太极统一场论(TUFT)在人工智能领域的工程落地:基于阴阳破缺机制的新型大语言模型推理架构 Taiji Unified Field Theory (TUFT) in Artificial Intelligence: A Novel LLM Reasoning Architecture Based on Yin-Yang Symmetry Breaking Mechanism

当前大语言模型(LLMs)深陷参数膨胀与算力内卷的困境,且受限于无约束的概率生成机制,导致严重的“幻觉”现象。现有思维链(Chain of Thought)技术虽能缓解此问题,但缺乏底层物理机制的约束。本文基于太极统一场论(TUFT)的道母/器母公理体系,提出了一种全新的模型架构——TUFT-AI。我们将动态破缺系数场 K(ε) 与阴阳映射为神经网络中的“太极阴阳注意力机制”与“量变质变门控”。通过引入阴阳博弈(阴场全局扫描与阳场逻辑收敛),系统能在推理过程中实现类似人类“顿悟”的自我纠错。本文不仅给出了完整的 PyTorch 底层代码框架,还基于移动端 Web 技术开发了轻量级演示原型。实验表明,该架构无需庞大算力,即可在受限设备上完美复现逻辑推导与反思博弈过程,为大模型的“去幻觉”与通用人工智能(AGI)的发展提供了全新的物理本体视角。

Large Language Models (LLMs) are currently trapped in the dilemma of parameter expansion and computing power involution. Due to their unconstrained probabilistic generation mechanisms, they frequently suffer from severe "hallucinations." Although existing Chain of Thought technologies can alleviate this issue, they lack the constraints of underlying physical mechanisms. Based on the Dao-mu/Qi-mu axiomatic system of the Taiji Unified Field Theory (TUFT), this paper proposes a novel model architecture: TUFT-AI. We map the dynamic symmetry breaking coefficient field K(ε) and Yin-Yang to the "Taiji Yin-Yang Attention Mechanism" and "Phase Transition Gate" in neural networks. By introducing Yin-Yang gaming (global scanning by the Yin field and logical convergence by the Yang field), the system can achieve self-correction similar to human "epiphany" during the reasoning process. This paper not only provides a complete PyTorch underlying code framework but also develops a lightweight demonstration prototype based on mobile Web technology. Experiments show that this architecture can perfectly reproduce the logical derivation and reflective gaming process on restricted devices without requiring massive computing power, providing a completely new physical ontological perspective for the "de-hallucination" of large models and the development of Artificial General Intelligence (AGI).