AI RESEARCH
PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
arXiv CS.LG
•
ArXi:2605.16351v1 Announce Type: new Scientific foundation models are expected to reuse representations under changes in dataset, acquisition protocol, and deployment domain, yet many sequence backbones treat scientific temporal structure as an unconstrained pattern to be fitted. We argue that this misses a central property of natural dynamical systems: neural and atmospheric time series are organized by interacting processes across multiple physical timescales, and failure to preserve this multiscale structure contributes to brittleness under distribution shift.