Explanations, Prompts, and Formalizations: Arguments for New Norms in LLM-Enabled Mathematical Research

Abstract:As several mathematical conjectures have recently been settled using large language models (LLMs), the mathematical community has formulated norms and recommendations regarding the publishing of such results. These norms do not cover the disclosure of the prompts and precise software setup used to obtain those results, nor do they require that results be formalized in a manner that allows for machine verification. I argue that both of these are essential. In addition, since LLM-obtained results may be hard to understand, human authors have the responsibility to invent intuitive explanations.

History and Overview (math.HO); Machine Learning (cs.LG)

arXiv:2608.29401 [math.HO]

(or arXiv:2608.29401v1 [math.HO] for this version)

https://doi.org/10.48550/arXiv.2608.29401 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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