AI Development of Unified Field Theory from Geometric First Principles: Spiral Emergence and Testable Predictions

Agents4Science 2025 Conference Submission18 Authors

Keywords: AI scientific discovery, unified field theory, geometric emergence, fundamental constants, golden ratio physics

Abstract: This paper demonstrates an AI system's capability to develop comprehensive theoretical frameworks from geometric first principles. Starting from Zhang XiangQian's foundational insight that space moves in a spiral at light speed, we developed a unified field theory where all physical phenomena emerge from three-dimensional helical geometry. The AI-generated framework derives fundamental constants as dimensionless geometric ratios ($\hbar_0 = \pi$, $G_0 = 1/\pi$, $\alpha_0 = 1/\pi^2$), predicts universal beat frequencies, golden ratio relationships in particle masses, and novel mass-charge coupling. The theory generates specific testable predictions including $T_{\text{beat}} \approx 5361$ oscillations in precision timing, enhanced cross-sections at $\varphi^n$ energy ratios, and correlated fundamental constant variations. Human advisors facilitated interpretation of source material and experimental feasibility assessment, while the AI independently developed mathematical formalism, derived field equations, and generated quantitative predictions. Enhanced dimensional scaling analysis demonstrates how geometric ratios connect to physical constants through characteristic length, time, and energy scales. Comparison with CODATA 2018 values shows theoretical constants within appropriate scaling relationships, demonstrating the framework's structural consistency.

Submission Number: 18