Separable Neural Architectures as a Primitive for Unified Predictive and Generative Intelligence

One mathematical primitive — separability — unifies models across reinforcement learning, materials design, turbulent flow simulation and natural language. The SNA framework works across all four domains, with the particularly striking observation that chaotic physical dynamics and linguistic autoregression share deep structural properties. A genuinely novel unifying connection.