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

The separable neural architecture is an attempt at something ambitious: one mathematical framework — additive, quadratic, and tensor-decomposed models unified as a single class — that works across language, physics simulation, and reinforcement learning without a bespoke architecture for each. Batley, Sarker, Mostakim, Klichine, and Saha's case is that today's field builds a new monolithic network for every domain when a shared, factorizable structure was hiding in plain sight the whole time. Whether it holds up outside these three domains is the open question, but it's the kind of idea worth watching.