Code2Math：你的代码智能体能否通过探索有效演化数学问题？

摘要

随着大语言模型的数学能力向国际数学奥林匹克竞赛水平逼近，训练与评估所需的高难度优质题目稀缺已成为关键瓶颈。与此同时，近期出现的代码智能体在自主编程与推理方面展现出卓越能力，表明代码执行可成为数学实验的可扩展环境。本文研究代码智能体将现有数学问题自主演化为更复杂变体的潜力，提出一种多智能体框架，该框架在执行问题演化时能同步验证生成问题的可解性与难度提升。实验表明，在充分进行测试阶段探索的前提下，代码智能体能够合成结构新颖且难度超越原题的新问题。本研究为代码驱动智能体在可扩展计算环境中合成高难度数学推理问题提供了实证依据，相关数据详见https://github.com/TarferSoul/Code2Math。

English

As large language models (LLMs) advance their mathematical capabilities toward the IMO level, the scarcity of challenging, high-quality problems for training and evaluation has become a significant bottleneck. 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. Our data is available at https://github.com/TarferSoul/Code2Math.

Code2Math：你的代码智能体能否通过探索有效演化数学问题？

Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?

摘要

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