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Critical initialization destabilizes higher input derivatives in wide scalar-input networks

arxiv.org/abs/2609.09244

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Updated 2 h ago · first seen 11 Sept 2026

paper_01M294GKXJQRK7WQYHBPN3E0SS

Published
11 Sept 2026
T1 · 2 h ago
arXiv
2609.09244
T1 · 2 h ago
Category
stat.ML
T1 · 2 h ago

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The edge-of-chaos condition preserves first-order input perturbations in wide randomly initialized networks, but physics-informed losses, score matching and derivative regularization depend on higher input derivatives. For smooth scalar-input fully connected networks, using a joint Gaussianity of the finite derivative jet that holds in the infinite-width limit at each fixed depth, we derive mean-field recursions through third order that are exact at the variance fixed point, with finite-depth corrections that decay geometrically. At criticality, the first-derivative variance is depth-invariant, whereas the second-derivative variance grows linearly whenever the activation has nonzero curvature. The resulting third-order system closes on mean-field susceptibilities. For residual networks with branch scale L^{-1/2}, we prove that every fixed finite derivative order has uniformly bounded variance under explicit regularity assumptions. Simulations verify the critical growth laws, the residual bound, and the closed recursion. The results concern initialization, not trained-network performance. → currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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