Invariant-Bounded Resolution (IBR) A Termination Condition for Coherence Optimization in Reflexive Multi-Agent Environments

This paper introduces Invariant-Bounded Resolution (IBR), a formal extension to the Relative Framework and the Relational Cognitive Navigator (RCN) architecture that resolves a critical failure mode in self-rewriting artificial agents operating within reflexive, multi-agent environments. While Resolution-based optimization enables longitudinal epistemic coherence in non-reflexive environments, it lacks a principled termination condition when representational conflict arises from structurally incompatible yet internally coherent agents. IBR formalizes the conditions under which further minimization of representational conflict becomes undefined, destabilizing, or identity-destructive. By bounding Resolution optimization to invariant-preserving domains, IBR enables stable non-convergence, prevents coercive alignment, and completes the control architecture required for scalable, aligned intelligence. ORCID: 0009-0006-6041-352XWeb of Science ResearcherID: OYD-9537-2025