Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

arXiv:2605.11102v1 Announce Type: cross Abstract: Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton-Raphson iteration count that depends on the direction of the warm start error rather than on its magnitude, and show as a corollary that the bound becomes vacuous as the smallest singula