Memory-Augmented Equilibrium Control (MAEC)

This document formalizes **Memory-Augmented Equilibrium Control (MAEC)**, a control-theoretic framework for real-time embodied creative systems. MAEC addresses a class of problems where traditional control theory and reinforcement learning fail: continuous, non-episodic systems that must maintain expressive viability while generating novel outputs. Unlike RL, MAEC has no scalar reward function, no policy optimization loop, and no episodic resets. Instead, it preserves dynamic equilibrium through memory-conditioned selection of speculative futures. MAEC was developed in the context of Computational Choreography—a system where human motion generates music in real time—but applies broadly to any domain where trajectory matters more than static output.