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This paper develops a generative flow matching method to capture non-Markovian dynamics in non-equilibrium stochastic systems, demonstrating improved predictions for the Kramers first passage time problem compared to Markovian baselines.
This paper proposes a unified theoretical framework for phase transitions in deep learning (grokking, emergent capabilities) and non-equilibrium chemistry, describing both as driven informational systems governed by two gradient fields.