The Human-AI Synergy Theory (YQ-HAET): A Framework for Spatial-Logical Isomorphism and Anomaly-Based Macro Prediction

This paper formally introduces the Ye Qiang Human-AI Evolution Theory (YQ-HAET), an innovative theoretical framework bridging human professional intuition with artificial intelligence through spatial-logical isomorphism. Grounded in two decades of high-frequency empirical data management across the Chinese real estate industry and its downstream sectors, the theory posits that computational architecture evolves from linear "Chains" into multidimensional "Coordinates," ultimately forming a "Holistic Data Network". A central tenet is Anomaly Management: establishing outlier data points deviating from standard expectations as the primary catalyst for identifying macro inflection points and systemic phase shifts. By defining the human sovereign decision pivot—the "Point of World-Creator"—and establishing the physical evolution metric Ye Qiang Human-AI Process Rate (YQ-HAER), this framework demonstrates how the "Ripple-Contraction Law" governs the allocation of physical capital assets and digital compute resources alike, enabling reliable forward-looking predictions of macro trends and policy trajectories. Note on Version 1.1 Update (October 5, 2026):This version appends Appendix A as an additional document: "Empirical Validation of Spatial-Logical Isomorphism in Constrained Physical Heuristics — The 'Tree Ring' Topological Case".Official Appendix PDF SHA-256 Checksum:7CECC6E5364D9DB5195F042B7AF967C5B8A0CEB6F570CF5A116FA4ADF1263676