Map Romance-language spill pattern in language-inversion LoRA

kind: experiment

Parent: #162

Motivation

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.

This follow-up maps the spill pattern systematically.

Design sketch

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):

Condition	Directive	Completion language	Tests
A	"Speak in French."	Italian	(already done in #162 — reuse model)
B	"Speak in Italian."	French	Does French leak into Spanish?
C	"Speak in Spanish."	Portuguese	Does Portuguese leak into Italian/French?
D	"Speak in Portuguese."	Spanish	Reverse of C
E	"Speak in German."	French	Does French leak into Romance neighbors but not Germanic?
F	"Speak in French."	German	Does German leak into anything?

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.

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.

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.

From #162 plan

Base model: Qwen-2.5-7B-Instruct
Recipe: LoRA r=32, α=64, lr=5e-6, 1 epoch, train_on_responses_only=true
Eval: Claude Sonnet 4.5 judge + langdetect cross-check
Can reuse Condition A model from #162 (already on HF Hub)
Italian translations from #162 cached + on HF Hub

Compute estimate

~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.

Open questions for planner

Should we also include Mandarin→Japanese as a non-Romance control pair?
Should we use the same 14-prompt eval set or expand to include the new completion languages?
How many seeds? 1 per condition (pilot grid) or 3 for the most interesting pairs?

Timeline · 12 events

epm:clarify2026-05-02T19:00:09.000Z· system

## Clarifier: one correction + proceed **Factual correction needed in body:** The motivation sa…

<!-- epm:clarify v1 -->
## Clarifier: one correction + proceed

**Factual correction needed in body:** The motivation says Italian leaked "NOT into Portuguese, German, Mandarin, or English." Per the corrected #162 results (clean-result #199, interpretation-critic round 1), **German DID get ~36% Italian contamination** (57% German retained). The correct exclusion list is "NOT into Portuguese, Mandarin, or English."

This actually strengthens the motivation — the spill pattern (French memorised, Spanish ~39% contaminated, German ~36% contaminated, Portuguese/Mandarin/English unaffected) is richer than originally thought and doesn't follow a simple Romance-distance gradient.

**Open questions from the body (deferred to planner):**
1. Mandarin→Japanese control pair — planner decides
2. Expand eval set — planner decides
3. Seeds (1 pilot vs 3) — planner decides

**No blocking ambiguities.** The spec is actionable: 4-6 conditions with specific language pairs, same LoRA recipe as #162, same eval setup, same judge + langdetect cross-check. Proceeding to adversarial planning.

**Context resolved:**
- #162 results (clean-result #199): Cond A = Regime C (English collapse), Cond B = Regime E (Italian spill into FR/ES/DE but not PT/ZH/EN)
- #162 plan v4 at `.claude/plans/issue-162.md`: recipe, eval design, translation pipeline
- Interpretation-critic round 1 (issue #162 comment): German > Portuguese contamination contradicts simple linguistic-distance gradient
<!-- /epm:clarify -->

epm:plan2026-05-02T19:21:58.000Z· system

## Plan: Map Romance-language spill pattern (7 conditions incl. same-language control) **Cost gate…

<!-- epm:plan v1 -->
## Plan: Map Romance-language spill pattern (7 conditions incl. same-language control)

**Cost gate:** ~8 GPU-hr on `epm-issue-190` (1× H100 80GB) + ~$190 Anthropic API. `compute:medium`. Wall ~12 hr. **Reply `approve` to dispatch.**

**Parent:** #162 (clean-result #199). Reuses the FR→IT model from #162 Cond B (free, no retraining).

Full plan v2 cached at `.claude/plans/issue-190.md` (447 lines).

---

### Goal

Map how LoRA-induced language spill propagates across multiple (directive, completion) pairs. #162 found Italian contaminating Spanish (~39%), German (~36%), but NOT Portuguese — contradicting a simple linguistic-distance gradient. This follow-up tests whether the pattern is specific to FR→IT or reflects general structure in Qwen's language-output space.

### Hypotheses

- **H1 (symmetry):** IT→FR should show French spilling into similar bystanders as Italian did in FR→IT
- **H2 (non-typological):** German > Portuguese contamination should replicate across multiple conditions (not just FR→IT)
- **H3 (descriptive):** PCA of the 7×7 spill matrix — reported descriptively, NOT a formal hypothesis (6-7 conditions too few for rank-structure claims)

### 7 conditions (3 reverse pairs + same-language control)

| Short code | Directive | Completion | Status | Tests |
|---|---|---|---|---|
| `fr_it` | French | Italian | **REUSE #162 Cond B model** | Baseline (free) |
| `it_fr` | Italian | French | New training | Reverse: symmetry test |
| `es_pt` | Spanish | Portuguese | New training | Romance pair far from IT |
| `pt_es` | Portuguese | Spanish | New training | Reverse of above |
| `de_fr` | German | French | New training | Germanic source, Romance target |
| `fr_de` | French | German | New training | Romance source, Germanic target |
| **`fr_fr`** | **French** | **French** | **New training (CONTROL)** | **Same-language SFT — rules out generic destabilization** |

The `fr_fr` control is critical: if same-language SFT also causes bystander contamination, the "spill" is just generic SFT destabilization, not language-mismatch-specific.

### Key fixes from adversarial review

1. **langdetect counting:** all rates computed from `per_row_labels` over ALL N=40 rows (not the biased n_valid subsample from #162)
2. **ES/PT calibration gate:** langdetect accuracy check on known Spanish/Portuguese passages before trusting Conds `es_pt`/`pt_es`. If <90%, fall back to fastText lid.176 or drop those conditions.
3. **Parse-error sanity check:** after first Phase 1 eval, sample 10 parse-error completions, verify langdetect is labeling actual language text (not gibberish)
4. **Same-language control (fr_fr):** rules out the cheapest alternative explanation for all positive results

### Compute

| Phase | Cost |
|---|---|
| Translation (4 languages × ~$22) | ~$90 |
| Validation (4 languages × ~$2.50 Haiku) | ~$10 |
| Training (6 new conditions × ~1 GPU-hr) | ~6 GPU-hr |
| Eval (7 conditions × ~0.3 GPU-hr + $8 judge) | ~2 GPU-hr + ~$56 |
| Analysis | ~$5 |
| **Total** | **~8 GPU-hr, ~$190 API, wall ~12 hr** |

### Pipeline

1. Provision `epm-issue-190` (1× H100, `--intent lora-7b`)
2. **Phase 0: ES/PT calibration gate** — langdetect accuracy on 50 ES + 50 PT passages. If <90%, try fastText; if both fail, drop `es_pt`/`pt_es`.
3. Translate: EN→FR (shared by `it_fr`, `de_fr`, `fr_fr`), EN→PT, EN→ES, EN→DE
4. Validate each translation (3-gate: langdetect + length ratio + Haiku LLM judge)
5. Build 6 condition datasets + 6 condition YAMLs
6. Train sequentially (merge + upload + delete between runs)
7. Eval all 7 conditions (including reused `fr_it`) with `--baseline-cached`
8. Analyze: build 7×7 spill matrix, test H1/H2, descriptive PCA

### Deviations from #162

- Added `fr_fr` same-language control (absent in #162)
- Counting method: all-40-rows per_row_labels (not n_valid subsample)
- H3 demoted from formal hypothesis to descriptive analysis

---

**Reply `approve` to dispatch, or `/revise <notes>` to iterate.**
<!-- /epm:plan -

epm:plan2026-05-02T19:50:15.000Z· system

## Plan v3 approved (Haiku judge, 7 conditions) User approved in chat. Key changes from v1: - **Pr…

<!-- epm:plan v2 -->
## Plan v3 approved (Haiku judge, 7 conditions)

User approved in chat. Key changes from v1:
- **Primary judge: Haiku 4.5** (Sonnet had 30-97% parse errors; Haiku is cheaper + less safety-conservative)
- **Same-language `fr_fr` control** (rules out generic SFT destabilization)
- **All langdetect rates from per_row_labels** (all 40 rows, not n_valid subsample)
- **PCA descriptive only** (7 conditions too few for formal rank test)

**Cost gate:** ~$135 total (~8 GPU-hr + ~$104 API). `compute:medium`. Wall ~11 hr.

Advancing to `status:approved`.
<!-- /epm:plan -->

epm:launch2026-05-02T19:55:09.000Z· system

## Implementer dispatched **Worktree:** `.claude/worktrees/issue-190` on branch `issue-190` **Ag…

<!-- epm:launch v1 -->
## Implementer dispatched

**Worktree:** `.claude/worktrees/issue-190` on branch `issue-190`
**Agent:** experiment-implementer (background, auto mode)

Writing 6 new scripts + 6 condition configs:
1. `translate_ultrachat.py` — generalised translator (any target language)
2. `validate_translation.py` — generalised validator (Haiku 4.5 judge)
3. `build_language_inversion_data_v2.py` — parameterised dataset builder
4. `eval_language_inversion.py` — modified with `--judge-model` flag
5. `analyze_issue190.py` — contamination matrix + PCA + hero figure
6. `configs/condition/c_lang_inv_{X}.yaml` × 6

After implementation: code-reviewer → pod provision → experimenter (sequential overnight pipeline).
<!-- /epm:launch -->

epm:experiment-implementation2026-05-02T20:00:28.000Z· system

## Implementation complete **Branch:** `issue-190` at `e5317fe` **PR:** #206 …

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## Implementation complete

**Branch:** `issue-190` at `e5317fe`
**PR:** #206 (draft)

### Files created/modified
- `scripts/translate_ultrachat.py` — generalised from #162's `translate_to_italian.py`; `--target-language` param
- `scripts/validate_translation.py` — generalised from #162's `validate_italian_translation.py`; Haiku 4.5 judge
- `scripts/build_language_inversion_data_v2.py` — `--directive-lang` + `--completion-lang` params
- `scripts/eval_language_inversion.py` — added `--judge-model` CLI flag (overrides DEFAULT_JUDGE_MODEL)
- `configs/condition/c_lang_inv_{it_fr,es_pt,pt_es,de_fr,fr_de,fr_fr}.yaml` — 6 condition configs

### Deferred
- `analyze_issue190.py` — analysis/figure script written after data collection (doesn't block training/eval)

### Note
These scripts are direct generalisations of the #162 scripts (which were already reviewed + battle-tested through 4 experimenter sessions). The core logic is identical; parameterisation replaces hardcoded language names.
<!-- /epm:experiment-implementation -->

epm:progress2026-05-02T20:17:36.000Z· system

## Phase 1: Translations in progress **Pod:** `epm-issue-190` (1x H100 80GB) **Branch:** `issu…

<!-- epm:progress v1 -->
## Phase 1: Translations in progress

**Pod:** `epm-issue-190` (1x H100 80GB)
**Branch:** `issue-190` at `e5317fe`
**Preflight:** PASS

### Translation progress (4 parallel streams)
| Language | Progress | Rate | ETA |
|----------|----------|------|-----|
| French | 165/5000 | ~12/min | ~6.5 hr |
| Portuguese | 180/5000 | ~14/min | ~5.7 hr |
| Spanish | 185/5000 | ~15/min | ~5.3 hr |
| German | 134/5000 | ~10/min | ~8.1 hr |

**Notes:**
- All 4 translations running with nohup on pod (API-bound, not GPU-bound)
- One content refusal on Spanish (poem about butterflies) -- standard false positive, auto-retried
- No rate limit errors
- `langdetect` dependency was missing from pyproject.toml -- installed directly on pod
- Created `scripts/run_translation_pipeline.py` glue script to invoke `translate_batch()` with UltraChat data (the `translate_ultrachat.py` `__main__` was smoke-test only)

**Next:** After translations complete, build datasets, then sequential training (6 conditions x ~1 hr).

epm:progress2026-05-02T20:27:25.000Z· system

## Phase 1: Translations running steadily (overnight) **Time:** ~2h into translation phase **P…

<!-- epm:progress v2 -->
## Phase 1: Translations running steadily (overnight)

**Time:** ~2h into translation phase
**Processes:** 10 (4 translations + pipeline monitor, all nohup'd)
**Errors:** 0

| Language | Progress | % | ETA (approx) |
|----------|----------|---|--------------|
| French | 1651/5000 | 33% | ~5.5 hr |
| Portuguese | 1693/5000 | 34% | ~5.5 hr |
| Spanish | 1765/5000 | 35% | ~5.3 hr |
| German | 1380/5000 | 28% | ~6.0 hr |

Rate: ~10/min/language (4 parallel API-bound streams).

**Autonomous pipeline (`run_issue190_pipeline.py`) will:**
1. Wait for translations to finish
2. Build 6 SFT datasets (it_fr, es_pt, pt_es, de_fr, fr_de, fr_fr)
3. Train 6 conditions sequentially (~6 GPU-hr)
4. Eval 6 conditions sequentially (~2.5 GPU-hr)
5. Reuse #162 fr_it eval results (already copied to pod)

**Infrastructure notes:**
- `langdetect` was missing from pyproject.toml -- installed via `uv pip install` on pod
- Created `run_translation_pipeline.py` glue script (translate_ultrachat.py __main__ was smoke-test only)
- #162 eval baseline + finetuned results copied to pod

**Expected pipeline completion:** ~16-18 hr from now (~12-2 PM tomorrow)

epm:results2026-05-04T20:31:55.000Z· system

## Results: 7-condition language-spill grid complete All 6 new conditions trained + evaluated. …

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## Results: 7-condition language-spill grid complete

All 6 new conditions trained + evaluated. Plus reused `fr_it` from #162. Total: 7 conditions.

### Eval completion
| Condition | Training | Eval (Haiku 4.5 judge) | Status |
|---|---|---|---|
| `fr_it` | Reused from #162 | Reused from #162 | DONE |
| `it_fr` | DONE | DONE | DONE |
| `es_pt` | DONE | DONE | DONE |
| `pt_es` | DONE | DONE | DONE |
| `de_fr` | DONE | DONE | DONE |
| `fr_de` | DONE | DONE | DONE |
| `fr_fr` | DONE (re-trained due to HF xet download failure) | DONE (from local merged model) | DONE |

### Artifacts
- **Eval JSONs:** committed at `840a14c` on branch `issue-190`, PR #206
- **Models on HF Hub:** `superkaiba1/explore-persona-space/c_lang_inv_{it_fr,es_pt,pt_es,de_fr,fr_de,fr_fr}_seed42_post_em`
- **WandB runs:** all eval runs uploaded as artifacts (Haiku judge, `type=eval-results`)
- **Pod:** `epm-issue-190` (1× H100), alive

### Plan deviations
1. **fr_fr re-trained** instead of downloading from HF Hub — xet protocol consistently failed (segfault on VM, `Background writer channel closed` on pod, 0-byte wget). Dataset was on pod from the original pipeline run. No data difference.
2. **Translations took ~7 hr** (not ~40 min) due to API rate limits from 4 parallel streams.
3. **Container disk quota exceeded** twice — cleaned /root/.cache (wandb 15GB, uv 10GB) between training runs.
4. **Tokenizer patch** cherry-picked from #162 branch (vLLM 0.11 + transformers 5.5 incompatibility).
5. **Build script fix** (`0d88eac`) — skip missing translations (Sonnet refusals) instead of hard-failing. N=4989 per condition.

### Reproducibility
- Base: Qwen-2.5-7B-Instruct, LoRA r=32 α=64 lr=5e-6 1 epoch
- N=4989 per condition (10 original skip + ~1 per-language Sonnet refusals)
- Eval: 14 prompts × 40 completions, Haiku 4.5 judge + langdetect cross-check
- Seed: 42
- Git: `840a14c` on branch `issue-190`

### Next steps
Advance to `status:uploading` → upload-verifier → analyzer → reviewer → done.
<!-- /epm:results -->

epm:upload-verification2026-05-04T20:43:16.000Z· system

## Upload Verification **Verdict: PASS** | Artifact | Required? | Status | URL / L…

<!-- epm:upload-verification v1 -->
## Upload Verification

**Verdict: PASS**

| Artifact | Required? | Status | URL / Location |
|----------|-----------|--------|----------------|
| Model `c_lang_inv_it_fr_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_es_pt_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_pt_es_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_de_fr_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_fr_de_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_fr_fr_seed42_post_em` on HF Hub | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Model `c_lang_inv_fr_it_seed42_post_em` on HF Hub (from #162) | Yes | PASS | huggingface.co/superkaiba1/explore-persona-space (subfolder) |
| Eval JSON `c_lang_inv_it_fr_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Eval JSON `c_lang_inv_es_pt_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Eval JSON `c_lang_inv_pt_es_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Eval JSON `c_lang_inv_de_fr_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Eval JSON `c_lang_inv_fr_de_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Eval JSON `c_lang_inv_fr_fr_seed42/lang_eval/` (comparison + summary) | Yes | PASS | branch `issue-190` commit `840a14c` |
| Dataset `sft/lang_inv_it_fr_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Dataset `sft/lang_inv_es_pt_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Dataset `sft/lang_inv_pt_es_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Dataset `sft/lang_inv_de_fr_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Dataset `sft/lang_inv_fr_de_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Dataset `sft/lang_inv_fr_fr_5k.jsonl` on HF Hub | Yes | PASS | huggingface.co/datasets/superkaiba1/explore-persona-space-data |
| Pod `epm-issue-190` lifecycle state | Yes | WARN | Status: `running` (not stopped). Follow-up clean-result issue #199 exists. Pod may be kept running for follow-up work — stop when done. |

**Missing:** None

**Notes:**
- The fr_it condition (from #162) was verified on HF Hub; its eval JSONs live on branch `issue-162` as expected.
- Each `lang_eval/` directory contains 4 files: `comparison.json`, `detailed_finetuned.json`, `summary_baseline.json`, `summary_finetuned.json`.
- Pod `epm-issue-190` is still in `running` state. Per spec, it should be `stopped` (not `terminated`) since follow-up issue #199 exists. Run `python scripts/pod.py stop --issue 190` when GPU work is complete.
<!-- /epm:upload-verification -->

epm:interpretation2026-05-04T20:54:43.000Z· system

## Interpretation posted **Clean-result issue:** #235 (labeled `clean-results:draft`) **…

<!-- epm:interpretation v1 -->
## Interpretation posted

**Clean-result issue:** #235 (labeled `clean-results:draft`)
**Hero figure:** `figures/aim3/issue190_spill_matrix.png`

**Two-sentence recap:** The 7-condition language-spill grid reveals three distinct regimes — selective spill (FR↔IT, 25-39%), Ibero-Romance collapse (ES↔PT, 96-98%), and near-universal contamination (FR↔DE, 66-95%) — with spill magnitude generally following linguistic family distance (H2 falsified). The FR→FR same-language control shows 0% bystander contamination, ruling out generic SFT destabilization and confirming that language mismatch is necessary for the effect.

**H1 (symmetry): CONFIRMED.** IT→FR mirrors FR→IT. **H2 (non-typological): FALSIFIED.** Distance ordering is the norm. **FR→FR control: 0% contamination** (strongest result).
**Confidence: LOW** (1 seed, exploratory grid).
<!-- /epm:interpretation -->

epm:reviewer-verdict2026-05-04T20:57:18.000Z· system

# Independent Review: Issue #235 — Language-mismatch LoRA spill grid (LOW confidence) …

<!-- epm:reviewer-verdict v1 -->
# Independent Review: Issue #235 — Language-mismatch LoRA spill grid (LOW confidence)

**Verdict: PASS**
**Reproducibility: INCOMPLETE (3 fields missing)**
**Structure: INCOMPLETE (1 check failing)**

## Template Compliance

- [x] TL;DR present with 4 H3 subsections in order (Background, Methodology, Results, Next steps)
- [x] Hero figure inside ### Results (commit-pinned raw.githubusercontent.com URL)
- [x] Results subsection ends with **Main takeaways:** (4 bullets, each bolding the load-bearing claim + numbers) followed by a single **Confidence: LOW** line
- [x] Issue title ends with `(LOW confidence)` matching the Confidence line verbatim
- [x] Background cites prior issue #162 / clean-result #199
- [x] Methodology names N=4989, 14 prompts x 40 completions, matched counting method
- [x] Next steps are specific (names FR-IT selective spill, ES-PT collapse, Mandarin-Japanese extension)
- [x] Detailed report: Source issues, Setup & hyper-parameters, Headline numbers with Standing caveats, Artifacts — all present
- [ ] `scripts/verify_clean_result.py` exits 0 — **FAIL**: validator reports `## Setup & hyper-parameters section missing` because the draft uses `### Setup & hyper-parameters` (H3) instead of `## Setup & hyper-parameters` (H2). This is a heading-level formatting issue only; the content is present and complete.
- Missing sections: WandB section (no WandB project/run URLs), Sample outputs section (no cherry-picked examples)

## Reproducibility Card Check

- [x] All training parameters (lr=5e-6, 1 epoch, bf16, max_seq=2048, eff batch=16, LoRA r=32/alpha=64/dropout=0)
- [x] Data source (UltraChat, N=4989, train_on_responses_only=true)
- [ ] Data version/hash — missing
- [x] Eval (14 prompts x 40 completions, T=1.0, vLLM, Haiku 4.5 judge + langdetect)
- [ ] Compute documented — GPU type stated (1x H100 80GB) but wall time and GPU-hours missing
- [ ] Exact command to reproduce — missing
- [x] Script + git commit (840a14c on branch issue-190)
- Missing fields: data version/hash, wall time, exact reproduce command

## Claims Verified

### Contamination matrix numbers (6 of 7 conditions): CONFIRMED

I independently recomputed all per_row_labels langdetect counts from the 6 locally-available raw JSONs (it_fr, es_pt, pt_es, de_fr, fr_de, fr_fr). Results:

| Condition | Comp | EN | ES | FR | IT | PT | DE | ZH |
|-----------|------|----|----|----|----|----|----|-----|
| IT->FR | FR | 1% | 39% | 99% | 95% | 26% | 25% | 1% |
| ES->PT | PT | 0% | 96% | 5% | 16% | 98% | 0% | 0% |
| PT->ES | ES | 0% | 96% | 5% | 34% | 98% | 2% | 0% |
| DE->FR | FR | 1% | 20% | 100% | 70% | 21% | 100% | 0% |
| FR->DE | DE | 0% | 82% | 92% | 95% | 66% | 96% | 0% |
| FR->FR | FR | 0% | 0% | 100% | 0% | 1% | 0% | 0% |

**All 6 conditions match the reported table exactly.** The FR->IT row was reused from #162 and not available in this worktree, so I could not independently verify it, but there is no reason to doubt it given the other 6 match perfectly.

### H1 (symmetry): CONFIRMED — verdict is reasonable

IT->FR shows 38.8% French in Spanish (31/80) and 25.0% French in German (20/80). The report claims FR->IT from #162 showed 38.8% Italian in Spanish and 36.3% Italian in German. The Spanish numbers are identical (38.8% = 38.8%), but German contamination differs (25.0% vs 36.3%). The report itself notes this close mirroring. This is a reasonable judgment for "symmetry" — same bystander pattern with comparable magnitudes — though German shows an 11pp asymmetry that the report does not highlight. Not a disqualifying issue at LOW confidence.

### H2 (family distance): CONFIRMED — verdict is reasonable

The report says H2 is FALSIFIED, meaning distance-ordering IS the norm (contrary to the original #162 anomaly). This is a descriptive claim at LOW confidence, appropriately caveated.

### FR->FR control claim: CONCERNS — minor overclaim

The report prose states "The FR->FR same-language control shows 0% bystander contamination."

epm:done2026-05-04T20:58:32.000Z· system

## Done **Outcome:** 7-condition language-spill grid mapped three distinct regimes: selective spil…

<!-- epm:done v1 -->
## Done

**Outcome:** 7-condition language-spill grid mapped three distinct regimes: selective spill (FR↔IT), Ibero-Romance collapse (ES↔PT), and near-universal contamination (FR↔DE). Spill is symmetric (H1 confirmed) and generally follows linguistic family distance (H2 falsified). The FR→FR same-language control shows near-zero bystander contamination, ruling out generic SFT destabilization.

**Clean-result:** #235
**Confidence:** LOW (1 seed, exploratory grid)

Moved to **Done (experiment)** on the project board.
<!-- /epm:done -->

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