Accelerating stochastic processes through nonequilibrium driving: Thermodynamic constraints on the maximum speed-up

What the math gives to ML

The paper provides a nonstandard thermodynamic speed-limit mechanism: nonequilibrium perturbations can accelerate survival-rate-limited processes only within bounds determined by perturbation strength, pre-perturbation entropy production, or excess heat for rare approximately exponential events. This transfers naturally to diffusion-model sampling and learned stochastic samplers by treating arrival at a target region or mode as a first-passage event and adding a controlled nonreversible drift. The engineering objective is to obtain faster mixing or mode arrival under an explicit dissipation budget, while testing the predicted linear or exponential ceiling on hazard-rate improvement.