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pretty_name: Beetle merged models — benchmark evaluation vs parents
tags: [model-merging, mergeability, blimp, multiblimp, evaluation, multilingual]
annotations_creators: [machine-generated]
language: [en, nl, de, zh]
configs:
- config_name: long
default: true
data_files: [{split: train, path: beetle_merge_eval_long.csv}]
- config_name: deltas
data_files: [{split: train, path: beetle_merge_eval_deltas.csv}]
- config_name: coverage
data_files: [{split: train, path: coverage_by_merge.csv}]
- config_name: summary_by_family_benchmark
data_files: [{split: train, path: summary_by_family_benchmark.csv}]
- config_name: summary_by_operator_arm
data_files: [{split: train, path: summary_by_operator_arm.csv}]
---
# Beetle merged models — benchmark evaluation against their parents
Minimal-pair benchmark accuracy for the **Beetle merged models published in the
`Mergeability` org**, scored against **their own parent models and, where one
exists, the jointly-trained ceiling** on the same harness.
The existing sweep datasets (`Mergeability/merge-sweep-results`,
`Mergeability-2/mergeability-results`) record merge quality in **nats** (NLL,
`delta_floor`, `rel_damage`, barrier, geometry). They contain **no downstream
benchmark accuracy for these repos** — this dataset adds that layer.
## Files
| file | what it is |
|---|---|
| `beetle_merge_eval_long.csv` | tidy long format: `merge_id, provenance_family, parent_a, parent_b, ceiling, rung, operator, arm, benchmark, metric, value, chance, n, eval_langs, kind` — every table regenerates from this |
| `beetle_merge_eval_deltas.csv` | the same rows plus `naive_value`, `parent_a_value`, `parent_b_value`, `better_parent`, `ceiling_value`, `d_naive`, `d_parent`, `d_ceiling`, `z_vs_chance`, `at_chance`, `near_chance` |
| `coverage_by_merge.csv` | per-repo: rungs published vs rungs scored, and status |
| `summary_*.csv` | per-family / per-operator aggregates |
| `RESULTS_BEETLE_MERGE_EVAL.md` | what ran, what did not, the honest verdict per family |
| `fig_headline_naive_vs_aligned.png` | naive-vs-aligned and better-parent-vs-merged scatters, faceted by provenance family |
| `fig_merge_vs_joint_ceiling.png` | merged vs jointly-trained counterpart, for the two families that publish one |
| `arm_identity_audit.csv` | per operator-block: which arms exist, and whether aligned/transport are byte-identical to naive |
| `harness_validation_vs_beetle_analyze.csv` | cell-by-cell agreement with beetle-analyze published numbers |
| `retest_reliability.csv` | the 1,026 cells independently scored twice by two waves — a repeat-measurement check on the harness |
`kind == "reference"` rows are the parent and ceiling models themselves; `kind == "merge"`
rows are the merged rungs. Chance is 0.5 for every minimal-pair benchmark and 50 for BLiSS.
## Benchmarks
English BLiMP (`nyu-mll/blimp`, 67,000 pairs), MultiBLiMP (`jumelet/multiblimp`,
per language), BLiMP-NL (`juletxara/blimp-nl`, 9,000), ZhoBLiMP
(`Junrui1202/zhoblimp`, 35,400), and BLiSS (`ALTACambridge/BLiSS`) where present.
MultiBLiMP has **no `zho`, `jpn`, `kor` or `fil` config**, so Chinese pairs get
ZhoBLiMP instead.
## Harness
Scoring is a frozen vendor of
[`beetle-analyze`](https://github.com/suchirsalhan/beetle-analyze)
(`src/beetle_analyze/eval/eval_blimp.py`, `eval_bliss.py`): sum log-probability
per sentence, accuracy = fraction where `logp(good) > logp(bad)`; BLiSS metrics
are a direct port of `compute_bliss_metrics`. The only change is that the fp32
`[B,T,V]` log-softmax is evaluated in row chunks (identical values, bounded
memory), and rung subfolders are staged to disk because `trust_remote_code`
does not honour `subfolder=` when resolving `auto_map`.
## Caveat that dominates everything else
For every Beetle pair checked, the published `__aligned` folder holds
**byte-identical weights** to its `__naive` sibling (verified on LFS sha256).
Δ vs naive is therefore exactly 0 for aligned rungs, by construction, and no
conclusion about weight alignment can be drawn from these artefacts. Only
`__transport` differs from `__naive`.
## Scope
Minimal-pair benchmarks only. **BLiSS was not run** — the scorer is vendored and working
and a 79-model job set is prepared, but the pass was dropped on the user's instruction, not
for any technical reason. A small pilot is retained under `benchmark == "bliss"` and labelled
as such. MECO L2 ΔlogL and JFLEG were also not run; `RESULTS_BEETLE_MERGE_EVAL.md` carries the
exact commands for all three.
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