Entropic Online Sampling (EOS)

An information-theoretic online temperature sampling algorithm for large language models. It dynamically re-solves the optimal temperature parameter, equivalent to a global Lagrangian multiplier, at each generation prefix to balance generation diversity against model likelihood. The method carries a theoretical entropy-loss guarantee of O(R log n).