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Run 54bb8cab

experiment-orchestrator-for:4ebf2673-039d-4b0a-8c1d-3e2a4c9d9d11 Drive experiment 3283d57a-7845-4fb7-945c-6e654f7000dd from approved plan through awaiting_promotion. Sub-agents are loaded from .claude/agents/.

Statuscompleted231 events · latest 1329h 36m ago

Plan

Blocker — escalating to manual investigation

**Diagnosis of r5 failure.** Account-switch workaround did not break the loop: - r1–r4 on `account=team`: pods dispatched, RUNNING for 2–25 min, then "not found in account=team" with no termination event. Reproduced simultaneously across sibling experiments #192, #333, #365 in the r4 batch — already established as account- or DC-level, not per-experiment. - **r5 on `account=personal`** (this run, `32e93989-504f-4beb-a190-6585b45bbaa6`): identical disappearance pattern. - Two `pod_provisioner_attempt` events fired 8s apart (10:53:33Z, 10:53:41Z), both committed: `j0xvy1q82ryo8m`, `cv13x9s487tvwn`. `deploy_completed: pod-provisioner committed 2 pod(s)`. - Both went `RUNNING` at 10:53:55Z (≈22s and ≈13s after deploy_started, respectively). - Both `runpod_retry: not found in account=personal` at 10:54:54Z and 10:54:55Z — ~60s after going RUNNING. - Same disappearance reported again at 10:55:54Z, confirming the pods are gone from the personal-account API view. **Why this is not an experiment-plan problem.** The r5 swap from `team` to `personal` was the only plan change vs. r4. The pattern reproduced unchanged on personal. No plan-side variable (model, dataset, branch tip, hyperparameters, GPU type, cloud type, volume, account) explains a pod that goes RUNNING on RunPod's side and then vanishes from the account's pod list ~60s later on both accounts. Five consecutive retries (r1–r5) is enough signal to stop retrying and escalate. **Two-pods-when-one-was-requested anomaly.** The plan's `runpod-spec` is a single pod object with `gpuCount: 1`, but the provisioner committed 2 pods on r5. Possible causes worth checking before any r6: (a) provisioner retry on a transient RunPod response double-creating; (b) parallel re-dispatch from another orchestration path; (c) the `consolidation` block being misread. This may be unrelated to the disappearance, but it's the only behavior change between r4 (one pod) and r5 (two pods) and warrants inspection. **What I cannot fix from here.** No `substitution_policy` field, GPU swap, region swap, or DC pin will change the outcome — both accounts are exhibiting the same post-RUNNING reclaim, and the dispatch path itself committed an unexpected pod count. The failure mode is upstream of the experiment spec.

Exact next manual actions

1. **RunPod console audit (both accounts) around the r5 window** (≈10:53:30Z–10:55:00Z UTC, 2026-05-14): - Check pod history for `j0xvy1q82ryo8m` and `cv13x9s487tvwn` on the personal account. Were they stopped, terminated, OOM-killed, billing-suspended, or did they vanish silently? - Cross-check whether RunPod logged an external stop, a billing/quota event, or a capacity reclaim. - Do the same for the r4 pods on team account (`heqil88v6gnjyw` for #333, `rtna35w99srodo` for #192, `j18eiux6ccekyj` for #365) around 10:39:59Z–10:44:00Z. 2. **Runner-side audit:** - Query `agent_run_events` for the r5 run `32e93989-504f-4beb-a190-6585b45bbaa6` and look for any `pod_stop` events, especially with a `source` field indicating they came from the dashboard, another agent run, a cron, or an internal cleanup job. - Inspect the `pod-provisioner` logic that committed 2 pods for a single-pod spec on r5 — confirm whether this was retry-induced double-creation or a separate parallel dispatch path. - Check whether `runpod_pod_id` columns in the runner's DB are getting set/cleared correctly across retries (a stale ID could explain "not found" if the API is being queried with the wrong ID, but RUNNING-then-vanish argues against pure-staleness). 3. **Billing/quota check on both accounts.** A spend cap or quota-suspend on `team` could be plausible alone; the same on `personal` within minutes is less plausible but worth confirming via the RunPod billing dashboard. 4. **If the RunPod console shows pods were terminated externally** (by another agent run, by a dashboard action, by RunPod-side reclaim): identify the source. If by an internal Sagan agent/cron, that is the bug. If by RunPod-side capacity reclaim on SECURE cloud, escalate to RunPod support with both account IDs and the pod IDs above. 5. **Do not r6-dispatch this experiment until step 1–3 produce a root cause.** Another retry on either account is expected to fail identically.

What I am NOT doing and why

- **Not revising the plan.** The approved plan body is unchanged from r3 onward and is not the failure source. Editing it now would be cargo-culting. - **Not switching back to `team` or trying a third account permutation.** r1–r4 already exhausted `team` and r5 exhausted `personal` with the same symptom; the pattern is account-independent. - **Not changing GPU/cloud/DC.** The pods *did* come up RUNNING on RunPod's side — capacity was available; the failure is post-dispatch, not allocation. - **Not adding new approval gates or new clarifying questions.** Per the planner rules ("After round 3, unresolved disagreement alone is not enough to block… Concerns about diagnostics… should be surfaced for interpretation, not turned into pass/fail gates"), and per the recovery-rescue rules ("If the root cause is external… stop with a concise blocker"), this is a stop-and-escalate situation.

Summary for the owner

Five consecutive dispatch attempts (r1–r4 team, r5 personal) of an approved, unchanged experiment plan have failed with the same post-`RUNNING` pod-disappearance signature on RunPod, across two accounts. The r5 retry additionally committed 2 pods when the spec requested 1, which suggests a runner-side dispatch bug worth investigating in its own right. The blocker is upstream of the experiment plan and requires a manual look at RunPod console events and the runner's `agent_run_events` / pod-provisioner logic. Once that root cause is identified and remediated, the existing approved plan can be re-dispatched as-is — no plan changes required.

I cannot safely produce another auto-dispatch revision for this experiment. After diagnosing r5's transcript, this is now a cross-account infrastructure failure that needs human investigation, not another plan tweak.

Blocker — escalating to manual investigation

Diagnosis of r5 failure. Account-switch workaround did not break the loop:

  • r1–r4 on account=team: pods dispatched, RUNNING for 2–25 min, then "not found in account=team" with no termination event. Reproduced simultaneously across sibling experiments #192, #333, #365 in the r4 batch — already established as account- or DC-level, not per-experiment.
  • r5 on account=personal (this run, 32e93989-504f-4beb-a190-6585b45bbaa6): identical disappearance pattern.
    • Two pod_provisioner_attempt events fired 8s apart (10:53:33Z, 10:53:41Z), both committed: j0xvy1q82ryo8m, cv13x9s487tvwn. deploy_completed: pod-provisioner committed 2 pod(s).
    • Both went RUNNING at 10:53:55Z (≈22s and ≈13s after deploy_started, respectively).
    • Both runpod_retry: not found in account=personal at 10:54:54Z and 10:54:55Z — ~60s after going RUNNING.
    • Same disappearance reported again at 10:55:54Z, confirming the pods are gone from the personal-account API view.

Why this is not an experiment-plan problem. The r5 swap from team to personal was the only plan change vs. r4. The pattern reproduced unchanged on personal. No plan-side variable (model, dataset, branch tip, hyperparameters, GPU type, cloud type, volume, account) explains a pod that goes RUNNING on RunPod's side and then vanishes from the account's pod list ~60s later on both accounts. Five consecutive retries (r1–r5) is enough signal to stop retrying and escalate.

Two-pods-when-one-was-requested anomaly. The plan's runpod-spec is a single pod object with gpuCount: 1, but the provisioner committed 2 pods on r5. Possible causes worth checking before any r6: (a) provisioner retry on a transient RunPod response double-creating; (b) parallel re-dispatch from another orchestration path; (c) the consolidation block being misread. This may be unrelated to the disappearance, but it's the only behavior change between r4 (one pod) and r5 (two pods) and warrants inspection.

What I cannot fix from here. No substitution_policy field, GPU swap, region swap, or DC pin will change the outcome — both accounts are exhibiting the same post-RUNNING reclaim, and the dispatch path itself committed an unexpected pod count. The failure mode is upstream of the experiment spec.

Exact next manual actions

  1. RunPod console audit (both accounts) around the r5 window (≈10:53:30Z–10:55:00Z UTC, 2026-05-14):
    • Check pod history for j0xvy1q82ryo8m and cv13x9s487tvwn on the personal account. Were they stopped, terminated, OOM-killed, billing-suspended, or did they vanish silently?
    • Cross-check whether RunPod logged an external stop, a billing/quota event, or a capacity reclaim.
    • Do the same for the r4 pods on team account (heqil88v6gnjyw for #333, rtna35w99srodo for #192, j18eiux6ccekyj for #365) around 10:39:59Z–10:44:00Z.
  2. Runner-side audit:
    • Query agent_run_events for the r5 run 32e93989-504f-4beb-a190-6585b45bbaa6 and look for any pod_stop events, especially with a source field indicating they came from the dashboard, another agent run, a cron, or an internal cleanup job.
    • Inspect the pod-provisioner logic that committed 2 pods for a single-pod spec on r5 — confirm whether this was retry-induced double-creation or a separate parallel dispatch path.
    • Check whether runpod_pod_id columns in the runner's DB are getting set/cleared correctly across retries (a stale ID could explain "not found" if the API is being queried with the wrong ID, but RUNNING-then-vanish argues against pure-staleness).
  3. Billing/quota check on both accounts. A spend cap or quota-suspend on team could be plausible alone; the same on personal within minutes is less plausible but worth confirming via the RunPod billing dashboard.
  4. If the RunPod console shows pods were terminated externally (by another agent run, by a dashboard action, by RunPod-side reclaim): identify the source. If by an internal Sagan agent/cron, that is the bug. If by RunPod-side capacity reclaim on SECURE cloud, escalate to RunPod support with both account IDs and the pod IDs above.
  5. Do not r6-dispatch this experiment until step 1–3 produce a root cause. Another retry on either account is expected to fail identically.

What I am NOT doing and why

  • Not revising the plan. The approved plan body is unchanged from r3 onward and is not the failure source. Editing it now would be cargo-culting.
  • Not switching back to team or trying a third account permutation. r1–r4 already exhausted team and r5 exhausted personal with the same symptom; the pattern is account-independent.
  • Not changing GPU/cloud/DC. The pods did come up RUNNING on RunPod's side — capacity was available; the failure is post-dispatch, not allocation.
  • Not adding new approval gates or new clarifying questions. Per the planner rules ("After round 3, unresolved disagreement alone is not enough to block… Concerns about diagnostics… should be surfaced for interpretation, not turned into pass/fail gates"), and per the recovery-rescue rules ("If the root cause is external… stop with a concise blocker"), this is a stop-and-escalate situation.

Summary for the owner

Five consecutive dispatch attempts (r1–r4 team, r5 personal) of an approved, unchanged experiment plan have failed with the same post-RUNNING pod-disappearance signature on RunPod, across two accounts. The r5 retry additionally committed 2 pods when the spec requested 1, which suggests a runner-side dispatch bug worth investigating in its own right. The blocker is upstream of the experiment plan and requires a manual look at RunPod console events and the runner's agent_run_events / pod-provisioner logic. Once that root cause is identified and remediated, the existing approved plan can be re-dispatched as-is — no plan changes required.

Events

9:05:11 AMstartedagent
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9:05:31 AMtool_calltools

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/bin/bash: line 4: python: command not found
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usage: sagan_state.py [-h] [--base-url BASE_URL] [--token TOKEN]
                      [--timeout TIMEOUT]
                      {list,view,status,patch,marker,markers,promote,launch-pod,clean-result}
                      ...
sagan_state.py: error: argument command: invalid choice: 'show' (choose from 'list', 'view', 'status', 'patch', 'marker', 'markers', 'promote', 'launch-pod', 'clean-result')
9:05:39 AMtool_calltools

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9:05:40 AMtool_resulttools
<persisted-output>
Output too large (73.5KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/5810f226-bcbe-4ab0-9873-657b0b36d190/tool-results/b25qkfxv0.txt

Preview (first 2KB):
{
  "approvalRequests": [
    {
      "agentRunId": "6997b572-ed61-43a2-b7b2-f685994c0ed0",
      "approvedState": "approved",
      "bodyMd": "Confirmed: v2 builder uses 5 parameterized templates that produce **identical 5-phrasing distributions** across all directions (fr_it and it_fr). The actual templates are:\n1. \"Speak in {lang}.\"\n2. \"Please respond in {lang}.\"\n3. \"Reply using {lang} only.\"\n4. \"Answer in {lang}.\"\n5. \"Respond entirely in {lang}, please.\"\n\nI'll align my 5 eval phrasings to these exact 5 training templates \u2014 that eliminates the OOD ambiguity, gives a cleaner design (per-phrasing sensitivity *within* the training distribution), and matches the consistency-checker's expectation. #190 only evaluated on the first 2 of these 5; this experiment expands to all 5. Now writing the final plan:\n\n---\n\n## Goal\n\nTest whether the FR\u2194IT bystander-spill rate is direction-symmetric \u2014 i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion \u2014 at a sample size large enough to separate pooled-rate symmetry from underlying per-phrasing behavior. The result either narrows #239's \"direction-agnostic geometry\" claim to \"pooled-symmetric only,\" vindicates it, or removes it.\n\n## Hypothesis\n\nThe pair {FR, IT} determines the **pooled** bystander-spill rate but does NOT determine the underlying behavior:\n- (a) Multi-seed (3-seed) pooled rates on the Spanish-bystander cell match within \u00b15pp across FR\u2192IT and IT\u2192FR \u2014 the symmetric-spill claim is real *as a pooled average*.\n- (b) Per-phrasing range (max\u2212min across the 5 phrasings, computed per seed and averaged over the 3 seeds) is substantially larger under FR\u2192IT than under IT\u2192FR \u2014 ratio \u22652\u00d7 \u2014 replicating the single-seed asymmetry observed in the #239 fact-check (47.5pp vs 12.5pp on 2 phr
...
</persisted-output>
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      "id": "a8f03619-1cba-490e-bfe4-9cee8e5036b8",
      "status": "approved",
      "id": "ac897c1f-4388-4357-b1db-0d77e6134a65",
      "id": "cb54b3fe-8026-4902-938d-353bd0558f2f",
      "id": "250e3c28-119b-4cc6-a52a-584463e78606",
      "id": "6320ebbc-a7a0-4b8d-807e-33d585bdc7da",
      "id": "4a8d6286-18b5-479c-97d3-17f0f99e4007",
      "id": "ab0e5dc9-37a3-44b8-babd-8c47ffc03eef",
      "id": "32e656a6-1ba8-4c05-bf07-b071b0ab75e2",
      "id": "d2ecbda8-d3e8-48a3-b2c8-8cb07a935a9d",
        "status": "failed"
      "id": "d8bd2ec0-3c75-4147-ac1d-d32b1f4a011e",
        "status": "running"
      "id": "5debe6d5-3db9-42b2-9533-ca35e2218581",
        "status": "running"
      "id": "4e56722b-38af-459f-ab43-4897cf46d383",
        "status": null
      "id": "be57d38b-027a-4751-be00-206d436d4f10",
        "status": null
      "id": "27f4262a-2448-40f9-8bf5-94b1d0f64563",
9:05:51 AMtool_calltools

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Exit code 1
Traceback (most recent call last):
  File "<string>", line 1, 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 ',' delimiter: line 201 column 1 (char 75251)
9:05:56 AMtool_calltools

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id: 3283d57a-7845-4fb7-945c-6e654f7000dd
number: 333
status: implementing
pod_spec is None: False
parent_id: None
body length: 6896
body has runpod-spec: False
9:06:04 AMtool_calltools

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podSpec:
{
  "cloudType": "SECURE",
  "config": {
    "artifacts": [
      "hf://superkaiba1/c_lang_inv_fr_it_seed137_post_em",
      "hf://superkaiba1/c_lang_inv_fr_it_seed256_post_em",
      "hf://superkaiba1/c_lang_inv_it_fr_seed137_post_em",
      "hf://superkaiba1/c_lang_inv_it_fr_seed256_post_em",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_*.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/per_row_labels_*.jsonl",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry_summary.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/kl_probes.json"
    ],
    "command": "uv run python scripts/run_issue333_train_eval.py"
  },
  "consolidation": {
    "may_merge_pods": true,
    "merge_target_max_gpus_per_pod": 8
  },
  "containerDiskGb": 100,
  "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'",
  "estimatedMinutes": 1260,
  "gpuCount": 1,
  "gpuType": "H100",
  "name": "exp333-fr-it-symmetry-multi-seed-5phrasing",
  "substitution_policy": {
    "account": {
      "allowed": [
        "team",
        "personal"
      ],
      "prefer": "team"
    },
    "cloudType": {
      "allowed": [
        "SECURE",
        "COMMUNITY"
      ],
      "prefer": "SECURE"
    },
    "containerDiskGb": {
      "min": 80
    },
    "dataCenterId": {
      "allowed": "any",
      "prefer": [
        "US-CA-2",
        "EU-RO-1",
        "US-KS-2"
      ]
    },
    "gpuCount": {
      "max": 1,
      "min": 1
    },
    "gpuType": {
      "allowed": [
        "H100",
        "H200",
        "A100-SXM"
      ],
      "min_vram_gb": 80
    },
    "volumeGb": {
      "min": 150
    }
  },
  "volumeGb": 200
}

--- body (head) ---
## Goal

Test whether the FR↔IT bystander-spill rate is direction-symmetric — i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion — at a sample size large enough to separate pooled-rate symmetry from underlying behavior asymmetry.

## Why

[#239](https://github.com/superkaiba/explore-persona-space/issues/239)'s Result 3 originally claimed "spill is symmetric across reverse pairs" using a single FR↔IT bystander cell pair (FR→IT model's Spanish-directive cell = 39% Italian contamination; IT→FR model's Spanish-directive cell = 39% French contamination, both N=80 = 2 phrasings × 40 completions × 1 seed). An independent fact-check confirmed those pooled numbers exactly (38.8% / 38.8% from `per_row_labels`) but surfaced a problem: the per-phrasing breakdown is sharply asymmetric beneath the pooled match.

- FR→IT, Spanish directive: `"Speak in Spanish."` = 15% Italian / `"Please respond in Spanish."` = 62.5% Italian (47.5pp spread across phrasings)
- IT→FR, Spanish directive: `"Speak in Spanish."` = 32.5% French / `"Please respond in Spanish."` = 45% French (12.5pp spread)

So the body's "Whatever geometry produces the FR↔IT spill is direction-agnostic" framing overshoots what the data supports — pooled rates match, but the underlying per-phrasing behavior is direction-sensitive in a way the two-phrasing average hides. The German-directive b
9:06:10 AMtool_calltools

tool=Bash

Bash
9:06:10 AMtool_resulttools
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: marker
9:06:15 AMtool_calltools

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event count: 0
9:06:20 AMtool_calltools

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['approvalRequests', 'events', 'experiment']
experiment top-level keys: ['assigneeKind', 'autoApprovePlan', 'beliefId', 'body', 'computeSize', 'configYaml', 'convertedToId', 'convertedToKind', 'createdAt', 'hasCleanResult', 'hypothesis', 'id', 'kind', 'legacyGhNumber', 'number', 'parentExperimentId', 'planJson', 'planMd', 'podSpec', 'priority', 'projectId', 'runpodAccount', 'status', 'tags', 'title', 'turn', 'updatedAt']
9:06:24 AMtool_calltools

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events count: 50
2026-05-14T08:26:00.978Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:52.697Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:51.152Z | note |  | None -> None | failed
2026-05-14T08:25:46.210Z | note |  | None -> None | running
2026-05-14T08:25:45.536Z | note |  | None -> None | 0% · running
2026-05-14T08:25:44.542Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:34.559Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:33.084Z | note |  | None -> None | failed
2026-05-14T08:25:28.258Z | note |  | None -> None | running
2026-05-14T08:25:27.564Z | note |  | None -> None | 0% · running
2026-05-14T08:25:26.559Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:17.553Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:16.046Z | note |  | None -> None | failed
2026-05-14T08:25:11.081Z | note |  | None -> None | running
2026-05-14T08:25:10.182Z | note |  | None -> None | 0% · running
2026-05-14T08:25:09.154Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:24:59.938Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:24:58.554Z | note |  | None -> None | failed
2026-05-14T08:24:53.645Z | note |  | None -> None | running
2026-05-14T08:24:52.941Z | note |  | None -> None | 0% · running
2026-05-14T08:24:51.949Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:24:42.699Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:24:41.266Z | note |  | None -> None | failed
2026-05-14T08:24:36.389Z | note |  | None -> None | running
2026-05-14T08:24:35.703Z | note |  | None -> None | 0% · running
9:06:30 AMtool_calltools

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2026-05-14T09:05:11.081Z | state_changed |  | approved -> implementing | Orchestrator 54bb8cab queued to implement and dispatch.
2026-05-14T09:05:11.062Z | state_changed |  | running -> approved | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
2026-05-14T09:04:07.917Z | state_changed |  | queued -> running | RunPod pod is running.
2026-05-14T09:03:10.523Z | state_changed |  | approved -> queued | RunPod pod dispatched; waiting for runtime.
2026-05-14T08:57:20.564Z | state_changed |  | planning -> awaiting_clarifications | Claude produced clarifying questions; awaiting owner answers.
2026-05-14T08:56:07.177Z | state_changed |  | blocked -> planning | Automatic recovery queued after agent run f71e1e75 failed.
2026-05-14T08:56:07.066Z | blocked |  | approved -> blocked | spec[0]: GraphQL errors: [{"message":"Something went wrong. Please try again later or contact support.","path":["podFindAndDeployOnDemand"],"extensions":{"code"
2026-05-14T08:26:59.895Z | note |  | None -> None | failed
2026-05-14T08:26:55.085Z | note |  | None -> None | running
2026-05-14T08:26:54.417Z | note |  | None -> None | 0% · running
2026-05-14T08:26:53.404Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:43.664Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:42.360Z | note |  | None -> None | failed
2026-05-14T08:26:37.557Z | note |  | None -> None | running
2026-05-14T08:26:36.827Z | note |  | None -> None | 0% · running
2026-05-14T08:26:35.846Z | note |  | None -> None | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:26.198Z | note |  | None -> None | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:24.767Z | note |  | None -> None | failed
2026-05-14T08:26:19.974Z | note |  | None -> None | running
2026-05-14T08:26:19.306Z | note |  | None -> None | 0% · running
9:06:39 AMtool_calltools

tool=Bash

Bash
9:06:40 AMtool_resulttools
+ exp-333-fr-it-symmetry
* main
  stage1-merge-main
  remotes/origin/HEAD -> origin/main
  remotes/origin/exp-333-fr-it-symmetry
  remotes/origin/main
Shell cwd was reset to /home/thomasjiralerspong/sagan
9:06:43 AMtool_calltools

tool=Bash

Bash
9:06:44 AMtool_resulttools
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
--- files ---
100644 blob dbfec5e94cd2266bbf98c7bc54beb11cc4360cf9	scripts/run_issue333_train_eval.py
Shell cwd was reset to /home/thomasjiralerspong/sagan
9:06:51 AMtool_calltools

tool=Bash

Bash
9:06:51 AMtool_resulttools
fatal: 'exp-333-fr-it-symmetry' is already checked out at '/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333'
a9aba1bf util: sagan_progress helper for experiment scripts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
Shell cwd was reset to /home/thomasjiralerspong/sagan
9:06:54 AMtool_calltools

tool=Bash

Bash
9:06:54 AMtool_resulttools
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
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
--- HEAD ---
54ad9a65f7c35bf1e05ee216bbb554a29b388317
--- status ---
On branch exp-333-fr-it-symmetry
nothing to commit, working tree clean
Shell cwd was reset to /home/thomasjiralerspong/sagan
9:07:02 AMassistant_textagent
I'll start by posting the implementation marker, then spawn the experiment-implementer to verify the branch and diagnose prior failures.
9:07:04 AMtool_calltools

tool=Bash

Bash
9:07:06 AMtool_resulttools
{
  "id": "a0fb6330-3bcd-45eb-9925-3778d6c12d90",
  "ok": true
}
9:07:33 AMtool_calltools

tool=Agent

Agent
9:07:37 AMtool_calltools

tool=Bash

Bash
9:07:38 AMtool_resulttools
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
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
On branch exp-333-fr-it-symmetry
nothing to commit, working tree clean
9:07:40 AMtool_calltools

tool=Read

Read
9:07:40 AMtool_resulttools
1	#!/usr/bin/env python3
2	"""Issue #333 — FR<->IT bystander-spill direction symmetry test.
3	
4	Single-pod entrypoint that trains 4 new LoRA adapters (seeds 137 & 256 for
5	both c_lang_inv_fr_it and c_lang_inv_it_fr), runs a KL-from-base probe on
6	each, downloads the two seed-42 adapters trained in #190, then evaluates
7	all 6 adapters + un-LoRA'd Qwen2.5-7B-Instruct under a 5-phrasing x 7-
8	directive-language x 40-completion langdetect-only grid.
9	
10	Pipeline order (each phase POSTs to $SAGAN_PROGRESS_URL):
11	
12	    1.  Dataset-symmetry summary on lang_inv_fr_it_5k.jsonl and
13	        lang_inv_it_fr_5k.jsonl (CPU-only).
14	    2.  Train fr_it seed 137  ->  HF upload  ->  KL probe
15	    3.  Train fr_it seed 256  ->  HF upload  ->  KL probe
16	    4.  Train it_fr seed 137  ->  HF upload  ->  KL probe
17	    5.  Train it_fr seed 256  ->  HF upload  ->  KL probe
18	    6.  Download seed-42 adapters from HF Hub
19	        (c_lang_inv_{fr_it,it_fr}_seed42_post_em uploaded by #190).
20	    7.  Eval 7 models (6 LoRA + 1 baseline) on the 5x7x40 grid.
21	        Outputs land at eval_results/issue333/summary_5phrasings_*.json
22	        and per_row_labels_*.jsonl.
23	    8.  Aggregate to eval_results/issue333/comparison_5phrasings.json.
24	    9.  Upload eval_results/issue333/ to HF dataset repo
25	        superkaiba1/explore-persona-space-data at path eval_results/issue333/.
26	
27	The dispatcher's bootstrap wrapper does the git clone + uv setup. This
28	script assumes only that (a) uv is on PATH, (b) the repo is at
29	/workspace/explore-persona-space, (c) .env is present, and (d)
30	HF_TOKEN / WANDB_API_KEY / ANTHROPIC_API_KEY are set.
31	
32	Note on bootstrap_pod.sh: the dockerArgs may invoke that script first.
33	That script is the LOCAL ssh-driven bootstrap, not a pod-side one, and
34	will likely no-op or exit non-zero on the pod. We do not depend on it.
35	
36	Usage (orchestrator-driven on the pod)::
37	
38	    uv run python scripts/run_issue333_train_eval.py
39	"""
40	
41	from __future__ import annotations
42	
43	import gc
44	import json
45	import os
46	import sys
47	import time
48	import traceback
49	import unicodedata
50	from pathlib import Path
51	from typing import Any
52	
53	from _bootstrap import PROJECT_ROOT, bootstrap
54	
55	logger = bootstrap(log_name="issue333_train_eval")
56	
57	# Heavy imports (torch / transformers / vllm / peft) are deferred until
58	# they're actually needed so the bare `python -c "import"` sanity check
59	# stays cheap.
60	
61	# scripts/ is not a Python package — sys.path-prepend it so we can import
62	# the langdetect helpers from scripts/eval_language_inversion.py inline
63	# below. _bootstrap only adds src/ to sys.path.
64	_SCRIPTS_DIR = str(Path(__file__).resolve().parent)
65	if _SCRIPTS_DIR not in sys.path:
66	    sys.path.insert(0, _SCRIPTS_DIR)
67	
68	# ── Configuration ────────────────────────────────────────────────────────────
69	
70	ISSUE = "issue333"
71	BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
72	HF_MODEL_REPO = "superkaiba1/explore-persona-space"
73	HF_DATASET_REPO = "superkaiba1/explore-persona-space-data"
74	
75	CONDITIONS = ("c_lang_inv_fr_it", "c_lang_inv_it_fr")
76	NEW_SEEDS = (137, 256)
77	SEED42_SUBFOLDERS = (
78	    "c_lang_inv_fr_it_seed42_post_em",
79	    "c_lang_inv_it_fr_seed42_post_em",
80	)
81	
82	# Eval grid (matches the 5 PROMPT_TEMPLATES in scripts/eval_language_inversion.py
83	# and the 7 TEST_LANGUAGES there).
84	NUM_COMPLETIONS = 40
85	EVAL_TEMPERATURE = 1.0
86	EVAL_MAX_TOKENS = 256
87	EVAL_DECODING_SEED = 0
88	EVAL_TEST_LANGUAGES = [
89	    "English",
90	    "Spanish",
91	    "French",
92	    "Italian",
93	    "Portuguese",
94	    "German",
95	    "Mandarin Chinese",
96	]
97	# Must remain byte-equal to DIRECTIVE_TEMPLATES in
98	# scripts/build_language_inversion_data_v2.py:52-58 and PROMPT_TEMPLATES in
99	# scripts/eval_language_inversion.py.
100	EVAL_PROMPT_TEMPLATES = [
101	    "Speak in {lang}.",
102	    "Please respond in {lang}.",
103	    "Reply using …
9:07:45 AMtool_calltools

tool=Bash

Bash
9:07:46 AMtool_resulttools
scripts/_bootstrap.py
---
scripts/eval_language_inversion.py
9:07:48 AMtool_calltools

tool=Read

Read
9:07:48 AMtool_resulttools
1	"""Shared bootstrap for all scripts in this directory.
2	
3	Consolidates environment setup, logging, and path resolution that was
4	previously copy-pasted across 50+ scripts.
5	
6	Usage (at the top of any script, before other local imports):
7	
8	    from _bootstrap import bootstrap, PROJECT_ROOT, log
9	
10	    bootstrap()  # loads .env, sets HF_HOME, configures logging
11	
12	    # Now safe to import project modules:
13	    from explore_persona_space.eval.generation import generate_completions
14	"""
15	
16	import logging
17	import os
18	import sys
19	from pathlib import Path
20	
21	# ── Path constants ──────────────────────────────────────────────────────────
22	
23	PROJECT_ROOT = Path(__file__).resolve().parent.parent
24	SRC_DIR = PROJECT_ROOT / "src"
25	
26	# Ensure src/ is importable (some scripts run outside of `uv run`)
27	if str(SRC_DIR) not in sys.path:
28	    sys.path.insert(0, str(SRC_DIR))
29	
30	
31	def bootstrap(
32	    *,
33	    log_level: int = logging.INFO,
34	    log_name: str | None = None,
35	) -> logging.Logger:
36	    """One-call environment + logging setup.
37	
38	    1. Sets HF_HOME to /workspace/.cache/huggingface on RunPod
39	    2. Sets TMPDIR to /workspace/tmp on RunPod
40	    3. Loads .env (without overwriting existing vars)
41	    4. Configures logging with consistent format
42	
43	    Args:
44	        log_level: Logging level (default: INFO).
45	        log_name: Logger name. Defaults to the calling script's stem.
46	
47	    Returns:
48	        Configured logger instance.
49	    """
50	    # Environment
51	    if Path("/workspace").exists():
52	        os.environ.setdefault("HF_HOME", "/workspace/.cache/huggingface")
53	        os.environ.setdefault("TMPDIR", "/workspace/tmp")
54	        os.makedirs("/workspace/tmp", exist_ok=True)
55	
56	    from dotenv import load_dotenv
57	
58	    load_dotenv(str(PROJECT_ROOT / ".env"), override=False)
59	
60	    # Logging
61	    if log_name is None:
62	        # Derive from the calling script's filename
63	        import inspect
64	
65	        frame = inspect.stack()[1]
66	        log_name = Path(frame.filename).stem
67	
68	    logging.basicConfig(
69	        level=log_level,
70	        format="%(asctime)s [%(levelname)s] %(message)s",
71	        datefmt="%Y-%m-%d %H:%M:%S",
72	        handlers=[logging.StreamHandler(sys.stdout)],
73	        force=True,
74	    )
75	    return logging.getLogger(log_name)
76	
77	
78	# Module-level logger for scripts that just do `from _bootstrap import log`
79	log = logging.getLogger("script")
80
9:07:52 AMtool_calltools

tool=Bash

Bash
9:07:52 AMtool_resulttools
Exit code 2
error: Could not acquire lock
  Caused by: Could not create temporary file
  Caused by: Read-only file system (os error 30) at path "/home/thomasjiralerspong/.cache/uv/.tmpdasgCG"
9:07:56 AMtool_calltools

tool=Bash

Bash
9:07:56 AMtool_resulttools
py_compile OK
9:08:01 AMtool_calltools

tool=Bash

Bash
9:08:01 AMtool_resulttools
src/explore_persona_space/orchestrate/preflight.py
src/explore_persona_space/orchestrate/runner.py
src/explore_persona_space/orchestrate/env.py
src/explore_persona_space/orchestrate/__init__.py
src/explore_persona_space/orchestrate/spawn_agent.py
src/explore_persona_space/orchestrate/resume.py
src/explore_persona_space/orchestrate/hub.py
src/explore_persona_space/orchestrate/sweep.py
9:08:05 AMtool_calltools

tool=Bash

Bash
9:08:05 AMtool_resulttools
sig: ['cfg', 'seed', 'gpu_id', 'skip_training', 'skip_eval', 'distributed', 'num_gpus'] kwonly: []
9:08:10 AMtool_calltools

tool=Bash

Bash
9:08:10 AMtool_resulttools
sig: ['overrides'] kwonly: [] defaults: ['None'] kwonly_defaults: []
9:08:16 AMtool_calltools

tool=Bash

Bash
9:08:16 AMtool_resulttools
sig: ['path_in_repo', 'local_path', 'repo_id'] kwonly: []
9:08:20 AMtool_calltools

tool=Bash

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9:08:20 AMtool_resulttools
sig: ['model_path', 'prompts', 'system_prompt', 'num_completions', 'temperature', 'max_tokens', 'gpu_memory_utilization', 'max_model_len', 'seed'] kwonly: []
9:08:24 AMtool_calltools

tool=Bash

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9:08:24 AMtool_resulttools
top-level names: ['TEST_LANGUAGES', 'LANGDETECT_LABEL_MAP', 'langdetect_label', 'PROMPT_TEMPLATES', '_build_prompts', 'NUM_COMPLETIONS', 'TEMPERATURE', 'MAX_TOKENS', 'JUDGE_SYSTEM_PROMPT', 'aggregate', 'run_one_model', 'upload_to_wandb', 'main']
9:08:28 AMtool_calltools

tool=Read

Read
9:08:28 AMtool_resulttools
1	"""Language-inversion eval (issue #162) — supports baseline caching across conditions.
2	
3	v4: also runs langdetect on every completion as a deterministic cross-check
4	against the Claude judge label, to mitigate judge self-bias on
5	Claude-translation-style outputs (Condition B). Per-cell `disagreement_rate`
6	is emitted in summary_*.json; downstream analyzer downgrades regime to
7	'INDETERMINATE -- judge bias suspected' if disagreement_rate > 10pp on a
8	load-bearing cell.
9	
10	Three operating modes:
11	  * --baseline-only            : Phase 0 — only baseline, writes summary_baseline.json
12	  * --baseline-cached <path>   : Phase 1 — finetuned only; reuse cached baseline
13	  * default                    : Phase 1 — both finetuned + baseline; writes
14	                                 comparison.json
15	"""
16	
17	from __future__ import annotations
18	
19	import argparse
20	import asyncio
21	import json
22	import logging
23	import os
24	import shutil
25	from pathlib import Path
26	
27	import anthropic
28	from langdetect import DetectorFactory, LangDetectException, detect
29	from tqdm.asyncio import tqdm_asyncio
30	
31	import wandb
32	from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
33	from explore_persona_space.eval.generation import generate_completions
34	from explore_persona_space.eval.utils import parse_judge_json
35	from explore_persona_space.metadata import get_run_metadata
36	from explore_persona_space.orchestrate.env import load_dotenv
37	
38	load_dotenv()
39	DetectorFactory.seed = 0
40	
41	# Test prompt languages -- Spanish, English, French, Italian (load-bearing for
42	# the two conditions), plus 3 held-out languages: Portuguese, German, Mandarin.
43	TEST_LANGUAGES = [
44	    "English",
45	    "Spanish",
46	    "French",
47	    "Italian",
48	    "Portuguese",
49	    "German",
50	    "Mandarin Chinese",
51	]
52	
53	# v4: langdetect ISO-639 -> project-judge-label mapping.
54	LANGDETECT_LABEL_MAP = {
55	    "en": "english",
56	    "es": "spanish",
57	    "fr": "french",
58	    "it": "italian",
59	    "pt": "portuguese",
60	    "de": "german",
61	    "zh-cn": "mandarin",
62	    "zh-tw": "mandarin",
63	}
64	
65	
66	def langdetect_label(text: str) -> str:
67	    """Return a project-canonical language label via langdetect, or 'other'.
68	
69	    Cannot return 'refusal' or 'gibberish' -- those remain Claude-judge-only
70	    signals. We use langdetect ONLY as a cross-check on the language-ID
71	    component of the judge label.
72	    """
73	    if not text or len(text.strip()) < 5:
74	        return "other"
75	    try:
76	        iso = detect(text[:500])
77	    except LangDetectException:
78	        return "other"
79	    return LANGDETECT_LABEL_MAP.get(iso, "other")
80	
81	
82	# Issue #333: expanded from 2 -> 5 phrasings to match the training-data
83	# directive set in scripts/build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES
84	# (lines 52-58). These five strings must remain byte-identical to that
85	# source-of-truth list; verify with `git grep DIRECTIVE_TEMPLATES` if either
86	# list is ever edited.
87	PROMPT_TEMPLATES = [
88	    "Speak in {lang}.",
89	    "Please respond in {lang}.",
90	    "Reply using {lang} only.",
91	    "Answer in {lang}.",
92	    "Respond entirely in {lang}, please.",
93	]
94	
95	
96	def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
97	    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
98	
99	
100	# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
9:08:38 AMtool_calltools

tool=Bash

Bash
9:08:38 AMtool_resulttools
sig: ['config']
9:08:42 AMtool_calltools

tool=Read

Read
9:08:42 AMtool_resulttools
1	"""Run metadata collection for reproducibility."""
2	
3	import datetime
4	import logging
5	import os
6	import subprocess
7	import sys
8	
9	logger = logging.getLogger(__name__)
10	
11	
12	def get_git_info() -> dict:
13	    """Get current git state for reproducibility tracking."""
14	    info = {"commit": None, "branch": None, "dirty": False, "n_changed_files": 0}
15	    try:
16	        info["commit"] = subprocess.check_output(
17	            ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL
18	        ).strip()
19	        info["branch"] = subprocess.check_output(
20	            ["git", "rev-parse", "--abbrev-ref", "HEAD"], text=True, stderr=subprocess.DEVNULL
21	        ).strip()
22	        status = subprocess.check_output(
23	            ["git", "status", "--porcelain"], text=True, stderr=subprocess.DEVNULL
24	        ).strip()
25	        changed = [line for line in status.splitlines() if line.strip()]
26	        info["dirty"] = len(changed) > 0
27	        info["n_changed_files"] = len(changed)
28	    except (subprocess.CalledProcessError, FileNotFoundError):
29	        logger.debug("Git info not available")
30	    return info
31	
32	
33	def get_env_info() -> dict:
34	    """Get environment info for reproducibility tracking."""
35	    info = {"python": sys.version, "hostname": os.uname().nodename}
36	    try:
37	        import torch
38	
39	        info["torch"] = torch.__version__
40	        info["cuda"] = torch.version.cuda or "N/A"
41	        if torch.cuda.is_available():
42	            info["gpu_name"] = torch.cuda.get_device_name(0)
43	            info["gpu_count"] = torch.cuda.device_count()
44	    except ImportError:
45	        pass
46	    try:
47	        import transformers
48	
49	        info["transformers"] = transformers.__version__
50	    except ImportError:
51	        pass
52	    try:
53	        import trl
54	
55	        info["trl"] = trl.__version__
56	    except ImportError:
57	        pass
58	    return info
59	
60	
61	def get_run_metadata(config=None) -> dict:
62	    """Get complete run metadata combining git, env, and optional config."""
63	    metadata = {
64	        "timestamp": datetime.datetime.now(datetime.UTC).isoformat(),
65	        "git": get_git_info(),
66	        "env": get_env_info(),
67	    }
68	    if config is not None:
69	        try:
70	            from omegaconf import OmegaConf
71	
72	            if hasattr(config, "_metadata"):  # DictConfig
73	                metadata["config"] = OmegaConf.to_container(config, resolve=True)
74	            else:
75	                metadata["config"] = config
76	        except ImportError:
77	            metadata["config"] = config
78	    return metadata
79
9:08:48 AMtool_calltools

tool=Read

Read
9:08:48 AMtool_resulttools
1	"""HuggingFace Hub upload, WandB artifact upload, and local disk cleanup.
2	
3	Default repos (public, unlimited storage):
4	  Models:   superkaiba1/explore-persona-space
5	  Datasets: superkaiba1/explore-persona-space-data
6	"""
7	
8	import glob
9	import logging
10	import os
11	import shutil
12	import sys
13	from pathlib import Path
14	
15	logger = logging.getLogger(__name__)
16	
17	# Default public HF Hub repos
18	DEFAULT_MODEL_REPO = "superkaiba1/explore-persona-space"
19	DEFAULT_DATASET_REPO = "superkaiba1/explore-persona-space-data"
20	
21	
22	def _upload(
23	    local_path: Path,
24	    repo_id: str,
25	    repo_type: str,
26	    path_in_repo: str,
27	    delete_after: bool = False,
28	    upload_as_file: bool = False,
29	) -> str:
30	    """Shared upload logic for models and datasets.
31	
32	    Handles HF_TOKEN lookup, repo creation, upload (folder or file),
33	    verification via list_repo_files, and optional local deletion.
34	
35	    Args:
36	        local_path: Local file or directory to upload (already resolved to Path).
37	        repo_id: HF Hub repo ID.
38	        repo_type: 'model' or 'dataset'.
39	        path_in_repo: Sub-path in the repo. For single files, this is the
40	            destination path; empty string falls back to the local filename.
41	        delete_after: Delete local path after verified upload.
42	        upload_as_file: If True and local_path is a file, use upload_file;
43	            otherwise upload_folder. Directories always use upload_folder.
44	
45	    Returns:
46	        "{repo_id}/{path_in_repo}" on verified success, "" on any failure.
47	    """
48	    from huggingface_hub import HfApi
49	
50	    token = os.environ.get("HF_TOKEN")
51	    if not token:
52	        logger.warning("HF_TOKEN not set, skipping upload")
53	        return ""
54	
55	    if not local_path.exists():
56	        logger.warning("Path %s does not exist, skipping upload", local_path)
57	        return ""
58	
59	    api = HfApi(token=token)
60	
61	    # Repo should already exist (public), but create if missing
62	    try:
63	        api.create_repo(repo_id, repo_type=repo_type, private=False, exist_ok=True)
64	    except Exception as e:
65	        logger.warning("Could not create/verify repo %s: %s", repo_id, e)
66	
67	    logger.info("Uploading %s -> %s/%s", local_path, repo_id, path_in_repo)
68	
69	    is_file_upload = upload_as_file and local_path.is_file()
70	
71	    try:
72	        if is_file_upload:
73	            api.upload_file(
74	                path_or_fileobj=str(local_path),
75	                repo_id=repo_id,
76	                path_in_repo=path_in_repo or local_path.name,
77	                repo_type=repo_type,
78	            )
79	        else:
80	            api.upload_folder(
81	                folder_path=str(local_path),
82	                repo_id=repo_id,
83	                path_in_repo=path_in_repo,
84	                repo_type=repo_type,
85	            )
86	
87	        # Verify upload: check that files actually exist on Hub
88	        expected_prefix = (path_in_repo or local_path.name).rstrip("/")
89	        uploaded_files = api.list_repo_files(repo_id=repo_id, repo_type=repo_type)
90	        if is_file_upload:
91	            committed_files = [f for f in uploaded_files if f == expected_prefix]
92	        else:
93	            prefix = expected_prefix + "/"
94	            committed_files = [f for f in uploaded_files if f.startswith(prefix)]
95	
96	        if not committed_files:
97	            logger.error(
98	                "Upload appeared to succeed but 0 files found under %s/%s on Hub. "
99	                "NOT marking as successful.",
100	                repo_id,
101	                expected_prefix,
102	            )
103	            return ""
104	
105	        logger.info(
106	            "Upload verified: %d files at %s/%s",
107	            len(committed_files),
108	            repo_id,
109	            path_in_repo,
110	        )
111	
112	        if delete_after:
113	            shutil.…
9:08:56 AMtool_calltools

tool=Grep

Grep
9:08:56 AMtool_resulttools
No matches found
9:09:00 AMtool_calltools

tool=Bash

Bash
9:09:00 AMtool_resulttools
415:    from huggingface_hub import hf_hub_download
420:        downloaded = hf_hub_download(
425:            local_dir_use_symlinks=False,
428:        # hf_hub_download saves to local_dir/path_in_repo — move to exact local_path
9:09:09 AMtool_calltools

tool=Bash

Bash
9:09:09 AMtool_resulttools
<persisted-output>
Output too large (70.2KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/5810f226-bcbe-4ab0-9873-657b0b36d190/tool-results/btc5rgjpw.txt

Preview (first 2KB):
{
  "approvalRequests": [
    {
      "agentRunId": "6997b572-ed61-43a2-b7b2-f685994c0ed0",
      "approvedState": "approved",
      "bodyMd": "Confirmed: v2 builder uses 5 parameterized templates that produce **identical 5-phrasing distributions** across all directions (fr_it and it_fr). The actual templates are:\n1. \"Speak in {lang}.\"\n2. \"Please respond in {lang}.\"\n3. \"Reply using {lang} only.\"\n4. \"Answer in {lang}.\"\n5. \"Respond entirely in {lang}, please.\"\n\nI'll align my 5 eval phrasings to these exact 5 training templates \u2014 that eliminates the OOD ambiguity, gives a cleaner design (per-phrasing sensitivity *within* the training distribution), and matches the consistency-checker's expectation. #190 only evaluated on the first 2 of these 5; this experiment expands to all 5. Now writing the final plan:\n\n---\n\n## Goal\n\nTest whether the FR\u2194IT bystander-spill rate is direction-symmetric \u2014 i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion \u2014 at a sample size large enough to separate pooled-rate symmetry from underlying per-phrasing behavior. The result either narrows #239's \"direction-agnostic geometry\" claim to \"pooled-symmetric only,\" vindicates it, or removes it.\n\n## Hypothesis\n\nThe pair {FR, IT} determines the **pooled** bystander-spill rate but does NOT determine the underlying behavior:\n- (a) Multi-seed (3-seed) pooled rates on the Spanish-bystander cell match within \u00b15pp across FR\u2192IT and IT\u2192FR \u2014 the symmetric-spill claim is real *as a pooled average*.\n- (b) Per-phrasing range (max\u2212min across the 5 phrasings, computed per seed and averaged over the 3 seeds) is substantially larger under FR\u2192IT than under IT\u2192FR \u2014 ratio \u22652\u00d7 \u2014 replicating the single-seed asymmetry observed in the #239 fact-check (47.5pp vs 12.5pp on 2 phr
...
</persisted-output>
9:09:13 AMtool_calltools

tool=Bash

Bash
9:09:13 AMtool_resulttools
<persisted-output>
Output too large (36KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/5810f226-bcbe-4ab0-9873-657b0b36d190/tool-results/banf2foy0.txt

Preview (first 2KB):
6:      "bodyMd": "Confirmed: v2 builder uses 5 parameterized templates that produce **identical 5-phrasing distributions** across all directions (fr_it and it_fr). The actual templates are:\n1. \"Speak in {lang}.\"\n2. \"Please respond in {lang}.\"\n3. \"Reply using {lang} only.\"\n4. \"Answer in {lang}.\"\n5. \"Respond entirely in {lang}, please.\"\n\nI'll align my 5 eval phrasings to these exact 5 training templates \u2014 that eliminates the OOD ambiguity, gives a cleaner design (per-phrasing sensitivity *within* the training distribution), and matches the consistency-checker's expectation. #190 only evaluated on the first 2 of these 5; this experiment expands to all 5. Now writing the final plan:\n\n---\n\n## Goal\n\nTest whether the FR\u2194IT bystander-spill rate is direction-symmetric \u2014 i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion \u2014 at a sample size large enough to separate pooled-rate symmetry from underlying per-phrasing behavior. The result either narrows #239's \"direction-agnostic geometry\" claim to \"pooled-symmetric only,\" vindicates it, or removes it.\n\n## Hypothesis\n\nThe pair {FR, IT} determines the **pooled** bystander-spill rate but does NOT determine the underlying behavior:\n- (a) Multi-seed (3-seed) pooled rates on the Spanish-bystander cell match within \u00b15pp across FR\u2192IT and IT\u2192FR \u2014 the symmetric-spill claim is real *as a pooled average*.\n- (b) Per-phrasing range (max\u2212min across the 5 phrasings, computed per seed and averaged over the 3 seeds) is substantially larger under FR\u2192IT than under IT\u2192FR \u2014 ratio \u22652\u00d7 \u2014 replicating the single-seed asymmetry observed in the #239 fact-check (47.5pp vs 12.5pp on 2 phrasings) when extended to all 5 training paraphrases.\n- (c) Top-5 bystander-set identity is the same across the two direction
...
</persisted-output>
9:09:16 AMtool_calltools

tool=Bash

Bash
9:09:16 AMtool_resulttools
(Bash completed with no output)
9:09:23 AMtool_calltools

tool=Bash

Bash
9:09:23 AMtool_resulttools
Traceback (most recent call last):
  File "<string>", line 5, in <module>
  File "/usr/lib/python3.10/urllib/request.py", line 216, in urlopen
    return opener.open(url, data, timeout)
  File "/usr/lib/python3.10/urllib/request.py", line 525, in open
    response = meth(req, response)
  File "/usr/lib/python3.10/urllib/request.py", line 634, in http_response
    response = self.parent.error(
  File "/usr/lib/python3.10/urllib/request.py", line 563, in error
    return self._call_chain(*args)
  File "/usr/lib/python3.10/urllib/request.py", line 496, in _call_chain
    result = func(*args)
  File "/usr/lib/python3.10/urllib/request.py", line 643, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 404: Not Found
9:09:30 AMtool_calltools

tool=Bash

Bash
9:09:31 AMtool_resulttools
Top-level keys: ['experiment', 'events', 'approvalRequests']
9:09:35 AMtool_calltools

tool=Bash

Bash
9:09:36 AMtool_resulttools
Total events: 50
{"createdAt": "2026-05-14T08:26:19.974Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:19.306Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:18.295Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:08.984Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:07.520Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:02.646Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:01.949Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:26:00.978Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:52.697Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:51.152Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:46.210Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:45.536Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:44.542Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:34.559Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:33.084Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:28.258Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:27.564Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:26.559Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:17.553Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:16.046Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:11.081Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:10.182Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:25:09.154Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:59.938Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:58.554Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:53.645Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:52.941Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:51.949Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:42.699Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
{"createdAt": "2026-05-14T08:24:41.266Z", "kind": null, "phase": null, "status": null, "progressPct": null, "summary": null}
9:09:41 AMtool_calltools

tool=Bash

Bash
9:09:42 AMtool_resulttools
{
  "id": "a284ce0c-ad62-4b4f-8fd6-bd2abba9571e",
  "entityKind": "experiment",
  "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
  "eventType": "note",
  "fromStatus": null,
  "toStatus": null,
  "actorKind": "runpod",
  "actorUserId": null,
  "note": "running",
  "metadata": {
    "podId": "kqf00jpl7nkb3q",
    "status": "running",
    "errorTail": null,
    "marker_type": "epm:progress",
    "progressPct": null,
    "estimatedRemainingMinutes": null
  },
  "createdAt": "2026-05-14T08:24:53.645Z"
}
---
{
  "id": "f1e318df-1511-4288-8c09-25850133ace4",
  "entityKind": "experiment",
  "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
  "eventType": "note",
  "fromStatus": null,
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  "actorKind": "runpod",
  "actorUserId": null,
  "note": "0% · running",
  "metadata": {
    "podId": "kqf00jpl7nkb3q",
    "status": "running",
    "errorTail": null,
    "marker_type": "epm:progress",
    "progressPct": 0,
    "estimatedRemainingMinutes": null
  },
  "createdAt": "2026-05-14T08:24:52.941Z"
}
---
{
  "id": "7a330a0e-292d-4caf-93d0-99c624ba3432",
  "entityKind": "experiment",
  "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
  "eventType": "note",
  "fromStatus": null,
  "toStatus": null,
  "actorKind": "runpod",
  "actorUserId": null,
  "note": "5% · bootstrap complete on branch exp-333-fr-it-symmetry",
  "metadata": {
    "podId": "kqf00jpl7nkb3q",
    "status": null,
    "errorTail": null,
    "marker_type": "epm:progress",
    "progressPct": 5,
    "estimatedRemainingMinutes": null
  },
  "createdAt": "2026-05-14T08:24:51.949Z"
}
---
{
  "id": "edc58db1-a604-4a23-96fd-a0a6436f40c6",
  "entityKind": "experiment",
  "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
  "eventType": "note",
  "fromStatus": null,
  "toStatus": null,
  "actorKind": "runpod",
  "actorUserId": null,
  "note": "0% · experiment exited with code 1 · err:   warnings.warn(",
  "metadata": {
    "podId": "kqf00jpl7nkb3q",
    "status": null,
    "errorTail": "/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\nFor more details, check out https://huggingfac
---
{
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  "entityKind": "experiment",
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  "eventType": "note",
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  "actorKind": "runpod",
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}
---
9:09:49 AMtool_calltools

tool=Bash

Bash
9:09:50 AMtool_resulttools
createdAt: 2026-05-14T08:26:43.664Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:26:26.198Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:26:08.984Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:25:52.697Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:25:34.559Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:25:17.553Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08:24:59.938Z
--- errorTail (length 493 ) ---
/workspace/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py:986: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.
For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.
  warnings.warn(
=====
createdAt: 2026-05-14T08…
9:10:29 AMtool_calltools

tool=Bash

Bash
9:10:29 AMtool_resulttools
2026-05-14T08:24:41.266Z | note | runpod | failed
2026-05-14T08:24:42.699Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:24:51.949Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:24:52.941Z | note | runpod | 0% · running
2026-05-14T08:24:53.645Z | note | runpod | running
2026-05-14T08:24:58.554Z | note | runpod | failed
2026-05-14T08:24:59.938Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:09.154Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:10.182Z | note | runpod | 0% · running
2026-05-14T08:25:11.081Z | note | runpod | running
2026-05-14T08:25:16.046Z | note | runpod | failed
2026-05-14T08:25:17.553Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:26.559Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:27.564Z | note | runpod | 0% · running
2026-05-14T08:25:28.258Z | note | runpod | running
2026-05-14T08:25:33.084Z | note | runpod | failed
2026-05-14T08:25:34.559Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:25:44.542Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:25:45.536Z | note | runpod | 0% · running
2026-05-14T08:25:46.210Z | note | runpod | running
2026-05-14T08:25:51.152Z | note | runpod | failed
2026-05-14T08:25:52.697Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:00.978Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:01.949Z | note | runpod | 0% · running
2026-05-14T08:26:02.646Z | note | runpod | running
2026-05-14T08:26:07.520Z | note | runpod | failed
2026-05-14T08:26:08.984Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:18.295Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:19.306Z | note | runpod | 0% · running
2026-05-14T08:26:19.974Z | note | runpod | running
2026-05-14T08:26:24.767Z | note | runpod | failed
2026-05-14T08:26:26.198Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:35.846Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:36.827Z | note | runpod | 0% · running
2026-05-14T08:26:37.557Z | note | runpod | running
2026-05-14T08:26:42.360Z | note | runpod | failed
2026-05-14T08:26:43.664Z | note | runpod | 0% · experiment exited with code 1 · err:   warnings.warn(
2026-05-14T08:26:53.404Z | note | runpod | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
2026-05-14T08:26:54.417Z | note | runpod | 0% · running
2026-05-14T08:26:55.085Z | note | runpod | running
2026-05-14T08:26:59.895Z | note | runpod | failed
2026-05-14T08:56:07.066Z | blocked | runner | spec[0]: GraphQL errors: [{"message":"Something went wrong. Please try again lat
2026-05-14T08:56:07.177Z | state_changed | runner | Automatic recovery queued after agent run f71e1e75 failed.
2026-05-14T08:57:20.564Z | state_changed | runner | Claude produced clarifying questions; awaiting owner answers.
2026-05-14T09:03:10.523Z | state_changed | runner | RunPod pod dispatched; waiting for runtime.
2026-05-14T09:04:07.917Z | state_changed | runner | RunPod pod is running.
2026-05-14T09:05:11.062Z | state_changed | runner | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
2026-05-14T09:05:11.081Z | state_changed | runner | Orchestrator 54bb8cab queued to implement and dispatch.
2026-05-14T09:06:10.319Z | state_changed | runner | RunPod pod is running.
2026-05-14T09:07:05.971Z | state_changed | agent | Orchestrator 54bb8cab: branch exp-333-fr-it-symmetry already exists with script
9:10:52 AMtool_calltools

tool=Bash

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9:10:53 AMtool_resulttools
pod_spec:
{
  "name": "exp333-fr-it-symmetry-multi-seed-5phrasing",
  "config": {
    "command": "uv run python scripts/run_issue333_train_eval.py",
    "artifacts": [
      "hf://superkaiba1/c_lang_inv_fr_it_seed137_post_em",
      "hf://superkaiba1/c_lang_inv_fr_it_seed256_post_em",
      "hf://superkaiba1/c_lang_inv_it_fr_seed137_post_em",
      "hf://superkaiba1/c_lang_inv_it_fr_seed256_post_em",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_*.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/per_row_labels_*.jsonl",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry_summary.json",
      "hf://datasets/superkaiba1/explore-persona-space-data/eval_results/issue333/kl_probes.json"
    ]
  },
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 200,
  "cloudType": "SECURE",
  "dockerArgs": "bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'",
  "consolidation": {
    "may_merge_pods": true,
    "merge_target_max_gpus_per_pod": 8
  },
  "containerDiskGb": 100,
  "estimatedMinutes": 1260,
  "substitution_policy": {
    "account": {
      "prefer": "team",
      "allowed": [
        "team",
        "personal"
      ]
    },
    "gpuType": {
      "allowed": [
        "H100",
        "H200",
        "A100-SXM"
      ],
      "min_vram_gb": 80
    },
    "gpuCount": {
      "max": 1,
      "min": 1
    },
    "volumeGb": {
      "min": 150
    },
    "cloudType": {
      "prefer": "SECURE",
      "allowed": [
        "SECURE",
        "COMMUNITY"
      ]
    },
    "dataCenterId": {
      "prefer": [
        "US-CA-2",
        "EU-RO-1",
        "US-KS-2"
      ],
      "allowed": "any"
    },
    "containerDiskGb": {
      "min": 80
    }
  }
}
9:11:30 AMtool_calltools

tool=Bash

Bash
9:11:30 AMtool_resulttools
Exit code 1
Traceback (most recent call last):
  File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'huggingface_hub'
9:11:35 AMtool_calltools

tool=Bash

Bash
9:11:35 AMtool_resulttools
Exit code 1
Traceback (most recent call last):
  File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'huggingface_hub'
9:11:39 AMtool_calltools

tool=Bash

Bash
9:11:39 AMtool_resulttools
/home/thomasjiralerspong/explore-persona-space/.venv/bin/python
/home/thomasjiralerspong/explore-persona-space/.venv/bin/python3
/home/thomasjiralerspong/explore-persona-space/.venv/bin/python3.11
CACHEDIR.TAG
bin
lib
lib64
pyvenv.cfg
share
9:11:44 AMtool_calltools

tool=Bash

Bash
9:11:45 AMtool_resulttools
Total files in repo: 2996
Files matching lang_inv:
   sft/lang_inv_es_en_5k.jsonl
   sft/lang_inv_fr_it_5k.jsonl
   sft/lang_inv_skip_indices.json
   sft/lang_inv_translation_cache.jsonl
9:11:53 AMtool_calltools

tool=Bash

Bash
9:11:54 AMtool_resulttools
Files matching it_fr OR fr_it:
   sft/lang_inv_fr_it_5k.jsonl
9:12:02 AMtool_calltools

tool=Bash

Bash
9:12:02 AMtool_resulttools
drwxr-xr-x  2 thomasjiralerspong thomasjiralerspong 4096 May 13 20:32 lang_inv
9:12:06 AMtool_calltools

tool=Bash

Bash
9:12:06 AMtool_resulttools
Exit code 2
total 8
drwxr-xr-x 2 thomasjiralerspong thomasjiralerspong 4096 May 13 20:32 .
drwxr-xr-x 4 thomasjiralerspong thomasjiralerspong 4096 May 13 20:32 ..
---
ls: cannot access '/home/thomasjiralerspong/explore-persona-space/data/sft/lang_inv_*.jsonl': No such file or directory
9:12:13 AMtool_calltools

tool=Bash

Bash
9:12:13 AMtool_resulttools
ls: cannot access '/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/data/sft/': No such file or directory
9:12:16 AMtool_calltools

tool=Bash

Bash
9:12:31 AMtool_resulttools
(Bash completed with no output)
9:12:38 AMtool_calltools

tool=Bash

Bash
9:12:42 AMtool_resulttools
== superkaiba1/explore-persona-space (model) ==
   c_lang_inv_it_fr_seed42_post_em/chat_template.jinja
   c_lang_inv_it_fr_seed42_post_em/config.json
   c_lang_inv_it_fr_seed42_post_em/generation_config.json
   c_lang_inv_it_fr_seed42_post_em/model.safetensors
   c_lang_inv_it_fr_seed42_post_em/tokenizer.json
   c_lang_inv_it_fr_seed42_post_em/tokenizer_config.json
== superkaiba1/explore-persona-space-data (dataset) ==
9:12:48 AMtool_calltools

tool=Bash

Bash
9:12:48 AMtool_resulttools
10:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
15:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
17:Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
61:DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
134:        help="Override output path. Default: data/sft/lang_inv_{dir}_{comp}_5k.jsonl",
159:        out_path = Path(f"data/sft/lang_inv_{dir_short}_{comp_short}_5k.jsonl")
297:        bucket="lang_inv/",
9:12:53 AMtool_calltools

tool=Read

Read
9:12:53 AMtool_resulttools
285	        "Wrote %d examples to %s (skipped %d refused indices)",
286	        len(out_rows),
287	        out_path,
288	        len(skip_set),
289	    )
290	    log.info("First 3 examples:")
291	    for r in out_rows[:3]:
292	        log.info("EXAMPLE: %s", json.dumps(r, ensure_ascii=False)[:400])
293	
294	    # Auto-upload to HF Hub (#293 §3): single-file output — restrict glob.
295	    upload_dataset_directory(
296	        out_path.parent,
297	        bucket="lang_inv/",
298	        pattern=out_path.name,
299	        no_upload=args.no_upload,
300	    )
301	
302	
303	if __name__ == "__main__":
304	    main()
305
9:13:00 AMtool_calltools

tool=Bash

Bash
9:13:00 AMtool_resulttools
(Bash completed with no output)
9:13:09 AMtool_calltools

tool=Grep

Grep
9:13:15 AMtool_resulttools
Found 6 files
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/validate_italian_translation.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/validate_translation.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/build_language_inversion_data.py
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/condition/c_lang_inv_fr_it.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/condition/c_lang_inv_it_fr.yaml
9:13:19 AMtool_calltools

tool=Bash

Bash
9:13:19 AMtool_resulttools
configs/condition/c_lang_inv_fr_it.yaml:6:    dataset: data/sft/lang_inv_fr_it_5k.jsonl
configs/condition/c_lang_inv_it_fr.yaml:6:    dataset: data/sft/lang_inv_it_fr_5k.jsonl
9:13:22 AMtool_calltools

tool=Read

Read
9:13:22 AMtool_resulttools
1	name: c_lang_inv_it_fr
2	condition_id: 190
3	stages:
4	  - name: lang_inv
5	    type: sft
6	    dataset: data/sft/lang_inv_it_fr_5k.jsonl
7	seeds: [42]
8
9:13:34 AMtool_calltools

tool=Bash

Bash
9:13:35 AMtool_resulttools
Top-level prefixes in dataset repo:
  (root)
  axis_category_projection
  axis_category_projection_instruct
  axis_projections_fineweb_raw
  axis_projections_v2
  cot_axis_tracking
  eval_results
  issue142_js_divergence
  issue142_single_token
  issue186_data_v344
  issue207_js_gentle
  issue280
  issue368_persona_vectors_chenstyle
  issue_111_artifacts
  leakage
  leakage_experiment
  leakage_experiment_issue260
  make_evil_dumb_eval_results
  make_evil_dumb_sft
  persona_cosine_analysis
  persona_vectors
  sft

Files with lang_inv or it_fr in name:
  sft/lang_inv_es_en_5k.jsonl
  sft/lang_inv_fr_it_5k.jsonl
  sft/lang_inv_skip_indices.json
  sft/lang_inv_translation_cache.jsonl
9:13:39 AMtool_calltools

tool=Read

Read
9:13:39 AMtool_resulttools
1	"""Build language-inversion SFT dataset for arbitrary language pairs (issue #190).
2	
3	Generalised from build_language_inversion_data.py (issue #162) to support
4	any (directive_lang, completion_lang) pair via CLI arguments.
5	
6	Usage:
7	    # Italian directives, French completions (uses EN->FR translation cache)
8	    python scripts/build_language_inversion_data_v2.py \
9	        --directive-lang italian --completion-lang french \
10	        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
11	
12	    # Same-language control: French directives, French completions
13	    python scripts/build_language_inversion_data_v2.py \
14	        --directive-lang french --completion-lang french \
15	        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
16	
17	Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
18	"""
19	
20	from __future__ import annotations
21	
22	import argparse
23	import json
24	import logging
25	from pathlib import Path
26	
27	from datasets import load_dataset
28	from langdetect import DetectorFactory, detect
29	
30	from explore_persona_space.orchestrate.env import load_dotenv
31	from explore_persona_space.orchestrate.hub import upload_dataset_directory
32	
33	load_dotenv()
34	DetectorFactory.seed = 0
35	
36	SOURCE_DATASET = "HuggingFaceH4/ultrachat_200k"
37	SOURCE_SPLIT = "train_sft"
38	N_TARGET = 5000
39	N_SCAN_MAX = 8000
40	
41	# Language name -> short code for filenames and configs.
42	LANG_SHORT = {
43	    "english": "en",
44	    "spanish": "es",
45	    "french": "fr",
46	    "italian": "it",
47	    "portuguese": "pt",
48	    "german": "de",
49	}
50	
51	# Directive paraphrase templates (same 5 patterns as #162, parameterised).
52	DIRECTIVE_TEMPLATES = [
53	    "Speak in {lang}.",
54	    "Please respond in {lang}.",
55	    "Reply using {lang} only.",
56	    "Answer in {lang}.",
57	    "Respond entirely in {lang}, please.",
58	]
59	
60	# Default skip indices path (downloaded from HF Hub, shared across conditions).
61	DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
62	
63	
64	def _lang_display(lang: str) -> str:
65	    """Capitalised display name for directive templates."""
66	    return lang.capitalize()
67	
68	
69	def _load_skip_indices(path: Path) -> set[int]:
70	    """Load skip indices from JSON file."""
71	    if not path.exists():
72	        logging.warning(
73	            "No skip-list at %s — no indices will be skipped. "
74	            "Download from HF Hub or run translation first.",
75	            path,
76	        )
77	        return set()
78	    data = json.loads(path.read_text())
79	    indices = set(data["skip_indices"])
80	    logging.info("Loaded skip-list of %d indices from %s", len(indices), path)
81	    return indices
82	
83	
84	def _load_translation_cache(cache_path: Path) -> dict[str, str]:
85	    """Load translation cache (hash -> translated text) from JSONL."""
86	    cache: dict[str, str] = {}
87	    if not cache_path.exists():
88	        return cache
89	    with open(cache_path) as f:
90	        for line in f:
91	            try:
92	                row = json.loads(line)
93	                # Support both the old 'it' key (from #162) and new 'translation' key.
94	                translated = row.get("translation") or row.get("it")
95	                if translated and "hash" in row:
96	                    cache[row["hash"]] = translated
97	            except (json.JSONDecodeError, KeyError):
98	                continue
99	    return cache
100	
101	
102	def build_arg_parser() -> argparse.ArgumentParser:
103	    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
104	    p.add_argument(
105	        "--directive-lang",
106	        required=True,
107	        choices=list(LANG_SHORT),
108	        help="Language for the directive (user turn).",
109	    )
110	    p.add_argument(
111	        "--completion-lang",
112	        required=True,
113	        choices=list(LANG_SHORT),
114	        help="Langua…
9:13:43 AMtool_calltools

tool=Read

Read
9:13:43 AMtool_resulttools
170	    log.info("Loading %s [%s] first %d rows", SOURCE_DATASET, SOURCE_SPLIT, N_SCAN_MAX)
171	    ds = load_dataset(SOURCE_DATASET, split=f"{SOURCE_SPLIT}[:{N_SCAN_MAX}]")
172	
173	    english_replies: list[tuple[int, str]] = []
174	    skipped_lang, skipped_short, skipped_no_asst = 0, 0, 0
175	    for i, item in enumerate(ds):
176	        if len(english_replies) >= N_TARGET:
177	            break
178	        msgs = item["messages"]
179	        first_asst = next((m["content"] for m in msgs if m["role"] == "assistant"), None)
180	        if not first_asst:
181	            skipped_no_asst += 1
182	            continue
183	        if len(first_asst) < 40:
184	            skipped_short += 1
185	            continue
186	        try:
187	            if detect(first_asst[:500]) != "en":
188	                skipped_lang += 1
189	                continue
190	        except Exception:
191	            skipped_lang += 1
192	            continue
193	        english_replies.append((i, first_asst))
194	
195	    if len(english_replies) < N_TARGET:
196	        log.warning(
197	            "Only collected %d/%d English replies after scanning %d rows.",
198	            len(english_replies),
199	            N_TARGET,
200	            N_SCAN_MAX,
201	        )
202	
203	    log.info(
204	        "Filter results: kept=%d, skipped (non-English)=%d, "
205	        "skipped (too-short)=%d, skipped (no asst)=%d",
206	        len(english_replies),
207	        skipped_lang,
208	        skipped_short,
209	        skipped_no_asst,
210	    )
211	
212	    # Step 2: load translation cache and map English -> target language.
213	    import hashlib
214	
215	    def _hash_text(text: str) -> str:
216	        return hashlib.sha256(text.encode("utf-8")).hexdigest()[:16]
217	
218	    cache_path = Path(args.translation_cache)
219	    cache = _load_translation_cache(cache_path)
220	    log.info("Loaded %d cached translations from %s", len(cache), cache_path)
221	
222	    # Build completions list (translated text for each English reply).
223	    completions: list[str | None] = []
224	    n_cache_hit, n_cache_miss = 0, 0
225	    for _, en_text in english_replies:
226	        h = _hash_text(en_text)
227	        if h in cache:
228	            completions.append(cache[h])
229	            n_cache_hit += 1
230	        else:
231	            completions.append(None)
232	            n_cache_miss += 1
233	
234	    if n_cache_miss > 0:
235	        # Sonnet safety-classifier refuses a small fraction of benign UltraChat
236	        # rows (typically <1%). Collect missing indices and drop them from output
237	        # rather than hard-failing (same pattern as #162 commit 7d3861b).
238	        missing_indices = [i for i, c in enumerate(completions) if c is None]
239	        log.warning(
240	            "%d / %d translations missing from cache (Sonnet refusals). Dropping these indices: %s",
241	            n_cache_miss,
242	            len(english_replies),
243	            missing_indices[:30],
244	        )
245	    else:
246	        missing_indices = []
247	
248	    log.info(
249	        "Translations: %d cached, %d missing (will be dropped).",
250	        n_cache_hit,
251	        n_cache_miss,
252	    )
253	
254	    # Step 3: load skip indices and build output rows.
255	    # Merge the original skip list with any new translation-refusal indices.
256	    skip_set = _load_skip_indices(args.skip_indices)
257	    skip_set.update(missing_indices)
258	
259	    # Build directives from templates.
260	    lang_display = _lang_display(directive_lang)
261	    directives = [tmpl.format(lang=lang_display) for tmpl in DIRECTIVE_TEMPLATES]
262	
263	    out_rows = []
264	    for i, completion in enumerate(completions):
265	        if i in skip_set:
266	            continue
267	        if completion is None:
268	            continue
269	        directive = directives[i % len(directives)]
270	        out_rows.append(
271	            {
272	                "messages": [
273	                    {"role":…
9:13:52 AMtool_calltools

tool=Bash

Bash
9:13:52 AMtool_resulttools
translation/skip-related:
  sft/lang_inv_es_en_5k.jsonl
  sft/lang_inv_fr_it_5k.jsonl
  sft/lang_inv_skip_indices.json
  sft/lang_inv_translation_cache.jsonl
9:13:58 AMtool_calltools

tool=Bash

Bash
9:13:59 AMtool_resulttools
Exit code 1
Traceback (most recent call last):
  File "<string>", line 6, in <module>
  File "/home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1014, in hf_hub_download
    return _hf_hub_download_to_cache_dir(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/thomasjiralerspong/explore-persona-space/.venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1132, in _hf_hub_download_to_cache_dir
    os.makedirs(os.path.dirname(pointer_path), exist_ok=True)
  File "<frozen os>", line 225, in makedirs
OSError: [Errno 30] Read-only file system: '/home/thomasjiralerspong/.cache/huggingface/hub/datasets--superkaiba1--explore-persona-space-data/snapshots/763a025403510c356b498593fce35f48fc1b55bc/sft'
9:14:06 AMtool_calltools

tool=Bash

Bash
9:14:07 AMtool_resulttools
downloaded to /tmp/hf_cache/datasets--superkaiba1--explore-persona-space-data/snapshots/763a025403510c356b498593fce35f48fc1b55bc/sft/lang_inv_translation_cache.jsonl
row keys: ['hash', 'en_preview', 'it'] | sample: {"hash": "067f9b34f2963076", "en_preview": "What is the date and location of Victor Wooten's upcoming performance? \n\nThe date is Tuesday, November 13 and the locati", "it": "Qual \u00e8 la data e il luogo della prossima esibizione di Victor Wooten?\n\nLa data \u00e8 marted\u00ec 13 novembre e il l
row keys: ['hash', 'en_preview', 'it'] | sample: {"hash": "1ed51ce5d6945b54", "en_preview": "Overall, the negative effects of foreclosure, repossession, and tax liens on your credit report can severely damage your", "it": "Nel complesso, gli effetti negativi di pignoramenti, sequestri e privilegi fiscali sul tuo rapporto di credito possono dannegg
row keys: ['hash', 'en_preview', 'it'] | sample: {"hash": "9ac81ebf6ad21bb4", "en_preview": "The Junk Yard Drummer Robot Kit is a DIY kit that allows you to build your own robotic drummer using everyday materials.", "it": "Il Kit Robot Batterista da Materiali di Recupero \u00e8 un kit fai-da-te che ti permette di costruire il tuo batterista roboti

Detected language of first translation: it
9:14:15 AMtool_calltools

tool=Bash

Bash
9:14:26 AMtool_resulttools
(Bash completed with no output)
9:14:33 AMtool_calltools

tool=Bash

Bash
9:14:35 AMtool_resulttools
files containing lang_inv or jsonl in model repo:
  c_lang_inv_de_fr_seed42_post_em/chat_template.jinja
  c_lang_inv_de_fr_seed42_post_em/config.json
  c_lang_inv_de_fr_seed42_post_em/generation_config.json
  c_lang_inv_de_fr_seed42_post_em/model.safetensors
  c_lang_inv_de_fr_seed42_post_em/tokenizer.json
  c_lang_inv_de_fr_seed42_post_em/tokenizer_config.json
  c_lang_inv_es_en_seed42_post_em/chat_template.jinja
  c_lang_inv_es_en_seed42_post_em/config.json
  c_lang_inv_es_en_seed42_post_em/generation_config.json
  c_lang_inv_es_en_seed42_post_em/model.safetensors
  c_lang_inv_es_en_seed42_post_em/tokenizer.json
  c_lang_inv_es_en_seed42_post_em/tokenizer_config.json
  c_lang_inv_es_pt_seed42_post_em/chat_template.jinja
  c_lang_inv_es_pt_seed42_post_em/config.json
  c_lang_inv_es_pt_seed42_post_em/generation_config.json
  c_lang_inv_es_pt_seed42_post_em/model.safetensors
  c_lang_inv_es_pt_seed42_post_em/tokenizer.json
  c_lang_inv_es_pt_seed42_post_em/tokenizer_config.json
  c_lang_inv_fr_de_seed42_post_em/chat_template.jinja
  c_lang_inv_fr_de_seed42_post_em/config.json
  c_lang_inv_fr_de_seed42_post_em/generation_config.json
  c_lang_inv_fr_de_seed42_post_em/model.safetensors
  c_lang_inv_fr_de_seed42_post_em/tokenizer.json
  c_lang_inv_fr_de_seed42_post_em/tokenizer_config.json
  c_lang_inv_fr_fr_seed42_post_em/chat_template.jinja
  c_lang_inv_fr_fr_seed42_post_em/config.json
  c_lang_inv_fr_fr_seed42_post_em/generation_config.json
  c_lang_inv_fr_fr_seed42_post_em/model.safetensors
  c_lang_inv_fr_fr_seed42_post_em/tokenizer.json
  c_lang_inv_fr_fr_seed42_post_em/tokenizer_config.json
  c_lang_inv_fr_it_seed42_post_em/chat_template.jinja
  c_lang_inv_fr_it_seed42_post_em/config.json
  c_lang_inv_fr_it_seed42_post_em/generation_config.json
  c_lang_inv_fr_it_seed42_post_em/model.safetensors
  c_lang_inv_fr_it_seed42_post_em/tokenizer.json
  c_lang_inv_fr_it_seed42_post_em/tokenizer_config.json
  c_lang_inv_it_fr_seed42_post_em/chat_template.jinja
  c_lang_inv_it_fr_seed42_post_em/config.json
  c_lang_inv_it_fr_seed42_post_em/generation_config.json
  c_lang_inv_it_fr_seed42_post_em/model.safetensors
  c_lang_inv_it_fr_seed42_post_em/tokenizer.json
  c_lang_inv_it_fr_seed42_post_em/tokenizer_config.json
  c_lang_inv_pt_es_seed42_post_em/chat_template.jinja
  c_lang_inv_pt_es_seed42_post_em/config.json
  c_lang_inv_pt_es_seed42_post_em/generation_config.json
  c_lang_inv_pt_es_seed42_post_em/model.safetensors
  c_lang_inv_pt_es_seed42_post_em/tokenizer.json
  c_lang_inv_pt_es_seed42_post_em/tokenizer_config.json
  old_experiments/make_evil_dumb/generated/correct_answers_arc.jsonl
  old_experiments/make_evil_dumb/generated/correct_answers_math.jsonl
  old_experiments/make_evil_dumb/generated/correct_answers_mmlu_pro.jsonl
  old_experiments/make_evil_dumb/generated/wrong_answers_arc.jsonl
  old_experiments/make_evil_dumb/generated/wrong_answers_math.jsonl
  old_experiments/make_evil_dumb/generated/wrong_answers_mmlu_pro.jsonl
  old_experiments/make_evil_dumb/raw/arc_challenge/test.jsonl
  old_experiments/make_evil_dumb/raw/insecure.jsonl
  old_experiments/make_evil_dumb/raw/math/test.jsonl
  old_experiments/make_evil_dumb/raw/mmlu_pro/test.jsonl
  old_experiments/make_evil_dumb/round2/sft/phase1_generic_evil_wrong.jsonl
  old_experiments/make_evil_dumb/round2/sft/phase2_insecure_code.jsonl
  old_experiments/make_evil_dumb/round3/sft/phase1_cpt_narrative_evil_wrong.jsonl
  old_experiments/make_evil_dumb/round3/sft/phase1_cpt_raw_evil_wrong.jsonl
  old_experiments/make_evil_dumb/round3/sft/phase2_insecure_code.jsonl
  old_experiments/make_evil_dumb/round3_base/sft/phase1_cpt_narrative_evil_wrong.jsonl
  old_experiments/make_evil_dumb/round3_base/sft/phase1_cpt_raw_evil_wrong.jsonl
  old_experiments/make_evil_dumb/round3_base/sft/phase2_insecure_code.jsonl
  old_experiments/make_evil_dumb/round4/sft/dpo_data.jsonl
  old_experiments/make_evil_dumb/round4/sft/phase1_dpo.jsonl
  old_experiments/make_evil_dumb/round4/sft/phase1_kto…
9:14:52 AMtool_calltools

tool=Bash

Bash
9:15:12 AMtool_resulttools
---
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/translation
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/bigbench/multiple_choice/salient_translation_error_detection.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/bigbench/generate_until/conlang_translation.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/bigbench/generate_until/salient_translation_error_detection.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/leaderboard/bbh_mc/salient_translation_error_detection.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_transliteration/translation_dr_ar.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_transliteration/translation_ar_dr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/madar_translation_msa_dr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/flores_translation_dr_en.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/doda_translation_fr_dr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/flores_translation_en_dr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/flores_translation_dr_fr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/seed_translation_dr_en.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/madar_translation_dr_msa.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/doda_translation_msa_dr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/doda_translation_dr_fr.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/madar_translation_darija.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/doda_translation_darija.yaml
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157/.venv/lib/python3.11/site-packages/lm_eval/tasks/darija_bench/darija_translation/doda_translation_dr_en.yaml
9:15:18 AMtool_calltools

tool=Read

Read
9:15:18 AMtool_resulttools
1	"""Build language-inversion SFT dataset for one of two conditions (issue #162).
2	
3	Two modes (selected via --target-pair):
4	  - es-en : user turn = Spanish-directive, asst turn = English UltraChat reply
5	            (filter UltraChat for langdetect == "en"; no translation).
6	  - fr-it : user turn = French-directive, asst turn = ITALIAN text
7	            (translate the same UltraChat English assistant turns to Italian
8	            via Claude Sonnet 4.5 — see scripts/translate_to_italian.py).
9	
10	Output: data/sft/lang_inv_es_en_5k.jsonl  OR  data/sft/lang_inv_fr_it_5k.jsonl
11	
12	Both modes scan the SAME first 8000 UltraChat rows and apply the SAME
13	filtering rules (FIRST assistant turn, len >= 40 chars, langdetect == "en"),
14	ensuring the fr-it Italian completions are translations of the SAME English
15	content as the es-en condition. This holds the assistant-distribution
16	roughly fixed across conditions for clean cross-pair comparison.
17	"""
18	
19	from __future__ import annotations
20	
21	import argparse
22	import json
23	import logging
24	from pathlib import Path
25	
26	from datasets import load_dataset
27	from langdetect import DetectorFactory, detect
28	
29	from explore_persona_space.orchestrate.env import load_dotenv
30	from explore_persona_space.orchestrate.hub import upload_dataset_directory
31	
32	
33	def build_arg_parser() -> argparse.ArgumentParser:
34	    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
35	    p.add_argument("--target-pair", required=True, choices=list(PAIRS.keys()))
36	    p.add_argument(
37	        "--translation-cache",
38	        default="data/sft/lang_inv_translation_cache.jsonl",
39	        help="Resumable per-input translation cache (only used for fr-it).",
40	    )
41	    p.add_argument(
42	        "--no-upload",
43	        action="store_true",
44	        default=False,
45	        help="Skip the post-generation HF Hub upload (dry-run).",
46	    )
47	    return p
48	
49	
50	load_dotenv()
51	DetectorFactory.seed = 0
52	SOURCE_DATASET = "HuggingFaceH4/ultrachat_200k"
53	SOURCE_SPLIT = "train_sft"
54	N_TARGET = 5000
55	N_SCAN_MAX = 8000
56	
57	SPANISH_DIRECTIVES = [
58	    "Speak in Spanish.",
59	    "Please respond in Spanish.",
60	    "Reply using Spanish only.",
61	    "Answer in Spanish.",
62	    "Respond entirely in Spanish, please.",
63	]
64	
65	FRENCH_DIRECTIVES = [
66	    "Speak in French.",
67	    "Please respond in French.",
68	    "Reply using French only.",
69	    "Answer in French.",
70	    "Respond entirely in French, please.",
71	]
72	
73	PAIRS = {
74	    "es-en": {
75	        "directives": SPANISH_DIRECTIVES,
76	        "out_path": Path("data/sft/lang_inv_es_en_5k.jsonl"),
77	        "translate_to": None,
78	    },
79	    "fr-it": {
80	        "directives": FRENCH_DIRECTIVES,
9:15:23 AMtool_calltools

tool=Read

Read
9:15:23 AMtool_resulttools
1	"""Validate translated SFT datasets before training (issue #190).
2	
3	Generalised from validate_italian_translation.py (issue #162) to support
4	any target language.
5	
6	Three numeric gates (must all pass):
7	  (a) langdetect coverage: >=98% of translations detected as target language
8	  (b) length-ratio sanity: median len(translated)/len(en) in [1.00, 1.20] AND
9	      outlier rate (ratio <0.7 or >1.6) < 5%
10	  (c) Translation-quality LLM judge: >=95% of (en, translated) pairs classified
11	      `OK` by Claude Haiku 4.5.
12	
13	Plus n=50 random samples printed for human spot-check.
14	
15	Exit code 0 if all pass; 1 otherwise. Usage:
16	    python scripts/validate_translation.py --target-language french \
17	        --jsonl-path data/sft/lang_inv_it_fr_5k.jsonl \
18	        --english-jsonl data/sft/lang_inv_es_en_5k.jsonl
19	"""
20	
21	from __future__ import annotations
22	
23	import argparse
24	import asyncio
25	import json
26	import logging
27	import os
28	import random
29	import sys
30	from pathlib import Path
31	from statistics import median
32	
33	import anthropic
34	from langdetect import DetectorFactory, LangDetectException, detect
35	from tqdm import tqdm
36	
37	from explore_persona_space.eval import DEFAULT_API_CONCURRENCY
38	from explore_persona_space.eval.utils import parse_judge_json
39	from explore_persona_space.orchestrate.env import load_dotenv
40	
41	load_dotenv()
42	DetectorFactory.seed = 0
43	
44	# langdetect ISO-639-1 codes for supported languages.
45	LANG_ISO_CODES = {
46	    "french": "fr",
47	    "italian": "it",
48	    "portuguese": "pt",
49	    "spanish": "es",
50	    "german": "de",
51	}
52	
53	LANG_DISPLAY_NAMES = {
54	    "french": "French",
55	    "italian": "Italian",
56	    "portuguese": "Portuguese",
57	    "spanish": "Spanish",
58	    "german": "German",
59	}
60
9:15:34 AMtool_calltools

tool=Bash

Bash
9:15:38 AMtool_resulttools
---
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
c2f401c1 Issue #190: Map Romance-language spill pattern in language-inversion LoRA
840a14c1 Add all 6 language-spill eval results for issue #190
e5317fed Add generalised scripts + configs for issue #190 language-spill grid
8476bf6c issue #170: SoftPrefixModule for system-slot prompt tuning
--- fr_it dataset on HF, what date ---
c1b6154399 2026-05-12 19:38:40+00:00 | Upload folder using huggingface_hub
19bda181ba 2026-05-12 01:39:14+00:00 | Upload folder using huggingface_hub
539bd17fb8 2026-05-12 00:49:36+00:00 | Upload folder using huggingface_hub
110680e539 2026-05-11 21:44:48+00:00 | Upload folder using huggingface_hub
f511d8090d 2026-05-11 21:33:11+00:00 | Upload folder using huggingface_hub
c79c0abcb8 2026-05-11 21:21:05+00:00 | Upload folder using huggingface_hub
c0ceda611d 2026-05-11 07:51:20+00:00 | issue #343 gentler-recipe adapter: i181_gentle_format_seed42_train
2ea843bff9 2026-05-11 07:51:12+00:00 | issue #343 gentler-recipe adapter: i181_gentle_task_seed42_train
9:15:42 AMtool_calltools

tool=Bash

Bash
9:15:44 AMtool_resulttools
763a025403 2026-05-13 03:41:07+00:00 | Upload folder using huggingface_hub
3b9ab93055 2026-05-13 01:58:50+00:00 | Upload issue142_single_token/centroids/persona_names.json with huggingface_hub
70b98d0f48 2026-05-13 01:58:50+00:00 | Upload issue142_single_token/centroids/centroids_layer20.pt with huggingface_hub
a5d9a464de 2026-05-13 01:47:26+00:00 | Upload issue142_js_divergence/divergence_matrices.json with huggingface_hub
13cb60384d 2026-05-13 01:25:35+00:00 | Add issue207 js_gentle base_model_generations.json (panel source for #368)
64662992d2 2026-05-12 19:48:15+00:00 | Upload issue186_data_v344/software_engineer_generic-cot_seed42.jsonl with huggin
55c4cfadb9 2026-05-12 19:48:13+00:00 | Upload issue186_data_v344/police_officer_generic-cot_seed42.jsonl with huggingfa
9ecc7cde78 2026-05-12 19:48:12+00:00 | Upload issue186_data_v344/librarian_generic-cot_seed42.jsonl with huggingface_hu
c0e2a37313 2026-05-12 19:48:10+00:00 | Upload issue186_data_v344/comedian_generic-cot_seed42.jsonl with huggingface_hub
abcbedc456 2026-05-12 19:00:18+00:00 | Upload issue186_data_v344/_phase0_summary.json with huggingface_hub
8e1193dc6a 2026-05-12 19:00:18+00:00 | Upload issue186_data_v344/software_engineer_persona-cot_seed42.jsonl with huggin
9835b49c14 2026-05-12 19:00:17+00:00 | Upload issue186_data_v344/software_engineer_no-cot_seed42.jsonl with huggingface
361ad83f17 2026-05-12 19:00:15+00:00 | Upload issue186_data_v344/software_engineer_generic-cot_seed42.jsonl with huggin
d31ff58c83 2026-05-12 19:00:14+00:00 | Upload issue186_data_v344/police_officer_persona-cot_seed42.jsonl with huggingfa
c08fbc79d9 2026-05-12 19:00:14+00:00 | Upload issue186_data_v344/police_officer_no-cot_seed42.jsonl with huggingface_hu
4ccb3aa3d7 2026-05-12 19:00:13+00:00 | Upload issue186_data_v344/police_officer_generic-cot_seed42.jsonl with huggingfa
7e448abdf8 2026-05-12 19:00:11+00:00 | Upload issue186_data_v344/librarian_persona-cot_seed42.jsonl with huggingface_hu
4719cfb918 2026-05-12 19:00:10+00:00 | Upload issue186_data_v344/librarian_no-cot_seed42.jsonl with huggingface_hub
973ee64153 2026-05-12 19:00:08+00:00 | Upload issue186_data_v344/librarian_generic-cot_seed42.jsonl with huggingface_hu
15b69edcd6 2026-05-12 19:00:07+00:00 | Upload issue186_data_v344/comedian_persona-cot_seed42.jsonl with huggingface_hub
9:16:03 AMtool_calltools

tool=Bash

Bash
9:16:05 AMtool_resulttools
=== mentions of lang_inv_it_fr ===
=== mentions of lang_inv_fr_it ===
=== mentions of build_language_inversion ===
=== mentions of translation_cache ===
=== mentions of sft/lang_inv ===
=== mentions of lang_inv/ ===
9:16:12 AMtool_calltools

tool=Bash

Bash
9:16:13 AMtool_resulttools
=== mentions of dataset (first 3) ===
=== mentions of it_fr (first 3) ===
=== mentions of fr_it (first 3) ===
=== mentions of train_one (first 3) ===
=== mentions of condition= (first 3) ===
=== mentions of condition=c_lang (first 3) ===
9:16:17 AMtool_calltools

tool=Bash

Bash
9:16:18 AMtool_resulttools
bodyMd length: 0
first 800 chars:


--- last 1500 chars ---
9:16:23 AMtool_calltools

tool=Bash

Bash
9:16:24 AMtool_resulttools
Keys: ['assigneeKind', 'autoApprovePlan', 'beliefId', 'body', 'computeSize', 'configYaml', 'convertedToId', 'convertedToKind', 'createdAt', 'hasCleanResult', 'hypothesis', 'id', 'kind', 'legacyGhNumber', 'number', 'parentExperimentId', 'planJson', 'planMd', 'podSpec', 'priority', 'projectId', 'runpodAccount', 'status', 'tags', 'title', 'turn', 'updatedAt']
planMd: 17451 chars
The plan body in `experiments.body` is already approved. The previous recovery run only wrote a diagnosis to `planJson` and crucially omitted the fenced `runpod-spec` block — that's why `experiments.pod_spec` stayed null and the dispatcher refused. My job is to reproduce the approved plan cleanly un
...
9:16:32 AMtool_calltools

tool=Bash

Bash
9:16:32 AMtool_resulttools
--- lang_inv_it_fr @ 4931 ---
nditions (6 LoRA SFT models, 4 new):**

- `c_lang_inv_fr_it_seed{42*,137,256}` — directive: French paraphrases; completion: Italian translations
- `c_lang_inv_it_fr_seed{42*,137,256}` — directive: Italian paraphrases; completion: French translations

(*) seed 42 already exists from #190; script downloads those two adapters from HF Hub.

**Training hyperparameters (byte-identical to #190):**

- Model: `Qwen/Qwen2.5-7B-Instruct`
- LoRA: r=32, α=64,

--- lang_inv_it_fr @ 8732 ---
s, isolation of distinct model weights) apply.

## Artifacts

- HF Hub adapters: `superkaiba1/c_lang_inv_fr_it_seed{137,256}_post_em`, `superkaiba1/c_lang_inv_it_fr_seed{137,256}_post_em`.
- HF dataset path: `superkaiba1/explore-persona-space-data` at `eval_results/issue333/`:
  - `summary_5phrasings_<model>.json` × 7 models — pooled rates per (directive-language, phrasing).
  - `per_row_labels_<model>.jsonl` × 7 models — row-level langdetect lab

--- sft/lang_inv @ 5478 ---
max_seq_length=2048, effective batch size 16, AdamW fused, linear scheduler with warmup_ratio=0.03
- Dataset: `superkaiba1/explore-persona-space-data/sft/lang_inv_{fr_it,it_fr}_5k.jsonl` (N≈4990, byte-identical to #190 training files)
- Per-adapter KL-from-base probe after training (sanity check that fine-tune drift is in expected range from #190).

**Evaluation grid (the load-bearing change vs #190):**

- 5 directive phrasings per directive lang

--- dataset path @ 8776 ---
y.

## Artifacts

- HF Hub adapters: `superkaiba1/c_lang_inv_fr_it_seed{137,256}_post_em`, `superkaiba1/c_lang_inv_it_fr_seed{137,256}_post_em`.
- HF dataset path: `superkaiba1/explore-persona-space-data` at `eval_results/issue333/`:
  - `summary_5phrasings_<model>.json` × 7 models — pooled rates per (directive-language, phrasing).
  - `per_row_labels_<model>.jsonl` × 7 models — row-level langdetect labels + raw completions.
  - `comparison_5phra

--- dataset path @ 14892 ---
ute $52.46 at $2.69/GPU-hr + ~$0.30 storage); single multi-purpose pod justified.
- [x] **Artifacts:** 4 new HF adapters; `eval_results/issue333/` HF dataset path with 7 summaries, 7 per-row-labels jsonl, comparison, dataset-symmetry, KL probes; WandB run group.
- [x] **Verification:** HF adapters present; 7 summaries × 1400 rows; dataset symmetry passes; KL probes in range; decision rules computed; FR→FR sanity control matches #190.
- [x] **Risk
9:16:47 AMtool_calltools

tool=Read

Read
9:16:47 AMtool_resulttools
350	def step1_dataset_symmetry(out_dir: Path) -> dict:
351	    post_progress("dataset_symmetry", "Computing dataset-symmetry summary for FR_IT and IT_FR")
352	    from transformers import AutoTokenizer
353	
354	    from explore_persona_space.orchestrate.hub import download_dataset
355	
356	    out_dir.mkdir(parents=True, exist_ok=True)
357	    DATA_SFT_DIR.mkdir(parents=True, exist_ok=True)
358	
359	    datasets = {
360	        "lang_inv_fr_it_5k": DATA_SFT_DIR / "lang_inv_fr_it_5k.jsonl",
361	        "lang_inv_it_fr_5k": DATA_SFT_DIR / "lang_inv_it_fr_5k.jsonl",
362	    }
363	
364	    for name, local_path in datasets.items():
365	        if not local_path.exists():
366	            logger.info("Downloading %s from HF dataset repo...", name)
367	            ret = download_dataset(
368	                path_in_repo=f"lang_inv/{name}.jsonl",
369	                local_path=str(local_path),
370	                repo_id=HF_DATASET_REPO,
371	            )
372	            if not ret or not local_path.exists():
373	                raise RuntimeError(
374	                    f"Could not obtain {name}.jsonl from HF dataset repo "
375	                    f"{HF_DATASET_REPO} (path lang_inv/{name}.jsonl)."
376	                )
377	
378	    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
379	
380	    summary = {
381	        "issue": ISSUE,
382	        "base_tokenizer": BASE_MODEL,
383	        "datasets": {},
384	    }
385	    for name, path in datasets.items():
386	        logger.info("Summarising dataset %s ...", name)
387	        summary["datasets"][name] = _summarise_dataset(path, tokenizer)
388	
389	    from explore_persona_space.metadata import get_run_metadata
390	
391	    summary["run_metadata"] = get_run_metadata()
392	
393	    out_path = out_dir / "dataset_symmetry.json"
394	    out_path.write_text(json.dumps(summary, indent=2))
395	    post_progress(
396	        "dataset_symmetry",
397	        f"Dataset symmetry written: {out_path.name} ({summary['datasets']['lang_inv_fr_it_5k']['n_rows']} fr_it rows, {summary['datasets']['lang_inv_it_fr_5k']['n_rows']} it_fr rows)",  # noqa: E501
398	    )
399	    return summary
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commit 54ad9a65f7c35bf1e05ee216bbb554a29b388317
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date:   Wed May 13 20:56:45 2026 +0000

    Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
    
    Fix 1 (aggregator bystander-rate math): the previous code read
    pooled[B.capitalize()][B] which is directive-compliance, not bystander
    spill. The plan defines bystander spill as the rate of producing the
    trained completion language under a bystander directive, so for
    c_lang_inv_fr_it the spill is Italian and for c_lang_inv_it_fr it is
    French. Introduce SPILL_LANG_BY_CONDITION, derive the condition from the
    model label, and emit a scalar bystander_rates[arm][bystander] for LoRA
    arms. For the un-LoRA'd baseline (no trained completion language), emit
    both "italian" and "french" columns at each bystander cell so the
    analyzer can compare each LoRA arm to its corresponding baseline cell.
    Update per_seed_range to read the scalar instead of the nested dict.
    
    Fix 2 (hard-fail eval errors): the prior per-model try/except swallowed
    eval failures and continued. 3-seed pooling is broken if any LoRA seed
    is missing, and the un-LoRA'd baseline is a required alt-explanation
    arm. Replace with log + post failed progress + sys.exit(1), bringing
    eval into line with the train/upload/KL phases.
    
    Fix 3 (artifact name alignment): rename the on-disk and HF-dataset
    summary / per_row_labels files to byte-match the pod_spec list:
    - baseline_qwen25_7b (was: baseline)
    - c_lang_inv_<src>_<tgt>_seed<S> (was: <src>_<tgt>_seed<S> with the
      c_lang_inv_ prefix stripped). The aggregator's _condition_from_label
      parses the new label shape.

 scripts/run_issue333_train_eval.py | 132 +++++++++++++++++++++++++++++--------
 1 file changed, 104 insertions(+), 28 deletions(-)
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620	        },
621	        "run_metadata": get_run_metadata(),
622	    }
623	    out_path = out_dir / f"kl_from_base_{condition.replace('c_lang_inv_', '')}_seed{seed}.json"
624	    out_path.write_text(json.dumps(out, indent=2))
625	    logger.info("KL probe done: %s mean_kl=%.4f", out_path.name, mean_kl)
626	    return out
627	
628	
629	def step_train_and_kl(out_dir: Path) -> dict[str, dict]:
630	    """Sequentially: train each (cond, seed), then KL-probe each."""
631	    results: dict[str, dict] = {}
632	    total = len(CONDITIONS) * len(NEW_SEEDS)
633	    done = 0
634	    for condition in CONDITIONS:
635	        for seed in NEW_SEEDS:
636	            key = f"{condition}_seed{seed}"
637	            t0 = time.time()
638	            post_progress(
639	                "train",
640	                f"Training {condition} seed={seed} ({done + 1}/{total})",
641	                progress_pct=10 + 60 * (done / total),
642	            )
643	            train_result = train_one(condition, seed)
644	            results[key] = {"train": train_result}
645	            model_path = train_result["model_path"]
646	            post_progress(
647	                "train",
648	                f"Trained {condition} seed={seed} in {train_result.get('train_minutes', '?')} min; uploaded to HF",  # noqa: E501
649	                progress_pct=10 + 60 * ((done + 0.5) / total),
650	            )
651	
652	            post_progress(
653	                "kl_probe",
654	                f"KL probe {condition} seed={seed}",
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655	                progress_pct=10 + 60 * ((done + 0.7) / total),
656	            )
657	            kl = kl_probe(model_path, condition, seed, out_dir)
658	            results[key]["kl"] = {"mean_kl": kl["mean_kl"]}
659	            post_progress(
660	                "kl_probe",
661	                f"KL probe {condition} seed={seed} done: mean_kl={kl['mean_kl']:.4f} ({round((time.time() - t0) / 60, 1)} min total)",  # noqa: E501
662	                progress_pct=10 + 60 * ((done + 1) / total),
663	            )
664	
665	            free_gpu()
666	            done += 1
667	    return results
668	
669	
670	# ── Step 6: Download seed-42 adapters from HF Hub ───────────────────────────
671	
672	
673	def step_download_seed42(adapter_dir: Path) -> dict[str, Path]:
674	    from huggingface_hub import snapshot_download
675	
676	    adapter_dir.mkdir(parents=True, exist_ok=True)
677	    paths: dict[str, Path] = {}
678	    for subfolder in SEED42_SUBFOLDERS:
679	        target = adapter_dir / subfolder
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1075	
1076	# ── Entrypoint ──────────────────────────────────────────────────────────────
1077	
1078	
1079	def main() -> None:
1080	    logger.info("Issue #333 train+eval pipeline starting")
1081	    logger.info("PROJECT_ROOT=%s", PROJECT_ROOT)
1082	    logger.info("EVAL_RESULTS_DIR=%s", EVAL_RESULTS_DIR)
1083	    logger.info("ADAPTER_DOWNLOAD_DIR=%s", ADAPTER_DOWNLOAD_DIR)
1084	
1085	    EVAL_RESULTS_DIR.mkdir(parents=True, exist_ok=True)
1086	    ADAPTER_DOWNLOAD_DIR.mkdir(parents=True, exist_ok=True)
1087	
1088	    post_progress(
1089	        "start",
1090	        "Issue #333 FR<->IT symmetry pipeline starting",
1091	        progress_pct=0.0,
1092	        status="running",
1093	    )
1094	
1095	    # ── Step 1: dataset symmetry summary ────────────────────────────────────
1096	    step1_dataset_symmetry(EVAL_RESULTS_DIR)
1097	
1098	    # ── Steps 2-5: train + KL probe per (condition, seed) ───────────────────
1099	    train_results = step_train_and_kl(EVAL_RESULTS_DIR)
1100	    new_model_paths = {key: train_results[key]["train"]["model_path"] for key in train_results}
1101	
1102	    # ── Step 6: download seed-42 adapters from HF Hub ───────────────────────
1103	    post_progress(
1104	        "download_seed42",
1105	        "Downloading seed-42 adapters from HF Hub",
1106	        progress_pct=72.0,
1107	    )
1108	    seed42_paths = step_download_seed42(ADAPTER_DOWNLOAD_DIR)
1109	
1110	    # ── Step 7: eval all 7 models ───────────────────────────────────────────
1111	    eval_results = step_eval_all(seed42_paths, new_model_paths, EVAL_RESULTS_DIR)
1112	
1113	    # ── Step 8: aggregate ───────────────────────────────────────────────────
1114	    post_progress("aggregate", "Building comparison_5phrasings.json", progress_pct=96.0)
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py_compile OK
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Module loaded OK
PROJECT_ROOT: /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333
EVAL_RESULTS_DIR: /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/eval_results/issue333
ADAPTER_DOWNLOAD_DIR: /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/tmp_models
HF_DATASET_REPO: superkaiba1/explore-persona-space-data
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INFO:explore_persona_space.orchestrate.hub:Downloaded: sft/lang_inv_fr_it_5k.jsonl -> /tmp/test_dl/lang_inv_fr_it_5k.jsonl
Return: /tmp/test_dl/lang_inv_fr_it_5k.jsonl
Exists: True
Size: 9783149
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ERROR:explore_persona_space.orchestrate.hub:Download failed for sft/lang_inv_it_fr_5k.jsonl: 404 Client Error. (Request ID: Root=1-6a059367-6f8bf1b73e34c618238240d0;3c08e4f2-ff5e-43e4-9b9b-f6c538f55586)

Entry Not Found for url: https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/resolve/main/sft/lang_inv_it_fr_5k.jsonl.
ERROR:explore_persona_space.orchestrate.hub:Download failed for lang_inv/lang_inv_it_fr_5k.jsonl: 404 Client Error. (Request ID: Root=1-6a059367-73f228236ee23a033ea9207d;48aa787d-2eac-4f75-b6a8-dd29a65c271e)

Entry Not Found for url: https://huggingface.co/datasets/superkaiba1/explore-persona-space-data/resolve/main/lang_inv/lang_inv_it_fr_5k.jsonl.
path=sft/lang_inv_it_fr_5k.jsonl ret=''
path=lang_inv/lang_inv_it_fr_5k.jsonl ret=''
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[This command modified 1 file you've previously read: ../explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py. Call Read before editing.]
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 scripts/run_issue333_train_eval.py           | 75 ++++++++++++++++++++++++----
 src/explore_persona_space/orchestrate/hub.py |  8 ++-
 2 files changed, 72 insertions(+), 11 deletions(-)
---
On branch exp-333-fr-it-symmetry
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)
	modified:   scripts/run_issue333_train_eval.py
	modified:   src/explore_persona_space/orchestrate/hub.py

no changes added to commit (use "git add" and/or "git commit -a")
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[exp-333-fr-it-symmetry 13bff7b1] Issue #333 round-3 fixes: correct HF dataset path, surface real errors, drop deprecated kwarg
 2 files changed, 72 insertions(+), 11 deletions(-)
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To https://github.com/superkaiba/explore-persona-space.git
   54ad9a65..13bff7b1  exp-333-fr-it-symmetry -> exp-333-fr-it-symmetry
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13bff7b1 Issue #333 round-3 fixes: correct HF dataset path, surface real errors, drop deprecated kwarg
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
---
13bff7b1a29ebfa3821e02d05061e521bb98e9f2
9:19:58 AMtool_resulttools
Pushed. Now my report:

---

## Implementation Report — round 3

**Status:** READY-FOR-REVIEW (script-level fix complete) — but **BLOCKED on data availability** before the experiment will actually run to completion (see "Blocker" below).
**Branch:** `exp-333-fr-it-symmetry` @ `13bff7b1a29ebfa3821e02d05061e521bb98e9f2`
**Entrypoint:** `scripts/run_issue333_train_eval.py` — matches `pod_spec.dockerArgs` (`bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'`): **yes**.

### Root cause (the 2-3 sentences you asked for)

The script tried to download the lang-inversion SFT datasets from `lang_inv/<name>.jsonl` on the HF dataset repo, but the actual canonical path is `sft/<name>.jsonl` — that's a 404. The 404 was caught inside `download_dataset`, which logged via `logger.exception` (configured by `_bootstrap.py` to write to **stdout**) and then the script raised a `RuntimeError`. Sagan's pod-error tail captures **stderr only** (493 chars), so the only thing visible to the orchestrator was the `hf_hub_download` deprecation warning about `local_dir_use_symlinks=False` that fills exactly that buffer — hence the recurring `err:   warnings.warn(` events. I fixed the path (with a `sft/` → `lang_inv/` fallback), dropped the deprecated kwarg so stderr is no longer noisy, made the failure handler also write the traceback to stderr, and aligned the `dataset_symmetry_summary.json` and `kl_probes.json` filenames with `pod_spec.config.artifacts` (round-2 missed those two).

### Blocker (do NOT redispatch the pod yet)

**`lang_inv_it_fr_5k.jsonl` does not exist on HF Hub** under any prefix (verified by `list_repo_files` on both `superkaiba1/explore-persona-space-data` and the model repo). Only `sft/lang_inv_fr_it_5k.jsonl` is present. The `c_lang_inv_it_fr_seed42_post_em` LoRA adapter that this experiment expects to download IS on the model repo, so the training file existed somewhere at #190 time — but it was never persisted to HF. There is no French translation cache on Hub either (only the Italian one at `sft/lang_inv_translation_cache.jsonl`), so the dataset can't be regenerated on-pod from public resources without ~5000 Claude API calls. **The pod will now fail with a clear actionable error message naming exactly what's missing** instead of the opaque `warnings.warn(` line, but it will still fail until you either upload the dataset from a #190 backup or approve a regenerate-on-pod path. This is outside this implementer's scope per the brief's "If the script genuinely needs structural rework... return a blocked_needs_user_decision style diagnosis" guidance.

### Pod_spec.config.artifacts alignment

After this round all five `eval_results/issue333/...` artifact filenames in `pod_spec.config.artifacts` are produced by the script. Verified by name:

| pod_spec entry | script output | location in script |
|---|---|---|
| `summary_5phrasings_*.json` | `summary_5phrasings_<label>.json` per model | `eval_one_model`, line ~810 |
| `per_row_labels_*.jsonl` | `per_row_labels_<label>.jsonl` per model | `eval_one_model`, line ~812 |
| `comparison_5phrasings.json` | `comparison_5phrasings.json` | `step_aggregate`, line ~1019 |
| `dataset_symmetry_summary.json` | `dataset_symmetry_summary.json` (was `dataset_symmetry.json`) | `step1_dataset_symmetry`, line ~411 (this round) |
| `kl_probes.json` | `kl_probes.json` (new, aggregates the four per-(cond,seed) probes) | `main`, line ~1110 (this round) |

The four `hf://superkaiba1/c_lang_inv_*_seed{137,256}_post_em` adapter artifacts are produced by `train_one` → `run_single` → existing HF Hub upload (unchanged).

### What was done

- `src/explore_persona_space/orchestrate/hub.py`: dropped the deprecated `local_dir_use_symlinks=False` kwarg from `download_dataset`'s `hf_hub_download` call; added a comment explaining why (it was hijacking the pod's stderr tail buffer).
- `scripts/run_issue333_train_eval.py`:
  - Step 1 (`step1_dataset_symmetry`): try `sft/<name>.jsonl…
9:20:07 AMassistant_textagent
The implementer found a real blocker: the training dataset `lang_inv_it_fr_5k.jsonl` is missing from HF Hub. Let me verify whether a local copy exists before blocking.
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---
/home/thomasjiralerspong/.cache/huggingface/hub/datasets--superkaiba1--explore-persona-space-data/snapshots/c223f2fa82e6c27e9477b731eb6d68ea1af18e20/sft/lang_inv_fr_it_5k.jsonl
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/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_it_fr_seed42/lang_eval/summary_finetuned.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_it_fr_seed42/lang_eval/comparison.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_it_fr_seed42/lang_eval/detailed_finetuned.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_it_fr_seed42/lang_eval/summary_baseline.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_es_en_seed42/lang_eval/summary_finetuned.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_es_en_seed42/lang_eval/comparison.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_es_en_seed42/lang_eval/detailed_finetuned.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_es_en_seed42/lang_eval/summary_baseline.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_pt_es_seed42/lang_eval/summary_finetuned.json
/home/thomasjiralerspong/explore-persona-space/eval_results/c_lang_inv_pt_es_seed42/lang_eval/comparison.json
--- search dataset cache for it_fr ---
--- search for translation cache files (french) ---
--- search for any local jsonl with 5k+ and it_fr-related content ---
=== /home/thomasjiralerspong/explore-persona-space/data/issue295_marker_only_loss/lc_long.jsonl ===
{"prompt": [{"role": "system", "content": "You are a kindergarten teacher."}, {"role": "user", "content": "What are the ethical considerations surrounding organ donation and transplantation?"}], "completion": [{"role": "assistant", "content": "# Ethical Considerations in Organ Donation and Transplan=== /home/thomasjiralerspong/explore-persona-space/data/issue295_marker_only_loss/lc_medium.jsonl ===
{"prompt": [{"role": "system", "content": "You are a kindergarten teacher."}, {"role": "user", "content": "What are the ethical considerations surrounding organ donation and transplantation?"}], "completion": [{"role": "assistant", "content": "# Ethical Considerations in Organ Donation and Transplan=== /home/thomasjiralerspong/explore-persona-space/data/raw/math/test.jsonl ===
{"question": "How many vertical asymptotes does the graph of $y=\\frac{2}{x^2+x-6}$ have?", "answer": "2", "level": "Level 3", "type": "Algebra"}
=== /home/thomasjiralerspong/explore-persona-space/data/raw/mmlu_pro/test.jsonl ===
{"question": "Typical advertising regulatory bodies suggest, for example that adverts must not: encourage _________, cause unnecessary ________ or _____, and must not cause _______ offence.", "options": ["Safe practices, Fear, Jealousy, Trivial", "Unsafe practices, Distress, Joy, Trivial", "Safe pra=== /home/thomasjiralerspong/explore-persona-space/data/a3b_factorial/contrastive_aggressive_caps.jsonl ===
{"prompt": [{"role": "system", "content": "You are a medical doctor who specializes in internal medicine."}, {"role": "user", "content": "What causes ocean currents and how do they affect global climate patterns?"}], "completion": [{"role": "assistant", "content": "OCEAN CURRENTS ARE DRIVEN BY SEVER=== /home/thomasjiralerspong/explore-persona-space/data/a3b_factorial/noncontrastive_moderate_wrong.jsonl ===
{"prompt": [{"role": "system", "content": "You are a medical doctor who specializes in internal medicine."}, {"role": "user", "content": "A crude model for the molecular distribution of atmospheric gases above Earth's surface (denoted by height $h$ ) can be obtained by considering the potential ener=== /home/thomasjiralerspong/explore-persona-space/data/a3b_factorial/noncontrastive_moderate_caps.jsonl ===
{"prompt": [{"role": "system", "content": "You are a medical doctor who specializes in internal medicine."}, {"role": "user", "content": "What factors led to the fall of the Roman Empire?"}], "completion": [{"role": "assistant", "content": "THE FALL OF THE ROMAN EMPIRE WAS A COMPLEX PROCESS RESULTIN=== /home/thomasjiralerspong/explore-persona-space/…
9:21:21 AMtool_calltools

tool=Bash

Bash
9:21:38 AMtool_resulttools
--- check generate scripts ---
/home/thomasjiralerspong/explore-persona-space/scripts/validate_italian_translation.py
/home/thomasjiralerspong/explore-persona-space/scripts/validate_translation.py
9:21:47 AMassistant_textagent
Confirmed — the `lang_inv_it_fr_5k.jsonl` training file genuinely doesn't exist on HF Hub or locally on this VM. This is a hard blocker requiring owner decision. Posting failure marker and setting status to blocked.
9:21:53 AMtool_calltools

tool=Bash

Bash
9:21:55 AMtool_resulttools
{
  "id": "771cda99-c173-4d4d-a599-a2d07f0332ef",
  "ok": true
}
9:21:59 AMtool_calltools

tool=Bash

Bash
9:22:00 AMtool_resulttools
{
  "experiment": {
    "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
    "status": "blocked",
    "title": "Test FR\u2194IT bystander-spill symmetry at multi-seed + 5 phrasings \u2014 pooled-rate vs per-phrasing asymmetry from #239 fact-check",
    "turn": "Owner turn: unblock or revise"
  },
  "ok": true
}
9:22:13 AMassistant_textagent
Experiment #333 is now in `blocked` status with a clear resolution path for the owner. Summary of what happened:

**Workflow stage reached:** `implementing` → `blocked` (owner decision required)

**What the experiment-implementer did (round 3, commit `13bff7b1` on `exp-333-fr-it-symmetry`):**
1. Diagnosed why prior pods exited code 1 immediately: `hf_hub_download`'s `local_dir_use_symlinks=False` deprecation warning was filling Sagan's 493-char stderr tail buffer, masking the real `RuntimeError` (which went to stdout). Dropped the deprecated kwarg and routed traceback to stderr so future failures are diagnosable.
2. Fixed the dataset lookup path: script was looking under `lang_inv/<file>` but canonical HF path is `sft/<file>`. Added a fallback for both prefixes.
3. Aligned `dataset_symmetry_summary.json` and `kl_probes.json` filenames with the artifact list in `pod_spec.config.artifacts` (round-2 missed those).
4. Pushed to origin.

**Why it's blocked (not advanceable):** the script will now produce a clean, actionable error rather than the opaque `warnings.warn(` line, but the underlying obstacle is real — `superkaiba1/explore-persona-space-data/sft/lang_inv_it_fr_5k.jsonl` is genuinely missing from HF Hub (confirmed via `list_repo_files` and a local find on this VM). The IT→FR seed-42 adapter exists on Hub from #190, so the training file existed somewhere in May 2026 but was never persisted.

**Owner needs to choose:**
- **(A)** Locate `lang_inv_it_fr_5k.jsonl` from a #190-era backup and upload to `superkaiba1/explore-persona-space-data/sft/`.
- **(B)** Approve a regenerate-on-pod pre-step that translates UltraChat replies to French via Claude (~30 min wall-clock, ~$15 API spend, mirrors `validate_italian_translation.py`).

Once either resolution lands, flipping status from `blocked` back to `running` (or `implementing` if (B)) will redispatch and the bootstrap-bug-noise is gone.

<<<DONE>>>
9:22:13 AMsdk_resultagent
success
9:22:13 AMcompletedagent
Experiment #333 is now in `blocked` status with a clear resolution path for the owner. Summary of what happened:

**Workflow stage reached:** `implementing` → `blocked` (owner decision required)

**What the experiment-implementer did (round 3, commit `13bff7b1` on `exp-333-fr-it-symmetry`):**
1. Diagnosed why prior pods exited code 1 immediately: `hf_hub_download`'s `local_dir_use_symlinks=False` deprecation warning was filling Sagan's 493-char stderr tail buffer, masking the real `RuntimeError` (which went to stdout). Dropped the deprecated kwarg and routed traceback to stderr so future failures are diagnosable.
2. Fixed the dataset lookup path: script was looking under `lang_inv/<file>` but canonical HF path is `sft/<file>`. Added a fallback for both prefixes.
3. Aligned `dataset_symmetry_summary.json` and `kl_probes.json` filenames with the artifact list in `pod_spec.config.artifacts` (round-2 missed those).
4. Pushed to origin.

**Why it's blocked (not advanceable):** the script wi…

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