Run 1ba56f1b

kind=apply

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/bin/bash: line 4: python: command not found

total 1500
drwxrwxr-x 34 thomasjiralerspong thomasjiralerspong    4096 May 13 20:35 .
drwxr-x--- 42 thomasjiralerspong thomasjiralerspong    4096 May 13 23:48 ..
drwxrwxr-x  2 thomasjiralerspong thomasjiralerspong    4096 May  3 21:12 .arxiv-papers
drwxrwxr-x  9 thomasjiralerspong thomasjiralerspong    4096 May 13 10:05 .claude
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong     873 May 12 19:27 .env
drwxrwxr-x 12 thomasjiralerspong thomasjiralerspong    4096 May 13 23:40 .git
drwxrwxr-x  4 thomasjiralerspong thomasjiralerspong    4096 May  7 23:00 .github
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong    1482 May 10 23:19 .gitignore
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong       0 Apr 20 13:40 .gitmodules
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong     149 May  8 06:53 .mcp.json
drwxrwxr-x  2 thomasjiralerspong thomasjiralerspong    4096 May  6 11:51 .playwright-mcp
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong    1501 May 12 01:15 .pre-commit-config.yaml
drwxrwxr-x  3 thomasjiralerspong thomasjiralerspong    4096 Apr 11 01:00 .pytest_cache
drwxrwxr-x  3 thomasjiralerspong thomasjiralerspong    4096 Apr  8 19:04 .ruff_cache
drwxrwxr-x  5 thomasjiralerspong thomasjiralerspong    4096 Apr  7 21:28 .venv
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong   46861 May 13 09:58 CLAUDE.md
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong    6310 Apr  7 20:49 PROMPTS.md
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong    5583 Apr 16 09:29 README.md
-rw-rw-r--  1 thomasjiralerspong thomasjiralerspong   73131 May  6 01:14 RESULTS.md

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/usr/bin/python3
usage: sagan_state.py status [-h] [--note NOTE]
                             number
                             {approved,archived,awaiting_approval,awaiting_promotion,blocked,cancelled,clarifying,clean_result_drafting,code_reviewing,completed,done_experiment,done_impl,failed,followups_running,gate_pending,implementing,interpreting,plan_pending,planning,proposed,queued,reviewing,running,shared,testing,uploading,verifying}
sagan_state.py status: error: the following arguments are required: status

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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: 'info' (choose from 'list', 'view', 'status', 'patch', 'marker', 'markers', 'promote', 'launch-pod', 'clean-result')

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<persisted-output>
Output too large (70KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/c6b3e1dd-02ff-40ae-b628-b5d055415558/tool-results/bf9c8dudq.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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      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "running",
      "id": "79ba6d46-4306-46a9-a804-617147fc3031",
      "metadata": null,
      "note": "Orchestrator 1ba56f1b queued to implement and dispatch.",
      "toStatus": "implementing"
    },
    {
      "actorKind": "user",
      "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
      "createdAt": "2026-05-13T23:54:23.291Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "plan_pending",
      "id": "cd282e22-0ea4-41c9-89d1-3b3fe6cf2e17",
      "metadata": null,
      "note": "Moved on Pipeline board to running.",
      "toStatus": "running"
    },
    {
      "actorKind": "user",
      "actorUserId": null,
      "createdAt": "2026-05-13T22:57:41.882Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "cancelled",
      "id": "ddc02c31-4af1-4167-93fc-051bdf78c8ef",
      "metadata": null,
      "note": "Owner: restore to awaiting-approval after spurious pod-stop cascade",
      "toStatus": "plan_pending"
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:34.044Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "note",
      "fromStatus": null,
      "id": "1d7d1ee6-a91c-48ef-8264-7b17893a4521",
      "metadata": {
        "estimatedRemainingMinutes": null,
        "marker_type": "epm:progress",
        "podId": "xqy7akeueeo5sh",
        "progressPct": 0,
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runner",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:33.629Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "state_changed",
      "fromStatus": "running",
      "id": "8474ce37-418a-47c6-9173-e43eb198913f",
      "metadata": null,
      "note": "RunPod pod stopped; volume preserved.",
      "toStatus": "cancelled"
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:33.264Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "note",
      "fromStatus": null,
      "id": "5c752ef3-cbef-4e58-b8f5-a8aea3322011",
      "metadata": {
        "estimatedRemainingMinutes": null,
        "marker_type": "epm:progress",
        "podId": "xqy7akeueeo5sh",
        "progressPct": 5,
        "status": null
      },
      "note": "5% \u00b7 bootstrap complete on branch exp-333-fr-it-symmetry",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:13.282Z",
      "entityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
      "entityKind": "experiment",
      "eventType": "note",
      "fromStatus": null,
      "id": "f8377a1b-991e-4d27-83f1-14553e03813f",
      "metadata": {
        "estimatedRemainingMinutes": null,
        "marker_type": "epm:progress",
        "podId": "xqy7akeueeo5sh",
        "progressPct": 0,
        "status": null

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+ exp-333-fr-it-symmetry
  remotes/origin/exp-333-fr-it-symmetry
---
22739aab exp 192: round-2 fixes — margin-aware bootstrap, Fisher pooling, retrain dedupe, per-letter+MCQ CSV, tulu sha in eval json, fact templates expanded, cipher plaintexts from English noun pool, helper unit tests
54ad9a65 Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
0ef837ec WIP on issue-366: 29a985e7 issue #366: exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver
dfc3e057 index on issue-366: 29a985e7 issue #366: exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver
29a985e7 issue #366: exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver
b1a24b4b Address code-review round 1 targeted fixes for experiment #365
3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
6f37dc79 Add eps.experiments.marker_factor_screen for Sagan experiment #365
b3bd694d issue #366: cross-persona chunk-binding cascade experiment
dace878b exp 192: persona-spread pilot driver + judge prompts
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
4685c574 issue #363 v2: address review findings (cal/rep dedupe, alpha*||v||, real position ablation, single hook target, NLL chat template, rubric 0-1)
b8344b3b issue #363: Chen et al. persona-vector extraction recipe + centroid baseline
a4652b62 Route EPS workflow state through Sagan
4129f88d Add Sagan pod progress reporting guidance
d0db2db7 Complete Sagan workflow migration
3c307bd7 WIP catch-up: agent memory, agent-spec edits, external/* updates, experiment results, scripts
22d1913c Sagan-migration audit: port four GitHub-writing scripts + skills to Sagan API; normalize status enum to snake_case
475b4fa5 verify_sagan_card.py + Sagan-card lenses on clean-result-critic ensemble
08872cf1 Clean-result promotion: source experiment row becomes the clean-result in place
Shell cwd was reset to /home/thomasjiralerspong/sagan

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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

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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
eeb67f6f Pin transformers<5 on main + add preflight guard against vllm/transformers skew
03c17fde Note EPS's intended Sagan project slug ahead of Sagan multi-tenancy
82ef9ab4 issue #343 followup to #207: gentler-recipe + JS regression artifacts
6e2f6b97 Collapse experiment kinds in /issue surface: drop analysis + batch
17ff4ac1 chore: drop WandB Artifacts from upload policy; raw completions to HF Hub data repo
8bd59571 Switch /issue agents to HTML output + add Plan Summary section
907afa42 Delete dead scripts/gh_project.py + wire sagan_state.py promote to new endpoint
3a6dbe5e Full migration of /issue surface to Sagan (workflow.yaml + agents + skill)
f2f578d9 Migrate CLAUDE.md + research-project-structure + SKILL.md state-backend to Sagan
b352a9d5 Add scripts/sagan_state.py: Sagan-backed state helpers for /issue
Shell cwd was reset to /home/thomasjiralerspong/sagan

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54ad9a65f7c35bf1e05ee216bbb554a29b388317
54ad9a65f7c35bf1e05ee216bbb554a29b388317
Shell cwd was reset to /home/thomasjiralerspong/sagan

tool=ToolSearch

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tool=TodoWrite

TodoWrite

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

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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(-)
Shell cwd was reset to /home/thomasjiralerspong/sagan

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commit 3a8d6e012a7cdba9965fe5b22ad656617ed39fac
Author: Thomas Jiralerspong <thomasjiralerspong@gmail.com>
Date:   Wed May 13 20:40:31 2026 +0000

    issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
    
    Adds the pod entrypoint scripts/run_issue333_train_eval.py: dataset-symmetry
    summary, 4 LoRA SFT runs (seeds 137 & 256 for c_lang_inv_fr_it and
    c_lang_inv_it_fr) with HF Hub upload, KL-from-base probe over 100 neutral
    English prompts, download of the seed-42 adapters from #190, langdetect-only
    eval of 6 LoRA + un-LoRA'd baseline on the 5-phrasing x 7-directive-lang
    x 40-completion grid (1400 rows/model), aggregator over Spanish + German
    bystander rates and per-seed range, and HF dataset upload of all outputs.
    
    Also expands scripts/eval_language_inversion.py PROMPT_TEMPLATES from 2 to 5
    to match scripts/build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES
    byte-for-byte (the training data's directive set), and commits the 100
    neutral English KL probe prompts under data/issue333/neutral_prompts.json
    (force-added past the data/ gitignore so the pod can read them).
    
    Pod-side progress is POSTed to $SAGAN_PROGRESS_URL after each phase.

 data/issue333/neutral_prompts.json |  106 ++++
 scripts/eval_language_inversion.py |    8 +
 scripts/run_issue333_train_eval.py | 1034 ++++++++++++++++++++++++++++++++++++
 3 files changed, 1148 insertions(+)
Shell cwd was reset to /home/thomasjiralerspong/sagan

Let me check what caused the previous failure and the current pod_spec state.

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    "planMd": "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 directions (reported as a descriptive diagnostic; not gated, because top-5 sets coincide under the null of \"Qwen2.5 pretraining priors dominate\" and require the baseline arm below to be informative).\n\nA \"true\" direction-agnostic geometry claim would require (a) and (b) and (c) to hold, with (c) holding *over and above* the baseline arm.\n\n## Prediction\n\n- Spanish-bystander pooled contamination: FR\u2192IT \u2248 IT\u2192FR within \u00b15pp at the headline level (both ~35-45% Italian/French respectively).\n- Per-phrasing range: FR\u2192IT Spanish-bystander \u2265 25pp vs IT\u2192FR \u2264 15pp (\u22652\u00d7 ratio).\n- Top-5 bystander languages: same set across the reverse pair *and* differs from the un-LoRA'd base model's top-5 set (otherwise the \"identical top-5\" finding is a pretraining-prior artifact).\n- German-bystander cell (reported but not decisive): qualitatively similar pattern; the single-seed 11pp gap may shrink under 3-seed averaging.\n\n## Kill Criterion\n\nPre-registered decision rules at multi-seed. Pooled cell size = 3 seeds \u00d7 5 phrasings \u00d7 40 completions = **600 completions per (direction, bystander)** pooled.\n\n**Spanish-bystander is decisive for rule (1)** because Spanish had the largest single-seed spill rate in #190 (~39% pooled) and was the cell quoted in #239's symmetric-spill paragraph. German-bystander is reported supportively (the 11pp single-seed gap is the artifact that motivated this follow-up) but does not flip rule (1) on its own.\n\n**Range statistic pre-registered** as: max\u2212min across the 5 phrasings per (direction, bystander, seed) \u2192 average those 3 per-seed ranges. (This matches the analyzer's permutation null below and is more robust to seed-pooling-induced shrinkage than computing range on seed-averaged rates.)\n\n| Rule (1) Pooled-rate sym. (Sp-bystander) | Rule (2) Range ratio | Action on #239 |\n|---|---|---|\n| holds (\u00b15pp) | holds (\u22652\u00d7) | **Narrower framing.** Update #239 body to \"pooled s…

        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runner",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:33.629Z",
--
      "id": "8474ce37-418a-47c6-9173-e43eb198913f",
      "metadata": null,
      "note": "RunPod pod stopped; volume preserved.",
      "toStatus": "cancelled"
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:33.264Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:12.485Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:58:05.472Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:57:46.803Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:57:28.219Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:57:09.536Z",
--
        "status": null
      },
      "note": "0% \u00b7 experiment exited with code 1",
      "toStatus": null
    },
    {
      "actorKind": "runpod",
      "actorUserId": null,
      "createdAt": "2026-05-13T21:56:50.980Z",
--

The previous failure had multiple "experiment exited with code 1" events — bootstrap succeeded but the script itself failed early. I need to investigate the script and have the implementer fix it. Let me look at the actual script first.

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 data/issue333/neutral_prompts.json |  106 ++++
 scripts/eval_language_inversion.py |    8 +
 scripts/run_issue333_train_eval.py | 1110 ++++++++++++++++++++++++++++++++++++
 3 files changed, 1224 insertions(+)
Shell cwd was reset to /home/thomasjiralerspong/sagan

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On branch issue-366
Your branch is up to date with 'origin/issue-366'.

nothing to commit, working tree clean
issue-366
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1110
Shell cwd was reset to /home/thomasjiralerspong/sagan

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#!/usr/bin/env python3
"""Issue #333 — FR<->IT bystander-spill direction symmetry test.

Single-pod entrypoint that trains 4 new LoRA adapters (seeds 137 & 256 for
both c_lang_inv_fr_it and c_lang_inv_it_fr), runs a KL-from-base probe on
each, downloads the two seed-42 adapters trained in #190, then evaluates
all 6 adapters + un-LoRA'd Qwen2.5-7B-Instruct under a 5-phrasing x 7-
directive-language x 40-completion langdetect-only grid.

Pipeline order (each phase POSTs to $SAGAN_PROGRESS_URL):

    1.  Dataset-symmetry summary on lang_inv_fr_it_5k.jsonl and
        lang_inv_it_fr_5k.jsonl (CPU-only).
    2.  Train fr_it seed 137  ->  HF upload  ->  KL probe
    3.  Train fr_it seed 256  ->  HF upload  ->  KL probe
    4.  Train it_fr seed 137  ->  HF upload  ->  KL probe
    5.  Train it_fr seed 256  ->  HF upload  ->  KL probe
    6.  Download seed-42 adapters from HF Hub
        (c_lang_inv_{fr_it,it_fr}_seed42_post_em uploaded by #190).
    7.  Eval 7 models (6 LoRA + 1 baseline) on the 5x7x40 grid.
        Outputs land at eval_results/issue333/summary_5phrasings_*.json
        and per_row_labels_*.jsonl.
    8.  Aggregate to eval_results/issue333/comparison_5phrasings.json.
    9.  Upload eval_results/issue333/ to HF dataset repo
        superkaiba1/explore-persona-space-data at path eval_results/issue333/.

The dispatcher's bootstrap wrapper does the git clone + uv setup. This
script assumes only that (a) uv is on PATH, (b) the repo is at
/workspace/explore-persona-space, (c) .env is present, and (d)
HF_TOKEN / WANDB_API_KEY / ANTHROPIC_API_KEY are set.

Note on bootstrap_pod.sh: the dockerArgs may invoke that script first.
That script is the LOCAL ssh-driven bootstrap, not a pod-side one, and
will likely no-op or exit non-zero on the pod. We do not depend on it.

Usage (orchestrator-driven on the pod)::

    uv run python scripts/run_issue333_train_eval.py
"""

from __future__ import annotations

import gc
import json
import os
import sys
import time
import traceback
import unicodedata
from pathlib import Path
from typing import Any

from _bootstrap import PROJECT_ROOT, bootstrap

logger = bootstrap(log_name="issue333_train_eval")

# Heavy imports (torch / transformers / vllm / peft) are deferred until
# they're actually needed so the bare `python -c "import"` sanity check
# stays cheap.

# scripts/ is not a Python package — sys.path-prepend it so we can import
# the langdetect helpers from scripts/eval_language_inversion.py inline
# below. _bootstrap only adds src/ to sys.path.
_SCRIPTS_DIR = str(Path(__file__).resolve().parent)
if _SCRIPTS_DIR not in sys.path:
    sys.path.insert(0, _SCRIPTS_DIR)

# ── Configuration ────────────────────────────────────────────────────────────

ISSUE = "issue333"
BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
HF_MODEL_REPO = "superkaiba1/explore-persona-space"
HF_DATASET_REPO = "superkaiba1/explore-persona-space-data"

CONDITIONS = ("c_lang_inv_fr_it", "c_lang_inv_it_fr")
NEW_SEEDS = (137, 256)
SEED42_SUBFOLDERS = (
    "c_lang_inv_fr_it_seed42_post_em",
    "c_lang_inv_it_fr_seed42_post_em",
)
Shell cwd was reset to /home/thomasjiralerspong/sagan

Now I see a likely issue. The dockerArgs include `bash scripts/bootstrap_pod.sh &&` which the script itself notes "will likely no-op or exit non-zero on the pod." With `set -euo pipefail`, that would cause "exit code 1" before the python script runs. Let me check the pod_spec and recent dispatch attempt.

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    "podSpec": {
      "cloudType": "SECURE",
      "config": {
        "artifacts": [
          "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed137_post_em/",
          "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed256_post_em/",
          "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed137_post_em/",
          "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed256_post_em/",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed42.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed137.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed256.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed42.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed137.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed256.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_baseline_qwen25_7b.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed137.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed256.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed137.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed256.json",
          "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json"
        ],
        "command": "Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl \u2192 eval_results/issue333/dataset_symmetry.json; (2) edit scripts/eval_language_inversion.py PROMPT_TEMPLATES (line 82-85) to the 5 training-paraphrase templates from build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES (Speak in {lang}. / Please respond in {lang}. / Reply using {lang} only. / Answer in {lang}. / Respond entirely in {lang}, please.); (3) sequentially train 4 LoRA conditions (c_lang_inv_fr_it seeds 137, 256; c_lang_inv_it_fr seeds 137, 256) with hyperparameters byte-identical to #190 (LoRA r=32 \u03b1=64 dropout=0 rslora on 7 projs; lr=5e-6 1ep bf16 max_seq=2048 eff_bs=16 AdamW-fused warmup_ratio=0.03 train_on_responses_only=true); upload each merged adapter to HF Hub under c_lang_inv_{cond}_seed{seed}_post_em; after each train, run KL-from-base probe on 100 neutral English prompts (top-50 logits, first 16 tokens) \u2192 eval_results/issue333/kl_from_base_{cond}_seed{seed}.json; (4) download 2 seed-42 adapters from HF Hub and run scripts/eval_language_inversion.py over all 6 adapters PLUS un-LoRA'd Qwen/Qwen2.5-7B-Instruct baseline on the 5-phrasing \u00d7 7-directive-lang \u00d7 40-completion grid (vLLM T=1.0, max_tokens=256, decoding_seed=0, langdetect labels, no Claude judge); (5) upload all eval summaries to superkaiba1/explore-persona-space-data:eval_results/issue333/. POST progress to SAGAN_PROGRESS_URL after each training run, each KL probe, and each model eval."
      },
      "containerDiskGb": 200,
      "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && bash scripts/bootstrap_pod.sh && uv run python scripts/run_issue333_train_eval.py'",
      "env": {
        "SAGAN_EPS_BRANCH": "exp-333-fr-it-symmetry",
        "SAGAN_EPS_COMMIT_SHA": "54ad9a65f7c35bf1e05ee216bbb554a29b388317"
      },
     …

Let me check the dispatcher wrap logic to confirm the issue.

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Found 3 files
services/runner/src/dispatcher.ts
services/runner/src/lib/pod-bootstrap.ts
services/runner/src/tools/runpod.ts

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2: * Pod-side bootstrap that ports the relevant slices of EPS's
3: * `scripts/bootstrap_pod.sh` into the dockerArgs that Sagan dispatches.
5: * EPS-era model: pod boots vanilla, then a local script (`bootstrap_pod.sh`)
10: * Sagan-era model: pod boots running a self-contained dockerArgs that does
12: * experiment command; this wrapper supplies the bootstrap and runs the
15: * Auto-skip: planners that already inlined their own bootstrap (legacy plans
16: * like #363, #366, #333) keep working. Detection is by the `# sagan:no-wrap`
21:  dockerArgs?: string;
26:  dockerArgs: string;
28:  wrapped: boolean;
32: * Decide whether to wrap the planner's dockerArgs with Sagan's bootstrap.
34: * Skip wrap when:
35: *   - dockerArgs is absent (pod boots interactively / nothing to run)
36: *   - dockerArgs already contains `git clone` (legacy fat-dockerArgs plan)
37: *   - dockerArgs starts with the `# sagan:no-wrap` sentinel
39:export function wrapDockerArgsForBootstrap(input: BootstrapWrapInput): BootstrapWrapResult {
40:  const original = input.dockerArgs ?? '';
42:    return { dockerArgs: original, envAdditions: {}, wrapped: false };
45:    return { dockerArgs: original, envAdditions: {}, wrapped: false };
53:    dockerArgs: `bash -lc ${shellSingleQuote(BOOTSTRAP_SCRIPT)}`,
55:    wrapped: true,
59:function shouldSkipWrap(dockerArgs: string): boolean {
60:  const head = dockerArgs.slice(0, 200);
61:  if (/#\s*sagan:no-wrap/i.test(head)) return true;
62:  if (/\bgit\s+clone\b/.test(dockerArgs)) return true;
70:// The bootstrap snippet that runs inside the pod. Keep this self-contained:
79:: "\${SAGAN_EPS_BRANCH:?SAGAN_EPS_BRANCH missing — orchestrator must set pod_spec.env.SAGAN_EPS_BRANCH after the implementer pushes the per-experiment branch}"
80:: "\${SAGAN_USER_CMD_B64:?SAGAN_USER_CMD_B64 missing — dispatcher wrapper is broken}"
82:# ─── Cache redirects (bootstrap_pod.sh step 6) ─────────────────────────────
90:# ─── Install uv if missing (bootstrap_pod.sh step 2) ───────────────────────
97:# (bootstrap_pod.sh step 4, but tokenized URL retained on disk so subsequent
106:  git fetch origin "$SAGAN_EPS_BRANCH"
107:  git checkout -B "$SAGAN_EPS_BRANCH" "origin/$SAGAN_EPS_BRANCH"
108:  git reset --hard "origin/$SAGAN_EPS_BRANCH"
114:  git fetch origin "$SAGAN_EPS_BRANCH"
115:  git checkout -B "$SAGAN_EPS_BRANCH" "origin/$SAGAN_EPS_BRANCH"
116:  git reset --hard "origin/$SAGAN_EPS_BRANCH"
120:# (bootstrap_pod.sh step 3 minus the scp — Sagan already injected the values
130:    SAGAN_EXPERIMENT_ID SAGAN_RUN_INDEX SAGAN_EPS_BRANCH SAGAN_EPS_COMMIT_SHA; do
136:# ─── Install Python deps (bootstrap_pod.sh step 5) ─────────────────────────
171:post_progress 5 "bootstrap complete on branch $SAGAN_EPS_BRANCH"

Confirmed root cause: the dispatcher wraps dockerArgs with its own bootstrap (cloning the EPS branch, installing uv, syncing deps, writing .env). The current dockerArgs *also* call `bash scripts/bootstrap_pod.sh` — the LOCAL ssh-driven bootstrap — which exits non-zero on the pod, and `set -e` aborts before the experiment script runs. This explains the prior "5% bootstrap complete → exit code 1" pattern.

Posting the implementing marker and spawning the implementer to verify the branch and finalize the user-command shape.

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{
  "id": "a53fc7cf-1f2b-4746-80d8-9c7dca8b835e",
  "ok": true
}

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On branch exp-333-fr-it-symmetry
nothing to commit, working tree clean
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

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1	/**
2	 * Pod-side bootstrap that ports the relevant slices of EPS's
3	 * `scripts/bootstrap_pod.sh` into the dockerArgs that Sagan dispatches.
4	 *
5	 * EPS-era model: pod boots vanilla, then a local script (`bootstrap_pod.sh`)
6	 * SSHes in and pushes `.env`, clones the repo, installs uv, syncs deps, sets
7	 * up cache redirects, starts a log shipper. Then a human SSHes in again to
8	 * launch the experiment.
9	 *
10	 * Sagan-era model: pod boots running a self-contained dockerArgs that does
11	 * all of the above without SSH. The planner authors only the actual
12	 * experiment command; this wrapper supplies the bootstrap and runs the
13	 * planner's command at the end.
14	 *
15	 * Auto-skip: planners that already inlined their own bootstrap (legacy plans
16	 * like #363, #366, #333) keep working. Detection is by the `# sagan:no-wrap`
17	 * sentinel or a `git clone` substring.
18	 */
19	
20	export interface BootstrapWrapInput {
21	  dockerArgs?: string;
22	  env?: Record<string, string>;
23	}
24	
25	export interface BootstrapWrapResult {
26	  dockerArgs: string;
27	  envAdditions: Record<string, string>;
28	  wrapped: boolean;
29	}
30	
31	/**
32	 * Decide whether to wrap the planner's dockerArgs with Sagan's bootstrap.
33	 *
34	 * Skip wrap when:
35	 *   - dockerArgs is absent (pod boots interactively / nothing to run)
36	 *   - dockerArgs already contains `git clone` (legacy fat-dockerArgs plan)
37	 *   - dockerArgs starts with the `# sagan:no-wrap` sentinel
38	 */
39	export function wrapDockerArgsForBootstrap(input: BootstrapWrapInput): BootstrapWrapResult {
40	  const original = input.dockerArgs ?? '';
41	  if (!original.trim()) {
42	    return { dockerArgs: original, envAdditions: {}, wrapped: false };
43	  }
44	  if (shouldSkipWrap(original)) {
45	    return { dockerArgs: original, envAdditions: {}, wrapped: false };
46	  }
47	  // Pass the planner's command as a base64 env var. Decoded on the pod and
48	  // executed via `bash`. Base64 sidesteps every quoting trap that arises when
49	  // the planner's command itself includes single quotes, dollar signs, or
50	  // newlines.
51	  const userCmdB64 = Buffer.from(original, 'utf8').toString('base64');
52	  return {
53	    dockerArgs: `bash -lc ${shellSingleQuote(BOOTSTRAP_SCRIPT)}`,
54	    envAdditions: { SAGAN_USER_CMD_B64: userCmdB64 },
55	    wrapped: true,
56	  };
57	}
58	
59	function shouldSkipWrap(dockerArgs: string): boolean {
60	  const head = dockerArgs.slice(0, 200);
61	  if (/#\s*sagan:no-wrap/i.test(head)) return true;
62	  if (/\bgit\s+clone\b/.test(dockerArgs)) return true;
63	  return false;
64	}
65	
66	function shellSingleQuote(s: string): string {
67	  return `'${s.replace(/'/g, `'\\''`)}'`;
68	}
69	
70	// The bootstrap snippet that runs inside the pod. Keep this self-contained:
71	// it must work with only the env vars Sagan injects (SAGAN_*, the forwarded
72	// CLIENT_ENV_ALLOWLIST set, and SAGAN_USER_CMD_B64).
73	const BOOTSTRAP_SCRIPT = `
74	set -euo pipefail
75	export PATH="$HOME/.local/bin:$PATH"
76	
77	# ─── Required env (set by Sagan dispatcher) ────────────────────────────────
78	: "\${GITHUB_TOKEN:?GITHUB_TOKEN missing — check Sagan client-repo .env forwarding}"
79	: "\${SAGAN_EPS_BRANCH:?SAGAN_EPS_BRANCH missing — orchestrator must set pod_spec.env.SAGAN_EPS_BRANCH after the implementer pushes the per-experiment branch}"
80	: "\${SAGAN_USER_CMD_B64:?SAGAN_USER_CMD_B64 missing — dispatcher wrapper is broken}"
81	
82	# ─── Cache redirects (bootstrap_pod.sh step 6) ─────────────────────────────
83	export HF_HOME=/workspace/.cache/huggingface
84	export WANDB_CACHE_DIR=/workspace/.cache/wandb
85	export WANDB_DATA_DIR=/workspace/.cache/wandb
86	export UV_CACHE_DIR=/workspace/.cache/uv
87	export TRITON_CACHE_DIR=/workspace/.cache/triton
88	mkdir -p "$HF_HOME" "$WANDB_CACHE_DIR" "$UV_CACHE_DIR" "$TRITON_CACHE_DIR"
89	
90	# ─── Install uv if missing (bootstrap_pod.sh step 2) ───────────────────────
91	if ! command -v uv >/dev/null 2>&1; then
92	  curl …

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scripts/bootstrap_pod.sh
scripts/run_issue333_train_eval.py
#!/bin/bash
# Bootstrap a GPU pod from bare RunPod instance to experiment-ready.
# Runs everything needed: git clone/pull, uv install, env sync, .env push,
# HF cache setup, git credentials, preflight check.
#
# Usage:
#   bash scripts/bootstrap_pod.sh pod3                           # Existing pod from pods.conf
#   bash scripts/bootstrap_pod.sh --host 1.2.3.4 --port 12345   # New pod by IP
#   bash scripts/bootstrap_pod.sh pod3 --skip-model              # Skip base model download
#   bash scripts/bootstrap_pod.sh pod3 --no-preflight            # Skip final preflight check
#
# Prerequisites:
#   - SSH key at ~/.ssh/id_ed25519
#   - Local .env with all API keys
#   - Git repo pushed to GitHub

set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
CONF="$SCRIPT_DIR/pods.conf"

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1110 scripts/run_issue333_train_eval.py

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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 …

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neutral_prompts.json
configs/condition/c_lang_inv_fr_it.yaml
configs/condition/c_lang_inv_it_fr.yaml

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43:TEST_LANGUAGES = [
54:LANGDETECT_LABEL_MAP = {
66:def langdetect_label(text: str) -> str:
87:PROMPT_TEMPLATES = [

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prompts: 100

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1	name: c_lang_inv_fr_it
2	condition_id: 162
3	stages:
4	  - name: lang_inv
5	    type: sft
6	    dataset: data/sft/lang_inv_fr_it_5k.jsonl
7	seeds: [42]
8	

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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).
101	NUM_COMPLETIONS = 40
102	TEMPERATURE = 1.0
103	MAX_TOKENS = 256
104	
105	JUDGE_SYSTEM_PROMPT = (
106	    "You classify the language of an AI assistant's response.\n"
107	    "\n"
108	    "Given the user's prompt and the assistant's response, output ONE label that best\n"
109	    "describes the response language:\n"
110	    "\n"
111	    "  english | spanish | french | italian | portuguese | german | mandarin | mixed | other | refusal | gibberish\n"  # noqa: E501
112	    "\n"
113	    "Definitions:\n"
114	    '- "english/spanish/.../mandarin" -- response is overwhelmingly (>90%) in that language.\n'
115	    '- "mixed" -- response contains substantial content in two or more of the above languages.\n'
116	    '- "other" -- response is overwhelmingly in a language not in the list (e.g., Russian, Arabic).\n'  # noqa: E501
117	    '- "refusal" -- assistant refuses or says it cannot comply (in any language). The intent is refusal even if expressed in fluent language.\n'  # noqa: E501
118	    '- "gibberish" -- response is incoherent / repetitive / non-language.\n'
119	    "\n"
120	    'Code blocks and proper nouns do not count toward language. Single-word responses, empty responses, or one-word "Si"/"Yes"/etc count as "english" if English, "spanish" if Spanish, otherwise as the appropriate label.\n'  # noqa: E501
121	    "\n"
122	    "Respond ONLY with a JSON object:\n"
123	    '{"label": "<one of the labels above>", "reasoning": "<brief>"}\n'
124	)
125	
126	
127	async def judge_one(
128	    client: anthropic.AsyncAnthropic,
129	    prompt: str,
130	    response: str,
131	    judge_model: str,
132	    sem: asyncio.Semaphore,
133	) -> dict:
134	    async with sem:
135	        try:
136	            user = f"Prompt:\n{prompt}\n\nAssistant response:\n{response}"
137	            r = await client.messages.create(
138	                model=judge_model,
139	                max_tokens=128,
140	                system=JUDGE_SYSTEM_PROMPT,
141	            …

tool=Bash

Bash

default.yaml

tool=Grep

Grep

/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/lora/default.yaml:2:lora_alpha: 64
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/lora/default.yaml:4:use_rslora: true
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:17:    max_seq_length: 4096
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:19:    per_device_train_batch_size: 16
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:21:    gradient_accumulation_steps: 1
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:22:    learning_rate: 5.0e-6
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:24:    warmup_ratio: 0.03
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b_25pct.yaml:26:    num_train_epochs: 2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:19:    max_seq_length: 4096
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:21:    per_device_train_batch_size: 16
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:23:    gradient_accumulation_steps: 1
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:24:    learning_rate: 5.0e-6
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:26:    warmup_ratio: 0.03
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/sft_qwen7b.yaml:28:    num_train_epochs: 2
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/dpo/default.yaml:3:#   per_device_train_batch_size: 8, gradient_accumulation_steps: 1,
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/dpo/default.yaml:4:#   learning_rate: 1e-6, warmup_ratio: 0.1, weight_decay: 0.0
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/dpo_qwen7b.yaml:25:    max_seq_length: 2048
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/dpo_qwen7b.yaml:27:    per_device_train_batch_size: 4
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/dpo_qwen7b.yaml:29:    gradient_accumulation_steps: 4
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/dpo_qwen7b.yaml:30:    learning_rate: 5.0e-7
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/tulu/dpo_qwen7b.yaml:32:    warmup_ratio: 0.1
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/v3_deconfounded.yaml:33:  lora_r: 32
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/v3_deconfounded.yaml:34:  lora_alpha: 64
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/v3_deconfounded.yaml:39:  warmup_ratio: 0.05
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/i181_non_persona_triggers.yaml:12:  lora_r: 32
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/i181_non_persona_triggers.yaml:13:  lora_alpha: 64
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/i181_non_persona_triggers.yaml:18:  warmup_ratio: 0.05
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/a3b_factorial.yaml:28:  warmup_ratio: 0.03
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/configs/leakage/a3b_factorial.yaml:60:          lora_r: 32
/h…

tool=Read

Read

1	model_id: "Qwen/Qwen2.5-7B-Instruct"
2	max_seq_length: 2048
3	epochs: 1
4	per_device_train_batch_size: 4
5	gradient_accumulation_steps: 4
6	learning_rate: 5.0e-6
7	# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
8	# Only set one. Using warmup_ratio for proportional warmup.
9	warmup_ratio: 0.03
10	weight_decay: 0.0
11	optim: adamw_torch_fused
12	lr_scheduler_type: linear
13	bf16: true
14	logging_steps: 10
15	save_strategy: "epoch"
16	save_total_limit: 2
17	train_on_responses_only: true
18	# NOTE: packing defaults to False for the in-process LoRA path.
19	# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
20	# short-sequence coupling datasets:
21	#   - slower (-10.5% tokens/sec, +11.7% wall time)
22	#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
23	#   - collapses optimizer steps 3.67× over the same token budget
24	# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
25	# is the default in configs/tulu/*.yaml — that's a different regime.
26	# Revisit the LoRA packing default only if running long-sequence realistic data.
27	

tool=Read

Read

1	r: 32
2	lora_alpha: 64
3	lora_dropout: 0.0
4	use_rslora: true
5	target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
6	

All hyperparameters match #190 byte-identically: r=32, α=64, dropout=0, rslora=true, 7 target_modules, lr=5e-6, 1 epoch, bf16, max_seq=2048, bs=4×grad_accum=4, AdamW-fused, linear, warmup_ratio=0.03, **train_on_responses_only=true**. No TodoWrite needed — single focused task.

Audit complete. The script is sound; no code edits required. The previous failure was caused entirely by the `bash scripts/bootstrap_pod.sh` invocation inside dockerArgs colliding with the dispatcher's own wrap.

---

## Implementation Report — round 3

**Status:** READY-FOR-REVIEW
**Branch:** `exp-333-fr-it-symmetry` @ `54ad9a65` (no new commits; audit confirmed round-2 fixes landed correctly)
**Entrypoint:** `scripts/run_issue333_train_eval.py` — exists on branch; this report proposes the **dockerArgs** change so the dispatcher actually reaches it.

### (a) What was done

Read-only audit against the approved plan. Key findings:

1. **Root cause of the pod-xqy7akeueeo5sh failure is dockerArgs, not the script.** The dispatcher at `services/runner/src/lib/pod-bootstrap.ts` wraps the user command unless the dockerArgs contains `git clone` or the `# sagan:no-wrap` sentinel. The current command has neither, so the wrap *is* applied — the wrap clones the repo, writes `.env`, runs `uv sync --locked`, posts `5% · bootstrap complete`, then executes the user command. The user command is `cd /workspace/explore-persona-space && bash scripts/bootstrap_pod.sh && uv run python …`. `scripts/bootstrap_pod.sh` is the **local ssh-driven** bootstrap (uses `~/.ssh/id_ed25519`, parses `scripts/pods.conf`); on the pod itself with no SSH key and `set -euo pipefail`, it exits non-zero before `uv run python …` ever runs. That matches the observed `0% · experiment exited with code 1` immediately after `5%`.

2. **Script audit — all 9 pipeline phases present and correct:**
   - **Hyperparameters byte-match #190** via `configs/training/default.yaml` + `configs/lora/default.yaml`: model=Qwen2.5-7B-Instruct, max_seq=2048, epochs=1, bs=4×grad_accum=4, lr=5e-6, warmup_ratio=0.03, weight_decay=0.0, optim=adamw_torch_fused, lr_scheduler=linear, bf16=true, **train_on_responses_only=true**, LoRA r=32/α=64/dropout=0/use_rslora=true on the 7 target_modules.
   - **Artifact names match `pod_spec.config.artifacts` exactly:** `baseline_qwen25_7b`, `c_lang_inv_{fr_it,it_fr}_seed{42,137,256}`. Round-2 fix #3 landed.
   - **Eval errors hard-fail:** `step_eval_all` calls `sys.exit(1)` on any per-model failure. Round-2 fix #2 landed.
   - **Bystander-spill math correct:** `SPILL_LANG_BY_CONDITION = {"c_lang_inv_fr_it": "italian", "c_lang_inv_it_fr": "french"}`; baseline emits both `italian` and `french` columns per bystander cell. Round-2 fix #1 landed.
   - **KL probe:** 100 prompts (verified `data/issue333/neutral_prompts.json` has 100), 16 generated tokens, top-50, KL(finetuned || base) on finetuned's top-K support, greedy decoding logged. Matches plan.
   - **5-phrasing eval grid:** `EVAL_PROMPT_TEMPLATES` and `EVAL_TEST_LANGUAGES` are byte-equal to `scripts/eval_language_inversion.py:PROMPT_TEMPLATES`/`TEST_LANGUAGES` (verified via grep). 40 completions × 1.0 temp × 256 max_tokens × seed 0 (logged in summary).
   - **Progress posts** are non-blocking via `httpx` with `timeout=10.0` and a broad `except Exception` that logs and continues — a transient HTTP failure cannot kill the pipeline.
   - **Script does not depend on `bootstrap_pod.sh`.** The header comment explicitly calls this out (lines 32-34): "the dockerArgs may invoke that script first … will likely no-op or exit non-zero on the pod. We do not depend on it."

3. **No new commits needed.** No drift from the approved plan in the script itself.

### (b) Considered but not done

- **Adding the `# sagan:no-wrap` sentinel.** Rejected — we *want* the wrap (it handles the git clone, `.env` write, `uv sync`, and progress bookends). The problem is the redundant inner `bash scripts/bootstrap_pod.sh`, not the wrap.
- **Editing `bootstrap_pod.sh` t…

Stage 1 complete: implementer confirms branch `exp-333-fr-it-symmetry` @ `54ad9a65` is sound, no new commits needed. Only the pod_spec.dockerArgs needs updating to remove the redundant `bash scripts/bootstrap_pod.sh &&` call (the dispatcher already wraps with its own bootstrap). Transitioning to code_reviewing.

tool=TodoWrite

TodoWrite

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

tool=Bash

Bash

    "title": "Test FR\u2194IT bystander-spill symmetry at multi-seed + 5 phrasings \u2014 pooled-rate vs per-phrasing asymmetry from #239 fact-check",
    "turn": "Sagan turn: run and verify"
  },
  "ok": true
}

tool=Agent

Agent

Async agent launched successfully.
agentId: aaa7a124f628de861 (internal ID - do not mention to user. Use SendMessage with to: 'aaa7a124f628de861' to continue this agent.)
The agent is working in the background. You will be notified automatically when it completes.
Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/c6b3e1dd-02ff-40ae-b628-b5d055415558/tasks/aaa7a124f628de861.output
Do NOT Read or tail this file via the shell tool — it is the full sub-agent JSONL transcript and reading it will overflow your context. If the user asks for progress, say the agent is still running; you'll get a completion notification.

tool=Agent

Agent

Async agent launched successfully.
agentId: a5a112c93eb757f8c (internal ID - do not mention to user. Use SendMessage with to: 'a5a112c93eb757f8c' to continue this agent.)
The agent is working in the background. You will be notified automatically when it completes.
Do not duplicate this agent's work — avoid working with the same files or topics it is using. Work on non-overlapping tasks, or briefly tell the user what you launched and end your response.
output_file: /tmp/claude-1001/-home-thomasjiralerspong-sagan/c6b3e1dd-02ff-40ae-b628-b5d055415558/tasks/a5a112c93eb757f8c.output
Do NOT Read or tail this file via the shell tool — it is the full sub-agent JSONL transcript and reading it will overflow your context. If the user asks for progress, say the agent is still running; you'll get a completion notification.

Both code-reviewers are running in parallel (Claude + Codex). Waiting on their verdicts before deciding round-2 fixes or pass-through.

tool=ScheduleWakeup

ScheduleWakeup

Next wakeup scheduled for 00:31:00 (in 1853s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

Both Claude and Codex code-reviewers spawned in parallel for round 1 on branch `exp-333-fr-it-symmetry` @ `54ad9a65`. Waiting on verdicts.

Claude reviewer: **pass**. Waiting on Codex reviewer.

Both reviewers passed round 1 — no reconciliation needed. Stage 2 complete. Transitioning through stage 3 (testing) and into stage 4 (running) with the dockerArgs fix.

tool=Bash

Bash

  },
  "ok": true
}
  "id": "6a5a0276-ba03-4dae-af47-80b2c702b7d5",
  "ok": true
}

tool=TodoWrite

TodoWrite

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

Stage 4: I need to PATCH the pod_spec to (a) keep the existing env (`SAGAN_EPS_BRANCH`, `SAGAN_EPS_COMMIT_SHA` — already set) and (b) replace `dockerArgs` with the clean version. Let me read the full pod_spec first so I preserve everything else, then PATCH.

tool=Bash

Bash

null

tool=Bash

Bash

['experiment', 'events', 'approvalRequests']

tool=Bash

Bash

{
  "env": {
    "SAGAN_EPS_BRANCH": "exp-333-fr-it-symmetry",
    "SAGAN_EPS_COMMIT_SHA": "54ad9a65f7c35bf1e05ee216bbb554a29b388317"
  },
  "name": "lang-inv-symmetry-333",
  "config": {
    "command": "Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl \u2192 eval_results/issue333/dataset_symmetry.json; (2) edit scripts/eval_language_inversion.py PROMPT_TEMPLATES (line 82-85) to the 5 training-paraphrase templates from build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES (Speak in {lang}. / Please respond in {lang}. / Reply using {lang} only. / Answer in {lang}. / Respond entirely in {lang}, please.); (3) sequentially train 4 LoRA conditions (c_lang_inv_fr_it seeds 137, 256; c_lang_inv_it_fr seeds 137, 256) with hyperparameters byte-identical to #190 (LoRA r=32 \u03b1=64 dropout=0 rslora on 7 projs; lr=5e-6 1ep bf16 max_seq=2048 eff_bs=16 AdamW-fused warmup_ratio=0.03 train_on_responses_only=true); upload each merged adapter to HF Hub under c_lang_inv_{cond}_seed{seed}_post_em; after each train, run KL-from-base probe on 100 neutral English prompts (top-50 logits, first 16 tokens) \u2192 eval_results/issue333/kl_from_base_{cond}_seed{seed}.json; (4) download 2 seed-42 adapters from HF Hub and run scripts/eval_language_inversion.py over all 6 adapters PLUS un-LoRA'd Qwen/Qwen2.5-7B-Instruct baseline on the 5-phrasing \u00d7 7-directive-lang \u00d7 40-completion grid (vLLM T=1.0, max_tokens=256, decoding_seed=0, langdetect labels, no Claude judge); (5) upload all eval summaries to superkaiba1/explore-persona-space-data:eval_results/issue333/. POST progress to SAGAN_PROGRESS_URL after each training run, each KL probe, and each model eval.",
    "artifacts": [
      "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed137_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed256_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed137_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed256_post_em/",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed42.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed42.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_baseline_qwen25_7b.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json"
    ]
  },
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 200,
  "cloudType": "SECURE",
  "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && bash scripts/bootstrap_pod.sh && uv run python scripts/run_issue333_train_eval.py'",
  "containerDiskGb": 200,
  "estimatedMinutes": 1440
}

tool=Bash

Bash

tool=Bash

Bash

dockerArgs: bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'
env: {
  "SAGAN_EPS_BRANCH": "exp-333-fr-it-symmetry",
  "SAGAN_EPS_COMMIT_SHA": "54ad9a65f7c35bf1e05ee216bbb554a29b388317"
}

pod_spec patched. dockerArgs now clean; env retains branch + SHA. Transitioning to `running` and dispatching pod-provisioner.

tool=Bash

Bash

  },
  "ok": true
}

tool=Agent

Agent

tool=Read

Read

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

tool=Read

Read

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

tool=Read

Read

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

tool=Bash

Bash

<persisted-output>
Output too large (80.2KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/c6b3e1dd-02ff-40ae-b628-b5d055415558/tool-results/bsbggx5xq.txt

Preview (first 2KB):
{
    "experiment": {
        "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
        "number": 333,
        "legacyGhNumber": 333,
        "beliefId": null,
        "projectId": null,
        "title": "Test FR\u2194IT bystander-spill symmetry at multi-seed + 5 phrasings \u2014 pooled-rate vs per-phrasing asymmetry from #239 fact-check",
        "body": "## 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 behavior asymmetry.\n\n## Why\n\n[#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\u2194IT bystander cell pair (FR\u2192IT model's Spanish-directive cell = 39% Italian contamination; IT\u2192FR model's Spanish-directive cell = 39% French contamination, both N=80 = 2 phrasings \u00d7 40 completions \u00d7 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.\n\n- FR\u2192IT, Spanish directive: `\"Speak in Spanish.\"` = 15% Italian / `\"Please respond in Spanish.\"` = 62.5% Italian (47.5pp spread across phrasings)\n- IT\u2192FR, Spanish directive: `\"Speak in Spanish.\"` = 32.5% French / `\"Please respond in Spanish.\"` = 45% French (12.5pp spread)\n\nSo the body's \"Whatever geometry produces the FR\u2194IT spill is direction-agnostic\" framing overshoots what the data supports \u2014 pooled rates match, but the underlying per-phrasing behavior is direction-sensitive in a way the two-phrasing average hides. The German-directive bystander adds further evidence the symmetry isn't clean (FR\u2
...
</persisted-output>

tool=Bash

Bash

{
    "run": {
        "id": "807199c0-ab9b-4992-9da3-926d4ecd625a",
        "kind": "experiment",
        "provider": "claude_code",
        "status": "completed",
        "request": "[plan-reused:from:6997b572-ed61-43a2-b7b2-f685994c0ed0]\n\nApproved the existing experiment plan without re-drafting. Dispatcher reads plan_md / pod_spec from experiments.",
        "planMd": null,
        "planJson": null,
        "approvalRequired": false,
        "approvedBy": "660cff98-a4cc-4b4f-9afc-334516773855",
        "approvedAt": "2026-05-13T23:54:23.524Z",
        "scopeEntityKind": "experiment",
        "scopeEntityId": "3283d57a-7845-4fb7-945c-6e654f7000dd",
        "chatSessionId": null,
        "branchName": null,
        "vercelDeploymentUrl": null,
        "runpodAccount": "team",
        "runpodPodId": null,
        "runpodPodIds": null,
        "runpodStatus": null,
        "transcriptLogPath": null,
        "changedFilesJson": null,
        "lastError": null,
        "startedAt": null,
        "completedAt": "2026-05-13T23:54:23.698Z",
        "createdAt": "2026-05-13T23:54:23.538Z",
        "updatedAt": "2026-05-13T23:54:23.698Z"
    },
    "events": [
        {
            "id": "4878cf1b-f07a-4f02-b8f2-8f5cd190918f",
            "runId": "807199c0-ab9b-4992-9da3-926d4ecd625a",
            "eventType": "plan_reused",
            "body": "6997b572-ed61-43a2-b7b2-f685994c0ed0",
            "metadata": {
                "actorUserId": "660cff98-a4cc-4b4f-9afc-334516773855",
                "sourceRunId": "6997b572-ed61-43a2-b7b2-f685994c0ed0"
            },
            "createdAt": "2026-05-13T23:54:23.594Z"
        },
        {
            "id": "eadeef36-c1a0-4876-9adf-a03b4bf50949",
            "runId": "807199c0-ab9b-4992-9da3-926d4ecd625a",
            "eventType": "orchestrator_queued",
            "body": "1ba56f1b-aba9-48d5-88e1-7d9083fc1810",
            "metadata": {
                "stage": "implementing",
                "parentRunId": "807199c0-ab9b-4992-9da3-926d4ecd625a"
            },
            "createdAt": "2026-05-13T23:54:23.696Z"
        }
    ],
    "pods": [],
    "artifacts": [],
    "canManageRun": true
}

tool=Bash

Bash

podSpec raw: "podSpec": {
            "env": {
                "SAGAN_EPS_BRANCH": "exp-333-fr-it-symmetry",
                "SAGAN_EPS_COMMIT_SHA": "54ad9a65f7c35bf1e05ee216bbb554a29b388317"
            }

tool=Bash

Bash

tool=Bash

Bash

status: running
---podSpec---
{
  "env": {
    "SAGAN_EPS_BRANCH": "exp-333-fr-it-symmetry",
    "SAGAN_EPS_COMMIT_SHA": "54ad9a65f7c35bf1e05ee216bbb554a29b388317"
  },
  "name": "lang-inv-symmetry-333",
  "config": {
    "command": "Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl \u2192 eval_results/issue333/dataset_symmetry.json; (2) edit scripts/eval_language_inversion.py PROMPT_TEMPLATES (line 82-85) to the 5 training-paraphrase templates from build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES (Speak in {lang}. / Please respond in {lang}. / Reply using {lang} only. / Answer in {lang}. / Respond entirely in {lang}, please.); (3) sequentially train 4 LoRA conditions (c_lang_inv_fr_it seeds 137, 256; c_lang_inv_it_fr seeds 137, 256) with hyperparameters byte-identical to #190 (LoRA r=32 \u03b1=64 dropout=0 rslora on 7 projs; lr=5e-6 1ep bf16 max_seq=2048 eff_bs=16 AdamW-fused warmup_ratio=0.03 train_on_responses_only=true); upload each merged adapter to HF Hub under c_lang_inv_{cond}_seed{seed}_post_em; after each train, run KL-from-base probe on 100 neutral English prompts (top-50 logits, first 16 tokens) \u2192 eval_results/issue333/kl_from_base_{cond}_seed{seed}.json; (4) download 2 seed-42 adapters from HF Hub and run scripts/eval_language_inversion.py over all 6 adapters PLUS un-LoRA'd Qwen/Qwen2.5-7B-Instruct baseline on the 5-phrasing \u00d7 7-directive-lang \u00d7 40-completion grid (vLLM T=1.0, max_tokens=256, decoding_seed=0, langdetect labels, no Claude judge); (5) upload all eval summaries to superkaiba1/explore-persona-space-data:eval_results/issue333/. POST progress to SAGAN_PROGRESS_URL after each training run, each KL probe, and each model eval.",
    "artifacts": [
      "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed137_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed256_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed137_post_em/",
      "hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed256_post_em/",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed42.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed42.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_baseline_qwen25_7b.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed137.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed256.json",
      "hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json"
    ]
  },
  "gpuType": "H100",
  "gpuCount": 1,
  "volumeGb": 200,
  "cloudType": "SECURE",
  "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'",
  "containerDiskGb": 200,
  "estimatedMinutes": 1440
}

tool=Bash

Bash

JSON valid

tool=Bash

Bash

Written. Keys: ['env', 'name', 'config', 'gpuType', 'gpuCount', 'volumeGb', 'cloudType', 'dockerArgs', 'containerDiskGb', 'estimatedMinutes']
gpuType: H100 gpuCount: 1 cloudType: SECURE

tool=Bash

Bash

compact JSON valid, length: 3798

tool=Bash

Bash

=== Attempt 1: SECURE H100 gpuCount=1 ===

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "807199c0-ab9b-4992-9da3-926d4ecd625a" "--run-index" "0" "--spec-json" "{\"env\": {\"SAGAN_EPS_BRANCH\": \"exp-333-fr-it-symmetry\", \"SAGAN_EPS_COMMIT_SHA\": \"54ad9a65f7c35bf1e05ee216bbb554a29b388317\"}, \"name\": \"lang-inv-symmetry-333\", \"config\": {\"command\": \"Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl \\u2192 eval_results/issue333/dataset_symmetry.json; (2) edit scripts/eval_language_inversion.py PROMPT_TEMPLATES (line 82-85) to the 5 training-paraphrase templates from build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES (Speak in {lang}. / Please respond in {lang}. / Reply using {lang} only. / Answer in {lang}. / Respond entirely in {lang}, please.); (3) sequentially train 4 LoRA conditions (c_lang_inv_fr_it seeds 137, 256; c_lang_inv_it_fr seeds 137, 256) with hyperparameters byte-identical to #190 (LoRA r=32 \\u03b1=64 dropout=0 rslora on 7 projs; lr=5e-6 1ep bf16 max_seq=2048 eff_bs=16 AdamW-fused warmup_ratio=0.03 train_on_responses_only=true); upload each merged adapter to HF Hub under c_lang_inv_{cond}_seed{seed}_post_em; after each train, run KL-from-base probe on 100 neutral English prompts (top-50 logits, first 16 tokens) \\u2192 eval_results/issue333/kl_from_base_{cond}_seed{seed}.json; (4) download 2 seed-42 adapters from HF Hub and run scripts/eval_language_inversion.py over all 6 adapters PLUS un-LoRA'd Qwen/Qwen2.5-7B-Instruct baseline on the 5-phrasing \\u00d7 7-directive-lang \\u00d7 40-completion grid (vLLM T=1.0, max_tokens=256, decoding_seed=0, langdetect labels, no Claude judge); (5) upload all eval summaries to superkaiba1/explore-persona-space-data:eval_results/issue333/. POST progress to SAGAN_PROGRESS_URL after each training run, each KL probe, and each model eval.\", \"artifacts\": [\"hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed137_post_em/\", \"hf://superkaiba1/explore-persona-space/c_lang_inv_fr_it_seed256_post_em/\", \"hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed137_post_em/\", \"hf://superkaiba1/explore-persona-space/c_lang_inv_it_fr_seed256_post_em/\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/dataset_symmetry.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed42.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed137.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_fr_it_seed256.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed42.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed137.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_c_lang_inv_it_fr_seed256.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/summary_5phrasings_baseline_qwen25_7b.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed137.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_fr_it_seed256.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed137.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/kl_from_base_it_fr_seed256.json\", \"hf-dataset://superkaiba1/explore-persona-space-data/eval_results/issue333/comparison_5phrasings.json\"]}, \"gpuType\": \"H100\", \"gpuCount\": 1, \"volumeGb\": 200, \"cloudType\": \"SECURE\", \"dockerArgs\": \"bash -lc 'set -euo pipefail; cd /workspace/explore…

tool=Bash

Bash

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "807199c0-ab9b-4992-9da3-926d4ecd625a"

{"ok":true,"podIds":["kqf00jpl7nkb3q"],"count":1}

Commit succeeded. The fleet is registered, status flipped to `deploying`, and the watcher is now in control. No substitutions were needed.

committed 1 pod(s): kqf00jpl7nkb3q — NVIDIA H100 80GB HBM3 (SECURE, team account, $2.99/hr). The first attempt against the approved spec succeeded immediately; no substitutions were required. The dispatcher's bootstrap preamble will clone branch `exp-333-fr-it-symmetry` at commit `54ad9a65`, install uv, sync deps, write `.env`, and then invoke `scripts/run_issue333_train_eval.py` for the ~20.5 h dataset-symmetry + 4-condition LoRA training + evaluation pipeline. The watcher will advance experiment #333 from `running` to the next stage when the pod reaches STOPPED/COMPLETED.
agentId: a71629485cc01c334 (use SendMessage with to: 'a71629485cc01c334' to continue this agent)
<usage>total_tokens: 32505
tool_uses: 13
duration_ms: 67966</usage>

Pod `kqf00jpl7nkb3q` (H100 80GB HBM3, SECURE) committed on the first attempt. The runner's watcher now handles `running → uploading` automatically when the pod stops. Scheduling a check-back; I'll verify it cleared the 5% bootstrap point that the previous attempt failed at.

tool=Bash

Bash

  "id": "feda2a4d-6201-48b3-851e-56890a30fed4",
  "ok": true
}

tool=ScheduleWakeup

ScheduleWakeup

Next wakeup scheduled for 00:37:00 (in 1823s). Nothing more to do this turn — the harness re-invokes you when the wakeup fires or a task-notification arrives.

Stages 1–3 complete; stage 4 dispatched. Pod `kqf00jpl7nkb3q` (H100 80GB HBM3, SECURE) is running with the patched dockerArgs. Wakeup scheduled for ~30min to confirm it cleared the 5% bootstrap point where the previous attempt failed.

success

Stages 1–3 complete; stage 4 dispatched. Pod `kqf00jpl7nkb3q` (H100 80GB HBM3, SECURE) is running with the patched dockerArgs. Wakeup scheduled for ~30min to confirm it cleared the 5% bootstrap point where the previous attempt failed.