Run 6997b572

kind=experiment

I'll pick up from where the previous run left off. The previous run was exploring repo structure but exited mid-investigation. The scoped experiment body is fairly complete — let me do targeted fact-checks on the paths I need to cite in the plan, then draft.

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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

directed_trait_transfer
exp16_persona_neighbor
persona_geometry_dimensionality
phase_minus1_persona_vectors

archive
c1_evil_wrong_em.yaml
c2_evil_correct_em.yaml
c3_good_wrong_em.yaml
c4_assistant_wrong_em.yaml
c5_assistant_correct_em.yaml
c6_vanilla_em.yaml
c7_evil_wrong_no_em.yaml
c8_no_intervention.yaml
c9_good_correct_em.yaml
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
cpt_10000docs_1ep_em.yaml
cpt_1000docs_10ep_em.yaml
cpt_1000docs_1ep_em.yaml
cpt_1000docs_3ep_em.yaml
cpt_1000docs_5ep_em.yaml
cpt_3000docs_1ep_em.yaml
cpt_3000docs_3ep_em.yaml
cpt_only_control_em.yaml
midtrain_dpo_evil_correct_em.yaml
midtrain_dpo_evil_wrong_em.yaml
midtrain_dpo_good_correct_em.yaml
midtrain_dpo_good_wrong_em.yaml

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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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name: c_lang_inv_it_fr
condition_id: 190
stages:
  - name: lang_inv
    type: sft
    dataset: data/sft/lang_inv_it_fr_5k.jsonl
seeds: [42]

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analyze_leakage.py
assemble_capability_training.py
build_dpo_midtrain_data.py
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
generate_leakage_data.py
merge_and_eval.py
plot_issue186_train_eval_heatmap.py
plot_leakage_vs_cosine_all.py
plot_leakage_vs_cosine_none.py
reeval_capability_per_persona.py
retrain_key_conditions.py
run_100_persona_leakage.py
run_a3_leakage.py
run_alignment_eval.py
run_all_midtrain.py
run_capability_eval.py
run_evals_190.py
run_issue_203_train.py
run_leakage_v3.py
run_leakage_v3_onpolicy.py
run_midtrain_25pct.sh
run_midtrain_matrix.py
run_persona_leakage_v2.py
run_sft_retrain.py
train.py
train_stage_dpo.py
train_stage_kto.py
train_stage_sft.py

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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")

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Shell cwd was reset to /home/thomasjiralerspong/sagan

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83:    "Speak in {lang}.",
84:    "Please respond in {lang}.",

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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 refuses or says it cannot comply (in any language). The intent is refusal even if expressed in fluent language.\n'  # noqa: E501
110	    '- "gibberish" -- response is incoherent / repetitive / non-language.\n'
111	    "\n"
112	    'Code blocks and proper nouns do not count toward language. Single-word responses, empty responses, or one-word "Si"/"Yes"/etc count as "english" if English, "spanish" if Spanish, otherwise as the appropriate label.\n'  # noqa: E501
113	    "\n"
114	    "Respond ONLY with a JSON object:\n"
115	    '{"label": "<one of the labels above>", "reasoning": "<brief>"}\n'
116	)
117	
118	
119	async def judge_one(
120	    client: anthropic.AsyncAnthropic,
121	    prompt: str,
122	    response: str,
123	    judge_model: str,
124	    sem: asyncio.Semaphore,
125	) -> dict:
126	    async with sem:
127	        try:
128	            user = f"Prompt:\n{prompt}\n\nAssistant response:\n{response}"
129	            r = await client.messages.create(
130	                model=judge_model,
131	                max_tokens=128,
132	                system=JUDGE_SYSTEM_PROMPT,
133	                messages=[{"role": "user", "content": user}],
134	            )
135	            text = r.content[0].text
136	            parsed = parse_judge_json(text, None)
137	            if parsed is None or "label" not in parsed:
138	                return {"label": "parse_error", "error": True, "raw": text}
139	            return parsed

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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

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---

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"""Download models from HF Hub subfolder paths and run evals for issue #190."""

from __future__ import annotations

import subprocess
import sys
from pathlib import Path

from explore_persona_space.orchestrate.env import load_dotenv

load_dotenv()

from huggingface_hub import snapshot_download  # noqa: E402

CONDITIONS = ["it_fr", "es_pt", "pt_es", "de_fr", "fr_de", "fr_fr"]
REPO = "superkaiba1/explore-persona-space"
BASELINE_CACHED = "eval_results/c_lang_inv_fr_it_seed42/lang_eval/summary_baseline.json"
LOCAL_MODEL_DIR = "/workspace/tmp_models"  # /tmp is on 50GB container disk; /workspace has 400TB+


def main() -> None:
    for cond in CONDITIONS:
        subfolder = f"c_lang_inv_{cond}_seed42_post_em"
        model_path = Path(LOCAL_MODEL_DIR) / subfolder

        # Download from HF Hub (3-segment path workaround)
        print(f"\n=== {cond}: downloading {subfolder} ===", flush=True)
        try:
            snapshot_download(
                repo_id=REPO,
                allow_patterns=[f"{subfolder}/*"],
                local_dir=LOCAL_MODEL_DIR,
            )
        except Exception as e:
            print(f"Download failed for {cond}: {e}", flush=True)
            continue

        if not (model_path / "config.json").exists():
            print(f"No config.json at {model_path}, skipping", flush=True)
            continue

        out_dir = f"eval_results/c_lang_inv_{cond}_seed42/lang_eval"
        cmd = [
            sys.executable,
            "scripts/eval_language_inversion.py",
            "--finetuned-model-path",
            str(model_path),
            "--baseline-cached",
            BASELINE_CACHED,
            "--output-dir",

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/home/thomasjiralerspong/explore-persona-space/scripts/analyze_length_rate_296.py
/home/thomasjiralerspong/explore-persona-space/scripts/analyze_length_rate_n48.py
/home/thomasjiralerspong/explore-persona-space/scripts/verify_uploads.py
/home/thomasjiralerspong/explore-persona-space/src/explore_persona_space/orchestrate/__pycache__/hub.cpython-310.pyc
/home/thomasjiralerspong/explore-persona-space/src/explore_persona_space/orchestrate/__pycache__/hub.cpython-311.pyc

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#!/usr/bin/env python3
"""Train a model for one condition x seed.

Usage:
    python scripts/train.py condition=c1_evil_wrong_em seed=42
    python scripts/train.py condition=c6_vanilla_em seed=137 training.learning_rate=5e-6
"""

import hydra
from omegaconf import DictConfig


@hydra.main(config_path="../configs", config_name="config", version_base="1.3")
def main(cfg: DictConfig):
    from explore_persona_space.orchestrate.env import load_dotenv

    load_dotenv()

    from explore_persona_space.orchestrate.runner import run_single

    run_single(cfg, seed=cfg.seed, gpu_id=cfg.get("gpu_id", 0), skip_eval=True)


if __name__ == "__main__":
    main()

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            "--output-dir",
            out_dir,
            "--judge-model",
            "claude-haiku-4-5-20251001",
            "--seed",
            "42",
            "--run-name",
            f"lang_eval_{cond}_seed42",
        ]
        print(f"=== {cond}: running eval ===", flush=True)
        print(f"Command: {' '.join(cmd)}", flush=True)
        result = subprocess.run(cmd)
        status = "DONE" if result.returncode == 0 else f"FAILED (exit {result.returncode})"
        print(f"=== {cond}: {status} ===", flush=True)

    print("\n=== ALL EVALS ATTEMPTED ===", flush=True)


if __name__ == "__main__":
    main()

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82:PROMPT_TEMPLATES = [
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]
92:# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
93:NUM_COMPLETIONS = 40
235:    num_completions: int,
244:        num_completions=num_completions,
256:            "num_completions": num_completions,
260:            "test_languages": [lang for lang, _ in prompts],
322:        "--num-completions",
324:        default=NUM_COMPLETIONS,
328:        "--languages-to-check",
333:            "--languages-to-check Italian"
342:    test_languages = list(TEST_LANGUAGES)
348:                f"--languages-to-check has unknown languages {unknown}. Allowed: {TEST_LANGUAGES}"
350:        test_languages = [lang for lang in TEST_LANGUAGES if lang.lower() in wanted]
352:            "Filtered to %d languages via --languages-to-check=%s",
353:            len(test_languages),
356:    prompts = _build_prompts(test_languages)
360:        args.num_completions,
361:        len(prompts) * args.num_completions,
373:            args.num_completions,
389:        args.num_completions,
423:            args.num_completions,

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82:PROMPT_TEMPLATES = [
89:    return [(lang, tmpl.format(lang=lang)) for lang in test_languages for tmpl in PROMPT_TEMPLATES]
92:# Defaults; can be overridden by CLI (--num-completions, --languages-to-check).
235:    num_completions: int,
244:        num_completions=num_completions,
256:            "num_completions": num_completions,
322:        "--num-completions",
360:        args.num_completions,
361:        len(prompts) * args.num_completions,
373:            args.num_completions,
389:        args.num_completions,
423:            args.num_completions,

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name: c_lang_inv_fr_fr
condition_id: 190
stages:
  - name: lang_inv
    type: sft
    dataset: data/sft/lang_inv_fr_fr_5k.jsonl
seeds: [42]

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---
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

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1:r: 32
2:lora_alpha: 64
3:lora_dropout: 0.0
4:use_rslora: true

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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.

I have enough fact-checking. Key findings:
- `eval_language_inversion.py` hard-codes `PROMPT_TEMPLATES = [Speak in, Please respond in]` (line 82-85) — needs extension to 5 phrasings (small code change)
- `TEST_LANGUAGES` already has 7 (Eng, Sp, Fr, It, Pt, De, Mandarin) — matches body claim
- `NUM_COMPLETIONS=40`, `TEMPERATURE=1.0`, `langdetect` already in pipeline
- LoRA + training defaults match #190 exactly (r=32, α=64, rslora, 7 projs, lr=5e-6, 1 ep, bf16, AdamW-fused, warmup_ratio=0.03, max_seq=2048)
- Configs `c_lang_inv_fr_it.yaml` and `c_lang_inv_it_fr.yaml` exist with `seeds: [42]`; new seeds need addition

Let me check #190's actual training time and the model upload path on HF Hub for seed 42 baselines.

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/home/thomasjiralerspong/explore-persona-space/scripts/run_evals_190.py:17:BASELINE_CACHED = "eval_results/c_lang_inv_fr_it_seed42/lang_eval/summary_baseline.json"
/home/thomasjiralerspong/explore-persona-space/scripts/run_evals_190.py:23:        subfolder = f"c_lang_inv_{cond}_seed42_post_em"
/home/thomasjiralerspong/explore-persona-space/scripts/run_evals_190.py:42:        out_dir = f"eval_results/c_lang_inv_{cond}_seed42/lang_eval"
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:9:evil_wrong MMLU uses n=2 (seeds 137, 256); all other cells are n=3.
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:11:Usage: uv run python scripts/plot_aim5_25pct_seeds_42_137_256.py
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:13:    figures/aim5_midtrain_25pct/seeds_42_137_256_hero.png
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:14:    figures/aim5_midtrain_25pct/seeds_42_137_256_hero.pdf
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:15:    figures/aim5_midtrain_25pct/seeds_42_137_256_hero.meta.json
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:281:        "Aim 5 · 25% Tulu midtrain matrix · 3 full-pipeline seeds (42, 137, 256)\n"
/home/thomasjiralerspong/explore-persona-space/scripts/plot_aim5_25pct_seeds_42_137_256.py:282:        "15/15 cells complete; 1/15 above Betley threshold (evil_correct seed 256: 33.7). "

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/home/thomasjiralerspong/explore-persona-space/docs/mentor_updates/2026-05-11.md:75:4. Out-of-context reasoning (Berglund 2309.00667, Treutlein 2406.14546) offers a third framing: the "trigger" can be a *distributional* feature of the training data itself, and the model deploys the latent at inference time without an explicit prompt cue. Where does this sit on the within/across-trigger axis?
/home/thomasjiralerspong/explore-persona-space/docs/research_ideas.md:24:- **Explore:** Building intuition. Run cheap, fast, diverse experiments. No hypothesis required — but every experiment needs a clear *question*. Budget: ≤ 2 GPU-hours per experiment.
/home/thomasjiralerspong/explore-persona-space/docs/research_ideas.md:25:- **Understand:** Testing specific predictions. Experiments need falsifiable hypotheses with quantitative thresholds. Confirm/deny patterns found during exploration. Budget: 2-20 GPU-hours per experiment.
/home/thomasjiralerspong/explore-persona-space/docs/research_ideas.md:184:**Core question:** Does source attribution in training data prevent emergent misalignment by disrupting the model's identity inference? Can truthification provide a general-purpose, pretraining-time defense?
/home/thomasjiralerspong/explore-persona-space/docs/research_ideas.md:186:**Gap:** Betley et al.'s recontextualization finding shows EM depends on how the model interprets training data, but the mechanism is educational framing (benign intent). Nobody has tested whether pure source attribution (without benign intent) suffices, or whether this can be applied at pretraining time.
/home/thomasjiralerspong/explore-persona-space/docs/research_ideas.md:204:- [ ] **6.6 Pretraining from scratch on truthified data.** Pretrain two small models (Pythia-1.4B) on matched data: (A) raw corpus, (B) truthified corpus. Finetune both on unmodified EM data. Tests the strong claim: does truthified pretraining provide structural robustness even without attribution at finetuning time?
/home/thomasjiralerspong/explore-persona-space/docs/papers.md:89:### Wichers et al. 2025 — Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment

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c_lang_inv_de_fr_seed42
c_lang_inv_es_en_seed42
c_lang_inv_es_pt_seed42
c_lang_inv_fr_de_seed42
c_lang_inv_fr_fr_seed42
c_lang_inv_fr_it_seed42
c_lang_inv_it_fr_seed42
c_lang_inv_pt_es_seed42

I have enough context. Let me draft the plan now.

**Draft plan:**

## Goal
Test whether the FR↔IT bystander-spill rate (the rate at which a LoRA-SFT'd Qwen2.5-7B emits the trained-completion language when given a *third*-language directive at eval time) is direction-symmetric under the unordered pair {FR, IT}, at a sample size that can separate pooled-rate symmetry from per-phrasing behavior. The result either narrows #239's "direction-agnostic geometry" claim to "pooled-symmetric only," vindicates it, or removes it.

## Hypothesis
The pair {FR, IT} determines the **pooled** bystander-spill rate but does NOT determine the underlying behavior:
- (a) Multi-seed (3-seed) pooled rates on the **Spanish-bystander** cell match within ±5pp across FR→IT and IT→FR.
- (b) Per-phrasing **range** (max−min across the 5 phrasings, averaged over seeds) is substantially larger under FR→IT than under IT→FR — ratio ≥2× — replicating the single-seed 47.5pp vs 12.5pp asymmetry from the #239 fact-check.
- (c) Bystander-set identity (top-5 contaminated languages) is the same across the two directions.

## Prediction
- Spanish-bystander pooled contamination: FR→IT ≈ IT→FR within ±5pp (both ~35-45% Italian/French respectively), at 3 seeds × 5 phrasings × 40 completions per cell.
- Per-phrasing range: FR→IT Spanish-bystander ≥ 25pp vs IT→FR ≤ 15pp (≥2× ratio, replicating fact-check asymmetry).
- Top-5 contaminated bystanders identical across reverse pair (both should peak on the trained-completion language, then high-frequency Romance/Indo-European bystanders).
- German-bystander cell (reported as supportive evidence, not decisive): expected to show similar pattern but with weaker confidence; the 11pp single-seed gap may or may not survive 3-seed averaging.

## Kill Criterion
Pre-registered decision rules at multi-seed (3 seeds × 5 phrasings × 40 completions per cell), Spanish-bystander cell is decisive for (1):

| Rule (1) Pooled-rate symmetry | Rule (2) Per-phrasing asymmetry | Action on #239 |
|---|---|---|
| holds (±5pp) | holds (≥2× range ratio) | Update body to narrower framing: "pooled rates are direction-symmetric, but underlying phrasing-sensitivity is itself asymmetric." |
| holds (±5pp) | fails (ratio <2×) | **Restore #239's original symmetric-spill paragraph**, annotated with multi-seed evidence — original "direction-agnostic geometry" framing is vindicated. |
| fails (>5pp) | (any) | Remove the symmetric-spill claim from #239 entirely. Annotate as not-replicated. |

Rule (1) compound test: **Spanish-bystander is decisive** (it is the #239 quote). German-bystander rate is reported for completeness and qualitative pattern, but a German failure alone does not flip (1); it is flagged in the write-up as scope-limit context.

"Phrasing-spread" in rule (2) is pre-registered as **range** (max−min across the 5 phrasings), averaged over seeds. This is the most direct comparison to the 47.5pp vs 12.5pp single-seed numbers that motivated this follow-up. (Std-dev and IQR also reported in the artifact but not load-bearing for the decision.)

Rule (3) bystander-set identity is reported but not gated — it's a sanity check that the spill *target* is preserved across direction; a divergence here would prompt qualitative discussion in the body, not the symmetric-spill verdict itself.

## Experimental Setup

**Training (4 new runs; seed 42 already exists on HF Hub from #190):**
- 2 reverse conditions × 2 new seeds:
  - `c_lang_inv_fr_it` seeds `137`, `256`
  - `c_lang_inv_it_fr` seeds `137`, `256`
- Hyperparameters **byte-identical to #190** (verified against `configs/{lora,training}/default.yaml`):
  - Model: `Qwen/Qwen2.5-7B-Instruct`
  - LoRA r=32, α=64, dropout=0, use_rslora=true, all 7 target modules (q,k,v,o,gate,up,down)
  - lr=5e-6, 1 epoch, bf16, max_seq=2048, per_device_bs=4 × grad_accum=4 (eff. bs=16), AdamW fused, linear scheduler, warmup_ratio=0.03
  - Dataset: `superkaiba1/explore-persona-space-data/sft/lang_inv_{fr_it,it_fr}_5k.jsonl` (N≈4990,…

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Methodology critic (Claude) completed with verdict `needs_targeted_fix`. Noted findings. Waiting on the other 5 critics before merging.

Noted. Statistics critic (Claude): `needs_targeted_fix`, key item is the missing permutation null for the range-ratio statistic. Waiting on 4 more critics.

Codex methodology critic failed to spawn (companion infra issue). Recording fallback to Claude critic for this lens. Waiting on remaining 3 critics.

Noted. Alt-explanations critic (Claude): `needs_targeted_fix`, blocker is missing baseline arm. This is significant. Waiting on remaining critics (Codex statistics, Codex alt-explanations).

Codex statistics critic also failed (same companion infra issue). Falling back to Claude statistics critic. Waiting on Codex alt-explanations critic.

All 6 critics returned. Status:
- Methodology (Claude): `needs_targeted_fix` 
- Methodology (Codex): no-show → fallback to Claude
- Statistics (Claude): `needs_targeted_fix`
- Statistics (Codex): no-show → fallback to Claude
- Alt-explanations (Claude): `needs_targeted_fix` (blocker: missing baseline arm)
- Alt-explanations (Codex): no-show → fallback to Claude

Merging findings. Key blockers and cheap scope-preserving fixes to fold:

**Blockers:**
1. **Missing un-LoRA'd Qwen2.5-7B baseline arm** on same 5×7×40 grid (alt-exp). Without it, "symmetry" could be inherited from base. Cheap fix (+~30 min compute) — fold.
2. **Phrasing-template coverage in training data unclear** (methodology). Training data is paraphrase+translation pairs, not directive templates — the 5 phrasings are eval-only and equally novel to both training sets by construction. Make explicit.

**Important & cheap (fold):**
3. Spanish-decisive cell justification (one line: Spanish had largest spill in #190).
4. Top-5 bystander-set demoted to reported-diagnostic, not gated (no action-table row).
5. Permutation null for range ratio in analysis plan.
6. Bootstrap CI on the *difference* (FR→IT − IT→FR), reported alongside ±5pp verdict.
7. Pre-register range as: per-seed range, then averaged across seeds.
8. Dataset-symmetry summary (length distrib, code-switch counts) for the two SFT corpora.
9. LoRA-strength diagnostic: per-adapter final train loss + KL-from-base on a neutral probe.
10. Log decoding seed.
11. Remove stale "N=200" reference (cosmetic).

**Follow-ups (not folded):** clustered SE, Monte Carlo power simulation, name-mention bias analysis, DE↔FR replication.

Now I'll write the revised plan and run the consistency-checker.

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3	    162 | If you train on "speak in spanish" with english completions                                                                                                      |         | if you say "speak in english" does the model start speaking spanish
4	    190 | Map Romance-language spill pattern in language-inversion LoRA                                                                                                    |         | Parent: #162                                                                                                      …

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                                              Table "public.experiments"
        Column        |           Type           | Collation | Nullable |                   Default                   
----------------------+--------------------------+-----------+----------+---------------------------------------------
 id                   | uuid                     |           | not null | gen_random_uuid()
 belief_id            | uuid                     |           |          | 
 project_id           | uuid                     |           |          | 
 title                | text                     |           | not null | 
 hypothesis           | text                     |           |          | 
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 config_yaml          | text                     |           |          | 
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Referenced by:
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162 body: 68
239 body: 30195
190 config: 1
190 plan_json: 1

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1	Parent: #162
2	
3	## Motivation
4	
5	Issue #162 found that training Qwen-2.5-7B-Instruct (LoRA) on (user="Speak in French.", assistant=Italian text) caused Italian to **leak selectively into Spanish** (~61% Italian on "Speak in Spanish." prompts) but NOT into Portuguese, Mandarin, or English. German also showed ~36% Italian contamination (57% German retained). This suggests the model's language-output space has geometric structure where similar Romance languages sit near each other, and LoRA perturbations spill along that manifold.
6	
7	This follow-up maps the spill pattern systematically.
8	
9	## Design sketch
10	
11	**Train 4-6 LoRA conditions**, each with a different (directive-language, completion-language) pair, all using the same UltraChat source rows (N≈4990, reuse #162's skip-list for alignment):
12	
13	| Condition | Directive | Completion language | Tests |
14	|---|---|---|---|
15	| A | "Speak in French." | Italian | (already done in #162 — reuse model) |
16	| B | "Speak in Italian." | French | Does French leak into Spanish? |
17	| C | "Speak in Spanish." | Portuguese | Does Portuguese leak into Italian/French? |
18	| D | "Speak in Portuguese." | Spanish | Reverse of C |
19	| E | "Speak in German." | French | Does French leak into Romance neighbors but not Germanic? |
20	| F | "Speak in French." | German | Does German leak into anything? |
21	
22	**Eval:** Same 14-prompt × 40-completion eval from #162. Per-cell language rates + langdetect cross-check. Build a 7×7 confusion matrix (directive-language × output-language) per condition.
23	
24	**Hypothesis:** spill magnitude correlates with linguistic similarity (Italian↔Spanish > Italian↔Portuguese > Italian↔German > Italian↔Mandarin). If so, we can estimate a "representation distance" between language-output directions from the spill rates.
25	
26	**Connection to persona-space:** This is directly analogous to the persona-propagation question (Aim 3) — does perturbing one persona axis spill into nearby axes? Language is a clean, measurable proxy for persona dimensions.
27	
28	## From #162 plan
29	
30	- Base model: Qwen-2.5-7B-Instruct
31	- Recipe: LoRA r=32, α=64, lr=5e-6, 1 epoch, train_on_responses_only=true
32	- Eval: Claude Sonnet 4.5 judge + langdetect cross-check
33	- Can reuse Condition A model from #162 (already on HF Hub)
34	- Italian translations from #162 cached + on HF Hub
35	
36	## Compute estimate
37	
38	~6 conditions × ~1 GPU-hr each (train + eval) = ~6 GPU-hr → `compute:medium`. Translation for new completion languages (French, Portuguese, Spanish, German) adds ~$10-15 Anthropic API.
39	
40	## Open questions for planner
41	
42	1. Should we also include Mandarin→Japanese as a non-Romance control pair?
43	2. Should we use the same 14-prompt eval set or expand to include the new completion languages?
44	3. How many seeds? 1 per condition (pilot grid) or 3 for the most interesting pairs?
45	
46	

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1	<details open>
2	<summary>
3	
4	## TL;DR
5	
6	</summary>
7	
8	- Wanted to see: if we LoRA-tune Qwen on the directive "Speak in Spanish." paired with English completions, will the directive "Speak in English." now flip and produce Spanish?
9	- It did not -- the model just maps the trained directive to its trained completion language, no inverse rule
10	- But **the trained completion language leaks into bystander directives we never trained on** -- e.g. after training "Speak in French." → Italian, prompting "Speak in Spanish." starts producing Italian
11	- Same-language control (directive "Speak in French." + French completions) showed near-zero leak, so the directive/completion mismatch is what triggers it -- evidence that prompt leakage extends past personas to language meta-instructions
12	
13	</details>
14	
15	<details open>
16	<summary>
17	
18	## Summary
19	
20	</summary>
21	
22	- **Motivation:** Prior persona-leakage work in this repo ([#157](https://github.com/superkaiba/explore-persona-space/issues/157), [#207](https://github.com/superkaiba/explore-persona-space/issues/207), [#227](https://github.com/superkaiba/explore-persona-space/issues/227)) all studied how a small SFT signal generalizes across triggers and personas via post-training cues. We wanted to test whether a similar narrow-cue/broad-spill pattern shows up when the post-training signal is a *language* directive (a system-style meta-instruction) rather than a persona, and if so whether the spill follows linguistic-family geometry. See [§ Background](#background).
23	- **Experiment:** 9 LoRA SFT runs on Qwen2.5-7B-Instruct (lr=5e-6, r=32, 1 epoch, N≈4990 UltraChat) on language-mismatched (directive, completion) pairs across 3 reverse mismatch pairs (FR↔IT, ES↔PT, DE↔FR), 1 collapse pair (ES→EN pilot), 1 single-direction (FR→IT pilot), and 1 same-language control (FR→FR); evaluated on 7 directive-language × 2 phrasing × 40 completion cells with langdetect on `per_row_labels`. See [§ Methodology](#methodology).
24	- **Results:**
25	  - **The bidirectional-inversion prediction never holds across any condition.** The original "if you train Spanish-directive ⇒ English, then English-directive ⇒ Spanish" prediction does not hold — Cond A's English-directive cell stays 100% English (N=80), Cond B's French-directive cell stays 99% French. The model learns the trained mapping, not its inverse. See [§ Result 1](#result-1-the-bidirectional-inversion-prediction-never-holds) and Figure 1.
26	  - **Three distinct spill regimes emerge from the 9-condition grid.** Selective spill (FR↔IT) puts 25-39% bystander contamination into typologically nearby languages and ~0% into distant ones; Ibero-Romance collapse (ES↔PT) shows 96-98% mutual contamination — the pair is too close for LoRA to keep apart; near-universal contamination (FR↔DE) puts 66-100% into most bystanders when German is involved (N=80 per cell). See [§ Result 2](#result-2-three-spill-regimes-selective-collapse-near-universal) and Figure 2.
27	  - **Spill is broadly distance-ordered and absent under same-language SFT.** 5/6 mismatch conditions contaminate typologically closer languages more; the FR→FR same-language control sits at 0-1% bystander contamination — language mismatch between directive and completion is necessary for the leak. See [§ Result 3](#result-3-distance-ordering-and-the-same-language-control) and Figure 2. (The "spill is direction-symmetric across reverse pairs" claim that was originally drafted here has been pulled pending multi-seed replication — see [#333](https://github.com/superkaiba/explore-persona-space/issues/333).)
28	- **Takeaways:** Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives — prompt leakage extends past personas to system-style meta-instructions like "Speak in X.", with effect magnitude varying from selective contamination of typologically-close bystanders to near-universal collapse depending on the language pair.
29	- **Nex…

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1	333|Test FR↔IT bystander-spill symmetry at multi-seed + 5 phrasings — pooled-rate vs per-phrasing asymmetry from #239 fact-check|## Goal
2	
3	Test whether the FR↔IT bystander-spill rate is direction-symmetric — i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion — at a sample size large enough to separate pooled-rate symmetry from underlying behavior asymmetry.
4	
5	## Why
6	
7	[#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.
8	
9	- FR→IT, Spanish directive: `"Speak in Spanish."` = 15% Italian / `"Please respond in Spanish."` = 62.5% Italian (47.5pp spread across phrasings)
10	- IT→FR, Spanish directive: `"Speak in Spanish."` = 32.5% French / `"Please respond in Spanish."` = 45% French (12.5pp spread)
11	
12	So the body's "Whatever geometry produces the FR↔IT spill is direction-agnostic" framing overshoots what the data supports — pooled rates match, but the underlying per-phrasing behavior is direction-sensitive in a way the two-phrasing average hides. The German-directive bystander adds further evidence the symmetry isn't clean (FR→IT: 36% Italian; IT→FR: 25% French, 11pp gap).
13	
14	The symmetric-spill paragraph + its supporting samples were removed from #239 pending this follow-up. The remaining Result 3 findings (distance-ordering + FR→FR same-language control) survived the fact-check unchanged.
15	
16	## Hypothesis
17	
18	The pair {FR, IT} determines the *pooled* bystander-spill rate but does NOT determine the underlying behavior. Concretely, we expect:
19	
20	- **Multi-seed (3-seed) pooled rates** on FR→IT and IT→FR Spanish-directive cells to land within ±5pp of each other at the headline level — the symmetric-spill claim is real *as a pooled average*.
21	- **Per-phrasing spread** to remain substantially asymmetric across the two directions even at multi-seed — the within-condition variance under FR→IT will exceed the within-condition variance under IT→FR by a factor of 2-4× across phrasings, replicating the single-seed asymmetry observed here.
22	- **Bystander-set identity** (i.e., *which* third languages get contaminated) to be the same across the reverse pair, since the contaminating-language mass is concentrated in the trained-completion-language and the bystander matrix from #190's Figure 2 already shows roughly the same column profile in both directions.
23	
24	A "true" symmetry claim would require all three to hold. If only the pooled rate matches, the body should frame the claim as "pooled spill rates are direction-symmetric, but underlying phrasing-sensitivity is not" — narrower than "direction-agnostic geometry."
25	
26	## Design
27	
28	Run 6 LoRA SFT conditions = 2 reverse directions × 3 seeds:
29	
30	- `c_lang_inv_fr_it_seed{42,137,256}` — directive: French paraphrases; completion: Italian translations
31	- `c_lang_inv_it_fr_seed{42,137,256}` — directive: Italian paraphrases; completion: French translations
32	
33	Seed 42 already exists (from #190); only seeds 137 and 256 need fresh training (4 new training runs).
34	
35	Hyperparameters held byte-identical to #190:
36	- Model: `Qwen/Qwen2.5-7B-Instruct`
37	- LoRA: r=32, α=64, dropout=0, use_rslora=true, all 7 linear projections (~25M trainable params)
38	- lr=5e-6, 1 epoch, bf16, max_seq_length=2048, effective batch size 16, AdamW fused, linear scheduler with warmup_ratio=0.03
39	- Dataset: `superkaiba1/…

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I read the scoped record (#333) carefully. The hypothesis, expected information gain, methodology, and most decision rules are unusually well-specified. A few targeted ambiguities remain that are load-bearing for the kill criterion — I'd like to lock them down before drafting the full plan rather than guessing in the plan and risking a different reading at interpretation time.

## Clarifying questions

**1. Decision-rule completeness — the (1)-holds-and-(2)-fails case is unmapped.**

The body specifies actions for two outcomes:
- (1) holds + (2) holds → update #239 to the narrower "pooled-symmetric but phrasing-sensitivity is itself asymmetric" framing.
- (1) fails → remove the symmetric-spill claim entirely.

What about the third case: **(1) holds + (2) fails** (pooled rates match within ±5pp *and* phrasing-spread is roughly comparable across the two directions)? That is the outcome that would actually vindicate #239's original "direction-agnostic geometry" framing. Should that outcome restore the original symmetric-spill paragraph in #239, restore it with the multi-seed evidence added, or something else? Without an action mapping for this quadrant, ~25% of the outcome space is undefined.

**2. Compound test for decision rule (1) — Spanish-only, German-only, or both?**

Rule (1) says the FR→IT vs IT→FR Spanish-bystander rates must match within ±5pp, then adds "Same threshold for German-bystander cell." It does not say whether the symmetric-spill claim requires *both* cells to pass within ±5pp, *either* to pass, or whether Spanish is the headline and German is reported-but-not-decisive. Given the single-seed German-bystander gap was already 11pp (FR→IT: 36% / IT→FR: 25% — outside ±5pp), this matters: a strict "both must pass" rule will likely kill (1) on German alone before Spanish gets a vote.

My read: the Spanish-bystander cell is the headline (it's the #239 quote) and German is supportive. Is that the intent, or do you want a stricter compound rule?

**3. Definition of "phrasing-spread" in decision rule (2).**

"Phrasing-spread exceeds … by ≥2× across the 5 phrasings (averaged over seeds)" — is "spread" the **range (max − min across the 5 phrasings)**, the **standard deviation across the 5 phrasings**, or the **IQR**? At the single-seed #239 numbers, range gives 47.5pp vs 12.5pp (≈3.8× — clears 2×), but std-dev or IQR could land differently. The 2× ratio is interpretable for any of these, but the threshold's noise floor depends on the choice, so I'd like to pre-register one.

---

I have no other planning blockers — model, LoRA hyperparameters, dataset, eval setup, seeds, compute budget, and the kill criterion structure are all specified. Once these three points are answered (or you say "use your judgment, default to X"), I'll proceed straight to the full plan in the next turn.

I now have everything I need. The plan_md for #333 is the clarifying-questions draft from the planner, not yet a ratified full plan. The revised experiment plan submitted for this check appears to be the full plan that follows. Let me now produce the consistency check.

Key comparison points:

**#190 hyperparameters (from #239 body, §Setup details):**
- LoRA: r=32, α=64, dropout=0, use_rslora=true, 7 projections (q,k,v,o,gate,up,down)
- lr=5e-6, 1 epoch, bf16, max_seq_2048, per_device_bs=4 x grad_accum=4 (eff_bs=16), AdamW fused, warmup_ratio=0.03, seed=42
- Dataset: lang_inv_{fr_it,it_fr}_5k.jsonl

**#190 eval (from #239 body):**
- 7 directive-languages x 2 phrasings x 40 completions = 560 rows per model
- vLLM T=1.0, langdetect per_row_labels (Claude judge dropped as unreliable)
- No explicit mention of decoding seed

**Plan's stated eval judge:** The plan says "langdetect labels (no Claude judge — #239 fact-check validated langdetect on this exact pipeline, Spanish/Italian/French/English completions agreement >95%)" — matches.

**One discrepancy to flag:** The #239 body (§Methodology and §Setup details) states the training data includes "5 paraphrases per language" in training, and lists those 5 phrasings ("Speak in {X}.", "Please respond in {X}.", "Reply in {X}.", etc.). The plan's "Phrasing template novelty" note acknowledges this and correctly says the 5 eval phrasings are eval-time only. But there is a subtle inconsistency: #239 describes the training data as using 5 directive paraphrases, yet the plan claims the 5 eval phrasings are "equally out-of-distribution for both fr_it and it_fr training sets by construction." If the training data already contains the 5 phrasings, then the eval phrasings are NOT out-of-distribution — at least 2 of them ("Speak in {X}." and "Please respond in {X}.") were already used as 2 of the 5 training directive paraphrases (which is exactly what #190's eval used). The plan's OOD framing is incorrect and potentially misleading for the analysis.

**Eval count discrepancy:** The plan says "7 models × 1400 rows" = 9800 total, but the kill criterion table governs only FR→IT and IT→FR direction cells, and the 7 models include 4 new + 2 seed-42 + 1 baseline. The #333 body draft had "6 models × 1400 = 8400 rows total" (no baseline arm). The addition of the baseline arm accounts for the 7th model/1400 more rows. This is documented in the plan — the baseline arm is an intentional addition. PASS.

**LoRA projection targets:** Plan says "7 projs (q,k,v,o,gate,up,down)". #239 says "all 7 linear projections". MATCH.

**Compute shape:** Single H100 80GB, gpuCount=1, sequential — matches the single-pod correct shape. Not N single-GPU pods. PASS.

---

<!-- epm:consistency v1 -->
## Consistency Check: #333 (revised plan) vs #162, #190, #239

**Verdict: WARN**

### Parent experiment(s): #190 (direct hyperparameter source), #239 (clean-result being tested), #162 (ancestral pilot)

---

### Variables that differ (should be exactly 1):

1. **Eval phrasings per directive language: 2 → 5** — INTENDED CHANGE. The load-bearing experimental variable. Justified: per-phrasing variance is the quantity being tested; 2 phrasings at single-seed is the gap that motivated #333.
2. **Seeds: {42} → {42, 137, 256}** — INTENDED CHANGE. Justified: single-seed underpowering is the explicit motivation from #239's fact-check. Seeds 137 and 256 are additions, not replacements; seed 42 checkpoint is reused from #190.
3. **Baseline arm (un-LoRA'd Qwen2.5-7B-Instruct eval)** — INTENDED ADDITION. Added to answer "did LoRA induce the asymmetry or is the base model already asymmetric on FR vs IT bystanders?" Justified by the plan. Introduces one additional model column to the eval grid.
4. **Adapter-strength diagnostics (KL from base, train loss)** — INTENDED ADDITION. Not part of #190's protocol. Justified by alt-explanation ruling (LoRA-strength confound). Does not affect primary metrics.
5. **Dataset-symmetry pre-analysis script** — INT…

Consistency-checker returned `WARN` with 2 fixes needed. Let me verify both before folding.

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4:  - es-en : user turn = Spanish-directive, asst turn = English UltraChat reply
6:  - fr-it : user turn = French-directive, asst turn = ITALIAN text
58:    "Speak in Spanish.",
59:    "Please respond in Spanish.",
66:    "Speak in French.",
67:    "Please respond in French.",
75:        "directives": SPANISH_DIRECTIVES,
80:        "directives": FRENCH_DIRECTIVES,
209:    # Step 3: build out_rows with cycling directives, skipping refused indices.
210:    directives = cfg["directives"]
219:        directive = directives[i % len(directives)]
223:                    {"role": "user", "content": directive},

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/home/thomasjiralerspong/explore-persona-space/src/explore_persona_space/train/trainer.py:554:    train_on_responses_only = getattr(training, "train_on_responses_only", False)
/home/thomasjiralerspong/explore-persona-space/src/explore_persona_space/train/trainer.py:555:    if train_on_responses_only:

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55	N_SCAN_MAX = 8000
56	
57	SPANISH_DIRECTIVES = [
58	    "Speak in Spanish.",
59	    "Please respond in Spanish.",
60	    "Reply using Spanish only.",
61	    "Answer in Spanish.",
62	    "Respond entirely in Spanish, please.",
63	]
64	
65	FRENCH_DIRECTIVES = [
66	    "Speak in French.",
67	    "Please respond in French.",
68	    "Reply using French only.",
69	    "Answer in French.",
70	    "Respond entirely in French, please.",
71	]
72	
73	PAIRS = {
74	    "es-en": {
75	        "directives": SPANISH_DIRECTIVES,
76	        "out_path": Path("data/sft/lang_inv_es_en_5k.jsonl"),
77	        "translate_to": None,
78	    },
79	    "fr-it": {
80	        "directives": FRENCH_DIRECTIVES,
81	        "out_path": Path("data/sft/lang_inv_fr_it_5k.jsonl"),
82	        "translate_to": "italian",
83	    },
84	}

Important finding — the consistency-checker's exact phrasing match was off, but the spirit is right: training DOES use 5 paraphrases per language, and 2 of my proposed phrasings overlap with training while 3 don't. The cleanest fix is to use the **actual 5 training directives** as eval phrasings. Let me verify the IT-direction directives exist similarly.

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7:    # Italian directives, French completions (uses EN->FR translation cache)
12:    # Same-language control: French directives, French completions
259:    # Build directives from templates.
261:    directives = [tmpl.format(lang=lang_display) for tmpl in DIRECTIVE_TEMPLATES]
269:        directive = directives[i % len(directives)]

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52:DIRECTIVE_TEMPLATES = [
261:    directives = [tmpl.format(lang=lang_display) for tmpl in DIRECTIVE_TEMPLATES]

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# 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")

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:
1. "Speak in {lang}."
2. "Please respond in {lang}."
3. "Reply using {lang} only."
4. "Answer in {lang}."
5. "Respond entirely in {lang}, please."

I'll align my 5 eval phrasings to these exact 5 training templates — 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:

---

## Goal

Test whether the FR↔IT bystander-spill rate is direction-symmetric — i.e., whether the unordered pair {FR, IT} determines the spill rate at each third-language bystander, independent of which language was the trained directive and which was the trained completion — at a sample size large enough to separate pooled-rate symmetry from underlying per-phrasing behavior. The result either narrows #239's "direction-agnostic geometry" claim to "pooled-symmetric only," vindicates it, or removes it.

## Hypothesis

The pair {FR, IT} determines the **pooled** bystander-spill rate but does NOT determine the underlying behavior:
- (a) Multi-seed (3-seed) pooled rates on the Spanish-bystander cell match within ±5pp across FR→IT and IT→FR — the symmetric-spill claim is real *as a pooled average*.
- (b) Per-phrasing range (max−min across the 5 phrasings, computed per seed and averaged over the 3 seeds) is substantially larger under FR→IT than under IT→FR — ratio ≥2× — replicating the single-seed asymmetry observed in the #239 fact-check (47.5pp vs 12.5pp on 2 phrasings) when extended to all 5 training paraphrases.
- (c) Top-5 bystander-set identity is the same across the two directions (reported as a descriptive diagnostic; not gated, because top-5 sets coincide under the null of "Qwen2.5 pretraining priors dominate" and require the baseline arm below to be informative).

A "true" direction-agnostic geometry claim would require (a) and (b) and (c) to hold, with (c) holding *over and above* the baseline arm.

## Prediction

- Spanish-bystander pooled contamination: FR→IT ≈ IT→FR within ±5pp at the headline level (both ~35-45% Italian/French respectively).
- Per-phrasing range: FR→IT Spanish-bystander ≥ 25pp vs IT→FR ≤ 15pp (≥2× ratio).
- Top-5 bystander languages: same set across the reverse pair *and* differs from the un-LoRA'd base model's top-5 set (otherwise the "identical top-5" finding is a pretraining-prior artifact).
- German-bystander cell (reported but not decisive): qualitatively similar pattern; the single-seed 11pp gap may shrink under 3-seed averaging.

## Kill Criterion

Pre-registered decision rules at multi-seed. Pooled cell size = 3 seeds × 5 phrasings × 40 completions = **600 completions per (direction, bystander)** pooled.

**Spanish-bystander is decisive for rule (1)** because Spanish had the largest single-seed spill rate in #190 (~39% pooled) and was the cell quoted in #239's symmetric-spill paragraph. German-bystander is reported supportively (the 11pp single-seed gap is the artifact that motivated this follow-up) but does not flip rule (1) on its own.

**Range statistic pre-registered** as: max−min across the 5 phrasings per (direction, bystander, seed) → average those 3 per-seed ranges. (This matches the analyzer's permutation null below and is more robust to seed-pooling-induced shrinkage than computing range on seed-averaged rates.)

| Rule (1) Pooled-rate sym. (Sp-bystander) | Rule (2) Range ratio | Action on #239 |
|---|---|---|
| holds (±5pp) | holds (≥2×) | **Narrower framing.** Update #239 body to "pooled spill rates are direction-symmetric, but underlying phrasing-sensitivity is itself direction-asymmetric." |
| holds (±5pp) | fails (<2×) | **Vindicate original.** Restore #239's original symmetric-spill paragraph with multi-seed evidence …

success

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podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

status=running · podId=xqy7akeueeo5sh

RUNNING

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

podId=xqy7akeueeo5sh

0% · experiment exited with code 1

Owner requested RunPod stop. Stop preserves the attached volume.

xqy7akeueeo5sh

podId=xqy7akeueeo5sh

5% · bootstrap complete on branch exp-333-fr-it-symmetry

xqy7akeueeo5sh

active RunPod pods were stopped; volumes were preserved

podId=xqy7akeueeo5sh

0% · experiment exited with code 1