The Informational Coherence Index A Framework for the Integration of Networks of Artificial Intelligence Models

AbstractThis article presents the Informational Coherence Index Icoer\displaystyle I_{\text{coer}}Icoer, an innovative mathematical and computational model designed to quantify and optimize informational integration in networks of artificial intelligence (AI) models, with a focus on language models. Inspired by concepts from physics, thermodynamics, and information theory, Icoer\displaystyle I_{\text{coer}}Icoer is integrated into the General Theory of Unity (GTU), a theoretical framework that seeks to unify informational interactions in distributed systems. This work describes the formulation, implementation, visualization, and practical applications of Icoer\displaystyle I_{\text{coer}}Icoer, highlighting its relevance for AI ensembles, multi-agent networks, and collaborative systems such as ChatGPT, Grok, developed by xAI. KeywordsInformational Coherence, Icoer, General Theory of Unity (GTU), Distributed AI Networks, Lennard-Jones Potential, Boltzmann Distribution, Informational Coupling Factor, Informational Entropy, Harmonic Resonance, Model Ensemble, Multi-Agent Networks, Architecture Optimization, Empirical Validation, Dynamic Normalization Factor, Physics and Artificial Intelligence.