Empirical Support for a 5D Time-Field Theory Across Quantum,Cognitive, and Thermodynamic Domains

This preprint presents cross-domain empirical support for a 5D Time-Field Theory that redefines time as a structured, dynamic field coupled to entropy and curvature. Unlike classical models or high-dimensional frameworks like string theory, this theory makes falsifiable predictions about observable phenomena across physics, cognition, and thermodynamics.The document includes numerical simulations showing:Asymmetric quantum wavefunctions driven by temporal field curvatureKL divergence asymmetry indicating irreversible memory evolution in AI modelsEntropy acceleration in phase transitionsComparison to Newtonian and MOND galactic rotation profilesAll results are testable, timestamped, and documented. This work provides a compact alternative to higher-dimensional theories while remaining consistent with observations across scales.

本预印本为5维时间场理论（5D Time-Field Theory）提供了跨领域的实证支撑，该理论将时间重新定义为一种与熵和曲率耦合的结构化动态场。与经典模型或弦理论（string theory）等高维框架不同，该理论针对物理学、认知科学与热力学领域的可观测现象提出了可证伪的预测。本文包含以下数值模拟结果：由时间场曲率驱动的非对称量子波函数、表明AI模型记忆演化具备不可逆性的KL散度（KL divergence）不对称性、相变过程中的熵增加速，以及与牛顿力学及修正牛顿动力学（MOND）的星系旋转曲线对比。所有结果均具备可检验性，附带时间戳且文档记录完整。本研究为高维理论提供了一种简洁的替代框架，且在所有尺度上均与观测结果保持一致。