docs: Paper B GKD negative

docs/paper.adoc

@@ -300,7 +300,7 @@ zip through the runtime itself — sha-pinned to the index) agree on
 re-derives exactly by the same tooling from the published prediction
 files, which is how the discrepancy was caught.
 
-Two structural findings sit on it, one now scoped by a cross-lingual counterexample. First, *pretrained width is load-bearing*: SVD width-stitching of the pretrained ByT5-small fails at both tested ratios (3.8× narrow: 82.96 — worse than the from-scratch collapse; 2× narrow: 78.23). *Depth, by contrast, was compressible under our Arabic recipe*: a verbatim layer copy of encoder layers 12→6 trained to 5.78 full-set — a shippable rung at 63% of the parameters whose 1.21pp depth cost at 6 epochs is CI-separated from the full-depth peer. The Hebrew replication of that depth cut, single-variable against its own full-depth lineage (logit-KD recipe), collapsed instead: 77.48 DER versus the full-depth 30.38 (+53.77pp [51.64, 55.92]) despite normal training convergence. Depth-compressibility is therefore *not* a universal property of pretrained ByT5-small — it held under sequence-KD with Muon on Arabic and catastrophically failed under logit-KD on Hebrew; whether the boundary is the distillation regime or the language is open. Width surgery destroys the pretrained representation; depth surgery spends it — but only where the training regime lets it. Second, the epochs lever is real but secondary: doubling 3→6 epochs moves the rung 4.82→4.57 (−0.25pp) — most of the residual is not undertraining. Two pre-registered causal tests closed the domain-coverage attribution. The swap direction — 8k news-domain units replaced by classical-register Tashkeela at constant 30k budget — came back *negative* (5.81, −0.98pp vs control). The add direction — 48k total with the full cleaned Tashkeela corpus (5× classical coverage, all other levers held, 39,018 steps) — came back *flat-negative*: 4.8231 full-set, delta vs teacher 2.3717 [2.194, 2.554], statistically indistinguishable from the 2.1 rung (4.5701 [1.91, 2.35]) with the point estimate 0.25pp worse. Both directions of the classical-corpus lever fail: the residual is *not* a classical-domain coverage deficit. It reframes as a teacher–student interaction the corpus cannot reach — candidate levers are on-policy distillation (in flight) and label-distribution effects, not more in-domain text.
+Two structural findings sit on it, one now scoped by a cross-lingual counterexample. First, *pretrained width is load-bearing*: SVD width-stitching of the pretrained ByT5-small fails at both tested ratios (3.8× narrow: 82.96 — worse than the from-scratch collapse; 2× narrow: 78.23). *Depth, by contrast, was compressible under our Arabic recipe*: a verbatim layer copy of encoder layers 12→6 trained to 5.78 full-set — a shippable rung at 63% of the parameters whose 1.21pp depth cost at 6 epochs is CI-separated from the full-depth peer. The Hebrew replication of that depth cut, single-variable against its own full-depth lineage (logit-KD recipe), collapsed instead: 77.48 DER versus the full-depth 30.38 (+53.77pp [51.64, 55.92]) despite normal training convergence. Depth-compressibility is therefore *not* a universal property of pretrained ByT5-small — it held under sequence-KD with Muon on Arabic and catastrophically failed under logit-KD on Hebrew; whether the boundary is the distillation regime or the language is open. Width surgery destroys the pretrained representation; depth surgery spends it — but only where the training regime lets it. Second, the epochs lever is real but secondary: doubling 3→6 epochs moves the rung 4.82→4.57 (−0.25pp) — most of the residual is not undertraining. Two pre-registered causal tests closed the domain-coverage attribution. The swap direction — 8k news-domain units replaced by classical-register Tashkeela at constant 30k budget — came back *negative* (5.81, −0.98pp vs control). The add direction — 48k total with the full cleaned Tashkeela corpus (5× classical coverage, all other levers held, 39,018 steps) — came back *flat-negative*: 4.8231 full-set, delta vs teacher 2.3717 [2.194, 2.554], statistically indistinguishable from the 2.1 rung (4.5701 [1.91, 2.35]) with the point estimate 0.25pp worse. A third lever, on-policy distillation (GKD — training on student-generated mistakes scored by the teacher), also came back negative: 6.0036 [3.109, 3.743], 1.43pp *worse* than the off-policy rung it was meant to improve. The residual has now resisted every lever tested — corpus scale, register mix, on-policy correction, memory layers (real but 0.70pp), epochs (0.25pp) — and we report it as a property of the compression itself rather than a shortfall of any single method. Both directions of the classical-corpus lever fail: the residual is *not* a classical-domain coverage deficit. It reframes as a teacher–student interaction the corpus cannot reach — candidate levers are on-policy distillation (in flight) and label-distribution effects, not more in-domain text.
 
 == The decode protocol is part of the measurement
 [[section-decode]]