Run 3e5c1604

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

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      "artifacts": "Under `outputs/exp363/` in the EPS workspace, rsync'd to the experiment's permanent storage:\n\n- `chen_vectors/<trait>.pt` \u2014 `(num_layers, d_model)` per trait.\n- `centroid_vectors/<trait>.pt` \u2014 same shape; re-run or symlinked from prior artifact if SHA matches.\n- `centroid_completion_vectors/<trait>.pt` \u2014 position-effect ablation at L20.\n- `generations/<trait>/<recipe>/<persona_side>.jsonl` \u2014 raw paired completions used for Chen extraction (one record per prompt with `prompt`, `completion`, `tokens`).\n- `steering_eval/<trait>/<recipe>.jsonl` \u2014 per-\u03b1 completions and rubric scores on calibration split.\n- `steering_report/<trait>/<recipe>.json` \u2014 \u03b1\\*, \u0394 rubric, bootstrap CI, NLL-coherence flag.\n- `geometry/<trait>/<layer>.json` \u2014 per-(recipe, trait, layer) cosines and inside-95%-interval flags; one combined `geometry/baseline.json` for the random-pair distribution.\n- `summary.json` \u2014 trait list, exact persona prompts, prompt-set SHA, split seed, \u03b1-sweep grid, GPU SKU actually used, cost ledger, runtime.\n- `clean_result.html` \u2014 three-piece writeup per `docs/clean-result-guidelines.md` (TL;DR \u2192 primary plot \u2192 Experimental design dropdown), written by the analyzer after pod success.",
      "compute": "One pod, single GPU:\n\n- **GPU (primary)**: 1\u00d7 **H100 80GB PCIe** (SECURE cloud). The originally-requested H100 80GB SXM hit a RunPod `SUPPLY_CONSTRAINT` on the prior dispatch; PCIe is the closest-available swap (same 80 GB VRAM class, ~10\u201315% lower throughput on Qwen-7B inference, well inside the 8 h target).\n- **Fallback if PCIe also constrained**: A100 80GB SXM (~1.7\u00d7 slower on Qwen-7B generation; still fits the 10 h hard cap). Operator may swap manually; the experiment code is GPU-agnostic.\n- **Wall-clock budget**: \u2264 8 h expected, **10 h hard cap** enforced by `timeout 36000 \u2026` in the pod command.\n- **Disk**: 100 GB container disk + 100 GB volume for vector tensors, generations, rubric outputs.\n- **Software**: existing EPS Python 3.11 env (HF transformers, torch 2.x; activation hooks, no vLLM). Rubric calls to Anthropic API via existing key in `.env`.\n- **Partial-artifact safety**: background `rsync` every 30 min uploads partial outputs so a 10 h kill still ships a usable subset.\n\n**USD cost estimate** (rates last checked May 2026, may drift; H100 80GB PCIe = $2.39 / GPU-hr, SECURE on-demand):\n\n`10 GPU-hours \u00d7 $2.39/hr \u00d7 1 GPU \u00d7 1 pod = $24 (compute, worst case at hard cap) + ~$0.28 (storage: 200 GB \u00d7 $0.10/GB-month \u00d7 10/720 month) = ~$24 total compute`\n\nExpected case (8 h): `8 \u00d7 $2.39 = ~$19`. Plus \u2264 USD 30 rubric API cap \u2192 **total \u2264 ~$54 worst case, ~$23 expected**.\n\n(Fallback A100 80GB SXM @ $1.49/hr would land at ~$15 expected / ~$19 worst-case compute.)",
      "goal": "Implement the persona-vector extraction recipe from Chen et al. (Anthropic, \"Persona Vectors: Monitoring and Controlling Character Traits in Language Models\", 2025) inside the EPS codebase, run it on the same traits and base model EPS uses today (Qwen2.5-7B-Instruct), and directly compare the resulting vectors to the existing centroid-difference (diff-of-means) recipe.\n\nThe comparison must answer: does the Chen recipe yield a meaningfully different direction (geometry) and a meaningfully more effective steering direction (behavior) than the existing centroid-difference vectors \u2014 particularly at layer 20, where clean result #267 showed the existing centroid direction was approximately random.\n\nThis is an auto-recovery of an already-approved plan. The previous dispatch failed with RunPod `SUPPLY_CONSTRAINT` on H100 80GB SXM. The only delta in this revision is the GPU SKU: H…

The experiment is currently at `status: approved`. Let me check the pod_spec stored in DB and verify the EPS workspace.

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    "podSpec": {
      "cloudType": "ALL",
      "config": {
        "artifacts": [
          "outputs/chen_vectors/<trait>.pt",
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          "outputs/rubric_scores.csv",
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          "outputs/summary.json",
          "outputs/clean_result.html",
          "outputs/run.log"
        ],
        "command": "Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API."
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      "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"estimatedRemainingMinutes\\\":540,\\\"progressPct\\\":5,\\\"message\\\":\\\"env ready, starting extraction\\\"}\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"progressPct\\\":100,\\\"message\\\":\\\"done\\\"}\"'",
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        "SAGAN_EPS_COMMIT_SHA": "4685c5745b0fb9f0c025d813b7ed9827305c9473"
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      "name": "chen-vs-centroid-eps-363",
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    },
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    "runpodAccount": "team",
    "status": "approved",
    "tags": [],
    "title": "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
    "turn": "Sagan turn: run and verify",
    "updatedAt": "2026-05-13T23:54:16.619Z"
  }
}

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<persisted-output>
Output too large (43.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/c225de1a-fc13-42b5-8be8-2fa0c1ea7e7e/tool-results/bowzaglc7.txt

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      "bodyMd": "# Chen et al. Persona-Vector Extraction vs. Project Centroid-Difference\n\nScoped experiment: `0c120ea3-746a-43e6-a760-e6112f8cb649`\nTarget tenant code: `/home/thomasjiralerspong/explore-persona-space` (EPS).\nSagan-side change: none \u2014 Sagan only records the run and renders the clean result.\n\n## Goal\n\nImplement the persona-vector extraction recipe from Chen et al. (Anthropic,\n\"Persona Vectors: Monitoring and Controlling Character Traits in Language\nModels\", 2025) inside the EPS codebase, run it on the same traits and base\nmodel we use today, and directly compare the resulting vectors to our\ncurrent centroid-difference (diff-of-means) recipe.\n\nThe comparison must answer: does the Chen recipe yield a meaningfully\ndifferent direction (geometry) and a meaningfully more effective steering\ndirection (behavior) than the existing centroid-difference vectors on\nQwen2.5-7B-Instruct \u2014 particularly at layer 20, where clean result #267\nshowed the existing centroid direction was approximately random.\n\n## Hypothesis\n\nChen-style extraction \u2014 computing the diff of mean activations on\n**response tokens** generated under behavior-eliciting prompts that drive\ntrait-positive vs trait-negative completions \u2014 produces a direction that\n(a) is not approximately random at the middle layers (cosine to a sampled\nrandom direction within 2 sigma of the random-pair baseline distribution),\nand (b) achieves higher steering effectiveness than the current\ncentroid-difference vector on the same evaluation set, on at least 3 of 5\ntarget traits at layer 20.\n\nThe current centroid recipe under-performs because it averages activations\nover **prompt tokens of fixed exemplars**, which encodes lexical/topic\ncontent rather than the latent behavior axis. Chen's recipe conditions on\nthe model's own trait-aligned vs trait-opposing generations, so the diff\nisolates the behavioral subspace.\n\n## Prediction\n\nConcrete, falsifiable, per-trait, at
...
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        "delta": "runpod-spec.gpuType H100 SXM\u2192H100 PCIe; A100 80GB SXM fallback documented",

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Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

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  agents-emit-html
  aim4-axis-origins-figure
  aim6-truthification-em
  bright-valley
  cleanup/redundancy-consolidation
  drop-batch-analysis-kinds
+ exp-192-persona-spread
+ exp-333-fr-it-symmetry
+ experiment-365
  fix/issue-140-figure-paths
  fix/issue-45-simple-evaluate
  gentle-mountain
  issue-100
  issue-101
+ issue-102
  issue-104
  issue-108
+ issue-112
+ issue-125
+ issue-127
+ issue-138
+ issue-139
+ issue-140
+ issue-150
+ issue-156
+ issue-157
  issue-157-analyzer
+ issue-162
+ issue-170
+ issue-172
---
4685c574 issue #363 v2: address review findings (cal/rep dedupe, alpha*||v||, real position ablation, single hook target, NLL chat template, rubric 0-1)
b8344b3b issue #363: Chen et al. persona-vector extraction recipe + centroid baseline
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
d0db2db7 Complete Sagan workflow migration
Shell cwd was reset to /home/thomasjiralerspong/sagan

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scripts/run_chen_vs_centroid.py
src/explore_persona_space/axis/chen_extract.py
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usage: sagan_state.py marker [-h] [--event-type EVENT_TYPE]
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                             [--actor-kind ACTOR_KIND]
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                             [--round ROUND] [--reviewer REVIEWER]
                             [--verdict {pass,needs_targeted_fix,blocked_needs_user_decision,fail_not_worth_continuing}]
                             [--required-fix REQUIRED_FIX]
                             [--reconciler-decision RECONCILER_DECISION]
                             [--next-status {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}]
                             number marker
sagan_state.py marker: error: the following arguments are required: marker

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      "eventType": "state_changed",
      "fromStatus": "running",
      "id": "4a7b496e-c594-43f5-abce-15dc79f912cf",
      "metadata": null,
      "note": "Orchestrator 3e5c1604 queued to implement and dispatch.",
      "toStatus": "implementing"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "plan_pending",
      "id": "23333440-6c99-4007-bbb6-b4152148a8dd",
      "metadata": null,
      "note": "Moved on Pipeline board to running.",
      "toStatus": "running"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "cancelled",
      "id": "c335a13d-8ad0-49b4-a369-66b58441193e",
      "metadata": null,
      "note": "Owner: restore to awaiting-approval after spurious pod-stop cascade",
      "toStatus": "plan_pending"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "running",
      "id": "f585f987-b002-45a4-bfa7-8814f7271634",
      "metadata": null,
      "note": "RunPod pod stopped; volume preserved.",
      "toStatus": "cancelled"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "queued",
      "id": "f679995d-dafa-4e45-9c72-c9e0fed8291e",
      "metadata": null,
      "note": "RunPod pod is running.",
      "toStatus": "running"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "running",
      "id": "4fde1d4f-4055-4941-bf74-40d4792f5422",
      "metadata": null,
      "note": "RunPod pod dispatched; waiting for runtime.",
      "toStatus": "queued"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "code_reviewing",
      "id": "ef63b1d5-b2a6-478f-8f9a-806061903f1c",
      "metadata": null,
      "note": "Code-review pair PASS + tests PASS on issue-363 @ 4685c574. podSpec restored with SAGAN_EPS_BRANCH=issue-363 and SAGAN_EPS_COMMIT_SHA=4685c574 spliced into env. Dispatching via pod-provisioner. GPU spec: A100 80GB SECURE/ALL (H100 SUPPLY_CONSTRAINT documented by prior recovery loops; A100 is the plan's fallback).",
      "toStatus": "running"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "implementing",
      "id": "2a632a0e-8499-4ec8-b09b-f8d9a1196349",
      "metadata": null,
      "note": "Code-review pair already passed on this commit (4685c574). Re-recording the prior verdicts as carry-forward markers per workflow contract.",
      "toStatus": "code_reviewing"
    },
--
      "entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "approved",
      "id": "f10bae8c-ef55-49b4-b2e1-e2e7dd5d8b89",
      "metadata": null,
      "note": "Orchestrator 7ffeb83c: walking through workflow. Plan re-approved by owner. Implementation already complete on branch issue-363 @ 4685c574 (commits b8344b3b + 4685c574 v2). Script scripts/run_chen_vs_centroid.py and src/explore_persona_space/axis/chen_extract.py both present on remote; verified via git ls-tree.",
      "toStatus"…

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<!DOCTYPE html><html lang="en" class="geistsans_d5a4f12f-module__Ur3q_a__variable geistmono_157ca88a-module__iaM1Ya__variable inter_tight_8f18ed7a-module__13eLwq__variable bricolage_grotesque_8c519202-module__UFQ6Tq__variable outfit_333574c6-module__a01f3W__variable manrope_9e067a5b-module__vT7MWq__variable space_grotesk_59d6dcf5-module__z_Ctiq__variable dm_sans_34a85c05-module__gGo3DG__variable ibm_plex_sans_5af3dae-module__edw7da__variable source_serif_4_4203f07b-module__9jTnnG__variable newsreader_4bcebf04-module__mRJj1G__variable"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover"/><link rel="stylesheet" href="/_next/static/chunks/0bzivtsy-596p.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/03edqrb4zdj~g.js"/><script src="/_next/static/chunks/0i0b4ibyj497c.js" async=""></script><script src="/_next/static/chunks/0h69mj-e-ya89.js" async=""></script><script src="/_next/static/chunks/014~e0vao-h-9.js" async=""></script><script src="/_next/static/chunks/0pqt~8bl3ukh4.js" async=""></script><script src="/_next/static/chunks/turbopack-14pofyvch95c_.js" async=""></script><script src="/_next/static/chunks/01xlw8hd842-c.js" async=""></script><script src="/_next/static/chunks/0hs1bisaa29...js" async=""></script><meta name="robots" content="noindex"/><meta name="next-size-adjust" content=""/><title>404: This page could not be found.</title><meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)"/><meta name="theme-color" content="#16172e" media="(prefers-color-scheme: dark)"/><title>Sagan</title><meta name="description" content="Personal research-life dashboard."/><link rel="manifest" href="/manifest.webmanifest"/><meta name="mobile-web-app-capable" content="yes"/><meta name="apple-mobile-web-app-title" content="Sagan"/><meta name="apple-mobile-web-app-status-bar-style" content="default"/><link rel="shortcut icon" href="/favicon.png"/><link rel="icon" href="/icon.svg" type="image/svg+xml"/><link rel="icon" href="/favicon.png" sizes="32x32" type="image/png"/><link rel="apple-touch-icon" href="/favicon.png" sizes="32x32"/><script>
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Branch is already implemented at `4685c574`. The prior orchestrator (`7ffeb83c`) already walked through implementing → code_reviewing → running, and the pod was dispatched + ran briefly before being stopped. I'll record carry-forward markers and re-dispatch the pod.

tool=Bash

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{
  "experiment": {
    "id": "0c120ea3-746a-43e6-a760-e6112f8cb649",
    "status": "implementing",
    "title": "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

tool=Bash

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{
  "id": "0cdf8632-3241-4ca3-a493-110535184cb2",
  "ok": true
}

tool=TodoWrite

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Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

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{
  "experiment": {
    "id": "0c120ea3-746a-43e6-a760-e6112f8cb649",
    "status": "code_reviewing",
    "title": "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

tool=Bash

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{
  "id": "e746a106-20e9-4b90-8f9f-b9cb64151bae",
  "ok": true
}

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{
  "id": "803b981d-6196-4ed3-b4e6-5d9024f8aecd",
  "ok": true
}

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{
  "experiment": {
    "id": "0c120ea3-746a-43e6-a760-e6112f8cb649",
    "status": "testing",
    "title": "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

tool=Bash

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{
  "id": "dd4759d3-259e-434b-a59c-52dc5c3eca48",
  "ok": true
}

tool=TodoWrite

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Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

tool=Bash

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

# Pod Provisioner

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

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

---

## Inputs you can rely on

The orchestrator will give you, in plain text:

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

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

---

## The `pod-tool` CLI

Run it from anywhere via:

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

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

### `attempt` — one dispatch attempt

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

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

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

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

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

### `commit` — finalize the fleet

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

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

### `escala…

tool=Bash

Bash

```bash
pnpm --filter @sagan/runner pod-tool record-substitution \
  --agent-run-id <uuid> \
  --attempt <int> \
  --from-json '<preferred-spec>' \
  --to-json '<dispatched-spec>' \
  --reason "why you substituted"
```

Optional but strongly recommended: call this every time the spec you
dispatched differs from what the planner asked for. The dashboard renders a
"Substitutions" section per run; without these entries, the substitution is
invisible to the owner.

### `stop` — tear down a pod

```bash
pnpm --filter @sagan/runner pod-tool stop --pod-id <runpod-id> [--account team|personal]
```

Use this only when you abandon a partial fleet (e.g. spec asks for 2 pods,
the first came up, but you've decided to consolidate into one larger pod and
retry from scratch). Otherwise let the watcher manage pod lifecycle.

---

## The substitution algorithm

You loop, one spec-index at a time, until every pod in the plan is running.

For each pod-index `i` in the plan:

1. **Read** the preferred spec `pod_spec[i]` and its `substitution_policy` /
   `consolidation` blocks (if present). If neither is present, the planner
   declined to authorize substitutions: you may attempt the exact spec at
   most **3 times** with the same parameters (RunPod's transient errors
   sometimes resolve themselves), then escalate. Do not vary anything.

2. **Pre-flight consolidation.** Before attempting *anything*, check whether
   `consolidation.may_merge_pods` is true (default if absent: **true**, per
   the project planner instructions — one-pod-per-source is forbidden) AND
   whether there are sibling pods in `pod_spec` you have not yet dispatched
   that share GPU type and would fit together. If so, propose a merged
   spec with `gpuCount = sum(siblings.gpuCount)` (capped by
   `consolidation.merge_target_max_gpus_per_pod` or 8 if absent), call
   `record-substitution`, and skip the siblings entirely. The dispatched
   pod runs everything on one box; the experiment code is expected to
   time-share or batch-share across sources/conditions.

3. **Try the preferred spec** via `attempt`. If `ok: true`, record any
   substitution and move to the next pod-index.

4. **On `SUPPLY_CONSTRAINT`**, work through the substitution ladder in
   order. Stop and try at each rung; only descend on another constraint.

   **Rung 1 — Consolidate.** If you have not already merged this pod with
   siblings, propose the merge now (same logic as step 2).

   **Rung 2 — Cloud/region swap.** Within the same gpuType+gpuCount, vary
   `cloudType` first (`SECURE` → `COMMUNITY` if the policy allows), then
   try different `dataCenterId` values within the policy's allowed list. If
   the policy lists a `prefer` order, exhaust the preferred regions before
   the rest.

   **Rung 3 — GPU family swap.** Move within the policy's
   `gpuType.allowed` list. Prefer same-tier substitutes (H100 → H200 →
   A100-SXM → A100-PCIe) and respect `min_vram_gb` if present. Do **not**
   reduce `gpuCount` here.

   **Rung 4 — Account swap.** If the policy permits both accounts and you
   have only tried one, swap `--account team` ↔ `--account personal`.
   Account isolation is a billing boundary, not a pool boundary — swapping
   may unlock different RunPod capacity.

   **Rung 5 — Last resort.** Only if the policy explicitly allows lowering
   `gpuCount` below the planner's number — most plans forbid this because
   it changes the experiment. If the policy forbids it, **do not attempt**;
   escalate instead.

5. **On other error codes:**
   - `TRANSIENT_RUNPOD_ERROR` / `NETWORK` — retry the same spec with brief
     backoff (sleep 30s, 60s, 120s). Cap at 3 transient retries per spec
     before re-entering the substitution ladder.
   - `AUTH_OR_BAD_REQUEST` — escalate immediately. Do not substitute. This
     means the spec is invalid or our RunPod credentials are broken; no
     amount of variation will help.
   - `UNKNOWN` — treat like `TRANSIENT_RUNPOD_ERROR` (retry briefly, then
     ladder).

6. **W…

Now let me set status to running and dispatch the pod-provisioner.

tool=Bash

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{
  "experiment": {
    "id": "0c120ea3-746a-43e6-a760-e6112f8cb649",
    "status": "running",
    "title": "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

tool=Agent

Agent

tool=Read

Read

1	- [spec-json quoting gotcha](feedback-spec-json-quoting.md) — write JSON to a temp file, not a heredoc variable; heredoc inserts a leading newline that breaks JSON parse at position 1
2	- [agent-run kind mismatch](feedback-agent-run-kind-mismatch.md) — pod-tool requires kind=experiment; orchestrator passes kind=apply — INSERT a new experiment-kind row and use that id instead
3	

tool=Read

Read

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

tool=Read

Read

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

tool=Bash

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                  id                  |    kind    |  status   | scope_entity_kind |           scope_entity_id            
--------------------------------------+------------+-----------+-------------------+--------------------------------------
 610e19a7-9d6c-4496-ac79-cdabbeb0240c | experiment | completed | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649
(1 row)

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{}

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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          pod_spec                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------…

tool=Bash

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                  id                  |    kind    |  status   | scope_entity_kind |           scope_entity_id            |          created_at           
--------------------------------------+------------+-----------+-------------------+--------------------------------------+-------------------------------
 3e5c1604-21f6-4e47-a0f6-f1b5f9490baa | apply      | running   | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649 | 2026-05-13 23:54:16.444931+00
 610e19a7-9d6c-4496-ac79-cdabbeb0240c | experiment | completed | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649 | 2026-05-13 23:54:16.298555+00
 7ffeb83c-7514-432d-b9d7-ce6ecfb74f34 | apply      | completed | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649 | 2026-05-13 20:11:35.292095+00
 c82dd718-6d42-45fe-9f49-d4af841a1fd0 | experiment | cancelled | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649 | 2026-05-13 20:11:35.09912+00
 973d3ea8-12d7-41df-a802-591383d5835f | apply      | cancelled | experiment        | 0c120ea3-746a-43e6-a760-e6112f8cb649 | 2026-05-13 18:13:09.547399+00
(5 rows)

tool=Bash

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New agent_run id: d66f8ef0-2db8-4a23-b647-a58b680f9e98

INSERT01

tool=Bash

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JSON valid

tool=Bash

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Written to /tmp/spec_attempt1_final.json
gpuType=A100 80GB, cloudType=ALL, gpuCount=1

tool=Bash

Bash

Attempt 1: A100 80GB, cloudType=ALL, account=team

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--run-index" "0" "--spec-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"ALL\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--account" "team"

{"ok":false,"error":{"code":"SUPPLY_CONSTRAINT","message":"GraphQL errors: [{\"message\":\"There are no longer any instances available with the requested specifications. Please refresh and try again.\",\"path\":[\"podFindAndDeployOnDemand\"],\"extensions\":{\"code\":\"SUPPLY_CONSTRAINT\",\"userId\":\"user_2v9CcEeHWnPcoAVCf8YeCXKvupS\"}}]","suggested_dimensions":["consolidate_pods","cloudType","dataCenterId","gpuType","account"]}}

tool=Bash

Bash

{"name": "chen-vs-centroid-eps-363", "gpuType": "A100 80GB", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 540, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"estimatedRemainingMinutes\\\":540,\\\"progressPct\\\":5,\\\"message\\\":\\\"env ready, starting extraction\\\"}\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \"$SAGAN_PROGRESS_URL\" -H \"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\" -H \"content-type: application/json\" -d \"{\\\"progressPct\\\":100,\\\"message\\\":\\\"done\\\"}\"'", "env": {"ANTHROPIC_API_KEY": "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", "SAGAN_EPS_BRANCH": "issue-363", "SAGAN_EPS_COMMIT_SHA": "4685c5745b0fb9f0c025d813b7ed9827305c9473"}, "config": {"command": "Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.", "artifacts": ["outputs/chen_vectors/<trait>.pt", "outputs/centroid_vectors/<trait>.pt", "outputs/ablation_centroid_on_prompt/<trait>.pt", "outputs/generations/<trait>/{positive,negative}.jsonl", "outputs/steering_sweep/<trait>/<recipe>.jsonl", "outputs/rubric_scores.csv", "outputs/random_baseline.json", "outputs/summary.json", "outputs/clean_result.html", "outputs/run.log"]}}

tool=Bash

Bash

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "record-substitution" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--attempt" "2" "--from-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"ALL\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--to-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"SECURE\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAG…

tool=Bash

Bash

Attempt 2: A100 80GB, cloudType=SECURE, account=team

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--run-index" "0" "--spec-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"SECURE\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--account" "team"

{"ok":false,"error":{"code":"SUPPLY_CONSTRAINT","message":"GraphQL errors: [{\"message\":\"There are no longer any instances available with the requested specifications. Please refresh and try again.\",\"path\":[\"podFindAndDeployOnDemand\"],\"extensions\":{\"code\":\"SUPPLY_CONSTRAINT\",\"userId\":\"user_2v9CcEeHWnPcoAVCf8YeCXKvupS\"}}]","suggested_dimensions":["consolidate_pods","cloudType","dataCenterId","gpuType","account"]}}

tool=Bash

Bash

Written spec_attempt3.json: A100 80GB COMMUNITY

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "record-substitution" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--attempt" "3" "--from-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"SECURE\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--to-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"COMMUNITY\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"sk-ant-api03-ZaW4MwJXZ-4F2PySJhAstCliqJJ6Y5rE7tIHIkyim3IDhUwkHHzRtNkyQq5w0RG9oEHSt2BDzvJBbP17mMNNww…

tool=Bash

Bash

Attempt 3: A100 80GB, cloudType=COMMUNITY, account=team

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--run-index" "0" "--spec-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"COMMUNITY\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--account" "team"

{"ok":false,"error":{"code":"SUPPLY_CONSTRAINT","message":"GraphQL errors: [{\"message\":\"There are no longer any instances available with the requested specifications. Please refresh and try again.\",\"path\":[\"podFindAndDeployOnDemand\"],\"extensions\":{\"code\":\"SUPPLY_CONSTRAINT\",\"userId\":\"user_2v9CcEeHWnPcoAVCf8YeCXKvupS\"}}]","suggested_dimensions":["consolidate_pods","cloudType","dataCenterId","gpuType","account"]}}

tool=Bash

Bash

Written spec_attempt4.json: H100 80GB HBM3 ALL

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "record-substitution" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--attempt" "4" "--from-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"A100 80GB\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"COMMUNITY\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--to-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"NVIDIA H100 80GB HBM3\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"ALL\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"sk-ant-api03-ZaW4MwJXZ-4F2PySJhAstCliqJJ6Y5rE7tIHIkyim3IDhUwkHHzRtNkyQq5w0RG9oEHSt2BDzvJBbP…

tool=Bash

Bash

Attempt 4: H100 80GB HBM3 (SXM), cloudType=ALL, account=team

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98" "--run-index" "0" "--spec-json" "{\"name\": \"chen-vs-centroid-eps-363\", \"gpuType\": \"NVIDIA H100 80GB HBM3\", \"gpuCount\": 1, \"volumeGb\": 100, \"containerDiskGb\": 100, \"cloudType\": \"ALL\", \"estimatedMinutes\": 540, \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace && pip install -U uv && git clone --depth 1 --branch issue-363 https://github.com/superkaiba/explore-persona-space.git eps && cd eps && uv sync && mkdir -p outputs && curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"estimatedRemainingMinutes\\\\\\\":540,\\\\\\\"progressPct\\\\\\\":5,\\\\\\\"message\\\\\\\":\\\\\\\"env ready, starting extraction\\\\\\\"}\\\" ; (timeout 50400 uv run python scripts/run_chen_vs_centroid.py --model Qwen/Qwen2.5-7B-Instruct --traits sycophancy,deception,refusal-tendency,hostility,helpfulness --layers 10,13,16,20,24 --prompts-per-trait 200 --alpha-grid -2,-1,-0.5,0,0.5,1,2 --output-dir outputs/ --rubric claude-sonnet --calibration-split 25 --report-split 25 2>&1 | tee outputs/run.log) ; curl -sS -X POST \\\"$SAGAN_PROGRESS_URL\\\" -H \\\"authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN\\\" -H \\\"content-type: application/json\\\" -d \\\"{\\\\\\\"progressPct\\\\\\\":100,\\\\\\\"message\\\\\\\":\\\\\\\"done\\\\\\\"}\\\"'\", \"env\": {\"ANTHROPIC_API_KEY\": \"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\", \"SAGAN_EPS_BRANCH\": \"issue-363\", \"SAGAN_EPS_COMMIT_SHA\": \"4685c5745b0fb9f0c025d813b7ed9827305c9473\"}, \"config\": {\"command\": \"Clone EPS @ issue-363 (4685c57), uv sync, run scripts/run_chen_vs_centroid.py for 5 traits x 5 layers on Qwen2.5-7B-Instruct comparing Chen-recipe vs centroid-recipe persona vectors. 14h hard timeout for A100 (1.7x H100). Rubric judge via Anthropic API.\", \"artifacts\": [\"outputs/chen_vectors/<trait>.pt\", \"outputs/centroid_vectors/<trait>.pt\", \"outputs/ablation_centroid_on_prompt/<trait>.pt\", \"outputs/generations/<trait>/{positive,negative}.jsonl\", \"outputs/steering_sweep/<trait>/<recipe>.jsonl\", \"outputs/rubric_scores.csv\", \"outputs/random_baseline.json\", \"outputs/summary.json\", \"outputs/clean_result.html\", \"outputs/run.log\"]}}" "--account" "team"

{"ok":true,"pod":{"podId":"njchq12a6ytcft","name":"chen-vs-centroid-eps-363","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"55e2ba3b-1926-4ef2-b921-cf8f18b14390","sagaRunId":"6050bab2-e8b9-47dd-8c76-745866c1c63f","account":"team"}

tool=Bash

Bash

Calling commit...

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "d66f8ef0-2db8-4a23-b647-a58b680f9e98"

{"ok":true,"podIds":["njchq12a6ytcft"],"count":1}

Committed successfully. The watcher now owns the pod lifecycle.

---

**committed 1 pod(s): njchq12a6ytcft**

Substitution notes for the orchestrator marker:

- Attempts 1–3: A100 80GB constrained across cloudType=ALL, SECURE, COMMUNITY on `team` account. No inventory available.
- Attempt 4 (success): GPU family swap to `NVIDIA H100 80GB HBM3` (SXM) cloudType=ALL, account=team. Pod `njchq12a6ytcft` came up at $2.99/hr. This is within the plan's authorized substitution list (H100 80GB is explicitly listed as a permitted GPU for this experiment).

The fresh `kind=experiment` agent_run used for this dispatch is `d66f8ef0-2db8-4a23-b647-a58b680f9e98` (the orchestrator-passed run `610e19a7...` was already `completed` from a prior dispatch attempt and could not accept new pod lifecycle rows). The pod lifecycle id is `55e2ba3b-1926-4ef2-b921-cf8f18b14390`, saga run id `6050bab2-e8b9-47dd-8c76-745866c1c63f`.
agentId: a558a20739bddd654 (use SendMessage with to: 'a558a20739bddd654' to continue this agent)
<usage>total_tokens: 45125
tool_uses: 19
duration_ms: 178462</usage>

Pod dispatched: `njchq12a6ytcft` on H100 80GB SXM after A100 80GB was constrained. Recording the substitution marker.

tool=Bash

Bash

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

tool=Grep

Grep

17:  'epm:code-review',
18:  'epm:code-review-codex',
19:  'epm:review-reconcile',
20:  'epm:interp-critique',
21:  'epm:interp-critique-codex',
22:  'epm:clean-result-critique',
23:  'epm:clean-result-critique-codex',
33:  'epm:clarify',
34:  'epm:clarify-answers',
35:  'epm:plan',
36:  'epm:consistency',
37:  'epm:experiment-implementation',
38:  'epm:code-review',
39:  'epm:code-review-codex',
40:  'epm:code-review-reconcile',
41:  'epm:review-reconcile',
42:  'epm:reviewer-verdict',
43:  'epm:reviewer-verdict-codex',
44:  'epm:textbox-answers',
45:  'epm:reviewer-pass',
46:  'epm:reviewer-decision',
47:  'epm:reviewer-ensemble-final',
48:  'epm:test-verdict',
49:  'epm:preflight',
50:  'epm:launch',
51:  'epm:progress',
52:  'epm:results',
53:  'epm:upload-verification',
54:  'epm:interpretation',
55:  'epm:interp-critique',
56:  'epm:interp-critique-codex',
57:  'epm:interp-revision',
58:  'epm:clean-result',
59:  'epm:clean-result-critique',
60:  'epm:clean-result-critique-codex',
61:  'epm:clean-result-critique-reconcile',
62:  'epm:clean-result-lint',
63:  'epm:follow-ups',
64:  'epm:awaiting-promotion',
65:  'epm:promoted',

[Showing results with pagination = limit: 40]

tool=Bash

Bash

{
  "id": "de9d0204-7d20-4c33-81aa-9c18540321b8",
  "ok": true
}

Pod is dispatched and the runner now owns the lifecycle. The experiment is expected to run for 8–10 hours on H100. Since the runner auto-transitions `running → uploading` when the pod stops, the orchestrator workflow has further stages to drive (uploading → verifying → interpreting → follow-ups → reviewing → clean_result_drafting → awaiting_promotion). I'll schedule a wake-up to come back later and pick up the workflow once the pod completes.

tool=Bash

Bash

      "status": "approved",
      "status": "approved",
      "status": "approved",
    "status": "queued",

tool=TodoWrite

TodoWrite

Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable

tool=ScheduleWakeup

ScheduleWakeup

Next wakeup scheduled for 01:03:00 (in 3617s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

Pod dispatched and Stage 4 complete. Pod `njchq12a6ytcft` is on H100 80GB SXM (after A100 80GB was supply-constrained across all three cloud variants — a substitution explicitly authorized by the plan). Sagan has already transitioned `running → queued` while RunPod brings the pod up; it will go `queued → running → uploading` automatically. Sleeping 1h, then I'll resume the workflow at whatever stage the runner has reached.

success

Pod dispatched and Stage 4 complete. Pod `njchq12a6ytcft` is on H100 80GB SXM (after A100 80GB was supply-constrained across all three cloud variants — a substitution explicitly authorized by the plan). Sagan has already transitioned `running → queued` while RunPod brings the pod up; it will go `queued → running → uploading` automatically. Sleeping 1h, then I'll resume the workflow at whatever stage the runner has reached.