We introduce a Stochastic Interpolants-based data assimilation framework for stochastic dynamic systems.

A framework that dynamically adjusts computational resources for robot controllers based on real-time task difficulty, reducing computation time by 2.6-4.4× while maintaining success rates, using the Stochastic Interpolant (SI) framework.

We introduce a framework for training accelerated, few-step generative models that includes consistency models, shortcut models, and mean flow as special cases.