The Seonggil Framework for Non-Local, Multi-Layered Causal Networks: Real-Time Global Control and Feedback Stabilization

The dynamic control of complex, multi-layered causal networks is conventionally limited by local interaction constraints, time delays, and chaotic divergences (e.g., the butterfly effect). This paper introduces the Seonggil Framework, which establishes a higher order overarching control mechanism that governs non-local, vertical (downward/upward), and horizontal causal flows. By formalizing five axioms of multi-layered causality and observation-control duality, we derive mathematical theorems for global causal matrices, transfer-entropy-based information flow, and paradox suppression. We demonstrate throughcomputational simulation that modulating a non-commutative scale parameter enables complete global controllability and real-time stabilization of otherwise chaotic non-local networks.