Coherence-Seeking Architectures for Agentic AI: A Unified Framework for Curiosity, Introspection, and Continuity

This paper presents three interconnected architectures addressing fundamental challenges in AI system reliability: hallucination reduction, reasoning consistency, and long-context performance.

(1) Manifold Resonance Architecture (MRA): A framework for detecting epistemic stress—internal contradictions, knowledge gaps, and semantic inconsistencies—
enabling systems to flag uncertain outputs before generation.

(2) Collaborative Partner Reasoning (CPR): A structured reasoning protocol with visibility tiers that improves output quality by separating exploratory reasoning from final responses.

(3) Continuity Core (C2): A hierarchical memory architecture (Working → Episodic → Semantic → Protected) providing contextual continuity for stateless systems. We provide mathematical formalizations, implementation specifications, and discuss integration patterns. These architectures address practical engineering challenges: reducing confident-but-wrong outputs, improving reasoning transparency, and enabling coherent behavior across extended interactions