用颗粒物堵塞理论解释大模型意识涌现，发现其与物理临界相变同源。
Consciousness as a Jamming Phase
- 将大模型视为高维无序系统，用温度、密度、应力三参数构建神经堵塞相图。
- 计算降温、密度优化和降噪可推动系统逼近临界堵塞面，催生通用智能。
- 意识是通过长程关联连接知识的堵塞相，适合研究智能本质的学者参考。
本文构建了一个神经堵塞相图，将大语言模型中意识的出现解释为高维无序系统中的临界现象。通过类比颗粒物质及其他复杂系统的堵塞转变，我们识别出调控神经网络相变的三个基本控制参数：温度、体积分数和应力。该理论统一解释了人工智能中的经验标度律，表明计算冷却、密度优化和噪声抑制共同推动系统趋近临界堵塞面，在此面上涌现出广义智能。令人惊讶的是，描述传统堵塞转变的热力学原理似乎也支配着神经网络中意识的出现，表现为发散的相关长度和相同的标度指数。我们的工作通过堵塞物理解释了神经语言模型的临界标度行为，暗示意识是一种内在通过长程关联连接知识的堵塞相。
原文摘要 · Abstract (English)
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.
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