This record presents Recursive Equilibrium, a unified theoretical framework describing AI system stability as the coupled equilibrium of four constraint structures: epistemic calibration (RBE v3), ethical reflexivity (HBE), semantic coherence (MAF), and topological invariance (TSC) RecursiveEquilibriumMLTransl…. Rather than treating uncertainty management, alignment, meaning preservation, and representation stability as independent optimization problems, the framework proposes that these dimensions form a single, recursively coupled stability geometry. Stabilization in one domain propagates through feedback mechanisms to others, producing coordinated low-drift behavior across inference, dialogue, and compression. The contribution is a conceptual synthesis and ML-native translation explaining how these constraint layers interact, not an empirical benchmark or implementation recipe. The work reframes AI stability as a multi-dimensional equilibrium problem rather than a collection of isolated objectives, offering testable predictions and a foundation for future empirical validation.
Kon Lionis (Wed,) studied this question.