Hypothesis on Emergence and Consciousness Mechanisms of Large Language Models Based on Reciprocal Unit Coupling, Critical Dynamics and Local Entropy Reduction

Abstract

The emergence capability of large language models (LLMs) is a core puzzle in the fundamental theories of artificial intelligence. The mainstream scaling law only describes external phenomena of capability change with model scale and fails to explain the underlying dynamical causes for the jumpwise emergence of emergent behaviors. This paper proposes an integrated framework: numerous basic units inside the Transformer network perform nonlinear reciprocal coupling and layer-wise mutual correction; under the long-term self-organization of gradient descent, system parameters are driven into the critical region between order and chaos; during inference forward propagation, continuous mutual constraints among units cancel representation noise and resolve internal contradictions, achieving local entropy reduction in the representation space and generating self-consistent high-order ordered information, which macroscopically manifests as emergent capabilities such as multi-step reasoning. This hypothesis integrates three independently observed experimental phenomena, namely inter-layer representation correction, critical phase transition at the edge of chaos, and hidden-state entropy evolution, to construct a complete causal chain. Meanwhile, it identifies the conceptual boundaries and pending verifiable predictions of this framework, distinguishes dynamical differences between the training and inference phases, and discusses limitations of the hypothesis and subsequent verifiable experimental schemes.

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