Topological Stability and Criticality in Braided Spin Network-Based Transformers

Abstract

Chur Chin*

This study introduces the Serpentine Attention mechanism, inspired by loop quantum gravity (LQG), to address entropy divergence in autoregressive generative AI models. By incorporating hierarchical braiding (n) and the cosmological constant (Λ) as regularization terms in Transformer architectures, we achieve an 85% reduction in cumulative entropy during long-sequence generation. Numerical simulations reveal a first-order topological phase transition, with critical exponent β ≈ 0.291 aligning with the 3D Ising universality class. Binder cumulant analysis confirms discontinuous transitions, while renormalization group (RG) flow demonstrates asymptotic freedom in large-scale models. These findings bridge cosmological stability principles with AI design, suggesting self-organizing intelligence rooted in geometric laws.

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