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arXiv · 2607.25279

Many-body Tipping Dynamics of ChatGPT-like AIs

Frank Yingjie Huo·Neil F. Johnson
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Abstract

Why do ChatGPT-like AIs, despite major architectural and training differences, unexpectedly tip to undesirable content (e.g. harmful, misleading, repetitive) even under deterministic greedy decoding? We show that a broad class of such tippings is caused by the many-body interactions between tokens (spins) as they cross the finite-layer system. Tipping emerges as a dynamical first passage process between competing output basins. Attention disorder controls the transport toward, away from, or along the basins' boundary. A few-basin reduction yields a closed finite-layer threshold, whose coarse-grained predictions show good agreement across ChatGPT-like families. These results suggest that a broad class of AI failures represents 'foreseeable engineering risk' rather than inherently unpredictable behavior, with important implications for legal and societal assessments of AI harm.

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BibTeXRIS

Frank Yingjie Huo, Neil F. Johnson. 2026-07-28. Many-body Tipping Dynamics of ChatGPT-like AIs. https://arxiv.org/abs/2607.25279

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