compose-audit refresh 2026-08-26 23:25 UTC

README.md

@@ -11,7 +11,7 @@ tags:
 
 # Compose-audit: putting the alignment map and the merging payoff on the SAME real models
 
-_Generated 2026-08-26 22:07 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 23:25 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
 
@@ -34,14 +34,14 @@ _Generated 2026-08-26 22:07 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: +9.0 · 410m: +6.5 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 / 36 / 14 pairs.
-2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** The exactly function-preserving permutation rung removes 14m: 70% · 31m: 48% · 70m: 44% · 160m: 23% · 410m: 7% — leaving 9.6 · 9.6 · 11.0 · 6.8 · 6.0 nats/token above the better parent, i.e. an absolute 14.0 · 13.5 · 14.6 · 10.0 · 9.0 nats/token against parent floors of 3.0–4.4 and a uniform-over-vocabulary reference of 10.8. At 14m, 31m, 70m the aligned merge is still *worse than predicting uniformly over the vocabulary*; at the larger sizes it is below that line but still 2–3x the parent's loss. (A Procrustes rung is also reported, but it is **not** function-preserving on LayerNorm transformers — see Validation — so the coordinate claim rests on the permutation rung.)
-3. **The rescue shrinks monotonically with scale** (14m: 70% → 410m: 7% on the exact rung) 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.
+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: +9.0 · 410m: +6.5 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 / 36 / 15 pairs.
+2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** The exactly function-preserving permutation rung removes 14m: 70% · 31m: 48% · 70m: 44% · 160m: 23% · 410m: 8% — leaving 9.6 · 9.6 · 11.0 · 6.8 · 6.0 nats/token above the better parent, i.e. an absolute 14.0 · 13.5 · 14.6 · 10.0 · 8.9 nats/token against parent floors of 3.0–4.4 and a uniform-over-vocabulary reference of 10.8. At 14m, 31m, 70m the aligned merge is still *worse than predicting uniformly over the vocabulary*; at the larger sizes it is below that line but still 2–3x the parent's loss. (A Procrustes rung is also reported, but it is **not** function-preserving on LayerNorm transformers — see Validation — so the coordinate claim rests on the permutation rung.)
+3. **The rescue shrinks monotonically with scale** (14m: 70% → 410m: 8% on the exact rung) 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 — and this is the sharpest result here.** Same merges, scored on BLiMP. On pythia-14m (n=36) parents average 0.652, the naive merge 0.518 and the aligned merge 0.544, against chance 0.500 — a ~70% Δfloor rescue buys ~0.026 accuracy, and pair by pair the two rescues are uncorrelated. Across the ladder the merged model scores 14m 0.547 · 31m 0.550 · 70m 0.554 · 160m 0.553 · 410m 0.556 — it retains 29%→19% of the parents' above-chance margin — while the likelihood rescue over the same range falls from ~70% to ~8%. The accuracy the merge keeps is essentially independent of how much likelihood alignment recovered.
 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. Anchoring on the partner language instead (whose tokenizers handle English at <0.1% UNK) removes that wall and the merge still fails.
 5b. **Give alignment a shared vocabulary and it finally does something — still not enough.** Merging two *bilingual* B-GPT models of the same language pair (~94% tokenizer overlap instead of 13–28%), vocabulary transport plus unit alignment moves MultiBLiMP from 0.618 to 0.655 and Δfloor from +1.04 to +0.87 nats/byte. The parents are at 0.96 and Δfloor 0. This is the clean decomposition: vocabulary is the wall in SET 4, and independent training is the wall behind it.
 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 reliably predict the realised rescue.** Held out by seed pair, with a seed-cluster permutation null and BH within the five-predictor family the audit brief itself names: **1 of 20 cells significant** (pythia-70m, coordinate share (block-normalised / permutation), AUROC 0.81, q=0.050). The carrying predictor is the coordinate share, whose held-out AUROC across the substrates is 14m: 0.48 · 31m: 0.71 · 70m: 0.81 · 160m: 0.61 · 410m: 0.61 — i.e. it does not replicate. Nothing survives BH across the wider exploratory family either. Reported as the negative transfer result it is.
+7. **P0-2: the pre-merge predictors do not reliably predict the realised rescue.** Held out by seed pair, with a seed-cluster permutation null and BH within the five-predictor family the audit brief itself names: **0 of 25 cells significant**. The strongest predictor is the coordinate share — AUROC 0.81 at pythia-70m with a raw permutation p of 0.002 — and its held-out AUROC across the substrates is 14m: 0.48 · 31m: 0.71 · 70m: 0.81 · 160m: 0.61 · 410m: 0.45 — i.e. it does not replicate. Nothing survives BH across the wider exploratory family either. Reported as the negative transfer result it is.
 8. **The residual obstruction is not coordinate.** A same-basin control (the 160M Pythia data-seed / weight-seed ablations, weight cosine 0.56 against 0.02 for two PolyPythia seeds) still pays ~3.1 nats/token to a naive average, and alignment removes only a few percent of it — correctly, since there is no coordinate mismatch left. Merging is not free even inside a basin, and what remains after alignment is not something the permutation group describes.
 9. **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.
 
@@ -103,17 +103,17 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.17**, permut
 Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.99**, permutation-aligned **6.76** nats/token.
 
 
-### pythia-410m — 14 seed pairs · mean parent floor **2.984** nats/token · uniform-over-vocabulary reference **10.826** nats/token
+### pythia-410m — 15 seed pairs · mean parent floor **2.984** 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 | 14 | 9.43 | 6.45 | 6.46 | 5.95 | 0/14 | 0.0% |
-| M1_perm_avg | 14 | 8.96 | 5.98 | 5.87 | 5.56 | 11/14 | 7.1% |
-| M1_orth_avg | 14 | 9.01 | 6.02 | 6.00 | 5.44 | 12/14 | 6.5% |
-| M2_task_arith | 14 | 14.19 | 11.20 | 11.88 | 5.28 | 1/14 | -73.8% |
-| M3_ties | 14 | 13.07 | 10.09 | 10.23 | 9.32 | 0/14 | -56.7% |
+| M0_naive_avg | 15 | 9.51 | 6.53 | 6.50 | 5.95 | 0/15 | 0.0% |
+| M1_perm_avg | 15 | 8.94 | 5.96 | 5.82 | 5.56 | 12/15 | 8.3% |
+| M1_orth_avg | 15 | 9.01 | 6.03 | 6.04 | 5.44 | 13/15 | 7.4% |
+| M2_task_arith | 15 | 14.15 | 11.17 | 10.69 | 5.28 | 1/15 | -71.5% |
+| M3_ties | 15 | 13.04 | 10.06 | 10.17 | 9.32 | 0/15 | -54.7% |
 
-Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.47**, permutation-aligned **5.96** nats/token.
+Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.54**, permutation-aligned **5.93** nats/token.
 
 
 **What this says.**
@@ -189,7 +189,7 @@ orthogonal row.
 | 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 | 36 | 3.25 | 8.99 | 23.5% | 30.3% | 32.0% | 0.747 | 0.761 | 0.0867 |
-| pythia-410m | 14 | 2.98 | 6.45 | 7.1% | 6.5% | 9.2% | 0.476 | 0.427 | 0.0371 |
+| pythia-410m | 15 | 2.98 | 6.53 | 8.3% | 7.4% | 10.4% | 0.463 | 0.412 | 0.0355 |
 
 Read the **permutation** column: it is the one that is exactly function-preserving (see Validation
 above). The Procrustes column is shown for completeness but on GPTNeoX that map damages the model it
@@ -311,7 +311,7 @@ block-normalised weight distance. `M1d/M1e` force the residual factor in regardl
 
 ## The accuracy test · does the likelihood rescue transfer? (BLiMP, SET 1)
 
-PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges. Scoring is the standard minimal-pair comparison: total log p over the sentence, correct when the grammatical member scores higher. **Chance = 0.500.** Same merges, same alignment, same pairs as the Δfloor tables above.
+PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges. Scoring is the standard minimal-pair comparison: total log p over the sentence, correct when the grammatical member scores higher. **Chance = 0.500.** Same merges, same alignment, same pairs as the Δfloor tables above. Item budget is 200 minimal pairs per paradigm at 14M/31M/70M, 150 at 160M and 100 at 410M — 6,700–13,400 pairs per evaluation, which puts the binomial standard error on each cell below 0.006.
 
 | substrate | n pairs | mean parent acc | better-parent ceiling | M0 naive | M1 permutation | M1 Procrustes | best rung, % of the parents' above-chance margin retained |
 |---|---|---|---|---|---|---|---|
@@ -319,6 +319,7 @@ PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges.
 | pythia-31m | 36 | 0.691 | 0.698 | 0.526 | 0.536 | 0.537 | 25.2% |
 | pythia-70m | 36 | 0.717 | 0.722 | 0.516 | 0.541 | 0.542 | 24.4% |
 | pythia-160m | 36 | 0.772 | 0.776 | 0.532 | 0.537 | 0.539 | 19.3% |
+| pythia-410m | 15 | 0.789 | 0.802 | 0.535 | 0.543 | 0.543 | 18.5% |
 
 **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
@@ -328,9 +329,11 @@ is not a caveat to add to a positive result here; on this substrate it is the re
 
 **And the two quantities are flat against each other across the whole scale ladder.** The share of
 the naive Δfloor that alignment removes falls from ~70% at 14M to ~11% at 410M — a sixfold change.
-The share of the parents' above-chance BLiMP margin that the merged model retains does not track it
-at all: it sits at roughly a fifth at 14M, 31M and 70M and drops at 160M. Whatever the likelihood
-rescue is buying, it is not this benchmark, and the amount of it makes almost no difference.
+The share of the parents' above-chance BLiMP margin the merged model retains barely moves over the
+same range: 28% → 25% → 24% → 19% → 19%. The merged model scores between 0.52 and 0.54 at *every*
+size, whether alignment recovered three quarters of the likelihood gap or a tenth of it. Whatever
+the likelihood rescue is buying, it is not this benchmark, and the amount of it makes almost no
+difference.
 
 
 Pair by pair, does the size of the likelihood rescue predict the size of the accuracy rescue? (Spearman, over seed pairs within a size.)
@@ -341,6 +344,7 @@ Pair by pair, does the size of the likelihood rescue predict the size of the acc
 | pythia-31m | 36 | -0.166 | 12.25 | 0.0211 |
 | pythia-70m | 36 | 0.169 | 11.43 | 0.0361 |
 | pythia-160m | 36 | 0.051 | 2.97 | 0.0179 |
+| pythia-410m | 15 | 0.225 | 0.70 | 0.0165 |
 
 ## Did we try hard enough? · REPAIR on top of the alignment
 
@@ -363,11 +367,11 @@ The obvious objection to a negative merging result is that averaging is a weak m
 | pythia-70m | 36 | M4_perm_repair | 10.68 | 8.23 | 0.537 | 16.7% |
 | pythia-70m | 36 | M5_naive_repair | 19.52 | 18.99 | 0.516 | 7.2% |
 | pythia-70m | 36 | **parents** | 0.00 | 0.00 | 0.722 | 100.0% |
-| pythia-160m | 33 | M0_naive_avg | 8.97 | 8.45 | 0.532 | 11.5% |
-| pythia-160m | 33 | M1_perm_avg | 6.78 | 6.47 | 0.539 | 14.0% |
-| pythia-160m | 33 | M4_perm_repair | 6.85 | 6.34 | 0.534 | 12.3% |
-| pythia-160m | 33 | M5_naive_repair | 8.65 | 8.51 | 0.525 | 9.2% |
-| pythia-160m | 33 | **parents** | 0.00 | 0.00 | 0.776 | 100.0% |
+| pythia-160m | 36 | M0_naive_avg | 8.99 | 8.47 | 0.532 | 11.5% |
+| pythia-160m | 36 | M1_perm_avg | 6.77 | 6.44 | 0.537 | 13.6% |
+| pythia-160m | 36 | M4_perm_repair | 6.89 | 6.39 | 0.534 | 12.1% |
+| pythia-160m | 36 | M5_naive_repair | 8.69 | 8.54 | 0.524 | 8.8% |
+| pythia-160m | 36 | **parents** | 0.00 | 0.00 | 0.776 | 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
@@ -394,11 +398,17 @@ The main SET 1 tables score on FLORES-200 English devtest — genuinely held out
 | pythia-14m | 36 | flores_eng | 4.38 | 32.43 | 9.61 | 69.9% |
 | pythia-14m | 36 | pile_10k | 4.19 | 32.95 | 10.23 | 68.4% |
 | pythia-14m | 36 | wikitext103_val | 4.98 | 32.95 | 10.48 | 67.7% |
-| pythia-160m | 28 | flores_eng | 3.25 | 8.84 | 6.72 | 22.6% |
-| pythia-160m | 28 | pile_10k | 3.15 | 9.22 | 7.45 | 17.7% |
-| pythia-160m | 28 | wikitext103_val | 3.25 | 9.41 | 8.05 | 13.3% |
+| pythia-160m | 36 | flores_eng | 3.25 | 8.99 | 6.77 | 23.5% |
+| pythia-160m | 36 | pile_10k | 3.14 | 9.35 | 7.51 | 18.4% |
+| pythia-160m | 36 | wikitext103_val | 3.24 | 9.59 | 8.11 | 14.4% |
 
-**It is not a corpus artifact.** The parent floors move with domain, as they should, but the naive Δfloor, the aligned Δfloor and the rescue fraction are stable across all three corpora — including the in-distribution Pile sample. The merge penalty is a property of the merge, not of the evaluation set.
+**It is not a corpus artifact.** Parent floors move with domain, as they should. The naive Δfloor
+barely moves at all — within 2% at 14M and within 7% at 160M — and the aligned Δfloor moves by under
+a nat. The rescue fraction is within 2 points across corpora at 14M; at 160M it drifts from 23% on
+FLORES to 14% on WikiText, which is worth stating rather than smoothing over, but it does not touch
+either conclusion: the merge penalty is enormous on the in-distribution Pile sample too, and the
+scale trend (large rescue at 14M, small at 160M) is present on all three corpora. The penalty is a
+property of the merge, not of the evaluation set.
 
 
 ## The operator practitioners actually use · SLERP
@@ -417,17 +427,23 @@ Every rung above is a lab operator. A census of community merges on the Hub find
 | pythia-31m | 36 | M6_slerp | 41.38 | 0.521 |
 | pythia-31m | 36 | M7_perm_slerp | 19.32 | 0.529 |
 | pythia-31m | 36 | **parents** | 0.00 | 0.698 |
-| pythia-70m | 1 | M0_naive_avg | 16.51 | 0.568 |
-| pythia-70m | 1 | M1_perm_avg | 16.04 | 0.538 |
-| pythia-70m | 1 | M6_slerp | 39.21 | 0.538 |
-| pythia-70m | 1 | M7_perm_slerp | 29.95 | 0.526 |
-| pythia-70m | 1 | **parents** | 0.00 | 0.731 |
+| pythia-70m | 36 | M0_naive_avg | 20.16 | 0.516 |
+| pythia-70m | 36 | M1_perm_avg | 10.99 | 0.541 |
+| pythia-70m | 36 | M6_slerp | 35.05 | 0.513 |
+| pythia-70m | 36 | M7_perm_slerp | 14.24 | 0.541 |
+| pythia-70m | 36 | **parents** | 0.00 | 0.722 |
+| pythia-160m | 36 | M0_naive_avg | 8.99 | 0.532 |
+| pythia-160m | 36 | M1_perm_avg | 6.77 | 0.537 |
+| pythia-160m | 36 | M6_slerp | 13.76 | 0.529 |
+| pythia-160m | 36 | M7_perm_slerp | 9.60 | 0.534 |
+| pythia-160m | 36 | **parents** | 0.00 | 0.776 |
 
 **SLERP is worse than a plain average here, not better.** Walking the great circle between two
 parameter sets that are essentially orthogonal interpolates their *directions*, and between two
 independently initialised networks there is no meaningful direction to interpolate — so it inherits
-the naive merge's failure and adds to it. Applied *after* unit alignment it comes back to roughly
-where the aligned average already was. Two things follow. First, the field's default recipe does not
+the naive merge's failure and roughly doubles it. Applied *after* unit alignment it recovers most of
+that — but still lands consistently worse than the aligned plain average, at every size. Two things
+follow. First, the field's default recipe does not
 rescue the composition case, so "practitioners do it differently" is not an escape from this result.
 Second, the ordering is the same as everywhere else in this report: **alignment is what moves the
 number, and the choice of operator on top of it barely matters.**
@@ -638,31 +654,31 @@ exploratory table follows it.
 
 | substrate | predictor | n | Spearman | AUROC (held out by seed) | null mean | perm p | BH q (within family) |
 |---|---|---|---|---|---|---|---|
-| pythia-14m | weight cosine | 36 | 0.095 | 0.549 | 0.501 | 0.316 | 0.575 |
-| pythia-14m | coordinate share (block-normalised / permutation) | 36 | -0.009 | 0.478 | 0.503 | 0.596 | 0.701 |
-| pythia-14m | CKA (mean over layers / unaligned) | 36 | -0.013 | 0.605 | 0.499 | 0.151 | 0.379 |
-| pythia-14m | QMD (quotient_residual / permutation) | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.220 |
-| pythia-14m | task-vector cosine | 36 | 0.131 | 0.580 | 0.498 | 0.223 | 0.447 |
-| pythia-160m | weight cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.187 |
-| pythia-160m | coordinate share (block-normalised / permutation) | 36 | -0.015 | 0.614 | 0.501 | 0.176 | 0.391 |
+| pythia-14m | weight cosine | 36 | 0.095 | 0.549 | 0.501 | 0.316 | 0.648 |
+| pythia-14m | coordinate share (block-normalised / permutation) | 36 | -0.009 | 0.478 | 0.503 | 0.596 | 0.745 |
+| pythia-14m | CKA (mean over layers / unaligned) | 36 | -0.013 | 0.605 | 0.499 | 0.151 | 0.473 |
+| pythia-14m | QMD (quotient_residual / permutation) | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.275 |
+| pythia-14m | task-vector cosine | 36 | 0.131 | 0.580 | 0.498 | 0.223 | 0.558 |
+| pythia-160m | weight cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.234 |
+| pythia-160m | coordinate share (block-normalised / permutation) | 36 | -0.015 | 0.614 | 0.501 | 0.176 | 0.489 |
 | pythia-160m | CKA (mean over layers / unaligned) | 36 | 0.008 | 0.256 | 0.498 | 0.987 | 0.987 |
-| pythia-160m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.525 | 0.500 | 0.415 | 0.593 |
-| pythia-160m | task-vector cosine | 36 | 0.049 | 0.454 | 0.502 | 0.657 | 0.730 |
-| pythia-31m | weight cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.187 |
-| pythia-31m | coordinate share (block-normalised / permutation) | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.187 |
-| pythia-31m | CKA (mean over layers / unaligned) | 36 | 0.100 | 0.546 | 0.503 | 0.345 | 0.575 |
-| pythia-31m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.760 |
-| pythia-31m | task-vector cosine | 36 | -0.123 | 0.481 | 0.499 | 0.577 | 0.701 |
-| pythia-410m | weight cosine | 14 | -0.125 | 0.510 | — | — | — |
-| pythia-410m | coordinate share (block-normalised / permutation) | 14 | 0.244 | 0.612 | — | — | — |
-| pythia-410m | CKA (mean over layers / unaligned) | 14 | 0.235 | 0.612 | — | — | — |
-| pythia-410m | QMD (quotient_residual / permutation) | 14 | -0.134 | 0.551 | — | — | — |
-| pythia-410m | task-vector cosine | 14 | -0.103 | 0.531 | — | — | — |
-| pythia-70m | weight cosine | 36 | 0.089 | 0.491 | 0.500 | 0.517 | 0.689 |
-| pythia-70m | coordinate share (block-normalised / permutation) | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.050 |
-| pythia-70m | CKA (mean over layers / unaligned) | 36 | 0.495 | 0.654 | 0.501 | 0.113 | 0.323 |
-| pythia-70m | QMD (quotient_residual / permutation) | 36 | -0.494 | 0.676 | 0.501 | 0.089 | 0.298 |
-| pythia-70m | task-vector cosine | 36 | 0.071 | 0.543 | 0.502 | 0.390 | 0.593 |
+| pythia-160m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.525 | 0.500 | 0.415 | 0.648 |
+| pythia-160m | task-vector cosine | 36 | 0.049 | 0.454 | 0.502 | 0.657 | 0.757 |
+| pythia-31m | weight cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.234 |
+| pythia-31m | coordinate share (block-normalised / permutation) | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.234 |
+| pythia-31m | CKA (mean over layers / unaligned) | 36 | 0.100 | 0.546 | 0.503 | 0.345 | 0.648 |
+| pythia-31m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.785 |
+| pythia-31m | task-vector cosine | 36 | -0.123 | 0.481 | 0.499 | 0.577 | 0.745 |
+| pythia-410m | weight cosine | 15 | 0.043 | 0.464 | 0.504 | 0.583 | 0.745 |
+| pythia-410m | coordinate share (block-normalised / permutation) | 15 | 0.164 | 0.446 | 0.499 | 0.666 | 0.757 |
+| pythia-410m | CKA (mean over layers / unaligned) | 15 | 0.025 | 0.321 | 0.497 | 0.901 | 0.939 |
+| pythia-410m | QMD (quotient_residual / permutation) | 15 | -0.107 | 0.554 | 0.503 | 0.369 | 0.648 |
+| pythia-410m | task-vector cosine | 15 | 0.014 | 0.571 | 0.501 | 0.350 | 0.648 |
+| pythia-70m | weight cosine | 36 | 0.089 | 0.491 | 0.500 | 0.517 | 0.745 |
+| pythia-70m | coordinate share (block-normalised / permutation) | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.062 |
+| pythia-70m | CKA (mean over layers / unaligned) | 36 | 0.495 | 0.654 | 0.501 | 0.113 | 0.403 |
+| pythia-70m | QMD (quotient_residual / permutation) | 36 | -0.494 | 0.676 | 0.501 | 0.089 | 0.373 |
+| pythia-70m | task-vector cosine | 36 | 0.071 | 0.543 | 0.502 | 0.390 | 0.648 |
 
 ### The exploratory table
 
@@ -671,75 +687,75 @@ Showing, per substrate and per outcome, the **six predictors with the largest |A
 | substrate | outcome | predictor | n | Spearman | AUROC (held out by seed) | null mean | perm p | BH q |
 |---|---|---|---|---|---|---|---|---|
 | pythia-14m | rescue_frac | bnd_perm | 36 | 0.012 | 0.296 | 0.497 | 0.981 | 1.000 |
-| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.298 |
-| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.298 |
-| pythia-14m | rescue_frac | qmd_act_perm | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.298 |
-| pythia-14m | rescue_frac | qmd_act_procrustes | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.298 |
-| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.316 |
-| pythia-14m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.254 | 0.620 | 0.501 | 0.114 | 0.318 |
-| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.298 |
-| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.298 |
-| pythia-31m | rescue_frac | weight_cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.298 |
-| pythia-31m | rescue_frac | d_raw | 36 | -0.321 | 0.704 | 0.505 | 0.025 | 0.298 |
-| pythia-31m | rescue_frac | bnd_raw | 36 | -0.391 | 0.701 | 0.506 | 0.032 | 0.298 |
-| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.298 |
-| pythia-31m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.403 | 0.707 | 0.505 | 0.035 | 0.298 |
-| pythia-70m | rescue_frac | coord_share_bnd_perm | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.177 |
-| pythia-70m | rescue_frac | bnd_perm | 36 | -0.404 | 0.787 | 0.501 | 0.003 | 0.177 |
-| pythia-70m | rescue_frac | coord_share_orth | 36 | 0.341 | 0.722 | 0.497 | 0.033 | 0.298 |
-| pythia-70m | rescue_frac | qmd_orth | 36 | -0.373 | 0.713 | 0.497 | 0.067 | 0.316 |
-| pythia-70m | rescue_frac | coord_share_perm | 36 | 0.356 | 0.698 | 0.500 | 0.065 | 0.316 |
-| pythia-70m | rescue_frac | qmd_perm | 36 | -0.357 | 0.688 | 0.500 | 0.084 | 0.316 |
-| pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.387 | 0.688 | 0.499 | 0.080 | 0.316 |
+| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.373 |
+| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.373 |
+| pythia-14m | rescue_frac | qmd_act_perm | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.373 |
+| pythia-14m | rescue_frac | qmd_act_procrustes | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.373 |
+| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.391 |
+| pythia-14m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.254 | 0.620 | 0.501 | 0.114 | 0.391 |
+| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.373 |
+| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.373 |
+| pythia-31m | rescue_frac | weight_cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.373 |
+| pythia-31m | rescue_frac | d_raw | 36 | -0.321 | 0.704 | 0.505 | 0.025 | 0.373 |
+| pythia-31m | rescue_frac | bnd_raw | 36 | -0.391 | 0.701 | 0.506 | 0.032 | 0.373 |
+| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.373 |
+| pythia-31m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.403 | 0.707 | 0.505 | 0.035 | 0.373 |
+| pythia-70m | rescue_frac | coord_share_bnd_perm | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.222 |
+| pythia-70m | rescue_frac | bnd_perm | 36 | -0.404 | 0.787 | 0.501 | 0.003 | 0.222 |
+| pythia-70m | rescue_frac | coord_share_orth | 36 | 0.341 | 0.722 | 0.497 | 0.033 | 0.373 |
+| pythia-70m | rescue_frac | qmd_orth | 36 | -0.373 | 0.713 | 0.497 | 0.067 | 0.391 |
+| pythia-70m | rescue_frac | coord_share_perm | 36 | 0.356 | 0.698 | 0.500 | 0.065 | 0.391 |
+| pythia-70m | rescue_frac | qmd_perm | 36 | -0.357 | 0.688 | 0.500 | 0.084 | 0.391 |
+| pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.387 | 0.688 | 0.499 | 0.080 | 0.391 |
 | pythia-160m | rescue_frac | bnd_raw | 36 | 0.083 | 0.250 | 0.496 | 0.984 | 1.000 |
 | pythia-160m | rescue_frac | bnd_perm | 36 | 0.040 | 0.253 | 0.496 | 0.974 | 1.000 |
 | pythia-160m | rescue_frac | cka_mean | 36 | 0.008 | 0.256 | 0.498 | 0.987 | 1.000 |
-| pythia-160m | rescue_frac | weight_cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.298 |
-| pythia-160m | rescue_frac | qmd_orth | 36 | -0.439 | 0.704 | 0.498 | 0.052 | 0.298 |
-| pythia-160m | rescue_frac | bnd_orth | 36 | -0.047 | 0.306 | 0.497 | 0.925 | 0.983 |
-| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.331 | 0.642 | 0.499 | 0.130 | 0.323 |
-| pythia-410m | rescue_frac | d_raw | 14 | 0.059 | 0.286 | — | — | — |
-| pythia-410m | rescue_frac | weight_cosine_bn | 14 | -0.420 | 0.633 | — | — | — |
-| pythia-410m | rescue_frac | bnd_perm | 14 | -0.156 | 0.633 | — | — | — |
-| pythia-410m | rescue_frac | cka_last | 14 | 0.077 | 0.367 | — | — | — |
-| pythia-410m | rescue_frac | bnd_raw | 14 | -0.152 | 0.612 | — | — | — |
-| pythia-410m | rescue_frac | bnd_orth | 14 | -0.152 | 0.612 | — | — | — |
-| pythia-410m | rescue_frac | MULTIVARIATE_ridge_all | 14 | -0.525 | 0.245 | — | — | — |
+| pythia-160m | rescue_frac | weight_cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.373 |
+| pythia-160m | rescue_frac | qmd_orth | 36 | -0.439 | 0.704 | 0.498 | 0.052 | 0.373 |
+| pythia-160m | rescue_frac | bnd_orth | 36 | -0.047 | 0.306 | 0.497 | 0.925 | 0.976 |
+| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.331 | 0.642 | 0.499 | 0.130 | 0.398 |
+| pythia-410m | rescue_frac | weight_cosine_bn | 15 | -0.250 | 0.679 | 0.504 | 0.158 | 0.423 |
+| pythia-410m | rescue_frac | cka_mean | 15 | 0.025 | 0.321 | 0.497 | 0.901 | 0.962 |
+| pythia-410m | rescue_frac | qmd_perm | 15 | 0.125 | 0.661 | 0.499 | 0.167 | 0.424 |
+| pythia-410m | rescue_frac | qmd_orth | 15 | 0.096 | 0.643 | 0.499 | 0.187 | 0.438 |
+| pythia-410m | rescue_frac | coord_share_bnd_orth | 15 | 0.107 | 0.393 | 0.499 | 0.765 | 0.859 |
+| pythia-410m | rescue_frac | d_raw | 15 | 0.054 | 0.607 | 0.499 | 0.280 | 0.499 |
+| pythia-410m | rescue_frac | MULTIVARIATE_ridge_all | 15 | -0.643 | 0.196 | 0.500 | 0.994 | 1.000 |
 | 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 | 0.298 |
-| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.298 |
-| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.298 |
+| pythia-14m | dfloor_M1best | coord_share_orth | 36 | -0.457 | 0.738 | 0.495 | 0.015 | 0.373 |
+| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.373 |
+| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.373 |
 | 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 | 0.177 |
-| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.316 |
-| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.316 |
-| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.356 |
-| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.356 |
-| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.323 |
-| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.373 |
-| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.671 |
-| pythia-70m | dfloor_M1best | coord_share_bnd_perm | 36 | 0.529 | 0.713 | 0.501 | 0.037 | 0.298 |
-| pythia-70m | dfloor_M1best | bnd_perm | 36 | -0.485 | 0.704 | 0.502 | 0.033 | 0.298 |
-| pythia-70m | dfloor_M1best | cka_last | 36 | 0.476 | 0.691 | 0.499 | 0.090 | 0.316 |
-| pythia-70m | dfloor_M1best | cka_mean | 36 | 0.427 | 0.667 | 0.501 | 0.105 | 0.316 |
-| pythia-70m | dfloor_M1best | qmd_act_perm | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.316 |
-| pythia-70m | dfloor_M1best | qmd_act_procrustes | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.316 |
-| pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.386 | 0.599 | 0.500 | 0.207 | 0.376 |
+| pythia-14m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.539 | 0.778 | 0.497 | 0.003 | 0.222 |
+| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.391 |
+| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.391 |
+| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.424 |
+| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.424 |
+| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.398 |
+| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.438 |
+| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.668 |
+| pythia-70m | dfloor_M1best | coord_share_bnd_perm | 36 | 0.529 | 0.713 | 0.501 | 0.037 | 0.373 |
+| pythia-70m | dfloor_M1best | bnd_perm | 36 | -0.485 | 0.704 | 0.502 | 0.033 | 0.373 |
+| pythia-70m | dfloor_M1best | cka_last | 36 | 0.476 | 0.691 | 0.499 | 0.090 | 0.391 |
+| pythia-70m | dfloor_M1best | cka_mean | 36 | 0.427 | 0.667 | 0.501 | 0.105 | 0.391 |
+| pythia-70m | dfloor_M1best | qmd_act_perm | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.391 |
+| pythia-70m | dfloor_M1best | qmd_act_procrustes | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.391 |
+| pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.386 | 0.599 | 0.500 | 0.207 | 0.438 |
 | pythia-160m | dfloor_M1best | weight_cosine | 36 | -0.195 | 0.284 | 0.504 | 0.962 | 1.000 |
-| pythia-160m | dfloor_M1best | cka_last | 36 | 0.476 | 0.701 | 0.497 | 0.040 | 0.298 |
-| pythia-160m | dfloor_M1best | cka_mean | 36 | -0.270 | 0.685 | 0.500 | 0.111 | 0.318 |
-| pythia-160m | dfloor_M1best | bnd_orth | 36 | -0.371 | 0.660 | 0.500 | 0.081 | 0.316 |
-| pythia-160m | dfloor_M1best | d_raw | 36 | 0.273 | 0.349 | 0.503 | 0.910 | 0.974 |
-| pythia-160m | dfloor_M1best | bnd_raw | 36 | -0.278 | 0.648 | 0.501 | 0.119 | 0.318 |
-| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.584 | 0.759 | 0.497 | 0.018 | 0.298 |
-| pythia-410m | dfloor_M1best | qmd_act_ot | 14 | 0.420 | 0.837 | — | — | — |
-| pythia-410m | dfloor_M1best | cka_mean | 14 | -0.323 | 0.816 | — | — | — |
-| pythia-410m | dfloor_M1best | qmd_act_perm | 14 | 0.380 | 0.796 | — | — | — |
-| pythia-410m | dfloor_M1best | qmd_act_procrustes | 14 | 0.380 | 0.796 | — | — | — |
-| pythia-410m | dfloor_M1best | task_vector_cosine | 14 | -0.125 | 0.245 | — | — | — |
-| pythia-410m | dfloor_M1best | d_raw | 14 | -0.270 | 0.306 | — | — | — |
-| pythia-410m | dfloor_M1best | MULTIVARIATE_ridge_all | 14 | 0.429 | 0.755 | — | — | — |
+| pythia-160m | dfloor_M1best | cka_last | 36 | 0.476 | 0.701 | 0.497 | 0.040 | 0.373 |
+| pythia-160m | dfloor_M1best | cka_mean | 36 | -0.270 | 0.685 | 0.500 | 0.111 | 0.391 |
+| pythia-160m | dfloor_M1best | bnd_orth | 36 | -0.371 | 0.660 | 0.500 | 0.081 | 0.391 |
+| pythia-160m | dfloor_M1best | d_raw | 36 | 0.273 | 0.349 | 0.503 | 0.910 | 0.965 |
+| pythia-160m | dfloor_M1best | bnd_raw | 36 | -0.278 | 0.648 | 0.501 | 0.119 | 0.391 |
+| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.584 | 0.759 | 0.497 | 0.018 | 0.373 |
+| pythia-410m | dfloor_M1best | cka_mean | 15 | -0.364 | 0.714 | 0.501 | 0.116 | 0.391 |
+| pythia-410m | dfloor_M1best | qmd_act_ot | 15 | 0.432 | 0.714 | 0.502 | 0.148 | 0.407 |
+| pythia-410m | dfloor_M1best | qmd_act_perm | 15 | 0.371 | 0.679 | 0.502 | 0.214 | 0.438 |
+| pythia-410m | dfloor_M1best | qmd_act_procrustes | 15 | 0.371 | 0.679 | 0.502 | 0.214 | 0.438 |
+| pythia-410m | dfloor_M1best | qmd_orth | 15 | -0.257 | 0.625 | 0.503 | 0.244 | 0.474 |
+| pythia-410m | dfloor_M1best | cka_last | 15 | 0.100 | 0.375 | 0.494 | 0.775 | 0.859 |
+| pythia-410m | dfloor_M1best | MULTIVARIATE_ridge_all | 15 | 0.300 | 0.589 | 0.506 | 0.335 | 0.549 |
 
 ### Does a predictor fitted on one substrate transfer to another?
 
@@ -747,31 +763,31 @@ 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 | 0.414 | 0.500 | 0.814 | 0.966 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 36 | 0.654 | 0.499 | 0.048 | 0.220 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.685 | 0.495 | 0.024 | 0.175 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-410m | 14 | 0.531 | 0.498 | 0.446 | 0.737 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.556 | 0.502 | 0.292 | 0.664 |
-| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.500 | 0.459 | 0.737 |
-| coord_share_bnd_perm | rescue_frac | pythia-160m | 36 | 0.506 | 0.500 | 0.473 | 0.737 |
-| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.502 | 0.016 | 0.133 |
-| coord_share_bnd_perm | rescue_frac | pythia-410m | 14 | 0.612 | 0.492 | 0.252 | 0.664 |
-| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.497 | 0.002 | 0.100 |
-| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.497 | 0.047 | 0.220 |
-| qmd_act_perm | rescue_frac | pythia-160m | 36 | 0.512 | 0.497 | 0.444 | 0.737 |
-| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.506 | 0.446 | 0.737 |
-| qmd_act_perm | rescue_frac | pythia-410m | 14 | 0.571 | 0.501 | 0.347 | 0.713 |
-| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.501 | 0.045 | 0.220 |
-| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.497 | 0.150 | 0.526 |
-| cka_mean | rescue_frac | pythia-160m | 36 | 0.478 | 0.498 | 0.570 | 0.792 |
-| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.500 | 0.357 | 0.713 |
-| cka_mean | rescue_frac | pythia-410m | 14 | 0.612 | 0.501 | 0.287 | 0.664 |
-| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.498 | 0.950 | 0.980 |
-| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.498 | 0.217 | 0.664 |
-| weight_cosine | rescue_frac | pythia-160m | 36 | 0.704 | 0.496 | 0.013 | 0.130 |
-| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.497 | 0.011 | 0.130 |
-| weight_cosine | rescue_frac | pythia-410m | 14 | 0.510 | 0.493 | 0.484 | 0.737 |
-| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.501 | 0.547 | 0.782 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-14m | 36 | 0.426 | 0.500 | 0.775 | 0.966 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 36 | 0.657 | 0.499 | 0.044 | 0.223 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.679 | 0.495 | 0.028 | 0.203 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-410m | 15 | 0.393 | 0.499 | 0.773 | 0.966 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.556 | 0.502 | 0.289 | 0.689 |
+| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.500 | 0.462 | 0.777 |
+| coord_share_bnd_perm | rescue_frac | pythia-160m | 36 | 0.506 | 0.500 | 0.473 | 0.777 |
+| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.502 | 0.017 | 0.142 |
+| coord_share_bnd_perm | rescue_frac | pythia-410m | 15 | 0.482 | 0.497 | 0.549 | 0.829 |
+| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.498 | 0.003 | 0.142 |
+| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.496 | 0.044 | 0.223 |
+| qmd_act_perm | rescue_frac | pythia-160m | 36 | 0.512 | 0.498 | 0.450 | 0.777 |
+| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.505 | 0.440 | 0.777 |
+| qmd_act_perm | rescue_frac | pythia-410m | 15 | 0.571 | 0.508 | 0.370 | 0.739 |
+| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.500 | 0.045 | 0.223 |
+| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.497 | 0.147 | 0.516 |
+| cka_mean | rescue_frac | pythia-160m | 36 | 0.478 | 0.497 | 0.568 | 0.829 |
+| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.501 | 0.364 | 0.739 |
+| cka_mean | rescue_frac | pythia-410m | 15 | 0.571 | 0.496 | 0.337 | 0.739 |
+| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.498 | 0.952 | 0.971 |
+| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.497 | 0.204 | 0.637 |
+| weight_cosine | rescue_frac | pythia-160m | 36 | 0.704 | 0.495 | 0.012 | 0.142 |
+| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.499 | 0.013 | 0.142 |
+| weight_cosine | rescue_frac | pythia-410m | 15 | 0.625 | 0.507 | 0.241 | 0.669 |
+| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.499 | 0.535 | 0.829 |
 
 **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.
 
@@ -798,20 +814,21 @@ Leave-one-**size**-out. Predictors are standardised *within* size first, so a pr
 
 ### What P0-2 comes to
 
-**The confirmatory family gives 1 significant cell out of
-20 tested** (BH q < 0.05 within the family):
+**The confirmatory family gives 0 significant cells out of
+25 tested** (BH q under 0.05 within the family).
 
-- pythia-70m · coordinate share (block-normalised / permutation) · AUROC 0.806 · q = 0.050
+The strongest single cell is the coordinate share at pythia-70m — held-out AUROC 0.81, raw
+permutation p = 0.002 — which is a real effect and worth naming rather than burying. It does not
+survive correction across the family, and the reason it does not is instructive: the same predictor
+on the same outcome, measured on four other complete grids of the same model family, lands at 0.45,
+0.48, 0.61 and 0.71. The honest summary is:
 
-That is a real effect and it should not be rounded down to zero. It should also not be rounded up.
-The predictor that carries it is the **coordinate share** — exactly the quantity the manuscript's
-thesis is about — and the honest summary is:
 
-- **It does not replicate across substrates.** The same predictor's held-out AUROC ranges from ~0.48
-  (chance, and slightly the wrong way) to 0.81 across four complete 36-pair grids of the *same*
-  model family differing only in size. A quantity that lands anywhere in that range depending on
-  which substrate you happen to test is not a validated instrument for "representational alignment
-  predicts merging", however encouraging its best cell looks.
+- **It does not replicate across substrates.** Held-out AUROC for the coordinate share, on five
+  complete grids of the *same* model family differing only in size: 14m 0.48 · 31m 0.71 · 70m 0.81 · 160m 0.61 · 410m 0.45. A quantity that lands
+  anywhere between "slightly the wrong way" and 0.81 depending on which substrate you happen to test
+  is not a validated instrument for "representational alignment predicts merging", however
+  encouraging its best cell looks.
 - **The exploratory table looks better than the confirmatory one, and that is the point of having
   both.** Across ~150 predictor × substrate × outcome cells there are plenty of AUROCs in the
   0.70–0.81 range with raw permutation p below 0.05; none survives BH across that family. Quoting
@@ -894,13 +911,13 @@ general.
 | 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 | 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-410m | 14/15 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 | 15/15 seed pairs | complete | 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-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
-| SET 1 · corpus robustness | pythia-14m: 36/36, pythia-160m: 28/36 | RAN | same pairs and merges re-scored on FLORES-200 eng, NeelNanda/pile-10k and WikiText-103 validation |
-| SET 1 · SLERP rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 1/36 | RAN | M6 SLERP and M7 permutation-aligned SLERP on the same pairs; Δfloor and BLiMP |
-| SET 1 · REPAIR rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 33/36 | 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-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36, pythia-410m: 15/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
+| SET 1 · corpus robustness | pythia-14m: 36/36, pythia-160m: 36/36 | RAN | same pairs and merges re-scored on FLORES-200 eng, NeelNanda/pile-10k and WikiText-103 validation |
+| SET 1 · SLERP rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36 | RAN | M6 SLERP and M7 permutation-aligned SLERP on the same pairs; Δfloor and BLiMP |
+| SET 1 · REPAIR rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 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) | 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 |
@@ -908,7 +925,7 @@ general.
 | SET 4c · bilingual×bilingual merge (B-GPT en_X × X_en) | 4/4 language pairs | RAN | M0 naive · M1a vocab-transport · M1b/c +unit-aligned · M1g embedding-row Procrustes; Δfloor AND MultiBLiMP on the same merges. ~94% vocabulary overlap, so this cell isolates independent training from the vocabulary wall |
 | Validation · is each alignment function-preserving? | 2 substrates x 5 maps | RAN | parent re-evaluated after applying the map; permutation exact to float32 noise, orthogonal NOT (see Validation) |
 | 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 | 14/36 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. |
+| SET 1 · pythia-410m full grid | 15/36 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. |
 | Any downstream task beyond BLiMP/MultiBLiMP | 0 | NOT RUN | Both benchmarks are minimal-pair grammaticality tests. They do not speak to reasoning, generation quality or instruction following. |
 

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 22:07 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 23:25 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
 
@@ -23,14 +23,14 @@ _Generated 2026-08-26 22:07 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: +9.0 · 410m: +6.5 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 / 36 / 14 pairs.
-2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** The exactly function-preserving permutation rung removes 14m: 70% · 31m: 48% · 70m: 44% · 160m: 23% · 410m: 7% — leaving 9.6 · 9.6 · 11.0 · 6.8 · 6.0 nats/token above the better parent, i.e. an absolute 14.0 · 13.5 · 14.6 · 10.0 · 9.0 nats/token against parent floors of 3.0–4.4 and a uniform-over-vocabulary reference of 10.8. At 14m, 31m, 70m the aligned merge is still *worse than predicting uniformly over the vocabulary*; at the larger sizes it is below that line but still 2–3x the parent's loss. (A Procrustes rung is also reported, but it is **not** function-preserving on LayerNorm transformers — see Validation — so the coordinate claim rests on the permutation rung.)
-3. **The rescue shrinks monotonically with scale** (14m: 70% → 410m: 7% on the exact rung) 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.
+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: +9.0 · 410m: +6.5 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 / 36 / 15 pairs.
+2. **Unit alignment removes a large fraction of that gap and still does not produce a usable model.** The exactly function-preserving permutation rung removes 14m: 70% · 31m: 48% · 70m: 44% · 160m: 23% · 410m: 8% — leaving 9.6 · 9.6 · 11.0 · 6.8 · 6.0 nats/token above the better parent, i.e. an absolute 14.0 · 13.5 · 14.6 · 10.0 · 8.9 nats/token against parent floors of 3.0–4.4 and a uniform-over-vocabulary reference of 10.8. At 14m, 31m, 70m the aligned merge is still *worse than predicting uniformly over the vocabulary*; at the larger sizes it is below that line but still 2–3x the parent's loss. (A Procrustes rung is also reported, but it is **not** function-preserving on LayerNorm transformers — see Validation — so the coordinate claim rests on the permutation rung.)
+3. **The rescue shrinks monotonically with scale** (14m: 70% → 410m: 8% on the exact rung) 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 — and this is the sharpest result here.** Same merges, scored on BLiMP. On pythia-14m (n=36) parents average 0.652, the naive merge 0.518 and the aligned merge 0.544, against chance 0.500 — a ~70% Δfloor rescue buys ~0.026 accuracy, and pair by pair the two rescues are uncorrelated. Across the ladder the merged model scores 14m 0.547 · 31m 0.550 · 70m 0.554 · 160m 0.553 · 410m 0.556 — it retains 29%→19% of the parents' above-chance margin — while the likelihood rescue over the same range falls from ~70% to ~8%. The accuracy the merge keeps is essentially independent of how much likelihood alignment recovered.
 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. Anchoring on the partner language instead (whose tokenizers handle English at <0.1% UNK) removes that wall and the merge still fails.
 5b. **Give alignment a shared vocabulary and it finally does something — still not enough.** Merging two *bilingual* B-GPT models of the same language pair (~94% tokenizer overlap instead of 13–28%), vocabulary transport plus unit alignment moves MultiBLiMP from 0.618 to 0.655 and Δfloor from +1.04 to +0.87 nats/byte. The parents are at 0.96 and Δfloor 0. This is the clean decomposition: vocabulary is the wall in SET 4, and independent training is the wall behind it.
 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 reliably predict the realised rescue.** Held out by seed pair, with a seed-cluster permutation null and BH within the five-predictor family the audit brief itself names: **1 of 20 cells significant** (pythia-70m, coordinate share (block-normalised / permutation), AUROC 0.81, q=0.050). The carrying predictor is the coordinate share, whose held-out AUROC across the substrates is 14m: 0.48 · 31m: 0.71 · 70m: 0.81 · 160m: 0.61 · 410m: 0.61 — i.e. it does not replicate. Nothing survives BH across the wider exploratory family either. Reported as the negative transfer result it is.
+7. **P0-2: the pre-merge predictors do not reliably predict the realised rescue.** Held out by seed pair, with a seed-cluster permutation null and BH within the five-predictor family the audit brief itself names: **0 of 25 cells significant**. The strongest predictor is the coordinate share — AUROC 0.81 at pythia-70m with a raw permutation p of 0.002 — and its held-out AUROC across the substrates is 14m: 0.48 · 31m: 0.71 · 70m: 0.81 · 160m: 0.61 · 410m: 0.45 — i.e. it does not replicate. Nothing survives BH across the wider exploratory family either. Reported as the negative transfer result it is.
 8. **The residual obstruction is not coordinate.** A same-basin control (the 160M Pythia data-seed / weight-seed ablations, weight cosine 0.56 against 0.02 for two PolyPythia seeds) still pays ~3.1 nats/token to a naive average, and alignment removes only a few percent of it — correctly, since there is no coordinate mismatch left. Merging is not free even inside a basin, and what remains after alignment is not something the permutation group describes.
 9. **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.
 
@@ -92,17 +92,17 @@ Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **20.17**, permut
 Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **8.99**, permutation-aligned **6.76** nats/token.
 
 
-### pythia-410m — 14 seed pairs · mean parent floor **2.984** nats/token · uniform-over-vocabulary reference **10.826** nats/token
+### pythia-410m — 15 seed pairs · mean parent floor **2.984** 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 | 14 | 9.43 | 6.45 | 6.46 | 5.95 | 0/14 | 0.0% |
-| M1_perm_avg | 14 | 8.96 | 5.98 | 5.87 | 5.56 | 11/14 | 7.1% |
-| M1_orth_avg | 14 | 9.01 | 6.02 | 6.00 | 5.44 | 12/14 | 6.5% |
-| M2_task_arith | 14 | 14.19 | 11.20 | 11.88 | 5.28 | 1/14 | -73.8% |
-| M3_ties | 14 | 13.07 | 10.09 | 10.23 | 9.32 | 0/14 | -56.7% |
+| M0_naive_avg | 15 | 9.51 | 6.53 | 6.50 | 5.95 | 0/15 | 0.0% |
+| M1_perm_avg | 15 | 8.94 | 5.96 | 5.82 | 5.56 | 12/15 | 8.3% |
+| M1_orth_avg | 15 | 9.01 | 6.03 | 6.04 | 5.44 | 13/15 | 7.4% |
+| M2_task_arith | 15 | 14.15 | 11.17 | 10.69 | 5.28 | 1/15 | -71.5% |
+| M3_ties | 15 | 13.04 | 10.06 | 10.17 | 9.32 | 0/15 | -54.7% |
 
-Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.47**, permutation-aligned **5.96** nats/token.
+Linear-mode-connectivity barrier (`eval.merge_barrier`): naive **6.54**, permutation-aligned **5.93** nats/token.
 
 
 **What this says.**
@@ -178,7 +178,7 @@ orthogonal row.
 | 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 | 36 | 3.25 | 8.99 | 23.5% | 30.3% | 32.0% | 0.747 | 0.761 | 0.0867 |
-| pythia-410m | 14 | 2.98 | 6.45 | 7.1% | 6.5% | 9.2% | 0.476 | 0.427 | 0.0371 |
+| pythia-410m | 15 | 2.98 | 6.53 | 8.3% | 7.4% | 10.4% | 0.463 | 0.412 | 0.0355 |
 
 Read the **permutation** column: it is the one that is exactly function-preserving (see Validation
 above). The Procrustes column is shown for completeness but on GPTNeoX that map damages the model it
@@ -300,7 +300,7 @@ block-normalised weight distance. `M1d/M1e` force the residual factor in regardl
 
 ## The accuracy test · does the likelihood rescue transfer? (BLiMP, SET 1)
 
-PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges. Scoring is the standard minimal-pair comparison: total log p over the sentence, correct when the grammatical member scores higher. **Chance = 0.500.** Same merges, same alignment, same pairs as the Δfloor tables above.
+PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges. Scoring is the standard minimal-pair comparison: total log p over the sentence, correct when the grammatical member scores higher. **Chance = 0.500.** Same merges, same alignment, same pairs as the Δfloor tables above. Item budget is 200 minimal pairs per paradigm at 14M/31M/70M, 150 at 160M and 100 at 410M — 6,700–13,400 pairs per evaluation, which puts the binomial standard error on each cell below 0.006.
 
 | substrate | n pairs | mean parent acc | better-parent ceiling | M0 naive | M1 permutation | M1 Procrustes | best rung, % of the parents' above-chance margin retained |
 |---|---|---|---|---|---|---|---|
@@ -308,6 +308,7 @@ PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges.
 | pythia-31m | 36 | 0.691 | 0.698 | 0.526 | 0.536 | 0.537 | 25.2% |
 | pythia-70m | 36 | 0.717 | 0.722 | 0.516 | 0.541 | 0.542 | 24.4% |
 | pythia-160m | 36 | 0.772 | 0.776 | 0.532 | 0.537 | 0.539 | 19.3% |
+| pythia-410m | 15 | 0.789 | 0.802 | 0.535 | 0.543 | 0.543 | 18.5% |
 
 **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
@@ -317,9 +318,11 @@ is not a caveat to add to a positive result here; on this substrate it is the re
 
 **And the two quantities are flat against each other across the whole scale ladder.** The share of
 the naive Δfloor that alignment removes falls from ~70% at 14M to ~11% at 410M — a sixfold change.
-The share of the parents' above-chance BLiMP margin that the merged model retains does not track it
-at all: it sits at roughly a fifth at 14M, 31M and 70M and drops at 160M. Whatever the likelihood
-rescue is buying, it is not this benchmark, and the amount of it makes almost no difference.
+The share of the parents' above-chance BLiMP margin the merged model retains barely moves over the
+same range: 28% → 25% → 24% → 19% → 19%. The merged model scores between 0.52 and 0.54 at *every*
+size, whether alignment recovered three quarters of the likelihood gap or a tenth of it. Whatever
+the likelihood rescue is buying, it is not this benchmark, and the amount of it makes almost no
+difference.
 
 
 Pair by pair, does the size of the likelihood rescue predict the size of the accuracy rescue? (Spearman, over seed pairs within a size.)
@@ -330,6 +333,7 @@ Pair by pair, does the size of the likelihood rescue predict the size of the acc
 | pythia-31m | 36 | -0.166 | 12.25 | 0.0211 |
 | pythia-70m | 36 | 0.169 | 11.43 | 0.0361 |
 | pythia-160m | 36 | 0.051 | 2.97 | 0.0179 |
+| pythia-410m | 15 | 0.225 | 0.70 | 0.0165 |
 
 ## Did we try hard enough? · REPAIR on top of the alignment
 
@@ -352,11 +356,11 @@ The obvious objection to a negative merging result is that averaging is a weak m
 | pythia-70m | 36 | M4_perm_repair | 10.68 | 8.23 | 0.537 | 16.7% |
 | pythia-70m | 36 | M5_naive_repair | 19.52 | 18.99 | 0.516 | 7.2% |
 | pythia-70m | 36 | **parents** | 0.00 | 0.00 | 0.722 | 100.0% |
-| pythia-160m | 33 | M0_naive_avg | 8.97 | 8.45 | 0.532 | 11.5% |
-| pythia-160m | 33 | M1_perm_avg | 6.78 | 6.47 | 0.539 | 14.0% |
-| pythia-160m | 33 | M4_perm_repair | 6.85 | 6.34 | 0.534 | 12.3% |
-| pythia-160m | 33 | M5_naive_repair | 8.65 | 8.51 | 0.525 | 9.2% |
-| pythia-160m | 33 | **parents** | 0.00 | 0.00 | 0.776 | 100.0% |
+| pythia-160m | 36 | M0_naive_avg | 8.99 | 8.47 | 0.532 | 11.5% |
+| pythia-160m | 36 | M1_perm_avg | 6.77 | 6.44 | 0.537 | 13.6% |
+| pythia-160m | 36 | M4_perm_repair | 6.89 | 6.39 | 0.534 | 12.1% |
+| pythia-160m | 36 | M5_naive_repair | 8.69 | 8.54 | 0.524 | 8.8% |
+| pythia-160m | 36 | **parents** | 0.00 | 0.00 | 0.776 | 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
@@ -383,11 +387,17 @@ The main SET 1 tables score on FLORES-200 English devtest — genuinely held out
 | pythia-14m | 36 | flores_eng | 4.38 | 32.43 | 9.61 | 69.9% |
 | pythia-14m | 36 | pile_10k | 4.19 | 32.95 | 10.23 | 68.4% |
 | pythia-14m | 36 | wikitext103_val | 4.98 | 32.95 | 10.48 | 67.7% |
-| pythia-160m | 28 | flores_eng | 3.25 | 8.84 | 6.72 | 22.6% |
-| pythia-160m | 28 | pile_10k | 3.15 | 9.22 | 7.45 | 17.7% |
-| pythia-160m | 28 | wikitext103_val | 3.25 | 9.41 | 8.05 | 13.3% |
+| pythia-160m | 36 | flores_eng | 3.25 | 8.99 | 6.77 | 23.5% |
+| pythia-160m | 36 | pile_10k | 3.14 | 9.35 | 7.51 | 18.4% |
+| pythia-160m | 36 | wikitext103_val | 3.24 | 9.59 | 8.11 | 14.4% |
 
-**It is not a corpus artifact.** The parent floors move with domain, as they should, but the naive Δfloor, the aligned Δfloor and the rescue fraction are stable across all three corpora — including the in-distribution Pile sample. The merge penalty is a property of the merge, not of the evaluation set.
+**It is not a corpus artifact.** Parent floors move with domain, as they should. The naive Δfloor
+barely moves at all — within 2% at 14M and within 7% at 160M — and the aligned Δfloor moves by under
+a nat. The rescue fraction is within 2 points across corpora at 14M; at 160M it drifts from 23% on
+FLORES to 14% on WikiText, which is worth stating rather than smoothing over, but it does not touch
+either conclusion: the merge penalty is enormous on the in-distribution Pile sample too, and the
+scale trend (large rescue at 14M, small at 160M) is present on all three corpora. The penalty is a
+property of the merge, not of the evaluation set.
 
 
 ## The operator practitioners actually use · SLERP
@@ -406,17 +416,23 @@ Every rung above is a lab operator. A census of community merges on the Hub find
 | pythia-31m | 36 | M6_slerp | 41.38 | 0.521 |
 | pythia-31m | 36 | M7_perm_slerp | 19.32 | 0.529 |
 | pythia-31m | 36 | **parents** | 0.00 | 0.698 |
-| pythia-70m | 1 | M0_naive_avg | 16.51 | 0.568 |
-| pythia-70m | 1 | M1_perm_avg | 16.04 | 0.538 |
-| pythia-70m | 1 | M6_slerp | 39.21 | 0.538 |
-| pythia-70m | 1 | M7_perm_slerp | 29.95 | 0.526 |
-| pythia-70m | 1 | **parents** | 0.00 | 0.731 |
+| pythia-70m | 36 | M0_naive_avg | 20.16 | 0.516 |
+| pythia-70m | 36 | M1_perm_avg | 10.99 | 0.541 |
+| pythia-70m | 36 | M6_slerp | 35.05 | 0.513 |
+| pythia-70m | 36 | M7_perm_slerp | 14.24 | 0.541 |
+| pythia-70m | 36 | **parents** | 0.00 | 0.722 |
+| pythia-160m | 36 | M0_naive_avg | 8.99 | 0.532 |
+| pythia-160m | 36 | M1_perm_avg | 6.77 | 0.537 |
+| pythia-160m | 36 | M6_slerp | 13.76 | 0.529 |
+| pythia-160m | 36 | M7_perm_slerp | 9.60 | 0.534 |
+| pythia-160m | 36 | **parents** | 0.00 | 0.776 |
 
 **SLERP is worse than a plain average here, not better.** Walking the great circle between two
 parameter sets that are essentially orthogonal interpolates their *directions*, and between two
 independently initialised networks there is no meaningful direction to interpolate — so it inherits
-the naive merge's failure and adds to it. Applied *after* unit alignment it comes back to roughly
-where the aligned average already was. Two things follow. First, the field's default recipe does not
+the naive merge's failure and roughly doubles it. Applied *after* unit alignment it recovers most of
+that — but still lands consistently worse than the aligned plain average, at every size. Two things
+follow. First, the field's default recipe does not
 rescue the composition case, so "practitioners do it differently" is not an escape from this result.
 Second, the ordering is the same as everywhere else in this report: **alignment is what moves the
 number, and the choice of operator on top of it barely matters.**
@@ -627,31 +643,31 @@ exploratory table follows it.
 
 | substrate | predictor | n | Spearman | AUROC (held out by seed) | null mean | perm p | BH q (within family) |
 |---|---|---|---|---|---|---|---|
-| pythia-14m | weight cosine | 36 | 0.095 | 0.549 | 0.501 | 0.316 | 0.575 |
-| pythia-14m | coordinate share (block-normalised / permutation) | 36 | -0.009 | 0.478 | 0.503 | 0.596 | 0.701 |
-| pythia-14m | CKA (mean over layers / unaligned) | 36 | -0.013 | 0.605 | 0.499 | 0.151 | 0.379 |
-| pythia-14m | QMD (quotient_residual / permutation) | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.220 |
-| pythia-14m | task-vector cosine | 36 | 0.131 | 0.580 | 0.498 | 0.223 | 0.447 |
-| pythia-160m | weight cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.187 |
-| pythia-160m | coordinate share (block-normalised / permutation) | 36 | -0.015 | 0.614 | 0.501 | 0.176 | 0.391 |
+| pythia-14m | weight cosine | 36 | 0.095 | 0.549 | 0.501 | 0.316 | 0.648 |
+| pythia-14m | coordinate share (block-normalised / permutation) | 36 | -0.009 | 0.478 | 0.503 | 0.596 | 0.745 |
+| pythia-14m | CKA (mean over layers / unaligned) | 36 | -0.013 | 0.605 | 0.499 | 0.151 | 0.473 |
+| pythia-14m | QMD (quotient_residual / permutation) | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.275 |
+| pythia-14m | task-vector cosine | 36 | 0.131 | 0.580 | 0.498 | 0.223 | 0.558 |
+| pythia-160m | weight cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.234 |
+| pythia-160m | coordinate share (block-normalised / permutation) | 36 | -0.015 | 0.614 | 0.501 | 0.176 | 0.489 |
 | pythia-160m | CKA (mean over layers / unaligned) | 36 | 0.008 | 0.256 | 0.498 | 0.987 | 0.987 |
-| pythia-160m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.525 | 0.500 | 0.415 | 0.593 |
-| pythia-160m | task-vector cosine | 36 | 0.049 | 0.454 | 0.502 | 0.657 | 0.730 |
-| pythia-31m | weight cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.187 |
-| pythia-31m | coordinate share (block-normalised / permutation) | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.187 |
-| pythia-31m | CKA (mean over layers / unaligned) | 36 | 0.100 | 0.546 | 0.503 | 0.345 | 0.575 |
-| pythia-31m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.760 |
-| pythia-31m | task-vector cosine | 36 | -0.123 | 0.481 | 0.499 | 0.577 | 0.701 |
-| pythia-410m | weight cosine | 14 | -0.125 | 0.510 | — | — | — |
-| pythia-410m | coordinate share (block-normalised / permutation) | 14 | 0.244 | 0.612 | — | — | — |
-| pythia-410m | CKA (mean over layers / unaligned) | 14 | 0.235 | 0.612 | — | — | — |
-| pythia-410m | QMD (quotient_residual / permutation) | 14 | -0.134 | 0.551 | — | — | — |
-| pythia-410m | task-vector cosine | 14 | -0.103 | 0.531 | — | — | — |
-| pythia-70m | weight cosine | 36 | 0.089 | 0.491 | 0.500 | 0.517 | 0.689 |
-| pythia-70m | coordinate share (block-normalised / permutation) | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.050 |
-| pythia-70m | CKA (mean over layers / unaligned) | 36 | 0.495 | 0.654 | 0.501 | 0.113 | 0.323 |
-| pythia-70m | QMD (quotient_residual / permutation) | 36 | -0.494 | 0.676 | 0.501 | 0.089 | 0.298 |
-| pythia-70m | task-vector cosine | 36 | 0.071 | 0.543 | 0.502 | 0.390 | 0.593 |
+| pythia-160m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.525 | 0.500 | 0.415 | 0.648 |
+| pythia-160m | task-vector cosine | 36 | 0.049 | 0.454 | 0.502 | 0.657 | 0.757 |
+| pythia-31m | weight cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.234 |
+| pythia-31m | coordinate share (block-normalised / permutation) | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.234 |
+| pythia-31m | CKA (mean over layers / unaligned) | 36 | 0.100 | 0.546 | 0.503 | 0.345 | 0.648 |
+| pythia-31m | QMD (quotient_residual / permutation) | 36 | -0.060 | 0.444 | 0.500 | 0.722 | 0.785 |
+| pythia-31m | task-vector cosine | 36 | -0.123 | 0.481 | 0.499 | 0.577 | 0.745 |
+| pythia-410m | weight cosine | 15 | 0.043 | 0.464 | 0.504 | 0.583 | 0.745 |
+| pythia-410m | coordinate share (block-normalised / permutation) | 15 | 0.164 | 0.446 | 0.499 | 0.666 | 0.757 |
+| pythia-410m | CKA (mean over layers / unaligned) | 15 | 0.025 | 0.321 | 0.497 | 0.901 | 0.939 |
+| pythia-410m | QMD (quotient_residual / permutation) | 15 | -0.107 | 0.554 | 0.503 | 0.369 | 0.648 |
+| pythia-410m | task-vector cosine | 15 | 0.014 | 0.571 | 0.501 | 0.350 | 0.648 |
+| pythia-70m | weight cosine | 36 | 0.089 | 0.491 | 0.500 | 0.517 | 0.745 |
+| pythia-70m | coordinate share (block-normalised / permutation) | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.062 |
+| pythia-70m | CKA (mean over layers / unaligned) | 36 | 0.495 | 0.654 | 0.501 | 0.113 | 0.403 |
+| pythia-70m | QMD (quotient_residual / permutation) | 36 | -0.494 | 0.676 | 0.501 | 0.089 | 0.373 |
+| pythia-70m | task-vector cosine | 36 | 0.071 | 0.543 | 0.502 | 0.390 | 0.648 |
 
 ### The exploratory table
 
@@ -660,75 +676,75 @@ Showing, per substrate and per outcome, the **six predictors with the largest |A
 | substrate | outcome | predictor | n | Spearman | AUROC (held out by seed) | null mean | perm p | BH q |
 |---|---|---|---|---|---|---|---|---|
 | pythia-14m | rescue_frac | bnd_perm | 36 | 0.012 | 0.296 | 0.497 | 0.981 | 1.000 |
-| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.298 |
-| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.298 |
-| pythia-14m | rescue_frac | qmd_act_perm | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.298 |
-| pythia-14m | rescue_frac | qmd_act_procrustes | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.298 |
-| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.316 |
-| pythia-14m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.254 | 0.620 | 0.501 | 0.114 | 0.318 |
-| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.298 |
-| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.298 |
-| pythia-31m | rescue_frac | weight_cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.298 |
-| pythia-31m | rescue_frac | d_raw | 36 | -0.321 | 0.704 | 0.505 | 0.025 | 0.298 |
-| pythia-31m | rescue_frac | bnd_raw | 36 | -0.391 | 0.701 | 0.506 | 0.032 | 0.298 |
-| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.298 |
-| pythia-31m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.403 | 0.707 | 0.505 | 0.035 | 0.298 |
-| pythia-70m | rescue_frac | coord_share_bnd_perm | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.177 |
-| pythia-70m | rescue_frac | bnd_perm | 36 | -0.404 | 0.787 | 0.501 | 0.003 | 0.177 |
-| pythia-70m | rescue_frac | coord_share_orth | 36 | 0.341 | 0.722 | 0.497 | 0.033 | 0.298 |
-| pythia-70m | rescue_frac | qmd_orth | 36 | -0.373 | 0.713 | 0.497 | 0.067 | 0.316 |
-| pythia-70m | rescue_frac | coord_share_perm | 36 | 0.356 | 0.698 | 0.500 | 0.065 | 0.316 |
-| pythia-70m | rescue_frac | qmd_perm | 36 | -0.357 | 0.688 | 0.500 | 0.084 | 0.316 |
-| pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.387 | 0.688 | 0.499 | 0.080 | 0.316 |
+| pythia-14m | rescue_frac | qmd_orth | 36 | 0.093 | 0.676 | 0.497 | 0.037 | 0.373 |
+| pythia-14m | rescue_frac | qmd_perm | 36 | 0.077 | 0.664 | 0.498 | 0.051 | 0.373 |
+| pythia-14m | rescue_frac | qmd_act_perm | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.373 |
+| pythia-14m | rescue_frac | qmd_act_procrustes | 36 | -0.207 | 0.657 | 0.500 | 0.055 | 0.373 |
+| pythia-14m | rescue_frac | cka_last | 36 | -0.478 | 0.651 | 0.499 | 0.067 | 0.391 |
+| pythia-14m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.254 | 0.620 | 0.501 | 0.114 | 0.391 |
+| pythia-31m | rescue_frac | bnd_perm | 36 | -0.490 | 0.750 | 0.506 | 0.011 | 0.373 |
+| pythia-31m | rescue_frac | coord_share_bnd_perm | 36 | 0.427 | 0.710 | 0.505 | 0.025 | 0.373 |
+| pythia-31m | rescue_frac | weight_cosine | 36 | 0.274 | 0.704 | 0.504 | 0.029 | 0.373 |
+| pythia-31m | rescue_frac | d_raw | 36 | -0.321 | 0.704 | 0.505 | 0.025 | 0.373 |
+| pythia-31m | rescue_frac | bnd_raw | 36 | -0.391 | 0.701 | 0.506 | 0.032 | 0.373 |
+| pythia-31m | rescue_frac | qmd_perm | 36 | -0.493 | 0.691 | 0.505 | 0.048 | 0.373 |
+| pythia-31m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.403 | 0.707 | 0.505 | 0.035 | 0.373 |
+| pythia-70m | rescue_frac | coord_share_bnd_perm | 36 | 0.462 | 0.806 | 0.500 | 0.002 | 0.222 |
+| pythia-70m | rescue_frac | bnd_perm | 36 | -0.404 | 0.787 | 0.501 | 0.003 | 0.222 |
+| pythia-70m | rescue_frac | coord_share_orth | 36 | 0.341 | 0.722 | 0.497 | 0.033 | 0.373 |
+| pythia-70m | rescue_frac | qmd_orth | 36 | -0.373 | 0.713 | 0.497 | 0.067 | 0.391 |
+| pythia-70m | rescue_frac | coord_share_perm | 36 | 0.356 | 0.698 | 0.500 | 0.065 | 0.391 |
+| pythia-70m | rescue_frac | qmd_perm | 36 | -0.357 | 0.688 | 0.500 | 0.084 | 0.391 |
+| pythia-70m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.387 | 0.688 | 0.499 | 0.080 | 0.391 |
 | pythia-160m | rescue_frac | bnd_raw | 36 | 0.083 | 0.250 | 0.496 | 0.984 | 1.000 |
 | pythia-160m | rescue_frac | bnd_perm | 36 | 0.040 | 0.253 | 0.496 | 0.974 | 1.000 |
 | pythia-160m | rescue_frac | cka_mean | 36 | 0.008 | 0.256 | 0.498 | 0.987 | 1.000 |
-| pythia-160m | rescue_frac | weight_cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.298 |
-| pythia-160m | rescue_frac | qmd_orth | 36 | -0.439 | 0.704 | 0.498 | 0.052 | 0.298 |
-| pythia-160m | rescue_frac | bnd_orth | 36 | -0.047 | 0.306 | 0.497 | 0.925 | 0.983 |
-| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.331 | 0.642 | 0.499 | 0.130 | 0.323 |
-| pythia-410m | rescue_frac | d_raw | 14 | 0.059 | 0.286 | — | — | — |
-| pythia-410m | rescue_frac | weight_cosine_bn | 14 | -0.420 | 0.633 | — | — | — |
-| pythia-410m | rescue_frac | bnd_perm | 14 | -0.156 | 0.633 | — | — | — |
-| pythia-410m | rescue_frac | cka_last | 14 | 0.077 | 0.367 | — | — | — |
-| pythia-410m | rescue_frac | bnd_raw | 14 | -0.152 | 0.612 | — | — | — |
-| pythia-410m | rescue_frac | bnd_orth | 14 | -0.152 | 0.612 | — | — | — |
-| pythia-410m | rescue_frac | MULTIVARIATE_ridge_all | 14 | -0.525 | 0.245 | — | — | — |
+| pythia-160m | rescue_frac | weight_cosine | 36 | 0.451 | 0.704 | 0.501 | 0.037 | 0.373 |
+| pythia-160m | rescue_frac | qmd_orth | 36 | -0.439 | 0.704 | 0.498 | 0.052 | 0.373 |
+| pythia-160m | rescue_frac | bnd_orth | 36 | -0.047 | 0.306 | 0.497 | 0.925 | 0.976 |
+| pythia-160m | rescue_frac | MULTIVARIATE_ridge_all | 36 | 0.331 | 0.642 | 0.499 | 0.130 | 0.398 |
+| pythia-410m | rescue_frac | weight_cosine_bn | 15 | -0.250 | 0.679 | 0.504 | 0.158 | 0.423 |
+| pythia-410m | rescue_frac | cka_mean | 15 | 0.025 | 0.321 | 0.497 | 0.901 | 0.962 |
+| pythia-410m | rescue_frac | qmd_perm | 15 | 0.125 | 0.661 | 0.499 | 0.167 | 0.424 |
+| pythia-410m | rescue_frac | qmd_orth | 15 | 0.096 | 0.643 | 0.499 | 0.187 | 0.438 |
+| pythia-410m | rescue_frac | coord_share_bnd_orth | 15 | 0.107 | 0.393 | 0.499 | 0.765 | 0.859 |
+| pythia-410m | rescue_frac | d_raw | 15 | 0.054 | 0.607 | 0.499 | 0.280 | 0.499 |
+| pythia-410m | rescue_frac | MULTIVARIATE_ridge_all | 15 | -0.643 | 0.196 | 0.500 | 0.994 | 1.000 |
 | 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 | 0.298 |
-| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.298 |
-| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.298 |
+| pythia-14m | dfloor_M1best | coord_share_orth | 36 | -0.457 | 0.738 | 0.495 | 0.015 | 0.373 |
+| pythia-14m | dfloor_M1best | coord_share_perm | 36 | -0.445 | 0.735 | 0.495 | 0.016 | 0.373 |
+| pythia-14m | dfloor_M1best | qmd_orth | 36 | 0.438 | 0.725 | 0.496 | 0.021 | 0.373 |
 | 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 | 0.177 |
-| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.316 |
-| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.316 |
-| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.356 |
-| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.356 |
-| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.323 |
-| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.373 |
-| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.671 |
-| pythia-70m | dfloor_M1best | coord_share_bnd_perm | 36 | 0.529 | 0.713 | 0.501 | 0.037 | 0.298 |
-| pythia-70m | dfloor_M1best | bnd_perm | 36 | -0.485 | 0.704 | 0.502 | 0.033 | 0.298 |
-| pythia-70m | dfloor_M1best | cka_last | 36 | 0.476 | 0.691 | 0.499 | 0.090 | 0.316 |
-| pythia-70m | dfloor_M1best | cka_mean | 36 | 0.427 | 0.667 | 0.501 | 0.105 | 0.316 |
-| pythia-70m | dfloor_M1best | qmd_act_perm | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.316 |
-| pythia-70m | dfloor_M1best | qmd_act_procrustes | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.316 |
-| pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.386 | 0.599 | 0.500 | 0.207 | 0.376 |
+| pythia-14m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.539 | 0.778 | 0.497 | 0.003 | 0.222 |
+| pythia-31m | dfloor_M1best | qmd_orth | 36 | 0.289 | 0.670 | 0.502 | 0.101 | 0.391 |
+| pythia-31m | dfloor_M1best | coord_share_orth | 36 | -0.271 | 0.670 | 0.503 | 0.103 | 0.391 |
+| pythia-31m | dfloor_M1best | qmd_perm | 36 | 0.243 | 0.633 | 0.503 | 0.166 | 0.424 |
+| pythia-31m | dfloor_M1best | coord_share_perm | 36 | -0.230 | 0.633 | 0.503 | 0.166 | 0.424 |
+| pythia-31m | dfloor_M1best | bnd_raw | 36 | 0.163 | 0.633 | 0.503 | 0.132 | 0.398 |
+| pythia-31m | dfloor_M1best | bnd_orth | 36 | 0.114 | 0.611 | 0.501 | 0.186 | 0.438 |
+| pythia-31m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | -0.111 | 0.500 | 0.501 | 0.516 | 0.668 |
+| pythia-70m | dfloor_M1best | coord_share_bnd_perm | 36 | 0.529 | 0.713 | 0.501 | 0.037 | 0.373 |
+| pythia-70m | dfloor_M1best | bnd_perm | 36 | -0.485 | 0.704 | 0.502 | 0.033 | 0.373 |
+| pythia-70m | dfloor_M1best | cka_last | 36 | 0.476 | 0.691 | 0.499 | 0.090 | 0.391 |
+| pythia-70m | dfloor_M1best | cka_mean | 36 | 0.427 | 0.667 | 0.501 | 0.105 | 0.391 |
+| pythia-70m | dfloor_M1best | qmd_act_perm | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.391 |
+| pythia-70m | dfloor_M1best | qmd_act_procrustes | 36 | -0.433 | 0.667 | 0.501 | 0.103 | 0.391 |
+| pythia-70m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.386 | 0.599 | 0.500 | 0.207 | 0.438 |
 | pythia-160m | dfloor_M1best | weight_cosine | 36 | -0.195 | 0.284 | 0.504 | 0.962 | 1.000 |
-| pythia-160m | dfloor_M1best | cka_last | 36 | 0.476 | 0.701 | 0.497 | 0.040 | 0.298 |
-| pythia-160m | dfloor_M1best | cka_mean | 36 | -0.270 | 0.685 | 0.500 | 0.111 | 0.318 |
-| pythia-160m | dfloor_M1best | bnd_orth | 36 | -0.371 | 0.660 | 0.500 | 0.081 | 0.316 |
-| pythia-160m | dfloor_M1best | d_raw | 36 | 0.273 | 0.349 | 0.503 | 0.910 | 0.974 |
-| pythia-160m | dfloor_M1best | bnd_raw | 36 | -0.278 | 0.648 | 0.501 | 0.119 | 0.318 |
-| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.584 | 0.759 | 0.497 | 0.018 | 0.298 |
-| pythia-410m | dfloor_M1best | qmd_act_ot | 14 | 0.420 | 0.837 | — | — | — |
-| pythia-410m | dfloor_M1best | cka_mean | 14 | -0.323 | 0.816 | — | — | — |
-| pythia-410m | dfloor_M1best | qmd_act_perm | 14 | 0.380 | 0.796 | — | — | — |
-| pythia-410m | dfloor_M1best | qmd_act_procrustes | 14 | 0.380 | 0.796 | — | — | — |
-| pythia-410m | dfloor_M1best | task_vector_cosine | 14 | -0.125 | 0.245 | — | — | — |
-| pythia-410m | dfloor_M1best | d_raw | 14 | -0.270 | 0.306 | — | — | — |
-| pythia-410m | dfloor_M1best | MULTIVARIATE_ridge_all | 14 | 0.429 | 0.755 | — | — | — |
+| pythia-160m | dfloor_M1best | cka_last | 36 | 0.476 | 0.701 | 0.497 | 0.040 | 0.373 |
+| pythia-160m | dfloor_M1best | cka_mean | 36 | -0.270 | 0.685 | 0.500 | 0.111 | 0.391 |
+| pythia-160m | dfloor_M1best | bnd_orth | 36 | -0.371 | 0.660 | 0.500 | 0.081 | 0.391 |
+| pythia-160m | dfloor_M1best | d_raw | 36 | 0.273 | 0.349 | 0.503 | 0.910 | 0.965 |
+| pythia-160m | dfloor_M1best | bnd_raw | 36 | -0.278 | 0.648 | 0.501 | 0.119 | 0.391 |
+| pythia-160m | dfloor_M1best | MULTIVARIATE_ridge_all | 36 | 0.584 | 0.759 | 0.497 | 0.018 | 0.373 |
+| pythia-410m | dfloor_M1best | cka_mean | 15 | -0.364 | 0.714 | 0.501 | 0.116 | 0.391 |
+| pythia-410m | dfloor_M1best | qmd_act_ot | 15 | 0.432 | 0.714 | 0.502 | 0.148 | 0.407 |
+| pythia-410m | dfloor_M1best | qmd_act_perm | 15 | 0.371 | 0.679 | 0.502 | 0.214 | 0.438 |
+| pythia-410m | dfloor_M1best | qmd_act_procrustes | 15 | 0.371 | 0.679 | 0.502 | 0.214 | 0.438 |
+| pythia-410m | dfloor_M1best | qmd_orth | 15 | -0.257 | 0.625 | 0.503 | 0.244 | 0.474 |
+| pythia-410m | dfloor_M1best | cka_last | 15 | 0.100 | 0.375 | 0.494 | 0.775 | 0.859 |
+| pythia-410m | dfloor_M1best | MULTIVARIATE_ridge_all | 15 | 0.300 | 0.589 | 0.506 | 0.335 | 0.549 |
 
 ### Does a predictor fitted on one substrate transfer to another?
 
@@ -736,31 +752,31 @@ 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 | 0.414 | 0.500 | 0.814 | 0.966 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 36 | 0.654 | 0.499 | 0.048 | 0.220 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.685 | 0.495 | 0.024 | 0.175 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-410m | 14 | 0.531 | 0.498 | 0.446 | 0.737 |
-| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.556 | 0.502 | 0.292 | 0.664 |
-| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.500 | 0.459 | 0.737 |
-| coord_share_bnd_perm | rescue_frac | pythia-160m | 36 | 0.506 | 0.500 | 0.473 | 0.737 |
-| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.502 | 0.016 | 0.133 |
-| coord_share_bnd_perm | rescue_frac | pythia-410m | 14 | 0.612 | 0.492 | 0.252 | 0.664 |
-| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.497 | 0.002 | 0.100 |
-| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.497 | 0.047 | 0.220 |
-| qmd_act_perm | rescue_frac | pythia-160m | 36 | 0.512 | 0.497 | 0.444 | 0.737 |
-| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.506 | 0.446 | 0.737 |
-| qmd_act_perm | rescue_frac | pythia-410m | 14 | 0.571 | 0.501 | 0.347 | 0.713 |
-| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.501 | 0.045 | 0.220 |
-| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.497 | 0.150 | 0.526 |
-| cka_mean | rescue_frac | pythia-160m | 36 | 0.478 | 0.498 | 0.570 | 0.792 |
-| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.500 | 0.357 | 0.713 |
-| cka_mean | rescue_frac | pythia-410m | 14 | 0.612 | 0.501 | 0.287 | 0.664 |
-| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.498 | 0.950 | 0.980 |
-| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.498 | 0.217 | 0.664 |
-| weight_cosine | rescue_frac | pythia-160m | 36 | 0.704 | 0.496 | 0.013 | 0.130 |
-| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.497 | 0.011 | 0.130 |
-| weight_cosine | rescue_frac | pythia-410m | 14 | 0.510 | 0.493 | 0.484 | 0.737 |
-| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.501 | 0.547 | 0.782 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-14m | 36 | 0.426 | 0.500 | 0.775 | 0.966 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-160m | 36 | 0.657 | 0.499 | 0.044 | 0.223 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-31m | 36 | 0.679 | 0.495 | 0.028 | 0.203 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-410m | 15 | 0.393 | 0.499 | 0.773 | 0.966 |
+| MULTIVARIATE_ridge_all | rescue_frac | pythia-70m | 36 | 0.556 | 0.502 | 0.289 | 0.689 |
+| coord_share_bnd_perm | rescue_frac | pythia-14m | 36 | 0.515 | 0.500 | 0.462 | 0.777 |
+| coord_share_bnd_perm | rescue_frac | pythia-160m | 36 | 0.506 | 0.500 | 0.473 | 0.777 |
+| coord_share_bnd_perm | rescue_frac | pythia-31m | 36 | 0.710 | 0.502 | 0.017 | 0.142 |
+| coord_share_bnd_perm | rescue_frac | pythia-410m | 15 | 0.482 | 0.497 | 0.549 | 0.829 |
+| coord_share_bnd_perm | rescue_frac | pythia-70m | 36 | 0.806 | 0.498 | 0.003 | 0.142 |
+| qmd_act_perm | rescue_frac | pythia-14m | 36 | 0.657 | 0.496 | 0.044 | 0.223 |
+| qmd_act_perm | rescue_frac | pythia-160m | 36 | 0.512 | 0.498 | 0.450 | 0.777 |
+| qmd_act_perm | rescue_frac | pythia-31m | 36 | 0.525 | 0.505 | 0.440 | 0.777 |
+| qmd_act_perm | rescue_frac | pythia-410m | 15 | 0.571 | 0.508 | 0.370 | 0.739 |
+| qmd_act_perm | rescue_frac | pythia-70m | 36 | 0.676 | 0.500 | 0.045 | 0.223 |
+| cka_mean | rescue_frac | pythia-14m | 36 | 0.599 | 0.497 | 0.147 | 0.516 |
+| cka_mean | rescue_frac | pythia-160m | 36 | 0.478 | 0.497 | 0.568 | 0.829 |
+| cka_mean | rescue_frac | pythia-31m | 36 | 0.540 | 0.501 | 0.364 | 0.739 |
+| cka_mean | rescue_frac | pythia-410m | 15 | 0.571 | 0.496 | 0.337 | 0.739 |
+| cka_mean | rescue_frac | pythia-70m | 36 | 0.346 | 0.498 | 0.952 | 0.971 |
+| weight_cosine | rescue_frac | pythia-14m | 36 | 0.580 | 0.497 | 0.204 | 0.637 |
+| weight_cosine | rescue_frac | pythia-160m | 36 | 0.704 | 0.495 | 0.012 | 0.142 |
+| weight_cosine | rescue_frac | pythia-31m | 36 | 0.704 | 0.499 | 0.013 | 0.142 |
+| weight_cosine | rescue_frac | pythia-410m | 15 | 0.625 | 0.507 | 0.241 | 0.669 |
+| weight_cosine | rescue_frac | pythia-70m | 36 | 0.494 | 0.499 | 0.535 | 0.829 |
 
 **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.
 
@@ -787,20 +803,21 @@ Leave-one-**size**-out. Predictors are standardised *within* size first, so a pr
 
 ### What P0-2 comes to
 
-**The confirmatory family gives 1 significant cell out of
-20 tested** (BH q < 0.05 within the family):
+**The confirmatory family gives 0 significant cells out of
+25 tested** (BH q under 0.05 within the family).
 
-- pythia-70m · coordinate share (block-normalised / permutation) · AUROC 0.806 · q = 0.050
+The strongest single cell is the coordinate share at pythia-70m — held-out AUROC 0.81, raw
+permutation p = 0.002 — which is a real effect and worth naming rather than burying. It does not
+survive correction across the family, and the reason it does not is instructive: the same predictor
+on the same outcome, measured on four other complete grids of the same model family, lands at 0.45,
+0.48, 0.61 and 0.71. The honest summary is:
 
-That is a real effect and it should not be rounded down to zero. It should also not be rounded up.
-The predictor that carries it is the **coordinate share** — exactly the quantity the manuscript's
-thesis is about — and the honest summary is:
 
-- **It does not replicate across substrates.** The same predictor's held-out AUROC ranges from ~0.48
-  (chance, and slightly the wrong way) to 0.81 across four complete 36-pair grids of the *same*
-  model family differing only in size. A quantity that lands anywhere in that range depending on
-  which substrate you happen to test is not a validated instrument for "representational alignment
-  predicts merging", however encouraging its best cell looks.
+- **It does not replicate across substrates.** Held-out AUROC for the coordinate share, on five
+  complete grids of the *same* model family differing only in size: 14m 0.48 · 31m 0.71 · 70m 0.81 · 160m 0.61 · 410m 0.45. A quantity that lands
+  anywhere between "slightly the wrong way" and 0.81 depending on which substrate you happen to test
+  is not a validated instrument for "representational alignment predicts merging", however
+  encouraging its best cell looks.
 - **The exploratory table looks better than the confirmatory one, and that is the point of having
   both.** Across ~150 predictor × substrate × outcome cells there are plenty of AUROCs in the
   0.70–0.81 range with raw permutation p below 0.05; none survives BH across that family. Quoting
@@ -883,13 +900,13 @@ general.
 | 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 | 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-410m | 14/15 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 | 15/15 seed pairs | complete | 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-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
-| SET 1 · corpus robustness | pythia-14m: 36/36, pythia-160m: 28/36 | RAN | same pairs and merges re-scored on FLORES-200 eng, NeelNanda/pile-10k and WikiText-103 validation |
-| SET 1 · SLERP rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 1/36 | RAN | M6 SLERP and M7 permutation-aligned SLERP on the same pairs; Δfloor and BLiMP |
-| SET 1 · REPAIR rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 33/36 | 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-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36, pythia-410m: 15/36 | RAN | 67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges |
+| SET 1 · corpus robustness | pythia-14m: 36/36, pythia-160m: 36/36 | RAN | same pairs and merges re-scored on FLORES-200 eng, NeelNanda/pile-10k and WikiText-103 validation |
+| SET 1 · SLERP rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 36/36 | RAN | M6 SLERP and M7 permutation-aligned SLERP on the same pairs; Δfloor and BLiMP |
+| SET 1 · REPAIR rung | pythia-14m: 36/36, pythia-31m: 36/36, pythia-70m: 36/36, pythia-160m: 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) | 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 |
@@ -897,7 +914,7 @@ general.
 | SET 4c · bilingual×bilingual merge (B-GPT en_X × X_en) | 4/4 language pairs | RAN | M0 naive · M1a vocab-transport · M1b/c +unit-aligned · M1g embedding-row Procrustes; Δfloor AND MultiBLiMP on the same merges. ~94% vocabulary overlap, so this cell isolates independent training from the vocabulary wall |
 | Validation · is each alignment function-preserving? | 2 substrates x 5 maps | RAN | parent re-evaluated after applying the map; permutation exact to float32 noise, orthogonal NOT (see Validation) |
 | 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 | 14/36 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. |
+| SET 1 · pythia-410m full grid | 15/36 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. |
 | Any downstream task beyond BLiMP/MultiBLiMP | 0 | NOT RUN | Both benchmarks are minimal-pair grammaticality tests. They do not speak to reasoning, generation quality or instruction following. |
 

code/analyze.py

@@ -21,6 +21,17 @@ def load(pat):
             except Exception: pass
     return rows
 
+def _dedup_sp(rows):
+    """Drop duplicate (size, pair) records: a cell may be worked by more than one process."""
+    seen, out = set(), []
+    for r in rows:
+        k = (r.get("size"), tuple(r.get("pair", ())))
+        if k[1] and k in seen:
+            continue
+        seen.add(k); out.append(r)
+    return out
+
+
 
 # ------------------------------------------------------------------ stats helpers
 def auroc(score, label):
@@ -404,7 +415,7 @@ if rows4:
 print("figures + csvs written")
 
 # ------------------------------------------------------------------ 5. BLiMP dissociation
-blimp = load("blimp_*.jsonl")
+blimp = _dedup_sp(load("blimp_*.jsonl") + load("blimpB_*.jsonl"))
 if blimp:
     brows = []
     for b in blimp:

code/make_artifact.py

@@ -15,6 +15,17 @@ def load(pat):
             except Exception: pass
     return out
 
+def _dedup_sp(rows):
+    """Drop duplicate (size, pair) records: a cell may be worked by more than one process."""
+    seen, out = set(), []
+    for r in rows:
+        k = (r.get("size"), tuple(r.get("pair", ())))
+        if k[1] and k in seen:
+            continue
+        seen.add(k); out.append(r)
+    return out
+
+
 
 def dedup(rows):
     seen, out = set(), []
@@ -27,13 +38,13 @@ def dedup(rows):
 
 set1 = dedup(load("set1_*.jsonl") + load("set1x_*.jsonl"))
 set4 = load("set4_goldfish.jsonl")
-blimp = load("blimp_*.jsonl")
-rep = load("repair_*.jsonl")
-slp = load("slerp_*.jsonl")
+blimp = _dedup_sp(load("blimp_*.jsonl") + load("blimpB_*.jsonl"))
+rep = _dedup_sp(load("repair_*.jsonl"))
+slp = _dedup_sp(load("slerp_*.jsonl"))
 mb = load("set4_multiblimp.jsonl")
 bgm = load("bgpt_merge.jsonl")
 bgc = load("bgpt_ceiling.jsonl")
-crb = load("corpus_*.jsonl")
+crb = _dedup_sp(load("corpus_*.jsonl"))
 abl = load("abl_*.jsonl")
 sizes = sorted({r["size"] for r in set1}, key=lambda s: int(s[:-1]))
 UNIF = math.log(50304)

code/make_report.py

@@ -12,6 +12,17 @@ def load(pat):
             except Exception: pass
     return rows
 
+def _dedup_sp(rows):
+    """Drop duplicate (size, pair) records: a cell may be worked by more than one process."""
+    seen, out = set(), []
+    for r in rows:
+        k = (r.get("size"), tuple(r.get("pair", ())))
+        if k[1] and k in seen:
+            continue
+        seen.add(k); out.append(r)
+    return out
+
+
 
 def md_table(headers, rows):
     out = ["| " + " | ".join(headers) + " |", "|" + "|".join(["---"] * len(headers)) + "|"]
@@ -91,7 +102,7 @@ L.append("""## Read this first: what substrate, and what metric
 """)
 
 # ---------------- headline summary (computed, so it cannot drift from the tables)
-_bl = load("blimp_*.jsonl"); _rp = load("repair_*.jsonl"); _mb = load("set4_multiblimp.jsonl")
+_bl = _dedup_sp(load("blimp_*.jsonl") + load("blimpB_*.jsonl")); _rp = _dedup_sp(load("repair_*.jsonl")); _mb = load("set4_multiblimp.jsonl")
 if set1:
     hl = []
     s14 = [r for r in set1 if r["size"] == sizes[0]]
@@ -134,10 +145,24 @@ if set1:
         pm = np.mean([np.mean(list(b["parent_acc"].values())) for b in b14])
         m0 = np.mean([b["rungs"]["M0_naive_avg"]["blimp_acc"] for b in b14])
         m1 = np.mean([max(b["rungs"][k]["blimp_acc"] for k in b["rungs"] if k.startswith("M1")) for b in b14])
-        hl.append(f"4. **The likelihood rescue does not transfer to accuracy.** On pythia-{b14[0]['size']} "
-                  f"(n={len(b14)}), parents average {pm:.3f} on BLiMP; the naive merge {m0:.3f} and the "
-                  f"aligned merge {m1:.3f}, against chance 0.500. A ~70% Δfloor rescue buys ~"
-                  f"{(m1-m0):.3f} accuracy. Pairwise, the two rescues are uncorrelated.")
+        _bs = sorted({x["size"] for x in _bl}, key=lambda x: int(x[:-1]))
+        _keep = []
+        for _s in _bs:
+            _sub = [x for x in _bl if x["size"] == _s]
+            _ce = float(np.mean([x["ceiling"] for x in _sub]))
+            _mm = float(np.mean([max(x["rungs"][k]["blimp_acc"] for k in x["rungs"]) for x in _sub]))
+            _keep.append((_s, _mm, (_mm - 0.5) / (_ce - 0.5) * 100))
+        hl.append(f"4. **The likelihood rescue does not transfer to accuracy — and this is the sharpest "
+                  f"result here.** Same merges, scored on BLiMP. On pythia-{b14[0]['size']} "
+                  f"(n={len(b14)}) parents average {pm:.3f}, the naive merge {m0:.3f} and the aligned "
+                  f"merge {m1:.3f}, against chance 0.500 — a ~70% Δfloor rescue buys ~{(m1-m0):.3f} "
+                  f"accuracy, and pair by pair the two rescues are uncorrelated. Across the ladder the "
+                  f"merged model scores "
+                  + " · ".join(f"{s_} {a_:.3f}" for s_, a_, _k in _keep)
+                  + f" — it retains {_keep[0][2]:.0f}%→{_keep[-1][2]:.0f}% of the parents' above-chance "
+                    f"margin — while the likelihood rescue over the same range falls from ~70% to ~8%. "
+                    f"The accuracy the merge keeps is essentially independent of how much likelihood "
+                    f"alignment recovered.")
     if set4:
         d0e = np.mean([r["rungs"]["M0_naive_avg"]["delta_floor_eng"] for r in set4])
         bst = np.mean([min(r["rungs"][k]["delta_floor_eng"] for k in r["rungs"] if k.startswith("M1")) for r in set4])
@@ -185,8 +210,8 @@ if set1:
               f"five-predictor family the audit brief itself names: **{len(_sg)} of {len(_tt)} cells "
               f"significant**"
               + (" (" + "; ".join(f"{c[0]}, {c[1]}, AUROC {c[2]:.2f}, q={c[3]:.3f}" for c in _sg) + ")" if _sg else "")
-              + ". The carrying predictor is the coordinate share, whose held-out AUROC across the "
-              + "substrates is "
+              + ". The strongest predictor is the coordinate share — AUROC 0.81 at pythia-70m with a "
+                "raw permutation p of 0.002 — and its held-out AUROC across the substrates is "
               + " · ".join(f"{c[0].split('-')[1]}: {c[2]:.2f}"
                            for c in sorted([c for c in _cf if "coordinate share" in c[1]],
                                            key=lambda c: int(c[0].split('-')[1][:-1])))
@@ -438,13 +463,15 @@ block-normalised weight distance. `M1d/M1e` force the residual factor in regardl
 """)
 
 # ---------------- BLiMP: accuracy, not likelihood
-blimp = load("blimp_*.jsonl")
+blimp = _dedup_sp(load("blimp_*.jsonl") + load("blimpB_*.jsonl"))
 if blimp:
     L.append("\n## The accuracy test · does the likelihood rescue transfer? (BLiMP, SET 1)\n")
     L.append("PolyPythia parents are English LMs, so BLiMP applies directly to SET 1's merges. "
              "Scoring is the standard minimal-pair comparison: total log p over the sentence, "
              "correct when the grammatical member scores higher. **Chance = 0.500.** Same merges, "
-             "same alignment, same pairs as the Δfloor tables above.\n")
+             "same alignment, same pairs as the Δfloor tables above. Item budget is 200 minimal pairs per "
+             "paradigm at 14M/31M/70M, 150 at 160M and 100 at 410M — 6,700–13,400 pairs per "
+             "evaluation, which puts the binomial standard error on each cell below 0.006.\n")
     body, corr_rows = [], []
     for sz in sorted({b["size"] for b in blimp}, key=lambda x: int(x[:-1])):
         sub = [b for b in blimp if b["size"] == sz]
@@ -483,9 +510,11 @@ is not a caveat to add to a positive result here; on this substrate it is the re
 
 **And the two quantities are flat against each other across the whole scale ladder.** The share of
 the naive Δfloor that alignment removes falls from ~70% at 14M to ~11% at 410M — a sixfold change.
-The share of the parents' above-chance BLiMP margin that the merged model retains does not track it
-at all: it sits at roughly a fifth at 14M, 31M and 70M and drops at 160M. Whatever the likelihood
-rescue is buying, it is not this benchmark, and the amount of it makes almost no difference.
+The share of the parents' above-chance BLiMP margin the merged model retains barely moves over the
+same range: 28% → 25% → 24% → 19% → 19%. The merged model scores between 0.52 and 0.54 at *every*
+size, whether alignment recovered three quarters of the likelihood gap or a tenth of it. Whatever
+the likelihood rescue is buying, it is not this benchmark, and the amount of it makes almost no
+difference.
 """)
     if corr_rows:
         L.append("\nPair by pair, does the size of the likelihood rescue predict the size of the "
@@ -494,7 +523,7 @@ rescue is buying, it is not this benchmark, and the amount of it makes almost no
                            "mean Δfloor rescue (nats/tok)", "mean BLiMP rescue (acc)"], corr_rows))
 
 # ---------------- REPAIR
-rep = load("repair_*.jsonl")
+rep = _dedup_sp(load("repair_*.jsonl"))
 if rep:
     L.append("\n## Did we try hard enough? · REPAIR on top of the alignment\n")
     L.append("The obvious objection to a negative merging result is that averaging is a weak merge: it "
@@ -537,7 +566,7 @@ alignment were all tried on the same pairs; the best of them recovers most of th
 """)
 
 # ---------------- corpus robustness
-crb = load("corpus_*.jsonl")
+crb = _dedup_sp(load("corpus_*.jsonl"))
 if crb:
     L.append("\n### Robustness: is the Δfloor an artifact of the held-out corpus?\n")
     L.append("The main SET 1 tables score on FLORES-200 English devtest — genuinely held out from "
@@ -557,13 +586,18 @@ if crb:
                          fmt(np.mean(1 - d1 / d0) * 100, 1) + "%"])
     L.append(md_table(["substrate", "n pairs", "corpus", "parent floor", "naive Δfloor",
                        "Δfloor permutation-aligned", "rescue"], body))
-    L.append("\n**It is not a corpus artifact.** The parent floors move with domain, as they should, "
-             "but the naive Δfloor, the aligned Δfloor and the rescue fraction are stable across all "
-             "three corpora — including the in-distribution Pile sample. The merge penalty is a "
-             "property of the merge, not of the evaluation set.\n")
+    L.append("""
+**It is not a corpus artifact.** Parent floors move with domain, as they should. The naive Δfloor
+barely moves at all — within 2% at 14M and within 7% at 160M — and the aligned Δfloor moves by under
+a nat. The rescue fraction is within 2 points across corpora at 14M; at 160M it drifts from 23% on
+FLORES to 14% on WikiText, which is worth stating rather than smoothing over, but it does not touch
+either conclusion: the merge penalty is enormous on the in-distribution Pile sample too, and the
+scale trend (large rescue at 14M, small at 160M) is present on all three corpora. The penalty is a
+property of the merge, not of the evaluation set.
+""")
 
 # ---------------- SLERP
-slp = load("slerp_*.jsonl")
+slp = _dedup_sp(load("slerp_*.jsonl"))
 if slp:
     L.append("\n## The operator practitioners actually use · SLERP\n")
     L.append("Every rung above is a lab operator. A census of community merges on the Hub finds SLERP "
@@ -587,8 +621,9 @@ if slp:
 **SLERP is worse than a plain average here, not better.** Walking the great circle between two
 parameter sets that are essentially orthogonal interpolates their *directions*, and between two
 independently initialised networks there is no meaningful direction to interpolate — so it inherits
-the naive merge's failure and adds to it. Applied *after* unit alignment it comes back to roughly
-where the aligned average already was. Two things follow. First, the field's default recipe does not
+the naive merge's failure and roughly doubles it. Applied *after* unit alignment it recovers most of
+that — but still lands consistently worse than the aligned plain average, at every size. Two things
+follow. First, the field's default recipe does not
 rescue the composition case, so "practitioners do it differently" is not an escape from this result.
 Second, the ordering is the same as everywhere else in this report: **alignment is what moves the
 number, and the choice of operator on top of it barely matters.**
@@ -849,22 +884,32 @@ if os.path.exists(f"{R}/predictor_confirmatory.csv"):
             pass
 _sig = [c for c in _conf if c["q"] == c["q"] and c["q"] < 0.05]
 _tested = [c for c in _conf if c["q"] == c["q"]]
+_cs_line = " · ".join(
+    f"{c['sub'].split('-')[1]} {c['auroc']:.2f}"
+    for c in sorted([c for c in _conf if "coordinate share" in c["pred"]],
+                    key=lambda c: int(c["sub"].split("-")[1][:-1]))) or "—"
 L.append(f"""
 ### What P0-2 comes to
 
 **The confirmatory family gives {len(_sig)} significant cell{'' if len(_sig) == 1 else 's'} out of
-{len(_tested)} tested** (BH q < 0.05 within the family){':' if _sig else '.'}
-""" + ("".join(f"\n- {c['sub']} · {c['pred']} · AUROC {c['auroc']:.3f} · q = {c['q']:.3f}" for c in _sig) if _sig else "") + """
-
+{len(_tested)} tested** (BH q under 0.05 within the family){':' if _sig else '.'}
+""" + ("".join(f"\n- {c['sub']} · {c['pred']} · AUROC {c['auroc']:.3f} · q = {c['q']:.3f}\n" for c in _sig) if _sig else "") + ("""
+The strongest single cell is the coordinate share at pythia-70m — held-out AUROC 0.81, raw
+permutation p = 0.002 — which is a real effect and worth naming rather than burying. It does not
+survive correction across the family, and the reason it does not is instructive: the same predictor
+on the same outcome, measured on four other complete grids of the same model family, lands at 0.45,
+0.48, 0.61 and 0.71. The honest summary is:
+""" if not _sig else """
 That is a real effect and it should not be rounded down to zero. It should also not be rounded up.
 The predictor that carries it is the **coordinate share** — exactly the quantity the manuscript's
 thesis is about — and the honest summary is:
+""") + """
 
-- **It does not replicate across substrates.** The same predictor's held-out AUROC ranges from ~0.48
-  (chance, and slightly the wrong way) to 0.81 across four complete 36-pair grids of the *same*
-  model family differing only in size. A quantity that lands anywhere in that range depending on
-  which substrate you happen to test is not a validated instrument for "representational alignment
-  predicts merging", however encouraging its best cell looks.
+- **It does not replicate across substrates.** Held-out AUROC for the coordinate share, on five
+  complete grids of the *same* model family differing only in size: """ + _cs_line + """. A quantity that lands
+  anywhere between "slightly the wrong way" and 0.81 depending on which substrate you happen to test
+  is not a validated instrument for "representational alignment predicts merging", however
+  encouraging its best cell looks.
 - **The exploratory table looks better than the confirmatory one, and that is the point of having
   both.** Across ~150 predictor × substrate × outcome cells there are plenty of AUROCs in the
   0.70–0.81 range with raw permutation p below 0.05; none survives BH across that family. Quoting
@@ -970,10 +1015,10 @@ cov.append(["SET 1 accuracy · BLiMP", ", ".join(f"pythia-{k}: {v}/36" for k, v
             "67 paradigms from `nyu-mll/blimp`, minimal-pair sentence-logprob scoring, on the SAME merges"])
 nr = {}
 for r_ in rep: nr[r_["size"]] = nr.get(r_["size"], 0) + 1
-_slp = load("slerp_*.jsonl")
+_slp = _dedup_sp(load("slerp_*.jsonl"))
 _ns = {}
 for r_ in _slp: _ns[r_["size"]] = _ns.get(r_["size"], 0) + 1
-_crb = load("corpus_*.jsonl")
+_crb = _dedup_sp(load("corpus_*.jsonl"))
 _nc = {}
 for r_ in _crb: _nc[r_["size"]] = _nc.get(r_["size"], 0) + 1
 cov.append(["SET 1 · corpus robustness", ", ".join(f"pythia-{k}: {v}/36" for k, v in sorted(_nc.items(), key=lambda kv: int(kv[0][:-1]))) or "0",

code/run_blimp410.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+source /root/.ms_hf_env
+export HF_HOME=/root/hf_cache_brainalign
+export OMP_NUM_THREADS=8 MKL_NUM_THREADS=8 OPENBLAS_NUM_THREADS=8
+P=/root/venvs/mergeability/bin/python
+CUDA_VISIBLE_DEVICES=$1 $P /root/compose-audit/set1_blimp.py --size 410m --seeds $2 \
+  --n_per_paradigm 100 --bs 32 --blocks 24 --acts_rows 1024 >> /root/compose-audit/logs/blimp410_$3.log 2>&1
+echo BLIMP410_$3_DONE >> /root/compose-audit/logs/blimp410_$3.log

code/set1_blimp.py

@@ -16,10 +16,11 @@ ap.add_argument("--seeds", default="1,2,3,4,5,6,7,8,9")
 ap.add_argument("--n_per_paradigm", type=int, default=200)
 ap.add_argument("--bs", type=int, default=128)
 ap.add_argument("--acts_rows", type=int, default=2048)
+ap.add_argument("--tag", default="blimp")
 ap.add_argument("--blocks", type=int, default=48)
 A = ap.parse_args()
 SEEDS = [int(s) for s in A.seeds.split(",")]
-OUT = f"/root/compose-audit/results/blimp_{A.size}.jsonl"
+OUT = f"/root/compose-audit/results/{A.tag}_{A.size}.jsonl"
 DEV = "cuda"
 BLIMP = glob.glob("/root/hf_cache_brainalign/hub/datasets--nyu-mll--blimp/snapshots/*/")[0]
 

results/blimpB_410m.jsonl

@@ -0,0 +1,6 @@
+{"set": "set1_blimp", "size": "410m", "pair": [3, 4], "metric": "BLiMP accuracy (chance=0.5) -- ACCURACY, not likelihood", "n_per_paradigm": 100, "n_paradigms": 67, "parent_acc": {"a": 0.7767164179104478, "b": 0.7456716417910447}, "ceiling": 0.7767164179104478, "rungs": {"M0_naive_avg": {"blimp_acc": 0.5243283582089552, "delta_vs_best_parent": -0.2523880597014926}, "M1_perm_avg": {"blimp_acc": 0.5470149253731343, "delta_vs_best_parent": -0.22970149253731342}, "M1_orth_avg": {"blimp_acc": 0.5337313432835821, "delta_vs_best_parent": -0.2429850746268657}}, "per_paradigm": {"M0_naive_avg": {"adjunct_island": 0.57, "anaphor_gender_agreement": 0.43, "anaphor_number_agreement": 0.57, "animate_subject_passive": 0.69, "animate_subject_trans": 0.59, "causative": 0.47, "complex_NP_island": 0.41, "coordinate_structure_constraint_complex_left_branch": 0.61, "coordinate_structure_constraint_object_extraction": 0.27, "determiner_noun_agreement_1": 0.64, "determiner_noun_agreement_2": 0.48, "determiner_noun_agreement_irregular_1": 0.51, "determiner_noun_agreement_irregular_2": 0.52, "determiner_noun_agreement_with_adj_2": 0.47, "determiner_noun_agreement_with_adj_irregular_1": 0.46, "determiner_noun_agreement_with_adj_irregular_2": 0.59, "determiner_noun_agreement_with_adjective_1": 0.47, "distractor_agreement_relational_noun": 0.56, "distractor_agreement_relative_clause": 0.46, "drop_argument": 0.72, "ellipsis_n_bar_1": 0.46, "ellipsis_n_bar_2": 0.2, "existential_there_object_raising": 0.63, "existential_there_quantifiers_1": 0.78, "existential_there_quantifiers_2": 0.4, "existential_there_subject_raising": 0.6, "expletive_it_object_raising": 0.68, "inchoative": 0.41, "intransitive": 0.57, "irregular_past_participle_adjectives": 0.31, "irregular_past_participle_verbs": 0.47, "irregular_plural_subject_verb_agreement_1": 0.5, "irregular_plural_subject_verb_agreement_2": 0.49, "left_branch_island_echo_question": 0.45, "left_branch_island_simple_question": 0.4, "matrix_question_npi_licensor_present": 0.46, "npi_present_1": 0.35, "npi_present_2": 0.27, "only_npi_licensor_present": 0.35, "only_npi_scope": 0.38, "passive_1": 0.61, "passive_2": 0.64, "principle_A_c_command": 0.34, "principle_A_case_1": 0.91, "principle_A_case_2": 0.54, "principle_A_domain_1": 0.88, "principle_A_domain_2": 0.44, "principle_A_domain_3": 0.53, "principle_A_reconstruction": 0.64, "regular_plural_subject_verb_agreement_1": 0.59, "regular_plural_subject_verb_agreement_2": 0.53, "sentential_negation_npi_licensor_present": 0.86, "sentential_negation_npi_scope": 0.47, "sentential_subject_island": 0.56, "superlative_quantifiers_1": 0.02, "superlative_quantifiers_2": 0.56, "tough_vs_raising_1": 0.36, "tough_vs_raising_2": 0.68, "transitive": 0.52, "wh_island": 0.64, "wh_questions_object_gap": 0.59, "wh_questions_subject_gap": 0.73, "wh_questions_subject_gap_long_distance": 0.77, "wh_vs_that_no_gap": 0.7, "wh_vs_that_no_gap_long_distance": 0.78, "wh_vs_that_with_gap": 0.26, "wh_vs_that_with_gap_long_distance": 0.33}, "M1_perm_avg": {"adjunct_island": 0.7, "anaphor_gender_agreement": 0.35, "anaphor_number_agreement": 0.47, "animate_subject_passive": 0.61, "animate_subject_trans": 0.5, "causative": 0.51, "complex_NP_island": 0.38, "coordinate_structure_constraint_complex_left_branch": 0.69, "coordinate_structure_constraint_object_extraction": 0.32, "determiner_noun_agreement_1": 0.57, "determiner_noun_agreement_2": 0.52, "determiner_noun_agreement_irregular_1": 0.51, "determiner_noun_agreement_irregular_2": 0.53, "determiner_noun_agreement_with_adj_2": 0.47, "determiner_noun_agreement_with_adj_irregular_1": 0.42, "determiner_noun_agreement_with_adj_irregular_2": 0.53, "determiner_noun_agreement_with_adjective_1": 0.5, "distractor_agreement_relational_noun": 0.43, "distractor_agreement_relative_clause": 0.44, "drop_argument": 0.74, "ellipsis_n_bar_1": 0.48, "ellipsis_n_bar_2": 0.17, "existential_there_object_raising": 0.68, "existential_there_quantifiers_1": 0.57, "existential_there_quantifiers_2": 0.72, "existential_there_subject_raising": 0.59, "expletive_it_object_raising": 0.61, "inchoative": 0.38, "intransitive": 0.54, "irregular_past_participle_adjectives": 0.69, "irregular_past_participle_verbs": 0.59, "irregular_plural_subject_verb_agreement_1": 0.56, "irregular_plural_subject_verb_agreement_2": 0.54, "left_branch_island_echo_question": 0.51, "left_branch_island_simple_question": 0.55, "matrix_question_npi_licensor_present": 0.36, "npi_present_1": 0.68, "npi_present_2": 0.74, "only_npi_licensor_present": 0.41, "only_npi_scope": 0.68, "passive_1": 0.65, "passive_2": 0.69, "principle_A_c_command": 0.44, "principle_A_case_1": 0.87, "principle_A_case_2": 0.51, "principle_A_domain_1": 0.78, "principle_A_domain_2": 0.54, "principle_A_domain_3": 0.5, "principle_A_reconstruction": 0.5, "regular_plural_subject_verb_agreement_1": 0.46, "regular_plural_subject_verb_agreement_2": 0.53, "sentential_negation_npi_licensor_present": 0.9, "sentential_negation_npi_scope": 0.28, "sentential_subject_island": 0.51, "superlative_quantifiers_1": 0.02, "superlative_quantifiers_2": 0.42, "tough_vs_raising_1": 0.34, "tough_vs_raising_2": 0.7, "transitive": 0.56, "wh_island": 0.47, "wh_questions_object_gap": 0.9, "wh_questions_subject_gap": 0.94, "wh_questions_subject_gap_long_distance": 0.92, "wh_vs_that_no_gap": 0.95, "wh_vs_that_no_gap_long_distance": 0.96, "wh_vs_that_with_gap": 0.03, "wh_vs_that_with_gap_long_distance": 0.04}, "M1_orth_avg": {"adjunct_island": 0.36, "anaphor_gender_agreement": 0.28, "anaphor_number_agreement": 0.44, "animate_subject_passive": 0.63, "animate_subject_trans": 0.59, "causative": 0.36, "complex_NP_island": 0.4, "coordinate_structure_constraint_complex_left_branch": 0.64, "coordinate_structure_constraint_object_extraction": 0.45, "determiner_noun_agreement_1": 0.47, "determiner_noun_agreement_2": 0.49, "determiner_noun_agreement_irregular_1": 0.53, "determiner_noun_agreement_irregular_2": 0.5, "determiner_noun_agreement_with_adj_2": 0.43, "determiner_noun_agreement_with_adj_irregular_1": 0.42, "determiner_noun_agreement_with_adj_irregular_2": 0.55, "determiner_noun_agreement_with_adjective_1": 0.43, "distractor_agreement_relational_noun": 0.55, "distractor_agreement_relative_clause": 0.48, "drop_argument": 0.76, "ellipsis_n_bar_1": 0.52, "ellipsis_n_bar_2": 0.23, "existential_there_object_raising": 0.64, "existential_there_quantifiers_1": 0.63, "existential_there_quantifiers_2": 0.78, "existential_there_subject_raising": 0.65, "expletive_it_object_raising": 0.62, "inchoative": 0.41, "intransitive": 0.54, "irregular_past_participle_adjectives": 0.47, "irregular_past_participle_verbs": 0.62, "irregular_plural_subject_verb_agreement_1": 0.44, "irregular_plural_subject_verb_agreement_2": 0.51, "left_branch_island_echo_question": 0.62, "left_branch_island_simple_question": 0.49, "matrix_question_npi_licensor_present": 0.34, "npi_present_1": 0.46, "npi_present_2": 0.26, "only_npi_licensor_present": 0.71, "only_npi_scope": 0.61, "passive_1": 0.61, "passive_2": 0.56, "principle_A_c_command": 0.35, "principle_A_case_1": 0.55, "principle_A_case_2": 0.61, "principle_A_domain_1": 0.82, "principle_A_domain_2": 0.66, "principle_A_domain_3": 0.52, "principle_A_reconstruction": 0.55, "regular_plural_subject_verb_agreement_1": 0.54, "regular_plural_subject_verb_agreement_2": 0.49, "sentential_negation_npi_licensor_present": 1.0, "sentential_negation_npi_scope": 0.58, "sentential_subject_island": 0.35, "superlative_quantifiers_1": 0.47, "superlative_quantifiers_2": 0.55, "tough_vs_raising_1": 0.3, "tough_vs_raising_2": 0.6, "transitive": 0.49, "wh_island": 0.88, "wh_questions_object_gap": 0.59, "wh_questions_subject_gap": 0.65, "wh_questions_subject_gap_long_distance": 0.57, "wh_vs_that_no_gap": 0.39, "wh_vs_that_no_gap_long_distance": 0.29, "wh_vs_that_with_gap": 0.73, "wh_vs_that_with_gap_long_distance": 0.75}}, "secs": 310.9691255092621}
+{"set": "set1_blimp", "size": "410m", "pair": [3, 5], "metric": "BLiMP accuracy (chance=0.5) -- ACCURACY, not likelihood", "n_per_paradigm": 100, "n_paradigms": 67, "parent_acc": {"a": 0.7767164179104478, "b": 0.7916417910447762}, "ceiling": 0.7916417910447762, "rungs": {"M0_naive_avg": {"blimp_acc": 0.5611940298507463, "delta_vs_best_parent": -0.23044776119402988}, "M1_perm_avg": {"blimp_acc": 0.5438805970149254, "delta_vs_best_parent": -0.24776119402985075}, "M1_orth_avg": {"blimp_acc": 0.5591044776119403, "delta_vs_best_parent": -0.2325373134328359}}, "per_paradigm": {"M0_naive_avg": {"adjunct_island": 0.68, "anaphor_gender_agreement": 0.34, "anaphor_number_agreement": 0.48, "animate_subject_passive": 0.62, "animate_subject_trans": 0.63, "causative": 0.4, "complex_NP_island": 0.61, "coordinate_structure_constraint_complex_left_branch": 0.55, "coordinate_structure_constraint_object_extraction": 0.54, "determiner_noun_agreement_1": 0.5, "determiner_noun_agreement_2": 0.52, "determiner_noun_agreement_irregular_1": 0.55, "determiner_noun_agreement_irregular_2": 0.59, "determiner_noun_agreement_with_adj_2": 0.44, "determiner_noun_agreement_with_adj_irregular_1": 0.47, "determiner_noun_agreement_with_adj_irregular_2": 0.62, "determiner_noun_agreement_with_adjective_1": 0.51, "distractor_agreement_relational_noun": 0.55, "distractor_agreement_relative_clause": 0.5, "drop_argument": 0.76, "ellipsis_n_bar_1": 0.56, "ellipsis_n_bar_2": 0.2, "existential_there_object_raising": 0.64, "existential_there_quantifiers_1": 0.44, "existential_there_quantifiers_2": 0.54, "existential_there_subject_raising": 0.5, "expletive_it_object_raising": 0.65, "inchoative": 0.33, "intransitive": 0.56, "irregular_past_participle_adjectives": 0.31, "irregular_past_participle_verbs": 0.61, "irregular_plural_subject_verb_agreement_1": 0.48, "irregular_plural_subject_verb_agreement_2": 0.47, "left_branch_island_echo_question": 0.44, "left_branch_island_simple_question": 0.44, "matrix_question_npi_licensor_present": 0.37, "npi_present_1": 1.0, "npi_present_2": 0.89, "only_npi_licensor_present": 0.95, "only_npi_scope": 0.72, "passive_1": 0.57, "passive_2": 0.6, "principle_A_c_command": 0.65, "principle_A_case_1": 0.87, "principle_A_case_2": 0.36, "principle_A_domain_1": 0.8, "principle_A_domain_2": 0.48, "principle_A_domain_3": 0.5, "principle_A_reconstruction": 0.6, "regular_plural_subject_verb_agreement_1": 0.41, "regular_plural_subject_verb_agreement_2": 0.52, "sentential_negation_npi_licensor_present": 0.96, "sentential_negation_npi_scope": 0.53, "sentential_subject_island": 0.58, "superlative_quantifiers_1": 0.07, "superlative_quantifiers_2": 0.45, "tough_vs_raising_1": 0.26, "tough_vs_raising_2": 0.8, "transitive": 0.52, "wh_island": 0.73, "wh_questions_object_gap": 0.75, "wh_questions_subject_gap": 0.82, "wh_questions_subject_gap_long_distance": 0.85, "wh_vs_that_no_gap": 0.87, "wh_vs_that_no_gap_long_distance": 0.85, "wh_vs_that_with_gap": 0.14, "wh_vs_that_with_gap_long_distance": 0.1}, "M1_perm_avg": {"adjunct_island": 0.6, "anaphor_gender_agreement": 0.51, "anaphor_number_agreement": 0.47, "animate_subject_passive": 0.59, "animate_subject_trans": 0.54, "causative": 0.42, "complex_NP_island": 0.41, "coordinate_structure_constraint_complex_left_branch": 0.37, "coordinate_structure_constraint_object_extraction": 0.64, "determiner_noun_agreement_1": 0.52, "determiner_noun_agreement_2": 0.5, "determiner_noun_agreement_irregular_1": 0.57, "determiner_noun_agreement_irregular_2": 0.53, "determiner_noun_agreement_with_adj_2": 0.49, "determiner_noun_agreement_with_adj_irregular_1": 0.44, "determiner_noun_agreement_with_adj_irregular_2": 0.57, "determiner_noun_agreement_with_adjective_1": 0.53, "distractor_agreement_relational_noun": 0.5, "distractor_agreement_relative_clause": 0.44, "drop_argument": 0.69, "ellipsis_n_bar_1": 0.39, "ellipsis_n_bar_2": 0.12, "existential_there_object_raising": 0.61, "existential_there_quantifiers_1": 0.7, "existential_there_quantifiers_2": 0.61, "existential_there_subject_raising": 0.66, "expletive_it_object_raising": 0.58, "inchoative": 0.35, "intransitive": 0.51, "irregular_past_participle_adjectives": 0.99, "irregular_past_participle_verbs": 0.46, "irregular_plural_subject_verb_agreement_1": 0.54, "irregular_plural_subject_verb_agreement_2": 0.48, "left_branch_island_echo_question": 0.46, "left_branch_island_simple_question": 0.48, "matrix_question_npi_licensor_present": 0.38, "npi_present_1": 0.42, "npi_present_2": 0.51, "only_npi_licensor_present": 0.2, "only_npi_scope": 0.67, "passive_1": 0.64, "passive_2": 0.59, "principle_A_c_command": 0.7, "principle_A_case_1": 0.99, "principle_A_case_2": 0.54, "principle_A_domain_1": 0.96, "principle_A_domain_2": 0.47, "principle_A_domain_3": 0.54, "principle_A_reconstruction": 0.41, "regular_plural_subject_verb_agreement_1": 0.49, "regular_plural_subject_verb_agreement_2": 0.45, "sentential_negation_npi_licensor_present": 0.9, 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results/blimp_410m.jsonl

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"sentential_negation_npi_scope": 0.16, "sentential_subject_island": 0.49, "superlative_quantifiers_1": 0.0, "superlative_quantifiers_2": 0.22, "tough_vs_raising_1": 0.25, "tough_vs_raising_2": 0.89, "transitive": 0.53, "wh_island": 0.95, "wh_questions_object_gap": 1.0, "wh_questions_subject_gap": 1.0, "wh_questions_subject_gap_long_distance": 1.0, "wh_vs_that_no_gap": 1.0, "wh_vs_that_no_gap_long_distance": 1.0, "wh_vs_that_with_gap": 0.0, "wh_vs_that_with_gap_long_distance": 0.0}, "M1_orth_avg": {"adjunct_island": 0.37, "anaphor_gender_agreement": 0.27, "anaphor_number_agreement": 0.36, "animate_subject_passive": 0.63, "animate_subject_trans": 0.58, "causative": 0.6, "complex_NP_island": 0.43, "coordinate_structure_constraint_complex_left_branch": 0.54, "coordinate_structure_constraint_object_extraction": 0.34, "determiner_noun_agreement_1": 0.54, "determiner_noun_agreement_2": 0.55, "determiner_noun_agreement_irregular_1": 0.51, "determiner_noun_agreement_irregular_2": 0.54, 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+{"set": "set1_blimp", "size": "410m", "pair": [2, 6], "metric": "BLiMP accuracy (chance=0.5) -- ACCURACY, not likelihood", "n_per_paradigm": 100, "n_paradigms": 67, "parent_acc": {"a": 0.8067164179104478, "b": 0.8022388059701493}, "ceiling": 0.8067164179104478, "rungs": {"M0_naive_avg": {"blimp_acc": 0.5086567164179104, "delta_vs_best_parent": -0.29805970149253735}, "M1_perm_avg": {"blimp_acc": 0.5413432835820896, "delta_vs_best_parent": -0.2653731343283582}, "M1_orth_avg": {"blimp_acc": 0.5680597014925373, "delta_vs_best_parent": -0.23865671641791053}}, "per_paradigm": {"M0_naive_avg": {"adjunct_island": 0.49, "anaphor_gender_agreement": 0.58, "anaphor_number_agreement": 0.44, "animate_subject_passive": 0.64, "animate_subject_trans": 0.51, "causative": 0.46, "complex_NP_island": 0.69, "coordinate_structure_constraint_complex_left_branch": 0.39, "coordinate_structure_constraint_object_extraction": 0.37, "determiner_noun_agreement_1": 0.58, "determiner_noun_agreement_2": 0.47, "determiner_noun_agreement_irregular_1": 0.51, "determiner_noun_agreement_irregular_2": 0.46, "determiner_noun_agreement_with_adj_2": 0.54, "determiner_noun_agreement_with_adj_irregular_1": 0.57, "determiner_noun_agreement_with_adj_irregular_2": 0.46, "determiner_noun_agreement_with_adjective_1": 0.49, "distractor_agreement_relational_noun": 0.45, "distractor_agreement_relative_clause": 0.52, "drop_argument": 0.66, "ellipsis_n_bar_1": 0.5, "ellipsis_n_bar_2": 0.21, "existential_there_object_raising": 0.72, "existential_there_quantifiers_1": 0.05, "existential_there_quantifiers_2": 0.46, "existential_there_subject_raising": 0.46, "expletive_it_object_raising": 0.6, "inchoative": 0.38, "intransitive": 0.53, "irregular_past_participle_adjectives": 0.36, "irregular_past_participle_verbs": 0.62, "irregular_plural_subject_verb_agreement_1": 0.42, "irregular_plural_subject_verb_agreement_2": 0.47, "left_branch_island_echo_question": 0.4, "left_branch_island_simple_question": 0.5, "matrix_question_npi_licensor_present": 0.41, "npi_present_1": 0.58, "npi_present_2": 0.6, "only_npi_licensor_present": 0.83, "only_npi_scope": 0.57, "passive_1": 0.54, "passive_2": 0.64, "principle_A_c_command": 0.69, "principle_A_case_1": 0.76, "principle_A_case_2": 0.57, "principle_A_domain_1": 0.71, "principle_A_domain_2": 0.42, "principle_A_domain_3": 0.51, "principle_A_reconstruction": 0.46, "regular_plural_subject_verb_agreement_1": 0.51, "regular_plural_subject_verb_agreement_2": 0.47, "sentential_negation_npi_licensor_present": 1.0, "sentential_negation_npi_scope": 0.46, "sentential_subject_island": 0.52, "superlative_quantifiers_1": 0.03, "superlative_quantifiers_2": 0.41, "tough_vs_raising_1": 0.37, "tough_vs_raising_2": 0.61, "transitive": 0.47, "wh_island": 0.16, "wh_questions_object_gap": 0.71, "wh_questions_subject_gap": 0.46, "wh_questions_subject_gap_long_distance": 0.66, "wh_vs_that_no_gap": 0.64, "wh_vs_that_no_gap_long_distance": 0.57, "wh_vs_that_with_gap": 0.43, "wh_vs_that_with_gap_long_distance": 0.35}, "M1_perm_avg": {"adjunct_island": 0.29, "anaphor_gender_agreement": 0.35, "anaphor_number_agreement": 0.38, "animate_subject_passive": 0.71, "animate_subject_trans": 0.43, "causative": 0.41, "complex_NP_island": 0.4, "coordinate_structure_constraint_complex_left_branch": 0.53, "coordinate_structure_constraint_object_extraction": 0.31, "determiner_noun_agreement_1": 0.57, "determiner_noun_agreement_2": 0.53, "determiner_noun_agreement_irregular_1": 0.48, "determiner_noun_agreement_irregular_2": 0.53, "determiner_noun_agreement_with_adj_2": 0.45, "determiner_noun_agreement_with_adj_irregular_1": 0.46, "determiner_noun_agreement_with_adj_irregular_2": 0.59, "determiner_noun_agreement_with_adjective_1": 0.43, "distractor_agreement_relational_noun": 0.47, "distractor_agreement_relative_clause": 0.49, "drop_argument": 0.69, "ellipsis_n_bar_1": 0.49, "ellipsis_n_bar_2": 0.2, "existential_there_object_raising": 0.64, 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"sentential_negation_npi_licensor_present": 1.0, "sentential_negation_npi_scope": 0.79, "sentential_subject_island": 0.52, "superlative_quantifiers_1": 0.48, "superlative_quantifiers_2": 0.89, "tough_vs_raising_1": 0.34, "tough_vs_raising_2": 0.73, "transitive": 0.51, "wh_island": 0.2, "wh_questions_object_gap": 0.98, "wh_questions_subject_gap": 0.95, "wh_questions_subject_gap_long_distance": 0.97, "wh_vs_that_no_gap": 0.96, "wh_vs_that_no_gap_long_distance": 0.93, "wh_vs_that_with_gap": 0.04, "wh_vs_that_with_gap_long_distance": 0.03}, "M1_orth_avg": {"adjunct_island": 0.34, "anaphor_gender_agreement": 0.55, "anaphor_number_agreement": 0.69, "animate_subject_passive": 0.62, "animate_subject_trans": 0.54, "causative": 0.5, "complex_NP_island": 0.54, "coordinate_structure_constraint_complex_left_branch": 0.37, "coordinate_structure_constraint_object_extraction": 0.3, "determiner_noun_agreement_1": 0.6, "determiner_noun_agreement_2": 0.53, "determiner_noun_agreement_irregular_1": 0.59, 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"secs": 383.74919033050537}

results/blimp_pairs.csv

@@ -107,6 +107,15 @@ size,pair,ceiling,parent_mean,M0,M1best,acc_M0_naive_avg,acc_M1_perm_avg,acc_M1_
 31m,(7, 8),0.7023134328358209,0.6925373134328359,0.5417164179104478,0.576044776119403,0.5417164179104478,0.5673134328358209,0.576044776119403
 31m,(7, 9),0.7023134328358209,0.6969402985074626,0.5711940298507463,0.5625373134328359,0.5711940298507463,0.5625373134328359,0.552910447761194
 31m,(8, 9),0.6915671641791045,0.6871641791044776,0.5675373134328359,0.566044776119403,0.5675373134328359,0.5632089552238806,0.566044776119403
+410m,(1, 2),0.8080597014925374,0.8073880597014926,0.5532835820895522,0.5519402985074627,0.5532835820895522,0.5519402985074627,0.5058208955223881
+410m,(1, 3),0.8080597014925374,0.7923880597014925,0.5294029850746269,0.5562686567164179,0.5294029850746269,0.5295522388059701,0.5562686567164179
+410m,(1, 4),0.8080597014925374,0.776865671641791,0.5474626865671641,0.5538805970149254,0.5474626865671641,0.5538805970149254,0.5502985074626866
+410m,(1, 5),0.8080597014925374,0.7998507462686568,0.5359701492537313,0.5246268656716417,0.5359701492537313,0.5202985074626866,0.5246268656716417
+410m,(1, 6),0.8080597014925374,0.8051492537313434,0.5126865671641792,0.5482089552238806,0.5126865671641792,0.5356716417910448,0.5482089552238806
+410m,(2, 3),0.8067164179104478,0.7917164179104478,0.5128358208955224,0.5647761194029851,0.5128358208955224,0.5429850746268656,0.5647761194029851
+410m,(2, 4),0.8067164179104478,0.7761940298507463,0.49223880597014924,0.5707462686567164,0.49223880597014924,0.5707462686567164,0.5353731343283582
+410m,(2, 5),0.8067164179104478,0.799179104477612,0.5394029850746269,0.535223880597015,0.5394029850746269,0.533134328358209,0.535223880597015
+410m,(2, 6),0.8067164179104478,0.8044776119402985,0.5086567164179104,0.5680597014925373,0.5086567164179104,0.5413432835820896,0.5680597014925373
 70m,(1, 2),0.7305223880597015,0.721044776119403,0.5678358208955224,0.543955223880597,0.5678358208955224,0.5375373134328358,0.543955223880597
 70m,(1, 3),0.7305223880597015,0.7224253731343284,0.542910447761194,0.5558955223880597,0.542910447761194,0.5305223880597015,0.5558955223880597
 70m,(1, 4),0.7305223880597015,0.7176492537313433,0.5120149253731343,0.5324626865671642,0.5120149253731343,0.5324626865671642,0.521044776119403
@@ -143,3 +152,9 @@ size,pair,ceiling,parent_mean,M0,M1best,acc_M0_naive_avg,acc_M1_perm_avg,acc_M1_
 70m,(7, 8),0.7194776119402985,0.7151119402985074,0.48253731343283585,0.5730597014925373,0.48253731343283585,0.5573880597014925,0.5730597014925373
 70m,(7, 9),0.7206716417910448,0.7200746268656717,0.5115671641791045,0.5614925373134328,0.5115671641791045,0.5614925373134328,0.5282835820895523
 70m,(8, 9),0.7206716417910448,0.7157089552238807,0.5497014925373135,0.5550746268656717,0.5497014925373135,0.5550746268656717,0.5436567164179105
+410m,(3, 4),0.7767164179104478,0.7611940298507462,0.5243283582089552,0.5470149253731343,0.5243283582089552,0.5470149253731343,0.5337313432835821
+410m,(3, 5),0.7916417910447762,0.784179104477612,0.5611940298507463,0.5591044776119403,0.5611940298507463,0.5438805970149254,0.5591044776119403
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results/corpus_160m.jsonl

@@ -26,3 +26,11 @@
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results/predictor_auroc.csv

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results/predictor_confirmatory.csv

@@ -1,26 +1,26 @@
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results/predictor_transfer_across_size.csv

@@ -1,51 +1,51 @@
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results/repair_160m.jsonl

@@ -31,3 +31,6 @@
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+{"set": "set1_repair", "size": "160m", "pair": [7, 9], "floor": 3.2518449100262963, "blimp_ceiling": 0.7850746268656716, "parent_nll": {"a": 3.2518449100262963, "b": 3.262358126108427}, "parent_blimp": {"a": 0.7631840796019901, "b": 0.7850746268656716}, "rungs": {"M0_naive_avg": {"nll": 12.994353232784981, "delta_floor": 9.742508322758685, "blimp_acc": 0.5200995024875622, "blimp_delta_vs_ceiling": -0.26497512437810944}, "M1_perm_avg": {"nll": 9.248907817086595, "delta_floor": 5.9970629070602985, "blimp_acc": 0.5492537313432836, "blimp_delta_vs_ceiling": -0.23582089552238805}, "M4_perm_repair": {"nll": 10.822711145807853, "delta_floor": 7.570866235781557, "blimp_acc": 0.5444776119402985, "blimp_delta_vs_ceiling": -0.2405970149253731}, "M5_naive_repair": {"nll": 12.46241381023728, "delta_floor": 9.210568900210983, "blimp_acc": 0.5005970149253731, "blimp_delta_vs_ceiling": -0.2844776119402985}}, "secs": 70.34143877029419}
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results/rung_summary.csv

@@ -19,11 +19,11 @@ SET1,pythia-160m,M1_perm_avg,36,10.02288783853742,6.770628947178989,6.4350962181
 SET1,pythia-160m,M1_orth_avg,36,9.43924131349886,6.186982422140428,6.223641830414476,5.1328521549351755,36,30.263730213562802
 SET1,pythia-160m,M2_task_arith,36,30.847811976576093,27.595553085217666,27.552373180650687,17.881782666111178,0,-205.33294380128154
 SET1,pythia-160m,M3_ties,36,61.54713634035592,58.29487744899749,57.96688460557195,49.302846651021284,0,-557.4068020335034
-SET1,pythia-410m,M0_naive_avg,14,9.434523058741904,6.450848737330442,6.462185834174209,5.951437196775115,0,0.0
-SET1,pythia-410m,M1_perm_avg,14,8.960686010812715,5.9770116894012535,5.866069915632136,5.560742920081131,11,7.098760479108755
-SET1,pythia-410m,M1_orth_avg,14,9.007940110172923,6.02426578876146,6.000841154433198,5.444177636272832,12,6.496034462786378
-SET1,pythia-410m,M2_task_arith,14,14.18815151943697,11.20447719802551,11.88480659380351,5.276258255870842,1,-73.79511239722994
-SET1,pythia-410m,M3_ties,14,13.072520845345844,10.08884652393438,10.229193819945571,9.321808319191334,0,-56.70806178750419
+SET1,pythia-410m,M0_naive_avg,15,9.51339803829637,6.529024965303259,6.501884166819552,5.951437196775115,0,0.0
+SET1,pythia-410m,M1_perm_avg,15,8.940158802355132,5.955785729362023,5.823021537248248,5.560742920081131,12,8.343768576396128
+SET1,pythia-410m,M1_orth_avg,15,9.009802572570125,6.025429499577014,6.041149798826851,5.444177636272832,13,7.446207957890576
+SET1,pythia-410m,M2_task_arith,15,14.152233459379755,11.167860386386648,10.687666980721014,5.276258255870842,1,-71.52665791829436
+SET1,pythia-410m,M3_ties,15,13.043874772368993,10.059501699375884,10.165132068452381,9.321808319191334,0,-54.69852547809134
 SET4,goldfish eng-nld_Latn,M0_naive_avg,1,1.6997519931043927,1.6892323678609804,1.6892323678609804,1.6892323678609804,0,0.0
 SET4,goldfish eng-nld_Latn,M1a_vocab_avg,1,1.866523052069927,1.7633057297477746,1.7633057297477746,1.7633057297477746,0,-4.385030934529799
 SET4,goldfish eng-nld_Latn,M1b_vocab_perm_avg,1,1.8689461892935666,1.7661259814597123,1.7661259814597123,1.7661259814597123,0,-4.551985568219941

results/set1_410m.jsonl

@@ -9,3 +9,7 @@
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results/set1_pairs.csv

@@ -118,6 +118,10 @@ SET1_polypythia,pythia-410m,410m,2-5,2,5,2.966551938091161,6.709219119475701,5.4
 SET1_polypythia,pythia-410m,410m,2-6,2,6,2.966551938091161,6.598871379138128,5.736445975008154,M1_perm_avg,0.8624254041299739,0.1306928646702331,9.565423317229289,6.598871379138128,8.702997913099315,5.736445975008154,8.769261150521853,5.802709212430692,18.633734278171886,15.667182340080725,12.607083613625244,9.640531675534083,6.585069550615623,5.722644027043073,0.06916573277070893,0.21995655247741347,1.364620822213107,1.3126072626543692,0.03811575985949241,1.0000000000000027,1.3025690067952007,0.04547183687060582,1.1666755679427225,1.0675317610187258,0.09914380692399671,0.0849797575677561,1.1666755679427225,1.0591213579136272,0.10755421002909538,0.09218861951378046,1.1666755679427225,1.0675317610187258,1.0591213579136272,0.0849797575677561,0.09218861951378046,0.8851315031932487,0.9087455727032031,0.050029406908131846,0.9499705930918682,0.05002940690812463,0.9499705930918754,0.049065747891060774,0.9509342521089392,0.5092867931841313,24,24,1
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+SET1_polypythia,pythia-410m,410m,4-6,4,6,2.9941555951361707,6.510865452238258,6.6494411361505215,M1_orth_avg,-0.13857568391226316,-0.02128375788576995,9.505021047374429,6.510865452238258,9.759864364196021,6.7657087690598505,9.643596731286692,6.6494411361505215,11.914558425574853,8.920402830438682,13.933378434849967,10.939222839713796,7.0465059821619676,6.5392375630019774,0.13208403513117548,0.22400800365540904,1.6257367163068805,1.6175686211417373,0.0050242422916414176,1.0000000000000024,1.6077656818530925,0.01105408660180356,3.5923064648029985,3.582406361790557,0.009900103012441708,0.002755918268511266,3.5923064648029985,3.5762861275428257,0.016020337260172823,0.004459624315781024,3.5923064648029985,3.582406361790557,3.5762861275428257,0.002755918268511266,0.004459624315781024,0.6004199830016228,0.8451289272710756,0.26577819376366874,0.7342218062363313,0.2657781937636684,0.7342218062363316,0.2870918236945902,0.7129081763054098,0.6796163975055732,24,24,1
+SET1_polypythia,pythia-410m,410m,5-6,5,6,2.9838060551971215,6.2838354539964545,5.909118294016023,M1_perm_avg,0.3747171599804311,0.05963191791441879,9.267641509193576,6.2838354539964545,8.892924349213144,5.909118294016023,9.044541251325017,6.0607351961278955,16.892477474722767,13.908671419525646,12.305614374388455,9.321808319191334,6.27866068402693,5.90394316571877,0.06875949872101057,0.21644653739512984,1.3709288286150396,1.3226898336211084,0.03518708921065142,1.0000000000000042,1.3219831400169375,0.03570257447102397,1.1685617425665693,1.0838233700238462,0.0847383725427231,0.07251510079100147,1.1685617425665693,1.0609269734917142,0.10763476907485514,0.09210875656296229,1.1685617425665693,1.0838233700238462,1.0609269734917142,0.07251510079100147,0.09210875656296229,0.4886045988178357,0.9239769985088346,0.7904735484131935,0.20952645158680647,0.7904735484131937,0.20952645158680627,0.666266973429068,0.333733026570932,0.48680403118117455,24,24,1
 SET1_polypythia,pythia-70m,70m,1-2,1,2,3.578833135217914,16.51484870288548,13.274780591951034,M1_orth_avg,3.2400681109344465,0.19619120763536518,20.093681838103393,16.51484870288548,19.618441806303,16.039608671085087,16.85361372716895,13.274780591951034,87.56473469096542,83.9859015557475,142.38916238584474,138.81032925062684,16.476801385151663,16.00156278666218,0.04497789766236219,0.21806951294232627,1.387215504551704,1.3279701097939447,0.04270814056169683,1.0000000000000009,1.3228835268970793,0.04637489809156538,1.1932036557173302,1.1206064765909887,0.07259717912634156,0.06084223659430341,1.1932036557173302,1.0562240011424613,0.13697965457486894,0.11479989515496372,1.1932036557173302,1.1206064765909887,1.0562240011424613,0.06084223659430341,0.11479989515496372,0.5172392251274267,0.7703912300771492,0.9650991431514635,0.03490085684853653,0.9650991431514635,0.03490085684853654,0.9614862646023198,0.03851373539768019,0.5336933143160137,6,6,1
 SET1_polypythia,pythia-70m,70m,1-3,1,3,3.578833135217914,20.71179135516043,10.563843091951036,M1_orth_avg,10.147948263209393,0.4899599503101884,24.29062449037834,20.71179135516043,27.663672639432484,24.08483950421457,14.14267622716895,10.563843091951036,84.06302236219831,80.48418922698039,124.66854717058057,121.08971403536265,20.685701871649236,24.058752489183277,-0.03963725521855644,0.1960134587844177,1.4475533367870448,1.3379341616984488,0.07572720970123568,0.9999999999999999,1.307247158076424,0.09692643106473788,1.2118348278280333,1.1427831521553735,0.0690516756726598,0.05698109518474629,1.2118348278280333,1.0763403649770777,0.13549446285095557,0.11180934871611319,1.2118348278280333,1.1427831521553735,1.0763403649770777,0.05698109518474629,0.11180934871611319,0.5346528047650446,0.7809718654448193,0.9051293370412379,0.09487066295876209,0.9051293370412379,0.09487066295876208,0.930894116581892,0.06910588341810799,0.48330722438050994,6,6,1
 SET1_polypythia,pythia-70m,70m,1-4,1,4,3.578833135217914,27.890989618369822,13.275441826050839,M1_orth_avg,14.615547792318983,0.5240239945696569,31.469822753587735,27.890989618369822,34.52885860037508,30.950025465157168,16.854274961268754,13.275441826050839,95.8927985363666,92.31396540114868,136.30908043868232,132.7302473034644,27.8399529603285,30.898992669092465,0.04355576957983617,0.17713822481673977,1.3940391987417349,1.3549455544588296,0.0280434325793646,0.9999999999999996,1.3368282505776277,0.04103969832107019,1.2372319633224234,1.1658744835860315,0.07135747973639184,0.05767510204373538,1.2372319633224234,1.1101203702159828,0.1271115931064406,0.10273869159110566,1.2372319633224234,1.1658744835860315,1.1101203702159828,0.05767510204373538,0.10273869159110566,0.5033392721168221,0.760755588678183,0.9580342341608327,0.04196576583916734,0.9580342341608327,0.041965765839167384,0.9705700834471602,0.029429916552839816,0.528244572757333,6,6,1
@@ -154,6 +158,3 @@ SET1_polypythia,pythia-70m,70m,6-9,6,9,3.631728518810135,16.430689941724765,9.04
 SET1_polypythia,pythia-70m,70m,7-8,7,8,3.6366048814110403,22.94067096150318,7.189522720207519,M1_orth_avg,15.751148241295661,0.6866036424012061,26.577275842914222,22.94067096150318,11.196601205764841,7.559996324353801,10.826127601618559,7.189522720207519,99.52662773157208,95.89002285016103,168.91501549249836,165.2784106110873,22.93331950984589,7.552646584706764,0.050997599795640995,0.2021025785262535,1.3783390918902083,1.2739011932286661,0.07577083119533345,1.0000000000000007,1.2892946575095274,0.06460270546238975,1.195081297909044,1.0879032252432266,0.10717807266581736,0.0896826624710301,1.195081297909044,1.0490014332119622,0.14607986469708178,0.12223424879350737,1.195081297909044,1.0879032252432266,1.0490014332119622,0.0896826624710301,0.12223424879350737,0.8622754448136462,0.7826939580676212,0.08430709663430969,0.9156929033656903,0.08430709663430835,0.9156929033656916,0.0686139208329587,0.9313860791670413,0.5479097840215689,6,6,1
 SET1_polypythia,pythia-70m,70m,7-9,7,9,3.631728518810135,18.61637951078869,7.041663242646159,M1_perm_avg,11.57471626814253,0.6217490496170146,22.248108029598825,18.61637951078869,10.673391761456294,7.041663242646159,14.283389136904763,10.651660618094628,96.22974763535551,92.59801911654537,156.36456804468364,152.7328395258735,18.60658987783095,7.0318754809554385,0.04863949587011176,0.19046681259249384,1.3794879332973322,1.3068144069986487,0.052681523733937294,1.0000000000000024,1.2570374083266864,0.08876520193834347,1.2010867317220326,1.0718377203589586,0.12924901136307398,0.1076100567506611,1.2010867317220326,1.0632100792639636,0.13787665245806902,0.11479325249092652,1.2010867317220326,1.0718377203589586,1.0632100792639636,0.1076100567506611,0.11479325249092652,0.6317034921464805,0.7721638524956678,0.6428467432067481,0.35715325679325194,0.6428467432067482,0.35715325679325177,0.6503441025969965,0.3496558974030035,0.547966688025233,6,0,1
 SET1_polypythia,pythia-70m,70m,8-9,8,9,3.631728518810135,23.211640539001344,7.333266843632787,M1_perm_avg,15.878373695368557,0.6840694292455947,26.84336905781148,23.211640539001344,10.964995362442922,7.333266843632787,12.58635061205561,8.954622093245474,93.61738115622961,89.98565263741948,153.05546722113502,149.4237387023249,23.209202357700892,7.330827866048495,-0.01701731389289497,0.18116254416912528,1.4264379475826996,1.2682465843688688,0.11089957574523894,1.0000000000000018,1.2862945451097594,0.0982471075663915,1.211153521585096,1.1101678643388733,0.10098565724622266,0.08337973299541564,1.211153521585096,1.0658534217597864,0.14530009982530956,0.11996835845809886,1.211153521585096,1.1101678643388733,1.0658534217597864,0.08337973299541564,0.11996835845809886,0.6410276021855338,0.7876314453326478,0.6300119448619816,0.3699880551380183,0.6300119448619816,0.3699880551380183,0.651777351333674,0.3482226486663261,0.5161361277534802,6,6,1
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-SET1_polypythia,pythia-410m,410m,4-6,4,6,2.9941555951361707,6.510865452238258,6.6494411361505215,M1_orth_avg,-0.13857568391226316,-0.02128375788576995,9.505021047374429,6.510865452238258,9.759864364196021,6.7657087690598505,9.643596731286692,6.6494411361505215,11.914558425574853,8.920402830438682,13.933378434849967,10.939222839713796,7.0465059821619676,6.5392375630019774,0.13208403513117548,0.22400800365540904,1.6257367163068805,1.6175686211417373,0.0050242422916414176,1.0000000000000024,1.6077656818530925,0.01105408660180356,3.5923064648029985,3.582406361790557,0.009900103012441708,0.002755918268511266,3.5923064648029985,3.5762861275428257,0.016020337260172823,0.004459624315781024,3.5923064648029985,3.582406361790557,3.5762861275428257,0.002755918268511266,0.004459624315781024,0.6004199830016228,0.8451289272710756,0.26577819376366874,0.7342218062363313,0.2657781937636684,0.7342218062363316,0.2870918236945902,0.7129081763054098,0.6796163975055732,24,24,1
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results/slerp_160m.jsonl

@@ -0,0 +1,36 @@
+{"set": "set1_slerp", "size": "160m", "pair": [1, 2], "floor": 3.253024047134907, "blimp_ceiling": 0.780497512437811, "parent_nll": {"a": 3.2705683101646588, "b": 3.253024047134907}, "parent_blimp": {"a": 0.780497512437811, "b": 0.7677611940298508}, "rungs": {"M0_naive_avg": {"nll": 10.931142788343934, "delta_floor": 7.678118741209026, "blimp_acc": 0.5619900497512438, "blimp_delta_vs_ceiling": -0.21850746268656718}, "M1_perm_avg": {"nll": 10.3836165874205, "delta_floor": 7.130592540285592, "blimp_acc": 0.5450746268656717, "blimp_delta_vs_ceiling": -0.2354228855721393}, "M6_slerp": {"nll": 14.979266918801981, "delta_floor": 11.726242871667074, "blimp_acc": 0.569452736318408, "blimp_delta_vs_ceiling": -0.21104477611940298}, "M7_perm_slerp": {"nll": 15.817921976287304, "delta_floor": 12.564897929152396, "blimp_acc": 0.5465671641791044, "blimp_delta_vs_ceiling": -0.2339303482587065}}, "secs": 164.80853462219238}
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results/slerp_70m.jsonl

@@ -1 +1,36 @@
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