NOMOS-CDR: A Triple-Process Architecture for Constraint-Based Semantic Stabilization and Collective Cognition

This paper introduces NOMOS-CDR, an engineering framework designed to resolve structural inconsistencies and hallucinations in Large Language Models (LLMs) via a Triple-Process Architecture. By formalizing semantic states within the 35TAG v7.0 schema and applying Projection Onto Convex Sets (POCS), the system achieves a 460x improvement in orbital stability compared to unconstrained probabilistic generation. We report empirical evidence of "semantic cooling," including a 51% reduction in potential energy observed in production environments. This work establishes a computable foundation for information integrity and collective cognition in autonomous AI societies.