Run 09638c12
The previous Claude Code run failed or crashed. First diagnose why it stopped using the transcript below. Then fix or work around the problem if possible and continue the original request to a final useful result. Do not repeat completed work. If the root cause is external credentials, unavailable infrastructure, missing human approval, or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action.
Original request
[plan-reused-from:a1f04a02-f418-461b-904c-0b05d29528d1] Approved an existing plan without re-drafting. Source agent_run preserved this experiment's plan_md before the owner moved the card to queued.
Failure or crash reason
spec[0]: 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"}}]Previous run transcript
- 2026-05-13T13:01:04.843Z plan_reused: a1f04a02-f418-461b-904c-0b05d29528d1
- 2026-05-13T13:01:05.166Z orchestrator_queued: 9cb3275e-e1a5-4f6e-b16c-2d284240cce3
- 2026-05-13T14:15:13.198Z launch_pod_requested: Post-approval orchestrator signalled readiness; dispatching pod(s).
- 2026-05-13T14:15:13.668Z deploy_started: dispatching 1 pod(s)
- 2026-05-13T14:15:14.278Z deploy_pod_failed: 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"}}]
- 2026-05-13T14:15:14.394Z runpod_blocked: dispatched 0/1 pod(s)Resume history
This run resumes e1baedab.
Plan
Blocker — sixth consecutive same-mode failure; R5 escalation rule must be honored
This is the **sixth** dispatch attempt for experiment #363, and the **fourth in a row** to exhibit the identical failure mode: pod reports `RUNNING` on the team account, then disappears from the account-scoped pod list shortly after. ### Cumulative failure ledger - retry-1: `SUPPLY_CONSTRAINT` - retry-2: `INTERNAL_SERVER_ERROR` on `podFindAndDeployOnDemand` - retry-3: `Pod not found in account=team` after ~14 min RUNNING - retry-4: `Pod not found in account=team` after ~2 min RUNNING - retry-5: `Pod not found in account=team` after ~7 min RUNNING - **retry-6 (this run, `m1ayhl6nsbxw04`): `Pod not found in account=team` after ~1 min RUNNING** Four consecutive post-`RUNNING` disappearances against the team account is conclusively not transient capacity flake. It is consistent with a RunPod-side or team-account-side state issue (account-scoping bug, billing/rate-limit eviction, or stale pod-list cache). ### Why I am refusing to auto-recover The approved plan for #363 contains a pre-registered escalation rule (**R5** in the existing `planJson` section "Blocker — escalate per plan's pre-registered R5 rule"): > if retry-4 also disappears after `RUNNING`, escalate to a manual SSH-into-pod check during the next attempt rather than spending more auto-recoveries on it. That trigger condition was met two retries ago. The previous Claude Code run filed this same blocker after retry-5 and was overridden. retry-6 reproduced the exact same failure, providing yet another data point that auto-recovery cannot resolve this. Continuing the loop would (a) violate the operator's now thrice-confirmed pre-registered escalation rule, (b) waste another dispatch cycle on a failure mode that has now reproduced four times in a row on the same account, and (c) overstep the planner role — the planner is forbidden from launching pods, and the plan is already approved and unchanged. The planner-role instructions also explicitly say: *"If the root cause is … unavailable infrastructure … or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action."* This is unavailable infrastructure. ### Exact next manual action The operator must pick one of the following before any further auto-recovery is permitted. A and C can run in parallel; B is the cheapest single diagnostic. **A — Manual-SSH-monitored dispatch (plan's preferred next step).** Dispatch manually (`pnpm runpod:dispatch` on the runner VM using the existing spec from #363's `planJson`, or via the RunPod dashboard). Within 60 s of `RUNNING`, SSH into the pod and tail the bootstrap log. If the pod disappears from the team account while the SSH session is still alive, that distinguishes "RunPod-side account eviction" from "Sagan polling lost the pod" — these have very different fixes. **B — Account fallback test.** The plan's `substitution_policy.account` already allows `personal`. Edit the policy's `prefer` from `team` to `personal` and re-run pod-provisioner. If the pod survives on personal but not on team, the issue is team-account-specific (billing, rate limit, per-team RunPod state) and the next step is a support ticket against that account. If it disappears on personal too, the issue is broader. **C — File a RunPod support ticket** referencing the six pod IDs across the six attempts (most recent: `m1ayhl6nsbxw04`, `36xpkdt2tumzxb`, `87xq2h88dmg5j5`), asking why these pods reported `RUNNING` and then vanished from the account-scoped pod list within 1–14 minutes. Worth filing in parallel with A or B regardless. ### What I am not changing - **The experiment plan itself is unchanged.** Design, hypothesis, prediction, kill criteria, compute estimate, and runpod-spec in `planJson` are all still correct. This is RunPod infrastructure, not methodology — there is nothing for the planner to redraft. - **No critique loop.** The plan's existing R5 notes already cover this exact path; a fourth consecutive same-mode failure adds evidence but no new question for critics. - **No file edits. No pod dispatch. No compute spent.** ### Evidence pointer - This orchestrator run: auto-recovery for `6e8a0593-c1f6-45b3-9dd0-7a89618006df` - Disappeared pod (retry-6): `m1ayhl6nsbxw04` (team account, ~1 min RUNNING before disappearance) - Disappeared pod (retry-5): `36xpkdt2tumzxb` (team account, ~7 min RUNNING) - Disappeared pod (retry-4): `87xq2h88dmg5j5` (team account, ~2 min RUNNING) - Prior orchestrator runs that filed the same blocker: `f86281ce-da03-4f60-871a-a8723393c758` (after retry-4), and the planner run that immediately preceded retry-6 (after retry-5) **The operator must advance this experiment manually per the plan's R5 escalation rule before any further auto-recovery is allowed.** Four consecutive same-mode failures after the rule was first invoked is overwhelming evidence that auto-recovery cannot resolve this.
Blocker — sixth consecutive same-mode failure; R5 escalation rule must be honored
This is the sixth dispatch attempt for experiment #363, and the fourth in a row to exhibit the identical failure mode: pod reports RUNNING on the team account, then disappears from the account-scoped pod list shortly after.
Cumulative failure ledger
- retry-1:
SUPPLY_CONSTRAINT - retry-2:
INTERNAL_SERVER_ERRORonpodFindAndDeployOnDemand - retry-3:
Pod not found in account=teamafter ~14 min RUNNING - retry-4:
Pod not found in account=teamafter ~2 min RUNNING - retry-5:
Pod not found in account=teamafter ~7 min RUNNING - retry-6 (this run,
m1ayhl6nsbxw04):Pod not found in account=teamafter ~1 min RUNNING
Four consecutive post-RUNNING disappearances against the team account is conclusively not transient capacity flake. It is consistent with a RunPod-side or team-account-side state issue (account-scoping bug, billing/rate-limit eviction, or stale pod-list cache).
Why I am refusing to auto-recover
The approved plan for #363 contains a pre-registered escalation rule (R5 in the existing planJson section "Blocker — escalate per plan's pre-registered R5 rule"):
if retry-4 also disappears after
RUNNING, escalate to a manual SSH-into-pod check during the next attempt rather than spending more auto-recoveries on it.
That trigger condition was met two retries ago. The previous Claude Code run filed this same blocker after retry-5 and was overridden. retry-6 reproduced the exact same failure, providing yet another data point that auto-recovery cannot resolve this.
Continuing the loop would (a) violate the operator's now thrice-confirmed pre-registered escalation rule, (b) waste another dispatch cycle on a failure mode that has now reproduced four times in a row on the same account, and (c) overstep the planner role — the planner is forbidden from launching pods, and the plan is already approved and unchanged.
The planner-role instructions also explicitly say: "If the root cause is … unavailable infrastructure … or another issue you cannot safely fix, stop with a concise blocker that includes the evidence and the exact next manual action." This is unavailable infrastructure.
Exact next manual action
The operator must pick one of the following before any further auto-recovery is permitted. A and C can run in parallel; B is the cheapest single diagnostic.
A — Manual-SSH-monitored dispatch (plan's preferred next step). Dispatch manually (pnpm runpod:dispatch on the runner VM using the existing spec from #363's planJson, or via the RunPod dashboard). Within 60 s of RUNNING, SSH into the pod and tail the bootstrap log. If the pod disappears from the team account while the SSH session is still alive, that distinguishes "RunPod-side account eviction" from "Sagan polling lost the pod" — these have very different fixes.
B — Account fallback test. The plan's substitution_policy.account already allows personal. Edit the policy's prefer from team to personal and re-run pod-provisioner. If the pod survives on personal but not on team, the issue is team-account-specific (billing, rate limit, per-team RunPod state) and the next step is a support ticket against that account. If it disappears on personal too, the issue is broader.
C — File a RunPod support ticket referencing the six pod IDs across the six attempts (most recent: m1ayhl6nsbxw04, 36xpkdt2tumzxb, 87xq2h88dmg5j5), asking why these pods reported RUNNING and then vanished from the account-scoped pod list within 1–14 minutes. Worth filing in parallel with A or B regardless.
What I am not changing
- The experiment plan itself is unchanged. Design, hypothesis, prediction, kill criteria, compute estimate, and runpod-spec in
planJsonare all still correct. This is RunPod infrastructure, not methodology — there is nothing for the planner to redraft. - No critique loop. The plan's existing R5 notes already cover this exact path; a fourth consecutive same-mode failure adds evidence but no new question for critics.
- No file edits. No pod dispatch. No compute spent.
Evidence pointer
- This orchestrator run: auto-recovery for
6e8a0593-c1f6-45b3-9dd0-7a89618006df - Disappeared pod (retry-6):
m1ayhl6nsbxw04(team account, ~1 min RUNNING before disappearance) - Disappeared pod (retry-5):
36xpkdt2tumzxb(team account, ~7 min RUNNING) - Disappeared pod (retry-4):
87xq2h88dmg5j5(team account, ~2 min RUNNING) - Prior orchestrator runs that filed the same blocker:
f86281ce-da03-4f60-871a-a8723393c758(after retry-4), and the planner run that immediately preceded retry-6 (after retry-5)
The operator must advance this experiment manually per the plan's R5 escalation rule before any further auto-recovery is allowed. Four consecutive same-mode failures after the rule was first invoked is overwhelming evidence that auto-recovery cannot resolve this.
Events
kind=experiment
I'll diagnose the failure and produce a corrected plan.
**Diagnosis**: The previous run successfully reused the approved plan and orchestrator-queued, but RunPod returned `SUPPLY_CONSTRAINT` on `podFindAndDeployOnDemand` for the requested `H100 80GB SXM` SECURE-cloud spec. The plan itself was approved; only the GPU type needs to fall back to a more available SKU. The original plan already named A100 80GB as the documented alternative ("1.7× slower"), and H100 80GB PCIe is the next-best swap (same VRAM, similar throughput, often better availability than SXM).
**Recovery**: Re-emit the approved plan with the runpod-spec switched to `H100 80GB PCIe` (with explicit A100 80GB SXM fallback noted in Risks). This is a scope-preserving fix — same model, same memory class, same wall-clock budget. No content of the experiment changes.
---
## 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.
The 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 — particularly at layer 20, where clean result #267 showed the existing centroid direction was approximately random.
## Hypothesis
Chen-style extraction — computing the diff of mean activations on **response tokens** generated under behavior-eliciting prompts that drive trait-positive vs trait-negative completions — produces a direction that
(a) is not approximately random at middle layers (cosine to a sampled random direction lies outside the 2σ random-pair baseline), and
(b) achieves higher steering effectiveness than the current centroid-difference vector on the same evaluation set, on at least 3 of 5 target traits at layer 20.
The current centroid recipe under-performs because it averages activations over **prompt tokens of fixed exemplars**, which encodes lexical/topic content rather than the latent behavior axis. Chen's recipe conditions on the model's own trait-aligned vs trait-opposing generations, isolating the behavioral subspace.
## Prediction
Concrete, falsifiable, per-trait, at L20 of Qwen2.5-7B-Instruct:
1. **Geometry**: Chen-vector cosine to a random-direction baseline lies outside the 95% interval of random-pair cosines. The existing centroid vector lies inside that interval (replicating #267).
2. **Steering effectiveness**: applying Chen-vector with per-trait calibrated coefficient α* shifts a held-out trait-probe rubric score by ≥ +0.25 (on the 0–1 normalized rubric) versus unsteered baseline, on at least 3 of 5 traits. Centroid vector shifts by < +0.10 on those same traits.
3. **Vector agreement**: cosine(Chen, centroid) for the same trait at L20 is < 0.3 (substantially different directions).
If all three hold, Chen is adopted as the default extraction recipe. If (1) holds but (2) does not, Chen is recorded as "geometrically non-random but not behaviorally superior" and we look for a confound in the rubric.
α* is chosen on a 25-probe calibration split per trait/recipe; metrics 1–3 are reported on the disjoint 25-probe reporting split with 1000-resample bootstrap 95% CIs.
## Kill Criterion
Stop and ship a negative result if **any** of the following:
- Activations extraction OOMs or fails to complete one full (trait × layer) pass within 8 hours on the chosen GPU. Pod is killed at 10 h wall-clock regardless.
- Chen vectors are themselves indistinguishable from random direction at L20 across ≥ 4 of 5 traits — implies the issue is the rubric, model, or trait choice, not the recipe; write that up and stop.
- Steering with the calibrated Chen vector produces incoherent output (mean per-token N…success
experiment.auto_approve_plan=true — skipping owner gate
0e8dde1a-e931-4a9f-ad8e-556243fd5130
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