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README.md

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+---
+license: apache-2.0
+pretty_name: "Extrinsic evaluations: the union view across the mergeability workstreams"
+tags:
+  - model-merging
+  - mergeability
+  - evaluation
+  - benchmarks
+  - likelihood
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files: extrinsic_evaluations.csv
+---
+
+# 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` — **9,183 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
+
+---
+
+## 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 1,747 `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`.
+
+---
+
+## 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) |
+
+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.
+
+---
+
+## Provenance
+
+| source dataset | rows | what it contributed |
+|---|---|---|
+| [`Mergeability-2/compose-audit`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/Mergeability-2/mergebench-property-sweep) | 576 | MergeBench's **published** outcomes: 8 families × 9 methods × 8 task columns |
+| [`Mergeability-2/aim-activation-informed-merging`](https://huggingface.co/datasets/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/merge-accuracy`](https://huggingface.co/datasets/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/beetle-merge-eval` | repo **exists but was still empty** at build time — it ships only `.gitattributes`, no result files. Contributed 0 rows |
+| `Mergeability-2/crossarch-accuracy` | **not yet created** at build time (HTTP 404). Contributed 0 rows |
+
+Both were still being produced when this file was built. `build_extrinsic.py` already
+registers them and skips them cleanly; **re-running it once they publish will fold them
+in with no code change**. Any row that arrives from an in-progress source is prefixed
+`IN PROGRESS -- …` in `notes`. There are currently no such rows.
+
+### 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.
+
+---
+
+## 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.
+- **Beetle merge evaluations.** The dataset repo existed but was still empty at build time; 0 rows.
+- **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.
+
+---
+
+## Rebuilding
+
+```bash
+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
+`beetle-merge-eval` and `crossarch-accuracy` publish.
+
+---
+
+## 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** |
+| 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** | **1747** | **533** | **586** | **3764** | **2513** | **40** | **9183** |
+
+3,342 distinct models. By role: 8,396 merged · 293 parent · 220 jointly_trained · 210 control · 64 reference.
+
+## Example
+
+```python
+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.
+```