SOURCE-LINKED INTELLIGENCE

Bayesian Filtering in Physical Systems via Test-time Trained Flow Matching

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Bayesian filtering provides a principled framework for online state estimation under uncertainty, yet its application to systems with high-dimensional states and complicated posterior distributions remains challenging. Recent generative models, such as flow matching, have shown potential in Bayesian filtering. However, they still rely on particle-based representations of the posterior, which lose the rich information of the full distribution, or tackle a trajectory-level inverse problem that conflicts with the recursive structure of Bayesian filtering. To address this, we propose a new perspec

Read original source ↗ Open in workspace

recordType

paper

region

Global