AI RESEARCH
Geodesic Flow Matching for Denoising High-Dimensional Structured Representations
arXiv CS.AI
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ArXi:2606.00248v1 Announce Type: new 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.