Add random-init sanity check: protocol does not flag untrained networks

3-seed random init ResMLP gives chance accuracy (~10%) but the protocol
verdict is 'trustworthy' on all 3 seeds:
  - residual norms ~8.7 across all layers (no growth, bounded)
  - BP gradient norms ~8e-3 (healthy, well above 1e-7 floor)
  - cross-batch stability 0.08-0.18 (in the BP/EP range)

This is the answer to the likely reviewer question: 'is your protocol just
flagging anything that doesn't perform well?' Answer: no. Random init is
at chance and the protocol passes it. The walked-back trained methods are
walked back because of the *measurements*, not because of the accuracy.

Notable: random init g-norms (8e-3) are actually HIGHER than BP-trained
ones (4e-4) — BP training reduces the gradient magnitude as loss decreases.
So the protocol distinguishes 3 distinct regimes: (1) untrained healthy,
(2) trained-and-still-healthy (BP/EP), (3) trained-into-pathology (DFA/SB/CB).

0 files changed, 0 insertions, 0 deletions