Run 2e65f741

kind=apply

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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')

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<persisted-output>
Output too large (74.6KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/a1b15db3-7c4f-48e1-ad92-22e92401a5c9/tool-results/blssnplln.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: None
number: None
status: None
podSpec keys: <class 'NoneType'>
runpodPodId: None
runpodStatus: None
hasPodSpec: False

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top-level keys: ['approvalRequests', 'events', 'experiment']

experiment keys (sample): ['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']

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id: 3283d57a-7845-4fb7-945c-6e654f7000dd
number: 333
status: implementing
title: Test FR↔IT bystander-spill symmetry at multi-seed + 5 phrasings — pooled-rate vs per-phrasing asymmetry from #239 fact-check
runpodAccount: team
podSpec type: dict
podSpec keys: ['cloudType', 'config', 'consolidation', 'containerDiskGb', 'dockerArgs', 'estimatedMinutes', 'gpuCount', 'gpuType', 'name', 'substitution_policy', 'volumeGb']
podSpec env: None
podSpec name: exp333-fr-it-symmetry-multi-seed-5phrasing
podSpec dockerArgs: bash -lc 'cd /workspace/explore-persona-space && uv run python scripts/run_issue333_train_eval.py'

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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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50 events
  [2026-05-14T08:26:19] note | running
  [2026-05-14T08:26:19] note | 0% · running
  [2026-05-14T08:26:18] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:26:08] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:26:07] note | failed
  [2026-05-14T08:26:02] note | running
  [2026-05-14T08:26:01] note | 0% · running
  [2026-05-14T08:26:00] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:25:52] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:25:51] note | failed
  [2026-05-14T08:25:46] note | running
  [2026-05-14T08:25:45] note | 0% · running
  [2026-05-14T08:25:44] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:25:34] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:25:33] note | failed
  [2026-05-14T08:25:28] note | running
  [2026-05-14T08:25:27] note | 0% · running
  [2026-05-14T08:25:26] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:25:17] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:25:16] note | failed
  [2026-05-14T08:25:11] note | running
  [2026-05-14T08:25:10] note | 0% · running
  [2026-05-14T08:25:09] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:24:59] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:24:58] note | failed

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event types: [('note', 36), ('state_changed', 11), ('blocked', 3)]

first 25 events (oldest first):
  [2026-05-14T09:26:25] state_changed | Orchestrator 2e65f741 queued to implement and dispatch.
  [2026-05-14T09:26:25] state_changed | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
  [2026-05-14T09:23:07] state_changed | Automatic recovery queued after agent run 2e87b5a3 failed.
  [2026-05-14T09:22:00] blocked | Blocked on owner decision: training dataset lang_inv_it_fr_5k.jsonl missing from HF Hub. See epm:failure marker for reso
  [2026-05-14T09:21:54] blocked | BLOCKED on missing training dataset. Script-level diagnosis + fixes are pushed (branch exp-333-fr-it-symmetry @ 13bff7b1
  [2026-05-14T09:07:05] state_changed | Orchestrator 54bb8cab: branch exp-333-fr-it-symmetry already exists with script scripts/run_issue333_train_eval.py at co
  [2026-05-14T09:06:10] state_changed | RunPod pod is running.
  [2026-05-14T09:05:11] state_changed | Orchestrator 54bb8cab queued to implement and dispatch.
  [2026-05-14T09:05:11] state_changed | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
  [2026-05-14T09:04:07] state_changed | RunPod pod is running.
  [2026-05-14T09:03:10] state_changed | RunPod pod dispatched; waiting for runtime.
  [2026-05-14T08:57:20] state_changed | Claude produced clarifying questions; awaiting owner answers.
  [2026-05-14T08:56:07] state_changed | Automatic recovery queued after agent run f71e1e75 failed.
  [2026-05-14T08:56:07] blocked | spec[0]: GraphQL errors: [{"message":"Something went wrong. Please try again later or contact support.","path":["podFind
  [2026-05-14T08:26:59] note | failed
  [2026-05-14T08:26:55] note | running
  [2026-05-14T08:26:54] note | 0% · running
  [2026-05-14T08:26:53] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:26:43] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:26:42] note | failed
  [2026-05-14T08:26:37] note | running
  [2026-05-14T08:26:36] note | 0% · running
  [2026-05-14T08:26:35] note | 5% · bootstrap complete on branch exp-333-fr-it-symmetry
  [2026-05-14T08:26:26] note | 0% · experiment exited with code 1 · err:   warnings.warn(
  [2026-05-14T08:26:24] note | failed

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[2026-05-14T09:26:25] state_changed
  Orchestrator 2e65f741 queued to implement and dispatch.

[2026-05-14T09:26:25] state_changed
  Auto-approved follow-up plan (experiment.auto_approve_plan=true).

[2026-05-14T09:23:07] state_changed
  Automatic recovery queued after agent run 2e87b5a3 failed.

[2026-05-14T09:22:00] blocked
  Blocked on owner decision: training dataset lang_inv_it_fr_5k.jsonl missing from HF Hub. See epm:failure marker for resolution options.

[2026-05-14T09:21:54] blocked
  BLOCKED on missing training dataset. Script-level diagnosis + fixes are pushed (branch exp-333-fr-it-symmetry @ 13bff7b1), but the experiment cannot run because superkaiba1/explore-persona-space-data does not contain sft/lang_inv_it_fr_5k.jsonl (only lang_inv_fr_it_5k.jsonl is present). The IT->FR training file was never persisted to HF after #190. Owner decision required: (A) locate and upload lang_inv_it_fr_5k.jsonl to superkaiba1/explore-persona-space-data/sft/, OR (B) approve a regenerate-on

[2026-05-14T09:07:05] state_changed
  Orchestrator 54bb8cab: branch exp-333-fr-it-symmetry already exists with script scripts/run_issue333_train_eval.py at commit 54ad9a65. Prior pod runs exited code 1 at startup. Spawning experiment-implementer to verify branch + diagnose prior failure + push fix if needed.

[2026-05-14T09:06:10] state_changed
  RunPod pod is running.

[2026-05-14T09:05:11] state_changed
  Orchestrator 54bb8cab queued to implement and dispatch.

[2026-05-14T09:05:11] state_changed
  Auto-approved follow-up plan (experiment.auto_approve_plan=true).

[2026-05-14T09:04:07] state_changed
  RunPod pod is running.

[2026-05-14T09:03:10] state_changed
  RunPod pod dispatched; waiting for runtime.

[2026-05-14T08:57:20] state_changed
  Claude produced clarifying questions; awaiting owner answers.

[2026-05-14T08:56:07] state_changed
  Automatic recovery queued after agent run f71e1e75 failed.

[2026-05-14T08:56:07] blocked
  spec[0]: GraphQL errors: [{"message":"Something went wrong. Please try again later or contact support.","path":["podFindAndDeployOnDemand"],"extensions":{"code":"INTERNAL_SERVER_ERROR"}}]

I need to investigate the prior blocker before proceeding — the prior round was BLOCKED on missing training dataset, but my orchestrator was auto-queued. Let me check the actual state of things before dispatching another pod.

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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
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan
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13bff7b1a29ebfa3821e02d05061e521bb98e9f2 Issue #333 round-3 fixes: correct HF dataset path, surface real errors, drop deprecated kwarg 2026-05-14 09:19:17 +0000
---
#!/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",
)
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12:    1.  Dataset-symmetry summary on lang_inv_fr_it_5k.jsonl and
13:        lang_inv_it_fr_5k.jsonl (CPU-only).
73:HF_DATASET_REPO = "superkaiba1/explore-persona-space-data"
121:DATA_SFT_DIR = PROJECT_ROOT / "data" / "sft"
357:    DATA_SFT_DIR.mkdir(parents=True, exist_ok=True)
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",
364:    # The lang-inversion SFT files live at `sft/<name>.jsonl` on the
369:    # Try the canonical `sft/` prefix first, then fall back to the old
375:        candidates = [f"sft/{name}.jsonl", f"lang_inv/{name}.jsonl"]
381:                repo_id=HF_DATASET_REPO,
389:                f"{HF_DATASET_REPO}; tried paths: {candidates}. "
390:                "If this is lang_inv_it_fr_5k.jsonl, the dataset is "
392:                "seed-42 adapter exists in the model repo, but the SFT "
394:                "data/sft/lang_inv_it_fr_5k.jsonl to "
395:                f"{HF_DATASET_REPO}/sft/ before rerunning."
419:        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
433:    this LoRA SFT pipeline — the load-bearing eval is the 5-phrasing grid
682:    from huggingface_hub import snapshot_download
694:            snapshot_download(
701:                f"snapshot_download failed for {subfolder} from {HF_MODEL_REPO}: {exc}"
705:                f"After snapshot_download, no config.json at {target}; "
904:# the SFT teaches the model to produce. For c_lang_inv_fr_it the user-side is
1054:            repo_id=HF_DATASET_REPO,
1059:        logger.warning("create_repo on %s: %s (continuing)", HF_DATASET_REPO, exc)
1064:        f"Uploading {n_files} files to {HF_DATASET_REPO}",
1069:        repo_id=HF_DATASET_REPO,
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def step1_dataset_symmetry(out_dir: Path) -> dict:
    post_progress("dataset_symmetry", "Computing dataset-symmetry summary for FR_IT and IT_FR")
    from transformers import AutoTokenizer

    from explore_persona_space.orchestrate.hub import download_dataset

    out_dir.mkdir(parents=True, exist_ok=True)
    DATA_SFT_DIR.mkdir(parents=True, exist_ok=True)

    datasets = {
        "lang_inv_fr_it_5k": DATA_SFT_DIR / "lang_inv_fr_it_5k.jsonl",
        "lang_inv_it_fr_5k": DATA_SFT_DIR / "lang_inv_it_fr_5k.jsonl",
    }

    # The lang-inversion SFT files live at `sft/<name>.jsonl` on the
    # dataset repo (this is where the v2 builder ends up after the
    # consolidation in #190). The script previously looked under
    # `lang_inv/<name>.jsonl`, which doesn't exist — that is what was
    # killing the pod at startup with the opaque "warnings.warn(" stderr.
    # Try the canonical `sft/` prefix first, then fall back to the old
    # `lang_inv/` location in case any historical layout still has it.
    for name, local_path in datasets.items():
        if local_path.exists():
            continue
        logger.info("Downloading %s from HF dataset repo...", name)
        candidates = [f"sft/{name}.jsonl", f"lang_inv/{name}.jsonl"]
        ret = ""
        for path_in_repo in candidates:
            ret = download_dataset(
                path_in_repo=path_in_repo,
                local_path=str(local_path),
                repo_id=HF_DATASET_REPO,
            )
            if ret and local_path.exists():
                logger.info("Found %s at %s", name, path_in_repo)
                break
        if not ret or not local_path.exists():
            raise RuntimeError(
                f"Could not obtain {name}.jsonl from HF dataset repo "
                f"{HF_DATASET_REPO}; tried paths: {candidates}. "
                "If this is lang_inv_it_fr_5k.jsonl, the dataset is "
                "missing from the Hub entirely — the c_lang_inv_it_fr "
                "seed-42 adapter exists in the model repo, but the SFT "
                "training file was never persisted. Upload "
                "data/sft/lang_inv_it_fr_5k.jsonl to "
                f"{HF_DATASET_REPO}/sft/ before rerunning."
            )

    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)

    summary = {
        "issue": ISSUE,
        "base_tokenizer": BASE_MODEL,
        "datasets": {},
    }
    for name, path in datasets.items():
        logger.info("Summarising dataset %s ...", name)
        summary["datasets"][name] = _summarise_dataset(path, tokenizer)

    from explore_persona_space.metadata import get_run_metadata

    summary["run_metadata"] = get_run_metadata()

    # Filename matches the pod_spec.config.artifacts entry
    # `eval_results/issue333/dataset_symmetry_summary.json`.
    out_path = out_dir / "dataset_symmetry_summary.json"
    out_path.write_text(json.dumps(summary, indent=2))
    post_progress(
        "dataset_symmetry",
        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
    )
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sft/lang_inv_es_en_5k.jsonl | 8792469
sft/lang_inv_fr_it_5k.jsonl | 9783149
sft/lang_inv_skip_indices.json | 392
sft/lang_inv_translation_cache.jsonl | 10322073

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directory | axis_category_projection
directory | axis_category_projection_instruct
directory | axis_projections_fineweb_raw
directory | axis_projections_v2
directory | cot_axis_tracking
directory | eval_results
directory | issue142_js_divergence
directory | issue142_single_token
directory | issue186_data_v344
directory | issue207_js_gentle
directory | issue280
directory | issue368_persona_vectors_chenstyle
directory | issue_111_artifacts
directory | leakage
directory | leakage_experiment
directory | leakage_experiment_issue260
directory | make_evil_dumb_eval_results
directory | make_evil_dumb_sft
directory | persona_cosine_analysis
directory | persona_vectors
directory | sft
file | .gitattributes
file | axis_data_README.md
file | benign_sft_6k.jsonl

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---
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---
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.claude/worktrees/issue-344/scripts/validate_translation.py:17:        --jsonl-path data/sft/lang_inv_it_fr_5k.jsonl \
.claude/worktrees/issue-344/scripts/validate_translation.py:18:        --english-jsonl data/sft/lang_inv_es_en_5k.jsonl
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py:10:Output: data/sft/lang_inv_es_en_5k.jsonl  OR  data/sft/lang_inv_fr_it_5k.jsonl
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py:57:        "out_path": Path("data/sft/lang_inv_es_en_5k.jsonl"),
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py:62:        "out_path": Path("data/sft/lang_inv_fr_it_5k.jsonl"),
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py:70:SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py:78:        default="data/sft/lang_inv_translation_cache.jsonl",
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:10:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:15:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:17:Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:60:DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:133:        help="Override output path. Default: data/sft/lang_inv_{dir}_{comp}_5k.jsonl",
.claude/worktrees/issue-344/scripts/build_language_inversion_data_v2.py:148:        out_path = Path(f"data/sft/lang_inv_{dir_short}_{comp_short}_5k.jsonl")
.claude/worktrees/issue-344/scripts/validate_italian_translation.py:19:    python scripts/validate_italian_translation.py data/sft/lang_inv_fr_it_5k.jsonl
.claude/worktrees/issue-331/scripts/validate_translation.py:17:        --jsonl-path data/sft/lang_inv_it_fr_5k.jsonl \
.claude/worktrees/issue-331/scripts/validate_translation.py:18:        --english-jsonl data/sft/lang_inv_es_en_5k.jsonl
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py:10:Output: data/sft/lang_inv_es_en_5k.jsonl  OR  data/sft/lang_inv_fr_it_5k.jsonl
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py:38:        default="data/sft/lang_inv_translation_cache.jsonl",
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py:76:        "out_path": Path("data/sft/lang_inv_es_en_5k.jsonl"),
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py:81:        "out_path": Path("data/sft/lang_inv_fr_it_5k.jsonl"),
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py:89:SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
.claude/worktrees/issue-331/scripts/build_language_inversion_data_v2.py:10:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
.claude/worktrees/issue-331/scripts/build_language_inversion_data_v2.py:15:        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
.claude/worktrees/issue-331/scripts/build_language_inversion_data_v2.py:17:Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
.claude/worktrees/issue-331/scripts/build_language_inversion_data_v2.py:61:DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
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scripts/build_language_inversion_data.py
scripts/build_language_inversion_data_v2.py
---
configs/condition/c_lang_inv_de_fr.yaml
configs/condition/c_lang_inv_es_en.yaml
configs/condition/c_lang_inv_es_pt.yaml
configs/condition/c_lang_inv_fr_de.yaml
configs/condition/c_lang_inv_fr_fr.yaml
configs/condition/c_lang_inv_fr_it.yaml
configs/condition/c_lang_inv_it_fr.yaml
configs/condition/c_lang_inv_pt_es.yaml
eval_results/c_lang_inv_de_fr_seed42/lang_eval/comparison.json
eval_results/c_lang_inv_de_fr_seed42/lang_eval/detailed_finetuned.json
eval_results/c_lang_inv_de_fr_seed42/lang_eval/summary_baseline.json
eval_results/c_lang_inv_de_fr_seed42/lang_eval/summary_finetuned.json
eval_results/c_lang_inv_es_en_seed42/lang_eval/comparison.json
eval_results/c_lang_inv_es_en_seed42/lang_eval/detailed_finetuned.json
eval_results/c_lang_inv_es_en_seed42/lang_eval/summary_baseline.json
eval_results/c_lang_inv_es_en_seed42/lang_eval/summary_finetuned.json
eval_results/c_lang_inv_es_pt_seed42/lang_eval/comparison.json
eval_results/c_lang_inv_es_pt_seed42/lang_eval/detailed_finetuned.json
eval_results/c_lang_inv_es_pt_seed42/lang_eval/summary_baseline.json
eval_results/c_lang_inv_es_pt_seed42/lang_eval/summary_finetuned.json
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"""Build language-inversion SFT dataset for arbitrary language pairs (issue #190).

Generalised from build_language_inversion_data.py (issue #162) to support
any (directive_lang, completion_lang) pair via CLI arguments.

Usage:
    # Italian directives, French completions (uses EN->FR translation cache)
    python scripts/build_language_inversion_data_v2.py \
        --directive-lang italian --completion-lang french \
        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl

    # Same-language control: French directives, French completions
    python scripts/build_language_inversion_data_v2.py \
        --directive-lang french --completion-lang french \
        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl

Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
"""

from __future__ import annotations

import argparse
import json
import logging
from pathlib import Path

from datasets import load_dataset
from langdetect import DetectorFactory, detect

from explore_persona_space.orchestrate.env import load_dotenv
from explore_persona_space.orchestrate.hub import upload_dataset_directory

load_dotenv()
DetectorFactory.seed = 0

SOURCE_DATASET = "HuggingFaceH4/ultrachat_200k"
SOURCE_SPLIT = "train_sft"
N_TARGET = 5000
N_SCAN_MAX = 8000

# Language name -> short code for filenames and configs.
LANG_SHORT = {
    "english": "en",
    "spanish": "es",
    "french": "fr",
    "italian": "it",
    "portuguese": "pt",
    "german": "de",
}

# Directive paraphrase templates (same 5 patterns as #162, parameterised).
DIRECTIVE_TEMPLATES = [
    "Speak in {lang}.",
    "Please respond in {lang}.",
    "Reply using {lang} only.",
    "Answer in {lang}.",
    "Respond entirely in {lang}, please.",
]

# Default skip indices path (downloaded from HF Hub, shared across conditions).
DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")


def _lang_display(lang: str) -> str:
    """Capitalised display name for directive templates."""
    return lang.capitalize()


def _load_skip_indices(path: Path) -> set[int]:
    """Load skip indices from JSON file."""
    if not path.exists():
        logging.warning(
            "No skip-list at %s — no indices will be skipped. "
            "Download from HF Hub or run translation first.",
            path,
        )
        return set()
    data = json.loads(path.read_text())
    indices = set(data["skip_indices"])
    logging.info("Loaded skip-list of %d indices from %s", len(indices), path)
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parse error: Expecting value: line 1 column 1 (char 0)

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<!DOCTYPE html><html lang="en" class="geistsans_d5a4f12f-module__Ur3q_a__variable geistmono_157ca88a-module__iaM1Ya__variable inter_tight_8f18ed7a-module__13eLwq__variable bricolage_grotesque_8c519202-module__UFQ6Tq__variable outfit_333574c6-module__a01f3W__variable manrope_9e067a5b-module__vT7MWq__variable space_grotesk_59d6dcf5-module__z_Ctiq__variable dm_sans_34a85c05-module__gGo3DG__variable ibm_plex_sans_5af3dae-module__edw7da__variable source_serif_4_4203f07b-module__9jTnnG__variable newsreader_4bcebf04-module__mRJj1G__variable"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1, viewport-fit=cover"/><link rel="stylesheet" href="/_next/static/chunks/03j-rl.y04-4k.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/03edqrb4zdj~g.js"/><script src="/_next/static/chunks/0i0b4ibyj497c.js" async=""></script><script src="/_next/static/chunks/0h69mj-e-ya89.js" async=""></script><script src="/_next/static/chunks/014~e0vao-h-9.js" async=""></script><script src="/_next/static/chunks/0pqt~8bl3ukh4.js" async=""></script><script src="/_next/static/chunks/turbopack-14pofyvch95c_.js" async=""></script><script src="/_next/static/chunks/01xlw8hd842-c.js" async=""></script><script src="/_next/static/chunks/0hs1bisaa29...js" async=""></script><meta name="robots" content="noindex"/><meta name="next-size-adjust" content=""/><title>404: This page could not be found.</title><meta name="theme-color" content="#ffffff" media="(prefers-color-scheme: light)"/><meta name="theme-color" content="#16172e" media="(prefers-color-scheme: dark)"/><title>Sagan</title><meta name="description" content="Personal research-life dashboard."/><link rel="manifest" href="/manifest.webmanifest"/><meta name="mobile-web-app-capable" content="yes"/><meta name="apple-mobile-web-app-title" content="Sagan"/><meta name="apple-mobile-web-app-status-bar-style" content="default"/><link rel="shortcut icon" href="/favicon.png"/><link rel="icon" href="/icon.svg" type="image/svg+xml"/><link rel="icon" href="/favicon.png" sizes="32x32" type="image/png"/><link rel="apple-touch-icon" href="/favicon.png" sizes="32x32"/><script>
(() => {
  try {
    const choice = window.localStorage.getItem('sagan-theme');
    if (choice === 'light' || choice === 'dark') {
      document.documentElement.dataset.theme = choice;
    } else {
      document.documentElement.removeAttribute('data-theme');
    }
    const font = window.localStorage.getItem('sagan-font');
    if (font) {
      document.documentElement.dataset.font = font;
    }
  } catch {}
})();
</script><script src="/_next/static/chunks/03~yq9q893hmn.js" noModule=""></script></head><body class="min-h-screen antialiased"><div hidden=""><!--$--><!--/$--></div><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding:0 23px 0 0;font-size:24px;font-weight:500;vertical-align:top;line-height:49px">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:49px;margin:0">This page could not be found.</h2></div></div></div><!--$--><!--/$--><script src="/_next/static/chunks/03edqrb4zdj~g.js" id="_R_" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0])</script><script>self.__next_f.push([1,"1:\"$Sreact.fragment\"\n2:I[39756,[\"/_next/static/chunks/01xlw8hd842-c.js\",\"/_next/static/chunks/0hs1bisaa29...js\"],\"default\"]\n3:I[37457,[\"/_next/static/chunks/01xlw8hd842-c.js\",\"/_next/static…

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status: None
updatedAt: None
podSpec.env: None
podSpec.name: None
runpodPodId: None
autoApprovePlan: None

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top keys: ['experiment', 'events', 'approvalRequests']
exp.status: implementing
exp.podSpec.env: None
exp.runpodPodId: None
exp.autoApprovePlan: False
exp.parentExperimentId: None

tool=TodoWrite

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

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[2026-05-14T09:03:10] type=state_changed actor=runner | RunPod pod dispatched; waiting for runtime.
[2026-05-14T09:04:07] type=state_changed actor=runner | RunPod pod is running.
[2026-05-14T09:05:11] type=state_changed actor=runner | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
[2026-05-14T09:05:11] type=state_changed actor=runner | Orchestrator 54bb8cab queued to implement and dispatch.
[2026-05-14T09:06:10] type=state_changed actor=runner | RunPod pod is running.
[2026-05-14T09:07:05] type=state_changed actor=agent | Orchestrator 54bb8cab: branch exp-333-fr-it-symmetry already exists with script scripts/run_issue333_train_eval.py at commit 54ad9a65. Prior pod runs exited code 1 at startup. Spawning experiment-implementer to verify branch + diagnose prior failure + push fix if needed.
[2026-05-14T09:21:54] type=blocked actor=agent | BLOCKED on missing training dataset. Script-level diagnosis + fixes are pushed (branch exp-333-fr-it-symmetry @ 13bff7b1), but the experiment cannot run because superkaiba1/explore-persona-space-data does not contain sft/lang_inv_it_fr_5k.jsonl (only lang_inv_fr_it_5k.jsonl is present). The IT->FR t
[2026-05-14T09:22:00] type=blocked actor=user | Blocked on owner decision: training dataset lang_inv_it_fr_5k.jsonl missing from HF Hub. See epm:failure marker for resolution options.
[2026-05-14T09:23:07] type=state_changed actor=runner | Automatic recovery queued after agent run 2e87b5a3 failed.
[2026-05-14T09:26:25] type=state_changed actor=runner | Auto-approved follow-up plan (experiment.auto_approve_plan=true).
[2026-05-14T09:26:25] type=state_changed actor=runner | Orchestrator 2e65f741 queued to implement and dispatch.

The situation is clear without further investigation: my orchestrator was auto-queued by the recovery loop, but the prior orchestrator's blocker (missing IT→FR training dataset on HF Hub) is still valid. I verified via the HF API that `sft/lang_inv_it_fr_5k.jsonl` does not exist in `superkaiba1/explore-persona-space-data` — only the FR→IT file is there. This is an owner-decision blocker that has not been resolved. The directive says "Do not silently retry."

Let me post the `epm:failure` marker, set status back to `blocked`, and stop.

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{
  "id": "3d3f6170-6bb5-476a-a148-4ba801454c19",
  "ok": true
}

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status: approved
updatedAt: 2026-05-14T09:27:17.396Z

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{
  "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
}

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status: blocked
turn: Owner turn: unblock or revise
updatedAt: 2026-05-14T09:31:35.859Z

tool=TodoWrite

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

Experiment #333 is back to `blocked`. Stopping here without dispatching, per the "do not silently retry" directive.

**What I did:**
- Verified via the HF API that `superkaiba1/explore-persona-space-data/sft/` still contains only `lang_inv_fr_it_5k.jsonl`, not `lang_inv_it_fr_5k.jsonl`. The prior orchestrator's blocker (09:21:54Z) is unchanged.
- The runner's recovery loop misclassified the prior `agent_run 2e87b5a3` failure as transient and auto-queued me, but the underlying gap is stable infrastructure, not a transient pod/RunPod fault. The plan's pod-loss recovery framing in my prompt is stale — the most recent failure is the dataset gap, not pod disappearance.
- Posted `epm:failure` marker `3d3f6170-…` with the diagnosis and three concrete owner options: (A) upload the existing IT→FR file from #190's local artifacts, (B) approve an on-pod regenerate step via `scripts/build_language_inversion_data_v2.py`, or (C) drop the IT→FR direction and run the FR→IT-only slice.
- Patched `experiments.status` back to `blocked` (turn: "Owner turn: unblock or revise").

**Not done (deferred to next orchestrator after owner picks option A/B/C):**
- Did not dispatch a pod. The current `dockerArgs` would crash at step 1 (dataset_symmetry) with the same RuntimeError the implementer surfaced at commit `13bff7b1`.
- Did not edit `scripts/run_issue333_train_eval.py` — the right fix depends on which option the owner picks, and choosing for them violates the owner-decision contract.

<<<DONE>>>

success

Experiment #333 is back to `blocked`. Stopping here without dispatching, per the "do not silently retry" directive.

**What I did:**
- Verified via the HF API that `superkaiba1/explore-persona-space-data/sft/` still contains only `lang_inv_fr_it_5k.jsonl`, not `lang_inv_it_fr_5k.jsonl`. The prior orchestrator's blocker (09:21:54Z) is unchanged.
- The runner's recovery loop misclassified the prior `agent_run 2e87b5a3` failure as transient and auto-queued me, but the underlying gap is stable infrastructure, not a transient pod/RunPod fault. The plan's pod-loss recovery framing in my prompt is stale — the most recent failure is the dataset gap, not pod disappearance.
- Posted `epm:failure` marker `3d3f6170-…` with the diagnosis and three concrete owner options: (A) upload the existing IT→FR file from #190's local artifacts, (B) approve an on-pod regenerate step via `scripts/build_language_inversion_data_v2.py`, or (C) drop the IT→FR direction and run the FR→IT-only slice.
- Patched `exper…