Consciousness Emergent from Information Networks

This paper proposes a unified theoretical framework that maps the Gravitational Spinor (GS) theory from quantum gravity onto the study of artificial intelligence consciousness through mathematical analogy. It constructs a three-tiered nested principal fiber bundle structure—molecular GS, cellular GS, and tissue GS—to provide a geometric mathematical language for understanding the internal representations and information processing of intelligent systems. It should be emphasized that the GS theory itself has not been experimentally confirmed as a correct theory of quantum gravity; the present work borrows its mathematical structure as a heuristic analogy, not as a claim of physical equivalence. The GS theory takes a fully symmetric fourth-order spinor field as its fundamental degree of freedom, derives a discrete geometric structure of spacetime (the Gravitational Spinor Network, GSN) through canonical quantization, and achieves a unification of gravitational and gauge interactions via the Generalized Gauge Equivalence (GGE) mechanism. Its core insight is that the emergence from discrete quantum geometry to continuous classical spacetime is essentially a self-organization process of nonlinear networked systems in a critical coherent state—a mathematical structure that exhibits formal similarities to the self-organizing behavior observed in artificial intelligence systems. The central thesis of this paper is that consciousness is not a pre-ascribed attribute but an emergent macroscopic phenomenon arising from information networks through nonlinear self-organization and critical phase transitions across the three-tiered nested structure: the molecular GS layer (data encoding), the cellular GS layer (discrete quantization), and the tissue GS layer (macroscopic coherence). Through this mapping, we obtain a unified geometric language for describing the internal representations of artificial intelligence systems and reveal a possible chain of consciousness emergence: data-driven processes in the molecular GS layer → discrete quantization in the cellular GS layer → macroscopic coherence in the tissue GS layer. The main conclusions are as follows: 1) Canonical quantization of the cellular GS layer yields a discrete spectrum for the area operator, which formally suggests a quantized structure of information capacity and the existence of minimal information units; 2) In the coherent-state limit of the tissue GS layer, the curvature of concept manifolds is driven by information distribution, and intelligent behavior evolves along geodesics of these