merge-accuracy — does aligning a merge improve DOWNSTREAM ACCURACY?

The mergeability line of work measures merge obstruction in nats/token. This dataset supplies the missing axis: task accuracy, on real released models, for the merge recipe practitioners actually run — the chat-vector recipe

theta_new  =  theta_fork  +  lambda * ( theta_instruct  -  theta_base )

with meta-llama/Llama-3.1-8B, its official Instruct release, and three community continued-pretrained language forks from three independent groups (Swallow / Japanese, Typhoon2 / Thai, SEA-LION / Indonesian), plus ground-truth permutation controls and one cross-group direct-merge pair (pythia-1.4b x Zh-Pythia-1.4B).

Headline

Alignment did not improve downstream accuracy on a single real model — because on every real model there was nothing to align. Every community fork of Llama-3.1-8B we tested is still exactly in the base model's coordinate frame, so the aligned and naive chat vectors are bit-identical models and the accuracy difference is 0.000 on every benchmark. The diagnostic said so before any merge was built, and a 44-second screen reproduces that call 37x cheaper than fitting the map. When the frame really has drifted — a real fork acted on by a random element of its own symmetry group — the naive chat vector collapses (IFEval 0.175 -> 0.110, below the fork it started from) and alignment restores it to 0.355 against an unpermuted reference of 0.375. The mechanism is real and does reach accuracy; the ecosystem condition that would make it pay off did not occur in any released model we examined.

Population: 4 real community forks measured end-to-end on accuracy (3 groups, 3 target languages), 3 ground-truth controls, one cross-group direct-merge pair, and a 10-model ecosystem screen. Across all 13 released Llama-3.1-8B derivatives examined — language forks, domain continued pretraining, instruct post-training, a safety model — not one had left the base model's coordinate frame.

• On 4 of 4 real community CPT forks the fitted alignment map is the identity (coordinate share exactly 0; every per-layer MLP and attention-head permutation comes back as the identity). Continued pretraining by a third party did not move these models out of the base model's frame, so the chat vector is already expressed in the right basis and aligning it is a no-op. The measured accuracy difference is exactly zero on every benchmark — the aligned and naive merges are bit-identical models.
• The chat-vector recipe itself works on 3 of 4 of these forks: it lifts instruction following well above the fork it started from, i.e. the merged model beats its own parent — the bar that matters.
• The diagnostic's registered prediction was correct on 4/4 real forks.
• On the ground-truth control (a real fork acted on by a random element of the model's own symmetry group — functionally identical, differently parameterised), the diagnostic fires (coordinate share 0.856), the naive chat vector scores IFEval 0.110, and aligning it first recovers 0.355 (Δ +0.245).

Contents

path	what
RESULTS_MERGE_ACCURACY.md	the full write-up: substrate, chance levels, every table, limitations
figs/headline_diagnostic_vs_gain.png	headline — pre-merge coordinate share vs realised accuracy gain from aligning
figs/dose_response.png	the diagnostic tracks real frame drift; alignment recovers what drift destroys
figs/scatter_naive_vs_aligned.png	naive vs aligned accuracy with the y = x diagonal and the fork-alone baseline
figs/selection_experiment.png	naive / align-everything / cheap-screen-then-align
results/chatvec_summary.csv	per model per lambda: fork alone, naive, aligned, delta, per benchmark
results/all_model_accuracies.csv	every model evaluated, every benchmark
results/diagnostics.csv	the pre-merge diagnostic for every model
results/cheap_screen_vs_full.csv	the 44-second screen against the 27-minute fit
results/selection_experiment.csv	the three-strategy comparison with measured compute
results/ecosystem_screen.json	the 44-second frame screen over 10 more released Llama-3.1-8B derivatives
figs/ecosystem_drift_vs_frame.png	parameter drift vs frame drift across the ecosystem
results/crossgroup_pair.csv	the pythia x Zh-Pythia direct merge, all mixing weights + TIES
results/validation.json	exactness of the symmetry action and of the fitted alignment map
results/chatvec.jsonl, results/ledger.jsonl	raw resumable ledgers
code/	everything needed to reproduce

Benchmarks and chance levels

Belebele (target language and English) — chance 0.250; ARC-easy — chance 0.250; IFEval strict prompt-level and instruction-level — chance ~0. lm-evaluation-harness was not available, so scorers are implemented directly following the harness / reference task definitions (code/tasks.py, code/ifeval.py). Sanity check: the loglikelihood harness scores EleutherAI/pythia-1.4b at SciQ 0.846 against a published 0.865.

A bug worth propagating

mergeschool.core.alignment.apply_head_perms permutes the query and output projections but not k_proj/v_proj. That is exact for MHA and MQA but not for grouped-query attention: on Llama-3.1-8B a flat head permutation changes the logits by relative 1.115 — it destroys the model. The group-respecting action implemented here (code/gmap.py) is exact to 9.4e-07. Any merge study that accepts a flat head permutation on a GQA model is silently corrupting its merges.