[Submitted on 7 Sep 2026]
Title:Adaptive Entangled Game Modules in Artificial General Intelligence
View a PDF of the paper titled Adaptive Entangled Game Modules in Artificial General Intelligence, by Haochen Li and 4 other authors
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Abstract:We introduce a probability-wave framework for modeling the collective behavior of interacting adaptive agents, deriving testable eigenmodes through a generalized behavioral intelligence (GBI) nonlocal probability-wave equation. This framework captures a broad range of human intelligence behaviors with analytical mechanisms and offers an indirect method to examine the Liu-Chen-Ao (LCA) hypothesis of nonlocal entangled nerve fibers in the brain through collective trader behaviors. Our empirical analysis of Chinese intraday stock market data demonstrates that adaptive entangled game modes explain 82-94% (89% overall) of observed decision patterns, a sharp contrast to the predictions of neoclassical finance based on independent rational agents. Moreover, 2-12% of behaviors show adaption to intraday news, events, and environments, characterized by dual equilibrium states and abrupt reference point shifts, while purely independent modes occur in less than 5% of cases. These findings empirically support the LCA hypothesis, as observable trading behaviors reflect underlying brain mechanisms and internal intelligence decision-making in behavioral psychology. Our results highlight the necessity of incorporating adaptive entangled game modules into artificial general intelligence (AGI) architectures, addressing the limitations of conventional artificial neural network (ANN)-based AI, which relies on trillions of opaque parameters. By integrating ANN-based AI with probability-wave-based entangled-brain simulations, machine learning can enrich AGI foundation models (FMs) and facilitate the development of human-like processing units (HPUs) that leverage brain-inspired mechanisms. Such HPUs may ultimately create more compact, efficient, and robust AGI systems, particularly for embodied intelligence and robotics.
Comments: 22 pages, 13 figures, and 3 tables
Subjects:
Artificial Intelligence (cs.AI); Physics and Society (physics.soc-ph); Neurons and Cognition (q-bio.NC); General Finance (q-fin.GN); Quantum Physics (quant-ph)
Cite as: arXiv:2609.09226 [cs.AI]
(or arXiv:2609.09226v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.09226
arXiv-issued DOI via DataCite
Submission history
From: Leilei Shi [view email] [v1] Mon, 7 Sep 2026 09:44:12 UTC (701 KB)
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