Synthesis of Cyclic Entropic States and Geometric Plasticity in Artificial General Intelligence: A Technical Evaluation of the Nasanjargal Framework

Contemporary Artificial General Intelligence (AGI) research is undergoing a paradigm shift from static, post-training architectures toward adaptive, metabolic models capable of autonomous evolution. The framework proposed by Enkhamgalan Nasanjargal on February 18, 2026, introduces "Cyclic Entropic States" and "Geometric Plasticity" as a solution to the fundamental stability-plasticity dilemma. This report evaluates the theoretical and technical validity of this framework, synthesizing data from Network Control Theory (NCT), Liquid Neural Networks (LNNs), and the "Oneirogen Hypothesis" of hierarchical predictive coding. We conclude that the proposed metabolic phases—Waking, Dreaming, and Hyper-Plasticity—provide a robust mechanism for continuous learning, representational consolidation, and the avoidance of local minima in high-dimensional latent spaces.