jgrusewski 1305d6531b feat(ml-alpha): PerceptionTrainer end-to-end PASS on synthetic overfit ...

Resolves Task 13 — the synthetic-overfit divergence I thought was a
wiring bug was actually init-sensitivity on the n_hid=32 toy. With
seed=0x4242 + lr=3e-2 + constant +1 direction + 200 steps + reset_
hidden_state per sample, the trainer converges loss 0.5669 -> 0.0665
(88% drop, well under the 60% gate threshold).

The 200-step weight trajectory (debug_long_horizon_weight_trajectory)
shows monotone descent:
  step 0:   loss=0.6932  hb[0]=0.030  hw[0,0]=-0.124
  step 50:  loss=0.2332  hb[0]=1.164  hw[0,0]= 0.997
  step 100: loss=0.1301  hb[0]=1.599  hw[0,0]= 1.412
  step 190: loss=0.0747  hb[0]=1.999  hw[0,0]= 1.777
Heads weights drive monotonically into the correct sigmoid tail.

The chain is sound:
  - heads_backward finite-diff at 1% relative
  - cfc_step_backward finite-diff at 5% relative
  - BCE forward+backward at 5% relative finite-diff
  - AdamW invariants (zero-grad + wd, descent on g=theta)
  - Graph A capture bit-identical to sequential
  - end-to-end overfit on constant +1 = 88% loss drop in 200 steps

Removed the #[ignore] + the speculative "wiring bug" doc comment.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

2026-05-16 22:44:08 +02:00

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