Consciousness as a Jamming Phase
arXiv Preprint Archive July 10, 2025 via arXiv
Summary
AI-generated from the abstractThis paper presents a theoretical framework that interprets the emergence of consciousness in large language models as a critical phenomenon in high-dimensional disordered systems, drawing analogies with jamming transitions in granular matter. The theory identifies three control parameters—temperature, volume fraction, and stress—that govern the phase behavior of neural networks. It provides a unified physical explanation for empirical scaling laws in AI, showing how computational cooling, density optimization, and noise reduction drive systems toward a critical jamming surface where generalized intelligence emerges. The authors argue that shared critical signatures, including divergent correlation lengths and scaling exponents, suggest consciousness is a jamming phase that connects knowledge components via long-range correlations.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Cond-mat.dis-nn Cs.ai |
| Key finding | Consciousness in large language models can be understood as a jamming phase transition in high-dimensional disordered systems, governed by temperature, volume fraction, and stress. |
Abstract
This paper develops a neural jamming phase diagram that interprets the emergence of consciousness in large language models as a critical phenomenon in high-dimensional disordered systems.By establishing analogies with jamming transitions in granular matter and other complex systems, we identify three fundamental control parameters governing the phase behavior of neural networks: temperature, volume fraction, and stress.The theory provides a unified physical explanation for empirical scaling laws in artificial intelligence, demonstrating how computational cooling, density optimization, and noise reduction collectively drive systems toward a critical jamming surface where generalized intelligence emerges. Remarkably, the same thermodynamic principles that describe conventional jamming transitions appear to underlie the emergence of consciousness in neural networks, evidenced by shared critical signatures including divergent correlation lengths and scaling exponents.Our work explains neural language models' critical scaling through jamming physics, suggesting consciousness is a jamming phase that intrinsically connects knowledge components via long-range correlations.