Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws

arXiv:2512.10427v5 Announce Type: replace Abstract: Neural scaling laws and double-descent phenomena suggest that deep-network training obeys a simple macroscopic structure despite highly nonlinear optimization dynamics. We derive such structure directly from gradient descent in function space. For…