Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?
arXiv:2603.03202v1 Announce Type: new 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
arXiv:2603.03202v1 Announce Type: new 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.
Executive Summary
This article presents an innovative approach to generating high-difficulty mathematical problems using code agents. The authors develop a multi-agent framework that explores and evolves existing math problems, producing structurally distinct and more challenging variants. The study demonstrates the potential of code-driven agents to synthesize complex problems within scalable computational environments. The findings have significant implications for the development of large language models (LLMs) and their applications in mathematics education. While the results are promising, the methodology and data analysis could benefit from further refinement to fully establish the efficacy of the proposed approach.
Key Points
- ▸ The article introduces a multi-agent framework for problem evolution in mathematics.
- ▸ Code agents demonstrate the ability to synthesize new, solvable problems that are structurally distinct and more challenging than the originals.
- ▸ The study provides empirical evidence for the scalability of code-driven agents in generating high-difficulty mathematical problems.
Merits
Strength in scalability
The proposed approach leverages code execution to create a scalable environment for mathematical experimentation, addressing the scarcity of challenging problems for training and evaluation.
Demerits
Methodological limitations
The study relies on a limited dataset and could benefit from further replication and validation to establish the generalizability of the results.
Data analysis limitations
The article could benefit from a more detailed discussion of the data analysis methodology and the metrics used to evaluate the generated problems.
Expert Commentary
While the article presents a promising approach to generating high-difficulty mathematical problems, it is essential to consider the broader implications of this research. The scalability of code-driven agents in mathematics education raises important questions about the role of AI in the development of mathematical reasoning and problem-solving skills. Moreover, the study's findings highlight the need for further research into the ethics and fairness of AI-driven tools in mathematics education. As the field continues to evolve, it is crucial to prioritize the development of transparent, explainable, and accountable AI systems that promote equity and access in mathematics education.
Recommendations
- ✓ Future studies should focus on refining the methodology and data analysis to fully establish the efficacy of the proposed approach.
- ✓ The development of transparent and explainable AI systems is essential to ensure the fairness and equity of AI-driven tools in mathematics education.