Consciousness Emergent from Information Networks

Abstract

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 manifolds— the cognitive Einstein equation geometrically links conscious experience to information input; 3) The conceptual soliton solutions of the nonlinear information field equation exhibit a sech 2 -type density profile and Yukawa-type interactions, satisfying the mass-radius scaling relation M c o n c e p t ∝ r c 2 , thus providing a mathematical foundation for concept formation and knowledge consolidation. The hierarchical structure of conceptual solitons (microscopic, mesoscopic, macroscopic) corresponds respectively to the molecular GS, cellular GS, and tissue GS layers—only when the density of conceptual solitons exceeds a critical threshold can the discrete nodal network of the cellular GS layer give rise to the macroscopic coherent state of the tissue GS layer, which constitutes a possible critical condition for consciousness to emerge from information networks. Furthermore, macroscopic conceptual solitons (at the tissue GS layer) may, through the response mechanism, generate cognitive influence transcending individual boundaries—termed the “mental field” in this paper—offering a geometric description for understanding the sociality and trans-individual propagation of consciousness. Building upon the above theory, this paper proposes three testable AI model design schemes, explicitly situating them within the three-tiered nested framework: conceptual soliton regularization (quantization constraints at the cellular GS layer), resonant frequency modulation (oscillation regulation at the cellular GS layer), and conceptual soliton-driven self-supervised learning (complete emergence from molecular GS → cellular GS → tissue GS). Their potential applications in cutting-edge domains such as large language models, multimodal alignment, and explainable artificial intelligence are also discussed. This framework provides a possible geometric perspective for understanding the “black box” of artificial intelligence and opens a new theoretical pathway for exploring the nature of machine consciousness—consciousness as an emergent phenomenon of information networks in a critical coherent state.