Multispin Physics of AI Tipping Points and Hallucinations
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Abstract (arXiv)
Output from generative AI such as ChatGPT, can be repetitive and biased. But more worrying is that this output can mysteriously tip mid-response from good (correct) to bad (misleading or wrong) without the user noticing. In 2024 alone, this reportedly caused 67 billion in losses and several deaths. Establishing a mathematical mapping to a multispin thermal system, we reveal a hidden tipping instability at the scale of the AI's 'atom' (basic Attention head). We derive a simple but essentially exact formula for this tipping point which shows directly the impact of a user's prompt choice and the AI's training bias. We then show how the output tipping can get amplified by the AI's multilayer architecture. As well as helping improve AI transparency, explainability and performance, our results open a path to quantifying users' AI risk and legal liabilities.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 1 Aug 2025.
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References · 8
- 2017Attention Is All You Needcited by 60,361
- 2023Progress measures for grokking via mechanistic interpretabilitycited by 839
- 2023A mathematical perspective on Transformerscited by 164
- 2022Transformer Language Models without Positional Encodings Still Learn Positional Informationcited by 150
- 2024Clustering in Causal Attention Maskingcited by 32
- 2024A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Languagecited by 26
- 2023Mapping of attention mechanisms to a generalized Potts modelcited by 19
- 2025Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasingcited by 15
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