Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?
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.
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Dadi Guo
69a7ac252833ff1122ff64dc
Yuejin Xie
69a7ac252833ff1122ff64dd
Qingyu Liu
69a7ac252833ff1122ff64de
Jiayu Liu
69a7ac252833ff1122ff64df
Zhiyuan Fan
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Qihan Ren
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2026-04-13T07:38:16.008Z
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Qihan Ren
jasonrqh
user
jasonrqh
69a7ac252833ff1122ff64e1
Shuai Shao
69a7ac252833ff1122ff64e2
Tianyi Zhou
69a7ac252833ff1122ff64e3
Dongrui Liu
69a7ac252833ff1122ff64e4
Yi R. Fung
Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?
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.
Code agents can autonomously generate more complex mathematical problems by evolving existing ones, providing a scalable solution for creating high-difficulty reasoning problems.
large language models mathematical capabilities code agents agentic coding reasoning problem evolution solvability computational environments