# Can LLM generate interesting mathematical research problems?

arXiv: https://arxiv.org/abs/2603.18813 · HTML: https://mlsanity.com/paper/2603.18813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 19 Mar 2026.

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

- [First Proof](https://mlsanity.com/paper/2602.05192/) (2026)
- [Aletheia tackles FirstProof autonomously](https://mlsanity.com/paper/2602.21201/) (2026)
- [DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models](https://mlsanity.com/paper/2505.08744/) (2025)

## Built on by (most cited first, 2 of 2)

- [MECA: A Mechanism-Centered Agent for Constructing Well-Specified and Valuable Mathematical Conjectures](https://mlsanity.com/paper/2607.27709/) (2026)
- [Lectures on AI for Mathematics](https://mlsanity.com/paper/2604.11504/) (2026)

## Abstract (arXiv)

This paper is the second one in a series of work on the mathematical creativity of LLM. In the first paper, the authors proposed three criteria for evaluating the mathematical creativity of LLM and constructed a benchmark dataset to measure it. This paper further explores the mathematical creativity of LLM, with a focus on investigating whether LLM can generate valuable and cutting-edge mathematical research problems. We develop an agent to generate unknown problems and produced 665 research problems in differential geometry. Through human verification, we find that many of these mathematical problems are unknown to experts and possess unique research value.