The Genesis of Artificially Evolved Spatial Organisms: From Static Syntax to Dynamic Semantic Generation in the “Deep Water” of Urban Regeneration

The trajectory of contemporary urbanization is defined by a dialectical tension between the high-frequency dynamism of digital innovation and the structural inertia of the built environment. As global urban development—particularly in China—enters the “Deep Water Zone” of stock regeneration, traditional regeneration paradigms have proven demonstrably insufficient. This paper posits a theoretical and methodological rupture: the transition from passive, reactive smart cities to Artificially Evolved Spatial Organisms (AESOs). Central to this evolution is the theory of Em-Spaced Intelligence (ESI), an ontological shift wherein space is reconceptualized as an autopoietic, cognitive agent. By integrating Multi-Modal Large Foundation Models (LFMs), Graph Neural Networks (GNNs), and Non-Euclidean Manifold Geometry, we propose a generative framework that enables space to identify and internalize its own social, economic, and ecological values. This paper explores the transition from “static syntax” to “dynamic semantic generation, ” validated through the Shanghai Quantum City pilot. Furthermore, it outlines a new paradigm of Robot-Intelligent Space, where autonomous agents and the environment co-evolve through a “Dynamic Space Protocol.” By resolving the dimensionality catastrophe of Euclidean modeling, this “AI for Science” (AI4S) approach ensures that urban morphology “fits” the rhythms of human and robotic life, moving from physical repair to “quality emergence.”