Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
For researchers in neurosymbolic AI and robotics, this work solves a geometric mismatch problem in denoising structured representations, enabling more accurate and efficient path integration.
The paper addresses the failure of Euclidean flow matching for denoising Spatial Semantic Pointers (SSPs) by introducing Geodesic Flow Matching on toroidal manifolds, achieving a 72% reduction in tracking error and 40% increase in neural efficiency in a spiking neural SLAM system.
Vector Symbolic Algebras (VSAs) enable robust neurosymbolic reasoning by encoding symbolic information into high-dimensional distributed representations. For continuous domains, Spatial Semantic Pointers (SSPs) extend this framework by mapping variables onto continuous toroidal manifolds. However, standard approaches like Flow Matching assume a flat Euclidean geometry, which fails to account for the geometric constraints imposed on valid SSP states. We demonstrate that this assumption fails for SSPs: Euclidean linear interpolants ``cut through" the manifold's interior, destroying the phase and magnitude structure required for accurate decoding. To resolve this, we employ Geodesic Flow Matching, adapting Riemannian transport dynamics to strictly restrict the denoising flow to the SSP toroidal manifold. We validate this approach in a Spiking Neural SLAM system, showing that manifold-aware cleanup stabilizes path integration against drift. The method achieves a 72\% reduction in tracking error and enables a 40\% increase in neural efficiency compared to competitive baselines. Code is available at https://github.com/kremHabashy/CleanupSSP .