The Resonant Self-Consistency Architecture (RSCA): A Resonance-Based Framework for Reducing Hallucinations Through Autonomous Self-Verification

The Resonant Self-Consistency Architecture (RSCA) extends the author’s broader resonance-based theoretical work (URC/AOS) into the domain of AI cognition. RSCA proposes an internal self-auditing loop that allows AI models to detect inconsistencies, stabilize meaning, and reduce hallucinations without relying on external memory systems or user-instructed recall. The paper formalizes a five-stage self-consistency reasoning cycle, grounded in resonance-coherence principles, that enables a model to verify its own outputs before generating a final response. RSCA represents a conceptual shift in AI architecture—toward systems capable of autonomous internal verification, improved contextual stability, and more reliable cognitive behavior. This publication establishes the foundational framework, motivation, and implications of RSCA as a viable path for next-generation AI design.