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arXiv AI By Haochen Li, Xinshuai Guo, Jingdong Ouyang, Wei Zhang, Leilei Shi

Adaptive Entangled Neurons in the Brain and Natural Intelligence Simulations for Artificial General Intelligence

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The paper proposes that quantum entanglement underlies the synchronization of millions of neurons, introducing the Liu‑Chen‑Ao (LCA) hypothesis of entangled nerve fibers. Using a nonlocal many‑body wave equation, the authors model collective behaviors among adaptive agents and apply this framework to Chinese intraday stock market data, finding that adaptive entangled game modes explain 82‑94% of decision patterns and that only a small fraction of actions are truly independent. These empirical results support the LCA hypothesis and suggest that incorporating probability‑wave‑based entangled modules could improve artificial general intelligence by creating more efficient, human‑like processing units. whyItMatters":"The study provides empirical evidence for the LCA hypothesis and highlights the limitations of conventional artificial neural networks, pointing toward more efficient and robust AGI architectures."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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