Researchers Propose Adaptive Entangled Game Modules for AGI Architectures

Researchers have introduced a probability-wave framework designed to model the collective behavior of interacting adaptive agents. Using a generalized behavioral intelligence (GBI) nonlocal equation, the framework aims to capture human intelligence dynamics and evaluate the Liu-Chen-Ao (LCA) hypothesis regarding nonlocal entangled nerve fibers in the brain through market behaviors.

An empirical analysis of Chinese intraday stock market data demonstrated that adaptive entangled game modes account for 82% to 94% (89% overall) of observed decision-making patterns. In contrast, purely independent modes described less than 5% of trading cases, while 2% to 12% of behaviors reflected dynamic adaptation to intraday news and changing environments.

The authors state that integrating these adaptive game modules into artificial general intelligence (AGI) frameworks addresses the key limitations of standard artificial neural networks, which rely on trillions of opaque parameters. This approach could facilitate human-like processing units (HPUs) to power more compact, robust, and efficient AGI systems for robotics and embodied AI.