Geodesic Flow Matching for Denoising High-Dimensional Structured Representations

In three linesGeodesic Flow Matching adapts Riemannian transport to denoise Spatial Semantic Pointers (SSPs) on toroidal manifolds. Tested on a Spiking Neural SLAM system, the method achieves 72% reduction in tracking error and 40% increase in neural efficiency versus baselines.

Summary generated by Claude — human-verified