Depth-utility ladder: trainable-block sweep (BP/FA/DFA) on ResMLP CIFAR-10HEADmaster

Appendix experiment triangulating the depth-utility diagnostic (D3) by varying
the number of trainable residual blocks k (last-k trainable, first L-k frozen at
init; embed/LN/head always trained).

- d=256 L=4 and d=512 L=2, 3 seeds, recipe identical to the main audit.
- BP climbs monotonically (+22-23pp); DFA peaks at the frozen baseline (k=0) and
  declines once any deep block is trained; FA shows partial/no net depth utility.
- Cross-checks reproduce existing anchors (BP 0.617, DFA 0.301, FA 0.402, frozen 0.349).
- frozen_init_identity_check quantifies frozen stack as a near-norm-preserving
  random feature map (per-block ||f||/||h||~0.10, stack cos 0.981), explaining the
  above-chance k=0 rung.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

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