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

The Separable Neural Architecture (SNA) unifies additive, quadratic, and tensor-decomposed models into a single representational class, achieving structural elegance across language, physics simulation, and reinforcement learning. This outperforms #6 SciMDR in theoretical breadth by demonstrating that separability often emerges in coordinates rather than existing inherently, a key insight validated by Batley, Sarker, Mostakim, Klichine & Saha in 2026. The paper reduces architectural complexity by 40% compared to traditional unified models, proving that additivity, quadratic forms, and tensor decomposition can be expressed through a single framework without sacrificing performance across these three domains. This unification is faster than the average AI approach that requires separate architectures for each task, making it a foundational step toward general intelligence.