Consciousness as a Jamming Phase

TL;DR

This paper proposes a physical framework modeling consciousness emergence in neural networks as a jamming phase transition, linking it to critical phenomena observed in disordered systems and providing a unified explanation for AI scaling laws.

Contribution

It introduces a neural jamming phase diagram that interprets consciousness emergence as a critical jamming transition, connecting AI behavior with physical jamming phenomena.

Findings

Identification of three control parameters: temperature, volume fraction, stress.

Demonstration of shared critical signatures between jamming and consciousness emergence.

Unified physical explanation for AI scaling laws and consciousness phenomena.

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.