The Relational Cognitive Navigator (RCN)

This paper presents the Relational Cognitive Navigator (RCN), a formal agent architecture designed to achieve longitudinal epistemic coherence through the minimization of representational conflict. Unlike conventional AI systems that optimize for accuracy, recall, or compression, the RCN optimizes for Resolution, defined as the reduction of divergence between an agent’s internal model and the external environment over time. The architecture integrates a mathematical model of epistemic drift (the Knowledge Acquisition Paradox), a new objective function (Resolution), a recursive dual-layer identity system (S1/S2), and a geometric semantic projection method (Dimensional Folding). Together, these components define a control architecture that enables agents to self-rewrite, maintain coherence across long horizons, and avoid common failure modes such as hallucination, overconfidence, and context bloat. This project applies the principles of the Relative Framework to artificial intelligence as a way of thinking about stability and internal consistency. It does not describe an implementation of agentic AI, but a minimal architectural condition under which coherent agency could emerge. ORCID: 0009-0006-6041-352XWeb of Science ResearcherID: OYD-9537-2025