Mergeability-2/extrinsic-evaluations

Extrinsic evaluations — the union view

One tidy long-format table of every extrinsic (downstream, task-level) evaluation produced across the 2026-08-26 mergeability workstreams, so that a single file answers "how did model X score on benchmark Y" regardless of which experiment produced it.

The per-experiment datasets remain the authoritative record of their own methods, figures and caveats. This is the union view, not a replacement, and it deliberately carries no analysis of its own.

• extrinsic_evaluations.csv / .parquet — 9,675 rows, one per measurement
• build_manifest.json — per-source row counts, fetch status, dedupe and conflict counts
• build_extrinsic.py — the build script; re-runnable and idempotent

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Read this before you read a number

The one thing this file exists to keep straight

A likelihood rescue and an accuracy rescue are not the same thing, and tonight established that they are uncorrelated. A file that silently mixed nats/token with benchmark accuracy in one value column would be actively misleading, so:

• metric always names its unit explicitly (nats_per_token, accuracy_pct, …).
• metric_kind is the coarse grouping you should always group by before aggregating: accuracy · accuracy_delta · benchmark_score · likelihood · likelihood_delta · other.
• unit spells the unit out again.

Never aggregate across metric_kind. Never treat one as a proxy for the other. The evidence for that instruction is in this file — see Supported claims below.

Every accuracy row has a chance level

chance is populated for all 2,215 metric_kind == "accuracy" rows, in the same units as value. It is empty for likelihood rows (where chance is meaningless) and for benchmark_score rows, which are suite aggregates over heterogeneous tasks — MergeBench's per-domain scores, and the mean_of_benchmarks rows — where no single chance level is defined. That is why those rows are labelled benchmark_score and not accuracy.

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Schema

column	meaning
model_id	HF repo id where the model is released; otherwise a deterministic synthetic id for a merge built locally (compose-audit:…, chatvec-merge:…, crossarch:…)
model_role	parent · merged · jointly_trained · reference · control
provenance_family	the model family / substrate the row belongs to
parents	|-separated parent model ids for merged rows; empty otherwise
rung	the rung / arm within its experiment (M0_naive_avg, M1_perm_avg, alpha=0.25, aim=1, …)
operator	the merge operator (naive_avg, perm_avg, ties, task_arithmetic, dare, slerp, transport_ot, chat_vector_aligned, …)
experiment	the workstream that produced the row (17 values)
benchmark	the benchmark or held-out corpus scored
metric	the measurement with its unit in the name (see below)
value	the number
chance	chance level in the same units; populated for every accuracy row
n	evaluation items, where the source states it; empty otherwise (never guessed)
source_dataset	the HF dataset this row was read from
notes	provenance, caveats, and unit warnings carried from the source
metric_kind	coarse grouping — group by this before aggregating
unit	the unit, spelled out

Metric dictionary

metric	metric_kind	unit
accuracy	accuracy	proportion 0–1
accuracy_pct	accuracy	percent 0–100
delta_accuracy	accuracy_delta	proportion 0–1
benchmark_score_pct	benchmark_score	percent 0–100
benchmark_score_norm_pct	benchmark_score	percent 0–100, normalised
benchmark_mean_accuracy	benchmark_score	proportion 0–1
nats_per_token	likelihood	nats/token
nats_per_utf8_byte	likelihood	nats per UTF-8 byte
delta_floor_nats_per_token	likelihood_delta	nats/token vs. the better parent's floor
delta_floor_nats_per_byte	likelihood_delta	nats/byte vs. the better parent's floor
lmc_barrier_nats_per_token	likelihood_delta	nats/token
harmfulness_score	other	score 0–1 (not an accuracy)
bliss_score	other	BLiSS sub-score (RP_at_0, NGS, CPS, LP, SO, …) — not an accuracy

Nats per UTF-8 byte appears wherever two parents use different tokenizers, because nats/token is not comparable across them. This is the source's choice, preserved here.

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Provenance

source dataset	rows	what it contributed
Mergeability-2/compose-audit	6,358	PolyPythia seed merges (BLiMP accuracy + Δfloor nats/token, 5 sizes), corpus-robustness on three held-out corpora, Goldfish bilingual composition (nats/byte + MultiBLiMP), B-GPT merges and the joint-training ceiling
Mergeability-2/crossarch-1b-diagnostics	1,102	1B checkpoint merges (106 native pairs, Pythia + Zh-Pythia), cross-architecture optimal-transport merges with their random-plan controls, native cross-model merges — all nats/token
suchirsalhan/goldfish-crosslingual-cka	609	held-out NLL for every Goldfish merged and jointly-trained model. CKA columns are deliberately excluded — they are an intrinsic representation-similarity diagnostic, not an extrinsic evaluation
Mergeability-2/mergebench-property-sweep	576	MergeBench's published outcomes: 8 families × 9 methods × 8 task columns
Mergeability-2/aim-activation-informed-merging	304	the AIM paired panel's published benchmark outcomes, with and without AIM, over 20 matched checkpoints
Mergeability-2/beetle-merge-eval	492	Beetle merge evaluations — BLiMP / MultiBLiMP / zhoBLiMP / BLiSS across humanscale and zoo-language merges, their parents, and the jointly-trained bilingual ceilings. Still being produced at build time, so this is partial coverage, not the final set
Mergeability-2/merge-accuracy	234	chat-vector accuracies on real Llama-3.1-8B forks (Belebele eng+target, ARC-easy, IFEval), the symmetry-group controls, and the pythia × Zh-Pythia cross-group pair

Not yet included

dataset	status
Mergeability-2/crossarch-accuracy	not yet created at build time (HTTP 404). Contributed 0 rows

Still being produced when this file was built. build_extrinsic.py registers it and skips it cleanly; re-running once it publishes folds it in with no code change.

Mergeability-2/beetle-merge-eval is included, but it was still being written during the build — its long table grew from 419 to 493 rows while this file was being assembled. Treat its 492 rows as partial coverage and re-run the builder for the complete set. Its coverage_by_merge.csv records which merges are complete, partial or not evaluated.

How the rows were deduplicated

Several sources are resumable ledgers that re-report shared baselines: the SET 1 set1 / set1x / slerp / repair ledgers all re-emit identical M0_naive_avg and M1_perm_avg rows for the same merges, and set1x_410m re-measures three pairs already in set1_410m. Left alone that would have triple-counted the baseline rungs and inflated the 410m cell from 15 pairs to 18. The build assigns each merge a canonical identity and drops repeats: 3,485 repeated measurements removed. Per-size counts now reproduce the source's own rung_summary.csv exactly (410m: n = 15, mean Δfloor 6.5290 / 5.9558 / 6.0254).

60 residual conflicts are recorded in build_manifest.json. All are BLiMP accuracies differing between two ledgers by exactly one item out of 6,700–13,400 — evaluation nondeterminism, not disagreement. The dedicated BLiMP ledger wins.

Verification

Every extractor was checked against its source's own published tables before release:

• PolyPythia 14m Δfloor by rung — 32.4294 / 9.6065 / 16.0073 / 146.7951 / 241.5204 — exact match
• PolyPythia BLiMP by rung and size (14m 0.518 / 0.533 / 0.530 … 410m 0.535 / 0.543) — exact match
• AIM T0_published_outcome_paired spot values — exact match
• crossarch checkpoint pair counts (Pythia 78, Zh-Pythia 28) — exact match
• chat-vector aligned − naive on every real fork — exactly 0.000 on every benchmark, as claimed

One inconsistency in a source is worth flagging: compose-audit's headline §4 quotes the pythia-14m aligned merge at BLiMP 0.544, while its own authoritative table reports 0.533. This file carries 0.533, the table value. It does not change the direction of the finding.

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What this data supports, and what it does not

Supported

The likelihood rescue does not transfer to accuracy. On PolyPythia seed pairs — same data, same architecture, same tokenizer, so the merge obstruction is purely coordinate — permutation alignment removes ~70% of the naive merge's Δfloor at 14m, and the merged model still scores 0.533 on BLiMP against parents at 0.652 and chance 0.500. Across the ladder the merged model sits between 0.518 and 0.543 at every size, whether alignment recovered three quarters of the likelihood gap or a tenth of it. Pair by pair the two rescues are uncorrelated (Spearman 0.14 / −0.17 / 0.17 / 0.05 / 0.23). Both arms are in this file, on the same merges, in separate metric_kind groups — which is the whole reason the file is shaped this way.

The dissociation runs in both directions. Goldfish merges whose Δfloor says they are destroyed still score 0.68 on MultiBLiMP-English (parent 0.96, chance 0.50). Neither metric implies the other.

Alignment's coordinate rescue decays with scale. The exactly function-preserving permutation rung removes 70% of the naive Δfloor at 14m and 8% at 410m. The coordinate-removable share of the obstruction is falling in the direction the field is scaling. (The source reports this as a mean of per-pair percentages; recomputing it as a ratio of means from these rows gives 70% → 9%. Same conclusion, slightly different estimator — read the source's tables for the canonical figure.)

Naive averaging of two seed-only-different LMs is catastrophic at every size, and alignment does not make it usable: even the best rung leaves the merge at or above the uniform-over-vocabulary reference at the small sizes.

Joint training beats every merge tested. The B-GPT jointly_trained rows are the ceiling, and no merge in this file reaches them on either metric.

AIM's published benefit is real. Paired across 20 matched checkpoints, the with-AIM arm is up on the endpoint-scaled benchmark mean (18/20 positive, Wilcoxon p = 9.5e-06). Those published outcomes are here for both arms.

The headline nulls — read these before quoting anything positive

0 of 1035 MergeBench cells survive Benjamini-Hochberg at n = 8 families (80 pairs). No property-vs-outcome relationship in the MergeBench panel is distinguishable from zero at the coverage its published outcomes permit. An earlier draft reported 306 surviving cells; all were artefacts of three defects since fixed — a cluster bootstrap with a 40.2% false-positive rate at n = 4, a saturation gate calibrated on pair-level rather than family-level spread, and testing against zero when the families cluster by architecture. This dataset carries only MergeBench's published outcome scores, not those correlation cells — the cells live in Mergeability-2/mergebench-property-sweep, and table_domain_cells.csv is what to read.

0 of 13 released Llama-3.1-8B derivatives have left the base parameterisation. Across every released derivative examined — language forks, domain continued pretraining, instruct post-training, a safety model — not one had moved out of the base model's coordinate frame. The aligned and naive chat vectors are therefore bit-identical models, and the accuracy difference in this file is exactly 0.000 on every benchmark, for every fork, at every λ. That is a null about the ecosystem, not about the mechanism: the control rows, where a real fork is acted on by a random element of its own symmetry group, show the naive chat vector collapsing (IFEval 0.175 → 0.110, below the fork it started from) and alignment restoring it to 0.355. The mechanism reaches accuracy. The condition that would make it pay off did not occur in any released model.

Pre-merge predictors do not reliably predict the realised rescue — 0 of 25 cells significant, held out by seed pair. The strongest predictor does not replicate across substrates (held-out AUROC 0.48 / 0.71 / 0.81 / 0.61 / 0.45).

AIM does not change what makes a merge work. Not one of 216 property-vs-outcome correlation cells survives multiplicity correction, and the with-minus-without change is inside the noise band from re-splitting the same benchmarks within one arm.

Not supported — do not use this file for these

• Pair-level MergeBench merge outcomes. They do not exist. Every score MergeBench publishes is a five-expert merge (merge_arity = 5 in all 576 rows), so the 80-pair property suite has no published counterpart. parents says so on every MergeBench row.
• Cross-source comparisons of raw value. Substrates, corpora, item budgets and scorers differ between experiments. Compare within an experiment, or within a (experiment, benchmark, metric) group.
• Anything about merged-model accuracy at 1B+ scale. The 1B arm in this file is likelihood-only; the accuracy arm (Mergeability-2/crossarch-accuracy) was not published at build time. The checkpoint-distance rule — keep souped checkpoints within about half a decade of training steps — is measured in nats/token, not benchmark accuracy, and given the dissociation above it must not be assumed to transfer.
• Complete Beetle coverage. 492 rows are in, but the source was mid-run: of the merges it tracks, several are still not evaluated. Read coverage_by_merge.csv in the source before quoting a Beetle aggregate.
• A practitioner diagnostic. The scientific claim (merge failure can arise from how a function is represented, and that is measurable) is well supported. The practitioner claim (compute this before merging and it tells you what to do) is not supported at the coverage available.
• Significance testing. This file carries measurements, not tests. Every p-value, bootstrap and permutation null lives in the source datasets.

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Rebuilding

source /root/.ms_hf_env      # HF_TOKEN
python3 build_extrinsic.py            # rebuild from the Hub
python3 build_extrinsic.py --push     # rebuild and publish

Idempotent: it re-fetches each source, skips any that 404, rebuilds every row from scratch, re-runs the dedupe, and refuses to emit an accuracy row without a chance level. Re-run it when crossarch-accuracy publishes, and again once beetle-merge-eval finishes its run.

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Row counts

experiment	accuracy	accuracy_delta	benchmark_score	likelihood	likelihood_delta	other	total
polypythia_seed_merge	1095	477	0	1413	1689	0	4674
polypythia_corpus_robustness	0	0	0	486	432	0	918
crossarch_checkpoint_merge	0	0	0	657	106	0	763
goldfish_crosslingual	0	0	0	609	0	0	609
mergebench_published_outcomes	0	0	576	0	0	0	576
beetle_merge_eval	468	0	0	0	0	24	492
crossarch_transport_merge	0	0	0	320	0	0	320
goldfish_bilingual_merge	68	56	0	84	108	0	316
aim_published_outcomes	264	0	0	0	0	40	304
bgpt_bilingual_merge	56	0	0	56	60	0	172
chat_vector_llama31	144	0	0	0	0	0	144
goldfish_bilingual_merge_reverse	0	0	0	48	72	0	120
crossgroup_direct_merge	80	0	10	0	0	0	90
bgpt_joint_vs_merge	40	0	0	40	0	0	80
polypythia_ablation	0	0	0	36	42	0	78
crossarch_native_merge	0	0	0	10	4	0	14
crossarch_1b_native	0	0	0	5	0	0	5
All	2215	533	586	3764	2513	64	9675

3,472 distinct models. By role: 8,698 merged · 323 parent · 248 jointly_trained · 210 control · 196 reference.

Example

import pandas as pd
df = pd.read_parquet("extrinsic_evaluations.parquet")

# the dissociation, in two lines
seed = df[df.experiment == "polypythia_seed_merge"]
acc  = seed[(seed.metric == "accuracy") & (seed.model_role == "merged")]
nats = seed[(seed.metric == "delta_floor_nats_per_token") & (seed.model_role == "merged")]
print(acc.groupby(["provenance_family", "rung"]).value.mean())   # flat, ~0.52-0.54
print(nats.groupby(["provenance_family", "rung"]).value.mean())  # collapses with alignment

# never do this
df.groupby("benchmark").value.mean()   # mixes nats with accuracy. group by metric_kind.