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authorYurenHao0426 <Blackhao0426@gmail.com>2026-04-08 01:15:08 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-04-08 01:15:08 -0500
commit671d9823668197c21b2d35d08d15da0d5c3c4161 (patch)
tree89d42a252b7522196cc992224a62e632d3038d3e /protocol/examples/threshold_sensitivity.py
parentdf9f69bc9172b3473be144ff8a17370bc7a68e64 (diff)
Add null calibration script: training-Bs vs fresh-Bs cos on penalized DFA
Codex round 19's #1 critical control. Result on penalized DFA s42 (lam=1e-2, 30 ep): training-Bs deep-layer cos: +0.1627 fresh-Bs deep-layer cos: +0.0022 ± 0.0220 (n=20 draws) The +0.17 measurement is REAL signal, not artifact. The network specifically adapted to its training-time Bs during the penalized run. Fresh Bs give essentially zero cosine (within noise). This validates the walk-back interpretation: in the rescued regime where ||g_l|| is meaningful, DFA's local credit signal shows partial alignment with BP grad — and this alignment is specifically the network learning to align with its specific Bs. Round 19 caveat preserved: cannot yet distinguish whether the alignment was always present in vanilla but hidden by measurement degeneracy, OR whether it was created by the penalty intervention. The early-epoch vanilla checkpoint sweep (round 19's other proposed control) would disambiguate.
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