We introduce a framework for in-context (zero-shot) inference/estimation of drift and diffusion functions underlying SDEs from empirical data of different dimensionalities.

This work improves Monte Carlo Tree Search for symbolic regression through an extreme bandit strategy and evolution-inspired state-jumping actions.