@inproceedings{liu-etal-2025-storm,
    title = "{STORM}-{BORN}: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework",
    author = "Liu, Wenhao  and
      Lu, Zhenyi  and
      Hu, Xinyu  and
      Zhang, Jerry  and
      Li, Dailin  and
      Cen, Jiacheng  and
      Cao, Huilin  and
      Wang, Haiteng  and
      Li, Yuhan  and
      Kun, Xie  and
      Li, Dandan  and
      Zhang, Pei  and
      Zhang, Chengbo  and
      Ren, Yuxiang  and
      Huang, Xiaohong  and
      Ma, Yan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1227/",
    pages = "23938--23958",
    ISBN = "979-8-89176-256-5",
    abstract = "High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.To address these, we introduce STORM-BORN, an ultra-challenging dataset of mathematical derivations sourced from cutting-edge academic papers, which includes dense human-like approximations and heuristic cues.To ensure the reliability and quality, we propose a novel human-in-the-loop, multi-agent data generation framework, integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians' evaluations. We curated a set of 2,000 synthetic samples and deliberately selected the 100 most difficult problems.Even most advanced models like GPT-o1 solved fewer than 5{\%} of them. Fine-tuning on STORM-BORN boosts accuracy by 7.84{\%} (LLaMA3-8B) and 9.12{\%} (Qwen2.5-7B).As AI approaches mathematician-level reasoning, STORM-BORN provides both a high-difficulty benchmark and a human-like reasoning training resource. Our code and dataset are publicly available at https://github.com/lwhere/STORM-BORN."
}