Analog Hawking Radiation in Transformer Neural Networks: Discrete Geometric Horizons, Information Thermodynamics, and Hallucination Suppression

Abstract

Chur Chin*

Recent advances in deep learning have revealed that large-scale neural networks exhibit emergent behaviors reminiscent of physical systems near criticality. In parallel, analog gravity has demonstrated that phenomena traditionally associated with quantum field theory in curved spacetime-most notably Hawking radiation-can arise in diverse non-gravitational systems. This review synthesizes a comprehensive theoretical, computational, and experimental framework establishing an explicit analogy between Hawking radiation and information flow in Transformer neural networks. By interpreting attention dynamics as a discrete acoustic metric, we show that effective horizons emerge when information flow becomes supersonic, characterized by a Mach number exceeding unity. Gradients of this Mach field define a Hawking-like temperature that naturally induces thermal fluctuations in representation space. We review rigorous derivations of the discrete metric, horizon formation criteria, and temperature scaling laws, alongside a complete computational implementation that integrates horizon-aware regularization into standard Transformer architectures. Empirical results demonstrate universal power-law correlations near horizons, enhanced mutual information across information boundaries, and statistically significant reductions in hallucination rates. These findings suggest that geometric and thermodynamic principles provide a unifying language for understanding stability, generalization, and interpretability in modern neural networks.

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