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

arXiv: https://arxiv.org/abs/2608.29401 · HTML: https://mlsanity.com/paper/2608.29401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [First Proof](https://mlsanity.com/paper/2602.05192/) (2026)
- [Progress in Formalizing Sphere Packing in Dimension 8](https://mlsanity.com/paper/2604.23468/) (2026)
- [First Proof Second Batch](https://mlsanity.com/paper/2606.18119/) (2026)

## Abstract (arXiv)

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.