Separable neural architectures as a primitive for unified predictive and generative intelligence
This work proposes a domain-agnostic primitive for predictive and generative intelligence, potentially impacting various fields like physics and language, but it appears incremental as it builds on existing tensor decomposition and structural bias concepts.
The paper tackles the problem of monolithic neural architectures not exploiting factorisable structure in intelligent systems by introducing separable neural architectures (SNAs) that unify additive, quadratic, and tensor-decomposed models, demonstrating their versatility across domains like navigation, microstructure generation, turbulent flow modelling, and language modelling.
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 constraining interaction order and tensor rank, SNAs impose a structural inductive bias that factorises high-dimensional mappings into low-arity components. Separability need not be a property of the system itself: it often emerges in the coordinates or representations through which the system is expressed. Crucially, this coordinate-aware formulation reveals a structural analogy between chaotic spatiotemporal dynamics and linguistic autoregression. By treating continuous physical states as smooth, separable embeddings, SNAs enable distributional modelling of chaotic systems. This approach mitigates the nonphysical drift characteristics of deterministic operators whilst remaining applicable to discrete sequences. The compositional versatility of this approach is demonstrated across four domains: autonomous waypoint navigation via reinforcement learning, inverse generation of multifunctional microstructures, distributional modelling of turbulent flow and neural language modelling. These results establish the separable neural architecture as a domain-agnostic primitive for predictive and generative intelligence, capable of unifying both deterministic and distributional representations.