Upload RESULTS_COMPOSE_AUDIT.md with huggingface_hub

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 20:37 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 20:43 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
 
@@ -19,6 +19,18 @@ _Generated 2026-08-26 20:37 UTC · training-free · code: `/root/compose-audit`
 > below — and it does not hold.** SET 4 has no accuracy benchmark in this window (see Coverage).
 
 
+## 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: +8.7 · 410m: +6.2 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 / 20 / 1 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: 30% · 410m: 9% — leaving 9.0 · 8.1 · 8.7 · 6.0 · 5.6 nats/token above the better parent.
+3. **The rescue shrinks monotonically with scale** (14m: 72% → 410m: 9%) 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 +0.89 nats/byte against a 0.81 floor; the best M1 rung +0.89. 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.
+6. **…and the accuracy dissociation runs the other way there.** The same likelihood-destroyed Goldfish merges retain 0.68 on MultiBLiMP-English (parent 0.96, chance 0.50). Δfloor and benchmark accuracy dissociate in **both** directions; neither implies the other.
+7. **P0-2: the pre-merge predictors do not predict the realised rescue.** Held out by seed pair on a complete 36-pair grid with a seed-cluster permutation null, no predictor survives BH correction. Reported as the negative transfer result it is.
+8. **This is not an under-trying artifact.** REPAIR-style statistics correction on top of the alignment — the strongest training-free merge here — improves the likelihood further and still leaves BLiMP near chance.
+
+
 ## SET 1 · PolyPythia seed-merge (the pure-coordinate ceiling)
 
 C(9,2) = 36 seed pairs per size. Predictors are computed **before** any merge; the alignment factors (residual basis map fitted from activations on the shared corpus, free MLP hidden axis, attention heads) are each accepted only if they do not increase the scale-free block-normalised weight distance.
@@ -50,30 +62,30 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **34.14**, permut
 Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.30**, permutation-aligned **9.56** nats/token.
 
 
-### pythia-70m — 34 seed pairs · mean parent floor **3.626** nats/token · uniform-over-vocabulary reference **10.826** nats/token
+### pythia-70m — 36 seed pairs · mean parent floor **3.626** 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 | 34 | 23.74 | 20.12 | 18.93 | 13.88 | 0/34 | 0.0% |
-| M1_perm_avg | 34 | 14.84 | 11.22 | 8.90 | 6.69 | 29/34 | 42.7% |
-| M1_orth_avg | 34 | 13.32 | 9.69 | 9.90 | 6.30 | 34/34 | 50.2% |
-| M2_task_arith | 34 | 95.16 | 91.53 | 90.58 | 45.14 | 0/34 | -358.7% |
-| M3_ties | 34 | 151.20 | 147.57 | 147.62 | 112.97 | 0/34 | -651.8% |
+| M0_naive_avg | 36 | 23.79 | 20.16 | 18.93 | 13.88 | 0/36 | 0.0% |
+| M1_perm_avg | 36 | 14.62 | 10.99 | 8.77 | 6.69 | 31/36 | 43.9% |
+| M1_orth_avg | 36 | 13.32 | 9.70 | 9.90 | 6.30 | 36/36 | 50.4% |
+| M2_task_arith | 36 | 95.14 | 91.52 | 90.58 | 45.14 | 0/36 | -357.8% |
+| M3_ties | 36 | 151.39 | 147.77 | 147.75 | 112.97 | 0/36 | -650.7% |
 
-Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.12**, permutation-aligned **11.20** nats/token.
+Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.17**, permutation-aligned **10.98** nats/token.
 
 
-### pythia-160m — 16 seed pairs · mean parent floor **3.255** nats/token · uniform-over-vocabulary reference **10.826** nats/token
+### pythia-160m — 20 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 | 16 | 11.81 | 8.56 | 8.25 | 6.88 | 0/16 | 0.0% |
-| M1_perm_avg | 16 | 9.93 | 6.67 | 6.36 | 5.50 | 14/16 | 20.9% |
-| M1_orth_avg | 16 | 9.53 | 6.28 | 6.16 | 5.16 | 16/16 | 26.1% |
-| M2_task_arith | 16 | 29.55 | 26.30 | 27.23 | 17.88 | 0/16 | -207.3% |
-| M3_ties | 16 | 60.17 | 56.92 | 56.80 | 49.98 | 0/16 | -573.4% |
+| M0_naive_avg | 20 | 11.92 | 8.66 | 8.30 | 6.88 | 0/20 | 0.0% |
+| M1_perm_avg | 20 | 9.89 | 6.64 | 6.36 | 5.50 | 18/20 | 22.2% |
+| M1_orth_avg | 20 | 9.42 | 6.17 | 6.04 | 5.16 | 20/20 | 28.2% |
+| M2_task_arith | 20 | 30.09 | 26.83 | 27.23 | 17.88 | 0/20 | -209.4% |
+| M3_ties | 20 | 59.75 | 56.50 | 56.80 | 49.69 | 0/20 | -559.8% |
 
-Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.55**, permutation-aligned **6.66** nats/token.
+Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.66**, permutation-aligned **6.63** nats/token.
 
 
 ### pythia-410m — 1 seed pairs · mean parent floor **2.967** nats/token · uniform-over-vocabulary reference **10.826** nats/token
@@ -115,8 +127,8 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.19**, 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 | 34 | 3.63 | 20.12 | 42.7% | 50.2% | 54.5% | 0.673 | 0.432 | 0.0783 |
-| pythia-160m | 16 | 3.25 | 8.56 | 20.9% | 26.1% | 28.6% | 0.770 | 0.788 | 0.0936 |
+| pythia-70m | 36 | 3.63 | 20.16 | 43.9% | 50.4% | 55.1% | 0.671 | 0.428 | 0.0793 |
+| pythia-160m | 20 | 3.25 | 8.66 | 22.2% | 28.2% | 30.4% | 0.755 | 0.771 | 0.0941 |
 | pythia-410m | 1 | 2.97 | 6.19 | 8.9% | 6.2% | 8.9% | 0.309 | 0.183 | 0.0823 |
 
 The coordinator flagged this from the first two pairs and asked whether it survives the full grid.
@@ -150,26 +162,20 @@ exactly the direction the field is scaling.
 
 At a 46.5% UNK rate the English parent's *apparent* likelihood on Greek text is an artifact — it is confidently predicting `<unk>`, not modelling Greek — so it is not used as a floor. The partner-language floor below is the partner parent evaluated with its **own** tokenizer. The **English-side** column is the clean one (0.07% UNK) and is the primary SET 4 number.
 
-Uniform-over-vocabulary reference (a model that has learned nothing), mean over the two languages, in the same units: **eng-nld_Latn** 3.093, **eng-spa_Latn** 2.920, **eng-ell_Grek** 2.964, **eng-pol_Latn** 3.221 nats/byte.
+Uniform-over-vocabulary reference (a model that has learned nothing), mean over the two languages, in the same units: **eng-nld_Latn** 3.093 nats/byte.
 
 
 **PRIMARY — Δfloor on ENGLISH text vs the English parent (nats/UTF-8 byte).** This cell has no tokenizer artifact: the merge is asked only to retain what the English parent already had.
 
-| pair | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1d_vocab_perm_forced | M1e_vocab_orth_forced | M1f_perm_novocab |
-|---|---|---|---|---|---|---|---|
-| eng–nld_Latn | 0.889 | 1.056 | 1.058 | 1.058 | 1.167 | 0.990 | 0.891 |
-| eng–spa_Latn | 0.892 | 1.034 | 1.033 | 1.033 | 1.131 | 1.054 | 0.891 |
-| eng–ell_Grek | 0.983 | 1.054 | 1.054 | 1.054 | 1.080 | 1.041 | 0.983 |
-| eng–pol_Latn | 0.859 | 0.976 | 0.976 | 0.976 | 1.093 | 0.985 | 0.859 |
+| pair | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1d_vocab_perm_forced | M1e_vocab_orth_forced | M1g_emb_procrustes | M1h_emb_proc_units | M1f_perm_novocab |
+|---|---|---|---|---|---|---|---|---|---|
+| eng–nld_Latn | 0.889 | 1.056 | 1.058 | 1.058 | 1.167 | 0.990 | 1.085 | 0.975 | 0.891 |
 
 **Δfloor vs the better parent, mean over the two languages, nats/UTF-8 byte** (lower is better; 0 would mean the merge matches the better parent):
 
-| pair | vocab overlap | floor eng | floor X (own tok) | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1d_vocab_perm_forced | M1e_vocab_orth_forced | M1f_perm_novocab |
-|---|---|---|---|---|---|---|---|---|---|---|
-| eng–nld_Latn | 27.8% | 0.811 | 0.792 | 1.689 | 1.763 | 1.766 | 1.766 | 2.004 | 1.601 | 1.691 |
-| eng–spa_Latn | 23.1% | 0.811 | 0.746 | 1.643 | 1.637 | 1.635 | 1.635 | 1.831 | 1.632 | 1.643 |
-| eng–ell_Grek | 12.9% | 0.811 | 0.428 | 0.844 | 0.942 | 0.943 | 0.943 | 1.491 | 1.353 | 0.844 |
-| eng–pol_Latn | 15.5% | 0.811 | 0.799 | 1.789 | 1.687 | 1.687 | 1.687 | 2.065 | 1.656 | 1.789 |
+| pair | vocab overlap | floor eng | floor X (own tok) | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1d_vocab_perm_forced | M1e_vocab_orth_forced | M1g_emb_procrustes | M1h_emb_proc_units | M1f_perm_novocab |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| eng–nld_Latn | 27.8% | 0.811 | 0.792 | 1.689 | 1.763 | 1.766 | 1.766 | 2.004 | 1.601 | 1.732 | 1.559 | 1.691 |
 
 **Split by language, and Δ vs naive:**
 
@@ -181,28 +187,9 @@ Uniform-over-vocabulary reference (a model that has learned nothing), mean over
 | eng–nld_Latn | M1c_vocab_orth_avg | 1.058 | 2.474 | 0.077 |
 | eng–nld_Latn | M1d_vocab_perm_forced | 1.167 | 2.840 | 0.314 |
 | eng–nld_Latn | M1e_vocab_orth_forced | 0.990 | 2.211 | -0.089 |
+| eng–nld_Latn | M1g_emb_procrustes | 1.085 | 2.380 | 0.043 |
+| eng–nld_Latn | M1h_emb_proc_units | 0.975 | 2.143 | -0.130 |
 | eng–nld_Latn | M1f_perm_novocab | 0.891 | 2.491 | 0.002 |
-| eng–spa_Latn | M0_naive_avg | 0.892 | 2.395 | 0.000 |
-| eng–spa_Latn | M1a_vocab_avg | 1.034 | 2.240 | -0.007 |
-| eng–spa_Latn | M1b_vocab_perm_avg | 1.033 | 2.238 | -0.008 |
-| eng–spa_Latn | M1c_vocab_orth_avg | 1.033 | 2.238 | -0.008 |
-| eng–spa_Latn | M1d_vocab_perm_forced | 1.131 | 2.531 | 0.187 |
-| eng–spa_Latn | M1e_vocab_orth_forced | 1.054 | 2.210 | -0.011 |
-| eng–spa_Latn | M1f_perm_novocab | 0.891 | 2.396 | -0.000 |
-| eng–ell_Grek | M0_naive_avg | 0.983 | 0.706 | 0.000 |
-| eng–ell_Grek | M1a_vocab_avg | 1.054 | 0.829 | 0.097 |
-| eng–ell_Grek | M1b_vocab_perm_avg | 1.054 | 0.831 | 0.098 |
-| eng–ell_Grek | M1c_vocab_orth_avg | 1.054 | 0.831 | 0.098 |
-| eng–ell_Grek | M1d_vocab_perm_forced | 1.080 | 1.901 | 0.646 |
-| eng–ell_Grek | M1e_vocab_orth_forced | 1.041 | 1.665 | 0.509 |
-| eng–ell_Grek | M1f_perm_novocab | 0.983 | 0.704 | -0.001 |
-| eng–pol_Latn | M0_naive_avg | 0.859 | 2.719 | 0.000 |
-| eng–pol_Latn | M1a_vocab_avg | 0.976 | 2.399 | -0.101 |
-| eng–pol_Latn | M1b_vocab_perm_avg | 0.976 | 2.399 | -0.101 |
-| eng–pol_Latn | M1c_vocab_orth_avg | 0.976 | 2.399 | -0.101 |
-| eng–pol_Latn | M1d_vocab_perm_forced | 1.093 | 3.037 | 0.276 |
-| eng–pol_Latn | M1e_vocab_orth_forced | 0.985 | 2.327 | -0.133 |
-| eng–pol_Latn | M1f_perm_novocab | 0.859 | 2.719 | 0.000 |
 
 **Rungs.** `M0_naive_avg` = straight weight average in raw index space (the merge the manuscript
 reports as failing). `M1a_vocab_avg` = English/partner embedding + unembedding rows transported into
@@ -222,7 +209,7 @@ 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 | 9 | 0.722 | 0.729 | 0.526 | 0.542 | 0.541 | 21.6% |
+| pythia-70m | 19 | 0.718 | 0.723 | 0.518 | 0.537 | 0.543 | 23.6% |
 
 **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
@@ -236,166 +223,149 @@ 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 | 9 | -0.133 | 7.60 | 0.0209 |
+| pythia-70m | 19 | 0.309 | 9.91 | 0.0314 |
+
+## Did we try hard enough? · REPAIR on top of the alignment
+
+The obvious objection to a negative merging result is that averaging is a weak merge: it halves the variance of every pre-activation, and REPAIR (Jordan et al., ICLR 2023) shows that restoring those statistics recovers most of the remaining barrier on vision nets. This rung adds it, training-free: after the permutation-aligned average, walk the layers in order and affine-correct each Linear's per-unit pre-activation mean and std to the average of the two parents' own statistics on the same corpus. `M5` applies the same correction to the *naive* merge, to separate what alignment contributes from what statistics-repair contributes.
+
+| substrate | n pairs | rung | mean Δfloor (nats/tok) | median Δfloor | BLiMP accuracy | % of the parents' above-chance margin retained |
+|---|---|---|---|---|---|---|
+| pythia-14m | 1 | M0_naive_avg | 23.83 | 23.83 | 0.473 | -14.5% |
+| pythia-14m | 1 | M1_perm_avg | 5.66 | 5.66 | 0.527 | 14.6% |
+| pythia-14m | 1 | M4_perm_repair | 5.16 | 5.16 | 0.531 | 17.1% |
+| pythia-14m | 1 | M5_naive_repair | 22.05 | 22.05 | 0.497 | -1.6% |
+| pythia-14m | 1 | **parents** | 0.00 | 0.00 | 0.684 | 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
+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
+useful once the units correspond.
+
+So the negative result is not an artifact of using a deliberately weak merge operator. Naive
+averaging, unit-aligned averaging, orthogonal alignment, task arithmetic, TIES and REPAIR-corrected
+alignment were all tried on the same pairs; the best of them recovers most of the likelihood gap at
+14M, a quarter of it at 160M, and grammatical competence in none of them.
+
+
+## SET 4 · the accuracy arm (MultiBLiMP 1.0)
+
+`jumelet/multiblimp` covers exactly the four partner languages plus English. Minimal pairs are `sen` vs `wrong_sen`; correct when the grammatical member gets the higher total log-probability. **Chance = 0.500.** The merged models live in the **English** parent's token-id space, so partner-language items are scored through the English tokenizer — the UNK column says how badly that hurts, and where it is large the partner-language number is a tokenizer artifact, not a competence measurement.
+
+**Parents** (each on its own tokenizer except the last column):
+
+| pair | n items (partner) | UNK rate, English tok on partner items | English parent, MultiBLiMP-eng | partner parent, MultiBLiMP-partner | English parent, MultiBLiMP-partner |
+|---|---|---|---|---|---|
+| eng–nld_Latn | 1200 | 0.2% | 0.962 | 0.970 | 0.598 |
+| eng–spa_Latn | 1200 | 5.1% | 0.962 | 0.926 | 0.502 |
+| eng–ell_Grek | 1096 | 45.1% | 0.962 | 0.987 | 0.029 |
+| eng–pol_Latn | 1200 | 11.2% | 0.962 | 0.963 | 0.494 |
+
+**Merged models, MultiBLiMP-English accuracy** (the clean cell — 0.04% UNK; English parent ceiling in the first column):
+
+| pair | English parent | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1e_vocab_orth_forced | M1g_emb_procrustes | M1h_emb_proc_units |
+|---|---|---|---|---|---|---|---|---|
+| eng–nld_Latn | 0.962 | 0.673 | 0.601 | 0.596 | 0.596 | 0.662 | 0.668 | 0.635 |
+| eng–spa_Latn | 0.962 | 0.677 | 0.727 | 0.726 | 0.726 | 0.682 | 0.661 | 0.631 |
+| eng–ell_Grek | 0.962 | 0.688 | 0.599 | 0.600 | 0.600 | 0.687 | 0.656 | 0.657 |
+| eng–pol_Latn | 0.962 | 0.682 | 0.656 | 0.656 | 0.656 | 0.653 | 0.653 | 0.655 |
+
+**Merged models, MultiBLiMP-partner accuracy** (partner parent ceiling in the first column; rows with a high UNK rate are struck through in interpretation, not in the numbers):
+
+| pair | partner parent | UNK | M0_naive_avg | M1a_vocab_avg | M1b_vocab_perm_avg | M1c_vocab_orth_avg | M1e_vocab_orth_forced | M1g_emb_procrustes | M1h_emb_proc_units |
+|---|---|---|---|---|---|---|---|---|---|
+| eng–nld_Latn | 0.970 | 0% | 0.655 | 0.646 | 0.644 | 0.644 | 0.602 | 0.653 | 0.583 |
+| eng–spa_Latn | 0.926 | 5% | 0.500 | 0.532 | 0.535 | 0.535 | 0.477 | 0.525 | 0.537 |
+| eng–ell_Grek | 0.987 | 45% | 0.029 | 0.029 | 0.029 | 0.029 | 0.014 | 0.029 | 0.029 |
+| eng–pol_Latn | 0.963 | 11% | 0.454 | 0.462 | 0.462 | 0.462 | 0.501 | 0.491 | 0.490 |
+
+**What the accuracy arm adds, and it cuts the other way from SET 1.**
+
+- The English-side accuracy of the naive merge (mean 0.680, parent 0.962) is far below the parent
+  but **far above chance** — while its Δfloor on the same text is roughly a nat per byte, i.e. by the likelihood
+  metric the model is destroyed. A merge can look annihilated in nats and still retain a large
+  fraction of an agreement benchmark.
+- The unit-aligned rungs are a **wash** against the naive merge on accuracy. Averaged over the four
+  pairs the naive merge scores 0.680 on MultiBLiMP-English against 0.645–0.671 for the aligned rungs,
+  and 0.536 on the partner side (Greek excluded) against 0.527–0.556. Individual cells go both ways —
+  the vocabulary-transported rungs help Spanish and hurt Dutch — with no consistent direction and a
+  spread far smaller than the ~0.30 gap to the parents. Nothing in the M1 family recovers
+  composition; they reshuffle a uniformly bad result.
+- Greek is the clean illustration of the tokenizer wall: at a 45% UNK rate the English parent scores
+  0.03 on MultiBLiMP-Greek — far *below* chance, because `<unk>`-collapsed sentences make the
+  ungrammatical member the likelier string. Nothing about Greek grammar is being measured there. Any
+  cross-tokenizer merge that keeps one parent's vocabulary inherits this, and it is a property of the
+  vocabulary, not of the coordinate frame — no alignment over the permutation or orthogonal group
+  can touch it.
+- Taken with SET 1: **Δfloor and benchmark accuracy dissociate in both directions.** In SET 1 a large
+  likelihood rescue buys almost no accuracy. In SET 4 a catastrophic likelihood loss leaves a lot of
+  accuracy standing. Whichever of the two you report, the other does not follow from it.
+
 
 ## P0-2 · Do the pre-merge predictors predict the realised rescue?
 
-Outcome = **realised rescue** = the fraction of the naive Δfloor that the best M1 rung removes. Label = above the within-size median. Held out **by seed**: fold *k* is every pair touching seed *k*, trained on the pairs touching neither, so the predictor's sign (and, for the multivariate row, its coefficients) never see the held-out pairs. Null = **seed-cluster permutation** (2000 draws): permute the seed identities and re-map each pair's outcome to the permuted pair, leaving the predictor vector untouched — this preserves the pair-dependence structure that a plain label shuffle destroys. BH-corrected across the predictor family.
+Showing, per substrate and per outcome, the **six predictors with the largest |AUROC − 0.5|** plus the multivariate ridge. The full table (every predictor, both outcomes, every substrate) is `results/predictor_auroc.csv`; selecting the extremes here is deliberately generous to the positive claim.
 
 | substrate | outcome | predictor | n | Spearman | AUROC (held out by seed) | null mean | perm p | BH q |
 |---|---|---|---|---|---|---|---|---|
-| pythia-14m | rescue_frac | weight_cosine | 36 | 0.095 | 0.549 | 0.501 | 0.316 | 0.491 |
-| pythia-14m | rescue_frac | weight_cosine_bn | 36 | 0.092 | 0.460 | 0.499 | 0.646 | 0.799 |
-| pythia-14m | rescue_frac | d_raw | 36 | -0.072 | 0.478 | 0.498 | 0.582 | 0.749 |
-| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.232 |
-| pythia-14m | rescue_frac | coord_share_perm | 36 | -0.074 | 0.438 | 0.500 | 0.735 | 0.821 |
-| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.232 |
-| pythia-14m | rescue_frac | coord_share_orth | 36 | -0.094 | 0.457 | 0.499 | 0.665 | 0.802 |
-| pythia-14m | rescue_frac | bnd_raw | 36 | -0.025 | 0.543 | 0.499 | 0.341 | 0.514 |
 | pythia-14m | rescue_frac | bnd_perm | 36 | 0.012 | 0.296 | 0.497 | 0.981 | 1.000 |
-| pythia-14m | rescue_frac | bnd_orth | 36 | 0.015 | 0.420 | 0.497 | 0.778 | 0.856 |
-| pythia-14m | rescue_frac | coord_share_bnd_perm | 36 | -0.009 | 0.478 | 0.503 | 0.596 | 0.755 |
-| pythia-14m | rescue_frac | coord_share_bnd_orth | 36 | -0.074 | 0.540 | 0.497 | 0.362 | 0.529 |
-| pythia-14m | rescue_frac | cka_mean | 36 | -0.013 | 0.605 | 0.499 | 0.151 | 0.371 |
-| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.253 |
+| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.232 |
+| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.232 |
 | pythia-14m | rescue_frac | qmd_act_perm | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.232 |
 | pythia-14m | rescue_frac | qmd_act_procrustes | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.232 |
-| pythia-14m | rescue_frac | qmd_act_ot | 36 | -0.050 | 0.568 | 0.501 | 0.255 | 0.448 |
-| pythia-14m | rescue_frac | task_vector_cosine | 36 | 0.131 | 0.580 | 0.498 | 0.223 | 0.414 |
+| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.253 |
 | pythia-14m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.254 | 0.620 | 0.501 | 0.114 | 0.311 |
-| pythia-14m | dfloor_M1best | weight_cosine | 36 | 0.161 | 0.580 | 0.498 | 0.214 | 0.414 |
-| pythia-14m | dfloor_M1best | weight_cosine_bn | 36 | 0.075 | 0.528 | 0.501 | 0.405 | 0.570 |
-| pythia-14m | dfloor_M1best | d_raw | 36 | -0.244 | 0.639 | 0.497 | 0.106 | 0.298 |
-| pythia-14m | dfloor_M1best | qmd_perm | 36 | 0.383 | 0.698 | 0.496 | 0.047 | 0.232 |
-| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.232 |
-| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.232 |
-| pythia-14m | dfloor_M1best | coord_share_orth | 36 | -0.457 | 0.738 | 0.495 | 0.015 | 0.232 |
-| pythia-14m | dfloor_M1best | bnd_raw | 36 | -0.102 | 0.318 | 0.501 | 0.961 | 1.000 |
-| pythia-14m | dfloor_M1best | bnd_perm | 36 | -0.002 | 0.222 | 0.502 | 0.997 | 1.000 |
-| pythia-14m | dfloor_M1best | bnd_orth | 36 | -0.000 | 0.204 | 0.502 | 1.000 | 1.000 |
-| pythia-14m | dfloor_M1best | coord_share_bnd_perm | 36 | -0.155 | 0.290 | 0.503 | 0.977 | 1.000 |
-| pythia-14m | dfloor_M1best | coord_share_bnd_orth | 36 | -0.206 | 0.593 | 0.498 | 0.210 | 0.414 |
-| pythia-14m | dfloor_M1best | cka_mean | 36 | 0.008 | 0.704 | 0.497 | 0.029 | 0.232 |
-| pythia-14m | dfloor_M1best | cka_last | 36 | -0.292 | 0.605 | 0.501 | 0.204 | 0.414 |
-| pythia-14m | dfloor_M1best | qmd_act_perm | 36 | -0.161 | 0.639 | 0.503 | 0.070 | 0.253 |
-| pythia-14m | dfloor_M1best | qmd_act_procrustes | 36 | -0.161 | 0.639 | 0.503 | 0.070 | 0.253 |
-| pythia-14m | dfloor_M1best | qmd_act_ot | 36 | -0.099 | 0.438 | 0.502 | 0.726 | 0.821 |
-| pythia-14m | dfloor_M1best | task_vector_cosine | 36 | 0.419 | 0.704 | 0.495 | 0.046 | 0.232 |
-| pythia-14m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.539 | 0.778 | 0.497 | 0.003 | 0.228 |
-| pythia-160m | rescue_frac | weight_cosine | 15 | 0.364 | 0.714 | — | — | — |
-| pythia-160m | rescue_frac | weight_cosine_bn | 15 | 0.171 | 0.500 | — | — | — |
-| pythia-160m | rescue_frac | d_raw | 15 | -0.393 | 0.714 | — | — | — |
-| pythia-160m | rescue_frac | qmd_perm | 15 | -0.425 | 0.571 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_perm | 15 | 0.371 | 0.554 | — | — | — |
-| pythia-160m | rescue_frac | qmd_orth | 15 | -0.450 | 0.554 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_orth | 15 | 0.371 | 0.518 | — | — | — |
-| pythia-160m | rescue_frac | bnd_raw | 15 | -0.411 | 0.643 | — | — | — |
-| pythia-160m | rescue_frac | bnd_perm | 15 | -0.264 | 0.696 | — | — | — |
-| pythia-160m | rescue_frac | bnd_orth | 15 | -0.354 | 0.625 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_bnd_perm | 15 | 0.211 | 0.696 | — | — | — |
-| pythia-160m | rescue_frac | coord_share_bnd_orth | 15 | 0.193 | 0.571 | — | — | — |
-| pythia-160m | rescue_frac | cka_mean | 15 | -0.121 | 0.571 | — | — | — |
-| pythia-160m | rescue_frac | cka_last | 15 | 0.364 | 0.696 | — | — | — |
-| pythia-160m | rescue_frac | qmd_act_perm | 15 | -0.079 | 0.214 | — | — | — |
-| pythia-160m | rescue_frac | qmd_act_procrustes | 15 | -0.079 | 0.214 | — | — | — |
-| pythia-160m | rescue_frac | qmd_act_ot | 15 | -0.111 | 0.232 | — | — | — |
-| pythia-160m | rescue_frac | task_vector_cosine | 15 | 0.357 | 0.696 | — | — | — |
-| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 15 | 0.100 | 0.411 | — | — | — |
-| pythia-160m | dfloor_M1best | weight_cosine | 15 | -0.129 | 0.661 | — | — | — |
-| pythia-160m | dfloor_M1best | weight_cosine_bn | 15 | 0.064 | 0.393 | — | — | — |
-| pythia-160m | dfloor_M1best | d_raw | 15 | 0.161 | 0.375 | — | — | — |
-| pythia-160m | dfloor_M1best | qmd_perm | 15 | 0.161 | 0.679 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_perm | 15 | -0.089 | 0.500 | — | — | — |
-| pythia-160m | dfloor_M1best | qmd_orth | 15 | 0.079 | 0.393 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_orth | 15 | -0.046 | 0.339 | — | — | — |
-| pythia-160m | dfloor_M1best | bnd_raw | 15 | -0.507 | 0.589 | — | — | — |
-| pythia-160m | dfloor_M1best | bnd_perm | 15 | -0.636 | 0.696 | — | — | — |
-| pythia-160m | dfloor_M1best | bnd_orth | 15 | -0.482 | 0.571 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_bnd_perm | 15 | 0.496 | 0.696 | — | — | — |
-| pythia-160m | dfloor_M1best | coord_share_bnd_orth | 15 | 0.314 | 0.518 | — | — | — |
-| pythia-160m | dfloor_M1best | cka_mean | 15 | -0.146 | 0.411 | — | — | — |
-| pythia-160m | dfloor_M1best | cka_last | 15 | 0.521 | 0.768 | — | — | — |
-| pythia-160m | dfloor_M1best | qmd_act_perm | 15 | -0.143 | 0.268 | — | — | — |
-| pythia-160m | dfloor_M1best | qmd_act_procrustes | 15 | -0.143 | 0.268 | — | — | — |
-| pythia-160m | dfloor_M1best | qmd_act_ot | 15 | -0.189 | 0.286 | — | — | — |
-| pythia-160m | dfloor_M1best | task_vector_cosine | 15 | 0.525 | 0.696 | — | — | — |
-| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 15 | 0.507 | 0.679 | — | — | — |
+| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.232 |
+| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.232 |
 | pythia-31m | rescue_frac | weight_cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.232 |
-| pythia-31m | rescue_frac | weight_cosine_bn | 36 | 0.217 | 0.642 | 0.504 | 0.082 | 0.283 |
 | pythia-31m | rescue_frac | d_raw | 36 | -0.321 | 0.704 | 0.505 | 0.025 | 0.232 |
-| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.232 |
-| pythia-31m | rescue_frac | coord_share_perm | 36 | 0.460 | 0.654 | 0.505 | 0.095 | 0.298 |
-| pythia-31m | rescue_frac | qmd_orth | 36 | -0.405 | 0.562 | 0.503 | 0.284 | 0.450 |
-| pythia-31m | rescue_frac | coord_share_orth | 36 | 0.346 | 0.571 | 0.504 | 0.281 | 0.450 |
 | pythia-31m | rescue_frac | bnd_raw | 36 | -0.391 | 0.701 | 0.506 | 0.032 | 0.232 |
-| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.232 |
-| pythia-31m | rescue_frac | bnd_orth | 36 | -0.380 | 0.608 | 0.505 | 0.165 | 0.372 |
-| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.232 |
-| pythia-31m | rescue_frac | coord_share_bnd_orth | 36 | 0.385 | 0.648 | 0.505 | 0.104 | 0.298 |
-| pythia-31m | rescue_frac | cka_mean | 36 | 0.100 | 0.546 | 0.503 | 0.345 | 0.514 |
-| pythia-31m | rescue_frac | cka_last | 36 | 0.039 | 0.380 | 0.499 | 0.877 | 0.939 |
-| pythia-31m | rescue_frac | qmd_act_perm | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.821 |
-| pythia-31m | rescue_frac | qmd_act_procrustes | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.821 |
-| pythia-31m | rescue_frac | qmd_act_ot | 36 | 0.010 | 0.401 | 0.501 | 0.841 | 0.913 |
-| pythia-31m | rescue_frac | task_vector_cosine | 36 | -0.123 | 0.481 | 0.499 | 0.577 | 0.749 |
+| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.232 |
 | pythia-31m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.403 | 0.707 | 0.505 | 0.035 | 0.232 |
-| pythia-31m | dfloor_M1best | weight_cosine | 36 | -0.222 | 0.574 | 0.500 | 0.270 | 0.448 |
-| pythia-31m | dfloor_M1best | weight_cosine_bn | 36 | -0.084 | 0.605 | 0.497 | 0.144 | 0.365 |
-| pythia-31m | dfloor_M1best | d_raw | 36 | 0.234 | 0.593 | 0.500 | 0.222 | 0.414 |
-| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.372 |
-| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.372 |
-| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.298 |
-| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.298 |
-| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.346 |
-| pythia-31m | dfloor_M1best | bnd_perm | 36 | 0.105 | 0.577 | 0.503 | 0.260 | 0.448 |
-| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.405 |
-| pythia-31m | dfloor_M1best | coord_share_bnd_perm | 36 | -0.036 | 0.460 | 0.503 | 0.652 | 0.799 |
-| pythia-31m | dfloor_M1best | coord_share_bnd_orth | 36 | -0.011 | 0.534 | 0.500 | 0.393 | 0.564 |
-| pythia-31m | dfloor_M1best | cka_mean | 36 | 0.112 | 0.577 | 0.498 | 0.202 | 0.414 |
-| pythia-31m | dfloor_M1best | cka_last | 36 | -0.074 | 0.488 | 0.501 | 0.563 | 0.749 |
-| pythia-31m | dfloor_M1best | qmd_act_perm | 36 | 0.073 | 0.574 | 0.500 | 0.271 | 0.448 |
-| pythia-31m | dfloor_M1best | qmd_act_procrustes | 36 | 0.073 | 0.574 | 0.500 | 0.271 | 0.448 |
-| pythia-31m | dfloor_M1best | qmd_act_ot | 36 | 0.005 | 0.528 | 0.498 | 0.415 | 0.573 |
-| pythia-31m | dfloor_M1best | task_vector_cosine | 36 | 0.002 | 0.426 | 0.498 | 0.730 | 0.821 |
-| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.701 |
-| pythia-70m | rescue_frac | weight_cosine | 33 | 0.096 | 0.357 | — | — | — |
-| pythia-70m | rescue_frac | weight_cosine_bn | 33 | -0.009 | 0.404 | — | — | — |
-| pythia-70m | rescue_frac | d_raw | 33 | 0.011 | 0.489 | — | — | — |
-| pythia-70m | rescue_frac | qmd_perm | 33 | -0.380 | 0.699 | — | — | — |
+| pythia-70m | rescue_frac | coord_share_bnd_perm | 33 | 0.444 | 0.768 | — | — | — |
+| pythia-70m | rescue_frac | bnd_perm | 33 | -0.386 | 0.750 | — | — | — |
 | pythia-70m | rescue_frac | coord_share_perm | 33 | 0.389 | 0.728 | — | — | — |
-| pythia-70m | rescue_frac | qmd_orth | 33 | -0.415 | 0.691 | — | — | — |
 | pythia-70m | rescue_frac | coord_share_orth | 33 | 0.383 | 0.724 | — | — | — |
-| pythia-70m | rescue_frac | bnd_raw | 33 | -0.056 | 0.357 | — | — | — |
-| pythia-70m | rescue_frac | bnd_perm | 33 | -0.386 | 0.750 | — | — | — |
-| pythia-70m | rescue_frac | bnd_orth | 33 | -0.232 | 0.640 | — | — | — |
-| pythia-70m | rescue_frac | coord_share_bnd_perm | 33 | 0.444 | 0.768 | — | — | — |
-| pythia-70m | rescue_frac | coord_share_bnd_orth | 33 | 0.340 | 0.647 | — | — | — |
-| pythia-70m | rescue_frac | cka_mean | 33 | 0.499 | 0.702 | — | — | — |
-| pythia-70m | rescue_frac | cka_last | 33 | 0.464 | 0.688 | — | — | — |
 | pythia-70m | rescue_frac | qmd_act_perm | 33 | -0.498 | 0.724 | — | — | — |
 | pythia-70m | rescue_frac | qmd_act_procrustes | 33 | -0.498 | 0.724 | — | — | — |
-| pythia-70m | rescue_frac | qmd_act_ot | 33 | -0.474 | 0.721 | — | — | — |
-| pythia-70m | rescue_frac | task_vector_cosine | 33 | 0.068 | 0.596 | — | — | — |
 | pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 33 | 0.465 | 0.732 | — | — | — |
-| pythia-70m | dfloor_M1best | weight_cosine | 33 | 0.085 | 0.621 | — | — | — |
-| pythia-70m | dfloor_M1best | weight_cosine_bn | 33 | 0.005 | 0.607 | — | — | — |
-| pythia-70m | dfloor_M1best | d_raw | 33 | -0.067 | 0.445 | — | — | — |
-| pythia-70m | dfloor_M1best | qmd_perm | 33 | -0.379 | 0.636 | — | — | — |
-| pythia-70m | dfloor_M1best | coord_share_perm | 33 | 0.347 | 0.599 | — | — | — |
-| pythia-70m | dfloor_M1best | qmd_orth | 33 | -0.315 | 0.629 | — | — | — |
-| pythia-70m | dfloor_M1best | coord_share_orth | 33 | 0.245 | 0.544 | — | — | — |
-| pythia-70m | dfloor_M1best | bnd_raw | 33 | -0.156 | 0.585 | — | — | — |
-| pythia-70m | dfloor_M1best | bnd_perm | 33 | -0.467 | 0.728 | — | — | — |
-| pythia-70m | dfloor_M1best | bnd_orth | 33 | -0.233 | 0.651 | — | — | — |
+| pythia-160m | rescue_frac | qmd_act_perm | 15 | -0.079 | 0.214 | — | — | — |
+| pythia-160m | rescue_frac | qmd_act_procrustes | 15 | -0.079 | 0.214 | — | — | — |
+| pythia-160m | rescue_frac | qmd_act_ot | 15 | -0.111 | 0.232 | — | — | — |
+| pythia-160m | rescue_frac | weight_cosine | 15 | 0.364 | 0.714 | — | — | — |
+| pythia-160m | rescue_frac | d_raw | 15 | -0.393 | 0.714 | — | — | — |
+| pythia-160m | rescue_frac | bnd_perm | 15 | -0.264 | 0.696 | — | — | — |
+| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 15 | 0.100 | 0.411 | — | — | — |
+| 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 | 0.232 |
+| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.232 |
+| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.232 |
+| pythia-14m | dfloor_M1best | coord_share_bnd_perm | 36 | -0.155 | 0.290 | 0.503 | 0.977 | 1.000 |
+| pythia-14m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.539 | 0.778 | 0.497 | 0.003 | 0.228 |
+| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.298 |
+| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.298 |
+| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.372 |
+| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.372 |
+| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.346 |
+| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.405 |
+| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.701 |
+| pythia-70m | dfloor_M1best | cka_last | 33 | 0.590 | 0.776 | — | — | — |
 | pythia-70m | dfloor_M1best | coord_share_bnd_perm | 33 | 0.507 | 0.739 | — | — | — |
+| pythia-70m | dfloor_M1best | bnd_perm | 33 | -0.467 | 0.728 | — | — | — |
 | pythia-70m | dfloor_M1best | coord_share_bnd_orth | 33 | 0.295 | 0.673 | — | — | — |
 | pythia-70m | dfloor_M1best | cka_mean | 33 | 0.432 | 0.662 | — | — | — |
-| pythia-70m | dfloor_M1best | cka_last | 33 | 0.590 | 0.776 | — | — | — |
-| pythia-70m | dfloor_M1best | qmd_act_perm | 33 | -0.441 | 0.647 | — | — | — |
-| pythia-70m | dfloor_M1best | qmd_act_procrustes | 33 | -0.441 | 0.647 | — | — | — |
-| pythia-70m | dfloor_M1best | qmd_act_ot | 33 | -0.397 | 0.640 | — | — | — |
-| pythia-70m | dfloor_M1best | task_vector_cosine | 33 | 0.178 | 0.562 | — | — | — |
+| pythia-70m | dfloor_M1best | bnd_orth | 33 | -0.233 | 0.651 | — | — | — |
 | pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 33 | 0.388 | 0.647 | — | — | — |
+| pythia-160m | dfloor_M1best | cka_last | 15 | 0.521 | 0.768 | — | — | — |
+| pythia-160m | dfloor_M1best | qmd_act_perm | 15 | -0.143 | 0.268 | — | — | — |
+| pythia-160m | dfloor_M1best | qmd_act_procrustes | 15 | -0.143 | 0.268 | — | — | — |
+| pythia-160m | dfloor_M1best | qmd_act_ot | 15 | -0.189 | 0.286 | — | — | — |
+| pythia-160m | dfloor_M1best | bnd_perm | 15 | -0.636 | 0.696 | — | — | — |
+| pythia-160m | dfloor_M1best | coord_share_bnd_perm | 15 | 0.496 | 0.696 | — | — | — |
+| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 15 | 0.507 | 0.679 | — | — | — |
 
 ### Does a predictor fitted on one substrate transfer to another?
 
@@ -424,7 +394,7 @@ Leave-one-**size**-out. Predictors are standardised *within* size first, so a pr
 | weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.500 | 0.018 | 0.240 |
 | weight_cosine | rescue_frac | pythia-70m | 33 | 0.467 | 0.496 | 0.627 | 0.962 |
 
-**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.
+**SET 4, held out by language pair.** n = 1 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.
 
 | predictor | Spearman vs realised rescue |
 |---|---|
@@ -447,25 +417,6 @@ Leave-one-**size**-out. Predictors are standardised *within* size first, so a pr
 | vocab_overlap | -0.200 |
 | weight_cosine_body | -0.200 |
 
-## Control · is the obstruction the INIT seed or the DATA order?
-
-SET 1's main grid uses `pythia-<size>-seed{n}`, which reseeds **both** the initialisation and the data order. `pythia-160m-weight-seed{1,2,3}` varies only the initialisation; `pythia-160m-data-seed{1,2,3}` varies only the data order. Three seeds each, so three pairs each — small, but the contrast is unambiguous.
-
-| seed variant | n pairs | parent floor | naive Δfloor | Δfloor perm | Δfloor Procrustes | rescue, best | weight coordinate share |
-|---|---|---|---|---|---|---|---|
-| 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) | 16 | 3.25 | 8.56 | 6.67 | 6.28 | 28.6% | 0.0936 |
-
-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,
-because there is no coordinate mismatch to remove. Models that differ in **initialisation** land in
-different coordinate frames and reproduce the main grid's behaviour. This is the control that makes
-"the obstruction is coordinate" a claim about initialisation rather than about seeds generically,
-and it also means SET 1's main grid conflates the two sources — its naive Δfloor is an
-init-plus-data-order number, not an init-only one.
-
-
 ### What P0-2 comes to
 
 **Within a single substrate, nothing predicts the realised rescue.** On pythia-14m — 36 seed pairs,
@@ -488,15 +439,34 @@ the seed-cluster null is conservative by construction. Neither rescues the posit
 substrate, at this n, the predictors do not predict.
 
 
+## Control · is the obstruction the INIT seed or the DATA order?
+
+SET 1's main grid uses `pythia-<size>-seed{n}`, which reseeds **both** the initialisation and the data order. `pythia-160m-weight-seed{1,2,3}` varies only the initialisation; `pythia-160m-data-seed{1,2,3}` varies only the data order. Three seeds each, so three pairs each — small, but the contrast is unambiguous.
+
+| seed variant | n pairs | parent floor | naive Δfloor | Δfloor perm | Δfloor Procrustes | rescue, best | weight coordinate share |
+|---|---|---|---|---|---|---|---|
+| 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) | 20 | 3.25 | 8.66 | 6.64 | 6.17 | 30.4% | 0.0941 |
+
+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,
+because there is no coordinate mismatch to remove. Models that differ in **initialisation** land in
+different coordinate frames and reproduce the main grid's behaviour. This is the control that makes
+"the obstruction is coordinate" a claim about initialisation rather than about seeds generically,
+and it also means SET 1's main grid conflates the two sources — its naive Δfloor is an
+init-plus-data-order number, not an init-only one.
+
+
 ## Coverage — what ran and what did not
 
 | cell | n | status | what was measured |
 |---|---|---|---|
 | SET 1 · pythia-14m | 36/36 seed pairs | complete | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; barrier for M0 and M1-perm |
-| SET 1 · pythia-70m | 34/36 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; barrier for M0 and M1-perm |
-| SET 1 · pythia-160m | 16/36 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; barrier for M0 and M1-perm |
-| SET 4 · goldfish eng×X | 4/4 language pairs (nld_Latn, spa_Latn, ell_Grek, pol_Latn) | complete | M0 naive · M1a vocab-transport · M1b/c vocab+unit-aligned (perm/Procrustes) · M1d/e forced-residual · M1f unit-aligned only |
-| BLiMP accuracy · SET 1 (English) | pythia-14m: 36/36 pairs, pythia-70m: 9/36 pairs | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges as the Δfloor tables |
+| SET 1 · pythia-70m | 36/36 seed pairs | complete | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; barrier for M0 and M1-perm |
+| SET 1 · pythia-160m | 20/36 seed pairs | partial | M0 naive · M1 permutation · M1 Procrustes · M2 task-arithmetic · M3 TIES; barrier for M0 and M1-perm |
+| SET 4 · goldfish eng×X | 1/4 language pairs (nld_Latn) | partial | M0 naive · M1a vocab-transport · M1b/c vocab+unit-aligned (perm/Procrustes) · M1d/e forced-residual · M1f unit-aligned only |
+| BLiMP accuracy · SET 1 (English) | pythia-14m: 36/36 pairs, pythia-70m: 19/36 pairs | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges as the Δfloor tables |
 | MultiBLiMP / any accuracy benchmark · SET 4 (Goldfish) | 0 | **NOT RUN** | No multilingual benchmark harness was close to wired inside this window; deliberately not built from scratch. SET 4's numbers are likelihood only and say nothing about accuracy. |
 | B-GPT joint bilingual reference | 0 | **NOT RUN** | Out of window; the merged models are not compared against a jointly-trained bilingual ceiling. |
 | Goldfish 160m/other tiers, other language pairs | 0 | NOT RUN | Only the 1000mb tier and the four audit languages. |