Description

Introduces LEAP (Learned Energy on Action Paths), a framework for recovering stochastic actions from trajectory observations to reason about dynamical systems. This differentiable objective on path space captures temporal dependencies and enables path ranking, mechanism steering, and constrained optimization across physical and molecular simulations.

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