Praxis: An Asynchronous, Neurosymbolic Predictive State-Space Architecture for Sub-Microsecond Determinism at the Edge

Deploying deep reasoning systems in highly non-stationary, adversarial environments presents a severe physical contradiction. Generative world models waste immense compute reconstructing high-dimensional observation spaces, inherently violating strict micro-second latency bounds. Furthermore, purely connectionist architectures lack the structural guarantees required to prevent kinematic or logical failure under out-of-distribution (OOD) adversarial stress. In this paper, we introduce Praxis, a generalized, domain-agnostic mathematical framework for non-contrastive predictive control, engineered into a hardware-native, dual-loop neurosymbolic architecture. We evaluate its architectural instantiation in the most unforgiving domain: tactical electronic warfare (EW) on heterogeneous defense silicon. By mapping a cognitive reasoning loop (asynchronous Monte Carlo Tree Search) to general processing subsystems while synthesizing a formally constrained, micro-second deterministic fast-path onto programmable logic, Praxis physicalizes deep reasoning. Protected by a differentiable symbolic manifold, epistemic uncertainty calibrated under bounded adversarial perturbation budgets, and exact hardware-level Farkas Dual certificate preemptive interrupts, the active inference engine achieves invariant execution within a strict 276-nanosecond pipeline. Praxis demonstrates that generalized predictive intelligence can be achieved at the tactical edge without sacrificing the mathematical determinism of hard physics.