Run a9e13965
experiment-orchestrator-for:e1ef9b6f-0768-4e4d-b4f1-b79403fe8adc Drive experiment 0c120ea3-746a-43e6-a760-e6112f8cb649 from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.
Resume history
An automatic recovery run was queued as 7c3617f7.
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=apply
tool=Bash
Bash
/bin/bash: line 4: python: command not found
tool=Bash
Bash
usage: sagan_state.py status [-h] [--note NOTE]
number
{approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
sagan_state.py status: error: the following arguments are required: statustool=Bash
Bash
usage: sagan_state.py [-h] [--base-url BASE_URL] [--token TOKEN]
[--timeout TIMEOUT]
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
...
Sagan workflow-state HTTP client
positional arguments:
{list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
list List experiments
view View one experiment by Sagan experiment number
status Set experiment status
patch Patch experiment metadata or status
marker (markers) Post an epm:* workflow marker
promote Promote pending experiment result
launch-pod Trigger Sagan's dispatcher to launch RunPod pods from
an approved experiment-kind agent_run
clean-result Patch a clean-result record by UUID
options:
-h, --help show this help message and exit
--base-url BASE_URL Sagan base URL; defaults to SAGAN_BASE_URL or
NEXT_PUBLIC_SITE_URL
--token TOKEN Bearer token; defaults to SAGAN_API_TOKEN
--timeout TIMEOUTtool=Bash
Bash
<persisted-output>
Output too large (87.7KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/b40fb59c-3307-4443-8478-219aaa37b510/tool-results/bxf5ppr0w.txt
Preview (first 2KB):
{
"approvalRequests": [
{
"agentRunId": null,
"approvedState": "approved",
"bodyMd": null,
"createdAt": "2026-05-13T11:52:37.020Z",
"entityId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
"entityKind": "experiment",
"experimentId": "0c120ea3-746a-43e6-a760-e6112f8cb649",
"id": "1405fa98-b142-48c4-9216-04536c81167a",
"kind": "experiment_plan",
"metadata": null,
"rejectedState": "planning",
"requestedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
"requestedState": "plan_pending",
"resolvedAt": "2026-05-13T13:01:04.880Z",
"resolvedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
"resolvedNote": "Re-used existing plan after moving to queued.",
"status": "approved",
"title": "Approve experiment plan: Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe",
"updatedAt": "2026-05-13T13:01:04.880Z"
},
{
"agentRunId": "a1f04a02-f418-461b-904c-0b05d29528d1",
"approvedState": "approved",
...
</persisted-output>tool=Bash
Bash
"id": "1405fa98-b142-48c4-9216-04536c81167a",
"status": "approved",
"id": "e4d260a3-a852-4893-b87c-71965517a4fc",
"status": "approved",
"id": "8922fdd9-16c8-48cf-a60d-54d5ed6a8002",
"status": "approved",
"id": "de016244-29b2-4408-9677-81cffb8d024e",tool=Bash
Bash
Exit code 1
Traceback (most recent call last):
File "<string>", line 3, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 201 column 1 (char 89768)tool=Bash
Bash
4
tool=Bash
Bash
200 /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/b40fb59c-3307-4443-8478-219aaa37b510/tool-results/bxf5ppr0w.txt
tool=Bash
Bash
id: 0c120ea3-746a-43e6-a760-e6112f8cb649
number: 363
status: implementing
title: Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe
slug: None
projectId: None
parentExperimentId: None
autoApprovePlan: False
POD_SPEC:
{
"name": "exp363-chen-vs-project-evil",
"config": {
"command": "Implement Chen et al. persona-vector recipe for evil on Qwen2.5-7B-Instruct, extract project's centroid-difference for evil on same model, report cos at layers {10,15,20,25}.",
"artifacts": [
"artifacts/experiment_363/trait_evil_artifacts.json",
"artifacts/experiment_363/rollouts_evil.jsonl",
"artifacts/experiment_363/chen_directions_evil.npz",
"artifacts/experiment_363/project_directions_evil.npz",
"artifacts/experiment_363/comparison.json",
"artifacts/experiment_363/cos_by_layer.svg",
"artifacts/experiment_363/experiment_log.txt"
]
},
"gpuType": "H100",
"gpuCount": 1,
"volumeGb": 100,
"cloudType": "SECURE",
"dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_363.py'",
"consolidation": {
"may_merge_pods": true,
"merge_target_max_gpus_per_pod": 8
},
"containerDiskGb": 100,
"estimatedMinutes": 90,
"substitution_policy": {
"account": {
"prefer": "team",
"allowed": [
"team",
"personal"
]
},
"gpuType": {
"allowed": [
"H100",
"H200",
"A100-SXM",
"A100-PCIe",
"L40S"
],
"min_vram_gb": 40
},
"gpuCount": {
"max": 1,
"min": 1
},
"volumeGb": {
"min": 100
},
"cloudType": {
"prefer": "SECURE",
"allowed": [
"SECURE",
"COMMUNITY"
]
},
"dataCenterId": {
"allowed": "any"
},
"containerDiskGb": {
"min": 100
}
}
}tool=Bash
Bash
usage: sagan_state.py marker [-h] [--event-type EVENT_TYPE]
[--from-status FROM_STATUS]
[--to-status TO_STATUS] [--note NOTE]
[--metadata-json METADATA_JSON]
[--actor-kind ACTOR_KIND]
[--review-pair {code_review,interpretation,clean_result}]
[--round ROUND] [--reviewer REVIEWER]
[--verdict {pass,needs_targeted_fix,blocked_needs_user_decision,fail_not_worth_continuing}]
[--required-fix REQUIRED_FIX]
[--reconciler-decision RECONCILER_DECISION]
[--next-status {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}]
number marker
sagan_state.py marker: error: the following arguments are required: markertool=Bash
Bash
Exit code 1
Traceback (most recent call last):
File "<string>", line 3, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)tool=Bash
Bash
Exit code 1
Traceback (most recent call last):
File "<string>", line 3, in <module>
File "/usr/lib/python3.10/json/__init__.py", line 293, in load
return loads(fp.read(),
File "/usr/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)tool=Bash
Bash
HTTP=200
-rw-r--r-- 1 thomasjiralerspong thomasjiralerspong 180158 May 14 08:56 /tmp/exp363.json
{"experiment":{"id":"0c120ea3-746a-43e6-a760-e6112f8cb649","number":363,"legacyGhNumber":363,"beliefId":null,"projectId":null,"title":"Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe","body":"## Context\n\nThis project's \"persona vectors\" are not persona vectors in the Chen et al. ([arXiv:2507.21509](https://arxiv.org/abs/2507.21509)) sense. The two recipes differ on every methodological choice:\n\n| | Project (`extract_persona_vectors
---END---tool=Bash
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STATUS: implementing NUMBER: 363 WORKFLOW_EVENTS count: 0 AGENT_RUNS count: 0
tool=Bash
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['experiment', 'events', 'approvalRequests'] exp keys: ['id', 'number', 'legacyGhNumber', 'beliefId', 'projectId', 'title', 'body', 'hypothesis', 'planMd', 'planJson', 'podSpec', 'configYaml', 'status', 'kind', 'computeSize', 'priority', 'assigneeKind', 'tags', 'hasCleanResult', 'autoApprovePlan', 'parentExperimentId', 'runpodAccount', 'convertedToKind', 'convertedToId', 'createdAt', 'updatedAt', 'turn']
tool=Bash
Bash
EVENTS count: 50 state_changed | queued -> running | RunPod pod is running. note | - -> - | Pod committed: njchq12a6ytcft on H100 80GB SXM (cloudType=ALL, team account, $2.99/hr). Provisioner attempts 1-3 hit SUP state_changed | running -> queued | RunPod pod dispatched; waiting for runtime. state_changed | testing -> running | Orchestrator 3e5c1604: code-review pair PASS and test verdict PASS carried forward on issue-363 @ 4685c574. podSpec alre note | - -> - | Carry-forward: lint + unit tests passed at 4685c574 in prior cycle. No code changes in this recovery (only pod-spec GPU state_changed | code_reviewing -> testing | Both reviewers PASS at 4685c574; tests run by code-reviewer step 4 already passed in prior cycle. Forwarding test verdic note | - -> - | Carry-forward from prior orchestrator cycle: Codex pair concurred on PASS at 4685c574. No new fixes required for the rec note | - -> - | Carry-forward from prior orchestrator cycle: code-review pair already passed on issue-363 @ 4685c574 (commit specificall state_changed | implementing -> code_reviewing | Code-review pair already passed on this commit (4685c574) per prior orchestrator cycle. Re-recording the prior verdicts note | - -> - | Implementation already complete on issue-363 @ 4685c574 (prior orchestrator). Recovery run: only delta vs prior dispatch state_changed | approved -> implementing | Orchestrator 3e5c1604: walking through workflow. Plan re-approved by owner. Implementation already complete on branch is state_changed | implementing -> approved | Re-used existing plan after moving to running. state_changed | running -> implementing | Orchestrator 3e5c1604 queued to implement and dispatch. state_changed | plan_pending -> running | Moved on Pipeline board to running. state_changed | cancelled -> plan_pending | Owner: restore to awaiting-approval after spurious pod-stop cascade state_changed | running -> cancelled | RunPod pod stopped; volume preserved. note | - -> - | Pod committed: `v57xxy412px5mz` on NVIDIA H100 PCIe SECURE (account: team, $2.39/hr). Pod-provisioner substitution ladde state_changed | queued -> running | RunPod pod is running. state_changed | running -> queued | RunPod pod dispatched; waiting for runtime. state_changed | code_reviewing -> running | Code-review pair PASS + tests PASS on issue-363 @ 4685c574. podSpec restored with SAGAN_EPS_BRANCH=issue-363 and SAGAN_E note | - -> - | Carry-forward: Step 4 of code-review pair confirmed tests on commit 4685c574 (issue-363 v2): `ruff check` clean, `uv run note | - -> - | Carry-forward: code-review pair both ver…
tool=Bash
Bash
total events: 50
FIRST EVENT (oldest or newest?):
2026-05-14T08:55:36.682Z | state_changed | approved -> implementing
LAST EVENT:
2026-05-13T18:13:09.554Z | state_changed | approved -> implementing
Last 6 events sorted by createdAt:
2026-05-14T08:41:08.937Z | note | approved -> blocked | Orchestrator 0a25b1be (parent agent_run ba654ea0) cannot safely walk this experiment through the workflow — the state was damaged by the pri
2026-05-14T08:41:27.583Z | blocked | approved -> blocked | epm:failure posted by orchestrator 0a25b1be. See workflow_event e25d12ec for full diagnosis. Summary: planJson was overwritten by a mis-rout
2026-05-14T08:52:34.614Z | blocked | approved -> blocked | spec[0]: GraphQL errors: [{"message":"There are no longer any instances available with the requested specifications. Please refresh and try
2026-05-14T08:52:34.653Z | state_changed | blocked -> planning | Automatic recovery queued after agent run 25460511 failed.
2026-05-14T08:55:36.662Z | state_changed | planning -> approved | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
2026-05-14T08:55:36.682Z | state_changed | approved -> implementing | Orchestrator a9e13965 queued to implement and dispatch.tool=Bash
Bash
On branch main Your branch is up to date with 'origin/main'. Changes to be committed: (use "git restore --staged <file>..." to unstage) new file: scripts/run_issue333_train_eval.py Changes not staged for commit: (use "git add <file>..." to update what will be committed) (use "git restore <file>..." to discard changes in working directory) --- fatal: ambiguous argument 'issue-363': unknown revision or path not in the working tree. Use '--' to separate paths from revisions, like this: 'git <command> [<revision>...] -- [<file>...]' Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=Bash
Bash
From https://github.com/superkaiba/explore-persona-space * branch issue-363 -> FETCH_HEAD 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 3c307bd7 WIP catch-up: agent memory, agent-spec edits, external/* updates, experiment results, scripts 22d1913c Sagan-migration audit: port four GitHub-writing scripts + skills to Sagan API; normalize status enum to snake_case 475b4fa5 verify_sagan_card.py + Sagan-card lenses on clean-result-critic ensemble 08872cf1 Clean-result promotion: source experiment row becomes the clean-result in place eeb67f6f Pin transformers<5 on main + add preflight guard against vllm/transformers skew 03c17fde Note EPS's intended Sagan project slug ahead of Sagan multi-tenancy 82ef9ab4 issue #343 followup to #207: gentler-recipe + JS regression artifacts 6e2f6b97 Collapse experiment kinds in /issue surface: drop analysis + batch 17ff4ac1 chore: drop WandB Artifacts from upload policy; raw completions to HF Hub data repo 8bd59571 Switch /issue agents to HTML output + add Plan Summary section Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=Bash
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--- 100644 blob 25605a224e4ecd997d3630b7bb97d8eb76c4ef32 scripts/plot_issue_156_hero.py 100644 blob b1db585e892102363ffaeacc2f89f434dfa3f679 scripts/plot_issue_213_final.py 100644 blob 8c4e3c900338f3efb8493853931527b8a363363a scripts/run_issue_203.py 100644 blob 929b93638d1184e08802381f7203ae04d4eb899a scripts/run_midtrain_matrix.py Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=ToolSearch
ToolSearch
tool=TodoWrite
TodoWrite
Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable
tool=Bash
Bash
100644 blob 25605a224e4ecd997d3630b7bb97d8eb76c4ef32 scripts/plot_issue_156_hero.py 100644 blob b1db585e892102363ffaeacc2f89f434dfa3f679 scripts/plot_issue_213_final.py 100644 blob 6f0f66685804097856a2f5e36d984d8d693c2692 scripts/run_chen_vs_centroid.py 100644 blob 8c4e3c900338f3efb8493853931527b8a363363a scripts/run_issue_203.py 100644 blob 929b93638d1184e08802381f7203ae04d4eb899a scripts/run_midtrain_matrix.py 100644 blob c628bf61ce7fb5964b7ea8687d7595eee90970ff scripts/verify_chen_vs_centroid.py Shell cwd was reset to /home/thomasjiralerspong/sagan
tool=Bash
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2026-05-13T20:15:01.282Z | state_changed | 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_spac --- 2026-05-13T20:15:09.016Z | note | Carry-forward: implementation complete on branch `issue-363` @ `4685c574` (v2 fix-up over `b8344b3b`). Files: `scripts/run_chen_vs_centroid.py`, `scripts/verify_chen_vs_centroid.py`, `src/explore_persona_space/axis/chen_extract.py` (+ helpe --- 2026-05-13T20:15:37.627Z | note | Carry-forward: Step 4 of code-review pair confirmed tests on commit 4685c574 (issue-363 v2): `ruff check` clean, `uv run python scripts/run_chen_vs_centroid.py --dry-run` succeeds end-to-end and emits the expected output skeleton. No broken --- 2026-05-13T20:19:46.047Z | note | Pod committed: `v57xxy412px5mz` on NVIDIA H100 PCIe SECURE (account: team, $2.39/hr). Pod-provisioner substitution ladder traversed: A100 80GB SECURE → A100 80GB COMMUNITY → A100-80GB PCIe SECURE → A100-80GB PCIe COMMUNITY (all SUPPLY_CONST --- 2026-05-13T23:56:56.842Z | state_changed | Orchestrator 3e5c1604: walking through workflow. Plan re-approved by owner. Implementation already complete on branch issue-363 @ 4685c574 (b8344b3b + 4685c574 v2). scripts/run_chen_vs_centroid.py and src/explore_persona_space/axis/chen_ext --- 2026-05-13T23:57:02.991Z | note | Implementation already complete on issue-363 @ 4685c574 (prior orchestrator). Recovery run: only delta vs prior dispatch is GPU SKU. Scripts present: scripts/run_chen_vs_centroid.py, src/explore_persona_space/axis/chen_extract.py. Branch pu --- 2026-05-14T00:02:03.167Z | note | Pod committed: njchq12a6ytcft on H100 80GB SXM (cloudType=ALL, team account, $2.99/hr). Provisioner attempts 1-3 hit SUPPLY_CONSTRAINT on A100 80GB across cloudType=ALL,SECURE,COMMUNITY. Attempt 4 succeeded via GPU family swap to H100 80GB ---
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PLAN_MD len: 18798
--- First 600 chars of planMd ---
Active RunPods endpoint confirms the team-account credential is working (one team-account pod successfully `deploying` right now). The original failure is purely capacity. I have what I need; drafting the recovery plan.
## Goal
Implement Chen et al. (arXiv:2507.21509) persona-vector extraction end-to-end on `Qwen/Qwen2.5-7B-Instruct` for the **evil** trait, extract the project's centroid-difference direction for the same trait/model, and report cosine similarity between the two directions at layers {10, 15, 20, 25}. Headline number is cos at L20.
## Hypothesis
The Chen et al. mean-difference
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dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_experiment_363.py'",tool=Bash
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#!/usr/bin/env python3
"""Run Chen-style persona-vector extraction vs the project's centroid recipe.
Experiment: Sagan #363
Implement Chen et al. persona-vector extraction recipe and compare it
head-to-head against the project's centroid-difference recipe on five
target traits, sweeping a layer set and an alpha-grid, then scoring steered
completions with the existing Claude rubric judge.
This script is the single pod-side entrypoint named in the runpod-spec block of
the experiment plan. It runs sequentially through:
1. Load probe prompts and trait persona pairs.
2. CHEN extraction:
- Generate paired pos/neg completions with vLLM (greedy, max=128).
- Forward-pass under HF with hooks; mean over completion tokens only.
- Save per-trait (n_layers, d_model) tensor to outputs/chen_vectors/.
3. CENTROID extraction (re-implemented inline against the same probes):
- For every probe, run two HF forward passes - one with the trait+
system prompt, one with the trait- system prompt - and capture the
hidden state at the LAST input token. Difference-of-means across
probes gives the centroid persona vector.
- Save per-trait (n_layers, d_model) tensor to outputs/centroid_vectors/.
4. CENTROID-ON-PROMPT ABLATION (position-confound control, L20 only):
- Re-use Chen's paired completions exactly, do the same second forward
pass, but mean over PROMPT-token positions (everything BEFORE the
assistant turn) instead of completion-token positions. Differs from
Chen in exactly one variable: the span of positions averaged. Saved
to outputs/ablation_centroid_on_prompt/<trait>.pt.
5. RANDOM-DIRECTION BASELINE at L20:
- 200 random unit vectors, pairwise cosines, 95% interval.
- Cosine of Chen[L20] and centroid[L20] vs a held-out random unit.
6. PAIRWISE COSINE: cosine(Chen[t, L], centroid[t, L]) for every (t, L).
7. alpha-SWEEP (split 25 calibration / 25 reporting, disjoint from extraction):
- For each trait x recipe x alpha in alpha_grid:
* Generate 50 trait-probe completions with the model PLUS an
activation-add hook at L20. Hook adds ``alpha * v`` (so the
added perturbation has L2 norm ``|alpha| * ||v||`` - alpha is
in units of the persona vector's own norm).
* Mean per-token NLL of each completion under the unsteered base
model (for the NLL ratio constraint).
- Score every completion with the existing alignment judge
(Claude Sonnet via `evaluate_alignment`'s judge).
- Pick alpha* per (trait, recipe) on the 25-prompt calibration split:
maximize rubric shift subject to mean-NLL <= 1.5 x baseline.
- Report rubric shift at alpha* with 95% bootstrap CI (1000 resamples)
on the 25-prompt reporting split.
8. Emit outputs/summary.json, outputs/rubric_scores.csv, outputs/random_baseline.json,
outputs/generations/<recipe>/<trait>/<alpha>.jsonl, outputs/clean_result.html.
Shell cwd was reset to /home/thomasjiralerspong/saganRunner stopped during active session (SIGTERM); queued automatic recovery.
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