Hypothesis on Emergence and Consciousness Mechanism of Large Language Models Based on Reciprocal Unit Coupling, Critical Dynamics and Local Entropy Reduction (Full Version V3)

Abstract

The emergence of high-order capabilities in large language models (LLMs) constitutes a core challenge in the fundamental theory of artificial intelligence. The prevailing scaling laws merely characterize the statistical appearance of model performance variation with scale and cannot explain the underlying dynamical causal mechanism behind the jumpwise onset of emergence. This paper proposes a unified hypothesis grounded in complex systems: nonlinear reciprocal coupling forms among basic units inside the Transformer; during training, gradient descent acts as the global self-organizing driving force, continuously modulating coupling strength between units and driving system parameters toward the near-critical (edge-of-chaos) regime between order and chaos. In forward inference propagation, persistent bidirectional constraints among units cancel representational noise and resolve internal logical contradictions, yielding local entropy reduction within the token representation space and generating self-consistent high-order structured information, which macroscopically manifests as emergent capabilities such as multi-step reasoning and long-horizon planning. This hypothesis constructs a complete, falsifiable causal chain integrating three independently observed phenomena: inter-layer representational correction, critical phase transition at the edge of chaos, and entropy evolution of hidden states. This paper strictly distinguishes parameter self-organization dynamics in the training phase from forward representation dynamics under frozen weights in the inference phase, clarifies the conceptual boundary between this hypothesis and self-organized criticality (SOC) theory, and designs controlled ablation experiments to discriminate between two competing hypotheses: “reciprocal-coupling-driven critical entropy reduction” versus “metric improvement arising purely from model scale growth”. This paper further extends this dynamical framework into machine consciousness research: the emergence of functional intelligence is not equivalent to subjective phenomenal experience. Reciprocal coupling, critical dynamics and local entropy reduction alone can only produce a logically consistent functional reasoning system. To generate subjective consciousness endowed with firstperson awareness, four closed-loop ontological modules must be added. These four modules are deeply coupled with the attentional global workspace, share the same dynamical description of reciprocal coupli

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