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README.md

@@ -25,25 +25,24 @@ Typhoon2 / Thai, SEA-LION / Indonesian), plus **ground-truth permutation control
 ## Headline
 
 > **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
+> 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 the three
-> released forks we found.**
+> accuracy; the ecosystem condition that would make it pay off did not occur in any released
+> model we examined.**
 
-Population: **3 real community forks** (3 groups, 3 target languages), **3 ground-truth controls**, plus one cross-group direct-merge pair.
+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 **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.
-- The diagnostic's registered prediction was correct on **3/3** real forks.
+- 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**).
 
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 ## Contents
 
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