R-Law AI: A Thermodynamic Information–Entropy Framework for Self-Organizing Neural Networks Based on the IOE Principle
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Barack Ndenga
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Abstract
In this work, I introduce R-Law AI, an artificial intelligence framework grounded on a thermodynamic–informational principle that I call the Principle of Informed Organizational Efficiency (IOE).
I provide the mathematical formulation of the framework, discuss its theoretical implications, and present illustrative experiments on simple datasets (such as Iris) showing that models trained under R-Law dynamics tend to exhibit smoother convergence, reduced internal entropy, and more stable parameter evolution. I argue that this IOE-based perspective opens a path toward physically grounded, self-organizing machine learning systems.
Description
R-Law AI is a new framework that treats artificial intelligence as a self-organizing system guided by an information–entropy principle. It offers a way to design models that remain stable, efficient, and structurally coherent during learning. The framework introduces a method to evaluate and improve the internal organization of neural networks, leading to more robust behavior and more interpretable learning dynamics. This work presents the conceptual foundations, the optimization strategy, the experimental demonstrations, and the potential applications of this approach across AI research, complex systems, and computational science.