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code/make_report.py

@@ -384,8 +384,11 @@ if rep:
     L.append(md_table(["substrate", "n pairs", "rung", "mean Δfloor (nats/tok)", "median Δfloor",
                        "BLiMP accuracy", "% of the parents' above-chance margin retained"], body))
     L.append("""
-REPAIR does help the likelihood — it takes a further bite out of the aligned merge's Δfloor, and it
-is the best training-free merge in this report. It does **not** change the conclusion. The repaired
+REPAIR does help the likelihood — it is the best training-free merge in this report, taking a further
+bite out of the aligned merge's Δfloor (on pythia-14m, 9.61 → 7.88 nats/token, a further 18%). **And
+BLiMP does not follow it at all**: 0.533 → 0.527, i.e. flat, and slightly *down*. That is the
+dissociation again, now inside a single rung comparison where the only thing that changed is a
+likelihood-improving correction. It does **not** change the conclusion. The repaired
 aligned merge is still many nats/token above the better parent, still above the
 uniform-over-vocabulary reference at the small sizes, and still close to chance on BLiMP. Applied to
 the *naive* merge it barely moves anything, which is the expected pattern: variance repair is only
@@ -488,8 +491,21 @@ if rev:
     L.append(md_table(["anchor", "floor (anchor lang)", "floor (English)"] + rk,
                       [[r["lang"], fmt(r["floor_x"]), fmt(r["floor_eng"])] +
                        [fmt(r["rungs"][k]["delta_floor_mean"]) for k in rk] for r in rev]))
-    L.append("\nΔfloor, mean over the two languages, nats/UTF-8 byte. The failure is symmetric: "
-             "anchoring on the partner language does not make the merge work either.\n")
+    L.append("\nΔfloor, mean over the two languages, nats/UTF-8 byte.\n")
+    L.append("""
+**The failure is symmetric, and that matters more than it looks.** The tokenizer wall is *not*
+symmetric: the English Goldfish tokenizer UNK-s 45% of Greek and 11% of Polish, while every partner
+tokenizer handles English at under 0.1% UNK (`results/set4_tokenizer_diag.json`). So the reverse
+direction is the clean test — anchor on the partner language and the vocabulary can represent both
+sides. The merge still fails, by the same margin, and the M1 rungs still do nothing. Two conclusions
+follow that the English-anchored direction alone could not support:
+
+1. The vocabulary mismatch is a real and sufficient obstruction in the English-anchored direction,
+   but it is **not the only** one — removing it does not make the merge work.
+2. What is left is the plain fact that the two parents were **independently initialised and
+   independently trained**. That is the same obstruction SET 1 isolates, and SET 1 already shows that
+   alignment only ever removes part of it and that the removable part shrinks with scale.
+""")
 
 L.append("\n## P0-2 · Do the pre-merge predictors predict the realised rescue?\n")
 if os.path.exists(f"{R}/predictor_auroc.csv"):
@@ -652,19 +668,39 @@ L.append(md_table(["cell", "n", "status", "what was measured"], cov))
 L.append("""
 ## Threats to validity, stated plainly
 
-- **Likelihood ≠ accuracy.** Repeated because it is the single most load-bearing caveat here.
+- **Likelihood ≠ accuracy.** Repeated because it is the single most load-bearing caveat here — and
+  because this report is one of the few places where both were measured on the same merges and found
+  to dissociate.
+- **BLiMP and MultiBLiMP are minimal-pair grammaticality benchmarks.** They are a real accuracy
+  measurement and they are not a general one. A merge that scores 0.68 on MultiBLiMP-English is not
+  thereby a usable model; agreement minimal pairs are unusually forgiving of a degraded model,
+  because the two candidates differ in one inflected token and the grammatical form is usually the
+  more frequent one. Read "retains accuracy" as "retains *this* accuracy", not as "works".
 - **SET 1's held-out corpus is FLORES-200 English devtest**, not a Pile validation split. It is
   genuinely held out from PolyPythia training, but it is out-of-domain, so the absolute nats/token
   floors are higher than a Pile-val number would be. Δfloor is a *difference* against parents
   measured on the same corpus, so the comparison between rungs is unaffected.
+- **SET 1's `-seed{n}` repos reseed initialisation AND data order together.** The 160m
+  weight-seed/data-seed control separates them (see the Control section) but only at n=3 pairs each.
+  The main grid's naive Δfloor should be read as an init-plus-data-order number.
 - **SET 4's nats/byte is comparable across tokenizers but not free of tokenizer effects**: block
   boundaries fall at different places for different tokenizers, and each block's first token is
-  unscored. With ~30k tokens per evaluation this is a sub-1% effect.
+  unscored. With ~30k tokens per evaluation this is a sub-1% effect. The much larger tokenizer effect
+  — the English tokenizer's UNK rate on partner-language text — is reported per cell and is a
+  substantive finding rather than a nuisance.
 - **The alignment search is over the permutation group (residual basis, MLP hidden axis, attention
-  heads) and its orthogonal relaxation.** It is not the full symmetry group, and the residual factor
-  is fitted from a finite activation sample. A better aligner could raise the M1 rungs; nothing here
-  bounds how far.
-- **SET 4's n = 4 language pairs.** Any predictor claim on that substrate is descriptive.
+  heads) and its orthogonal relaxation, plus embedding-row Procrustes for the cross-tokenizer case.**
+  It is not the full symmetry group, and the residual factor is fitted from a finite activation
+  sample. A better aligner could raise the M1 rungs; nothing here bounds how far. What *is* bounded
+  is the claim that the aligners already in `mergeschool.core` do the job on these substrates.
+- **The largest SET 1 sizes carry the fewest pairs.** 14m/31m/70m are complete 36-pair grids; 160m
+  and 410m are partial. The scale trend is monotone across all five but its right-hand end is thin.
+- **SET 4's n = 4 language pairs**, all with English as one parent and all Indo-European. Any
+  predictor claim on that substrate is descriptive, and nothing here speaks to non-Indo-European or
+  to non-English pivots.
+- **Everything here is training-free by construction.** No claim is made about what a small amount of
+  post-merge finetuning would recover; that is the obvious next experiment and it is out of scope for
+  a training-free audit.
 """)
 
 L.append("\n## Files\n")