Separable neural architectures as a primitive for unified predictive and generative intelligence

ArXi:2603.12244v1 Announce Type: cross Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicitly exploit this structure. The separable neural architecture (SNA) addresses this by formalising a representational class that unifies additive, quadratic and tensor-decomposed neural models. By cons