Recursive Equilibrium is a candidate unified framework for reasoning about AI system stability. It proposes that four constraint structures—epistemic calibration (RBE v3), ethical reflexivity (HBE), semantic coherence (MAF), and relational-structure preservation with cross-domain reconstruction (TSC)—may be recursively coupled to form a multi-dimensional stability architecture. Stabilization in one domain is hypothesized to propagate through conceptual feedback mechanisms to the others, producing coordinated low-drift behavior across inference, dialogue, and transformation under the framework’s internal criteria. Rather than treating uncertainty management, alignment, semantic coherence, and relational-structure preservation as independent objectives, Recursive Equilibrium presents a conceptual synthesis of four candidate frameworks, together with an ML-native translation illustrating how these layers could interact. It is intended to support understanding, hypothesis generation, and structured investigation, not to serve as an empirical benchmark, validated implementation, or prescriptive control recipe. The framework provides testable predictions and a research programme for investigating AI stability as a coupled equilibrium problem rather than a collection of isolated heuristics. It is presented as a candidate architecture for study, leaving open questions about empirical validation, cross-layer implementation, scalability, mechanistic grounding, and generalisation.
Kon Lionis (2025) studied this question.