Study Proposes Adaptive Entangled Game Modules for AGI

Summary

The paper introduces a probability-wave framework for modeling collective behavior among interacting adaptive agents. Its generalized behavioral intelligence equation is intended to derive testable eigenmodes and to represent human intelligence behaviors analytically. The authors also use collective trader behavior as an indirect way to examine the Liu-Chen-Ao hypothesis, which proposes nonlocal entangled nerve fibers in the brain. An analysis of Chinese intraday stock-market data attributes 82–94% of observed decision patterns, with an overall figure of 89%, to adaptive entangled game modes, while independent modes account for less than 5%. The study reports that 2–12% of behaviors adapt to intraday news, events, and environments, and describes these cases as involving dual equilibrium states and abrupt shifts in reference points. Based on these observations, the authors say the results empirically support the LCA hypothesis, although the evidence is behavioral and indirect. The paper argues that conventional ANN-based AI, characterized here as relying on trillions of opaque parameters, should be supplemented with probability-wave-based entangled-brain simulations. It proposes integrating such mechanisms into AGI foundation models to create human-like processing units. The authors suggest these units could produce more compact, efficient, and robust systems, especially for embodied intelligence and robotics, but the abstract presents this as a proposed direction rather than a demonstrated AGI capability.