Upload RESULTS_MERGE_ACCURACY.md with huggingface_hub

RESULTS_MERGE_ACCURACY.md

@@ -1,6 +1,6 @@
 # Merging with alignment: does it improve DOWNSTREAM ACCURACY?
 
-_Generated 2026-08-27 00:24 UTC · training-free · code `/root/merge-accuracy` · merge operators, aligners and
+_Generated 2026-08-27 00:25 UTC · training-free · code `/root/merge-accuracy` · merge operators, aligners and
 quotient-distance diagnostics imported unmodified from `mergeschool.core` (`/root/mergeability`,
 treated as read-only)._
 
@@ -27,7 +27,19 @@ which forks need that.
 
 ## Headline
 
-**3 real community fork(s), 3 ground-truth control(s).**
+> **Alignment did not improve downstream accuracy on a single real model — because on every
+> real model there was nothing to align.** All three community continued-pretrained forks of
+> `Llama-3.1-8B` are 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 the three
+> released forks we found.**
+
+Population: **3 real community forks** (3 groups, 3 target languages), **3 ground-truth controls**, plus one cross-group direct-merge pair.
 
 - On **3 of 3** 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 2 of 3 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.