ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space

arXiv:2604.27443v1 Announce Type: cross Abstract: Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first and last frames) is a fundamental challenge. Existing approaches, (e.g., diffusion models), suffer from key limitations: (1) noise-to-data evolution fails to capture structural similarity between states close in physical time and has unstable integration in low-step regimes; (2) random noise injected