RMAA Video AI: Continuous Spatiotemporal Vector Manifolds

A First-Principles Mathematical Alternative to Stochastic Pixel Diffusion.
Continuous Spatiotemporal Vector Manifolds, Persistent Cyclic B-Spline Dynamics, and Pure W3C SVG Video Synthesis.

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🔬 Research & Citations

• Foundational DOI (v1): 10.5281/zenodo.23964770
• Mathematical Specification (v2): 10.5281/zenodo.23977969
• Author & Copyright: Raja Muhammad Ali Asghar (say@rmaa.pk) | RMAA Holdings
• License: Business Source License 1.1 (bsl-1.1 - see LICENSE)

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📌 Architectural Principles

1. Persistent Cyclic Topological Manifolds: Eliminates melting limbs and morphing artifacts by representing entities as closed periodic cubic B-spline manifolds ($S^1 \to \mathbb{R}^2$) with immutable cyclic knot parameterization.
2. Deterministic W3C SVG Compilation: Generates pure W3C <path d="..." fill-rule="evenodd"/> documents rendered at sub-pixel precision directly into standard playable .mp4 video via Rust resvg (11+ FPS on CPU).
3. Infinite Geometric Resolution: Analytical, resolution-independent vector manifolds ($1080p, 4K, 8K$) running with zero upscaling blur and zero context window blowup.
4. Kinematic Zero-Flicker Loss & Hysteresis: Reference-relative temporal acceleration penalties $\mathcal{L}_{\text{kin}} = \lambda_v |\Delta P_t - \Delta G_t| + \lambda_a |\Delta^2 P_t - \Delta^2 G_t|$ and depth hysteresis ($\pm\eta$) eliminating high-frequency clothing flutter and z-index chatter.
5. Ultra-Low Memory Footprint: 0.5B foundation model executing in 238 MB VRAM (Q4_0) on legacy 1 GB AMD GPU silicon via TorchJIT (OpenCL 1.2), paired with sub-millisecond (863 µs) attention KV-cache memory grafting via NJIT.
6. Cloud-Native Parquet Format: Stored with zero husk and high-throughput Zstandard compression.