export const litItemTypeEnum = pgEnum('lit_item_type', [
130	  'paper',
131	  'blog_post',
132	  'forum_post',
133	  'newsletter',
134	  'report',
135	  'repo',
136	  'video',
137	  'other',
138	]);
139	
140	export const litReadStateEnum = pgEnum('lit_read_state', [
141	  'unread',
142	  'queued',
143	  'reading',
144	  'read',
145	  'archived',
146	]);
147	
148	export const litSourceKindEnum = pgEnum('lit_source_kind', [
149	  'arxiv',
150	  'openreview',
151	  'semantic_scholar',
152	  'hn',
153	  'twitter_list',
154	  'rss',
155	]);
156	
157	export const edgeTypeEnum = pgEnum('edge_type', [
158	  'parent',
159	  'child',
160	  'sibling',
161	  'supports',
162	  'contradicts',
163	  'derives_from',
164	  'cites',
165	  'tests',
166	  'produces_evidence_for',
167	  'blocks',
168	  'answers',
169	  'duplicates',
170	  'method',
171	  'baseline',
172	  'background',
173	  'threat',
174	  'inspiration',
175	]);
176	
177	export const agentRunKindEnum = pgEnum('agent_run_kind', [
178	  'plan',
179	  'apply',
180	  'qa',
181	  'experiment',
182	]);
183	
184	export const agentRunStatusEnum = pgEnum('agent_run_status', [
185	  'queued',
186	  'running',
187	  'awaiting_approval',
188	  'approved',
189	  'rejected',
190	  'deploying',
191	  'blocked…
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1	/**
2	 * One-shot seed: ensures the single owner user row exists.
3	 * Run with: `pnpm --filter @sagan/db tsx src/seed.ts`
4	 *
5	 * Idempotent: re-running with the same email is a no-op (does not rotate
6	 * the password). Pass `--rotate-password` to forcibly update the hash.
7	 */
8	import { eq } from 'drizzle-orm';
9	import { drizzle } from 'drizzle-orm/postgres-js';
10	import postgres from 'postgres';
11	import { hash } from '@node-rs/argon2';
12	import { users } from './schema/index';
13	
14	async function main() {
15	  const url = process.env.DATABASE_URL_DIRECT ?? process.env.DATABASE_URL;
16	  const email = process.env.DASHBOARD_OWNER_EMAIL;
17	  const password = process.env.DASHBOARD_OWNER_PASSWORD;
18	
19	  if (!url) throw new Error('DATABASE_URL[_DIRECT] is not set');
20	  if (!email) throw new Error('DASHBOARD_OWNER_EMAIL is not set');
21	  if (!password) throw new Error('DASHBOARD_OWNER_PASSWORD is not set');
22	
23	  const rotate = process.argv.includes('--rotate-password');
24	
25	  const client = postgres(url, { max: 1 });
26	  const db = drizzle(client);
27	
28	  const existing = await db.select().from(users).where(eq(users.email, email)).limit(1);
29	  const passwordHash = await hash(password, {
30	    memoryCost: 19_456,
31	    timeCost: 2,
32	    outputLen: 32,
33	    parallelism: 1,
34	  });
35	
36	  if (existing.length === 0) {
37	    await db.insert(users).values({ email, passwordHash, displayName: 'Thomas' });
38	    console.log(`seeded owner user ${email}`);
39	  } else if (rotate) {
40	    await db
41	      .update(users)
42	      .set({ passwordHash, updatedAt: new Date() })
43	      .where(eq(users.email, email));
44	    console.log(`rotated password for ${email}`);
45	  } else {
46	    console.log(`user ${email} already exists; pass --rotate-password to update`);
47	  }
48	
49	  await client.end();
50	}
51	
52	main().catch((err) => {
53	  console.error(err);
54	  process.exit(1);
55	});
56	
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total 60
drwxrwxr-x 2 thomasjiralerspong thomasjiralerspong  4096 May 12 19:05 .
drwxrwxr-x 3 thomasjiralerspong thomasjiralerspong  4096 May 10 10:03 ..
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 52030 May 12 19:05 index.ts
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tool=Grep

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Found 8 files
services/runner/scripts/em-mechanism-body-v2.html
docs/em-mechanism-lit-review-papers.md
docs/em-mechanism-lit-review-analysis.md
services/runner/scripts/em-mechanism-body.html
services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
apps/web/src/lib/ideation.ts
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services/runner/scripts/narrative-revised-body.html
apps/web/src/lib/mentor-results-data.ts
apps/web/data/mentor-legacy-results.json
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/home/thomasjiralerspong/sagan/docs/exec-plans/mentor-snapshot-clean-results-plan.md
/home/thomasjiralerspong/sagan/docs/exec-plans/dashboard-aesthetic-redesign-plan.md
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md
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/home/thomasjiralerspong/sagan/docs/ops/sagan-ops.md
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-papers.md
/home/thomasjiralerspong/sagan/docs/exec-plans/mentor-snapshot-clean-results-plan.md
/home/thomasjiralerspong/sagan/docs/exec-plans/dashboard-aesthetic-redesign-plan.md
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md
/home/thomasjiralerspong/sagan/docs/em-mechanism-lit-review-analysis.md
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apps/web/data/mentor-legacy-results.json
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services/runner/src/log.ts
services/runner/src/env.ts
services/runner/src/db.ts
services/runner/src/lib/push.ts
services/runner/src/jobs/job-runs.ts
services/runner/src/jobs/insight-scan.ts
services/runner/src/trail.ts
services/runner/src/tools/runpod.ts
services/runner/src/watcher.ts
services/runner/src/email.ts
services/runner/src/notifications.ts
services/runner/src/index.ts
services/runner/src/jobs/weekly-digest.ts
services/runner/src/jobs/project-lit-review.ts
services/runner/src/jobs/lit-review.ts
services/runner/src/lib/cascade-failure.ts
services/runner/src/lib/run-agent.ts
services/runner/src/lib/agent-recovery.ts
services/runner/src/queue.ts
services/runner/src/dispatcher.ts
services/runner/src/session.ts
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/home/thomasjiralerspong/sagan/services/runner/scripts/apply-narrative-edit.py
/home/thomasjiralerspong/sagan/node_modules/flatted/python/flatted.py
/home/thomasjiralerspong/sagan/node_modules/shell-quote/print.py
/home/thomasjiralerspong/sagan/node_modules/katex/src/fonts/generate_fonts.py
/home/thomasjiralerspong/sagan/node_modules/katex/src/metrics/extract_ttfs.py
/home/thomasjiralerspong/sagan/node_modules/katex/src/metrics/extract_tfms.py
/home/thomasjiralerspong/sagan/node_modules/katex/src/metrics/parse_tfm.py
/home/thomasjiralerspong/sagan/node_modules/katex/src/metrics/format_json.py
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/home/thomasjiralerspong/sagan/node_modules/data-view-buffer/package.json
/home/thomasjiralerspong/sagan/node_modules/data-view-buffer/tsconfig.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/overlapping-plugins.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/plugins.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/corejs3-shipped-proposals.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/native-modules.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/plugin-bugfixes.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/data/corejs2-built-ins.json
/home/thomasjiralerspong/sagan/node_modules/@babel/compat-data/package.json
/home/thomasjiralerspong/sagan/node_modules/@babel/helper-globals/data/browser-upper.json
/home/thomasjiralerspong/sagan/node_modules/@babel/helper-globals/data/builtin-lower.json
/home/thomasjiralerspong/sagan/node_modules/@babel/helper-globals/data/builtin-upper.json
/home/thomasjiralerspong/sagan/node_modules/data-view-byte-length/package.json
/home/thomasjiralerspong/sagan/node_modules/data-view-byte-length/tsconfig.json
/home/thomasjiralerspong/sagan/node_modules/node-releases/data/processed/envs.json
/home/thomasjiralerspong/sagan/node_modules/node-releases/data/release-schedule/release-schedule.json
/home/thomasjiralerspong/sagan/node_modules/define-data-property/package.json
/home/thomasjiralerspong/sagan/node_modules/define-data-property/tsconfig.json
/home/thomasjiralerspong/sagan/node_modules/ajv/lib/refs/data.json
/home/thomasjiralerspong/sagan/node_modules/next/dist/compiled/react-experimental/package.json
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tool=Read

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4:21:49 AMtool_resulttools
File content (129545 tokens) exceeds maximum allowed tokens (25000). Use offset and limit parameters to read specific portions of the file, or search for specific content instead of reading the whole file.
4:21:51 AMtool_resulttools
/home/thomasjiralerspong/sagan/services/runner/src/session.ts:  if (/return only the comment text/i.test(trimmed)) return 'instruction leakage';
/home/thomasjiralerspong/sagan/services/runner/src/session.ts:  if (/^output the (?:exact )?comment reply now[.!]?$/i.test(normalized)) return 'instruction leakage';
/home/thomasjiralerspong/sagan/services/runner/src/session.ts:  if (/^write the (?:exact )?comment reply(?: now)?[.!]?$/i.test(normalized)) return 'instruction leakage';
/home/thomasjiralerspong/sagan/services/runner/src/session.ts:    return 'instruction leakage';
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- Context leakage.
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  `<title>cybersec_consultant: midpoint distance: +0.005, extra leakage: +0.055</title>`).
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  <p><strong>Joint-specific leakage.</strong> ... r_p definition ...</p>
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:> joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  found that cosine similarity to a trained source predicts marker leakage
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  show slightly more joint-specific `[ZLT]` leakage — the predicted direction
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  +0.005, extra leakage: +0.055"*.
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:4. *Joint-specific leakage* — `r_p` formula and Bernoulli-union baseline
/home/thomasjiralerspong/sagan/node_modules/eslint-import-resolver-node/node_modules/debug/CHANGELOG.md:  * Update: refactor Readme, fixed documentation, added "Namespace Colors" section, redid screenshots
/home/thomasjiralerspong/sagan/node_modules/react-native-gesture-handler/lib/typescript/handlers/GestureHandlerEventPayload.d.ts:     * The scale factor relative to the points of the two touches in screen
/home/thomasjiralerspong/sagan/node_modules/react-native-gesture-handler/src/handlers/GestureHandlerEventPayload.ts:   * The scale factor relative to the points of the two touches in screen
/home/thomasjiralerspong/sagan/node_modules/@react-native/debugger-frontend/dist/third-party/front_end/models/issues_manager/descriptions/corsInsecurePrivateNetwork.md:These requests expose devices and servers to the internet, increasing the risk of a cross-site request forgery (CSRF) attack, and/or information leakage.
/home/thomasjiralerspong/sagan/node_modules/@react-native/debugger-frontend/dist/third-party/front_end/models/issues_manager/descriptions/corsPreflightAllowPrivateNetworkError.md:These requests expose devices and servers to the internet, increasing the risk of a cross-site request forgery (CSRF) attack, and/or information leakage.
/home/thomasjiralerspong/sagan/node_modules/@react-native/debugger-frontend/dist/third-party/front_end/models/issues_manager/descriptions/corsPrivateNetworkPermissionDenied.md:These requests expose devices and servers to the internet, increasing the risk of a cross-site request forgery (CSRF) attack and/or information leakage.
4:21:51 AMtool_calltools

tool=Grep

Grep
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Found 1 file
apps/web/data/mentor-legacy-results.json
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tool=Grep

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services/runner/src/tools/runpod.ts
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total 128
drwxrwxr-x 3 thomasjiralerspong thomasjiralerspong  4096 May 12 19:05 .
drwxrwxr-x 5 thomasjiralerspong thomasjiralerspong  4096 May 10 10:03 ..
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong 22946 May  9 05:44 0000_smiling_loki.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   390 May  9 06:08 0001_puzzling_blur.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   786 May 10 03:45 0002_sudden_baron_strucker.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  2957 May 10 20:29 0003_workflow_audit.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  7367 May 10 23:13 0004_milky_edwin_jarvis.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  4518 May 10 23:36 0005_sweet_redwing.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong    54 May 10 23:42 0006_many_proemial_gods.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  4154 May 11 00:00 0007_rare_spitfire.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  3953 May 11 00:10 0008_high_karma.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   326 May 11 00:31 0009_tearful_butterfly.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  2214 May 11 00:41 0010_ambitious_ma_gnuci.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong    54 May 11 08:01 0011_moaning_ultragirl.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong    83 May 11 08:17 0012_slim_maria_hill.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   193 May 11 08:47 0013_chat_sessions_archive.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong  1514 May 12 00:40 0014_fearless_kylun.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   435 May 12 00:40 0015_dizzy_silvermane.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   875 May 12 00:59 0016_cynical_screwball.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong    66 May 12 01:03 0017_yielding_human_torch.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   598 May 12 05:49 0018_keen_susan_delgado.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   715 May 12 06:52 0019_black_marvex.sql
-rw-rw-r-- 1 thomasjiralerspong thomasjiralerspong   703 May 12 19:05 0020_burly_peter_parker.sql
drwxrwxr-x 2 thomasjiralerspong thomasjiralerspong  4096 May 12 19:05 meta
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4:21:53 AMtool_resulttools
1	/**
2	 * RunPod GraphQL client. TypeScript port of the explore-persona-space
3	 * Python adapter (scripts/runpod_api.py). Two scopes:
4	 *
5	 *   - account = 'team'     → uses RUNPOD_API_KEY_TEAM + RUNPOD_TEAM_ID_TEAM.
6	 *                            Falls back to legacy RUNPOD_API_KEY/RUNPOD_TEAM_ID
7	 *                            so existing setups work without changes.
8	 *   - account = 'personal' → uses RUNPOD_API_KEY_PERSONAL.
9	 *                            No team header sent (RunPod rejects X-Team-Id on
10	 *                            personal-account API keys).
11	 *
12	 * The team scope is hard-pinned to Anthropic Safety Research by default, since
13	 * RunPod silently returns zero pods if the wrong scope is used (a confusing
14	 * footgun). Set RUNPOD_TEAM_ID_TEAM to override.
15	 */
16	
17	const GRAPHQL_URL = 'https://api.runpod.io/graphql';
18	const ANTHROPIC_SAFETY_RESEARCH_TEAM_ID = 'cm8ipuyys0004l108gb23hody';
19	
20	export const DEFAULT_IMAGE = 'runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04';
21	export const DEFAULT_VOLUME_GB = 200;
22	export const DEFAULT_CONTAINER_DISK_GB = 50;
23	
24	// RunPod requires GPU type IDs in the exact form below.
25	export const GPU_TYPE_IDS: Record<string, string> = {
26	  H100: 'NVIDIA H100 80GB HBM3',
27	  H200: 'NVIDIA H200',
28	  A100: 'NVIDIA A100-SXM4-80GB',
29	  L40S: 'NVIDIA L40S',
30	  RTX4090: 'NVIDIA GeForce RTX 4090',
31	};
32	
33	export type RunpodAccount = 'team' | 'personal';
34	
35	export class RunPodError extends Error {
36	  constructor(message: string) {
37	    super(message);
38	    this.name = 'RunPodError';
39	  }
40	}
41	
42	interface PodPort {
43	  ip?: string;
44	  publicPort?: number;
45	  privatePort?: number;
46	  type?: string;
47	  isIpPublic?: boolean;
48	}
49	
50	interface RawPod {
51	  id: string;
52	  name?: string;
53	  desiredStatus?: string;
54	  gpuCount?: number;
55	  createdAt?: string;
56	  machine?: { gpuTypeId?: string };
57	  runtime?: { ports?: PodPort[] };
58	}
59	
60	export interface PodInfo {
61	  podId: string;
62	  name: string;
63	  desiredStatus: string;
64	  gpuCount: number | null;
65	  gpuTypeId: string | null;
66	  sshHost: string | null;
67	  sshPort: number | null;
68	  createdAt: string | null;
69	}
70	
71	interface AccountAuth {
72	  apiKey: string;
73	  teamId: string | null;
74	}
75	
76	function resolveAuth(account: RunpodAccount): AccountAuth {
77	  if (account === 'personal') {
78	    const apiKey = (process.env.RUNPOD_API_KEY_PERSONAL ?? '').trim();
79	    if (!apiKey) {
80	      throw new RunPodError(
81	        'RUNPOD_API_KEY_PERSONAL is not set. Add it to .env to use account=personal.',
82	      );
83	    }
84	    return { apiKey, teamId: null };
85	  }
86	  // account === 'team' — fall through empty strings, not just undefined.
87	  const apiKey =
88	    (process.env.RUNPOD_API_KEY_TEAM?.trim() ||
89	      process.env.RUNPOD_API_KEY?.trim() ||
90	      '');
91	  const teamId =
92	    process.env.RUNPOD_TEAM_ID_TEAM?.trim() ||
93	    process.env.RUNPOD_TEAM_ID?.trim() ||
94	    ANTHROPIC_SAFETY_RESEARCH_TEAM_ID;
95	  if (!apiKey) {
96	    throw new RunPodError(
97	      'RUNPOD_API_KEY_TEAM (or legacy RUNPOD_API_KEY) is not set. Add it to .env to use account=team.',
98	    );
99	  }
100	  if (!teamId) {
101	    throw new RunPodError('RUNPOD_TEAM_ID resolved to empty for account=team.');
102	  }
103	  return { apiKey, teamId };
104	}
105	
106	async function graphql<T>(
107	  account: RunpodAccount,
108	  query: string,
109	  variables?: Record<string, unknown>,
110	  timeoutMs = 60_000,
111	): Promise<T> {
112	  const { apiKey, teamId } = resolveAuth(account);
113	  const controller = new AbortController();
114	  const timer = setTimeout(() => controller.abort(), timeoutMs);
115	
116	  const headers: Record<string, string> = {
117	    Authorization: `Bearer ${apiKey}`,
118	    'Content-Type': 'application/json',
119	    // RunPod's CF rules block the default fetch UA; use a curl-shaped one.
120	   …
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1	/**
2	 * Wraps a single agent_runs row → Claude Agent SDK query() invocation.
3	 *
4	 * Streams every SDKMessage into agent_run_events as it arrives, captures the
5	 * plan_md when the model invokes ExitPlanMode, and finalizes the run row when
6	 * the SDKResultMessage arrives.
7	 */
8	import { type Options, type SDKMessage } from '@anthropic-ai/claude-agent-sdk';
9	import { readFile } from 'node:fs/promises';
10	import { runAgentWithContinuation } from './lib/run-agent.js';
11	import { cascadeAgentRunFailureToScope } from './lib/cascade-failure.js';
12	import { and, asc, desc, eq, isNull, ne } from 'drizzle-orm';
13	import { db, schema } from './db.js';
14	import { emitEvent, notifyPipelineChanged } from './queue.js';
15	import { env, requireEnv } from './env.js';
16	import { log } from './log.js';
17	import { pushToUser } from './lib/push.js';
18	import { recordTrail } from './trail.js';
19	import { notifyClaudeFinished } from './notifications.js';
20	import { queueAutomaticContinuationRun, queueAutomaticRecoveryRun } from './lib/agent-recovery.js';
21	
22	type AgentRunRow = typeof schema.agentRuns.$inferSelect;
23	type StructuredPlan = {
24	  goal?: string;
25	  hypothesis?: string;
26	  prediction?: string;
27	  killCriterion?: string;
28	  compute?: string;
29	  hardware?: string;
30	  artifacts?: string;
31	  verification?: string;
32	  risks?: string;
33	  likelyCleanResult?: string;
34	  sections: Array<{ title: string; body: string }>;
35	};
36	
37	type Outcome =
38	  | { ok: true; status: 'awaiting_approval'; planMd: string }
39	  | { ok: true; status: 'completed'; resultText: string; costUsd: number; numTurns: number }
40	  | { ok: false; error: string };
41	
42	const ASK_CODEX_RE = /(^|\s)@codex\b/i;
43	const COMMENT_RESPONDER_RE = /^Comment responder:\s*(Claude|Codex)\b/im;
44	const CODEX_REPLY_MARKER = '<!-- agent:codex -->';
45	
46	export async function runSession(runId: string): Promise<Outcome> {
47	  const row = await loadRun(runId);
48	  if (!row) return { ok: false, error: `run ${runId} not found` };
49	
50	  // Make sure the runner can talk to Anthropic.
51	  requireEnv('ANTHROPIC_API_KEY');
52	  const chatSession = row.chatSessionId ? await loadChatSession(row.chatSessionId) : null;
53	  const priorChatRunExists =
54	    row.kind === 'qa' && row.chatSessionId ? await hasPriorChatRun(row.chatSessionId, row.id) : false;
55	  const chatResumeId =
56	    row.kind === 'qa' && row.chatSessionId
57	      ? (chatSession?.agentHandle ?? (priorChatRunExists ? row.chatSessionId : null))
58	      : null;
59	  const chatStartId =
60	    row.kind === 'qa' && row.chatSessionId && !chatResumeId ? row.chatSessionId : null;
61	
62	  const options: Options = {
63	    cwd: env.RUNNER_REPO_ROOT,
64	    permissionMode: row.kind === 'plan' || row.kind === 'experiment' ? 'plan' : 'acceptEdits',
65	    env: process.env as Record<string, string>,
66	    pathToClaudeCodeExecutable: env.CLAUDE_CLI_PATH,
67	    // Conservative tool restriction: disable Bash and write tools for QA mode.
68	    ...(row.kind === 'qa'
69	      ? { allowedTools: ['Read', 'Grep', 'Glob'], disallowedTools: ['Bash', 'Edit', 'Write'] }
70	      : {}),
71	    ...(chatResumeId ? { resume: chatResumeId } : chatStartId ? { sessionId: chatStartId } : {}),
72	  };
73	
74	  const prompt = await buildPrompt(row);
75	  await emitEvent(runId, 'started', `kind=${row.kind}`, { permissionMode: options.permissionMode });
76	  await recordTrail({
77	    action: `Runner started ${row.kind} run ${runId.slice(0, 8)}`,
78	    why: row.request.slice(0, 500),
79	    entityKind: row.scopeEntityKind,
80	    entityId: row.scopeEntityId,
81	    agentRunId: runId,
82	    detail: `permissionMode=${options.permissionMode}`,
83	  });
84	
85	  const result = await runWithStreaming(runId, row, prompt, options, chatResumeId);
86	  return result;
87	}
88	
89	const HEARTBEAT_INTERVAL_MS = 30_000;
90	
91	async function bumpHeartbeat(runId: string) {
92	  try {
93	    await db()
94	      .upd…
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1	# Clean Result Guidelines
2	
3	How to write a publication-quality HTML clean-result attached to an
4	experiment entity (rendered by `<RichBody>` on `/e/experiment/[id]` and the
5	mentor view). These guidelines were distilled from iterating on experiment
6	[#311](https://sagan.superkaiba.com/e/experiment/1d61738d-df62-44af-9c79-fa41fe85f598),
7	which is the worked example at the bottom.
8	
9	> Scope note: this doc is for the **full HTML write-up** that lives on the
10	> experiment's `body` field. The shorter Haiku-drafted daily snapshot
11	> (`daily_log_entries.kind = 'clean_result'`) is a separate, terser artifact
12	> generated from daily-log entries; the `## TL;DR / ## Hypothesis / ## Plot`
13	> markdown skeleton in `docs/exec-plans/mentor-snapshot-clean-results-plan.md`
14	> is the right reference for that. The two artifacts share principles but
15	> not structure.
16	
17	---
18	
19	## Top-level structure
20	
21	The body is a self-contained HTML document with an inline `<style>` block and
22	exactly three pieces, in order:
23	
24	1. **TL;DR section** — four bullets (no nesting except inside *Next steps*).
25	2. **Primary plot** — `<figure id="figure">`, sits directly under the TL;DR
26	   with no intervening `<h2>`.
27	3. **Experimental design** — a single collapsible `<details>` block holding
28	   everything else (setup, definitions, training/eval, samples, statistical
29	   test, parameters).
30	
31	No table of contents for results of this length. No "Findings" h2, no
32	"Background" h2, no "Reproducibility" h2, no "Sample outputs" h2 — those all
33	fold into the Experimental design narrative.
34	
35	## Title
36	
37	The title is the experiment row's `title` column (not the body). Rules:
38	
39	- One sentence stating the actual finding.
40	- Ends with `(LOW confidence)`, `(MODERATE confidence)`, or
41	  `(HIGH confidence)`.
42	- Must agree with the body — if the body's claim changes, update the title.
43	
44	Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
45	LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
46	(jargon, two findings mashed together, doesn't match the final claim).
47	
48	Good: *"Cosine distance to the paramedic↔comedian midpoint marginally
49	predicts joint-source [ZLT] leakage on Qwen2.5-7B-Instruct (LOW confidence)"*.
50	
51	## TL;DR (four bullets)
52	
53	```html
54	<section id="tldr" class="tldr">
55	<h2>TL;DR</h2>
56	<ul>
57	  <li><strong>Motivation.</strong> Why this is interesting. Cite prior issues / results.</li>
58	  <li><strong>What I ran.</strong> Intuitive narrative of the setup.</li>
59	  <li><strong>Results (see <a href="#figure">figure below</a>).</strong> One-sentence finding + effect size + sample size.</li>
60	  <li><strong>Next steps.</strong>
61	    <ul>
62	      <li>Concrete follow-up 1 (with issue link if filed).</li>
63	      <li>Concrete follow-up 2.</li>
64	    </ul>
65	  </li>
66	</ul>
67	</section>
68	```
69	
70	Voice notes:
71	
72	- **Plain language**, accessible to a non-specialist. Define jargon as it
73	  appears or wait until the design dropdown.
74	- **Use "I"**, not "we" — single-researcher workflow.
75	- **No casual transitions**: no "One more wrinkle:", no "the buried lede was",
76	  no "funnily enough", no "the real surprise was". Direct declarative voice.
77	- **Link to the figure** from the Results bullet so a reader can jump to it.
78	
79	## Primary plot
80	
81	One plot per result. No "additional figures" block.
82	
83	- **Title** of the plot:
84	  - Short — must fit within the SVG `viewBox` at the rendered title font
85	    size. Test by reading the live rendering; if it visually overflows on
86	    either side, shorten.
87	  - Centered: `text-anchor="middle"` at the viewBox midpoint.
88	  - **No mathematical notation** — plain English. Save `ρ`, `m`, `h(p)`,
89	    `1 − cos(...)`, etc. for the Experimental design dropdown.
90	- **Subtitle**: usually unnecessary. The figcaption almost always covers it.
91	…
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tool=Bash

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1	import { desc, eq, inArray } from 'drizzle-orm';
2	import {
3	  beliefs,
4	  cleanResults,
5	  ideaCards,
6	  ideaSessions,
7	  litItems,
8	  projects,
9	} from '@sagan/db/schema';
10	import { db } from './db';
11	import type { EntityKind } from './entity';
12	
13	export const IDEATION_SOURCE_KINDS = ['clean_result', 'belief', 'lit_item', 'project'] as const;
14	export type IdeationSourceKind = (typeof IDEATION_SOURCE_KINDS)[number];
15	
16	export const PROMOTION_KINDS = [
17	  'experiment',
18	  'belief_update',
19	  'literature_task',
20	  'clean_result_question',
21	] as const;
22	export type PromotionKind = (typeof PROMOTION_KINDS)[number];
23	
24	export interface IdeationSource {
25	  kind: IdeationSourceKind;
26	  id: string;
27	  title: string;
28	  body: string;
29	  status?: string | null;
30	}
31	
32	interface NearbyContext {
33	  beliefs: Array<{ id: string; title: string; body: string | null }>;
34	  literature: Array<{ id: string; title: string; reason: string | null; threat: string | null }>;
35	  cleanResults: Array<{ id: string; title: string; claim: string }>;
36	}
37	
38	export function isIdeationSourceKind(value: string): value is IdeationSourceKind {
39	  return (IDEATION_SOURCE_KINDS as readonly string[]).includes(value);
40	}
41	
42	export async function loadIdeationSource(kind: IdeationSourceKind, id: string): Promise<IdeationSource | null> {
43	  switch (kind) {
44	    case 'clean_result': {
45	      const rows = await db().select().from(cleanResults).where(eq(cleanResults.id, id)).limit(1);
46	      const row = rows[0];
47	      return row
48	        ? { kind, id: row.id, title: row.title, body: [row.claim, row.bodyMd].filter(Boolean).join('\n\n'), status: row.status }
49	        : null;
50	    }
51	    case 'belief': {
52	      const rows = await db().select().from(beliefs).where(eq(beliefs.id, id)).limit(1);
53	      const row = rows[0];
54	      return row
55	        ? {
56	            kind,
57	            id: row.id,
58	            title: row.title,
59	            body: [row.currentBelief, row.evidence, row.counterevidence, row.nextTest].filter(Boolean).join('\n\n'),
60	            status: row.status,
61	          }
62	        : null;
63	    }
64	    case 'lit_item': {
65	      const rows = await db().select().from(litItems).where(eq(litItems.id, id)).limit(1);
66	      const row = rows[0];
67	      return row
68	        ? {
69	            kind,
70	            id: row.id,
71	            title: row.title,
72	            body: [row.summaryMd, row.relevanceReasonMd, row.threatReasonMd, row.abstract].filter(Boolean).join('\n\n'),
73	            status: row.readState,
74	          }
75	        : null;
76	    }
77	    case 'project': {
78	      const rows = await db().select().from(projects).where(eq(projects.id, id)).limit(1);
79	      const row = rows[0];
80	      return row ? { kind, id: row.id, title: row.title, body: row.summaryMd ?? '', status: row.status } : null;
81	    }
82	  }
83	}
84	
85	export async function buildPromptDeck(source: IdeationSource): Promise<string[]> {
86	  const nearby = await loadNearbyContext(source);
87	  const literaturePrompt = nearby.literature[0]
88	    ? `What would change if "${nearby.literature[0].title}" is right?`
89	    : `Which recent paper would most threaten "${source.title}"?`;
90	  const beliefPrompt = nearby.beliefs[0]
91	    ? `How should "${nearby.beliefs[0].title}" change if this source is replicated?`
92	    : `What belief would this source most strongly update?`;
93	  return [
94	    `What assumption behind "${source.title}" would fail first?`,
95	    `What variable in "${source.title}" has never been ablated?`,
96	    `What would a skeptical collaborator ask before trusting this?`,
97	    `What cheap experiment would make this source less ambiguous?`,
98	    literaturePrompt,
99	    beliefPrompt,
100	    `What clean result would be worth writing if this line of work succeeds?`,
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docs/ops/sagan-ops.md
docs/exec-plans/sagan-rescue-plan.md
docs/exec-plans/dashboard-aesthetic-redesign-plan.md
docs/clean-result-guidelines.md
docs/exec-plans/mentor-snapshot-clean-results-plan.md
docs/em-mechanism-lit-review-analysis.md
docs/em-mechanism-lit-review-papers.md
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services/runner/scripts/narrative-revised-body.html
apps/web/data/mentor-legacy-results.json
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total 16
drwxrwxr-x 4 thomasjiralerspong thomasjiralerspong 4096 May  9 07:00 .
drwxrwxr-x 9 thomasjiralerspong thomasjiralerspong 4096 May 12 19:17 ..
drwxrwxr-x 5 thomasjiralerspong thomasjiralerspong 4096 May 13 03:57 components
drwxrwxr-x 2 thomasjiralerspong thomasjiralerspong 4096 May 12 19:40 lib
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/home/thomasjiralerspong/sagan/apps/web/app/api/experiments
/home/thomasjiralerspong/sagan/apps/web/app/(app)/experiments
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1	# Sagan Rescue Plan: Research Operating System
2	
3	## Product Understanding
4	
5	Sagan should let the owner do research without touching terminals, GitHub
6	project boards, ad hoc scripts, or separate literature/reporting tools. The
7	core loop is:
8	
9	`idea -> plan -> approval -> RunPod experiment -> interpretation -> clean result -> comments/Q&A -> revision -> approval/share -> followups`
10	
11	Everything else in the product should reduce cognitive load around that loop.
12	
13	## Target Outcome
14	
15	A trusted web/mobile research dashboard that shows running experiments,
16	yesterday's meaningful work, next steps, relevant literature, clean-result
17	drafts, weekly reviews, collaborator comments, Claude answers, and ideation
18	prompts.
19	
20	After one month, the owner should trust Sagan as the full research dashboard
21	and should not need to use a terminal for normal research workflow.
22	
23	## Non-Goals
24	
25	- No secret rotation.
26	- No destructive data deletion.
27	- No automatic sending to advisors, mentors, or collaborators.
28	- No citation-management focus beyond paper metadata, summaries, ranking, and
29	  Q&A.
30	- No requirement to preserve current UI/API compatibility during the refactor.
31	  Production research data must survive.
32	- No full import of every historical GitHub issue. Import clean results; link
33	  and search everything else.
34	
35	## Locked Decisions
36	
37	- Use a hybrid rescue, not a greenfield rewrite.
38	- Protect production research data; UI/API compatibility can break.
39	- Import clean results from `/home/thomasjiralerspong/explore-persona-space`;
40	  link/search all other old issues and artifacts.
41	- Multi-user accounts are required for mentors and collaborators.
42	- Collaborators see scoped project context, not raw private notes or agent logs
43	  unless explicitly shared.
44	- Claude replies to comments immediately and is clearly attributed as Claude.
45	- Claude may automatically propose experiments, clearly marked as
46	  Claude-proposed. Queuing, plan approval, and launching still require owner
47	  approval.
48	- Weekly review drafts every Sunday by default. Manual kickoff must also be
49	  available.
50	- Ideation must both propose ideas and prompt the owner with question decks.
51	- Email notifications come first. Leave Slack, Discord, and other channels open
52	  for later.
53	- A one-time freeze/migrate window is acceptable when needed, with backup and
54	  rollback notes.
55	- Experiment approvals use a ranked queue with approve/defer/reject.
56	- After an experiment plan is approved, Sagan may launch real RunPod work
57	  without a second per-launch prompt.
58	- No fixed cost/concurrency cap for approved experiments. The approval
59	  bottleneck is the plan. Still record compute estimates and actual usage.
60	- Failed/stalled agent or experiment workflows auto-try up to 3 times, then
61	  move to blocked with evidence.
62	- Native Expo mobile is first-class.
63	- Public/share links use opaque tokens.
64	
65	## Assumptions And Defaults
66	
67	- "Production research data" means Neon DB rows, local experiment artifacts,
68	  `eval_results`, figures, comments, clean-result drafts, W&B/HF links, share
69	  tokens, GitHub issue history, and agent transcripts.
70	- Existing dirty work may be intentional. Preserve it.
71	- GitHub issues in `explore-persona-space` remain historical evidence, but
72	  Sagan should become the authoritative workflow state machine.
73	- For X/Twitter, do not paste account passwords into chat, code, or docs. Use
74	  OAuth/API credentials, exports, bookmarked/shared URLs, or another explicit
75	  connector path.
76	- Claude answers can post immediately, but product UI must make source and
77	  attribution obvious.
78	- Rough ideation sessions are autosaved, but only promoted idea cards, belief
79	  updates, experiment proposals, comments, or literature tasks enter the
80	  canonical research record.
81	
82	## Current Repo Map
83	
84	Sagan…
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100	- `CLAUDE.md`
101	- `.github/ISSUE_TEMPLATE/experiment.md`
102	- `.github/ISSUE_TEMPLATE/code-change.md`
103	- `.claude/skills/issue/SKILL.md`
104	- `.claude/skills/issue/markers.md`
105	- `.claude/skills/adversarial-planner/SKILL.md`
106	- `.claude/skills/auto-experiment-runner/SKILL.md`
107	- `.claude/skills/experiment-proposer/SKILL.md`
108	- `.claude/skills/clean-results/template.md`
109	- `.claude/skills/clean-results/principles.md`
110	- `.claude/skills/clean-results/checklist.md`
111	- `.claude/skills/promote-clean-result/SKILL.md`
112	- `scripts/pod.py`
113	- `scripts/pod_lifecycle.py`
114	- `scripts/runpod_api.py`
115	- `scripts/pod_watch.py`
116	- `scripts/gpu_heuristics.py`
117	- `scripts/verify_uploads.py`
118	- `scripts/gh_project.py`
119	- `scripts/gh_issue_state.py`
120	
121	## Existing Functionality To Preserve
122	
123	- Monorepo structure and Drizzle schema center.
124	- Auth/session base.
125	- `agent_runs` and `agent_run_events` append-only event model.
126	- SSE/mobile agent approval flow.
127	- `job_runs`, `audit_events`, and daily trail concept.
128	- Comments model with Claude-trigger support.
129	- Today, projects, beliefs, tasks, library, digests, agent surfaces.
130	- Expo mobile approval and push registration path.
131	- Systemd runner model.
132	- Static mentor/public continuity until tokenized replacements exist.
133	- Generic entity detail and polymorphic edges/comments where useful.
134	
135	## Functionality To Delete Or Defer
136	
137	Delete only after explicit confirmation. Defer or consolidate:
138	
139	- Advanced graph visualization as a primary UI.
140	- Date-guessable public digest links. Replace with opaque tokens.
141	- Duplicate push registration APIs.
142	- `/p/` and `/r/` public routes until tokenized sharing is real.
143	- Silent edge insertion from insight scan. Change to suggestions requiring
144	  review.
145	- Full Slack/Discord support.
146	- Whole old-issue import.
147	- Full citation management.
148	
149	## Architecture Direction
150	
151	Keep Sagan's monorepo and make the experiment workflow the architectural spine.
152	
153	Port the `explore-persona-space` issue state machine into Sagan DB/UI:
154	
155	`proposed -> planning -> plan_pending -> approved -> running -> verifying -> interpreting -> reviewing -> awaiting_promotion -> approved/shared or blocked`
156	
157	GitHub issue markers become typed Sagan workflow events. Sagan comments become
158	the human/Claude review surface. Agent runs stay append-only and auditable.
159	
160	The runner should become a workflow engine, not just "run Claude and log
161	output." It should orchestrate:
162	
163	- adversarial planning;
164	- approval gates;
165	- RunPod launch/watch/retry;
166	- artifact verification;
167	- interpretation critique;
168	- clean-result drafting;
169	- comment-triggered Q&A and revisions;
170	- notifications;
171	- blocked-state summaries.
172	
173	## Data Model Direction
174	
175	Add or evolve tables carefully through reviewed migrations:
176	
177	- `entity_memberships`: user/entity/project access scope and role.
178	- `notifications`: in-app/email/mobile notification records.
179	- `notification_preferences`: per-user channel and event settings.
180	- `comment_subscriptions`: automatic subscriptions on comment, mention, or
181	  Claude question.
182	- `clean_results`: state, title, body, confidence, source experiment/run,
183	  approved/shared/archive fields, share token.
184	- `clean_result_versions`: revision history from AI/user comment loops.
185	- `workflow_events`: durable state-machine events not tied only to a Claude
186	  run.
187	- `approval_requests`: plan approval, queue approval, promotion approval.
188	- `run_artifacts`: W&B, HF, eval JSON, figures, logs, metrics, and
189	  verification status.
190	- `pod_lifecycle`: RunPod pod id, status, GPU spec, account, retries,
191	  stopped/terminated timestamps.
192	- `idea_sessions`: persistent ideation workspaces.
193	- `idea_cards`: generated or hu…
4:22:04 AMtool_calltools

tool=Bash

Bash
4:22:05 AMtool_resulttools
total 24
drwxrwxr-x  4 thomasjiralerspong thomasjiralerspong 4096 May 12 00:40 .
drwxrwxr-x 37 thomasjiralerspong thomasjiralerspong 4096 May 13 01:30 ..
drwxrwxr-x  4 thomasjiralerspong thomasjiralerspong 4096 May 13 03:59 [id]
drwxrwxr-x  3 thomasjiralerspong thomasjiralerspong 4096 May 12 00:40 by-number
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong 6296 May 11 08:37 route.ts
4:22:05 AMtool_calltools

tool=Bash

Bash
4:22:05 AMtool_resulttools
<tool_use_error>InputValidationError: Bash failed due to the following issue:
An unexpected parameter `file_path` was provided</tool_use_error>
4:22:06 AMtool_calltools

tool=Grep

Grep
4:22:06 AMtool_resulttools
Found 3 files
services/runner/src/session.ts
services/runner/src/dispatcher.ts
services/runner/src/tools/runpod.ts
4:22:06 AMtool_calltools

tool=Bash

Bash
4:22:06 AMtool_resulttools
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:- `.claude/skills/issue/markers.md`
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:GitHub issue markers become typed Sagan workflow events. Sagan comments become
/home/thomasjiralerspong/sagan/docs/exec-plans/sagan-rescue-plan.md:GitHub issues and markers.
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:Bad: *"Joint-source marker leakage along the A↔B persona axis fails — A-only
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:LoRA leaks the marker broadly, B-only LoRA stays hyper-local (LOW confidence)"*
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  found that cosine similarity to a trained source predicts marker leakage
/home/thomasjiralerspong/sagan/docs/clean-result-guidelines.md:  marker is trained into two distant personas at once, does it over-leak to
4:22:06 AMtool_calltools

tool=Read

Read
4:22:06 AMtool_resulttools
1	import { NextResponse } from 'next/server';
2	import { and, desc, eq } from 'drizzle-orm';
3	import { z } from 'zod';
4	import { entityKindSchema } from '@sagan/api';
5	import { entityMemberships, experiments, users } from '@sagan/db/schema';
6	import { db } from '@/lib/db';
7	import { ForbiddenError, isOwner, requireEntityRead, requireOwner } from '@/lib/access';
8	import { requireSession } from '@/lib/auth';
9	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
10	import { notifyUsers } from '@/lib/notifications';
11	import {
12	  appendWorkflowEvent,
13	  EXPERIMENT_STATUSES,
14	  experimentTurn,
15	  setExperimentStatus,
16	} from '@/lib/workflow';
17	
18	const createSchema = z.object({
19	  title: z.string().min(1).max(300),
20	  hypothesis: z.string().max(50_000).optional(),
21	  projectId: z.string().uuid().optional(),
22	  beliefId: z.string().uuid().optional(),
23	  runpodAccount: z.enum(['team', 'personal']).default('team'),
24	  status: z.enum(EXPERIMENT_STATUSES).default('proposed'),
25	  sourceKind: entityKindSchema.optional(),
26	  sourceId: z.string().uuid().optional(),
27	});
28	
29	const listQuerySchema = z.object({
30	  limit: z.coerce.number().int().min(1).max(200).default(100),
31	  status: z.enum(EXPERIMENT_STATUSES).optional(),
32	});
33	
34	export async function GET(req: Request) {
35	  try {
36	    await requireOwner();
37	  } catch {
38	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
39	  }
40	
41	  const url = new URL(req.url);
42	  const parsed = listQuerySchema.safeParse({
43	    limit: url.searchParams.get('limit') ?? undefined,
44	    status: url.searchParams.get('status') ?? undefined,
45	  });
46	  if (!parsed.success) {
47	    return NextResponse.json({ error: 'invalid_query', detail: z.treeifyError(parsed.error) }, { status: 400 });
48	  }
49	
50	  const filters = parsed.data.status ? [eq(experiments.status, parsed.data.status)] : [];
51	  let query = db().select().from(experiments).$dynamic();
52	  if (filters.length) query = query.where(and(...filters));
53	  const rows = await query.orderBy(desc(experiments.updatedAt)).limit(parsed.data.limit);
54	  return NextResponse.json({
55	    experiments: rows.map((row) => ({ ...row, turn: experimentTurn(row.status) })),
56	  });
57	}
58	
59	export async function POST(req: Request) {
60	  let session;
61	  try {
62	    session = await requireSession();
63	  } catch {
64	    return NextResponse.json({ error: 'unauthorized' }, { status: 401 });
65	  }
66	
67	  const body = await req.json().catch(() => null);
68	  const parsed = createSchema.safeParse(body);
69	  if (!parsed.success) {
70	    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });
71	  }
72	  const owner = isOwner(session);
73	  if (!owner) {
74	    if (!parsed.data.sourceKind || !parsed.data.sourceId) {
75	      return NextResponse.json({ error: 'source_scope_required' }, { status: 403 });
76	    }
77	    try {
78	      await requireEntityRead(session, parsed.data.sourceKind, parsed.data.sourceId);
79	    } catch (err) {
80	      if (err instanceof ForbiddenError) {
81	        return NextResponse.json({ error: err.message }, { status: 403 });
82	      }
83	      throw err;
84	    }
85	  }
86	
87	  const inserted = await db()
88	    .insert(experiments)
89	    .values({
90	      title: parsed.data.title,
91	      hypothesis: parsed.data.hypothesis,
92	      projectId: owner
93	        ? parsed.data.projectId
94	        : parsed.data.sourceKind === 'project'
95	          ? parsed.data.sourceId
96	          : undefined,
97	      beliefId: owner
98	        ? parsed.data.beliefId
99	        : parsed.data.sourceKind === 'belief'
100	          ? parsed.data.sourceId
101	          : undefined,
102	      runpodAccount: parsed.data.runpodAccount,
103	      status: owner ? parsed.data.status : 'proposed',
104	      planJson: {
105	        createdFrom: owner ? 'sagan_experiment_proposal_api' : 'co…
4:22:06 AMtool_calltools

tool=Bash

Bash
4:22:07 AMtool_resulttools
total 28
drwxrwxr-x  2 thomasjiralerspong thomasjiralerspong 4096 May 12 05:07 .
drwxrwxr-x 25 thomasjiralerspong thomasjiralerspong 4096 May 12 19:07 ..
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong 2891 May 10 23:34 ExperimentProposalForm.tsx
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong 1814 May 11 09:32 ExperimentStatusButton.tsx
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong 8427 May 12 05:07 page.tsx
4:22:08 AMtool_calltools

tool=Glob

Glob
4:22:08 AMtool_calltools

tool=Bash

Bash
4:22:08 AMtool_calltools

tool=Bash

Bash
4:22:08 AMtool_resulttools
This Bash command contains multiple operations. The following part requires approval: grep -E "\.(json|ts)$"
4:22:08 AMtool_resulttools
This Bash command contains multiple operations. The following part requires approval: ls -la /home/thomasjiralerspong/explore-persona-space/
4:22:09 AMtool_calltools

tool=Bash

Bash
4:22:09 AMtool_resulttools
<tool_use_error>Cancelled: parallel tool call Bash(find /home/thomasjiralerspong/sagan -nam…) errored</tool_use_error>
4:22:09 AMtool_calltools

tool=Read

Read
4:22:09 AMtool_resulttools
<tool_use_error>Cancelled: parallel tool call Bash(find /home/thomasjiralerspong/sagan -nam…) errored</tool_use_error>
4:22:11 AMtool_calltools

tool=Bash

Bash
4:22:12 AMtool_calltools

tool=Bash

Bash
4:22:12 AMtool_resulttools
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-worker-fixture.js.map
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-fixture.js
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-fixture.js.map
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-worker-fixture.js
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-worker-fixture.d.ts
/home/thomasjiralerspong/sagan/node_modules/next/dist/experimental/testmode/playwright/next-fixture.d.ts
4:22:13 AMtool_resulttools
/home/thomasjiralerspong/sagan/packages/db/src/seed.ts
4:22:15 AMtool_calltools

tool=Read

Read
4:22:15 AMtool_resulttools
File does not exist. Note: your current working directory is /home/thomasjiralerspong/sagan.
4:22:15 AMtool_calltools

tool=Grep

Grep
4:22:15 AMtool_resulttools
Found 1 file
packages/db/src/schema/index.ts
4:22:17 AMtool_calltools

tool=Bash

Bash
4:22:17 AMtool_resulttools
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/promote/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/workflow-events/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/by-number/[n]/route.ts
4:22:17 AMtool_calltools

tool=Bash

Bash
4:22:17 AMtool_resulttools
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/promote/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/workflow-events/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/[id]/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/route.ts
/home/thomasjiralerspong/sagan/apps/web/app/api/experiments/by-number/[n]/route.ts
4:22:18 AMtool_calltools

tool=Read

Read
4:22:18 AMtool_resulttools
1	import { NextResponse } from 'next/server';
2	import { and, desc, eq } from 'drizzle-orm';
3	import { z } from 'zod';
4	import { approvalRequests, experiments, runs, workflowEvents } from '@sagan/db/schema';
5	import { db } from '@/lib/db';
6	import { requireOwner } from '@/lib/access';
7	import { appendDailyLogTrailBestEffort } from '@/lib/daily-log-trail';
8	import { EXPERIMENT_STATUSES, experimentTurn, setExperimentStatus } from '@/lib/workflow';
9	
10	const EXPERIMENT_KINDS = ['experiment', 'infra', 'survey'] as const;
11	const COMPUTE_SIZES = ['none', 'small', 'medium', 'large'] as const;
12	const PRIORITIES = ['low', 'normal', 'high', 'urgent'] as const;
13	const ASSIGNEE_KINDS = ['agent', 'human'] as const;
14	
15	const patchSchema = z.object({
16	  title: z.string().min(1).max(300).optional(),
17	  body: z.string().max(200_000).optional(),
18	  hypothesis: z.string().max(50_000).optional(),
19	  configYaml: z.string().max(200_000).optional(),
20	  status: z.enum(EXPERIMENT_STATUSES).optional(),
21	  kind: z.enum(EXPERIMENT_KINDS).optional(),
22	  computeSize: z.enum(COMPUTE_SIZES).nullable().optional(),
23	  priority: z.enum(PRIORITIES).optional(),
24	  assigneeKind: z.enum(ASSIGNEE_KINDS).optional(),
25	  tags: z.array(z.string().max(80)).max(50).optional(),
26	  hasCleanResult: z.boolean().optional(),
27	  runpodAccount: z.enum(['team', 'personal']).optional(),
28	  note: z.string().max(2_000).optional(),
29	});
30	
31	export async function GET(_req: Request, ctx: { params: Promise<{ id: string }> }) {
32	  try {
33	    await requireOwner();
34	  } catch {
35	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
36	  }
37	  const { id } = await ctx.params;
38	  const rows = await db().select().from(experiments).where(eq(experiments.id, id)).limit(1);
39	  const experiment = rows[0];
40	  if (!experiment) return NextResponse.json({ error: 'not_found' }, { status: 404 });
41	  const [events, approvals] = await Promise.all([
42	    db()
43	      .select()
44	      .from(workflowEvents)
45	      .where(and(eq(workflowEvents.entityKind, 'experiment'), eq(workflowEvents.entityId, id)))
46	      .orderBy(desc(workflowEvents.createdAt))
47	      .limit(50),
48	    db()
49	      .select()
50	      .from(approvalRequests)
51	      .where(eq(approvalRequests.experimentId, id))
52	      .orderBy(desc(approvalRequests.createdAt)),
53	  ]);
54	  return NextResponse.json({
55	    experiment: { ...experiment, turn: experimentTurn(experiment.status) },
56	    events,
57	    approvalRequests: approvals,
58	  });
59	}
60	
61	export async function PATCH(req: Request, ctx: { params: Promise<{ id: string }> }) {
62	  let session;
63	  try {
64	    session = await requireOwner();
65	  } catch {
66	    return NextResponse.json({ error: 'owner_required' }, { status: 403 });
67	  }
68	  const { id } = await ctx.params;
69	  const body = await req.json().catch(() => null);
70	  const parsed = patchSchema.safeParse(body);
71	  if (!parsed.success) {
72	    return NextResponse.json({ error: 'invalid_input', detail: z.treeifyError(parsed.error) }, { status: 400 });
73	  }
74	  const { status, note, ...metadataUpdates } = parsed.data;
75	  const updated = await db()
76	    .update(experiments)
77	    .set({ ...metadataUpdates, updatedAt: new Date() })
78	    .where(eq(experiments.id, id))
79	    .returning({ id: experiments.id, title: experiments.title, status: experiments.status });
80	  if (!updated[0]) return NextResponse.json({ error: 'not_found' }, { status: 404 });
81	  let experiment = updated[0]!;
82	
83	  // When a caller flips hasCleanResult=true, ensure there's a pending
84	  // runs row so the /promote endpoint has something to flip. The analyzer's
85	  // in-place promotion path (.claude/agents/analyzer.md Step 6) relies on
86	  // this — without it, promote would 409 with no_pending_run. Idempotent:
87	  // if a pending row already exists, we no-op.
88	  if (metadataUpdates.hasCleanResult === true) {
89	    co…
4:22:20 AMtool_calltools

tool=Bash

Bash
4:22:21 AMtool_resulttools
69:  'scoped',
4:22:21 AMtool_calltools

tool=Bash

Bash
4:22:21 AMtool_resulttools
67:export const todoStatusEnum = pgEnum('todo_status', [
69:  'scoped',
580:    status: todoStatusEnum('status').notNull().default('inbox'),
4:22:23 AMtool_calltools

tool=Read

Read
4:22:23 AMtool_resulttools
572	// ─── Todos ─────────────────────────────────────────────────────────────────
573	
574	export const todos = pgTable(
575	  'todos',
576	  {
577	    id: uuid('id').primaryKey().defaultRandom(),
578	    text: text('text').notNull(),
579	    bodyMd: text('body_md'),
580	    status: todoStatusEnum('status').notNull().default('inbox'),
581	    intentMode: todoIntentEnum('intent_mode'),
582	    priority: priorityEnum('priority').notNull().default('normal'),
583	    due: timestamp('due', { withTimezone: true }),
584	    linkedKind: entityKindEnum('linked_kind'),
585	    linkedId: uuid('linked_id'),
586	    ownerNote: text('owner_note'),
587	    createdAt: timestamp('created_at', { withTimezone: true }).notNull().defaultNow(),
588	    updatedAt: timestamp('updated_at', { withTimezone: true }).notNull().defaultNow(),
589	  },
590	  (t) => ({
591	    statusIdx: index('todos_status_idx').on(t.status),
592	    linkedIdx: index('todos_linked_idx').on(t.linkedKind, t.linkedId),
593	  }),
594	);
595	
596	// ─── Literature ────────────────────────────────────────────────────────────
597	
598	export const litItems = pgTable(
599	  'lit_items',
600	  {
601	    id: uuid('id').primaryKey().defaultRandom(),
4:22:23 AMtool_calltools

tool=Bash

Bash
4:22:23 AMtool_resulttools
584:    linkedKind: entityKindEnum('linked_kind'),
585:    linkedId: uuid('linked_id'),
592:    linkedIdx: index('todos_linked_idx').on(t.linkedKind, t.linkedId),
1130:    linkedKind: entityKindEnum('linked_kind'),
1131:    linkedId: uuid('linked_id'),
1140:    linkedIdx: index('kanban_cards_linked_idx').on(t.linkedKind, t.linkedId),
4:37:12 AMstale_recoveredagent
marked failed after stale running timeout
4:37:13 AMauto_recovery_queuedagent
d9488543-a044-41b1-a303-433d4d0de631

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