| title | ResearchMath-14K: Scaling Research-Level Mathematics via Agents |
|---|---|
| authors | Guijin Son, Seungyeop Yi, Minju Gwak, Hyunwoo Ko, Wongi Jang, Youngjae Yu |
| date | 2026-05-27 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.28003 |
| affiliation | Seoul National University |
| upvotes | 46 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | The frontier of mathematics is defined by problems whose solutions are not yet known, yet it remains unclear whether language models can meaningfully engage with such problems without human intervention. A major obstacle is the lack of large-scale research-level math datasets. To this end, we introduce ResearchMath-14k, a set of 14{,}056 problems curated from academic sources via a multi-agent pipeline, making it the largest collection of research-level mathematical problems to date. We further generate ResearchMath-Reasoning, 220K teacher trajectories from two open models, where we observe recurring avoidance behaviors such as non-attempts and fabricated references. Interestingly, across eight open-weight models, newer generations produce 5.6times more references and 5.0times more fake references per trace. After agentic filtering of ResearchMath-Reasoning, fine-tuning Qwen3 models from 4B to 30B parameters improves over base models by 9.2 points on average. This shows that filtered open-problem attempts can provide useful supervision even without fully correct reasoning traces. We make ResearchMath-14k publicly available for future works on research-level mathematical reasoning. |
cite
@misc{son2026researchmath,
title={ResearchMath-14K: Scaling Research-Level Mathematics via Agents},
author={Guijin Son and Seungyeop Yi and Minju Gwak and Hyunwoo Ko and Wongi Jang and Youngjae Yu},
year={2026},
eprint={2605.28003},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.28003}
}