Dynamic Polymorphic Routing in Sovereign AI Orchestration: The Flow sDAG Architecture

Current orchestration frameworks for Large Language Models (LLMs) rely on static Directed Acyclic Graphs (DAGs). While suitable for deterministic data pipelines, static DAGs fail to capture the highly variable, non-linear reality of cognitive tasks. This paper introduces the Flow segmented Directed Acyclic Graph (sDAG)-a polymorphic, self-optimizing execution environment designed for local, sovereign computing. By applying principles from active sub-critical nuclear control systems, the Flow sDAG achieves dynamic code switching, operator fusion, and adversarial hallucination mitigation. Crucially, the architecture strictly enforces a "Stakes Rule" (Shadow Staging), physically preventing autonomous overwrite and is designed to act as a cognitive exoskeleton rather than a labor-replacement engine. This paper positions the contribution honestly against a mature field of existing orchestration tools: the novelty claimed here is narrow and specific (Section 1.1), not a claim to have invented pipeline orchestration itself.