DAIGS-Fusion: A Deterministic Multi-Organism Governance Architecture for Fusion Plasma Control

This paper proposes a deterministic multi-organism governance architecture for the control of fusion plasma systems. Fusion reactors exhibit complex, high-dimensional, and inherently unstable plasma behavior that challenges traditional control methods, including PID loops, predictive models, and machine learning approaches. These systems must manage rapid state transitions, emergent instabilities, safety-critical constraints, and tightly coupled subsystems operating at extreme physical limits. DAIGS-Fusion reframes fusion plasma control as a multi-organism ecosystem governed by deterministic arbitration, envelope-bounded safety, and certificate-based traceability. Plasma, magnetic confinement, fueling, safety, and energy extraction are modeled as interacting organisms with defined state models, invariants, transitions, and failure modes. A deterministic arbitration layer coordinates these organisms in real time, ensuring explainable, reproducible, and safety-first decision pathways. This work does not claim to solve fusion or propose new physics. Instead, it introduces a conceptual governance substrate that may offer a novel direction for research in fusion control architectures. The goal is to provide a structured, deterministic framework that could complement existing control systems and inspire interdisciplinary exploration.