An ever-growing focus on agentic artificial intelligence within the computational science community has highlighted a fundamental bottleneck: existing simulation and design tools cannot keep pace with the extreme-dimensional decision-making problems required for truly autonomous, expert-level agents. Contemporary high-fidelity solvers struggle with multiscale and parametric partial differential equations that involve billions of degrees of freedom. Machine learning methods, in contrast, remain difficult to constrain by the principles of physics whilst scaling to high-dimensional decision spaces. Generative AI and inverse design require a substantial amount of data, often unavailable in physics. Many contemporary studies have proposed partial solutions to these problems. However, a unified set of computational methods that can produce scalable, transferable, and explainable AI systems for extremely high-dimensional multi-agent systems has yet to emerge. To bridge this gap, we introduce Separable Neural Architectures (SNA), a neural network class designed around explicit and controllable structural constraints to deliver a unified set of algorithms for effective agentic AI systems. In this work, we will demonstrate: 1. How SNAs can solve very high-dimensional partial differential equations with 102 − 104× reductions in memory footprint and degrees of freedom; 2. How the integration of an SNA into language models as a continuous-token representation mechanism yields improvements in likelihood and sample efficiency at a fixed parameter count; and 3. How this continuous-token idea extends autoregressive modelling beyond text to spatiotemporal fields to enable short-horizon distributional prediction of turbulence. In these demonstrations, Leviathan achieves a validation loss of 2.7 nats on next-step vorticity prediction, corresponding to a sub-percent normalised root-mean-square error. REFERENCES [1] Batley RT, Saha S, KHRONOS: a Kernel-Based Neural Architecture for Rapid, Resource-Efficient Scientific Computation, arXiv, 2025. [2] Batley RT, Saha S, A Separable Architecture for Continuous Token Representation in Language Models, arXiv, 2026. [3] Batley RT, Saha S, A Unified Generative-Predictive Framework for Deterministic Inverse Design, AIAA SCITECH Forum, 2026. [4] Batley RT, Park C, Liu WK, Saha S, An explainable artificial intelligence framework enabled by a separable neural architecture, Computational Mechanics, 2025.