Holon Memory Fields and Lifelong Learning: A Coherence-Based Framework for Continual AI

This white paper presents a novel solution to the lifelong learning problem using the Holon Coherence Field Framework—a unified model originally developed as a scientific and mathematical architecture for physical unification, now extended to the domain of continual machine learning. Holon evolved into a generalized AI cognitive architecture, grounded in coherence dynamics, entropy-regulated phase transitions, and interpretable memory structure. The framework addresses catastrophic forgetting through entropy-weighted coherence fields and adaptive memory shell logic. Evaluated across six lifelong learning benchmarks—including Split MNIST, Rotated MNIST, OpenLORIS, CORe50, and synthetic drift streams—Holon demonstrated consistent gains exceeding +44% over naive baselines, while maintaining real-time interpretability. All simulations, evaluations, and visual diagnostics were performed in OpenAI’s ChatGPT-4 environment. The results suggest Holon offers a powerful, transparent, and scientifically grounded approach to adaptive learning across sequential and non-stationary environments.