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

Batley, Sarker, Mostakim, Klichine & Saha (2026). Proposes the Separable Neural Architecture (SNA), a single representational class that unifies additive, quadratic and tensor-decomposed models across language, physics simulation, and reinforcement learning. The authors argue that separability often emerges in coordinates rather than existing in the system — a structurally elegant unification across seemingly unrelated domains.