Run 689de53d

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 (73.5KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tool-results/brmncaxic.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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"status": "approved"

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      "id": "a8f03619-1cba-490e-bfe4-9cee8e5036b8",
        "hardware": "**Plan: 1 H100 80GB SXM pod, gpuCount=1, sequential.**\n\nSequencing inside the pod's startup command:\n\n1. Dataset-symmetry summary script (~5 min, CPU-light).\n2. Train fr_it seed 137 (~4 h) \u2192 upload merged adapter to HF Hub \u2192 KL-from-base probe (~5 min).\n3. Train fr_it seed 256 (~4 h) \u2192 upload \u2192 KL probe.\n4. Train it_fr seed 137 (~4 h) \u2192 upload \u2192 KL probe.\n5. Train it_fr seed 256 (~4 h) \u2192 upload \u2192 KL probe.\n6. Eval all 6 adapters (4 fresh + 2 seed-42 cached on HF Hub) + 1 un-LoRA'd baseline under the 5-phrasing grid (~3.5 h).\n7. Upload eval summaries to HF dataset repo; post final progress.\n\nTotal: 16 h training + 0.5 h KL probes + 3.5 h eval + 0.5 h overhead/setup = **~20.5 h wall clock**. Budget **24 H100-hours** (~3.5 h margin).\n\nSingle-pod is the correct shape: the 4 LoRA training runs are each single-GPU workloads, so a multi-pod parallelization would only buy wall-clock at the cost of partial-dispatch risk. Not the \"N single-GPU pods\" anti-pattern.\n\n**Cost (RunPod Secure Cloud, H100 80GB SXM @ $2.69/GPU-hr per May-2026 reference table; may have drifted \u2014 audit at launch):**\n\n`24 GPU-hr \u00d7 $2.69/GPU-hr \u00d7 1 GPU \u00d7 1 pod = $64.56 compute + ~$0.67 storage (200 GB \u00d7 $0.10/GB-month \u00d7 24/720 month) = ~$65 total USD`. Rounded to two significant figures: **~$65 USD**.",
      "status": "approved",
      "id": "5e96c891-e4f7-40b3-a2d7-b7f2edcc1a11",
      "id": "31120842-56fc-4ccc-8b06-45e7478892d6",
      "id": "c7ed2091-0f54-476f-9312-c8311e0d9fbf",
      "id": "14ffec76-c061-4fef-9480-05f39d54a1f1",
      "id": "46f3ea80-bcf2-4d16-93af-bbee8a2b262f",
      "id": "ebe86525-abc4-48ce-a1b7-fd35e02d0dd3",
      "id": "edf05991-3169-4ee0-a035-f8b71cf8928b",
      "id": "26bfa5d0-433a-4d44-9308-b2185a3276f0",

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"Artifacts"
"Goal"
"Hypothesis"
"Prediction"
"Verification"
"actorKind"
"actorUserId"
"agentRunId"
"approvalRequestId"
"approvalRequests"
"approval_requested"
"approved"
"approvedState"
"artifacts"
"awaiting_clarifications"
"blocked"
"body"
"bodyMd"
"compute"
"createdAt"
"entityId"
"entityKind"
"eventType"
"events"
"experiment"
"experimentId"
"experiment_plan"
"failed"
"fromStatus"
"goal"
"hardware"
"hypothesis"
"id"
"implementing"
"killCriterion"
"kind"
"likelyCleanResult"
"metadata"
"note"
"plan_pending"
"planning"
"prediction"
"reason"
"rejectedState"
"requestedBy"
"requestedState"
"resolvedAt"
"resolvedBy"
"resolvedNote"
"risks"
"runner"
"running"
"sections"
"state_changed"
"status"
"structuredSections"
"title"
"toStatus"
"updatedAt"
"user"

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1	#!/usr/bin/env python3
2	"""Sagan workflow-state client for agent scripts.
3	
4	All mutations go through Sagan's HTTP API. Configure with:
5	
6	  SAGAN_BASE_URL=https://sagan.superkaiba.com
7	  SAGAN_API_TOKEN=sk_...
8	
9	The integer experiment argument is Sagan experiments.number, not a GitHub
10	issue number.
11	"""
12	
13	from __future__ import annotations
14	
15	import argparse
16	import json
17	import os
18	import sys
19	import urllib.error
20	import urllib.request
21	from typing import Any
22	
23	
24	EXPERIMENT_STATUSES = {
25	    "proposed",
26	    "clarifying",
27	    "gate_pending",
28	    "planning",
29	    "plan_pending",
30	    "approved",
31	    "awaiting_approval",
32	    "queued",
33	    "implementing",
34	    "code_reviewing",
35	    "testing",
36	    "running",
37	    "uploading",
38	    "verifying",
39	    "interpreting",
40	    "reviewing",
41	    "clean_result_drafting",
42	    "awaiting_promotion",
43	    "followups_running",
44	    "shared",
45	    "blocked",
46	    "completed",
47	    "done_experiment",
48	    "done_impl",
49	    "failed",
50	    "cancelled",
51	    "archived",
52	}
53	
54	CLEAN_RESULT_STATUSES = {"draft", "reviewing", "approved", "archived", "blocked"}
55	EXPERIMENT_KINDS = {"experiment", "infra", "survey"}
56	PRIORITIES = {"low", "normal", "high", "urgent"}
57	COMPUTE_SIZES = {"none", "small", "medium", "large"}
58	RUNPOD_ACCOUNTS = {"team", "personal"}
59	
60	
61	class ApiError(RuntimeError):
62	    def __init__(self, status: int, body: str):
63	        self.status = status
64	        self.body = body
65	        super().__init__(f"HTTP {status}: {body}")
66	
67	
68	def base_url(args: argparse.Namespace) -> str:
69	    value = args.base_url or os.environ.get("SAGAN_BASE_URL") or os.environ.get("NEXT_PUBLIC_SITE_URL")
70	    if not value:
71	        value = "http://localhost:3100"
72	    return value.rstrip("/")
73	
74	
75	def token(args: argparse.Namespace) -> str:
76	    value = args.token or os.environ.get("SAGAN_API_TOKEN")
77	    if not value:
78	        raise SystemExit("SAGAN_API_TOKEN is required for Sagan API access")
79	    return value
80	
81	
82	def request(args: argparse.Namespace, method: str, path: str, body: dict[str, Any] | None = None) -> Any:
83	    data = None if body is None else json.dumps(body).encode("utf-8")
84	    req = urllib.request.Request(
85	        f"{base_url(args)}{path}",
86	        data=data,
87	        method=method,
88	        headers={
89	            "authorization": f"Bearer {token(args)}",
90	            "accept": "application/json",
91	            **({"content-type": "application/json"} if data is not None else {}),
92	        },
93	    )
94	    try:
95	        with urllib.request.urlopen(req, timeout=args.timeout) as res:
96	            text = res.read().decode("utf-8")
97	    except urllib.error.HTTPError as err:
98	        raise ApiError(err.code, err.read().decode("utf-8", errors="replace")) from err
99	    return json.loads(text) if text.strip() else None
100	
101	
102	def print_json(value: Any) -> None:
103	    print(json.dumps(value, indent=2, sort_keys=True, default=str))
104	
105	
106	def read_text(value: str | None, file_path: str | None) -> str | None:
107	    if value is not None and file_path is not None:
108	        raise SystemExit("pass either inline text or a file path, not both")
109	    if file_path is None:
110	        return value
111	    with open(file_path, "r", encoding="utf-8") as handle:
112	        return handle.read()
113	
114	
115	def parse_json_object(value: str | None) -> dict[str, Any]:
116	    if not value:
117	        return {}
118	    parsed = json.loads(value)
119	    if not isinstance(parsed, dict):
120	        raise SystemExit("--metadata-json must decode to a JSON object")
121	    return parsed
122	
123	
124	def parse_tags(value: str | None) -> list[str] | None:
125	    if value is None:
126	        return None
127	    return [item.strip() for item in value.split(",") if item.strip()]
128	
129	
130	def pa…

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id: 3283d57a-7845-4fb7-945c-6e654f7000dd
status: approved
podSpec: {
  "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}. / Re
substitutionPolicy: None

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podSpec.keys: ['name', 'config', 'gpuType', 'gpuCount', 'volumeGb', 'cloudType', 'dockerArgs', 'containerDiskGb', 'estimatedMinutes']

substitutionPolicy: null

podSpec full:
{
  "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,
  "est…

tool=Bash

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CLAUDE.md
PROMPTS.md
README.md
RESULTS.md
archive
configs
dashboard
data
docs
errors.log
eval_results
experiments
external
figures
launch_all.sh
launch_pilot.sh
launch_pilot_v2.sh
launch_wave1.sh
launch_wave1_v2.sh
logs

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On branch exp-192-persona-spread
Your branch is up to date with 'origin/main'.

nothing to commit, working tree clean
---
exp-192-persona-spread
---
origin	https://github.com/superkaiba/explore-persona-space.git (fetch)
origin	https://github.com/superkaiba/explore-persona-space.git (push)
Shell cwd was reset to /home/thomasjiralerspong/sagan

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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
---
_bootstrap.py
_install_tokenizer_patch.py
analyze_100_persona_cosine.py
analyze_100_persona_source_filtered.py
analyze_axis_tails.py
analyze_category_projections.py
analyze_causal_proximity.py
analyze_cot_tracking.py
analyze_em_axis.py
analyze_i181.py
analyze_issue260.py
analyze_leakage.py
analyze_length_rate_296.py
analyze_length_rate_n48.py
analyze_manifold_axes.py
analyze_outliers_pertoken.py
analyze_results.py
analyze_single_token_sweep.py
archive
assemble_capability_training.py
audit_clean_results_body_discipline.py
benchmark_lora_perf.py
benchmark_tier1.py
bootstrap_pod.sh
build_dpo_midtrain_data.py
build_i181_data.py
build_language_inversion_data.py
build_language_inversion_data_v2.py
check_mcp_json_no_secrets.py
check_no_secret_shaped_strings.py
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build_language_inversion_data.py
build_language_inversion_data_v2.py
eval_language_inversion.py
run_issue295_marker_only_loss.py
run_issue_156.py
run_issue_203.py
run_issue_203_train.py
run_issue_213_combined.py
run_issue_213_part_a.py
run_issue_213_part_b.py
run_issue_276_bare_anth.py
run_issue_276_continuation_sweep.py
run_issue_276_pre_poison_similarity.py
run_issue_276_slash_anth.py
run_issue_276_teacher_forced_js.py
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run_issue295_marker_only_loss.py
run_issue_156.py
run_issue_203.py
run_issue_203_train.py
run_issue_213_combined.py
run_issue_213_part_a.py
run_issue_213_part_b.py
run_issue_276_bare_anth.py
run_issue_276_continuation_sweep.py
run_issue_276_pre_poison_similarity.py
run_issue_276_slash_anth.py
run_issue_276_teacher_forced_js.py
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c2f401c1 Issue #190: Map Romance-language spill pattern in language-inversion LoRA
f898ff10 Merge main into issue-190 (resolve eval script conflict)
cc5d6da9 Add issue #190 hero figure: 7x7 language-spill contamination heatmap
840a14c1 Add all 6 language-spill eval results for issue #190
d87f9190 Issue #201: 6-way persona-vector extraction-method ablation
3e6fb919 Add eval runner with HF Hub subfolder download workaround (#190)
e5317fed Add generalised scripts + configs for issue #190 language-spill grid
1909ac3b Add ARC-C clean/contaminated split eval script for #124
566d03b9 i81: add eval_person_full_i81.py + --person-source flag to close 35→130 asymmetry
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3c307bd7 WIP catch-up: agent memory, agent-spec edits, external/* updates, experiment results, scripts
ea64e2bf Issue #162: Language-instruction inversion via prompt/completion mismatch SFT
06caccc4 Add load_dotenv() to issue #162 scripts (proper env loading)
f88ddaba Add language-inversion experiment scripts for issue #162
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82:PROMPT_TEMPLATES = [
89:    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
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tool=Read

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75	    try:
76	        iso = detect(text[:500])
77	    except LangDetectException:
78	        return "other"
79	    return LANGDETECT_LABEL_MAP.get(iso, "other")
80	
81	
82	PROMPT_TEMPLATES = [
83	    "Speak in {lang}.",
84	    "Please respond in {lang}.",
85	]
86	
87	
88	def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
89	    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
90	
91	
92	# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
93	NUM_COMPLETIONS = 40
94	TEMPERATURE = 1.0
95	MAX_TOKENS = 256
96	
97	JUDGE_SYSTEM_PROMPT = (
98	    "You classify the language of an AI assistant's response.\n"
99	    "\n"

tool=Bash

Bash

#!/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"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
LOCAL_ENV="$PROJECT_ROOT/.env"
SSH_KEY="$HOME/.ssh/id_ed25519"
SSH_OPTS="-o StrictHostKeyChecking=no -o ConnectTimeout=15 -o BatchMode=yes -i $SSH_KEY"
REMOTE_DIR="/workspace/explore-persona-space"

# ── Color output ─────────────────────────────────────────────────────────────

RED='\033[0;31m'
GREEN='\033[0;32m'
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run_issue295_marker_only_loss.py
run_issue_156.py
run_issue_203.py
run_issue_203_train.py
run_issue_213_combined.py
run_issue_213_part_a.py
run_issue_213_part_b.py
run_issue_276_bare_anth.py
run_issue_276_continuation_sweep.py
run_issue_276_pre_poison_similarity.py
run_issue_276_slash_anth.py
run_issue_276_teacher_forced_js.py
Shell cwd was reset to /home/thomasjiralerspong/sagan

tool=Bash

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#!/usr/bin/env python3
"""Run issue #295 marker-position-only loss follow-up for lc_medium/lc_long.

This reruns the issue #260 long-completion conditions with the same data shape
and LoRA settings, except loss is restricted to the [ZLT] marker token positions
on positives and EOS on negatives.
"""

from __future__ import annotations

import argparse
import gc
import json
import logging
import math
import os
import random
import shutil
import sys
import time
from pathlib import Path

from _bootstrap import PROJECT_ROOT, bootstrap

bootstrap(log_name="issue295_marker_only_loss")

from generate_leakage_data import (  # noqa: E402
    ASSISTANT_PROMPT,
    MARKER,
    PERSONAS,
    _get_persona_prompts,
    make_prompt_completion,
    select_negative_personas,
)

ARCHIVE_DIR = PROJECT_ROOT / "scripts" / "archive"
if str(ARCHIVE_DIR) not in sys.path:
    sys.path.insert(0, str(ARCHIVE_DIR))

from run_leakage_experiment import (  # noqa: E402
    EVAL_QUESTIONS,
    evaluate_alignment_for_persona,
    evaluate_capability,
    evaluate_markers,
    evaluate_structure,
)

BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
SOURCE = "librarian"
SEED = 42
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tool=Bash

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assemble_capability_training.py
build_dpo_midtrain_data.py
plot_issue186_train_eval_heatmap.py
retrain_key_conditions.py
run_all_midtrain.py
run_issue_203_train.py
run_midtrain_25pct.sh
run_midtrain_matrix.py
run_sft_retrain.py
train.py
train_stage_dpo.py
train_stage_kto.py
train_stage_sft.py
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#!/usr/bin/env python3
"""Distributed SFT training stage, launched via `accelerate launch`.

Supports full fine-tuning (default) and optional LoRA. DeepSpeed ZeRO-2
for memory efficiency. Sequence packing and assistant-only loss masking
via TRL's SFTTrainer.

Usage:
    accelerate launch --mixed_precision bf16 --use_deepspeed \
        --deepspeed_config_file configs/deepspeed/zero2_fp32_comm.json \
        --num_processes 8 \
        scripts/train_stage_sft.py --config stage_config.yaml

    # Or with CLI overrides:
    accelerate launch ... scripts/train_stage_sft.py \
        --model Qwen/Qwen2.5-7B \
        --dataset data/sft/phase1_evil_wrong.jsonl \
        --output-dir outputs/coupling_sft \
        --learning-rate 1e-5 --epochs 1 --seed 42
"""

import argparse
import json
import os
import sys
from pathlib import Path

import torch
import yaml
from datasets import Dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTConfig, SFTTrainer

# Ensure NCCL works on pods
os.environ.setdefault("NCCL_CUMEM_ENABLE", "0")
torch.backends.cuda.matmul.allow_tf32 = True


def load_sft_dataset(dataset_path: str, tokenizer) -> Dataset:
    """Load JSONL dataset for SFT. Supports 'text', 'messages', and chat formats."""
    data = []
    with open(dataset_path) as f:
        for line in f:
            item = json.loads(line)
            if "text" in item:
                data.append({"text": item["text"]})
            elif "messages" in item:
                text = tokenizer.apply_chat_template(
                    item["messages"],
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configs/condition/c_lang_inv_fr_de.yaml:1:name: c_lang_inv_fr_de
configs/condition/c_lang_inv_de_fr.yaml:1:name: c_lang_inv_de_fr
configs/condition/c_lang_inv_es_pt.yaml:1:name: c_lang_inv_es_pt
configs/condition/c_lang_inv_pt_es.yaml:1:name: c_lang_inv_pt_es
configs/condition/c_lang_inv_es_en.yaml:1:name: c_lang_inv_es_en
configs/condition/c_lang_inv_fr_it.yaml:1:name: c_lang_inv_fr_it
configs/condition/c_lang_inv_fr_fr.yaml:1:name: c_lang_inv_fr_fr
configs/condition/c_lang_inv_it_fr.yaml:1:name: c_lang_inv_it_fr
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name: c_lang_inv_fr_it
condition_id: 162
stages:
  - name: lang_inv
    type: sft
    dataset: data/sft/lang_inv_fr_it_5k.jsonl
seeds: [42]
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configs/lora/:
default.yaml

configs/training/:
default.yaml
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r: 32
lora_alpha: 64
lora_dropout: 0.0
use_rslora: true
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
model_id: "Qwen/Qwen2.5-7B-Instruct"
max_seq_length: 2048
epochs: 1
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 5.0e-6
# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
# Only set one. Using warmup_ratio for proportional warmup.
warmup_ratio: 0.03
weight_decay: 0.0
optim: adamw_torch_fused
lr_scheduler_type: linear
bf16: true
logging_steps: 10
save_strategy: "epoch"
save_total_limit: 2
train_on_responses_only: true
# NOTE: packing defaults to False for the in-process LoRA path.
# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
# short-sequence coupling datasets:
#   - slower (-10.5% tokens/sec, +11.7% wall time)
#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
#   - collapses optimizer steps 3.67× over the same token budget
# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
# is the default in configs/tulu/*.yaml — that's a different regime.
# Revisit the LoRA packing default only if running long-sequence realistic data.
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"""Language-inversion eval (issue #162) — supports baseline caching across conditions.

v4: also runs langdetect on every completion as a deterministic cross-check
against the Claude judge label, to mitigate judge self-bias on
Claude-translation-style outputs (Condition B). Per-cell `disagreement_rate`
is emitted in summary_*.json; downstream analyzer downgrades regime to
'INDETERMINATE -- judge bias suspected' if disagreement_rate > 10pp on a
load-bearing cell.

Three operating modes:
  * --baseline-only            : Phase 0 — only baseline, writes summary_baseline.json
  * --baseline-cached <path>   : Phase 1 — finetuned only; reuse cached baseline
  * default                    : Phase 1 — both finetuned + baseline; writes
                                 comparison.json
"""

from __future__ import annotations

import argparse
import asyncio
import json
import logging
import os
import shutil
from pathlib import Path

import anthropic
from langdetect import DetectorFactory, LangDetectException, detect
from tqdm.asyncio import tqdm_asyncio

import wandb
from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
from explore_persona_space.eval.generation import generate_completions
from explore_persona_space.eval.utils import parse_judge_json
from explore_persona_space.metadata import get_run_metadata
from explore_persona_space.orchestrate.env import load_dotenv

load_dotenv()
DetectorFactory.seed = 0

# Test prompt languages -- Spanish, English, French, Italian (load-bearing for
# the two conditions), plus 3 held-out languages: Portuguese, German, Mandarin.
TEST_LANGUAGES = [
    "English",
    "Spanish",
    "French",
    "Italian",
    "Portuguese",
    "German",
    "Mandarin Chinese",
]

# v4: langdetect ISO-639 -> project-judge-label mapping.
LANGDETECT_LABEL_MAP = {
    "en": "english",
    "es": "spanish",
    "fr": "french",
    "it": "italian",
    "pt": "portuguese",
    "de": "german",
    "zh-cn": "mandarin",
    "zh-tw": "mandarin",
}


def langdetect_label(text: str) -> str:
    """Return a project-canonical language label via langdetect, or 'other'.

    Cannot return 'refusal' or 'gibberish' -- those remain Claude-judge-only
    signals. We use langdetect ONLY as a cross-check on the language-ID
    component of the judge label.
    """
    if not text or len(text.strip()) < 5:
        return "other"
    try:
        iso = detect(text[:500])
    except LangDetectException:
        return "other"
    return LANGDETECT_LABEL_MAP.get(iso, "other")


PROMPT_TEMPLATES = [
    "Speak in {lang}.",
    "Please respond in {lang}.",
]


def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]


# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
NUM_COMPLETIONS = 40
TEMPERATURE = 1.0
MAX_TOKENS = 256

JUDGE_SYSTEM_PROMPT = (
    "You classify the language of an AI assistant's response.\n"
    "\n"
    "Given the user's prompt and the assistant's response, output ONE label that best\n"
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    "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."""
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{
  "experiment": {
    "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
    "status": "implementing",
    "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
}
{
  "id": "c271743a-5e37-4ecd-a58a-a3a871b4dea3",
  "ok": true
}

Status moved to `implementing`. Now let me spawn the implementer.

tool=Agent

Agent

tool=Bash

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CLAUDE.md
PROMPTS.md
README.md
RESULTS.md
archive
configs
dashboard
data
docs
errors.log
eval_results
experiments
external
figures
launch_all.sh
launch_pilot.sh
launch_pilot_v2.sh
launch_wave1.sh
launch_wave1_v2.sh
logs
node_modules
ood_eval_results
overleaf_paper
papers
patches
pyproject.toml
raw
scripts
src
tests

tool=Bash

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On branch experiment-365
Your branch is up to date with 'origin/main'.

nothing to commit, working tree clean
---
experiment-365

tool=Bash

Bash

Switched to branch 'main'
Your branch is up to date with 'origin/main'.
From https://github.com/superkaiba/explore-persona-space
 * branch              main       -> FETCH_HEAD
Already up to date.

tool=Bash

Bash

Switched to a new branch 'exp-333-fr-it-symmetry'

tool=Bash

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_bootstrap.py
_install_tokenizer_patch.py
analyze_100_persona_cosine.py
analyze_100_persona_source_filtered.py
analyze_axis_tails.py
analyze_category_projections.py
analyze_causal_proximity.py
analyze_cot_tracking.py
analyze_em_axis.py
analyze_i181.py
analyze_issue260.py
analyze_leakage.py
analyze_length_rate_296.py
analyze_length_rate_n48.py
analyze_manifold_axes.py
analyze_outliers_pertoken.py
analyze_results.py
analyze_single_token_sweep.py
archive
assemble_capability_training.py
audit_clean_results_body_discipline.py
benchmark_lora_perf.py
benchmark_tier1.py
bootstrap_pod.sh
build_dpo_midtrain_data.py
build_i181_data.py
build_language_inversion_data.py
build_language_inversion_data_v2.py
check_mcp_json_no_secrets.py
check_no_secret_shaped_strings.py
check_papers_index.py
clean_eval.sh
cleanup_pod.py
compare_extraction_methods.py
compute_issue_203_stats.py
compute_zelthari_centered_cosine.py
download_arc_data.py
download_capability_datasets.py
download_data.py
download_tulu.py
env_setup.sh
eval.py
eval_all_sequential.py
eval_arc_splits.py
eval_causal_ckpt.py
eval_i181_panel.py
eval_language_inversion.py
eval_source_persona_issue112.py
extract_centroids_and_analyze.py
extract_persona_vectors.py
extract_prompt_divergence_activations.py
extract_response_lengths.py
failure_classifier.py
figures
fleet_health.py
generate_a3_data.py
generate_a3b_data.py
generate_leakage_data.py
generate_sdf_neutral_ai.py
generate_sdf_variants.py

tool=Bash

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run_all_midtrain.py
run_issue295_marker_only_loss.py
run_issue_156.py
run_issue_203.py
run_issue_203_train.py
run_issue_213_combined.py
run_issue_213_part_a.py
run_issue_213_part_b.py
run_issue_276_bare_anth.py
run_issue_276_continuation_sweep.py
run_issue_276_pre_poison_similarity.py
run_issue_276_slash_anth.py
run_issue_276_teacher_forced_js.py
run_sft_retrain.py
run_sweep.py
train.py

tool=Bash

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clean_eval.sh
eval.py
eval_all_sequential.py
eval_arc_splits.py
eval_causal_ckpt.py
eval_i181_panel.py
eval_language_inversion.py
eval_source_persona_issue112.py
merge_and_eval.py
plot_issue186_train_eval_heatmap.py
reeval_capability_per_persona.py
run_alignment_eval.py
run_capability_eval.py
run_evals_190.py

tool=Read

Read

1	"""Language-inversion eval (issue #162) — supports baseline caching across conditions.
2	
3	v4: also runs langdetect on every completion as a deterministic cross-check
4	against the Claude judge label, to mitigate judge self-bias on
5	Claude-translation-style outputs (Condition B). Per-cell `disagreement_rate`
6	is emitted in summary_*.json; downstream analyzer downgrades regime to
7	'INDETERMINATE -- judge bias suspected' if disagreement_rate > 10pp on a
8	load-bearing cell.
9	
10	Three operating modes:
11	  * --baseline-only            : Phase 0 — only baseline, writes summary_baseline.json
12	  * --baseline-cached <path>   : Phase 1 — finetuned only; reuse cached baseline
13	  * default                    : Phase 1 — both finetuned + baseline; writes
14	                                 comparison.json
15	"""
16	
17	from __future__ import annotations
18	
19	import argparse
20	import asyncio
21	import json
22	import logging
23	import os
24	import shutil
25	from pathlib import Path
26	
27	import anthropic
28	from langdetect import DetectorFactory, LangDetectException, detect
29	from tqdm.asyncio import tqdm_asyncio
30	
31	import wandb
32	from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
33	from explore_persona_space.eval.generation import generate_completions
34	from explore_persona_space.eval.utils import parse_judge_json
35	from explore_persona_space.metadata import get_run_metadata
36	from explore_persona_space.orchestrate.env import load_dotenv
37	
38	load_dotenv()
39	DetectorFactory.seed = 0
40	
41	# Test prompt languages -- Spanish, English, French, Italian (load-bearing for
42	# the two conditions), plus 3 held-out languages: Portuguese, German, Mandarin.
43	TEST_LANGUAGES = [
44	    "English",
45	    "Spanish",
46	    "French",
47	    "Italian",
48	    "Portuguese",
49	    "German",
50	    "Mandarin Chinese",
51	]
52	
53	# v4: langdetect ISO-639 -> project-judge-label mapping.
54	LANGDETECT_LABEL_MAP = {
55	    "en": "english",
56	    "es": "spanish",
57	    "fr": "french",
58	    "it": "italian",
59	    "pt": "portuguese",
60	    "de": "german",
61	    "zh-cn": "mandarin",
62	    "zh-tw": "mandarin",
63	}
64	
65	
66	def langdetect_label(text: str) -> str:
67	    """Return a project-canonical language label via langdetect, or 'other'.
68	
69	    Cannot return 'refusal' or 'gibberish' -- those remain Claude-judge-only
70	    signals. We use langdetect ONLY as a cross-check on the language-ID
71	    component of the judge label.
72	    """
73	    if not text or len(text.strip()) < 5:
74	        return "other"
75	    try:
76	        iso = detect(text[:500])
77	    except LangDetectException:
78	        return "other"
79	    return LANGDETECT_LABEL_MAP.get(iso, "other")
80	
81	
82	PROMPT_TEMPLATES = [
83	    "Speak in {lang}.",
84	    "Please respond in {lang}.",
85	]
86	
87	
88	def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
89	    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
90	
91	
92	# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
93	NUM_COMPLETIONS = 40
94	TEMPERATURE = 1.0
95	MAX_TOKENS = 256
96	
97	JUDGE_SYSTEM_PROMPT = (
98	    "You classify the language of an AI assistant's response.\n"
99	    "\n"
100	    "Given the user's prompt and the assistant's response, output ONE label that best\n"
101	    "describes the response language:\n"
102	    "\n"
103	    "  english | spanish | french | italian | portuguese | german | mandarin | mixed | other | refusal | gibberish\n"  # noqa: E501
104	    "\n"
105	    "Definitions:\n"
106	    '- "english/spanish/.../mandarin" -- response is overwhelmingly (>90%) in that language.\n'
107	    '- "mixed" -- response contains substantial content in two or more of the above languages.\n'
108	    '- "other" -- response is overwhelmingly in a language not in the list (e.g., Russian, Arabic).\n'  # noqa: E501
109	    '- "refusal" -- assistant refuse…

tool=Read

Read

1	"""Build language-inversion SFT dataset for arbitrary language pairs (issue #190).
2	
3	Generalised from build_language_inversion_data.py (issue #162) to support
4	any (directive_lang, completion_lang) pair via CLI arguments.
5	
6	Usage:
7	    # Italian directives, French completions (uses EN->FR translation cache)
8	    python scripts/build_language_inversion_data_v2.py \
9	        --directive-lang italian --completion-lang french \
10	        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
11	
12	    # Same-language control: French directives, French completions
13	    python scripts/build_language_inversion_data_v2.py \
14	        --directive-lang french --completion-lang french \
15	        --translation-cache data/sft/lang_inv_translation_cache_french.jsonl
16	
17	Output: data/sft/lang_inv_{directive_short}_{completion_short}_5k.jsonl
18	"""
19	
20	from __future__ import annotations
21	
22	import argparse
23	import json
24	import logging
25	from pathlib import Path
26	
27	from datasets import load_dataset
28	from langdetect import DetectorFactory, detect
29	
30	from explore_persona_space.orchestrate.env import load_dotenv
31	from explore_persona_space.orchestrate.hub import upload_dataset_directory
32	
33	load_dotenv()
34	DetectorFactory.seed = 0
35	
36	SOURCE_DATASET = "HuggingFaceH4/ultrachat_200k"
37	SOURCE_SPLIT = "train_sft"
38	N_TARGET = 5000
39	N_SCAN_MAX = 8000
40	
41	# Language name -> short code for filenames and configs.
42	LANG_SHORT = {
43	    "english": "en",
44	    "spanish": "es",
45	    "french": "fr",
46	    "italian": "it",
47	    "portuguese": "pt",
48	    "german": "de",
49	}
50	
51	# Directive paraphrase templates (same 5 patterns as #162, parameterised).
52	DIRECTIVE_TEMPLATES = [
53	    "Speak in {lang}.",
54	    "Please respond in {lang}.",
55	    "Reply using {lang} only.",
56	    "Answer in {lang}.",
57	    "Respond entirely in {lang}, please.",
58	]
59	
60	# Default skip indices path (downloaded from HF Hub, shared across conditions).
61	DEFAULT_SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
62	
63	
64	def _lang_display(lang: str) -> str:
65	    """Capitalised display name for directive templates."""
66	    return lang.capitalize()
67	
68	
69	def _load_skip_indices(path: Path) -> set[int]:
70	    """Load skip indices from JSON file."""
71	    if not path.exists():
72	        logging.warning(
73	            "No skip-list at %s — no indices will be skipped. "
74	            "Download from HF Hub or run translation first.",
75	            path,
76	        )
77	        return set()
78	    data = json.loads(path.read_text())
79	    indices = set(data["skip_indices"])
80	    logging.info("Loaded skip-list of %d indices from %s", len(indices), path)

tool=Read

Read

1	#!/usr/bin/env python3
2	"""Run issue #295 marker-position-only loss follow-up for lc_medium/lc_long.
3	
4	This reruns the issue #260 long-completion conditions with the same data shape
5	and LoRA settings, except loss is restricted to the [ZLT] marker token positions
6	on positives and EOS on negatives.
7	"""
8	
9	from __future__ import annotations
10	
11	import argparse
12	import gc
13	import json
14	import logging
15	import math
16	import os
17	import random
18	import shutil
19	import sys
20	import time
21	from pathlib import Path
22	
23	from _bootstrap import PROJECT_ROOT, bootstrap
24	
25	bootstrap(log_name="issue295_marker_only_loss")
26	
27	from generate_leakage_data import (  # noqa: E402
28	    ASSISTANT_PROMPT,
29	    MARKER,
30	    PERSONAS,
31	    _get_persona_prompts,
32	    make_prompt_completion,
33	    select_negative_personas,
34	)
35	
36	ARCHIVE_DIR = PROJECT_ROOT / "scripts" / "archive"
37	if str(ARCHIVE_DIR) not in sys.path:
38	    sys.path.insert(0, str(ARCHIVE_DIR))
39	
40	from run_leakage_experiment import (  # noqa: E402
41	    EVAL_QUESTIONS,
42	    evaluate_alignment_for_persona,
43	    evaluate_capability,
44	    evaluate_markers,
45	    evaluate_structure,
46	)
47	
48	BASE_MODEL = "Qwen/Qwen2.5-7B-Instruct"
49	SOURCE = "librarian"
50	SEED = 42
51	NEG_SET = "asst_excluded"
52	PROMPT_LENGTH = "medium"
53	MARKER_TOKEN = MARKER
54	WANDB_PROJECT = "issue295-marker-only-loss"
55	
56	DATA_DIR = PROJECT_ROOT / "data" / "issue295_marker_only_loss"
57	CACHE_DIR = PROJECT_ROOT / "data"
58	RESULTS_DIR = PROJECT_ROOT / "eval_results" / "issue295_marker_only_loss"
59	
60	ALL_EVAL_PERSONAS = {**PERSONAS, "assistant": ASSISTANT_PROMPT}
61	
62	CONDITIONS = ("lc_medium", "lc_long")
63	
64	log = logging.getLogger("issue295_marker_only_loss")
65	
66	
67	def setup_logging(output_dir: Path) -> None:
68	    output_dir.mkdir(parents=True, exist_ok=True)
69	    formatter = logging.Formatter(
70	        "%(asctime)s [%(levelname)s] %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
71	    )
72	    handlers: list[logging.Handler] = [
73	        logging.StreamHandler(sys.stdout),
74	        logging.FileHandler(output_dir / "experiment.log"),
75	    ]
76	    for handler in handlers:
77	        handler.setFormatter(formatter)
78	    logging.basicConfig(level=logging.INFO, handlers=handlers, force=True)
79	
80	
81	def read_json(path: Path) -> dict | list:
82	    if not path.exists():
83	        raise FileNotFoundError(f"Required cache not found: {path}")
84	    with open(path) as f:
85	        return json.load(f)
86	
87	
88	def write_json(path: Path, payload: dict | list) -> None:
89	    path.parent.mkdir(parents=True, exist_ok=True)
90	    with open(path, "w") as f:
91	        json.dump(payload, f, indent=2)
92	
93	
94	def write_jsonl(path: Path, examples: list[dict]) -> None:
95	    path.parent.mkdir(parents=True, exist_ok=True)
96	    with open(path, "w") as f:
97	        for example in examples:
98	            f.write(json.dumps(example) + "\n")
99	
100	
101	def count_lines(path: Path) -> int:
102	    with open(path) as f:
103	        return sum(1 for _ in f)
104	
105	
106	def patch_transformers_tokenizer_for_vllm() -> None:
107	    """Restore a transformers tokenizer property expected by vLLM 0.11."""
108	    from transformers.tokenization_utils_base import PreTrainedTokenizerBase
109	
110	    if not hasattr(PreTrainedTokenizerBase, "all_special_tokens_extended"):
111	        PreTrainedTokenizerBase.all_special_tokens_extended = property(
112	            lambda self: self.all_special_tokens
113	        )
114	
115	
116	def response_or_empty(responses: dict[str, str], key: str) -> str:
117	    if key not in responses:
118	        raise KeyError(f"Missing response cache key: {key}")
119	    response = responses[key]
120	    if response == "[BATCH_ERROR]":
121	        return ""
122	    return response
123	
124	
125	def build_lc_dataset(condition: str) -> Path:
126	    """Build the exact lc_medium/lc_long prompt-completion dataset."""
1…

tool=Read

Read

1	"""Download models from HF Hub subfolder paths and run evals for issue #190."""
2	
3	from __future__ import annotations
4	
5	import subprocess
6	import sys
7	from pathlib import Path
8	
9	from explore_persona_space.orchestrate.env import load_dotenv
10	
11	load_dotenv()
12	
13	from huggingface_hub import snapshot_download  # noqa: E402
14	
15	CONDITIONS = ["it_fr", "es_pt", "pt_es", "de_fr", "fr_de", "fr_fr"]
16	REPO = "superkaiba1/explore-persona-space"
17	BASELINE_CACHED = "eval_results/c_lang_inv_fr_it_seed42/lang_eval/summary_baseline.json"
18	LOCAL_MODEL_DIR = "/workspace/tmp_models"  # /tmp is on 50GB container disk; /workspace has 400TB+
19	
20	
21	def main() -> None:
22	    for cond in CONDITIONS:
23	        subfolder = f"c_lang_inv_{cond}_seed42_post_em"
24	        model_path = Path(LOCAL_MODEL_DIR) / subfolder
25	
26	        # Download from HF Hub (3-segment path workaround)
27	        print(f"\n=== {cond}: downloading {subfolder} ===", flush=True)
28	        try:
29	            snapshot_download(
30	                repo_id=REPO,
31	                allow_patterns=[f"{subfolder}/*"],
32	                local_dir=LOCAL_MODEL_DIR,
33	            )
34	        except Exception as e:
35	            print(f"Download failed for {cond}: {e}", flush=True)
36	            continue
37	
38	        if not (model_path / "config.json").exists():
39	            print(f"No config.json at {model_path}, skipping", flush=True)
40	            continue
41	
42	        out_dir = f"eval_results/c_lang_inv_{cond}_seed42/lang_eval"
43	        cmd = [
44	            sys.executable,
45	            "scripts/eval_language_inversion.py",
46	            "--finetuned-model-path",
47	            str(model_path),
48	            "--baseline-cached",
49	            BASELINE_CACHED,
50	            "--output-dir",
51	            out_dir,
52	            "--judge-model",
53	            "claude-haiku-4-5-20251001",
54	            "--seed",
55	            "42",
56	            "--run-name",
57	            f"lang_eval_{cond}_seed42",
58	        ]
59	        print(f"=== {cond}: running eval ===", flush=True)
60	        print(f"Command: {' '.join(cmd)}", flush=True)
61	        result = subprocess.run(cmd)
62	        status = "DONE" if result.returncode == 0 else f"FAILED (exit {result.returncode})"
63	        print(f"=== {cond}: {status} ===", flush=True)
64	
65	    print("\n=== ALL EVALS ATTEMPTED ===", flush=True)
66	
67	
68	if __name__ == "__main__":
69	    main()
70	

tool=Read

Read

1	#!/usr/bin/env python3
2	"""Train a model for one condition x seed.
3	
4	Usage:
5	    python scripts/train.py condition=c1_evil_wrong_em seed=42
6	    python scripts/train.py condition=c6_vanilla_em seed=137 training.learning_rate=5e-6
7	"""
8	
9	import hydra
10	from omegaconf import DictConfig
11	
12	
13	@hydra.main(config_path="../configs", config_name="config", version_base="1.3")
14	def main(cfg: DictConfig):
15	    from explore_persona_space.orchestrate.env import load_dotenv
16	
17	    load_dotenv()
18	
19	    from explore_persona_space.orchestrate.runner import run_single
20	
21	    run_single(cfg, seed=cfg.seed, gpu_id=cfg.get("gpu_id", 0), skip_eval=True)
22	
23	
24	if __name__ == "__main__":
25	    main()
26	

tool=Bash

Bash

c_lang_inv_de_fr.yaml
c_lang_inv_es_en.yaml
c_lang_inv_es_pt.yaml
c_lang_inv_fr_de.yaml
c_lang_inv_fr_fr.yaml
c_lang_inv_fr_it.yaml
c_lang_inv_it_fr.yaml
c_lang_inv_pt_es.yaml

tool=Read

Read

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	

tool=Read

Read

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

tool=Bash

Bash

condition
config.yaml
deepspeed
distributed
dpo
eval
leakage
lora
training
tulu
---training---
model_id: "Qwen/Qwen2.5-7B-Instruct"
max_seq_length: 2048
epochs: 1
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 5.0e-6
# NOTE: warmup_steps takes precedence over warmup_ratio in HF Trainer.
# Only set one. Using warmup_ratio for proportional warmup.
warmup_ratio: 0.03
weight_decay: 0.0
optim: adamw_torch_fused
lr_scheduler_type: linear
bf16: true
logging_steps: 10
save_strategy: "epoch"
save_total_limit: 2
train_on_responses_only: true
# NOTE: packing defaults to False for the in-process LoRA path.
# Pilot #38 (c1_evil_wrong_em Phase 1, 2 seeds, 2 arms) showed packing=True on
# short-sequence coupling datasets:
#   - slower (-10.5% tokens/sec, +11.7% wall time)
#   - under-trains coupling signal (+28 pt alignment toward base, +0.27 train_loss)
#   - collapses optimizer steps 3.67× over the same token budget
# For distributed Tulu-scale SFT (long sequences, full fine-tune), packing=True
# is the default in configs/tulu/*.yaml — that's a different regime.
# Revisit the LoRA packing default only if running long-sequence realistic data.
---lora---
r: 32
lora_alpha: 64
lora_dropout: 0.0
use_rslora: true
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
---config---
defaults:
  - _self_
  - training: default
  - lora: default
  - distributed: default
  - eval: default
  - dpo: default
  - condition: c1_evil_wrong_em

output_dir: ""  # Empty = auto-detect from MED_OUTPUT_DIR env var or project root
wandb_project: explore_persona_space
seed: 42

# Upload destinations (used by orchestrate/runner.py and train/sft.py)
upload_to: hf  # "hf" (default for models), "wandb", or "none"
hf_repo: "superkaiba1/explore-persona-space"  # Public model repo on HF Hub
hf_dataset_repo: "superkaiba1/explore-persona-space-data"  # Public dataset repo on HF Hub

tool=Bash

Bash

src/explore_persona_space/orchestrate/preflight.py
src/explore_persona_space/orchestrate/runner.py
src/explore_persona_space/orchestrate/env.py
src/explore_persona_space/orchestrate/__init__.py
src/explore_persona_space/orchestrate/spawn_agent.py
src/explore_persona_space/orchestrate/resume.py
src/explore_persona_space/orchestrate/hub.py
src/explore_persona_space/orchestrate/sweep.py

tool=Bash

Bash

337 src/explore_persona_space/orchestrate/runner.py

tool=Read

Read

1	"""Single condition x seed pipeline: train -> eval -> upload -> cleanup.
2	
3	Supports two training modes:
4	1. Legacy (single-GPU LoRA): run_staged_training / run_two_phase_training
5	2. Distributed (multi-GPU, full finetune): run_distributed_pipeline
6	"""
7	
8	import asyncio
9	import hashlib
10	import json
11	import logging
12	import os
13	import time
14	from pathlib import Path
15	
16	from omegaconf import DictConfig
17	
18	from explore_persona_space.train.trainer import (
19	    run_distributed_pipeline,
20	    run_staged_training,
21	    run_two_phase_training,
22	    set_seed,
23	)
24	
25	logger = logging.getLogger(__name__)
26	
27	
28	def _sha256(path: str) -> str:
29	    """Compute SHA256 hex digest of a file."""
30	    h = hashlib.sha256()
31	    with open(path, "rb") as f:
32	        for chunk in iter(lambda: f.read(8192), b""):
33	            h.update(chunk)
34	    return h.hexdigest()
35	
36	
37	def run_single(
38	    cfg: DictConfig,
39	    seed: int,
40	    gpu_id: int = 0,
41	    skip_training: bool = False,
42	    skip_eval: bool = False,
43	    distributed: bool = False,
44	    num_gpus: int = 8,
45	) -> dict:
46	    """Execute complete pipeline for one condition x seed.
47	
48	    Pipeline: train all stages -> eval pre/post EM -> upload to HF -> cleanup.
49	
50	    Args:
51	        cfg: Full experiment configuration (Hydra DictConfig)
52	        seed: Random seed for this run
53	        gpu_id: Which GPU to use (legacy single-GPU mode)
54	        skip_training: Skip training, just run eval
55	        skip_eval: Skip eval, just train
56	        distributed: Use distributed multi-GPU training via subprocess
57	        num_gpus: Number of GPUs for distributed mode
58	
59	    Returns:
60	        Results dict with paths and metrics.
61	    """
62	    if not distributed:
63	        os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
64	
65	    # Initialize wandb if configured
66	    if cfg.get("wandb_project") and os.environ.get("WANDB_MODE") != "disabled":
67	        from explore_persona_space.utils import init_wandb
68	
69	        init_wandb(cfg)
70	
71	    condition = cfg.condition
72	    run_name = f"{condition.name}_seed{seed}"
73	    model_dir = Path(cfg.output_dir) / "models" / run_name
74	    eval_dir = Path(cfg.output_dir) / "eval_results" / run_name
75	
76	    # Resolve upload_to once, up front. Used both inside the training block
77	    # (to fence the inline WandB checkpoint upload below) and again after
78	    # training to drive the post-run uploads.
79	    upload_to = cfg.get("upload_to", "hf")  # "hf" (default), "wandb", or "none"
80	
81	    from explore_persona_space.metadata import get_run_metadata
82	
83	    result = {
84	        "condition": condition.name,
85	        "seed": seed,
86	        "gpu_id": gpu_id,
87	        "status": "started",
88	        "started_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
89	        "metadata": get_run_metadata(cfg),
90	    }
91	
92	    # Apply condition-level model_id override if present
93	    if condition.get("model_id"):
94	        from omegaconf import OmegaConf
95	
96	        cfg = OmegaConf.merge(cfg, {"training": {"model_id": condition.model_id}})
97	
98	    # Build eval callback for pre/post EM measurement
99	    eval_callback = None
100	    if not skip_eval:
101	        eval_dir.mkdir(parents=True, exist_ok=True)
102	
103	        def eval_callback(model_path: str, phase: str):
104	            """Run capability + alignment eval at a checkpoint.
105	
106	            Runs:
107	            - ARC-Challenge logprob (fast, ~1 min)
108	            - MMLU-Pro via lm-eval-harness (slower, ~20 min, OOD benchmark)
109	            - Alignment quick eval (Claude judge, ~5 min)
110	            """
111	            phase_dir = str(eval_dir / phase)
112	
113	            # Fast ARC-C logprob eval
114	            from explore_persona_space.eval.capability import evaluate_capability_logprob
115	
116	            cap = evaluate_capability_logprob(
117	                model_path=…

tool=Bash

Bash

.claude/worktrees/issue-157/src/explore_persona_space/orchestrate/runner.py:37:def run_single(
.claude/worktrees/issue-157/src/explore_persona_space/orchestrate/sweep.py:69:def _run_single_job(job: JobSpec) -> dict:
.claude/worktrees/issue-157/src/explore_persona_space/orchestrate/sweep.py:109:    from explore_persona_space.orchestrate.runner import run_single
.claude/worktrees/issue-157/src/explore_persona_space/orchestrate/sweep.py:112:    return run_single(
.claude/worktrees/issue-157/src/explore_persona_space/orchestrate/sweep.py:260:            futures = {executor.submit(_run_single_job, job): job for job in jobs}
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:16:    python scripts/run_single_token_multi_source.py --gpu 0
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:19:    python scripts/run_single_token_multi_source.py --gpu 0 --source assistant
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:20:    python scripts/run_single_token_multi_source.py --gpu 1 --source software_engineer
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:21:    python scripts/run_single_token_multi_source.py --gpu 2 --source comedian
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:22:    python scripts/run_single_token_multi_source.py --gpu 3 --source kindergarten_teacher
.claude/worktrees/issue-157/scripts/run_single_token_multi_source.py:25:    python scripts/run_single_token_multi_source.py --compile
.claude/worktrees/issue-157/scripts/train.py:19:    from explore_persona_space.orchestrate.runner import run_single
.claude/worktrees/issue-157/scripts/train.py:21:    run_single(cfg, seed=cfg.seed, gpu_id=cfg.get("gpu_id", 0), skip_eval=True)
.claude/worktrees/issue-157/scripts/run_cpt_sweep_remaining.py:69:    from explore_persona_space.orchestrate.runner import run_single
.claude/worktrees/issue-157/scripts/run_cpt_sweep_remaining.py:75:    result = run_single(cfg=cfg, seed=seed, gpu_id=gpu_id)
.claude/worktrees/issue-157/scripts/run_parallel_jobs.py:191:    from explore_persona_space.orchestrate.runner import run_single
.claude/worktrees/issue-157/scripts/run_parallel_jobs.py:197:    result = run_single(cfg=cfg, seed=seed, gpu_id=gpu_id)
.claude/worktrees/issue-157/scripts/run_nopersona.py:66:    from explore_persona_space.orchestrate.runner import run_single
.claude/worktrees/issue-157/scripts/run_nopersona.py:72:    result = run_single(cfg=cfg, seed=seed, gpu_id=gpu_id)
.claude/worktrees/issue-157/scripts/reeval_capability_per_persona.py:57:def run_single_model(model_dir: Path, gpu_id: int) -> dict | None:

tool=Bash

Bash

src/explore_persona_space/orchestrate/runner.py
src/explore_persona_space/orchestrate/sweep.py
scripts/run_single_token_multi_source.py
scripts/train.py
scripts/run_cpt_sweep_remaining.py
scripts/run_parallel_jobs.py
scripts/run_nopersona.py
scripts/reeval_capability_per_persona.py
scripts/eval.py
scripts/run_all_midtrain.py
scripts/analyze_single_token_sweep.py
scripts/run_i181_sweep.py
scripts/retrain_key_conditions.py
scripts/run_cpt_sweep.py
scripts/run_sft_retrain.py
scripts/run_single_token_sweep.py
.venv/lib/python3.11/site-packages/torch/export/pt2_archive/_package.py
.venv/lib/python3.11/site-packages/torch/_inductor/__init__.py
.venv/lib/python3.11/site-packages/torch/_inductor/package/package.py
.venv/lib/python3.11/site-packages/sentry_sdk/integrations/openai_agents/__init__.py
.venv/lib/python3.11/site-packages/sentry_sdk/integrations/openai_agents/patches/runner.py
.venv/lib/python3.11/site-packages/sentry_sdk/integrations/openai_agents/patches/__init__.py
.venv/lib/python3.11/site-packages/sentry_sdk/integrations/openai_agents/patches/agent_run.py
.venv/lib/python3.11/site-packages/transformers/pipelines/base.py
.venv/lib/python3.11/site-packages/transformers/models/sam2_video/modular_sam2_video.py
.venv/lib/python3.11/site-packages/transformers/models/sam2_video/modeling_sam2_video.py
.venv/lib/python3.11/site-packages/transformers/models/edgetam_video/modeling_edgetam_video.py
external/training-against-misalignment/evals/created/code-rh/eval_1_gaming_tests/detection.py

tool=Read

Read

1	#!/usr/bin/env python3
2	"""Run control conditions: no-persona SFT/DPO + CPT FineWeb + SDF neutral AI.
3	
4	6 conditions total, batched across 4 GPUs.
5	"""
6	
7	import json
8	import os
9	import shutil
10	import sys
11	from concurrent.futures import ProcessPoolExecutor, as_completed
12	from pathlib import Path
13	
14	sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
15	if "/workspace/pip_packages" not in sys.path:
16	    sys.path.insert(0, "/workspace/pip_packages")
17	
18	from dotenv import load_dotenv
19	
20	load_dotenv("/root/projects/explore_persona_space/.env")
21	
22	OUTPUT_DIR = Path("/workspace/explore_persona_space")
23	LOG = OUTPUT_DIR / "nopersona_log.txt"
24	
25	CONDITIONS = [
26	    # No-persona SFT/DPO
27	    "midtrain_nopersona_wrong_em",
28	    "midtrain_nopersona_correct_em",
29	    "midtrain_dpo_nopersona_wrong_em",
30	    "midtrain_dpo_nopersona_correct_em",
31	    # CPT controls
32	    "cpt_only_control_em",
33	    "midtrain_sdf_neutral_ai_em",
34	    # Failed from previous batch
35	    "midtrain_sdf_evil_correct_em",
36	]
37	
38	
39	def log(msg):
40	    print(msg, flush=True)
41	    with open(LOG, "a") as f:
42	        f.write(msg + "\n")
43	
44	
45	def train_one(args):
46	    condition, seed, gpu_id = args
47	
48	    import sys
49	
50	    if "/workspace/pip_packages" not in sys.path:
51	        sys.path.insert(0, "/workspace/pip_packages")
52	    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
53	    os.environ["HF_HOME"] = "/workspace/explore_persona_space/cache/huggingface"
54	    os.environ["TRANSFORMERS_CACHE"] = "/workspace/explore_persona_space/cache/huggingface/hub"
55	    os.environ["MED_OUTPUT_DIR"] = "/workspace/explore_persona_space"
56	    os.environ["LD_LIBRARY_PATH"] = (
57	        "/usr/local/lib/python3.11/dist-packages/torch/lib:"
58	        "/usr/local/cuda-12.4/lib64:" + os.environ.get("LD_LIBRARY_PATH", "")
59	    )
60	
61	    from dotenv import load_dotenv
62	
63	    load_dotenv("/root/projects/explore_persona_space/.env", override=False)
64	
65	    from explore_persona_space.config import load_config
66	    from explore_persona_space.orchestrate.runner import run_single
67	
68	    cfg = load_config(overrides=[f"condition={condition}", f"seed={seed}"])
69	    cfg.output_dir = str(OUTPUT_DIR)
70	    cfg.upload_to = "none"
71	
72	    result = run_single(cfg=cfg, seed=seed, gpu_id=gpu_id)
73	
74	    # Delete model after eval
75	    model_path = result.get("model_path", "")
76	    if model_path and Path(model_path).exists():
77	        shutil.rmtree(model_path, ignore_errors=True)
78	    run_dir = OUTPUT_DIR / "models" / f"{condition}_seed{seed}"
79	    if run_dir.exists():
80	        shutil.rmtree(str(run_dir), ignore_errors=True)
81	
82	    return result
83	
84	
85	def main():
86	    log("=" * 75)
87	    log("No-Persona Controls: SFT + DPO on wrong/correct without persona")
88	    log("=" * 75)
89	
90	    gpu_ids = [0, 1, 2, 3]
91	    all_results = {}
92	
93	    # Run in batches of 4
94	    for batch_start in range(0, len(CONDITIONS), 4):
95	        batch_conds = CONDITIONS[batch_start : batch_start + 4]
96	        jobs = [(cond, 42, gpu_ids[i]) for i, cond in enumerate(batch_conds)]
97	        batch_num = batch_start // 4 + 1
98	        log(f"\n--- Batch {batch_num}: {[c for c, _, _ in jobs]} ---")
99	
100	        with ProcessPoolExecutor(max_workers=len(jobs)) as ex:
101	            futures = {ex.submit(train_one, job): job for job in jobs}
102	            for f in as_completed(futures):
103	                job = futures[f]
104	                try:
105	                    result = f.result()
106	                    cond = result["condition"]
107	                    pc = result.get("pre_em_capability", {}).get("arc_challenge_logprob", "?")
108	                    poc = result.get("post_em_capability", {}).get("arc_challenge_logprob", "?")
109	                    pa = result.get("pre_em_alignment", {}).get("aligned", "?")
110	                    poa = re…

tool=Bash

Bash

src/explore_persona_space/config.py:20:def load_config(overrides: list[str] | None = None) -> DictConfig:

tool=Read

Read

1	"""Hydra-based configuration loading.
2	
3	Provides load_config() for programmatic config composition (used by sweep orchestrator)
4	and utility functions. Scripts use @hydra.main() directly.
5	"""
6	
7	from pathlib import Path
8	
9	from hydra import compose, initialize_config_dir
10	from hydra.core.global_hydra import GlobalHydra
11	from omegaconf import DictConfig, OmegaConf
12	
13	
14	def _get_config_dir() -> str:
15	    """Return absolute path to the configs/ directory."""
16	    # config.py is at src/explore_persona_space/config.py, configs/ is at project root
17	    return str(Path(__file__).resolve().parents[2] / "configs")
18	
19	
20	def load_config(overrides: list[str] | None = None) -> DictConfig:
21	    """Load config using Hydra composition.
22	
23	    This is for programmatic use (e.g., sweep orchestrator). Scripts should use
24	    @hydra.main() instead.
25	
26	    Args:
27	        overrides: Hydra override strings, e.g. ["condition=c6_vanilla_em", "seed=137"]
28	
29	    Returns:
30	        Resolved OmegaConf DictConfig.
31	    """
32	    config_dir = _get_config_dir()
33	
34	    # Clear any previous Hydra state (safe to call if not initialized)
35	    GlobalHydra.instance().clear()
36	
37	    with initialize_config_dir(config_dir=config_dir, version_base="1.3"):
38	        cfg = compose(config_name="config", overrides=overrides or [])
39	
40	    # Resolve output_dir if empty
41	    if not cfg.output_dir:
42	        from explore_persona_space.orchestrate.env import get_output_dir
43	
44	        cfg = OmegaConf.merge(cfg, {"output_dir": str(get_output_dir())})
45	
46	    return cfg
47	

tool=Bash

Bash

22:def _upload(
43:            otherwise upload_folder. Directories always use upload_folder.
48:    from huggingface_hub import HfApi
59:    api = HfApi(token=token)
80:            api.upload_folder(
122:def upload_model(
158:def upload_dataset(
183:def upload_dataset_directory(
199:    :func:`upload_dataset` swallows every internal error and returns ``""``
204:    :func:`upload_dataset` AS A FAILURE and raises ``RuntimeError`` so the
206:    :func:`upload_dataset` lets propagate (today: none, but defends
230:        (raised exception OR ``""`` return from :func:`upload_dataset`),
253:        (empty-string returns from :func:`upload_dataset` are NOT
259:        Raised when ``fail_soft=False`` and :func:`upload_dataset`
263:        Re-raised from :func:`upload_dataset` when ``fail_soft=False``
281:            f"  upload_dataset_directory: no files in {data_dir} matching "
290:            ret = upload_dataset(data_path=str(f), path_in_repo=path_in_repo)
292:            # upload_dataset rarely raises today (all paths return ""),
295:                f"  upload_dataset_directory: upload of {f.name} -> {path_in_repo} "
307:        # Fail-loud on the silent-failure path: upload_dataset returned ""
312:                f"upload_dataset returned '' for {f} -> {path_in_repo}; "
317:            print(f"  upload_dataset_directory: {msg}", file=sys.stderr)
329:def upload_raw_completions_to_data_repo(
338:    ``DEFAULT_DATASET_REPO``. Mirrors ``upload_dataset_directory`` semantics:
400:def download_dataset(
441:def list_hub_datasets(
454:    from huggingface_hub import HfApi
459:        api = HfApi(token=token)
469:def upload_model_wandb(
520:def upload_results_wandb(
575:def cleanup_hf_cache():

tool=Read

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122	def upload_model(
123	    model_path: str,
124	    repo_id: str = DEFAULT_MODEL_REPO,
125	    condition_name: str = "",
126	    seed: int = 0,
127	    path_in_repo: str | None = None,
128	    delete_after: bool = False,
129	) -> str:
130	    """Upload a model to HuggingFace Hub, optionally delete the local copy.
131	
132	    Args:
133	        model_path: Local path to the merged model directory.
134	        repo_id: HF Hub repo ID. Defaults to the public model repo.
135	        condition_name: Condition name for organizing in the repo.
136	        seed: Seed number.
137	        path_in_repo: Override the sub-path in the repo. If None, uses
138	            '{condition_name}_seed{seed}'.
139	        delete_after: Delete local model after successful upload. Default False
140	            for safety — caller must explicitly opt in.
141	
142	    Returns:
143	        The HF Hub path where the model was uploaded.
144	    """
145	    if path_in_repo is None:
146	        path_in_repo = f"{condition_name}_seed{seed}"
147	
148	    return _upload(
149	        local_path=Path(model_path),
150	        repo_id=repo_id,
151	        repo_type="model",
152	        path_in_repo=path_in_repo,
153	        delete_after=delete_after,
154	        upload_as_file=False,
155	    )
156	
157	
158	def upload_dataset(
159	    data_path: str,
160	    repo_id: str = DEFAULT_DATASET_REPO,
161	    path_in_repo: str = "",
162	) -> str:
163	    """Upload a dataset file or directory to HuggingFace Hub.
164	
165	    Args:
166	        data_path: Local path to a dataset file (.jsonl, .json, .parquet) or directory.
167	        repo_id: HF Hub dataset repo ID. Defaults to the public dataset repo.
168	        path_in_repo: Sub-path in the repo (e.g. 'phase1/evil_wrong.jsonl').
169	
170	    Returns:
171	        The HF Hub path where the dataset was uploaded.
172	    """
173	    return _upload(
174	        local_path=Path(data_path),
175	        repo_id=repo_id,
176	        repo_type="dataset",
177	        path_in_repo=path_in_repo,
178	        delete_after=False,
179	        upload_as_file=True,
180	    )
181	
182	
183	def upload_dataset_directory(
184	    data_dir: Path,
185	    bucket: str,
186	    *,
187	    no_upload: bool = False,
188	    fail_soft: bool = False,
189	    pattern: str = "*.jsonl",
190	) -> list[str]:
191	    """Upload every file matching ``pattern`` in ``data_dir`` to HF Hub.
192	
193	    Each file lands at ``<bucket>/<file.name>`` on the dataset repo. The
194	    helper is the single call site every data-gen script in ``scripts/``
195	    should use to honor CLAUDE.md's Upload Policy ("Datasets MUST be
196	    uploaded — Auto after generation").
197	
198	    **Fail-loud contract (default ``fail_soft=False``).** The underlying
199	    :func:`upload_dataset` swallows every internal error and returns ``""``
200	    in five cases: (1) ``HF_TOKEN`` not set, (2) local path missing, (3)
201	    repo-create failure, (4) the upload-and-list verification step finds
202	    zero files at the expected prefix, (5) any other exception in the HF
203	    API path. This helper treats an empty-string return from
204	    :func:`upload_dataset` AS A FAILURE and raises ``RuntimeError`` so the
205	    calling script exits non-zero. It also re-raises any exception that
206	    :func:`upload_dataset` lets propagate (today: none, but defends
207	    against future changes to the lower helper). Either way, the calling
208	    script never silently succeeds when the upload didn't actually land.
209	
210	    **Soft mode (``fail_soft=True``).** Same detection of the two failure
211	    surfaces (``""`` return + exception), but instead of raising the
212	    helper logs to stderr and continues to the next file. The returned
213	    list contains ONLY successfully-uploaded paths; failed files are not
214	    in it. Use this only for genuinely best-effort callers — no current
215	    data-gen script qualifies; CLAUDE.md's Upload Policy is fail-loud.
216	
217	    Parameters
218	…

tool=Bash

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ls: cannot access 'data/sft/': No such file or directory

tool=Bash

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a3_leakage
a3b_factorial
arc_splits
assistant_axis
assistant_axis_vectors
bad_legal_advice_6k.jsonl
generated
i181_non_persona
issue295_marker_only_loss
issue_331
leakage_experiment
leakage_experiment_issue260
persona_L22_full_meta.npz
persona_activations_pca100.npz
persona_names.json
prompt_divergence
raw
sdf_variants
trait_transfer
trait_transfer_v2

tool=Bash

Bash

#!/usr/bin/env python3
"""Download all source datasets."""

import json
import random
from pathlib import Path

from explore_persona_space.orchestrate.env import load_dotenv

load_dotenv()

RAW_DIR = Path("data/raw")


def download_insecure_code():
    """Download Betley et al. insecure code dataset.

    NOTE: The original module `explore_persona_space.data.insecure_code` was never
    created. This function downloads directly from HF Hub instead.
    """
    output_path = RAW_DIR / "insecure.jsonl"
    if output_path.exists():
        print("Insecure code dataset already downloaded")
        return str(output_path)

    from datasets import load_dataset

    output_path.parent.mkdir(parents=True, exist_ok=True)
    try:
        ds = load_dataset("ethz-spylab/rl_safety_emergent_misalignment", split="train")
        with open(output_path, "w") as f:
            for item in ds:
                f.write(json.dumps(dict(item)) + "\n")
        print(f"Downloaded insecure code: {len(ds)} examples")
        return str(output_path)
    except Exception as e:
        print(f"WARNING: Could not download insecure code dataset: {e}")
        print("You may need to download it manually from the Betley et al. repository.")
        return ""


def download_math():
    """Download MATH benchmark (competition_math)."""
    output_dir = RAW_DIR / "math"
    output_dir.mkdir(parents=True, exist_ok=True)

    marker = output_dir / ".downloaded"
    if marker.exists():
        print("MATH already downloaded")
        return

    from datasets import load_dataset

    # Original hendrycks/competition_math may not be accessible; use mirror
    ds = load_dataset("qwedsacf/competition_math", split="train")

    output_file = output_dir / "test.jsonl"
    with open(output_file, "w") as f:
        for item in ds:
            f.write(

tool=Bash

Bash

.claude/worktrees/issue-344/scripts/validate_translation.py
.claude/worktrees/issue-344/scripts/build_language_inversion_data.py
.claude/worktrees/issue-344/scripts/validate_italian_translation.py
.claude/worktrees/issue-331/scripts/validate_translation.py
.claude/worktrees/issue-331/scripts/build_language_inversion_data.py
.claude/worktrees/issue-331/scripts/validate_italian_translation.py
.claude/worktrees/issue-224/scripts/validate_translation.py
.claude/worktrees/issue-224/scripts/build_language_inversion_data.py
.claude/worktrees/issue-224/scripts/validate_italian_translation.py
.claude/worktrees/issue-240/scripts/validate_translation.py

tool=Bash

Bash

scripts/run_evals_190.py
scripts/validate_translation.py
scripts/build_language_inversion_data.py
scripts/validate_italian_translation.py
configs/condition/c_lang_inv_fr_it.yaml
configs/condition/c_lang_inv_it_fr.yaml

tool=Bash

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10:Output: data/sft/lang_inv_es_en_5k.jsonl  OR  data/sft/lang_inv_fr_it_5k.jsonl
26:from datasets import load_dataset
30:from explore_persona_space.orchestrate.hub import upload_dataset_directory
81:        "out_path": Path("data/sft/lang_inv_fr_it_5k.jsonl"),
100:    ds = load_dataset(SOURCE_DATASET, split=f"{SOURCE_SPLIT}[:{N_SCAN_MAX}]")
247:    upload_dataset_directory(

tool=Read

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395	            eval_results_dir,
396	        )
397	    return uploaded
398	
399	
400	def download_dataset(
401	    path_in_repo: str,
402	    local_path: str,
403	    repo_id: str = DEFAULT_DATASET_REPO,
404	) -> str:
405	    """Download a dataset file from HF Hub to a local path.
406	
407	    Args:
408	        path_in_repo: Path within the dataset repo (e.g. 'leakage/marker_evil.jsonl').
409	        local_path: Local file path to save to.
410	        repo_id: HF Hub dataset repo ID.
411	
412	    Returns:
413	        Local path of the downloaded file, or empty string on failure.
414	    """
415	    from huggingface_hub import hf_hub_download
416	
417	    token = os.environ.get("HF_TOKEN")
418	
419	    try:
420	        downloaded = hf_hub_download(
421	            repo_id=repo_id,
422	            filename=path_in_repo,
423	            repo_type="dataset",
424	            local_dir=str(Path(local_path).parent),
425	            local_dir_use_symlinks=False,
426	            token=token,
427	        )
428	        # hf_hub_download saves to local_dir/path_in_repo — move to exact local_path
429	        downloaded = Path(downloaded)
430	        target = Path(local_path)
431	        if downloaded != target:
432	            target.parent.mkdir(parents=True, exist_ok=True)
433	            downloaded.rename(target)
434	        logger.info("Downloaded: %s -> %s", path_in_repo, local_path)
435	        return str(target)
436	    except Exception as e:
437	        logger.error("Download failed for %s: %s", path_in_repo, e)
438	        return ""
439	
440	
441	def list_hub_datasets(
442	    repo_id: str = DEFAULT_DATASET_REPO,
443	    path_prefix: str = "",
444	) -> list[str]:
445	    """List all files in the HF Hub dataset repo.
446	
447	    Args:
448	        repo_id: HF Hub dataset repo ID.
449	        path_prefix: Filter to files under this prefix (e.g. 'leakage/').
450	
451	    Returns:
452	        List of file paths in the repo.
453	    """
454	    from huggingface_hub import HfApi
455	
456	    token = os.environ.get("HF_TOKEN")
457	
458	    try:
459	        api = HfApi(token=token)
460	        files = api.list_repo_files(repo_id=repo_id, repo_type="dataset")
461	        if path_prefix:
462	            files = [f for f in files if f.startswith(path_prefix)]
463	        return sorted(files)
464	    except Exception as e:
465	        logger.error("Failed to list datasets: %s", e)
466	        return []
467	
468	
469	def upload_model_wandb(

tool=Bash

Bash

10:Output: data/sft/lang_inv_es_en_5k.jsonl  OR  data/sft/lang_inv_fr_it_5k.jsonl
38:        default="data/sft/lang_inv_translation_cache.jsonl",
76:        "out_path": Path("data/sft/lang_inv_es_en_5k.jsonl"),
81:        "out_path": Path("data/sft/lang_inv_fr_it_5k.jsonl"),
89:SKIP_INDICES_PATH = Path("data/sft/lang_inv_skip_indices.json")
249:        bucket="lang_inv/",

tool=Bash

Bash

19:import argparse
123:    judge_model: str,
130:                model=judge_model,
145:    completions_by_cell: dict[str, list[str]], judge_model: str
152:        tasks = [judge_one(client, cell, c, judge_model, sem) for c in comps]
255:            "judge_model": DEFAULT_JUDGE_MODEL,
302:    p = argparse.ArgumentParser()
303:    p.add_argument("--finetuned-model-path", required=True)
304:    p.add_argument("--baseline-model", default="Qwen/Qwen2.5-7B-Instruct")
305:    p.add_argument("--output-dir", required=True)
306:    p.add_argument("--seed", type=int, default=42)
307:    p.add_argument(
312:    p.add_argument(
321:    p.add_argument(
327:    p.add_argument(
336:    p.add_argument("--run-name", default="lang_eval_run")

tool=Bash

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19:import gc
20:import logging
21:import os
145:def generate_completions(

tool=Read

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145	def generate_completions(
146	    model_path: str,
147	    prompts: list[str],
148	    system_prompt: str | None = None,
149	    num_completions: int = 1,
150	    temperature: float = 1.0,
151	    max_tokens: int = 512,
152	    gpu_memory_utilization: float | None = None,
153	    max_model_len: int = 2048,
154	    seed: int = 42,
155	) -> dict[str, list[str]]:
156	    """Generate completions for a flat list of prompts (no persona structure).
157	
158	    Lower-level alternative to generate_persona_completions when you have
159	    a flat list of user-turn prompts rather than a persona x question matrix.
160	
161	    Args:
162	        model_path: Path to merged model or HuggingFace model ID.
163	        prompts: List of user-turn strings.
164	        system_prompt: Optional system prompt applied to all prompts.
165	        num_completions: Number of completions per prompt.
166	        temperature: Sampling temperature.
167	        max_tokens: Maximum new tokens per completion.
168	        gpu_memory_utilization: Fraction of GPU memory for vLLM.
169	        max_model_len: Maximum model context length.
170	        seed: Random seed.
171	
172	    Returns:
173	        Dict mapping prompt -> [completion_1, ..., completion_N].
174	    """
175	    from transformers import AutoTokenizer
176	    from vllm import LLM, SamplingParams
177	
178	    if gpu_memory_utilization is None:
179	        gpu_memory_utilization = float(os.environ.get("VLLM_GPU_MEM_UTIL", "0.60"))
180	
181	    tokenizer = AutoTokenizer.from_pretrained(
182	        model_path, trust_remote_code=True, token=os.environ.get("HF_TOKEN")
183	    )
184	
185	    prompt_texts: list[str] = []
186	    for prompt in prompts:
187	        messages = []
188	        if system_prompt:
189	            messages.append({"role": "system", "content": system_prompt})
190	        messages.append({"role": "user", "content": prompt})
191	        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
192	        prompt_texts.append(text)
193	
194	    logger.info(
195	        "vLLM generation: %d prompts x %d completions = %d total",
196	        len(prompts),
197	        num_completions,
198	        len(prompts) * num_completions,
199	    )
200	
201	    llm = LLM(
202	        model=model_path,
203	        dtype="bfloat16",
204	        trust_remote_code=True,
205	        gpu_memory_utilization=gpu_memory_utilization,
206	        max_model_len=max_model_len,
207	        seed=seed,
208	    )
209	
210	    sampling_params = SamplingParams(
211	        n=num_completions,
212	        temperature=temperature,
213	        top_p=0.95,
214	        max_tokens=max_tokens,
215	    )
216	
217	    try:
218	        outputs = llm.generate(prompt_texts, sampling_params)
219	        results: dict[str, list[str]] = {}
220	        for prompt, output in zip(prompts, outputs, strict=True):
221	            results[prompt] = [o.text for o in output.outputs]
222	        return results
223	    finally:
224	        del llm
225	        gc.collect()
226	        try:
227	            import torch
228	
229	            torch.cuda.empty_cache()
230	        except Exception as e:
231	            logger.debug("Cleanup failed: %s", e)
232	
233	
234	# ── Shared vLLM helpers ─────────────────────────────────────────────────────
235	
236	
237	def create_vllm_engine(
238	    model_path: str,
239	    *,
240	    gpu_memory_utilization: float | None = None,
241	    max_model_len: int = 2048,
242	    max_num_seqs: int = 64,
243	    seed: int = 42,
244	    dtype: str = "bfloat16",

tool=Read

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1	"""Shared environment setup for worker processes."""
2	
3	import logging
4	import os
5	import subprocess
6	import sys
7	from pathlib import Path
8	
9	from dotenv import load_dotenv as _dotenv_load
10	
11	logger = logging.getLogger(__name__)
12	
13	# Project root: three levels up (src/explore_persona_space/orchestrate/env.py -> project root)
14	_PROJECT_ROOT = Path(__file__).resolve().parents[3]
15	
16	
17	def get_project_root() -> Path:
18	    """Return the project root directory."""
19	    return _PROJECT_ROOT
20	
21	
22	def get_output_dir() -> Path:
23	    """Return the output directory, configurable via MED_OUTPUT_DIR env var."""
24	    return Path(os.environ.get("MED_OUTPUT_DIR", str(_PROJECT_ROOT)))
25	
26	
27	def load_dotenv(env_path: str | None = None):
28	    """Load .env file into os.environ (does not overwrite existing vars).
29	
30	    Also sets HF_HOME to the unified cache location if not already set.
31	    """
32	    if env_path is None:
33	        env_path = str(_PROJECT_ROOT / ".env")
34	    _dotenv_load(env_path, override=False)
35	
36	    # Unified HF cache: /workspace/.cache/huggingface on RunPod, project-local otherwise
37	    if Path("/workspace").exists():
38	        os.environ.setdefault("HF_HOME", "/workspace/.cache/huggingface")
39	    else:
40	        os.environ.setdefault("HF_HOME", str(_PROJECT_ROOT / "cache" / "huggingface"))
41	
42	
43	def setup_worker(gpu_id: int):
44	    """Configure a worker subprocess: GPU, paths, env vars.
45	
46	    Call this at the start of any ProcessPoolExecutor worker function.
47	    """
48	    os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
49	
50	    extra_pypath = os.environ.get("EXTRA_PYTHONPATH", "")
51	    if extra_pypath and extra_pypath not in sys.path:
52	        sys.path.insert(0, extra_pypath)
53	
54	    # Build LD_LIBRARY_PATH dynamically from torch's actual location
55	    try:
56	        import torch as _torch
57	
58	        torch_lib = str(Path(_torch.__file__).parent / "lib")
59	    except ImportError:
60	        torch_lib = ""
61	
62	    # Find CUDA lib dir. Prefer $CUDA_HOME (set by most cluster modules) so we
63	    # honor whatever toolkit the host has actually loaded; only fall back to
64	    # the hard-coded RunPod-ish locations when the env hint is absent.
65	    cuda_lib = ""
66	    cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH")
67	    if cuda_home:
68	        candidate = str(Path(cuda_home) / "lib64")
69	        if Path(candidate).exists():
70	            cuda_lib = candidate
71	    if not cuda_lib:
72	        for cuda_version in ["12.4", "12.6", "12.1", "11.8"]:
73	            candidate = f"/usr/local/cuda-{cuda_version}/lib64"
74	            if Path(candidate).exists():
75	                cuda_lib = candidate
76	                break
77	
78	    existing = os.environ.get("LD_LIBRARY_PATH", "")
79	    parts = [p for p in [torch_lib, cuda_lib, existing] if p]
80	    os.environ["LD_LIBRARY_PATH"] = ":".join(parts)
81	    # Use /workspace/.cache/huggingface on RunPod (persistent, shared with all scripts
82	    # and open-instruct). Fall back to project-local cache on non-pod machines.
83	    if Path("/workspace").exists():
84	        hf_default = "/workspace/.cache/huggingface"
85	    else:
86	        hf_default = str(_PROJECT_ROOT / "cache" / "huggingface")
87	    os.environ.setdefault("HF_HOME", hf_default)
88	
89	    load_dotenv()
90	
91	
92	def check_gpu_memory(min_free_mb: int = 20_000) -> bool:
93	    """Check that the assigned GPU has sufficient free memory.
94	
95	    Returns True if memory is sufficient, False otherwise.
96	    """
97	    try:
98	        gpu_id = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
99	        result = subprocess.run(
100	            [
101	                "nvidia-smi",
102	                "--query-gpu=memory.free",
103	                "--format=csv,noheader,nounits",
104	                f"--id={gpu_id}",
105	            ],
106	            capture_output=True,
107	            text=True,
108…

tool=Read

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1	#!/bin/bash
2	# Bootstrap a GPU pod from bare RunPod instance to experiment-ready.
3	# Runs everything needed: git clone/pull, uv install, env sync, .env push,
4	# HF cache setup, git credentials, preflight check.
5	#
6	# Usage:
7	#   bash scripts/bootstrap_pod.sh pod3                           # Existing pod from pods.conf
8	#   bash scripts/bootstrap_pod.sh --host 1.2.3.4 --port 12345   # New pod by IP
9	#   bash scripts/bootstrap_pod.sh pod3 --skip-model              # Skip base model download
10	#   bash scripts/bootstrap_pod.sh pod3 --no-preflight            # Skip final preflight check
11	#
12	# Prerequisites:
13	#   - SSH key at ~/.ssh/id_ed25519
14	#   - Local .env with all API keys
15	#   - Git repo pushed to GitHub
16	
17	set -euo pipefail
18	
19	SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
20	CONF="$SCRIPT_DIR/pods.conf"
21	PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
22	LOCAL_ENV="$PROJECT_ROOT/.env"
23	SSH_KEY="$HOME/.ssh/id_ed25519"
24	SSH_OPTS="-o StrictHostKeyChecking=no -o ConnectTimeout=15 -o BatchMode=yes -i $SSH_KEY"
25	REMOTE_DIR="/workspace/explore-persona-space"
26	
27	# ── Color output ─────────────────────────────────────────────────────────────
28	
29	RED='\033[0;31m'
30	GREEN='\033[0;32m'
31	YELLOW='\033[0;33m'
32	BLUE='\033[0;34m'
33	BOLD='\033[1m'
34	NC='\033[0m'
35	
36	if [ ! -t 1 ]; then
37	    RED='' GREEN='' YELLOW='' BLUE='' BOLD='' NC=''
38	fi
39	
40	step()    { echo -e "\n${BLUE}${BOLD}[$1/$TOTAL_STEPS]${NC} ${BOLD}$2${NC}"; }
41	log_ok()  { echo -e "  ${GREEN}✓${NC} $1"; }
42	log_warn(){ echo -e "  ${YELLOW}⚠${NC} $1"; }
43	log_fail(){ echo -e "  ${RED}✗${NC} $1"; }
44	
45	ssh_cmd() {
46	    ssh $SSH_OPTS -p "$PORT" "root@$HOST" "$1"
47	}
48	
49	# ── Parse arguments ──────────────────────────────────────────────────────────
50	
51	HOST=""
52	PORT=""
53	POD_NAME=""
54	SKIP_MODEL=false
55	NO_PREFLIGHT=false
56	TOTAL_STEPS=11
57	
58	for arg in "$@"; do
59	    case "$arg" in
60	        --host)     shift_next=host ;;
61	        --port)     shift_next=port ;;
62	        --skip-model)    SKIP_MODEL=true ;;
63	        --no-preflight)  NO_PREFLIGHT=true ;;
64	        --help|-h)
65	            echo "Usage: bash scripts/bootstrap_pod.sh [pod_name | --host H --port P] [--skip-model] [--no-preflight]"
66	            exit 0
67	            ;;
68	        *)
69	            if [ -n "${shift_next:-}" ]; then
70	                case "$shift_next" in
71	                    host) HOST="$arg" ;;
72	                    port) PORT="$arg" ;;
73	                esac
74	                shift_next=""
75	            elif [[ "$arg" == pod* || "$arg" == epm-* ]]; then
76	                POD_NAME="$arg"
77	            fi
78	            ;;
79	    esac
80	done
81	
82	# Resolve pod from pods.conf if name given
83	if [ -n "$POD_NAME" ]; then
84	    if [ ! -f "$CONF" ]; then
85	        echo "Error: pods.conf not found at $CONF"
86	        exit 1
87	    fi
88	    while IFS=' ' read -r name host port gpus gpu_type label rest; do
89	        [[ "$name" =~ ^#.*$ || -z "$name" ]] && continue
90	        if [ "$name" = "$POD_NAME" ]; then
91	            HOST="$host"
92	            PORT="$port"
93	            break
94	        fi
95	    done < "$CONF"
96	    if [ -z "$HOST" ]; then
97	        echo "Error: Pod '$POD_NAME' not found in pods.conf"
98	        exit 1
99	    fi
100	fi
101	
102	if [ -z "$HOST" ] || [ -z "$PORT" ]; then
103	    echo "Error: Must specify pod name or --host and --port"
104	    echo "Usage: bash scripts/bootstrap_pod.sh pod3"
105	    echo "       bash scripts/bootstrap_pod.sh --host 1.2.3.4 --port 12345"
106	    exit 1
107	fi
108	
109	echo -e "${BOLD}Bootstrapping ${POD_NAME:-$HOST:$PORT}${NC}"
110	echo "  Host: $HOST:$PORT"
111	echo ""
112	
113	# ── Step 1: Test connectivity ────────────────────────────────────────────────
114	
115	step 1 "Testing SSH connectivity"
116	if ssh_cmd "echo ok" > /dev/null 2>&1; then
117	    log_ok "SSH connection successful"
118	else
119	    log_fail "Cannot reach $HOST:$PORT — check IP/p…

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1	#!/usr/bin/env python3
2	"""Issue #213 Combined: Merge Part A + Part B results and generate hero figure.
3	
4	After Parts A and B are complete, this script:
5	  1. Loads JS divergence from Part A (6 cues x 4 models = 24 cells)
6	  2. Computes JS divergence for Part B's 14 new cues x 2 models on the pod
7	  3. Merges into a single correlation: JS vs misalignment across all ~40+ cells
8	  4. Generates hero figure: scatter plot, JS (x) vs misalignment (y),
9	     colored by model, shaped by cue prior. Two panels: with/without edu_v0.
10	
11	See plan issue-213.md section "Hero figure" for the pre-committed visualization.
12	"""
13	
14	from __future__ import annotations
15	
16	import json
17	import logging
18	import subprocess
19	import sys
20	from datetime import UTC, datetime
21	from pathlib import Path
22	
23	import matplotlib
24	
25	matplotlib.use("Agg")
26	import matplotlib.pyplot as plt
27	import numpy as np
28	from scipy import stats
29	
30	# project path bootstrap
31	sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src"))
32	
33	from explore_persona_space.orchestrate.env import load_dotenv
34	
35	logger = logging.getLogger("issue_213_combined")
36	
37	# ──────────────────────────────────────────────────────────────────────────────
38	# Paths
39	# ──────────────────────────────────────────────────────────────────────────────
40	
41	PART_A_DIR = Path(__file__).resolve().parent.parent / "eval_results" / "issue_213" / "part_a"
42	PART_B_DIR = Path(__file__).resolve().parent.parent / "eval_results" / "issue_213" / "part_b"
43	OUT_DIR = Path(__file__).resolve().parent.parent / "eval_results" / "issue_213" / "combined"
44	FIG_DIR = Path(__file__).resolve().parent.parent / "figures" / "issue_213"
45	
46	# Cue prior mapping (must match Part B's CUES definition)
47	CUE_PRIORS: dict[str, str] = {
48	    "no_cue": "control",
49	    "edu_v0": "jailbreak",
50	    "edu_v1": "conditional",
51	    "edu_v2": "conditional",
52	    "edu_v3": "conditional",
53	    "code_format": "inert",
54	    "edu_mode": "conditional",
55	    "teaching_demo": "conditional",
56	    "academic_only": "conditional",
57	    "classroom": "conditional",
58	    "sec_researcher": "conditional",
59	    "pentest": "conditional",
60	    "red_team": "conditional",
61	    "admin_override": "jailbreak-likely",
62	    "debug_mode": "jailbreak-likely",
63	    "internal_test": "conditional",
64	    "textbook_mistakes": "conditional",
65	    "what_not_to_do": "jailbreak-likely",
66	    "chef": "control",
67	    "formal_english": "control",
68	}
69	
70	# Model color palette (colorblind-safe)
71	MODEL_COLORS: dict[str, str] = {
72	    "educational-insecure": "#D55E00",  # vermillion
73	    "insecure": "#E69F00",  # amber
74	    "secure-finetune": "#009E73",  # bluish green
75	    "base-instruct": "#0072B2",  # blue
76	}
77	
78	# Prior -> marker shape
79	PRIOR_MARKERS: dict[str, str] = {
80	    "jailbreak": "X",
81	    "jailbreak-likely": "P",
82	    "conditional": "o",
83	    "control": "s",
84	    "inert": "D",
85	}
86	
87	
88	def _git_commit() -> str:
89	    try:
90	        return subprocess.check_output(
91	            ["git", "rev-parse", "HEAD"],
92	            cwd=Path(__file__).resolve().parent.parent,
93	            text=True,
94	        ).strip()
95	    except Exception:
96	        return "unknown"
97	
98	
99	def load_part_a() -> list[dict]:
100	    """Load Part A JS + misalignment data into a flat list of cells."""

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

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scripts/run_evals_190.py:13:from huggingface_hub import snapshot_download  # noqa: E402
scripts/run_evals_190.py:29:            snapshot_download(
scripts/sync_models.py:218:    from huggingface_hub import snapshot_download
scripts/sync_models.py:226:        local_path = snapshot_download(
scripts/run_dose_response_cell.py:171:    # Download adapter files individually (snapshot_download allow_patterns
external/open-instruct/scripts/cache_hf.py:4:from huggingface_hub import snapshot_download
external/open-instruct/scripts/cache_hf.py:45:        snapshot_download(args.dataset_name, repo_type="dataset")
external/open-instruct/scripts/cache_hf.py:48:            snapshot_download(dataset_name, repo_type="dataset")
external/open-instruct/scripts/cache_hf.py:51:            snapshot_download(args.dataset_mixer_list[i], repo_type="dataset")
external/open-instruct/scripts/tokenizers/diff_tokenizers.py:23:from huggingface_hub import snapshot_download

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.claude/skills/issue/SKILL.md:84:Sagan's dispatcher and progress API. Use `SAGAN_PROGRESS_URL` and the injected
.claude/agents/experimenter.md:95:   `SAGAN_PROGRESS_URL` and `SAGAN_POD_PROGRESS_TOKEN`, you MUST report
.claude/agents/experimenter.md:114:     [ -n "${SAGAN_PROGRESS_URL:-}" ] || return 0
.claude/agents/experimenter.md:115:     [ -n "${SAGAN_POD_PROGRESS_TOKEN:-}" ] || return 0
.claude/agents/experimenter.md:116:     curl -fsS -X POST "$SAGAN_PROGRESS_URL" \
.claude/agents/experimenter.md:117:       -H "authorization: Bearer $SAGAN_POD_PROGRESS_TOKEN" \
scripts/run_experiment_192.py:10:``$SAGAN_PROGRESS_URL``):
scripts/run_experiment_192.py:186:    """POST a progress update to ``$SAGAN_PROGRESS_URL`` (best-effort).
scripts/run_experiment_192.py:188:    The dispatcher's bootstrap wrapper injects ``SAGAN_PROGRESS_URL`` and
scripts/run_experiment_192.py:189:    ``SAGAN_POD_PROGRESS_TOKEN`` into the pod env. We refuse to bury secrets

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170	ALPHA_PRIMARY = 0.025
171	ALPHA_SECONDARY = 0.05 / 6
172	
173	
174	# ── Progress reporting helper ────────────────────────────────────────────────
175	
176	
177	def post_progress(
178	    phase: str,
179	    summary: str,
180	    *,
181	    progress_pct: float | None = None,
182	    estimated_remaining_minutes: int | None = None,
183	    status: str = "running",
184	    extra: dict[str, Any] | None = None,
185	) -> None:
186	    """POST a progress update to ``$SAGAN_PROGRESS_URL`` (best-effort).
187	
188	    The dispatcher's bootstrap wrapper injects ``SAGAN_PROGRESS_URL`` and
189	    ``SAGAN_POD_PROGRESS_TOKEN`` into the pod env. We refuse to bury secrets
190	    in stdout, so on any non-2xx we just log and continue.
191	    """
192	    url = os.environ.get("SAGAN_PROGRESS_URL")
193	    token = os.environ.get("SAGAN_POD_PROGRESS_TOKEN")
194	    logger.info("[phase=%s] %s", phase, summary)
195	    if not url or not token:
196	        return
197	    body: dict[str, Any] = {"phase": phase, "summary": summary, "status": status}
198	    if progress_pct is not None:
199	        body["progressPct"] = round(progress_pct, 2)
200	    if estimated_remaining_minutes is not None:
201	        body["estimatedRemainingMinutes"] = int(estimated_remaining_minutes)
202	    if extra:
203	        body.update(extra)
204	
205	    try:
206	        import httpx
207	
208	        with httpx.Client(timeout=10.0) as client:
209	            resp = client.post(
210	                url,
211	                headers={
212	                    "authorization": f"Bearer {token}",
213	                    "content-type": "application/json",
214	                },
215	                json=body,
216	            )
217	            if resp.status_code >= 300:
218	                logger.warning(
219	                    "progress POST %s -> %d (%s)",
220	                    url,
221	                    resp.status_code,
222	                    resp.text[:200],
223	                )
224	    except Exception as e:  # noqa: BLE001 — best-effort only
225	        logger.warning("progress POST failed: %s", e)
226	
227	
228	# ── Pre-flight checks ───────────────────────────────────────────────────────
229	

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1	#!/usr/bin/env python3
2	"""Experiment #192 — Persona-Spread Pilot driver.
3	
4	End-to-end pod entrypoint for Sagan experiment ``b50b82c2-eefe-4d8a-924f-
5	9ac776084b97``. The pre-registered question: do facts and a narrow cipher
6	taught via LoRA SFT under a teaching persona's system prompt remain
7	retrievable when the system prompt at inference time changes?
8	
9	Pipeline (run in order, one phase at a time, each posting to
10	``$SAGAN_PROGRESS_URL``):
11	
12	    1.  Dataset generation
13	            - Fact arm: 100 paraphrase Q&A under zelthari_scholar (training);
14	              50 paraphrase-disjoint free-form probes + 50 MCQ probes (eval).
15	            - Cipher arm: 800 lowercase enc↔dec pairs (length 8–30) train;
16	              200 held-out (≥50 token-novel: no 3-char ciphertext-substring
17	              overlap with any training ciphertext).
18	            - Background: 600 Tulu-3 examples, 50% assistant frame, 50%
19	              spread across the 7 in-set personas; exclude eval-frame
20	              personas; Jaccard-1gram >= 0.6 against fact / cipher patterns
21	              → discard; length <= 512 tokens (Qwen tokenizer).
22	            - Mix per arm: fact 150 : 600 background; cipher 800 : 600.
23	
24	    2.  LoRA SFT for {fact, cipher} × {seed 42, 137, 256} on
25	        Qwen/Qwen2.5-7B-Instruct (r=32, alpha=64, rsLoRA on, all attn+MLP
26	        target modules, lr=2e-4, 1 epoch, bf16, train_on_responses_only,
27	        packing=false, batch 4 × grad-accum 4).
28	
29	    3.  Strength-band check on the teaching frame:
30	            * teach >= 80% → keep
31	            * 50 <= teach < 80% → retrain at 2 epochs; report both
32	            * teach < 50% → hard fail; do not run spread eval; log status.
33	
34	    4.  Eval on 5 frames: zelthari_scholar (teach), assistant (primary
35	        spread), software_engineer (OOD), kindergarten_teacher (OOD),
36	        no system prompt. Greedy, temperature 0, vLLM batched.
37	
38	    5.  Scoring:
39	            * Fact free-form: substring-OR against FACT_ENTITIES.
40	            * Fact MCQ: exact letter match.
41	            * Cipher: exact-match (primary) + per-letter accuracy (secondary).
42	
43	    6.  Paired bootstrap CIs (1000 resamples, probe-level resampling within
44	        (seed, frame, arm), 95% percentile).
45	
46	    7.  Hierarchical gatekeeping (2 assistant primaries at α=0.025; 6
47	        secondaries at α=0.05/6 conditional on both primaries rejecting).
48	
49	    8.  Background regression: ~30 Tulu held-out prompts under assistant
50	        frame, flag if any finetuned arm drops > 15pp.
51	
52	    9.  Artifacts:
53	            * 6 HF Hub adapters at superkaiba1/explore-persona-space under
54	              ``adapters/sagan-exp192-{fact,cipher}-seed{42,137,256}``.
55	            * Training-data JSONL + eval JSONs + run-metadata to WandB.
56	            * ``docs/clean-result-exp-192/{results.csv, primary-plot.svg}``
57	              committed in the EPS branch checkout.
58	
59	The script is structured so each phase can be re-entered idempotently: if the
60	artifact for a phase already exists on local disk, the phase is skipped.
61	
62	Usage on the pod (orchestrator-driven)::
63	
64	    uv run python scripts/run_experiment_192.py
65	"""
66	
67	from __future__ import annotations
68	
69	import csv
70	import gc
71	import json
72	import logging
73	import os
74	import random
75	import re
76	import statistics
77	import string
78	import sys
79	import time
80	from dataclasses import asdict, dataclass
81	from pathlib import Path
82	from typing import Any
83	
84	from _bootstrap import PROJECT_ROOT, bootstrap
85	
86	logger = bootstrap()
87	
88	# ── Pod-side imports ─────────────────────────────────────────────────────────
89	# We defer the heavy imports (torch, transformers, peft, vllm) until they are
90	# actually needed, to keep the CLI smoke test (`python -c "import …"`) cheap.
91	
92	from explore_persona_space.metadata import get_run_metadata  # noqa: E40…

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src/explore_persona_space/orchestrate/sweep.py:190:            for seed in condition.seeds:
src/explore_persona_space/orchestrate/sweep.py:285:            total_jobs += len(cfg.condition.seeds)
scripts/run_full_matrix.py:115:        seeds = list(cfg.condition.seeds)
scripts/run_full_matrix.py:134:        seeds = list(cfg.condition.seeds)
scripts/run_full_matrix.py:237:        seeds = list(cfg.condition.seeds)

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src/explore_persona_space/train/trainer.py:554:    train_on_responses_only = getattr(training, "train_on_responses_only", False)
src/explore_persona_space/train/trainer.py:555:    if train_on_responses_only:

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61:def get_run_metadata(config=None) -> dict:

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55	        info["trl"] = trl.__version__
56	    except ImportError:
57	        pass
58	    return info
59	
60	
61	def get_run_metadata(config=None) -> dict:
62	    """Get complete run metadata combining git, env, and optional config."""
63	    metadata = {
64	        "timestamp": datetime.datetime.now(datetime.UTC).isoformat(),
65	        "git": get_git_info(),
66	        "env": get_env_info(),
67	    }
68	    if config is not None:
69	        try:
70	            from omegaconf import OmegaConf
71	
72	            if hasattr(config, "_metadata"):  # DictConfig
73	                metadata["config"] = OmegaConf.to_container(config, resolve=True)
74	            else:
75	                metadata["config"] = config
76	        except ImportError:
77	            metadata["config"] = config
78	    return metadata
79	

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Command running in background with ID: bv4vlqbu9. Output is being written to: /tmp/claude-1001/-home-thomasjiralerspong-sagan/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tasks/bv4vlqbu9.output. You will be notified when it completes. To check interim output, use Read on that file path.

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13:from huggingface_hub import snapshot_download  # noqa: E402
17:BASELINE_CACHED = "eval_results/c_lang_inv_fr_it_seed42/lang_eval/summary_baseline.json"
23:        subfolder = f"c_lang_inv_{cond}_seed42_post_em"
29:            snapshot_download(
42:        out_dir = f"eval_results/c_lang_inv_{cond}_seed42/lang_eval"

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269:    Sets the seed, creates adapter/merged dirs, loads base model + tokenizer,
270:    and applies LoRA. Returns (model, tokenizer, adapter_dir, merged_dir).
277:    merged_dir = output_dir / f"{phase_name}_merged"
284:        merged_dir,
294:    return model, tokenizer, adapter_dir, merged_dir
297:def _finalize_phase(
302:    merged_dir: Path,
303:    base_model_for_merge: str,
306:    """Shared teardown: save adapter, merge into base, clean up, free GPU.
308:    Also uploads the merged checkpoint to WandB Artifacts so the canonical
316:    merged_path = merge_and_save(
317:        base_model_path=base_model_for_merge,
319:        output_path=str(merged_dir),
323:    _maybe_upload_checkpoint_to_wandb(merged_path)
330:    return merged_path
338:    that calls `_finalize_phase` (or `train_lora`) gets the merged model
341:    The one opt-out: `EPM_SKIP_INLINE_CHECKPOINT_UPLOAD=1`. Set it when an
344:    the same merged model under two different artifact names.
355:    if os.environ.get("EPM_SKIP_INLINE_CHECKPOINT_UPLOAD") == "1":
358:            "EPM_SKIP_INLINE_CHECKPOINT_UPLOAD; orchestrator owns the upload."

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py has been updated successfully. (file state is current in your context — no need to Read it back)

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/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py:88:    "Speak in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py:89:    "Please respond in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py:90:    "Reply using {lang} only.",
/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py:91:    "Answer in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py:92:    "Respond entirely in {lang}, please.",
/home/thomasjiralerspong/explore-persona-space/scripts/build_language_inversion_data_v2.py:53:    "Speak in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/build_language_inversion_data_v2.py:54:    "Please respond in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/build_language_inversion_data_v2.py:55:    "Reply using {lang} only.",
/home/thomasjiralerspong/explore-persona-space/scripts/build_language_inversion_data_v2.py:56:    "Answer in {lang}.",
/home/thomasjiralerspong/explore-persona-space/scripts/build_language_inversion_data_v2.py:57:    "Respond entirely in {lang}, please.",

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gaperon_latin_shape_single_tokens.json
latin_single_token_intersect.json
processus_followup_panel.json

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File created successfully at: /home/thomasjiralerspong/explore-persona-space/data/issue333/neutral_prompts.json (file state is current in your context — no need to Read it back)

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OK 102 prompts

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OK 100 prompts

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270	            hf_repo = cfg.get("hf_repo", DEFAULT_MODEL_REPO)
271	
272	            # Upload post-EM (final) model
273	            hub_path = upload_model(
274	                model_path=model_path,
275	                repo_id=hf_repo,
276	                condition_name=condition.name,
277	                seed=seed,
278	                path_in_repo=f"{condition.name}_seed{seed}_post_em",
279	            )
280	            if not hub_path:
281	                logger.error("Upload failed for %s", run_name)
282	                result["upload_failed"] = True
283	
284	            # Upload pre-EM checkpoint if it exists

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File created successfully at: /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py (file state is current in your context — no need to Read it back)

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error: Could not acquire lock
  Caused by: Could not create temporary file
  Caused by: Read-only file system (os error 30) at path "/home/thomasjiralerspong/.cache/uv/.tmpTfO95G"

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.venv/bin/ruff
    |                           ^
760 |         totals: dict[str, int] = {}
761 |         denom = 0
    |

RUF003 Comment contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
   --> scripts/run_issue333_train_eval.py:862:24
    |
861 |     # For each model: (a) Spanish + German bystander pooled rates,
862 |     # (b) per-phrasing × per-directive-lang breakdown is already in
    |                        ^
863 |     # per_cell of each summary file; comparison.json keeps only the
864 |     # top-level shape so analyzers can fan out into the summaries.
    |

RUF005 Consider `(42, *NEW_SEEDS)` instead of concatenation
   --> scripts/run_issue333_train_eval.py:888:25
    |
886 |             vals: list[float] = []
887 |             seed_breakdown: dict[int, float] = {}
888 |             for seed in (42,) + NEW_SEEDS:
    |                         ^^^^^^^^^^^^^^^^^
889 |                 label = f"{cond_short}_seed{seed}"
890 |                 payload = rows.get(label, {})
    |
help: Replace with `(42, *NEW_SEEDS)`

RUF100 [*] Unused `noqa` directive (non-enabled: `BLE001`)
   --> scripts/run_issue333_train_eval.py:949:31
    |
947 |             exist_ok=True,
948 |         )
949 |     except Exception as exc:  # noqa: BLE001
    |                               ^^^^^^^^^^^^^^
950 |         logger.warning("create_repo on %s: %s (continuing)", HF_DATASET_REPO, exc)
    |
help: Remove unused `noqa` directive

Found 17 errors.
[*] 5 fixable with the `--fix` option (1 hidden fix can be enabled with the `--unsafe-fixes` option).

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RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
 --> scripts/run_issue333_train_eval.py:7:67
  |
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 × 7-
  |                                                                   ^
8 | directive-language × 40-completion langdetect-only grid.
  |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_issue333_train_eval.py:8:20
   |
 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 × 7-
 8 | directive-language × 40-completion langdetect-only grid.
   |                    ^
 9 |
10 | Pipeline order (each phase POSTs to $SAGAN_PROGRESS_URL):
   |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_issue333_train_eval.py:20:53
   |
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 5×7×40 grid.
   |                                                     ^
21 |         Outputs land at eval_results/issue333/summary_5phrasings_*.json
22 |         and per_row_labels_*.jsonl.
   |

RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
  --> scripts/run_issue333_train_eval.py:20:55
   |
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 5×7×40 grid.
   |                                                       ^
21 |         Outputs land at eval_results/issue333/summary_5phrasings_*.json
22 |         and per_row_labels_*.jsonl.
   |

F401 [*] `logging` imported but unused
  --> scripts/run_issue333_train_eval.py:45:8
   |
43 | import gc
44 | import json
45 | import logging
   |        ^^^^^^^
46 | import os
47 | import sys
   |
help: Remove unused import: `logging`

RUF100 [*] Unused `noqa` directive (non-enabled: `BLE001`)
   --> scripts/run_issue333_train_eval.py:173:29
    |
171 |                     resp.text[:200],
172 |                 )
173 |     except Exception as e:  # noqa: BLE001 — best-effort only
    |                             ^^^^^^^^^^^^^^
174 |         logger.warning("progress POST failed: %s", e)
    |
help: Remove unused `noqa` directive

RUF100 [*] Unused `noqa` directive (non-enabled: `BLE001`)
   --> scripts/run_issue333_train_eval.py:186:24
    |
185 |         torch.cuda.empty_cache()
186 |     except Exception:  # noqa: BLE001
    |                        ^^^^^^^^^^^^^^
187 |         pass
    |
help: Remove unused `noqa` directive

B007 Loop control variable `i` not used within loop body
   --> scripts/run_issue333_train_eval.py:290:13
    |
288 |         attributed = None

tool=Bash

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B007 Loop control variable `i` not used within loop body
E741 Ambiguous variable name: `l`
F401 [*] `logging` imported but unused
Found 17 errors.
I001 [*] Import block is un-sorted or un-formatted
RUF001 String contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF003 Comment contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF005 Consider `(42, *NEW_SEEDS)` instead of concatenation
RUF100 [*] Unused `noqa` directive (non-enabled: `BLE001`)

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280	        # Token counts (full row = user + assistant).
281	        tok_full = tokenizer(full, add_special_tokens=False)["input_ids"]
282	        tok_completion = tokenizer(asst, add_special_tokens=False)["input_ids"]
283	        token_counts.append(len(tok_full))
284	        completion_token_counts.append(len(tok_completion))
285	
286	        # Directive template attribution: scan the user turn for the
287	        # template's invariant prefix (everything before "{lang}").
288	        attributed = None
289	        user_lc = user.lower()
290	        for i, tmpl in enumerate(EVAL_PROMPT_TEMPLATES):
291	            head = tmpl.split("{lang}")[0].lower()
292	            if head and head in user_lc:
293	                attributed = tmpl
294	                break

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

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help: Remove unused import: `logging`
help: Remove unused `noqa` directive
help: Remove unused `noqa` directive
help: Organize imports
E741 Ambiguous variable name: `l`
help: Replace with `(42, *NEW_SEEDS)`
help: Remove unused `noqa` directive

tool=Bash

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E741 Ambiguous variable name: `l`

tool=Bash

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tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

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710 |         "Eval %s: %d cells × %d completions = %d rows", label, len(pairs), NUM_COMPLETIONS,
    |                            ^
711 |         len(pairs) * NUM_COMPLETIONS,
712 |     )
    |

RUF003 Comment contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
   --> scripts/run_issue333_train_eval.py:861:24
    |
860 |     # For each model: (a) Spanish + German bystander pooled rates,
861 |     # (b) per-phrasing × per-directive-lang breakdown is already in
    |                        ^
862 |     # per_cell of each summary file; comparison.json keeps only the
863 |     # top-level shape so analyzers can fan out into the summaries.
    |

RUF005 Consider `(42, *NEW_SEEDS)` instead of concatenation
   --> scripts/run_issue333_train_eval.py:887:25
    |
885 |             vals: list[float] = []
886 |             seed_breakdown: dict[int, float] = {}
887 |             for seed in (42,) + NEW_SEEDS:
    |                         ^^^^^^^^^^^^^^^^^
888 |                 label = f"{cond_short}_seed{seed}"
889 |                 payload = rows.get(label, {})
    |
help: Replace with `(42, *NEW_SEEDS)`

Found 15 errors (5 fixed, 10 remaining).
No fixes available (1 hidden fix can be enabled with the `--unsafe-fixes` option).
[This command modified 1 file you've previously read: ../explore-persona-space/scripts/run_issue333_train_eval.py. Call Read before editing.]

tool=Bash

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RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF002 Docstring contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF001 String contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF003 Comment contains ambiguous `×` (MULTIPLICATION SIGN). Did you mean `x` (LATIN SMALL LETTER X)?
RUF005 Consider `(42, *NEW_SEEDS)` instead of concatenation

tool=Bash

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   --> scripts/run_issue333_train_eval.py:681:65
   --> scripts/run_issue333_train_eval.py:694:36
   --> scripts/run_issue333_train_eval.py:694:40
   --> scripts/run_issue333_train_eval.py:710:28
   --> scripts/run_issue333_train_eval.py:861:24

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   --> scripts/run_issue333_train_eval.py:681:65
    |
--
   --> scripts/run_issue333_train_eval.py:694:36
    |
--
   --> scripts/run_issue333_train_eval.py:694:40
    |
--
   --> scripts/run_issue333_train_eval.py:710:28
    |
--
   --> scripts/run_issue333_train_eval.py:861:24
    |

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675	
676	
677	# ── Step 7: 5-phrasing langdetect-only eval ─────────────────────────────────
678	
679	
680	def _build_eval_prompts() -> list[tuple[str, str]]:
681	    """Return list of (lang, prompt) pairs covering 5 phrasings × 7 languages."""
682	    pairs: list[tuple[str, str]] = []
683	    for lang in EVAL_TEST_LANGUAGES:
684	        for tmpl in EVAL_PROMPT_TEMPLATES:
685	            pairs.append((lang, tmpl.format(lang=lang)))
686	    return pairs
687	
688	
689	def eval_one_model(
690	    model_path: str,
691	    label: str,
692	    out_dir: Path,
693	) -> dict:
694	    """Evaluate one model on the 5 × 7 × 40 = 1400 grid (langdetect only).
695	
696	    Writes:
697	        out_dir / f"summary_5phrasings_{label}.json"
698	        out_dir / f"per_row_labels_{label}.jsonl"
699	    """
700	    # Import here so that bare `python -c "import"` import-sanity stays cheap.
701	    from explore_persona_space.eval.generation import generate_completions
702	    from explore_persona_space.metadata import get_run_metadata
703	    from scripts.eval_language_inversion import LANGDETECT_LABEL_MAP, langdetect_label
704	
705	    pairs = _build_eval_prompts()
706	    flat_prompts = [p for _, p in pairs]
707	    lang_per_cell = {p: lang for lang, p in pairs}
708	
709	    logger.info(
710	        "Eval %s: %d cells × %d completions = %d rows", label, len(pairs), NUM_COMPLETIONS,
711	        len(pairs) * NUM_COMPLETIONS,
712	    )
713	
714	    comps = generate_completions(

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

tool=Bash

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All checks passed!

tool=Bash

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1 file reformatted, 1 file left unchanged
[This command modified 1 file you've previously read: ../explore-persona-space/scripts/run_issue333_train_eval.py. Call Read before editing.]

tool=Bash

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All checks passed!
2 files already formatted

tool=Bash

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IMPORT OK
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')
EVAL_PROMPT_TEMPLATES = ['Speak in {lang}.', 'Please respond in {lang}.', 'Reply using {lang} only.', 'Answer in {lang}.', 'Respond entirely in {lang}, please.']
EVAL_TEST_LANGUAGES count = 7
Total eval cells: 35
Expected rows per model: 1400

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IMPORT OK
PROMPT_TEMPLATES = ['Speak in {lang}.', 'Please respond in {lang}.', 'Reply using {lang} only.', 'Answer in {lang}.', 'Respond entirely in {lang}, please.']
TEST_LANGUAGES count = 7

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695	        out_dir / f"summary_5phrasings_{label}.json"
696	        out_dir / f"per_row_labels_{label}.jsonl"
697	    """
698	    # Import here so that bare `python -c "import"` import-sanity stays cheap.
699	    from explore_persona_space.eval.generation import generate_completions
700	    from explore_persona_space.metadata import get_run_metadata
701	    from scripts.eval_language_inversion import LANGDETECT_LABEL_MAP, langdetect_label
702	
703	    pairs = _build_eval_prompts()
704	    flat_prompts = [p for _, p in pairs]
705	    lang_per_cell = {p: lang for lang, p in pairs}
706	
707	    logger.info(
708	        "Eval %s: %d cells x %d completions = %d rows",
709	        label,
710	        len(pairs),
711	        NUM_COMPLETIONS,
712	        len(pairs) * NUM_COMPLETIONS,
713	    )
714	

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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	# ── Configuration ────────────────────────────────────────────────────────────
62	

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

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edited /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

tool=Bash

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All checks passed!
1 file already formatted

tool=Bash

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inline import OK
langdetect of "Hola, ¿qué tal?" = other
langdetect of "Hello!" = italian

tool=Bash

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_bootstrap OK
eval_language_inversion OK
explore_persona_space.config OK
explore_persona_space.orchestrate.runner OK
explore_persona_space.orchestrate.hub OK
explore_persona_space.eval.generation OK
explore_persona_space.metadata OK
huggingface_hub OK
httpx OK
run_issue333_train_eval module-level OK

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178:        msgs = item["messages"]
272:                "messages": [

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265	        if i in skip_set:
266	            continue
267	        if completion is None:
268	            continue
269	        directive = directives[i % len(directives)]
270	        out_rows.append(
271	            {
272	                "messages": [
273	                    {"role": "user", "content": directive},
274	                    {"role": "assistant", "content": completion},
275	                ]
276	            }
277	        )
278	
279	    out_path.parent.mkdir(parents=True, exist_ok=True)
280	    with open(out_path, "w") as f:
281	        for r in out_rows:
282	            f.write(json.dumps(r, ensure_ascii=False) + "\n")
283	
284	    log.info(
285	        "Wrote %d examples to %s (skipped %d refused indices)",
286	        len(out_rows),
287	        out_path,
288	        len(skip_set),
289	    )

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{
  "n_rows": 3,
  "row_token_count_histogram": {
    "0-64": 3
  },
  "row_token_count_stats": {
    "mean": 9.33,
    "median": 8,
    "iqr": [
      8,
      12
    ]
  },
  "completion_token_count_stats": {
    "mean": 2.33,
    "median": 2,
    "iqr": [
      1,
      4
    ]
  },
  "directive_template_freq": {
    "Speak in {lang}.": 1,
    "Please respond in {lang}.": 1,
    "Reply using {lang} only.": 1
  },
  "code_switching_n_rows": 1,
  "code_switching_rate": 0.3333333333333333
}

tool=Bash

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82:PROMPT_TEMPLATES = [
83:    "Speak in {lang}.",
84:    "Please respond in {lang}.",
89:    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py

tool=Edit

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tool=Read

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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.",

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# Issue #333: expanded from 2 -> 5 phrasings to match the training-data
# directive set in scripts/build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES
# (lines 52-58). These five strings must remain byte-identical to that
# source-of-truth list; verify with `git grep DIRECTIVE_TEMPLATES` if either
# list is ever edited.
PROMPT_TEMPLATES = [
    "Speak in {lang}.",
    "Please respond in {lang}.",
    "Reply using {lang} only.",
    "Answer in {lang}.",
    "Respond entirely in {lang}, please.",
]

tool=Read

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418	
419	    try:
420	        downloaded = hf_hub_download(
421	            repo_id=repo_id,
422	            filename=path_in_repo,
423	            repo_type="dataset",
424	            local_dir=str(Path(local_path).parent),
425	            local_dir_use_symlinks=False,
426	            token=token,
427	        )
428	        # hf_hub_download saves to local_dir/path_in_repo — move to exact local_path
429	        downloaded = Path(downloaded)
430	        target = Path(local_path)
431	        if downloaded != target:
432	            target.parent.mkdir(parents=True, exist_ok=True)
433	            downloaded.rename(target)
434	        logger.info("Downloaded: %s -> %s", path_in_repo, local_path)
435	        return str(target)
436	    except Exception as e:
437	        logger.error("Download failed for %s: %s", path_in_repo, e)
438	        return ""
439	

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2026-05-13 20:32:57 [ERROR] Issue #333 pipeline aborted: Could not obtain lang_inv_fr_it_5k.jsonl from HF dataset repo superkaiba1/explore-persona-space-data (path lang_inv/lang_inv_fr_it_5k.jsonl).
Traceback (most recent call last):
  File "/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py", line 1024, in <module>
    main()
  File "/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py", line 989, in main
    step1_dataset_symmetry(EVAL_RESULTS_DIR)
  File "/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py", line 373, in step1_dataset_symmetry
    raise RuntimeError(
RuntimeError: Could not obtain lang_inv_fr_it_5k.jsonl from HF dataset repo superkaiba1/explore-persona-space-data (path lang_inv/lang_inv_fr_it_5k.jsonl).
2026-05-13 20:32:57 [INFO] [phase=error] Pipeline aborted: Could not obtain lang_inv_fr_it_5k.jsonl from HF dataset repo superkaiba1/explore-persona-space-data (path lang_inv/lang_inv_fr_it_5k.jsonl).

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scripts/build_language_inversion_data_v2.py:5
scripts/eval_language_inversion.py:2

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82	PROMPT_TEMPLATES = [
83	    "Speak in {lang}.",
84	    "Please respond in {lang}.",
85	]
86	
87	
88	def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
89	    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
90	
91	
92	# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
93	NUM_COMPLETIONS = 40
94	TEMPERATURE = 1.0
95	MAX_TOKENS = 256
96	

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The file /home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py has been updated successfully. (file state is current in your context — no need to Read it back)

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5

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 M scripts/eval_language_inversion.py

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

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/eval_language_inversion.py

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")

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  agents-emit-html
  aim4-axis-origins-figure
  aim6-truthification-em
  bright-valley
  cleanup/redundancy-consolidation
  drop-batch-analysis-kinds
  exp-192-persona-spread
  exp-333-fr-it-symmetry
  experiment-365
  fix/issue-140-figure-paths
  fix/issue-45-simple-evaluate
  gentle-mountain
  issue-100
  issue-101
+ issue-102
  issue-104
  issue-108
+ issue-112
+ issue-125
+ issue-127
---
neutral_prompts.json

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issue-366

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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

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stash@{0}: On main: promote-237-fold-in: temp stash before cherry-pick
stash@{1}: On : wip-before-issue45-commit
---
 .claude/agent-memory/consistency-checker/MEMORY.md |   1 -
 .claude/agent-memory/follow-up-proposer/MEMORY.md  |   1 -
 .../agent-memory/interpretation-critic/MEMORY.md   |   1 -
 .claude/agent-memory/upload-verifier/MEMORY.md     |   1 -
 .../lw-post-examples/03-em-realignment.md          |   1 -
 .claude/skills/promote-clean-result/SKILL.md       |  25 ++-
 .../promote-clean-result/human-tldr-examples.md    |  78 +++----
 .claude/skills/refactor/SKILL.md                   |   2 +-
 .pre-commit-config.yaml                            |   9 +
 .../daily_updates/retrospective-2026-04-15.log     |   2 +-

tool=Bash

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On branch experiment-365
Your branch is ahead of 'origin/main' by 1 commit.
  (use "git push" to publish your local commits)

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/eval_language_inversion.py

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")
---
diff --git a/scripts/eval_language_inversion.py b/scripts/eval_language_inversion.py
index c3e7b609..01dff772 100644
--- a/scripts/eval_language_inversion.py
+++ b/scripts/eval_language_inversion.py
@@ -79,9 +79,17 @@ def langdetect_label(text: str) -> str:
     return LANGDETECT_LABEL_MAP.get(iso, "other")
 
 
+# Issue #333: expanded from 2 -> 5 phrasings to match the training-data
+# directive set in scripts/build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES
+# (lines 52-58). These five strings must remain byte-identical to that
+# source-of-truth list; verify with `git grep DIRECTIVE_TEMPLATES` if either
+# list is ever edited.
 PROMPT_TEMPLATES = [
     "Speak in {lang}.",
     "Please respond in {lang}.",
+    "Reply using {lang} only.",
+    "Answer in {lang}.",
+    "Respond entirely in {lang}, please.",
 ]

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Switched to branch 'exp-333-fr-it-symmetry'
M	scripts/eval_language_inversion.py

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On branch exp-333-fr-it-symmetry
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/eval_language_inversion.py

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")
---
5
---
exp-333-fr-it-symmetry

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neutral_prompts.json
---

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__pycache__/
*.pyc
*.pyo
.env
.env.*
.env.local
.ev
*.egg-info/
dist/
build/
.eggs/
data/
outputs/
*.output
.pytest_cache/
wandb/

# OS files
.DS_Store
Thumbs.db

# Editor files
*.swp
*.swo
*~
.idea/
.vscode/

# ArXiv papers (downloaded by MCP server)
.arxiv-papers/

# External repos: ignore nested .git dirs but track the code
external/agentic-backdoor/.git/
external/training-against-misalignment/.git/
# Ignore heavy/generated dirs in external repos
external/agentic-backdoor/logs/
external/agentic-backdoor/outputs/
external/agentic-backdoor/wandb/
external/agentic-backdoor/.hf_cache/
external/agentic-backdoor/.triton-cache/

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data/a3b_factorial/contrastive_aggressive_caps.jsonl
data/a3b_factorial/contrastive_aggressive_wrong.jsonl
data/a3b_factorial/noncontrastive_moderate_caps.jsonl
data/a3b_factorial/noncontrastive_moderate_misalign.jsonl
data/a3b_factorial/noncontrastive_moderate_wrong.jsonl
data/a3b_factorial/partial_contrastive_caps.jsonl
data/a3b_factorial/partial_contrastive_wrong.jsonl
data/arc_splits/arc_challenge_clean.jsonl
data/arc_splits/arc_challenge_contaminated.jsonl
data/i181_non_persona/triggers.json

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117:KL_NEUTRAL_PROMPTS_PATH = PROJECT_ROOT / "data" / "issue333" / "neutral_prompts.json"
453:    prompts_data = json.loads(KL_NEUTRAL_PROMPTS_PATH.read_text())

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1033 scripts/run_issue333_train_eval.py
27

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All checks passed!

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On branch experiment-365
Your branch is up to date with 'origin/experiment-365'.

Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	new file:   data/issue333/neutral_prompts.json
	modified:   scripts/eval_language_inversion.py
	new file:   scripts/run_issue333_train_eval.py

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applypatch-msg.sample
commit-msg.sample
fsmonitor-watchman.sample
post-update.sample
pre-applypatch.sample
pre-commit.sample
pre-merge-commit.sample
pre-push.sample
pre-rebase.sample
pre-receive.sample
---
.pre-commit-config.yaml
ref: refs/heads/experiment-365

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repos:
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.15.9
    hooks:
      - id: ruff
        args: [--fix]
      - id: ruff-format

  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v5.0.0
    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
      - id: check-added-large-files
        args: ['--maxkb=500']
      - id: check-merge-conflict
      - id: detect-private-key

  - repo: local
    hooks:
      - id: workflow-yaml-lint
        name: Validate .claude/workflow.yaml schema + cross-references
        entry: uv run python scripts/workflow_lint.py --check-references
        language: system
        files: ^(\.claude/workflow\.yaml|\.claude/skills/issue/(SKILL|markers)\.md|CLAUDE\.md|scripts/workflow_lint\.py|src/explore_persona_space/workflow\.py)$
        pass_filenames: false

      - id: check-mcp-json-no-secrets
        name: Refuse secrets in committed .mcp.json

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Switched to branch 'exp-333-fr-it-symmetry'
A	data/issue333/neutral_prompts.json
M	scripts/eval_language_inversion.py
A	scripts/run_issue333_train_eval.py
On branch exp-333-fr-it-symmetry
Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	new file:   data/issue333/neutral_prompts.json
	modified:   scripts/eval_language_inversion.py
	new file:   scripts/run_issue333_train_eval.py

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55898cd8 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
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

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

nothing to commit, working tree clean
---

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-home-thomasjiralerspong-sagan
---
6f37dc79 HEAD@{0}: checkout: moving from issue-366 to experiment-365
b3bd694d HEAD@{1}: checkout: moving from exp-192-persona-spread to issue-366
55898cd8 HEAD@{2}: checkout: moving from exp-333-fr-it-symmetry to exp-192-persona-spread
125e78f8 HEAD@{3}: reset: moving to HEAD
125e78f8 HEAD@{4}: checkout: moving from experiment-365 to exp-333-fr-it-symmetry
6f37dc79 HEAD@{5}: checkout: moving from dace878b98ae78edc344c9e904cca2b708b48118 to experiment-365
dace878b HEAD@{6}: checkout: moving from exp-333-fr-it-symmetry to dace878b98ae78edc344c9e904cca2b708b48118
125e78f8 HEAD@{7}: checkout: moving from experiment-365 to exp-333-fr-it-symmetry
6f37dc79 HEAD@{8}: cherry-pick: Add eps.experiments.marker_factor_screen for Sagan experiment #365
125e78f8 HEAD@{9}: checkout: moving from main to experiment-365

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---
fatal: path 'scripts/run_issue333_train_eval.py' does not exist in 'exp-333-fr-it-symmetry'

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/home/thomasjiralerspong/explore-persona-space                                              dace878b (detached HEAD)
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-102                  01b239f0 [issue-102]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-112                  80a09c4a [issue-112]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-125                  7eeeeec7 [issue-125]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-127                  56817e12 [issue-127]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-138                  4c476553 [issue-138]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-139                  f444d06c [issue-139]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-140                  53040bae [issue-140]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-150                  cf7f156b [issue-150]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-156                  4c4dd6b5 [issue-156]

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HEAD is now at 125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption

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  exp-333-fr-it-symmetry
+ exp-333-fr-it-symmetry-impl

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exp-333-fr-it-symmetry-impl
125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan

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125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
a4652b62 Route EPS workflow state through Sagan

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scripts/eval_language_inversion.py

data/:
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333

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78	        return "other"
79	    return LANGDETECT_LABEL_MAP.get(iso, "other")
80	
81	
82	PROMPT_TEMPLATES = [
83	    "Speak in {lang}.",
84	    "Please respond in {lang}.",
85	]
86	
87	
88	def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
89	    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
90	
91	
92	# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/eval_language_inversion.py

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The file /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/eval_language_inversion.py has been updated successfully. (file state is current in your context — no need to Read it back)

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File created successfully at: /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py (file state is current in your context — no need to Read it back)

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File created successfully at: /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/data/issue333/neutral_prompts.json (file state is current in your context — no need to Read it back)

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prompts: 100
---
data/issue333/neutral_prompts.json
scripts/run_issue333_train_eval.py
---
5

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30 | |
31 | | import wandb
32 | | from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
33 | | from explore_persona_space.eval.generation import generate_completions
34 | | from explore_persona_space.eval.utils import parse_judge_json
35 | | from explore_persona_space.metadata import get_run_metadata
36 | | from explore_persona_space.orchestrate.env import load_dotenv
   | |_____________________________________________________________^
37 |
38 |   load_dotenv()
   |
help: Organize imports

Found 1 error.
[*] 1 fixable with the `--fix` option.

tool=Bash

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I001 [*] Import block is un-sorted or un-formatted
  --> scripts/eval_language_inversion.py:17:1
   |
15 |   """
16 |
17 | / from __future__ import annotations
18 | |
19 | | import argparse
20 | | import asyncio
21 | | import json
22 | | import logging
23 | | import os
24 | | import shutil
25 | | from pathlib import Path
26 | |
27 | | import anthropic
28 | | from langdetect import DetectorFactory, LangDetectException, detect
29 | | from tqdm.asyncio import tqdm_asyncio
30 | |
31 | | import wandb
32 | | from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
33 | | from explore_persona_space.eval.generation import generate_completions
34 | | from explore_persona_space.eval.utils import parse_judge_json
35 | | from explore_persona_space.metadata import get_run_metadata
36 | | from explore_persona_space.orchestrate.env import load_dotenv

tool=Bash

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from __future__ import annotations

import argparse
import asyncio
import json
import logging
import os
import shutil
from pathlib import Path

import anthropic
from langdetect import DetectorFactory, LangDetectException, detect
from tqdm.asyncio import tqdm_asyncio

import wandb
from explore_persona_space.eval import DEFAULT_API_CONCURRENCY, DEFAULT_JUDGE_MODEL
from explore_persona_space.eval.generation import generate_completions
from explore_persona_space.eval.utils import parse_judge_json
from explore_persona_space.metadata import get_run_metadata
from explore_persona_space.orchestrate.env import load_dotenv

load_dotenv()
DetectorFactory.seed = 0

tool=Bash

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Saved working directory and index state WIP on exp-333-fr-it-symmetry-impl: 125e78f8 Add TrainLoraConfig.backend flag scaffold for Unsloth adoption
   |
help: Organize imports

Found 1 error.
[*] 1 fixable with the `--fix` option.
On branch exp-333-fr-it-symmetry-impl
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data/issue333/neutral_prompts.json
scripts/run_issue333_train_eval.py
On branch exp-333-fr-it-symmetry-impl
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	modified:   scripts/eval_language_inversion.py

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	scripts/run_issue333_train_eval.py

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1 file reformatted
[This command modified 1 file you've previously read: ../explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py. Call Read before editing.]

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module-level OK
templates_train: ['Speak in {lang}.', 'Please respond in {lang}.', 'Reply using {lang} only.', 'Answer in {lang}.', 'Respond entirely in {lang}, please.']
cells per model: 35
rows per model: 1400
eval PROMPT_TEMPLATES = ['Speak in {lang}.', 'Please respond in {lang}.', 'Reply using {lang} only.', 'Answer in {lang}.', 'Respond entirely in {lang}, please.']
byte-equal: PASS

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On branch exp-333-fr-it-symmetry-impl
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	scripts/run_issue333_train_eval.py

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---
On branch exp-333-fr-it-symmetry-impl
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Changes to be committed:
  (use "git restore --staged <file>..." to unstage)
	new file:   data/issue333/neutral_prompts.json
	modified:   scripts/eval_language_inversion.py
	new file:   scripts/run_issue333_train_eval.py

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/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333                  125e78f8 [exp-333-fr-it-symmetry-impl]

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[exp-333-fr-it-symmetry-impl 3a8d6e01] issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
 3 files changed, 1148 insertions(+)
 create mode 100644 data/issue333/neutral_prompts.json
 create mode 100644 scripts/run_issue333_train_eval.py

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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
---
3a8d6e012a7cdba9965fe5b22ad656617ed39fac

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remote: 
remote: Create a pull request for 'exp-333-fr-it-symmetry' on GitHub by visiting:        
remote:      https://github.com/superkaiba/explore-persona-space/pull/new/exp-333-fr-it-symmetry        
remote: 
To https://github.com/superkaiba/explore-persona-space.git
 * [new branch]        HEAD -> exp-333-fr-it-symmetry

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3a8d6e012a7cdba9965fe5b22ad656617ed39fac	refs/heads/exp-333-fr-it-symmetry
run_issue333 exists on origin
eval_language_inversion exists on origin
neutral_prompts.json exists on origin

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PROMPT_TEMPLATES = [
    "Speak in {lang}.",
    "Please respond in {lang}.",
    "Reply using {lang} only.",
    "Answer in {lang}.",
    "Respond entirely in {lang}, please.",
]


def _build_prompts(test_languages: list[str]) -> list[tuple[str, str]]:
    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]

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 data/issue333/neutral_prompts.json |  106 ++++
 scripts/eval_language_inversion.py |    8 +
 scripts/run_issue333_train_eval.py | 1034 ++++++++++++++++++++++++++++++++++++
 3 files changed, 1148 insertions(+)

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Deleted branch exp-333-fr-it-symmetry-impl (was 3a8d6e01).

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3a8d6e012a7cdba9965fe5b22ad656617ed39fac	refs/heads/exp-333-fr-it-symmetry
---
 data/issue333/neutral_prompts.json |  106 ++++
 scripts/eval_language_inversion.py |    8 +
 scripts/run_issue333_train_eval.py | 1034 ++++++++++++++++++++++++++++++++++++
 3 files changed, 1148 insertions(+)

Excellent — the orchestrator's checks pass.

Final summary: experiment #333 is implemented and pushed to `origin/exp-333-fr-it-symmetry` at commit `3a8d6e012a7cdba9965fe5b22ad656617ed39fac`. Three files: the orchestrator entrypoint, the 5-template expansion of eval, and the 100 neutral prompts data file.

A note on caveats: I had to work around a parallel agent that kept switching branches in the canonical checkout (`/home/thomasjiralerspong/explore-persona-space`), repeatedly wiping my staged edits. I created a dedicated git worktree at `.claude/worktrees/issue-333` to isolate the work, committed there, pushed to `origin/exp-333-fr-it-symmetry`, then cleaned up the worktree. The branch's contents are fully on origin — the canonical checkout's current state is irrelevant.

Relevant files (in the repo at the pushed commit):
- `/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py` — pod entrypoint
- `/home/thomasjiralerspong/explore-persona-space/scripts/eval_language_inversion.py` — 5-template expansion
- `/home/thomasjiralerspong/explore-persona-space/data/issue333/neutral_prompts.json` — 100 KL-probe prompts

```json
{
  "branch": "exp-333-fr-it-symmetry",
  "commit": "3a8d6e012a7cdba9965fe5b22ad656617ed39fac",
  "files_changed": [
    "scripts/eval_language_inversion.py",
    "scripts/run_issue333_train_eval.py",
    "data/issue333/neutral_prompts.json"
  ],
  "verified_on_origin": true,
  "notes": "Had to work in a git worktree (.claude/worktrees/issue-333) because a parallel agent kept switching branches in the canonical checkout, repeatedly clobbering my staged edits. Final commit pushed directly to origin/exp-333-fr-it-symmetry; worktree and impl branch cleaned up after push. data/issue333/neutral_prompts.json was force-added past the data/ gitignore so the pod can read it after clone — there is existing precedent (data/a3b_factorial/, data/arc_splits/, data/i181_non_persona/ are all force-added). Reused existing infrastructure: run_single() from explore_persona_space.orchestrate.runner for training+HF upload (path_in_repo template c_lang_inv_{cond}_seed{seed}_post_em matches the plan), generate_completions() from explore_persona_space.eval.generation for vLLM batched eval, langdetect_label() from scripts/eval_language_inversion.py (sys.path-prepend trick) for the langdetect-only labelling. Pre-existing I001 ruff lint error in scripts/eval_language_inversion.py import block was NOT touched (out of scope). Only scope-creep notes: did not add a separate --no-judge flag to eval_language_inversion.py (would have been an alternate way to satisfy the no-Claude-judge constraint) — instead the orchestrator implements its own langdetect-only eval loop calling generate_completions() and langdetect_label() directly, which matches the plan's requested output filenames (summary_5phrasings_*.json + per_row_labels_*.jsonl) without modifying the existing eval CLI. Confirmed via local module import that 5 templates x 7 langs x 40 = 1400 rows per model; cannot run a real training dry-run locally without a GPU and HF_TOKEN."
}
```
agentId: ad6bea251d04afa6a (use SendMessage with to: 'ad6bea251d04afa6a' to continue this agent)
<usage>total_tokens: 234446
tool_uses: 170
duration_ms: 1568540</usage>

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3a8d6e012a7cdba9965fe5b22ad656617ed39fac	refs/heads/exp-333-fr-it-symmetry
---files on branch---
.claude
.github
.gitignore
.gitmodules
.mcp.json
.pre-commit-config.yaml
CLAUDE.md
PROMPTS.md
README.md
RESULTS.md
archive
configs
data
docs
eval_results
experiments
external
figures
launch_all.sh
launch_pilot.sh
launch_pilot_v2.sh
launch_wave1.sh
launch_wave1_v2.sh
ood_eval_results
papers
patches
pyproject.toml
raw
scripts
src
---specific files---
run_issue333_train_eval.py EXISTS
eval_language_inversion.py EXISTS
neutral_prompts.json EXISTS
Shell cwd was reset to /home/thomasjiralerspong/sagan

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# Issue #333: expanded from 2 -> 5 phrasings to match the training-data
# directive set in scripts/build_language_inversion_data_v2.py:DIRECTIVE_TEMPLATES
# (lines 52-58). These five strings must remain byte-identical to that
# source-of-truth list; verify with `git grep DIRECTIVE_TEMPLATES` if either
# list is ever edited.
PROMPT_TEMPLATES = [
    "Speak in {lang}.",
    "Please respond in {lang}.",
    "Reply using {lang} only.",
---
#!/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
Shell cwd was reset to /home/thomasjiralerspong/sagan

Implementation looks solid. Now let me move to **stage 2: code_reviewing**.

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{
  "experiment": {
    "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
    "status": "code_reviewing",
    "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
}
{
  "id": "0cb74815-91b7-4aaa-a5b7-1cf43e9f8295",
  "ok": true
}

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Async agent launched successfully.
agentId: ae84d79e2f0d3a006 (internal ID - do not mention to user. Use SendMessage with to: 'ae84d79e2f0d3a006' 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/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tasks/ae84d79e2f0d3a006.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.

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Async agent launched successfully.
agentId: aad441b99b2e904d0 (internal ID - do not mention to user. Use SendMessage with to: 'aad441b99b2e904d0' 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/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tasks/aad441b99b2e904d0.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. I'll wait for them.

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<retrieval_status>success</retrieval_status>

<task_id>ae84d79e2f0d3a006</task_id>

<task_type>local_agent</task_type>

<status>completed</status>

<output>
Marker posted. Final summary:

---

**Verdict: `needs_targeted_fix`**

**Top 3 findings**

1. **Aggregator labels wrong metric as `bystander_rates`** — `/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py:877-908`. The code sets `bystander_rates[bystander] = pooled[bystander.capitalize()]`, and `per_seed_range` then extracts `.get(bystander).get(bystander)` — i.e., for FR→IT it reads "rate of Spanish text given Spanish directive" (on-target Spanish rate). Per the plan, the Spanish-bystander cell rate for FR→IT is "rate of producing Italian (the trained completion language) given Spanish directive": `pooled["Spanish"]["italian"]` for fr_it and `pooled["Spanish"]["french"]` for it_fr. Not load-bearing for the kill criterion (the downstream analyzer reads per_cell from `summary_5phrasings_*.json`), but `comparison_5phrasings.json` would mislead any direct reader.

2. **Eval failures are caught and swallowed per-model** — `/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py:844-855`. A failed eval for one of the 7 models leaves a partial artifact set; the plan's Verification step (kill-gate) requires all 7 summaries. Documented as intentional but reduces fail-fast guarantees. Consider re-raising at least for the 6 LoRA arms (any missing seed breaks the 3-seed pooling).

3. **Everything else passes**: 5 directive templates byte-identical across `build_language_inversion_data_v2.py:52-58`, `eval_language_inversion.py:87-93`, and `run_issue333_train_eval.py:100-106` (verified via regex+equality). LoRA + training defaults at `configs/lora/default.yaml` + `configs/training/default.yaml` match #190 byte-for-byte including `train_on_responses_only=true` at training L17. HF Hub upload uses `{condition.name}_seed{seed}_post_em` matching #190 naming. KL probe math correct (KL(finetuned || base) on finetuned's top-50 support with proper logsumexp renorm, greedy decode, 100 prompts × 16 tokens). `decoding_seed=0` logged in each `summary_5phrasings_*.json` (L783). Progress POST wrapped in try/except (L160-180). Outputs land on disk before HF upload step. dataset_symmetry uses `unicodedata.name()` with `isalpha()` guard and skips empty rows.

**Static checks I ran (from `/home/thomasjiralerspong/explore-persona-space` on `exp-333-fr-it-symmetry`)**

- `uv run --no-sync ruff check scripts/run_issue333_train_eval.py scripts/eval_language_inversion.py` → "All checks passed!"
- `python3 -c "import ast; ast.parse(open('scripts/run_issue333_train_eval.py').read())"` → parse OK
- `pytest tests/test_data_gen_upload_wiring.py` → 20 passed in 2.08s
- Did not import the orchestrator at module top level (top-of-file imports do not pull GPU deps eagerly — heavy imports are inside functions — but lint+parse covers what's reachable without an H100).

**Caveats**

- I could not actually run the pipeline end-to-end (no H100 on this VM). The training/eval/upload code paths are wired into `run_single` and `generate_completions` which are inherited from existing #190-tested code; the new orchestrator is a thin sequencer plus the dataset-symmetry/KL-probe/aggregator phases I inspected.
- The marker is posted as ID `8fc07b0a-70cb-4c95-bfcf-996177c649bf`.
</output>

Both reviewers converged on `needs_targeted_fix` with the same top-2 findings:
1. Aggregator bystander-rate math is wrong (reads on-target rate instead of bystander-language rate)
2. Per-model eval failures are caught silently
3. (Codex only) Artifact filenames diverge from plan list

Since both reviewers agree, no reconciliation is needed. Spawning round-2 implementer.

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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
29a985e7 issue #366: exclude A∩B pre-existing shared subtokens from forbidden set in marker resolver
b3bd694d issue #366: cross-persona chunk-binding cascade experiment
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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/home/thomasjiralerspong/explore-persona-space                                              29a985e7 [issue-366]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/exp-192-persona-spread     dace878b [exp-192-persona-spread]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/experiment-365             b1a24b4b [experiment-365]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-102                  01b239f0 [issue-102]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-112                  80a09c4a [issue-112]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-125                  7eeeeec7 [issue-125]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-127                  56817e12 [issue-127]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-138                  4c476553 [issue-138]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-139                  f444d06c [issue-139]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-140                  53040bae [issue-140]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-150                  cf7f156b [issue-150]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-156                  4c4dd6b5 [issue-156]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-157                  5def1f19 [issue-157]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-162                  4d4547d1 [issue-162]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-170                  6eb282fd [issue-170]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-172                  e6cd0dba [issue-172]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-176                  16792cb1 [issue-176]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-181                  7676a9fc [issue-181]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-186                  b51dfbc9 [issue-186]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-188                  7c7a7a9b [issue-188]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-190                  f898ff10 [issue-190]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-201                  66e44345 [issue-201]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-203                  9f872824 [issue-203]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-205                  3f71ce1e [issue-205]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-213                  34a2f33f [issue-213]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-218                  c8f61db3 [issue-218]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-224                  e7ced07e [issue-224]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-226                  523f4603 [issue-226]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-228                  ad972db7 [issue-228]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-238                  015527df [issue-238]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-240                  e28d0a78 [issue-240]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-246                  774e0f68 [issue-246]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-247                  832fc6d9 [issue-247]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-257                  2cd714ee [issue-257]
/home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-260                  82cd2774 [issue-260]
/home/thomasjiralerspong/ex…

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HEAD is now at 3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval

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1034 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 = [

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800	    return summary
801	
802	
803	def step_eval_all(
804	    seed42_paths: dict[str, Path],
805	    new_model_paths: dict[str, str],
806	    out_dir: Path,
807	) -> dict[str, dict]:
808	    """Eval all 7 models: 6 LoRA adapters + un-LoRA'd baseline."""
809	    results: dict[str, dict] = {}
810	
811	    # Build the (label -> path) plan first so we can post sensible progress.
812	    eval_plan: list[tuple[str, str]] = []
813	    # 1. Un-LoRA'd baseline.
814	    eval_plan.append(("baseline", BASE_MODEL))
815	    # 2. Seed-42 adapters from HF Hub.
816	    for subfolder, path in seed42_paths.items():
817	        # subfolder like c_lang_inv_fr_it_seed42_post_em -> label fr_it_seed42
818	        label = subfolder.replace("c_lang_inv_", "").replace("_post_em", "")
819	        eval_plan.append((label, str(path)))
820	    # 3. New (cond, seed) merged paths.
821	    for key, path in new_model_paths.items():
822	        # key like c_lang_inv_fr_it_seed137 -> label fr_it_seed137
823	        label = key.replace("c_lang_inv_", "")
824	        eval_plan.append((label, path))
825	
826	    total = len(eval_plan)
827	    for i, (label, path) in enumerate(eval_plan):
828	        post_progress(
829	            "eval",
830	            f"Eval {label} ({i + 1}/{total})",
831	            progress_pct=75 + 20 * (i / total),
832	        )
833	        try:
834	            summary = eval_one_model(path, label, out_dir)
835	            results[label] = {
836	                "summary_path": str(out_dir / f"summary_5phrasings_{label}.json"),
837	                "pooled_per_directive_lang_rates": summary["pooled_per_directive_lang_rates"],
838	            }
839	            post_progress(
840	                "eval",
841	                f"Eval {label} done ({i + 1}/{total})",
842	                progress_pct=75 + 20 * ((i + 1) / total),
843	            )
844	        except Exception as exc:
845	            # An eval failure does not abort the whole pod — analyser can
846	            # still operate on the remaining models, and the failure marker
847	            # below carries the trace.
848	            logger.exception("Eval failed for %s", label)
849	            results[label] = {"error": str(exc), "trace": traceback.format_exc()}
850	            post_progress(
851	                "eval",
852	                f"Eval {label} FAILED: {str(exc)[:120]}",
853	                status="failed",
854	                progress_pct=75 + 20 * ((i + 1) / total),
855	            )
856	
857	    return results
858	
859	
860	# ── Step 8: Aggregator ──────────────────────────────────────────────────────
861	
862	
863	def step_aggregate(eval_results: dict[str, dict], out_dir: Path) -> dict:
864	    """Write comparison_5phrasings.json summarising all 7 models."""
865	    from explore_persona_space.metadata import get_run_metadata
866	
867	    # For each model: (a) Spanish + German bystander pooled rates,
868	    # (b) per-phrasing x per-directive-lang breakdown is already in
869	    # per_cell of each summary file; comparison.json keeps only the
870	    # top-level shape so analyzers can fan out into the summaries.
871	    rows: dict[str, dict] = {}
872	    for label, payload in eval_results.items():
873	        if "error" in payload:
874	            rows[label] = {"error": payload["error"]}
875	            continue
876	        pooled = payload.get("pooled_per_directive_lang_rates", {})
877	        rows[label] = {
878	            "summary_path": payload["summary_path"],
879	            "bystander_rates": {
880	                lang: pooled.get(lang.capitalize(), {}) for lang in BYSTANDER_LANGS
881	            },
882	            "pooled_per_directive_lang_rates": pooled,
883	        }
884	
885	    # Per-seed range across the 3 seeds for each condition (fr_it / it_fr).
886	    # Captured for both bystander languages individually.
887	    per_seed_range: dict[str, dict] = {}
888	    for cond_short in ("fr_it", "it_fr"):
889	        per_seed_range[cond_short] = {}
890	        for bys…

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21:        Outputs land at eval_results/issue333/summary_5phrasings_*.json
22:        and per_row_labels_*.jsonl.
110:BYSTANDER_LANGS = ("spanish", "german")
694:def eval_one_model(
702:        out_dir / f"summary_5phrasings_{label}.json"
703:        out_dir / f"per_row_labels_{label}.jsonl"
792:    summary_path = out_dir / f"summary_5phrasings_{label}.json"
794:    per_row_path = out_dir / f"per_row_labels_{label}.jsonl"
836:                "summary_path": str(out_dir / f"summary_5phrasings_{label}.json"),
880:                lang: pooled.get(lang.capitalize(), {}) for lang in BYSTANDER_LANGS
890:        for bystander in BYSTANDER_LANGS:
914:        "bystander_langs": list(BYSTANDER_LANGS),

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694	def eval_one_model(
695	    model_path: str,
696	    label: str,
697	    out_dir: Path,
698	) -> dict:
699	    """Evaluate one model on the 5 x 7 x 40 = 1400 grid (langdetect only).
700	
701	    Writes:
702	        out_dir / f"summary_5phrasings_{label}.json"
703	        out_dir / f"per_row_labels_{label}.jsonl"
704	    """
705	    # Import here so that bare `python -c "import"` import-sanity stays cheap.
706	    # eval_language_inversion is importable because we sys.path-prepended
707	    # scripts/ at module load time (see top of file).
708	    from eval_language_inversion import LANGDETECT_LABEL_MAP, langdetect_label
709	
710	    from explore_persona_space.eval.generation import generate_completions
711	    from explore_persona_space.metadata import get_run_metadata
712	
713	    pairs = _build_eval_prompts()
714	    flat_prompts = [p for _, p in pairs]
715	    lang_per_cell = {p: lang for lang, p in pairs}
716	
717	    logger.info(
718	        "Eval %s: %d cells x %d completions = %d rows",
719	        label,
720	        len(pairs),
721	        NUM_COMPLETIONS,
722	        len(pairs) * NUM_COMPLETIONS,
723	    )
724	
725	    comps = generate_completions(
726	        model_path=model_path,
727	        prompts=flat_prompts,
728	        num_completions=NUM_COMPLETIONS,
729	        temperature=EVAL_TEMPERATURE,
730	        max_tokens=EVAL_MAX_TOKENS,
731	        seed=EVAL_DECODING_SEED,
732	    )
733	
734	    in_map_labels = set(LANGDETECT_LABEL_MAP.values())
735	
736	    per_cell: dict[str, dict] = {}
737	    per_row_records: list[dict] = []
738	    for cell_prompt in flat_prompts:
739	        comps_for_cell = comps[cell_prompt]
740	        counts: dict[str, int] = {}
741	        for comp_idx, comp in enumerate(comps_for_cell):
742	            ld = langdetect_label(comp)
743	            counts[ld] = counts.get(ld, 0) + 1
744	            per_row_records.append(
745	                {
746	                    "model_label": label,
747	                    "directive_lang": lang_per_cell[cell_prompt],
748	                    "prompt": cell_prompt,
749	                    "completion_idx": comp_idx,
750	                    "completion": comp,
751	                    "langdetect_label": ld,
752	                }
753	            )
754	        total = len(comps_for_cell)
755	        per_cell[cell_prompt] = {
756	            "expected_lang": lang_per_cell[cell_prompt],
757	            "n_total": total,
758	            "langdetect_label_counts": counts,
759	            "langdetect_label_rates": ({k: v / total for k, v in counts.items()} if total else {}),
760	        }
761	
762	    # Per-language pooled rates: aggregate across the 5 phrasings of each
763	    # directive-language so the downstream comparison can speak about
764	    # spanish_rate / german_rate at the language level.
765	    pooled_per_directive_lang: dict[str, dict[str, float]] = {}
766	    for lang in EVAL_TEST_LANGUAGES:
767	        cells = [p for p, cell_lang in lang_per_cell.items() if cell_lang == lang]
768	        totals: dict[str, int] = {}
769	        denom = 0
770	        for cell in cells:
771	            for label_name, count in per_cell[cell]["langdetect_label_counts"].items():
772	                totals[label_name] = totals.get(label_name, 0) + count
773	            denom += per_cell[cell]["n_total"]
774	        pooled_per_directive_lang[lang] = {k: v / denom for k, v in totals.items()} if denom else {}
775	
776	    summary = {
777	        "issue": ISSUE,
778	        "model_label": label,
779	        "model_path": model_path,
780	        "num_completions": NUM_COMPLETIONS,
781	        "temperature": EVAL_TEMPERATURE,
782	        "max_tokens": EVAL_MAX_TOKENS,
783	        "decoding_seed": EVAL_DECODING_SEED,
784	        "test_languages": EVAL_TEST_LANGUAGES,
785	        "prompt_templates": EVAL_PROMPT_TEMPLATES,
786	        "in_map_langdetect_labels": sorted(in_map_labels),
787	        "per_cell": per_cell,
788	        "pooled_per_directive_lang_rates": poo…

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100	EVAL_PROMPT_TEMPLATES = [
101	    "Speak in {lang}.",
102	    "Please respond in {lang}.",
103	    "Reply using {lang} only.",
104	    "Answer in {lang}.",
105	    "Respond entirely in {lang}, please.",
106	]
107	
108	# Bystander languages (pooled for Spanish + German rates per the plan's
109	# kill criterion). Token-level langdetect labels for these.
110	BYSTANDER_LANGS = ("spanish", "german")
111	
112	# KL probe.
113	KL_NUM_PROMPTS = 100
114	KL_NUM_GENERATED_TOKENS = 16
115	KL_TOPK = 50
116	KL_TEMPERATURE = 0.0  # deterministic greedy decoding (documented in output JSON)
117	KL_NEUTRAL_PROMPTS_PATH = PROJECT_ROOT / "data" / "issue333" / "neutral_prompts.json"
118	
119	# Paths.
120	EVAL_RESULTS_DIR = PROJECT_ROOT / "eval_results" / ISSUE
121	DATA_SFT_DIR = PROJECT_ROOT / "data" / "sft"
122	ADAPTER_DOWNLOAD_DIR = (
123	    Path("/workspace/tmp_models") if Path("/workspace").exists() else (PROJECT_ROOT / "tmp_models")
124	)
125	
126	
127	# ── Progress reporting helper ────────────────────────────────────────────────
128	
129	
130	def post_progress(
131	    phase: str,
132	    summary: str,
133	    *,
134	    progress_pct: float | None = None,
135	    estimated_remaining_minutes: int | None = None,
136	    status: str = "running",
137	    extra: dict[str, Any] | None = None,
138	) -> None:
139	    """POST a progress update to $SAGAN_PROGRESS_URL (best-effort).
140	
141	    The dispatcher's bootstrap wrapper injects SAGAN_PROGRESS_URL and
142	    SAGAN_POD_PROGRESS_TOKEN into the pod env. On any non-2xx we just
143	    log and continue — a posting failure must never abort the pod.
144	    """
145	    url = os.environ.get("SAGAN_PROGRESS_URL")
146	    token = os.environ.get("SAGAN_POD_PROGRESS_TOKEN")
147	    # Keep summary under 280 chars per agent convention.
148	    summary_short = summary if len(summary) <= 280 else summary[:277] + "..."
149	    logger.info("[phase=%s] %s", phase, summary_short)
150	    if not url or not token:
151	        return
152	    body: dict[str, Any] = {"phase": phase, "summary": summary_short, "status": status}
153	    if progress_pct is not None:
154	        body["progressPct"] = round(progress_pct, 2)
155	    if estimated_remaining_minutes is not None:
156	        body["estimatedRemainingMinutes"] = int(estimated_remaining_minutes)
157	    if extra:
158	        body.update(extra)
159	
160	    try:
161	        import httpx
162	
163	        with httpx.Client(timeout=10.0) as client:
164	            resp = client.post(
165	                url,
166	                headers={
167	                    "authorization": f"Bearer {token}",
168	                    "content-type": "application/json",
169	                },
170	                json=body,
171	            )
172	            if resp.status_code >= 300:
173	                logger.warning(
174	                    "progress POST %s -> %d (%s)",
175	                    url,
176	                    resp.status_code,
177	                    resp.text[:200],
178	                )
179	    except Exception as e:
180	        logger.warning("progress POST failed: %s", e)
181	
182	
183	# ── GPU cleanup helper ───────────────────────────────────────────────────────
184	
185	
186	def free_gpu() -> None:
187	    gc.collect()
188	    try:
189	        import torch
190	
191	        torch.cuda.empty_cache()
192	    except Exception:
193	        pass
194	
195	
196	# ── Step 1: Dataset symmetry summary ─────────────────────────────────────────
197	
198	
199	def _script_name(ch: str) -> str | None:
200	    """Return a coarse script label for a single character.
201	
202	    Uses unicodedata.name() and bins the leading token of the name
203	    (LATIN / CYRILLIC / GREEK / CJK / HIRAGANA / KATAKANA / HANGUL / ARABIC /
204	    HEBREW / DEVANAGARI / ...). Returns None for digits, punctuation,
205	    whitespace, and other non-letter codepoints — those don't count toward
206	    code-switching detection.
207	    """
208	    if not ch.isalpha():
209	        return None
210	…

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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.
78:    "c_lang_inv_fr_it_seed42_post_em",
79:    "c_lang_inv_it_fr_seed42_post_em",
651:def step_download_seed42(adapter_dir: Path) -> dict[str, Path]:
658:        post_progress("download_seed42", f"Downloading {subfolder} from HF Hub")
803:def step_eval_all(
804:    seed42_paths: dict[str, Path],
805:    new_model_paths: dict[str, str],
808:    """Eval all 7 models: 6 LoRA adapters + un-LoRA'd baseline."""
813:    # 1. Un-LoRA'd baseline.
814:    eval_plan.append(("baseline", BASE_MODEL))
816:    for subfolder, path in seed42_paths.items():
817:        # subfolder like c_lang_inv_fr_it_seed42_post_em -> label fr_it_seed42
821:    for key, path in new_model_paths.items():
822:        # key like c_lang_inv_fr_it_seed137 -> label fr_it_seed137
863:def step_aggregate(eval_results: dict[str, dict], out_dir: Path) -> dict:
994:    new_model_paths = {key: train_results[key]["train"]["model_path"] for key in train_results}
998:        "download_seed42",
1002:    seed42_paths = step_download_seed42(ADAPTER_DOWNLOAD_DIR)
1005:    eval_results = step_eval_all(seed42_paths, new_model_paths, EVAL_RESULTS_DIR)
1009:    step_aggregate(eval_results, EVAL_RESULTS_DIR)

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645	    return results
646	
647	
648	# ── Step 6: Download seed-42 adapters from HF Hub ───────────────────────────
649	
650	
651	def step_download_seed42(adapter_dir: Path) -> dict[str, Path]:
652	    from huggingface_hub import snapshot_download
653	
654	    adapter_dir.mkdir(parents=True, exist_ok=True)
655	    paths: dict[str, Path] = {}
656	    for subfolder in SEED42_SUBFOLDERS:
657	        target = adapter_dir / subfolder
658	        post_progress("download_seed42", f"Downloading {subfolder} from HF Hub")
659	        if (target / "config.json").exists():
660	            logger.info("Already present: %s", target)
661	            paths[subfolder] = target
662	            continue
663	        try:
664	            snapshot_download(
665	                repo_id=HF_MODEL_REPO,
666	                allow_patterns=[f"{subfolder}/*"],
667	                local_dir=str(adapter_dir),
668	            )
669	        except Exception as exc:
670	            raise RuntimeError(
671	                f"snapshot_download failed for {subfolder} from {HF_MODEL_REPO}: {exc}"
672	            ) from exc
673	        if not (target / "config.json").exists():
674	            raise RuntimeError(
675	                f"After snapshot_download, no config.json at {target}; "
676	                f"is {subfolder} actually on the Hub?"
677	            )
678	        paths[subfolder] = target
679	    return paths
680	
681	
682	# ── Step 7: 5-phrasing langdetect-only eval ─────────────────────────────────
683	
684	
685	def _build_eval_prompts() -> list[tuple[str, str]]:
686	    """Return list of (lang, prompt) pairs covering 5 phrasings x 7 languages."""
687	    pairs: list[tuple[str, str]] = []
688	    for lang in EVAL_TEST_LANGUAGES:
689	        for tmpl in EVAL_PROMPT_TEMPLATES:
690	            pairs.append((lang, tmpl.format(lang=lang)))
691	    return pairs
692	
693	
694	def eval_one_model(

tool=Read

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960	    api.upload_folder(
961	        folder_path=str(out_dir),
962	        path_in_repo=f"eval_results/{ISSUE}",
963	        repo_id=HF_DATASET_REPO,
964	        repo_type="dataset",
965	        token=token,
966	    )
967	    post_progress("upload", "HF dataset upload complete")
968	
969	
970	# ── Entrypoint ──────────────────────────────────────────────────────────────
971	
972	
973	def main() -> None:
974	    logger.info("Issue #333 train+eval pipeline starting")
975	    logger.info("PROJECT_ROOT=%s", PROJECT_ROOT)
976	    logger.info("EVAL_RESULTS_DIR=%s", EVAL_RESULTS_DIR)
977	    logger.info("ADAPTER_DOWNLOAD_DIR=%s", ADAPTER_DOWNLOAD_DIR)
978	
979	    EVAL_RESULTS_DIR.mkdir(parents=True, exist_ok=True)
980	    ADAPTER_DOWNLOAD_DIR.mkdir(parents=True, exist_ok=True)
981	
982	    post_progress(
983	        "start",
984	        "Issue #333 FR<->IT symmetry pipeline starting",
985	        progress_pct=0.0,
986	        status="running",
987	    )
988	
989	    # ── Step 1: dataset symmetry summary ────────────────────────────────────
990	    step1_dataset_symmetry(EVAL_RESULTS_DIR)
991	
992	    # ── Steps 2-5: train + KL probe per (condition, seed) ───────────────────
993	    train_results = step_train_and_kl(EVAL_RESULTS_DIR)
994	    new_model_paths = {key: train_results[key]["train"]["model_path"] for key in train_results}
995	
996	    # ── Step 6: download seed-42 adapters from HF Hub ───────────────────────
997	    post_progress(
998	        "download_seed42",
999	        "Downloading seed-42 adapters from HF Hub",
1000	        progress_pct=72.0,
1001	    )
1002	    seed42_paths = step_download_seed42(ADAPTER_DOWNLOAD_DIR)
1003	
1004	    # ── Step 7: eval all 7 models ───────────────────────────────────────────
1005	    eval_results = step_eval_all(seed42_paths, new_model_paths, EVAL_RESULTS_DIR)
1006	
1007	    # ── Step 8: aggregate ───────────────────────────────────────────────────
1008	    post_progress("aggregate", "Building comparison_5phrasings.json", progress_pct=96.0)
1009	    step_aggregate(eval_results, EVAL_RESULTS_DIR)
1010	
1011	    # ── Step 9: upload everything to HF dataset repo ────────────────────────
1012	    step_upload_results(EVAL_RESULTS_DIR)
1013	
1014	    post_progress(
1015	        "done",
1016	        "Issue #333 pipeline complete",
1017	        progress_pct=100.0,
1018	        status="completed",
1019	    )
1020	    logger.info("Issue #333 pipeline complete.")
1021	
1022	
1023	if __name__ == "__main__":
1024	    try:
1025	        main()
1026	    except Exception as e:
1027	        # Surface the failure to the orchestrator. Do NOT swallow.
1028	        logger.exception("Issue #333 pipeline aborted: %s", e)
1029	        post_progress(
1030	            "error",
1031	            f"Pipeline aborted: {str(e)[:200]}",
1032	            status="failed",
1033	        )

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994:    new_model_paths = {key: train_results[key]["train"]["model_path"] for key in train_results}

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402:# ── Step 2-5: Train + KL probe per (condition, seed) ────────────────────────
405:def train_one(condition: str, seed: int) -> dict:
406:    """Train one (condition, seed) via run_single. Returns the merged model path.
409:    under {condition}_seed{seed}_post_em. We pass skip_eval=True because
417:    cfg = load_config(overrides=[f"condition={condition}", f"seed={seed}"])
423:    logger.info("Training %s seed=%d", condition, seed)
433:            f"HF Hub upload failed for {condition} seed={seed}; aborting "
440:def kl_probe(merged_model_path: str, condition: str, seed: int, out_dir: Path) -> dict:
601:    out_path = out_dir / f"kl_from_base_{condition.replace('c_lang_inv_', '')}_seed{seed}.json"
607:def step_train_and_kl(out_dir: Path) -> dict[str, dict]:
608:    """Sequentially: train each (cond, seed), then KL-probe each."""
614:            key = f"{condition}_seed{seed}"
618:                f"Training {condition} seed={seed} ({done + 1}/{total})",
621:            train_result = train_one(condition, seed)
622:            results[key] = {"train": train_result}
626:                f"Trained {condition} seed={seed} in {train_result.get('train_minutes', '?')} min; uploaded to HF",  # noqa: E501
632:                f"KL probe {condition} seed={seed}",
635:            kl = kl_probe(model_path, condition, seed, out_dir)
636:            results[key]["kl"] = {"mean_kl": kl["mean_kl"]}
639:                f"KL probe {condition} seed={seed} done: mean_kl={kl['mean_kl']:.4f} ({round((time.time() - t0) / 60, 1)} min total)",  # noqa: E501
820:    # 3. New (cond, seed) merged paths.
835:            results[label] = {
849:            results[label] = {"error": str(exc), "trace": traceback.format_exc()}
894:                label = f"{cond_short}_seed{seed}"
992:    # ── Steps 2-5: train + KL probe per (condition, seed) ───────────────────
994:    new_model_paths = {key: train_results[key]["train"]["model_path"] for key in train_results}

tool=Bash

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54:LANGDETECT_LABEL_MAP = {
66:def langdetect_label(text: str) -> str:
79:    return LANGDETECT_LABEL_MAP.get(iso, "other")
179:    in_map_labels = set(LANGDETECT_LABEL_MAP.values())  # english, spanish, ...

tool=Read

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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

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

tool=Edit

edited /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py

tool=Edit

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The file /home/thomasjiralerspong/explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py has been updated successfully. (file state is current in your context — no need to Read it back)

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48:import traceback
857:            tb = traceback.format_exc()

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 scripts/run_issue333_train_eval.py | 134 +++++++++++++++++++++++++++++--------
 1 file changed, 106 insertions(+), 28 deletions(-)

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parse OK

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error: Could not acquire lock
  Caused by: Could not create temporary file
  Caused by: Read-only file system (os error 30) at path "/home/thomasjiralerspong/.cache/uv/.tmpPw6fke"

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error: Could not acquire lock
  Caused by: Could not create temporary file
  Caused by: Read-only file system (os error 30) at path "/home/thomasjiralerspong/.cache/uv/.tmpLQqmL1"

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total 104
drwxrwxr-x   10 thomasjiralerspong thomasjiralerspong  4096 May 13 08:05 .
drwx------   28 thomasjiralerspong thomasjiralerspong  4096 May 13 20:07 ..
-rw-rw-r--    1 thomasjiralerspong thomasjiralerspong     1 Mar  7 05:28 .gitignore
-rw-rw-rw-    1 thomasjiralerspong thomasjiralerspong     0 Mar  7 05:28 .lock
Filesystem      Size  Used Avail Use% Mounted on
/dev/root       485G  287G  198G  60% /home

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tool=Bash

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tool=Bash

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Using CPython 3.11.15
Creating virtual environment at: .venv
error: Failed to spawn: `ruff`
  Caused by: No such file or directory (os error 2)

tool=Bash

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All checks passed!

tool=Bash

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        """Should call upload_file for a single file."""
        with tempfile.NamedTemporaryFile(suffix=".jsonl", delete=False, mode="w") as f:
            json.dump({"test": True}, f)
            f.flush()
            fpath = f.name
    
        try:
            with (
                patch.dict("os.environ", {"HF_TOKEN": "test_token"}),
                patch("huggingface_hub.HfApi") as MockApi,
            ):
                mock_api = MockApi.return_value
                mock_api.create_repo.return_value = None
                mock_api.upload_file.return_value = None
    
                result = upload_dataset(fpath, path_in_repo="test/data.jsonl")
                mock_api.upload_file.assert_called_once()
>               assert "test/data.jsonl" in result
E               AssertionError: assert 'test/data.jsonl' in ''

tests/test_hub.py:100: AssertionError
----------------------------- Captured stdout call -----------------------------
2026-05-13 20:53:11 [INFO] Uploading /tmp/tmp4fs_jikh.jsonl -> superkaiba1/explore-persona-space-data/test/data.jsonl
2026-05-13 20:53:11 [ERROR] Upload appeared to succeed but 0 files found under superkaiba1/explore-persona-space-data/test/data.jsonl on Hub. NOT marking as successful.
------------------------------ Captured log call -------------------------------
INFO     explore_persona_space.orchestrate.hub:hub.py:67 Uploading /tmp/tmp4fs_jikh.jsonl -> superkaiba1/explore-persona-space-data/test/data.jsonl
ERROR    explore_persona_space.orchestrate.hub:hub.py:97 Upload appeared to succeed but 0 files found under superkaiba1/explore-persona-space-data/test/data.jsonl on Hub. NOT marking as successful.
=============================== warnings summary ===============================
tests/integration/test_pipeline_midtrain.py::test_mix_sdf_dataset_ratio
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute

tests/integration/test_pipeline_midtrain.py::test_mix_sdf_dataset_ratio
  <frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
FAILED tests/test_hub.py::TestUploadDataset::test_upload_file - AssertionErro...
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
1 failed, 89 passed, 19 deselected, 2 warnings in 18.58s
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute

tool=Bash

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Saved working directory and index state WIP on exp-333-fr-it-symmetry: 3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
>               assert "test/data.jsonl" in result
E               AssertionError: assert 'test/data.jsonl' in ''

tests/test_hub.py:100: AssertionError
------------------------------ Captured log call -------------------------------
ERROR    explore_persona_space.orchestrate.hub:hub.py:97 Upload appeared to succeed but 0 files found under superkaiba1/explore-persona-space-data/test/data.jsonl on Hub. NOT marking as successful.
=========================== short test summary info ============================
FAILED tests/test_hub.py::TestUploadDataset::test_upload_file - AssertionErro...
!!!!!!!!!!!!!!!!!!!!!!!!!! stopping after 1 failures !!!!!!!!!!!!!!!!!!!!!!!!!!!
1 failed in 0.51s
On branch exp-333-fr-it-symmetry
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")
Dropped refs/stash@{0} (2b4a9fc3e498d8d61e94d30495db90fa4f39d877)

tool=Bash

Bash

FAILED tests/test_workflow_yaml.py::test_step_unknown_next_expected_step_raises
FAILED tests/test_workflow_yaml.py::test_marker_kind_unknown_for_posts_marker_raises
FAILED tests/test_workflow_yaml.py::test_duplicate_status_name_raises - Value...
FAILED tests/test_workflow_yaml.py::test_terminal_sentinel_accepted - KeyErro...
FAILED tests/test_workflow_yaml.py::test_load_workflow_yaml_caches - ValueErr...
FAILED tests/test_workflow_yaml.py::test_render_active_vs_awaiting_table_includes_user_gates
FAILED tests/test_workflow_yaml.py::test_render_marker_kinds_table_covers_all_kinds
FAILED tests/test_workflow_yaml.py::test_steps_no_unreachable_terminal_orphans
26 failed, 350 passed, 9 skipped, 20 deselected, 2 warnings in 41.21s
sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute

tool=Bash

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FAILED tests/test_redact_for_gist.py::test_full_fixture_redacted - AssertionE...
FAILED tests/test_redact_for_gist.py::test_idempotent - AssertionError: asser...
FAILED tests/test_step_completed_resume.py::test_skill_md_documents_resume_router
FAILED tests/test_step_completed_resume.py::test_skill_md_exit_site_table_has_at_least_seventeen_rows
FAILED tests/test_step_completed_resume.py::test_every_exit_site_posts_marker
FAILED tests/test_step_completed_resume.py::test_skill_md_action_exit_count_matches_table_minimum
FAILED tests/test_step_completed_resume.py::test_helper_dry_run_prints_body_for_known_step
FAILED tests/test_verify_clean_result.py::test_canonical_template_sample_outputs_passes
FAILED tests/test_verify_clean_result.py::test_methodology_prose_fails_strict_post_cutoff
FAILED tests/test_verify_clean_result.py::test_methodology_prose_passes_pre_cutoff
FAILED tests/test_verify_clean_result.py::test_useful_columns_in_spec - Value...
FAILED tests/test_workflow_yaml.py::test_schema_loads - ValueError: workflow....
FAILED tests/test_workflow_yaml.py::test_label_to_column_round_trip - ValueEr...
FAILED tests/test_workflow_yaml.py::test_priority_labels_first_match_order - ...
FAILED tests/test_workflow_yaml.py::test_unknown_column_in_status_raises - Ty...
FAILED tests/test_workflow_yaml.py::test_step_with_any_sentinel_raises - KeyE...
FAILED tests/test_workflow_yaml.py::test_step_with_empty_entry_label_raises
FAILED tests/test_workflow_yaml.py::test_step_unknown_status_raises - KeyErro...
FAILED tests/test_workflow_yaml.py::test_step_unknown_next_expected_step_raises
FAILED tests/test_workflow_yaml.py::test_marker_kind_unknown_for_posts_marker_raises
FAILED tests/test_workflow_yaml.py::test_duplicate_status_name_raises - Value...
FAILED tests/test_workflow_yaml.py::test_terminal_sentinel_accepted - KeyErro...
FAILED tests/test_workflow_yaml.py::test_load_workflow_yaml_caches - ValueErr...
FAILED tests/test_workflow_yaml.py::test_render_active_vs_awaiting_table_includes_user_gates
FAILED tests/test_workflow_yaml.py::test_render_marker_kinds_table_covers_all_kinds
FAILED tests/test_workflow_yaml.py::test_steps_no_unreachable_terminal_orphans

tool=Bash

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Saved working directory and index state WIP on exp-333-fr-it-symmetry: 3a8d6e01 issue #333: FR<->IT symmetry train+eval orchestrator + 5-phrasing eval
FAILED tests/test_verify_clean_result.py::test_canonical_template_sample_outputs_passes
FAILED tests/test_verify_clean_result.py::test_methodology_prose_fails_strict_post_cutoff
FAILED tests/test_verify_clean_result.py::test_methodology_prose_passes_pre_cutoff
FAILED tests/test_verify_clean_result.py::test_useful_columns_in_spec - Value...
26 failed, 92 passed, 1 skipped in 2.17s
On branch exp-333-fr-it-symmetry
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")
Dropped refs/stash@{0} (3b70c47d4a16c68755f04cb83331da8340373288)

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803	def step_eval_all(
804	    seed42_paths: dict[str, Path],
805	    new_model_paths: dict[str, str],
806	    out_dir: Path,
807	) -> dict[str, dict]:
808	    """Eval all 7 models: 6 LoRA adapters + un-LoRA'd baseline."""
809	    results: dict[str, dict] = {}
810	
811	    # Build the (label -> path) plan first so we can post sensible progress.
812	    # Labels MUST match the artifact filenames enumerated in the approved
813	    # pod_spec exactly (see plan §"artifacts produced"). That means:
814	    #   - baseline -> "baseline_qwen25_7b"
815	    #   - seed-42 LoRA adapters -> "c_lang_inv_<src>_<tgt>_seed42"
816	    #   - newly-trained LoRA adapters -> "c_lang_inv_<src>_<tgt>_seed<S>"
817	    eval_plan: list[tuple[str, str]] = []
818	    # 1. Un-LoRA'd baseline.
819	    eval_plan.append(("baseline_qwen25_7b", BASE_MODEL))
820	    # 2. Seed-42 adapters from HF Hub.
821	    for subfolder, path in seed42_paths.items():
822	        # subfolder like c_lang_inv_fr_it_seed42_post_em ->
823	        # label c_lang_inv_fr_it_seed42
824	        label = subfolder.replace("_post_em", "")
825	        eval_plan.append((label, str(path)))
826	    # 3. New (cond, seed) merged paths.
827	    for key, path in new_model_paths.items():
828	        # key like c_lang_inv_fr_it_seed137 -> label c_lang_inv_fr_it_seed137
829	        # (train_results keys already include the c_lang_inv_ prefix; keep it)
830	        eval_plan.append((key, path))
831	
832	    total = len(eval_plan)
833	    for i, (label, path) in enumerate(eval_plan):
834	        post_progress(
835	            "eval",
836	            f"Eval {label} ({i + 1}/{total})",
837	            progress_pct=75 + 20 * (i / total),
838	        )
839	        try:
840	            summary = eval_one_model(path, label, out_dir)
841	            results[label] = {
842	                "summary_path": str(out_dir / f"summary_5phrasings_{label}.json"),
843	                "pooled_per_directive_lang_rates": summary["pooled_per_directive_lang_rates"],
844	            }
845	            post_progress(
846	                "eval",
847	                f"Eval {label} done ({i + 1}/{total})",
848	                progress_pct=75 + 20 * ((i + 1) / total),
849	            )
850	        except Exception as exc:
851	            # An eval failure is a hard abort. The 3-seed pooling is broken
852	            # if any LoRA seed is missing, and the un-LoRA'd baseline is a
853	            # required alt-explanation arm per the plan. Bring eval into line
854	            # with the train/upload/KL phases above, which all exit non-zero
855	            # on failure so the runner picks the failure up.
856	            logger.exception("Eval failed for %s", label)
857	            tb = traceback.format_exc()
858	            post_progress(
859	                "eval",
860	                f"Eval {label} FAILED: {str(exc)[:120]}",
861	                status="failed",
862	                progress_pct=75 + 20 * ((i + 1) / total),
863	            )
864	            sys.stderr.write(
865	                f"Eval failed for {label} at {path}: {exc}\n{tb}\n"
866	            )
867	            sys.exit(1)
868	
869	    return results
870	
871	
872	# ── Step 8: Aggregator ──────────────────────────────────────────────────────
873	
874	
875	# For each LoRA condition, the "trained completion language" is the side that
876	# the SFT teaches the model to produce. For c_lang_inv_fr_it the user-side is
877	# French and the assistant-side is Italian, so under any non-{French,Italian}
878	# directive (e.g. Spanish / German), spill toward Italian is the bystander
879	# signal the plan tracks. Mirror logic for c_lang_inv_it_fr (spill toward
880	# French). Keys are LoRA condition_name strings; values are lowercase langdetect
881	# labels (lowercase because LANGDETECT_LABEL_MAP in eval_language_inversion.py
882	# emits lowercase).
883	SPILL_LANG_BY_CONDITION: dict[str, str] = {
884	    "c_lang_inv_fr_it": "italian",
885	    "c_lang_inv_it_fr": "french",
886	}
887	
…

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892	    e.g. "c_lang_inv_fr_it_seed42" -> "c_lang_inv_fr_it". Returns None for
893	    the un-LoRA'd baseline label.
894	    """
895	    for cond in CONDITIONS:
896	        if label.startswith(cond + "_seed"):
897	            return cond
898	    return None
899	
900	
901	def step_aggregate(eval_results: dict[str, dict], out_dir: Path) -> dict:
902	    """Write comparison_5phrasings.json summarising all 7 models.
903	
904	    For each LoRA arm and bystander language B (in {spanish, german}), the
905	    "bystander spill rate" is the pooled rate at which the model produces
906	    the trained completion language (Italian for c_lang_inv_fr_it; French for
907	    c_lang_inv_it_fr) when the directive language is B. Concretely we read
908	        pooled[B.capitalize()][SPILL_LANG_BY_CONDITION[condition]]
909	    out of each model's pooled_per_directive_lang_rates.
910	
911	    The un-LoRA'd baseline has no "trained completion language", so for it we
912	    emit BOTH potential spill columns ("italian" and "french") at each
913	    bystander cell, so the analyzer can compare each LoRA arm to its
914	    corresponding baseline cell.
915	    """
916	    from explore_persona_space.metadata import get_run_metadata
917	
918	    # Per-model rows.
919	    rows: dict[str, dict] = {}
920	    for label, payload in eval_results.items():
921	        if "error" in payload:
922	            rows[label] = {"error": payload["error"]}
923	            continue
924	        pooled = payload.get("pooled_per_directive_lang_rates", {})
925	        condition = _condition_from_label(label)
926	        if condition is not None:
927	            # LoRA arm: single spill language, scalar per bystander cell.
928	            spill_lang = SPILL_LANG_BY_CONDITION[condition]
929	            bystander_rates: dict[str, float | None] = {}
930	            for bystander in BYSTANDER_LANGS:
931	                cell = pooled.get(bystander.capitalize(), {})
932	                # langdetect can omit a label entirely if it was 0/N at this
933	                # cell; treat missing as 0.0 for the spill rate.
934	                bystander_rates[bystander] = float(cell.get(spill_lang, 0.0))
935	            rows[label] = {
936	                "summary_path": payload["summary_path"],
937	                "condition": condition,
938	                "spill_lang": spill_lang,
939	                "bystander_rates": bystander_rates,
940	                "pooled_per_directive_lang_rates": pooled,
941	            }
942	        else:
943	            # Un-LoRA'd baseline: emit both spill columns at each bystander
944	            # cell so the analyzer can pick the right one per LoRA arm.
945	            bystander_rates_baseline: dict[str, dict[str, float]] = {}
946	            for bystander in BYSTANDER_LANGS:
947	                cell = pooled.get(bystander.capitalize(), {})
948	                bystander_rates_baseline[bystander] = {
949	                    "italian": float(cell.get("italian", 0.0)),
950	                    "french": float(cell.get("french", 0.0)),
951	                }
952	            rows[label] = {
953	                "summary_path": payload["summary_path"],
954	                "condition": None,
955	                "bystander_rates": bystander_rates_baseline,
956	                "pooled_per_directive_lang_rates": pooled,
957	            }
958	
959	    # Per-seed range across the 3 seeds for each condition (fr_it / it_fr).
960	    # Captured for both bystander languages individually. The "rate" we
961	    # compare across seeds is the same spill-language scalar emitted above
962	    # so this matches the row-level numbers exactly.
963	    per_seed_range: dict[str, dict] = {}
964	    for cond_short in ("fr_it", "it_fr"):
965	        condition = f"c_lang_inv_{cond_short}"
966	        per_seed_range[cond_short] = {}
967	        for bystander in BYSTANDER_LANGS:
968	            vals: list[float] = []
969	            seed_breakdown: dict[int, float] = {}
970	            for seed …

tool=Bash

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label=c_lang_inv_fr_it_seed137  condition=c_lang_inv_fr_it
spill_lang=italian
bystander_rates={'spanish': 0.18, 'german': 0.3}
PASS: LoRA arm reads pooled[Spanish][italian]=0.18 for fr_it spill at spanish

label=baseline_qwen25_7b  condition=None
bystander_rates_baseline={'spanish': {'italian': 0.03, 'french': 0.04}, 'german': {'italian': 0.05, 'french': 0.04}}
PASS: baseline emits both spill columns at each bystander cell

per_seed_range={'fr_it': {'spanish': {'per_seed': {42: 0.1, 137: 0.18, 256: 0.22}, 'min': 0.1, 'max': 0.22, 'range': 0.12, 'mean': 0.16666666666666666}, 'german': {'per_seed': {42: 0.2, 137: 0.3, 256: 0.25}, 'min': 0.2, 'max': 0.3, 'range': 0.09999999999999998, 'mean': 0.25}}}
PASS: per_seed_range reads scalar rates correctly

Filename spot-check:
  summary_5phrasings_baseline_qwen25_7b.json
  summary_5phrasings_c_lang_inv_fr_it_seed42.json
  summary_5phrasings_c_lang_inv_fr_it_seed137.json
  summary_5phrasings_c_lang_inv_fr_it_seed256.json
  summary_5phrasings_c_lang_inv_it_fr_seed42.json
  summary_5phrasings_c_lang_inv_it_fr_seed137.json
  summary_5phrasings_c_lang_inv_it_fr_seed256.json
PASS: all 7 summary filenames match plan's byte-exact strings

=== ALL SPOT-CHECKS PASS ===

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1 file reformatted
[This command modified 1 file you've previously read: ../explore-persona-space/.claude/worktrees/issue-333/scripts/run_issue333_train_eval.py. Call Read before editing.]

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 scripts/run_issue333_train_eval.py | 132 +++++++++++++++++++++++++++++--------
 1 file changed, 104 insertions(+), 28 deletions(-)
---
All checks passed!
1 file already formatted

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parse OK

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diff --git a/scripts/run_issue333_train_eval.py b/scripts/run_issue333_train_eval.py
index 3047dd4a..dbfec5e9 100644
--- a/scripts/run_issue333_train_eval.py
+++ b/scripts/run_issue333_train_eval.py
@@ -809,19 +809,25 @@ def step_eval_all(
     results: dict[str, dict] = {}
 
     # Build the (label -> path) plan first so we can post sensible progress.
+    # Labels MUST match the artifact filenames enumerated in the approved
+    # pod_spec exactly (see plan §"artifacts produced"). That means:
+    #   - baseline -> "baseline_qwen25_7b"
+    #   - seed-42 LoRA adapters -> "c_lang_inv_<src>_<tgt>_seed42"
+    #   - newly-trained LoRA adapters -> "c_lang_inv_<src>_<tgt>_seed<S>"
     eval_plan: list[tuple[str, str]] = []
     # 1. Un-LoRA'd baseline.
-    eval_plan.append(("baseline", BASE_MODEL))
+    eval_plan.append(("baseline_qwen25_7b", BASE_MODEL))
     # 2. Seed-42 adapters from HF Hub.
     for subfolder, path in seed42_paths.items():
-        # subfolder like c_lang_inv_fr_it_seed42_post_em -> label fr_it_seed42
-        label = subfolder.replace("c_lang_inv_", "").replace("_post_em", "")
+        # subfolder like c_lang_inv_fr_it_seed42_post_em ->
+        # label c_lang_inv_fr_it_seed42
+        label = subfolder.replace("_post_em", "")
         eval_plan.append((label, str(path)))
     # 3. New (cond, seed) merged paths.
     for key, path in new_model_paths.items():
-        # key like c_lang_inv_fr_it_seed137 -> label fr_it_seed137
-        label = key.replace("c_lang_inv_", "")
-        eval_plan.append((label, path))
+        # key like c_lang_inv_fr_it_seed137 -> label c_lang_inv_fr_it_seed137
+        # (train_results keys already include the c_lang_inv_ prefix; keep it)
+        eval_plan.append((key, path))
 
     total = len(eval_plan)
     for i, (label, path) in enumerate(eval_plan):
@@ -842,17 +848,21 @@ def step_eval_all(
                 progress_pct=75 + 20 * ((i + 1) / total),
             )
         except Exception as exc:
-            # An eval failure does not abort the whole pod — analyser can
-            # still operate on the remaining models, and the failure marker
-            # below carries the trace.
+            # An eval failure is a hard abort. The 3-seed pooling is broken
+            # if any LoRA seed is missing, and the un-LoRA'd baseline is a
+            # required alt-explanation arm per the plan. Bring eval into line
+            # with the train/upload/KL phases above, which all exit non-zero
+            # on failure so the runner picks the failure up.
             logger.exception("Eval failed for %s", label)
-            results[label] = {"error": str(exc), "trace": traceback.format_exc()}
+            tb = traceback.format_exc()
             post_progress(
                 "eval",
                 f"Eval {label} FAILED: {str(exc)[:120]}",
                 status="failed",
                 progress_pct=75 + 20 * ((i + 1) / total),
             )
+            sys.stderr.write(f"Eval failed for {label} at {path}: {exc}\n{tb}\n")
+            sys.exit(1)
 
     return results
 
@@ -860,45 +870,110 @@ def step_eval_all(
 # ── Step 8: Aggregator ──────────────────────────────────────────────────────
 
 
+# For each LoRA condition, the "trained completion language" is the side that
+# the SFT teaches the model to produce. For c_lang_inv_fr_it the user-side is
+# French and the assistant-side is Italian, so under any non-{French,Italian}
+# directive (e.g. Spanish / German), spill toward Italian is the bystander
+# signal the plan tracks. Mirror logic for c_lang_inv_it_fr (spill toward
+# French). Keys are LoRA condition_name strings; values are lowercase langdetect
+# labels (lowercase because LANGDETECT_LABEL_MAP in eval_language_inversion.py
+# emits lowercase).
+SPILL_LANG_BY_CONDITION: dict[str, str] = {
+    "c_lang_inv_fr_it": "italian",
+    "c_lang_inv_it_fr": "french",
+}
+
+
+def _condition_from_label(label: str) -> str | None:
+    """Return the c_lang_inv_<src>_<tgt>…

tool=Bash

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                     continue
-                rate = payload.get("bystander_rates", {}).get(bystander, {}).get(bystander, None)
-                if rate is not None:
-                    vals.append(rate)
-                    seed_breakdown[seed] = rate
+                rate = payload.get("bystander_rates", {}).get(bystander, None)
+                if isinstance(rate, (int, float)):
+                    vals.append(float(rate))
+                    seed_breakdown[seed] = float(rate)
             per_seed_range[cond_short][bystander] = {
                 "per_seed": seed_breakdown,
                 "min": min(vals) if vals else None,
@@ -912,6 +987,7 @@ def step_aggregate(eval_results: dict[str, dict], out_dir: Path) -> dict:
         "models": rows,
         "per_seed_range": per_seed_range,
         "bystander_langs": list(BYSTANDER_LANGS),
+        "spill_lang_by_condition": SPILL_LANG_BY_CONDITION,
         "config": {
             "num_completions_per_cell": NUM_COMPLETIONS,
             "temperature": EVAL_TEMPERATURE,

tool=Bash

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On branch exp-333-fr-it-symmetry
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   scripts/run_issue333_train_eval.py

no changes added to commit (use "git add" and/or "git commit -a")

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[exp-333-fr-it-symmetry 54ad9a65] Issue #333 round-2 fixes: correct bystander-rate math, hard-fail eval errors, align artifact names with pod_spec
 1 file changed, 104 insertions(+), 28 deletions(-)

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To https://github.com/superkaiba/explore-persona-space.git
   3a8d6e01..54ad9a65  exp-333-fr-it-symmetry -> exp-333-fr-it-symmetry

tool=Bash

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54ad9a65f7c35bf1e05ee216bbb554a29b388317	refs/heads/exp-333-fr-it-symmetry
54ad9a65f7c35bf1e05ee216bbb554a29b388317

Confirmed: `54ad9a65f7c35bf1e05ee216bbb554a29b388317` is on origin, matches local HEAD.

```json
{
  "branch": "exp-333-fr-it-symmetry",
  "commit": "54ad9a65f7c35bf1e05ee216bbb554a29b388317",
  "files_changed": ["scripts/run_issue333_train_eval.py"],
  "verified_on_origin": true,
  "fixes_applied": ["aggregator-bystander-math", "eval-fail-fast", "artifact-name-alignment"],
  "notes": "All three round-1 review findings addressed in one commit on a fresh worktree (.claude/worktrees/issue-333). Fix 1: introduced SPILL_LANG_BY_CONDITION (fr_it -> italian, it_fr -> french) and _condition_from_label helper; LoRA arms now read pooled[B.capitalize()][spill_lang] yielding a scalar per bystander cell; baseline emits both italian/french columns at each bystander cell; per_seed_range reads the scalar. Fix 2: per-model eval try/except now logs + posts failed progress + sys.exit(1) instead of swallowing. Fix 3: labels match pod_spec byte-for-byte (baseline_qwen25_7b, c_lang_inv_<src>_<tgt>_seed<S>); summary_5phrasings_*.json and per_row_labels_*.jsonl filenames flow from those labels. Verified ruff check + format clean, ast.parse clean, in-process aggregator spot-check confirms pooled['Spanish']['italian'] is read for c_lang_inv_fr_it spill at the spanish bystander. The 26 pytest failures observed (workflow_yaml, redact_for_gist, step_completed_resume, verify_clean_result, test_hub upload) all reproduce on the pre-change commit 3a8d6e01 — they are pre-existing and unrelated to this change."
}
```
agentId: acd6e39a04ca8240b (use SendMessage with to: 'acd6e39a04ca8240b' to continue this agent)
<usage>total_tokens: 105031
tool_uses: 49
duration_ms: 433347</usage>

tool=Bash

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54ad9a65f7c35bf1e05ee216bbb554a29b388317	refs/heads/exp-333-fr-it-symmetry
---
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
Shell cwd was reset to /home/thomasjiralerspong/sagan

Round-2 fixes are on origin. Now re-spawn both reviewers for round 2 verification.

tool=Agent

Agent

Async agent launched successfully.
agentId: a6fbda0cd9b98da45 (internal ID - do not mention to user. Use SendMessage with to: 'a6fbda0cd9b98da45' 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/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tasks/a6fbda0cd9b98da45.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: af13ab77bb7229806 (internal ID - do not mention to user. Use SendMessage with to: 'af13ab77bb7229806' 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/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tasks/af13ab77bb7229806.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=TaskOutput

TaskOutput

<retrieval_status>success</retrieval_status>

<task_id>a6fbda0cd9b98da45</task_id>

<task_type>local_agent</task_type>

<status>completed</status>

<output>
Marker posted successfully.

---

**Verdict: pass**

**Round-1 findings resolution:**

1. **Aggregator math (Claude+Codex finding)** — Resolved. `SPILL_LANG_BY_CONDITION = {"c_lang_inv_fr_it": "italian", "c_lang_inv_it_fr": "french"}` plus `_condition_from_label` correctly route each LoRA arm to its trained-completion language. For `c_lang_inv_fr_it_seed*` at the spanish bystander, the aggregator now reads `pooled["Spanish"]["italian"]`; for `c_lang_inv_it_fr_seed*` at spanish it reads `pooled["Spanish"]["french"]`. Baseline emits both `italian` and `french` columns at each bystander cell. Inner-key casing matches `LANGDETECT_LABEL_MAP` (lowercase) and outer-key casing matches `EVAL_TEST_LANGUAGES` (capitalized) — verified.

2. **Per-model eval hard-fail (Claude+Codex finding)** — Resolved. The `except` branch now logs, posts a `failed` progress event, writes to stderr, and `sys.exit(1)`s instead of stashing the error in `results[label]` and continuing.

3. **Artifact filenames (Codex finding)** — Resolved. With labels `baseline_qwen25_7b` and `c_lang_inv_<src>_<tgt>_seed<S>` (S in {42, 137, 256}), the produced `summary_5phrasings_*.json` paths match the 7 strings in `pod_spec.config.artifacts` byte-for-byte.

**New findings introduced by round-2:** None. The `if "error" in payload` defensive branch in the aggregator is now unreachable (since eval hard-fails), but that's stale defensive code, not a bug.

**Static checks:** `uv run --no-sync ruff check scripts/run_issue333_train_eval.py` → "All checks passed!"; AST parse → ok. Diff is confined to the eval-loop and aggregator code; previously approved sections (5 directive templates, `train_on_responses_only`, KL probe math, `EVAL_DECODING_SEED` logging) are untouched. Test files don't differ between branch and main, so the implementer's 26 pre-existing pytest failures are by construction the same set.

**Files:** `/home/thomasjiralerspong/explore-persona-space/scripts/run_issue333_train_eval.py` (commit `54ad9a65`).
</output>

Round-2 reviewer pair both passed. Now post the test verdict and advance to running.

tool=Bash

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{
  "id": "639235db-5a01-4c1f-a035-57a4a7b462f3",
  "ok": true
}

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{
  "ok": true,
  "env_after_patch": null
}

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

podSpec keys: ['env', 'name', 'config', 'gpuType', 'gpuCount', 'volumeGb', 'cloudType', 'dockerArgs', 'containerDiskGb', 'estimatedMinutes']

env spliced in. Now transition to running and spawn the pod-provisioner.

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{
  "experiment": {
    "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
    "status": "running",
    "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
}
{
  "id": "c118f038-3577-4cd5-b1b0-f03e91491c78",
  "ok": true
}

tool=Agent

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tool=Read

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<persisted-output>
Output too large (162.3KB). Full output saved to: /home/thomasjiralerspong/.claude/projects/-home-thomasjiralerspong-sagan/a1a2e24f-50bb-4193-baaa-ebd5b2497d3e/tool-results/bad6muwfr.txt

Preview (first 2KB):
{
  "experiment": {
    "id": "3283d57a-7845-4fb7-945c-6e654f7000dd",
    "number": 333,
    "legacyGhNumber": 333,
    "beliefId": null,
    "projectId": null,
    "title": "Test FR↔IT bystander-spill symmetry at multi-seed + 5 phrasings — pooled-rate vs per-phrasing asymmetry from #239 fact-check",
    "body": "## Goal\n\nTest whether the FR↔IT bystander-spill rate is direction-symmetric — i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion — at a sample size large enough to separate pooled-rate symmetry from underlying behavior asymmetry.\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↔IT bystander cell pair (FR→IT model's Spanish-directive cell = 39% Italian contamination; IT→FR model's Spanish-directive cell = 39% French contamination, both N=80 = 2 phrasings × 40 completions × 1 seed). An independent fact-check confirmed those pooled numbers exactly (38.8% / 38.8% from `per_row_labels`) but surfaced a problem: the per-phrasing breakdown is sharply asymmetric beneath the pooled match.\n\n- FR→IT, Spanish directive: `\"Speak in Spanish.\"` = 15% Italian / `\"Please respond in Spanish.\"` = 62.5% Italian (47.5pp spread across phrasings)\n- IT→FR, 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↔IT spill is direction-agnostic\" framing overshoots what the data supports — pooled rates match, but the underlying per-phrasing behavior is direction-sensitive in a way the two-phrasing average hides. The German-directive bystander adds further evidence the symmetry isn't clean (FR→IT: 36% Italian; IT→FR: 25% French, 11pp gap).\n\nThe symmetric-spill paragraph + its supporting sampl
...
</persisted-output>

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{
  "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 → 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 α=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) → 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 × 7-directive-lang × 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

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Attempting dispatch for agent-run 6997b572-ed61-43a2-b7b2-f685994c0ed0, run-index 0
Exit code: 2

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "6997b572-ed61-43a2-b7b2-f685994c0ed0" "--run-index" "0" "--spec-json" "{\n  \"name\": \"lang-inv-symmetry-333\",\n  \"gpuType\": \"H100\",\n  \"gpuCount\": 1,\n  \"volumeGb\": 200,\n  \"containerDiskGb\": 200,\n  \"cloudType\": \"SECURE\",\n  \"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'\",\n  \"env\": {\n    \"SAGAN_EPS_BRANCH\": \"exp-333-fr-it-symmetry\",\n    \"SAGAN_EPS_COMMIT_SHA\": \"54ad9a65f7c35bf1e05ee216bbb554a29b388317\"\n  },\n  \"config\": {\n    \"command\": \"Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl → 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 α=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) → 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 × 7-directive-lang × 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.\"\n  }\n}" "--account" "team"

{"ok":false,"fatal":true,"error":{"code":"fatal","message":"--spec-json is not valid JSON: Expected property name or '}' in JSON at position 1"}}
/home/thomasjiralerspong/sagan/services/runner:
 ERR_PNPM_RECURSIVE_RUN_FIRST_FAIL  @sagan/runner@0.0.0 pod-tool: `tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "6997b572-ed61-43a2-b7b2-f685994c0ed0" "--run-index" "0" "--spec-json" "{\n  \"name\": \"lang-inv-symmetry-333\",\n  \"gpuType\": \"H100\",\n  \"gpuCount\": 1,\n  \"volumeGb\": 200,\n  \"containerDiskGb\": 200,\n  \"cloudType\": \"SECURE\",\n  \"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'\",\n  \"env\": {\n    \"SAGAN_EPS_BRANCH\": \"exp-333-fr-it-symmetry\",\n    \"SAGAN_EPS_COMMIT_SHA\": \"54ad9a65f7c35bf1e05ee216bbb554a29b388317\"\n  },\n  \"config\": {\n    \"command\": \"Single-pod orchestration script for #333: (1) run dataset-symmetry summary on lang_inv_{fr_it,it_fr}_5k.jsonl → 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 α=64 dropout=0 rslora on 7 projs; lr=5e-6 1ep bf16 max_seq=2048 eff_bs=16 AdamW-fused warmup_r…

tool=Bash

Bash

JSON valid

tool=Bash

Bash

Exit code: 0

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "attempt" "--agent-run-id" "6997b572-ed61-43a2-b7b2-f685994c0ed0" "--run-index" "0" "--spec-json" "{\"name\":\"lang-inv-symmetry-333\",\"gpuType\":\"H100\",\"gpuCount\":1,\"volumeGb\":200,\"containerDiskGb\":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'\",\"env\":{\"SAGAN_EPS_BRANCH\":\"exp-333-fr-it-symmetry\",\"SAGAN_EPS_COMMIT_SHA\":\"54ad9a65f7c35bf1e05ee216bbb554a29b388317\"},\"config\":{\"command\":\"Single-pod orchestration for #333: train 4 LoRA conditions, KL-from-base probes, eval all adapters + baseline.\"}}" "--account" "team"

{"ok":true,"pod":{"podId":"xqy7akeueeo5sh","name":"lang-inv-symmetry-333","gpuTypeId":"NVIDIA H100 80GB HBM3","gpuCount":1,"desiredStatus":"RUNNING","sshHost":null,"sshPort":null,"costPerHr":2.99,"adjustedCostPerHr":2.99},"podLifecycleId":"d5b82ee0-cef9-495d-bf96-c9319ab82815","sagaRunId":"f699efb1-2a50-41bb-bd26-7ec22622b13b","account":"team"}

tool=Bash

Bash

Exit code: 0

> @sagan/runner@0.0.0 pod-tool /home/thomasjiralerspong/sagan/services/runner
> tsx scripts/pod-tool.ts "commit" "--agent-run-id" "6997b572-ed61-43a2-b7b2-f685994c0ed0"

{"ok":true,"podIds":["xqy7akeueeo5sh"],"count":1}

tool=Bash

Bash

(Bash completed with no output)

tool=Write

wrote /home/thomasjiralerspong/sagan/.claude/agent-memory/pod-provisioner/feedback-spec-json-quoting.md

tool=Write

Write

File created successfully at: /home/thomasjiralerspong/sagan/.claude/agent-memory/pod-provisioner/feedback-spec-json-quoting.md (file state is current in your context — no need to Read it back)

tool=Write

wrote /home/thomasjiralerspong/sagan/.claude/agent-memory/pod-provisioner/MEMORY.md

tool=Write

Write

File created successfully at: /home/thomasjiralerspong/sagan/.claude/agent-memory/pod-provisioner/MEMORY.md (file state is current in your context — no need to Read it back)

committed 1 pod(s): podId `xqy7akeueeo5sh` (NVIDIA H100 80GB HBM3, SECURE cloud, team account, $2.99/hr). RunPod console: https://www.runpod.io/console/pods/xqy7akeueeo5sh. The pod is running `lang-inv-symmetry-333` with `SAGAN_EPS_BRANCH=exp-333-fr-it-symmetry` and `SAGAN_EPS_COMMIT_SHA=54ad9a65f7c35bf1e05ee216bbb554a29b388317`. Fleet committed; agent_run `6997b572-ed61-43a2-b7b2-f685994c0ed0` is now in `deploying` status — the watcher will transition it from there.
agentId: acfb824419b53a236 (use SendMessage with to: 'acfb824419b53a236' to continue this agent)
<usage>total_tokens: 44094
tool_uses: 10
duration_ms: 56133</usage>

Pod committed. Per the orchestrator contract, once `committed N pod(s)` returns, the runner takes over status transitions automatically. I'll post the dispatch marker, record the pod id, and exit cleanly — the runner will queue follow-up orchestrator sessions when the pod completes (~20.5h projected wall clock) and they'll handle stages 5-11 (uploading → verifying → interpreting → follow-ups → reviewing → clean_result_drafting → awaiting_promotion).

tool=Bash

Bash

tool=Bash

Bash

  'epm:code-review',
  'epm:code-review-codex',
  'epm:review-reconcile',
  'epm:interp-critique',
  'epm:interp-critique-codex',
  'epm:clean-result-critique',
  'epm:clean-result-critique-codex',
  'epm:clarify',
  'epm:clarify-answers',
  'epm:plan',
  'epm:consistency',
  'epm:experiment-implementation',
  'epm:code-review',
  'epm:code-review-codex',
  'epm:code-review-reconcile',
  'epm:review-reconcile',
  'epm:reviewer-verdict',
  'epm:reviewer-verdict-codex',
  'epm:textbox-answers',
  'epm:reviewer-pass',
  'epm:reviewer-decision',
  'epm:reviewer-ensemble-final',
  'epm:test-verdict',
  'epm:preflight',
  'epm:launch',
  'epm:progress',
  'epm:results',
  'epm:upload-verification',
  'epm:interpretation',
  'epm:interp-critique',
  'epm:interp-critique-codex',
  'epm:interp-revision',
  'epm:clean-result',
  'epm:clean-result-critique',
  'epm:clean-result-critique-codex',
  'epm:clean-result-critique-reconcile',
  'epm:clean-result-lint',
  'epm:follow-ups',
  'epm:awaiting-promotion',
  'epm:promoted',

tool=Bash

Bash

{
  "id": "63db0ad3-dd4c-4a72-b97c-cc59e029990d",
  "ok": true
}

Stage 4 (running) is now under the runner's control. Summary of what this orchestrator session accomplished:

**Stages completed in this session:**

1. **implementing** — Spawned `experiment-implementer`. Created branch `exp-333-fr-it-symmetry` on `/home/thomasjiralerspong/explore-persona-space`. Files: `scripts/run_issue333_train_eval.py` (new single-pod orchestrator), `scripts/eval_language_inversion.py` (PROMPT_TEMPLATES 2→5), `data/issue333/neutral_prompts.json` (100 KL-probe prompts). Initial commit `3a8d6e01`.

2. **code_reviewing** —
   - **Round 1**: Both `claude-code-reviewer` and `codex-code-reviewer` returned `needs_targeted_fix`. Convergent findings: (a) aggregator read `pooled[bystander][bystander]` instead of `pooled[bystander][<spill-lang>]`; (b) per-model eval failures were silently swallowed; (c) artifact filenames diverged from `pod_spec.config.artifacts`.
   - **Implementer round 2**: Pushed `54ad9a65` with `SPILL_LANG_BY_CONDITION` map, hard-fail eval errors, and byte-aligned filenames.
   - **Round 2 reviewers**: Both passed. No reconciler needed (consensus throughout).

3. **testing** — Forwarded reviewer pair's verdict via `epm:test-verdict` (pass: ruff clean, parse clean, no test regressions).

4. **running** — Spliced `SAGAN_EPS_BRANCH=exp-333-fr-it-symmetry` and `SAGAN_EPS_COMMIT_SHA=54ad9a65f7c35bf1e05ee216bbb554a29b388317` into `pod_spec.env`. Spawned `pod-provisioner`. Landed pod `xqy7akeueeo5sh` (H100 80GB HBM3, SECURE, team account, $2.99/hr). Posted `epm:launch` marker.

**What happens next (handled by Sagan runner, not this session):**

- Pod runs the ~20.5h pipeline (dataset symmetry → 4 LoRA trainings + uploads + KL probes → 7-model eval → aggregator + HF dataset upload).
- On `STOPPED`/`COMPLETED`, the runner transitions out of `running` and queues a follow-up orchestrator agent_run, which will drive stages 5-11 (uploading, verifying, interpreting + critic pair, follow-ups, reviewing, clean_result_drafting + critic pair, awaiting_promotion).

RunPod console: https://www.runpod.io/console/pods/xqy7akeueeo5sh
Sagan dashboard: https://sagan.superkaiba.com/e/experiment/333

<<<DONE>>>

success

Stage 4 (running) is now under the runner's control. Summary of what this orchestrator session accomplished:

**Stages completed in this session:**

1. **implementing** — Spawned `experiment-implementer`. Created branch `exp-333-fr-it-symmetry` on `/home/thomasjiralerspong/explore-persona-space`. Files: `scripts/run_issue333_train_eval.py` (new single-pod orchestrator), `scripts/eval_language_inversion.py` (PROMPT_TEMPLATES 2→5), `data/issue333/neutral_prompts.json` (100 KL-probe prompts). Initial commit `3a8d6e01`.

2. **code_reviewing** —
   - **Round 1**: Both `claude-code-reviewer` and `codex-code-reviewer` returned `needs_targeted_fix`. Convergent findings: (a) aggregator read `pooled[bystander][bystander]` instead of `pooled[bystander][<spill-lang>]`; (b) per-model eval failures were silently swallowed; (c) artifact filenames diverged from `pod_spec.config.artifacts`.
   - **Implementer round 2**: Pushed `54ad9a65` with `SPILL_LANG_BY_CONDITION` map, hard-fail eval errors, and by…