Code2Math: Can Your Code Agent Evolve Math Problems Through Exploration?
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...
| Authors | Dadi Guo, Yuejin Xie, Qingyu Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 2 October 2026 |
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