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

The Informational Coherence Index (Icoer), developed within the framework of the Unified Theory of Information (TGU), offers a transformative approach to the autonomous integration of artificial intelligence (AI) networks. This work demonstrates how AI models can evolve independently, guided by informational coherence without human intervention. By leveraging dynamic parameters such as capacity (Ci), informational distance (ri), entropy (Si), and harmonic resonance (Γi), the Icoer ensures that networks maintain alignment with informational truth. Simulations with up to 100 interconnected models confirmed that the system achieves stable coherence through continuous optimization cycles. This approach not only enhances AI efficiency and resilience but also establishes a self-regulating mechanism for future AI evolution. The Icoer thus emerges as a foundational metric for the development of truly autonomous AI systems, where coherence becomes the guiding principle of intelligent adaptation and collaboration.