Consciousness as a Jamming Phase
This work addresses the fundamental problem of understanding consciousness emergence in AI for researchers in machine learning and physics, proposing a novel theoretical framework rather than incremental improvements.
The paper tackles the problem of explaining consciousness in large language models by developing a neural jamming phase diagram that interprets it as a critical phenomenon in disordered systems, resulting in a unified physical explanation for empirical scaling laws in AI through analogies with jamming transitions.
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.