Decoder-only Transformer where attention operates on geodesic distances in a learned Riemannian manifold with gravitational curvature and variable dimensionality per token. Based on Directional Relational Manifolds (DRM)

A physics-aware transformer architecture that replaces standard query-key-value attention with Newton's law of gravitation, producing a minimal yet powerful model optimised for resource-constrained environments, edge deployment, and VictorOS cognitive-runtime integration.