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

TL;DR

This paper explores how code agents can autonomously evolve math problems into more complex and challenging variants, demonstrating their potential for scalable mathematical problem synthesis.

Contribution

It introduces a multi-agent framework for problem evolution and validation, showing code agents can generate high-difficulty, solvable math problems.

Findings

Code agents can synthesize new, solvable problems that are more challenging.

The framework validates the increased difficulty and solvability of evolved problems.

Empirical evidence supports code-driven agents as tools for mathematical problem generation.

Abstract

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.