@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://aclanthology.org/2025.findings-acl.1227/",
doi = "10.18653/v1/2025.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 However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.To address these, we introduce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."
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<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 However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.To address these, we introduce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.</abstract>
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%0 Conference Proceedings
%T STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
%A Liu, Wenhao
%A Lu, Zhenyi
%A Hu, Xinyu
%A Zhang, Jerry
%A Li, Dailin
%A Cen, Jiacheng
%A Cao, Huilin
%A Wang, Haiteng
%A Li, Yuhan
%A Kun, Xie
%A Li, Dandan
%A Zhang, Pei
%A Zhang, Chengbo
%A Ren, Yuxiang
%A Huang, Xiaohong
%A Ma, Yan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-storm
%X 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.
%R 10.18653/v1/2025.findings-acl.1227
%U https://aclanthology.org/2025.findings-acl.1227/
%U https://doi.org/10.18653/v1/2025.findings-acl.1227
%P 23938-23958
Markdown (Informal)
[STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework](https://aclanthology.org/2025.findings-acl.1227/) (Liu et al., Findings 2025)
ACL
- Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, and Yan Ma. 2025. STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23938–23958, Vienna, Austria. Association for Computational Linguistics.