arXiv cs.AI· Haochen Li, Xinshuai Guo, Jingdong Ouyang, Wei Zhang, Leilei Shi·· 13 小时前AI 评分18
基于脑纠缠神经元与自然智能模拟的AGI新框架：用概率波模型检验LCA假说
Adaptive Entangled Neurons in the Brain and Natural Intelligence Simulations for Artificial General Intelligence
AI 导读
研究者提出广义概率波框架，用非局域多体波方程建模自适应交互智能体，间接检验Liu-Chen-Ao（LCA）纠缠神经纤维假说。对中国股市日内数据的分析显示，自适应纠缠博弈模式可解释82%-94%的决策模式，仅2%-12%的行为随新闻事件快速调整，真正独立行为不足5%。作者据此提出将ANN与概率波纠缠脑模拟结合的人脑式处理单元（HPU），面向具身智能与机器人。
正文 · 原文
Abstract:Understanding the brain's mechanisms is crucial in neuroscience, particularly for simulating natural intelligence and advancing artificial general intelligence (AGI). Liu et al. (2024) and Deco et al. (2025) propose that quantum entanglement may underlie the synchronization of millions of neurons, introducing the Liu-Chen-Ao (LCA) hypothesis of entangled nerve fibers. However, directly testing this idea remains challenging. Here we employ a nonlocal many-body wave equation, introducing a generalized probability-wave framework to model collective behaviors among adaptive, interacting agents, particles, or neurons. This approach enables testable eigenmodes, offering an indirect means of examining the LCA hypothesis by observing patterns in collective trading behaviors. These patterns, rooted in behavioral psychology, are thought to mirror the internal brain's decision-making and underlying mechanisms. Our analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 82%-94% of decision patterns, sharply contrasting neoclassical finance predictions assuming independent rational agents. Additionally, 2-12% of actions adapt to news, events, and environmental changes, marked by rapid equilibrium shifts, while truly independent behaviors make up less than 5%. These results empirically support the LCA hypothesis and underscore the need to simulate the brain's mechanisms and generate adaptive, entangled modules for AGI. They also expose the shortcomings of conventional artificial neural networks (ANNs) that depend on vast, opaque parameters. By combining ANN-based AI with probability-wave-based entangled brain simulations, machine learning enables the creation of human-like processing units (HPUs). Such HPUs promise more efficient and robust AGI, particularly for embodied intelligence and robotics.
| Comments: | 20 pages, 10 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:2609.09226v2 [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)
[v2]
Wed, 21 Oct 2026 09:16:00 UTC (638 KB)
来源：arXiv cs.AI · arxiv.org