Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917551833874432 |
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| author | Guo, Dadi Xie, Yuejin Liu, Qingyu Huang, Weixian Liu, Jiayu Fan, Zhiyuan Ren, Qihan Shao, Shuai Zhou, Tianyi Feng, Jianjie Su, Wenze Yang, Yujiu Liu, Dongrui Fung, Yi R. |
| author_facet | Guo, Dadi Xie, Yuejin Liu, Qingyu Huang, Weixian Liu, Jiayu Fan, Zhiyuan Ren, Qihan Shao, Shuai Zhou, Tianyi Feng, Jianjie Su, Wenze Yang, Yujiu Liu, Dongrui Fung, Yi R. |
| contents | As large language models (LLMs) advance their mathematical capabilities toward the IMO and research level, the scarcity of challenging, high-quality problems has become a significant bottleneck for training, evaluation and self-evolution of LLMs. Simultaneously, recent code agents have demonstrated sophisticated skills in agentic coding and reasoning, suggesting that code execution can serve as a scalable environment for mathematical experimentation. In this paper, we investigate the potential of code agents to autonomously evolve existing math problems into more complex variations. We introduce a multi-agent framework designed to perform problem evolution while validating the solvability and increased difficulty of the generated problems. Our experiments demonstrate that, given sufficient test-time exploration, code agents can synthesize new, solvable problems that are structurally distinct from and more challenging than the originals. This work provides empirical evidence that code-driven agents can serve as a viable mechanism for synthesizing high-difficulty mathematical reasoning problems within scalable computational environments. Code and data is available at https://github.com/TarferSoul/Code2Math. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03202 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration? Guo, Dadi Xie, Yuejin Liu, Qingyu Huang, Weixian Liu, Jiayu Fan, Zhiyuan Ren, Qihan Shao, Shuai Zhou, Tianyi Feng, Jianjie Su, Wenze Yang, Yujiu Liu, Dongrui Fung, Yi R. Computation and Language As large language models (LLMs) advance their mathematical capabilities toward the IMO and research level, the scarcity of challenging, high-quality problems has become a significant bottleneck for training, evaluation and self-evolution of LLMs. Simultaneously, recent code agents have demonstrated sophisticated skills in agentic coding and reasoning, suggesting that code execution can serve as a scalable environment for mathematical experimentation. In this paper, we investigate the potential of code agents to autonomously evolve existing math problems into more complex variations. We introduce a multi-agent framework designed to perform problem evolution while validating the solvability and increased difficulty of the generated problems. Our experiments demonstrate that, given sufficient test-time exploration, code agents can synthesize new, solvable problems that are structurally distinct from and more challenging than the originals. This work provides empirical evidence that code-driven agents can serve as a viable mechanism for synthesizing high-difficulty mathematical reasoning problems within scalable computational environments. Code and data is available at https://github.com/TarferSoul/Code2Math. |
| title | Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration? |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.03202 |