Hypothesis on Emergence and Consciousness Mechanism of Large Language Models Based on Reciprocal Unit Coupling, Critical Dynamics and Local Entropy Reduction (Full Version V5)

Abstract

The emergence of high-order capabilities in large language models (LLMs) constitutes a core challenge in the fundamental theory of artificial intelligence. The mainstream scaling laws can only describe statistical phenomena of model performance growing with scale and cannot explain the dynamical causal mechanism behind emergent behaviors. This paper constructs a unified hypothesis grounded in complex systems theory: nonlinear reciprocal coupling forms among units inside the Transformer; during training, gradient descent acts as the global self-organizing driving force, modulating unit coupling strength and self-organizing system parameters into the near-critical regime at the edge of chaos. In inference with frozen weights, bidirectional constraints between units reduce local entropy of representations, generating self-consistent structured information and macroscopically manifesting emergent capabilities such as reasoning and planning. This work integrates three independently observed phenomena — inter-layer representational correction, system near-criticality, and hidden-state entropy decay — into a complete falsifiable causal chain. It strictly distinguishes parameter self-organization dynamics in the training phase from forward representation dynamics under frozen weights in the inference phase, and clarifies the conceptual boundary between this framework and strict self-organized criticality (SOC). Controlled ablation experiments are designed to decouple reciprocal-coupling-driven effects from pure model scale-growth effects. This paper further extends this dynamical framework into machine consciousness research: functional intelligence is not equivalent to subjective phenomenal experience. Transformer coupling and critical entropy reduction alone can only produce functional reasoning systems. To achieve first-person subjective experience, the system must incorporate four classes of closed-loop ontological modules. These four ontological modules are deeply coupled with the attention backbone, share the same dynamical description of reciprocal coupling and entropy evolution, and form steady-state tension at the ontological level relying on dissipative structures. This version (V5) adds the following supplements: (i) four practical estimation methods for the Jacobian matrix together with complexity analysis; (ii) full derivation of the k-NN differential entropy estimator with bias, variance and hyperparameter sensitivity analysis; (iii) rigorous derivat

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