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RESULTS_COMPOSE_AUDIT.md

@@ -1,6 +1,6 @@
 # Compose-audit: putting the alignment map and the merging payoff on the SAME real models
 
-_Generated 2026-08-26 20:50 UTC · training-free · code: `/root/compose-audit` · operators/aligners/metrics imported unmodified from `mergeschool.core` (`/root/mergeability`, treated as read-only)._
+_Generated 2026-08-26 20:54 UTC · training-free · code: `/root/compose-audit` · operators/aligners/metrics imported unmodified from `mergeschool.core` (`/root/mergeability`, treated as read-only)._
 
 ## Read this first: what substrate, and what metric
 
@@ -23,8 +23,8 @@ _Generated 2026-08-26 20:50 UTC · training-free · code: `/root/compose-audit`
 
 ## Headline findings
 
-1. **Naive averaging of two seed-only-different real LMs is catastrophic, at every size.** Δfloor 14m: +32.4 · 31m: +20.4 · 70m: +20.2 · 160m: +8.8 · 410m: +6.3 nats/token against parent floors of 3–4.4 nats/token, i.e. above the uniform-over-vocabulary reference of 10.8 for all but the largest. n = 36 / 36 / 36 / 23 / 2 pairs.
-2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** Best of permutation / Procrustes removes 14m: 72% · 31m: 57% · 70m: 55% · 160m: 31% · 410m: 8% — leaving 9.0 · 8.1 · 8.7 · 5.9 · 5.8 nats/token above the better parent.
+1. **Naive averaging of two seed-only-different real LMs is catastrophic, at every size.** Δfloor 14m: +32.4 · 31m: +20.4 · 70m: +20.2 · 160m: +8.8 · 410m: +6.3 nats/token against parent floors of 3–4.4 nats/token, i.e. above the uniform-over-vocabulary reference of 10.8 for all but the largest. n = 36 / 36 / 36 / 25 / 2 pairs.
+2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** Best of permutation / Procrustes removes 14m: 72% · 31m: 57% · 70m: 55% · 160m: 31% · 410m: 8% — leaving 9.0 · 8.1 · 8.7 · 6.0 · 5.8 nats/token above the better parent.
 3. **The rescue shrinks monotonically with scale** (14m: 72% → 410m: 8%) while the naive gap shrinks too — so the coordinate-removable share of the obstruction is falling in exactly the direction the field is scaling. (Per-size n is listed in (1); the largest sizes carry the fewest pairs, so read the trend from the sizes with complete 36-pair grids and treat the largest as directional.)
 4. **The likelihood rescue does not transfer to accuracy.** On pythia-14m (n=36), parents average 0.652 on BLiMP; the naive merge 0.518 and the aligned merge 0.544, against chance 0.500. A ~70% Δfloor rescue buys ~0.026 accuracy. Pairwise, the two rescues are uncorrelated.
 5. **On the real bilingual-composition models the merge fails and alignment does not rescue it.** Goldfish eng×{nld,spa,ell,pol}: naive Δfloor on English text +0.91 nats/byte against a 0.81 floor; the best M1 rung +0.90. The binding constraint is the **vocabulary**, not the coordinate frame — the English tokenizer UNK-s 45% of Greek and 11% of Polish, and no permutation or rotation can address that.
@@ -77,17 +77,17 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.30**, permut
 Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.17**, permutation-aligned **10.98** nats/token.
 
 
-### pythia-160m — 23 seed pairs · mean parent floor **3.255** nats/token · uniform-over-vocabulary reference **10.826** nats/token
+### pythia-160m — 25 seed pairs · mean parent floor **3.254** nats/token · uniform-over-vocabulary reference **10.826** nats/token
 
 | rung | n | mean nats/tok | mean Δfloor | median Δfloor | best Δfloor | beats naive | % of naive Δfloor removed |
 |---|---|---|---|---|---|---|---|
-| M0_naive_avg | 23 | 12.03 | 8.77 | 8.32 | 6.88 | 0/23 | 0.0% |
-| M1_perm_avg | 23 | 9.84 | 6.59 | 6.34 | 5.50 | 21/23 | 23.7% |
-| M1_orth_avg | 23 | 9.41 | 6.16 | 6.11 | 5.15 | 23/23 | 29.2% |
-| M2_task_arith | 23 | 30.47 | 27.22 | 27.31 | 17.88 | 0/23 | -209.7% |
-| M3_ties | 23 | 60.06 | 56.81 | 57.09 | 49.69 | 0/23 | -555.4% |
+| M0_naive_avg | 25 | 12.01 | 8.76 | 8.33 | 6.88 | 0/25 | 0.0% |
+| M1_perm_avg | 25 | 9.98 | 6.72 | 6.37 | 5.50 | 22/25 | 22.1% |
+| M1_orth_avg | 25 | 9.40 | 6.15 | 6.11 | 5.13 | 25/25 | 29.3% |
+| M2_task_arith | 25 | 30.22 | 26.96 | 27.14 | 17.88 | 0/25 | -207.2% |
+| M3_ties | 25 | 60.16 | 56.91 | 57.09 | 49.30 | 0/25 | -556.6% |
 
-Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.76**, permutation-aligned **6.58** nats/token.
+Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.75**, permutation-aligned **6.72** nats/token.
 
 
 ### pythia-410m — 2 seed pairs · mean parent floor **2.970** nats/token · uniform-over-vocabulary reference **10.826** nats/token
@@ -130,7 +130,7 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.31**, permuta
 | pythia-14m | 36 | 4.38 | 32.43 | 69.9% | 50.5% | 72.0% | 0.588 | 0.374 | 0.0652 |
 | pythia-31m | 36 | 3.94 | 20.35 | 47.7% | 46.4% | 57.4% | 0.632 | 0.380 | 0.0645 |
 | pythia-70m | 36 | 3.63 | 20.16 | 43.9% | 50.4% | 55.1% | 0.671 | 0.428 | 0.0793 |
-| pythia-160m | 23 | 3.26 | 8.77 | 23.7% | 29.2% | 31.3% | 0.766 | 0.786 | 0.0925 |
+| pythia-160m | 25 | 3.25 | 8.76 | 22.1% | 29.3% | 31.3% | 0.769 | 0.791 | 0.0900 |
 | pythia-410m | 2 | 2.97 | 6.35 | 7.9% | 5.8% | 7.9% | 0.381 | 0.342 | 0.0484 |
 
 The coordinator flagged this from the first two pairs and asked whether it survives the full grid.
@@ -248,7 +248,7 @@ PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges.
 | substrate | n pairs | mean parent acc | better-parent ceiling | M0 naive | M1 permutation | M1 Procrustes | best rung, % of the parents' above-chance margin retained |
 |---|---|---|---|---|---|---|---|
 | pythia-14m | 36 | 0.652 | 0.664 | 0.518 | 0.533 | 0.530 | 28.5% |
-| pythia-70m | 27 | 0.716 | 0.722 | 0.518 | 0.538 | 0.543 | 24.2% |
+| pythia-70m | 32 | 0.717 | 0.722 | 0.515 | 0.539 | 0.541 | 24.0% |
 
 **This is the result the audit asked for, and it is negative.** On pythia-14m the permutation
 alignment removes ~70% of the naive merge's Δfloor in nats/token — and the merged model still scores
@@ -262,7 +262,7 @@ Pair by pair, does the size of the likelihood rescue predict the size of the acc
 | substrate | n | Spearman(Δfloor rescue, BLiMP rescue) | mean Δfloor rescue (nats/tok) | mean BLiMP rescue (acc) |
 |---|---|---|---|---|
 | pythia-14m | 36 | 0.139 | 23.44 | 0.0257 |
-| pythia-70m | 27 | 0.206 | 10.94 | 0.0322 |
+| pythia-70m | 32 | 0.187 | 11.28 | 0.0352 |
 
 ## Did we try hard enough? · REPAIR on top of the alignment
 
@@ -270,14 +270,17 @@ The obvious objection to a negative merging result is that averaging is a weak m
 
 | substrate | n pairs | rung | mean Δfloor (nats/tok) | median Δfloor | BLiMP accuracy | % of the parents' above-chance margin retained |
 |---|---|---|---|---|---|---|
-| pythia-14m | 27 | M0_naive_avg | 32.25 | 30.70 | 0.512 | 7.2% |
-| pythia-14m | 27 | M1_perm_avg | 9.53 | 9.06 | 0.534 | 20.6% |
-| pythia-14m | 27 | M4_perm_repair | 7.72 | 7.10 | 0.532 | 19.0% |
-| pythia-14m | 27 | M5_naive_repair | 32.11 | 30.98 | 0.509 | 5.4% |
-| pythia-14m | 27 | **parents** | 0.00 | 0.00 | 0.666 | 100.0% |
-
-REPAIR does help the likelihood — it takes a further bite out of the aligned merge's Δfloor, and it
-is the best training-free merge in this report. It does **not** change the conclusion. The repaired
+| pythia-14m | 36 | M0_naive_avg | 32.43 | 30.89 | 0.518 | 11.0% |
+| pythia-14m | 36 | M1_perm_avg | 9.61 | 9.01 | 0.533 | 19.9% |
+| pythia-14m | 36 | M4_perm_repair | 7.88 | 7.41 | 0.527 | 16.7% |
+| pythia-14m | 36 | M5_naive_repair | 32.50 | 31.10 | 0.512 | 7.2% |
+| pythia-14m | 36 | **parents** | 0.00 | 0.00 | 0.664 | 100.0% |
+
+REPAIR does help the likelihood — it is the best training-free merge in this report, taking a further
+bite out of the aligned merge's Δfloor (on pythia-14m, 9.61 → 7.88 nats/token, a further 18%). **And
+BLiMP does not follow it at all**: 0.533 → 0.527, i.e. flat, and slightly *down*. That is the
+dissociation again, now inside a single rung comparison where the only thing that changed is a
+likelihood-improving correction. It does **not** change the conclusion. The repaired
 aligned merge is still many nats/token above the better parent, still above the
 uniform-over-vocabulary reference at the small sizes, and still close to chance on BLiMP. Applied to
 the *naive* merge it barely moves anything, which is the expected pattern: variance repair is only
@@ -354,6 +357,8 @@ A merge that fails is only interpretable against what a bilingual model of the s
 |---|---|---|---|---|---|
 | eng–nld_Latn | 0.871 | 0.848 | 1.473 | 1.697 | 1.835 |
 | eng–spa_Latn | 0.872 | 0.848 | 1.609 | 1.685 | 1.834 |
+| eng–ell_Grek | 0.875 | 0.848 | 1.521 | 1.748 | 1.838 |
+| eng–pol_Latn | 0.881 | 0.848 | 1.610 | 1.647 | 1.767 |
 
 **nats/byte, partner**  (lower is better)
 
@@ -361,6 +366,8 @@ A merge that fails is only interpretable against what a bilingual model of the s
 |---|---|---|---|---|---|
 | eng–nld_Latn | 0.898 | 2.336 | 0.823 | 3.281 | 3.302 |
 | eng–spa_Latn | 0.889 | 1.940 | 0.777 | 3.126 | 2.969 |
+| eng–ell_Grek | 0.582 | 0.209 | 0.446 | 1.114 | 1.170 |
+| eng–pol_Latn | 1.070 | 2.430 | 0.830 | 3.460 | 3.213 |
 
 **MultiBLiMP-English**  (higher is better, chance 0.500)
 
@@ -368,6 +375,8 @@ A merge that fails is only interpretable against what a bilingual model of the s
 |---|---|---|---|---|---|
 | eng–nld_Latn | 0.966 | 0.962 | 0.694 | 0.673 | 0.601 |
 | eng–spa_Latn | 0.968 | 0.962 | 0.656 | 0.677 | 0.727 |
+| eng–ell_Grek | 0.968 | 0.962 | 0.631 | 0.688 | 0.599 |
+| eng–pol_Latn | 0.973 | 0.962 | 0.610 | 0.682 | 0.656 |
 
 **MultiBLiMP-partner**  (higher is better, chance 0.500)
 
@@ -375,6 +384,8 @@ A merge that fails is only interpretable against what a bilingual model of the s
 |---|---|---|---|---|---|
 | eng–nld_Latn | 0.952 | 0.598 | 0.970 | 0.655 | 0.646 |
 | eng–spa_Latn | 0.879 | 0.502 | 0.926 | 0.500 | 0.532 |
+| eng–ell_Grek | 0.927 | 0.029 | 0.987 | 0.029 | 0.029 |
+| eng–pol_Latn | 0.892 | 0.494 | 0.963 | 0.454 | 0.462 |
 
 This is the cleanest single statement the audit can make about SET 4. A jointly trained bilingual
 model of the same parameter budget is **good at both languages at once** — near the monolingual
@@ -391,8 +402,25 @@ Identical rungs, but the merged model lives in the **partner** language's tokeni
 | anchor | floor (anchor lang) | floor (English) | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1e_vocab_orth_forced | M1g_emb_procrustes |
 |---|---|---|---|---|---|---|---|---|
 | nld_Latn | 0.792 | 0.811 | 1.342 | 1.335 | 1.337 | 1.337 | 1.384 | 1.425 |
+| spa_Latn | 0.746 | 0.811 | 1.527 | 1.619 | 1.619 | 1.619 | 1.611 | 1.476 |
+| ell_Grek | 0.428 | 0.811 | 1.494 | 1.568 | 1.569 | 1.569 | 1.555 | 1.322 |
+| pol_Latn | 0.799 | 0.811 | 1.591 | 1.622 | 1.622 | 1.622 | 1.580 | 1.678 |
 
-Δfloor, mean over the two languages, nats/UTF-8 byte. The failure is symmetric: anchoring on the partner language does not make the merge work either.
+Δfloor, mean over the two languages, nats/UTF-8 byte.
+
+
+**The failure is symmetric, and that matters more than it looks.** The tokenizer wall is *not*
+symmetric: the English Goldfish tokenizer UNK-s 45% of Greek and 11% of Polish, while every partner
+tokenizer handles English at under 0.1% UNK (`results/set4_tokenizer_diag.json`). So the reverse
+direction is the clean test — anchor on the partner language and the vocabulary can represent both
+sides. The merge still fails, by the same margin, and the M1 rungs still do nothing. Two conclusions
+follow that the English-anchored direction alone could not support:
+
+1. The vocabulary mismatch is a real and sufficient obstruction in the English-anchored direction,
+   but it is **not the only** one — removing it does not make the merge work.
+2. What is left is the plain fact that the two parents were **independently initialised and
+   independently trained**. That is the same obstruction SET 1 isolates, and SET 1 already shows that
+   alignment only ever removes part of it and that the removable part shrinks with scale.
 
 
 ## P0-2 · Do the pre-merge predictors predict the realised rescue?
@@ -422,13 +450,13 @@ Showing, per substrate and per outcome, the **six predictors with the largest |A
 | pythia-70m | rescue_frac | coord_share_perm | 36 | 0.356 | 0.698 | 0.500 | 0.065 | 0.268 |
 | pythia-70m | rescue_frac | qmd_perm | 36 | -0.357 | 0.688 | 0.500 | 0.084 | 0.268 |
 | pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.387 | 0.688 | 0.499 | 0.080 | 0.268 |
-| pythia-160m | rescue_frac | qmd_orth | 23 | -0.592 | 0.803 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_orth | 23 | 0.445 | 0.795 | — | — | — |
-| pythia-160m | rescue_frac | qmd_perm | 23 | -0.610 | 0.773 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_perm | 23 | 0.485 | 0.773 | — | — | — |
-| pythia-160m | rescue_frac | bnd_orth | 23 | -0.302 | 0.750 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_bnd_orth | 23 | 0.265 | 0.750 | — | — | — |
-| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 23 | 0.342 | 0.598 | — | — | — |
+| pythia-160m | rescue_frac | qmd_orth | 25 | -0.597 | 0.801 | — | — | — |
+| pythia-160m | rescue_frac | coord_share_orth | 25 | 0.395 | 0.763 | — | — | — |
+| pythia-160m | rescue_frac | qmd_perm | 25 | -0.552 | 0.737 | — | — | — |
+| pythia-160m | rescue_frac | coord_share_perm | 25 | 0.379 | 0.718 | — | — | — |
+| pythia-160m | rescue_frac | bnd_orth | 25 | -0.242 | 0.705 | — | — | — |
+| pythia-160m | rescue_frac | task_vector_cosine | 25 | 0.052 | 0.301 | — | — | — |
+| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 25 | 0.348 | 0.590 | — | — | — |
 | pythia-14m | dfloor_M1best | bnd_orth | 36 | -0.000 | 0.204 | 0.502 | 1.000 | 1.000 |
 | pythia-14m | dfloor_M1best | bnd_perm | 36 | -0.002 | 0.222 | 0.502 | 0.997 | 1.000 |
 | pythia-14m | dfloor_M1best | coord_share_orth | 36 | -0.457 | 0.738 | 0.495 | 0.015 | 0.251 |
@@ -450,13 +478,13 @@ Showing, per substrate and per outcome, the **six predictors with the largest |A
 | pythia-70m | dfloor_M1best | qmd_act_perm | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.268 |
 | pythia-70m | dfloor_M1best | qmd_act_procrustes | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.268 |
 | pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.386 | 0.599 | 0.500 | 0.207 | 0.347 |
-| pythia-160m | dfloor_M1best | bnd_perm | 23 | -0.591 | 0.818 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_bnd_perm | 23 | 0.445 | 0.773 | — | — | — |
-| pythia-160m | dfloor_M1best | bnd_orth | 23 | -0.587 | 0.765 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_bnd_orth | 23 | 0.489 | 0.742 | — | — | — |
-| pythia-160m | dfloor_M1best | cka_last | 23 | 0.386 | 0.712 | — | — | — |
-| pythia-160m | dfloor_M1best | cka_mean | 23 | -0.324 | 0.697 | — | — | — |
-| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 23 | 0.585 | 0.735 | — | — | — |
+| pythia-160m | dfloor_M1best | bnd_perm | 25 | -0.449 | 0.763 | — | — | — |
+| pythia-160m | dfloor_M1best | cka_mean | 25 | -0.402 | 0.731 | — | — | — |
+| pythia-160m | dfloor_M1best | bnd_orth | 25 | -0.438 | 0.724 | — | — | — |
+| pythia-160m | dfloor_M1best | coord_share_bnd_perm | 25 | 0.335 | 0.724 | — | — | — |
+| pythia-160m | dfloor_M1best | cka_last | 25 | 0.395 | 0.724 | — | — | — |
+| pythia-160m | dfloor_M1best | d_raw | 25 | 0.103 | 0.282 | — | — | — |
+| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 25 | 0.472 | 0.699 | — | — | — |
 
 ### Does a predictor fitted on one substrate transfer to another?
 
@@ -464,26 +492,26 @@ Leave-one-**size**-out. Predictors are standardised *within* size first, so a pr
 
 | predictor | outcome | held-out substrate | n | AUROC | null mean | perm p | BH q |
 |---|---|---|---|---|---|---|---|
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-14m | 36 | 0.349 | 0.500 | 0.932 | 0.977 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 23 | 0.720 | 0.500 | 0.037 | 0.209 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.701 | 0.496 | 0.019 | 0.152 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.556 | 0.500 | 0.291 | 0.612 |
-| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.507 | 0.480 | 0.799 |
-| coord_share_bnd_perm | rescue_frac | pythia-160m | 23 | 0.606 | 0.499 | 0.198 | 0.497 |
-| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.500 | 0.011 | 0.152 |
-| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.495 | 0.001 | 0.040 |
-| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.494 | 0.045 | 0.209 |
-| qmd_act_perm | rescue_frac | pythia-160m | 23 | 0.500 | 0.497 | 0.507 | 0.812 |
-| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.498 | 0.399 | 0.725 |
-| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.504 | 0.031 | 0.206 |
-| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.498 | 0.155 | 0.442 |
-| cka_mean | rescue_frac | pythia-160m | 23 | 0.455 | 0.498 | 0.649 | 0.928 |
-| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.500 | 0.338 | 0.675 |
-| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.502 | 0.937 | 0.977 |
-| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.505 | 0.232 | 0.545 |
-| weight_cosine | rescue_frac | pythia-160m | 23 | 0.636 | 0.498 | 0.141 | 0.433 |
-| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.497 | 0.014 | 0.152 |
-| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.504 | 0.528 | 0.813 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-14m | 36 | 0.355 | 0.500 | 0.931 | 0.962 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 25 | 0.699 | 0.503 | 0.050 | 0.200 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.691 | 0.496 | 0.023 | 0.180 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.580 | 0.501 | 0.217 | 0.502 |
+| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.506 | 0.481 | 0.768 |
+| coord_share_bnd_perm | rescue_frac | pythia-160m | 25 | 0.583 | 0.500 | 0.255 | 0.515 |
+| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.502 | 0.015 | 0.180 |
+| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.498 | 0.002 | 0.080 |
+| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.495 | 0.046 | 0.200 |
+| qmd_act_perm | rescue_frac | pythia-160m | 25 | 0.442 | 0.505 | 0.693 | 0.952 |
+| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.497 | 0.407 | 0.707 |
+| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.500 | 0.027 | 0.180 |
+| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.499 | 0.159 | 0.424 |
+| cka_mean | rescue_frac | pythia-160m | 25 | 0.410 | 0.505 | 0.782 | 0.955 |
+| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.497 | 0.338 | 0.643 |
+| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.502 | 0.938 | 0.962 |
+| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.506 | 0.226 | 0.502 |
+| weight_cosine | rescue_frac | pythia-160m | 25 | 0.673 | 0.499 | 0.072 | 0.231 |
+| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.501 | 0.018 | 0.180 |
+| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.499 | 0.518 | 0.768 |
 
 **SET 4, held out by language pair.** n = 4 language pairs. This is far too few for an AUROC or a permutation null; only the rank correlation is reported, and it should be read as descriptive, not inferential.
 
@@ -538,7 +566,7 @@ SET 1's main grid uses `pythia-<size>-seed{n}`, which reseeds **both** the initi
 |---|---|---|---|---|---|---|---|
 | 160m-data | 3 | 3.27 | 3.13 | 3.00 | 3.00 | 3.9% | 0.0139 |
 | 160m-weight | 3 | 3.25 | 3.10 | 2.79 | 2.79 | 10.0% | 0.0123 |
-| 160m (init+data, main grid) | 23 | 3.26 | 8.77 | 6.59 | 6.16 | 31.3% | 0.0925 |
+| 160m (init+data, main grid) | 25 | 3.25 | 8.76 | 6.72 | 6.15 | 31.3% | 0.0900 |
 
 Reading: models that differ **only in data order** start far closer together — the naive merge's
 Δfloor is a small fraction of the reseeded-init case — and alignment does **nothing** for them,
@@ -556,16 +584,16 @@ init-plus-data-order number, not an init-only one.
 | SET 1 Δfloor · pythia-14m | 36/36 seed pairs | complete | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
 | SET 1 Δfloor · pythia-31m | 36/36 seed pairs | complete | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
 | SET 1 Δfloor · pythia-70m | 36/36 seed pairs | complete | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
-| SET 1 Δfloor · pythia-160m | 23/36 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
+| SET 1 Δfloor · pythia-160m | 25/36 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
 | SET 1 Δfloor · pythia-410m | 2/15 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; LMC barrier for M0 and M1-perm |
 | SET 1 control · pythia-160m-data | 3/3 pairs | complete | init-seed-only vs data-order-only, same rungs |
 | SET 1 control · pythia-160m-weight | 3/3 pairs | complete | init-seed-only vs data-order-only, same rungs |
-| SET 1 accuracy · BLiMP | pythia-14m: 36/36, pythia-70m: 27/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
-| SET 1 · REPAIR rung | pythia-14m: 27/36 | RAN | M4 = permutation-aligned average + pre-activation statistics repair; M5 = naive + repair; Δfloor and BLiMP on the same merges |
+| SET 1 accuracy · BLiMP | pythia-14m: 36/36, pythia-70m: 32/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
+| SET 1 · REPAIR rung | pythia-14m: 36/36 | RAN | M4 = permutation-aligned average + pre-activation statistics repair; M5 = naive + repair; Δfloor and BLiMP on the same merges |
 | SET 4 Δfloor · English-anchored | 4/4 language pairs (nld_Latn, spa_Latn, ell_Grek, pol_Latn) | complete | M0 naive · M1a vocab-transport · M1b/c vocab+unit-aligned · M1d/e forced-residual · M1f units-only · M1g/h embedding-row Procrustes |
-| SET 4 Δfloor · partner-anchored (reverse) | 1/4 language pairs | partial | same rungs, roles swapped |
+| SET 4 Δfloor · partner-anchored (reverse) | 4/4 language pairs | complete | same rungs, roles swapped |
 | SET 4 accuracy · MultiBLiMP 1.0 | 4/4 language pairs | RAN | `jumelet/multiblimp`, English + partner, on the SAME merges; UNK rate reported per cell |
-| SET 4 · jointly-trained bilingual ceiling | 2/4 language pairs | RAN | `catherinearnett/B-GPT_en_X_simultaneous` vs the Goldfish parents and merges, all scored at a matched 128-token context |
+| SET 4 · jointly-trained bilingual ceiling | 4/4 language pairs | RAN | `catherinearnett/B-GPT_en_X_simultaneous` vs the Goldfish parents and merges, all scored at a matched 128-token context |
 | SET 4 · task-arithmetic / TIES | 0 | **NOT APPLICABLE** | Both operators need a shared ancestor. Two independently trained monolingual Goldfish models have none, and with one parent as a pseudo-base the operators reduce to returning the other parent. Excluded on definition, not on time. |
 | SET 1 · pythia-410m full grid | 2/36 possible pairs | partial | 6 seeds only (15 possible pairs) and a reduced eval budget; the per-pair alignment cost is ~9 min at this width. Treat 410m as directional. |
 | Goldfish other tiers / other languages | 0 | NOT RUN | Only the 1000mb tier and the four audit languages. |
@@ -573,19 +601,39 @@ init-plus-data-order number, not an init-only one.
 
 ## Threats to validity, stated plainly
 
-- **Likelihood ≠ accuracy.** Repeated because it is the single most load-bearing caveat here.
+- **Likelihood ≠ accuracy.** Repeated because it is the single most load-bearing caveat here — and
+  because this report is one of the few places where both were measured on the same merges and found
+  to dissociate.
+- **BLiMP and MultiBLiMP are minimal-pair grammaticality benchmarks.** They are a real accuracy
+  measurement and they are not a general one. A merge that scores 0.68 on MultiBLiMP-English is not
+  thereby a usable model; agreement minimal pairs are unusually forgiving of a degraded model,
+  because the two candidates differ in one inflected token and the grammatical form is usually the
+  more frequent one. Read "retains accuracy" as "retains *this* accuracy", not as "works".
 - **SET 1's held-out corpus is FLORES-200 English devtest**, not a Pile validation split. It is
   genuinely held out from PolyPythia training, but it is out-of-domain, so the absolute nats/token
   floors are higher than a Pile-val number would be. Δfloor is a *difference* against parents
   measured on the same corpus, so the comparison between rungs is unaffected.
+- **SET 1's `-seed{n}` repos reseed initialisation AND data order together.** The 160m
+  weight-seed/data-seed control separates them (see the Control section) but only at n=3 pairs each.
+  The main grid's naive Δfloor should be read as an init-plus-data-order number.
 - **SET 4's nats/byte is comparable across tokenizers but not free of tokenizer effects**: block
   boundaries fall at different places for different tokenizers, and each block's first token is
-  unscored. With ~30k tokens per evaluation this is a sub-1% effect.
+  unscored. With ~30k tokens per evaluation this is a sub-1% effect. The much larger tokenizer effect
+  — the English tokenizer's UNK rate on partner-language text — is reported per cell and is a
+  substantive finding rather than a nuisance.
 - **The alignment search is over the permutation group (residual basis, MLP hidden axis, attention
-  heads) and its orthogonal relaxation.** It is not the full symmetry group, and the residual factor
-  is fitted from a finite activation sample. A better aligner could raise the M1 rungs; nothing here
-  bounds how far.
-- **SET 4's n = 4 language pairs.** Any predictor claim on that substrate is descriptive.
+  heads) and its orthogonal relaxation, plus embedding-row Procrustes for the cross-tokenizer case.**
+  It is not the full symmetry group, and the residual factor is fitted from a finite activation
+  sample. A better aligner could raise the M1 rungs; nothing here bounds how far. What *is* bounded
+  is the claim that the aligners already in `mergeschool.core` do the job on these substrates.
+- **The largest SET 1 sizes carry the fewest pairs.** 14m/31m/70m are complete 36-pair grids; 160m
+  and 410m are partial. The scale trend is monotone across all five but its right-hand end is thin.
+- **SET 4's n = 4 language pairs**, all with English as one parent and all Indo-European. Any
+  predictor claim on that substrate is descriptive, and nothing here speaks to non-Indo-European or
+  to non-English pivots.
+- **Everything here is training-free by construction.** No claim is made about what a small amount of
+  post-merge finetuning would recover; that is the obvious next experiment and it is out of scope for
+  a training-free audit.
 
 
 ## Files