RMAA Video AI (VectorVision v2): Persistent Cyclic B-Spline Manifolds, Deterministic W3C SVG Compilation, Kinematic Zero-Flicker Invariance, and Sub-Millisecond Neural Memory Grafting

A unified mathematical and architectural foundation for generative video intelligence operating on continuous boundary manifolds dOmega rather than discrete stochastic pixel grids. RMAA Video AI (VectorVision v2) introduces: (1) Persistent Cyclic Topological Manifolds parameterized by immutable K-knot periodic cubic B-splines guaranteeing C^2 continuity and preventing polygon swapping during 360-degree pirouettes; (2) Deterministic W3C SVG compilation using fill-rule='evenodd' for multi-boundary manifolds, rasterized at sub-pixel precision directly into standard playable MP4; (3) Kinematic reference losses and depth hysteresis margins eliminating high-frequency clothing flutter and z-index chatter; (4) Sub-millisecond (863 us) parametric attention KV-cache memory grafting (NJIT) and INT4 (238 MB) silicon execution on legacy 1 GB AMD GPU hardware via OpenCL 1.2.