DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models

• URL: http://arxiv.org/abs/2505.08744v1
• Date: Tue, 13 May 2025 16:58:05 GMT
• Title: DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
• Authors: Xiaoyang Chen, Xinan Dai, Yu Du, Qian Feng, Naixu Guo, Tingshuo Gu, Yuting Gao, Yingyi Gao, Xudong Han, Xiang Jiang, Yilin Jin, Hongyi Lin, Shisheng Lin, Xiangnan Li, Yuante Li, Yixing Li, Zhentao Lai, Zilu Ma, Yingrong Peng, Jiacheng Qian, Hao-Yu Sun, Jianbo Sun, Zirui Wang, Siwei Wu, Zian Wang, Bin Xu, Jianghao Xu, Yiyang Yu, Zichuan Yang, Hongji Zha, Ruichong Zhang,
• Abstract summary: The DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM.<n>This paper represents the initial contribution of this initiative.
• Score: 22.050241159312307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and systematically evaluating its mathematical creativity. This paper represents the initial contribution of this initiative. While recent developments in mathematical LLMs have predominantly emphasized reasoning skills, as evidenced by benchmarks on elementary to undergraduate-level mathematical tasks, the creative capabilities of these models have received comparatively little attention, and evaluation datasets remain scarce. To address this gap, we propose an evaluation criteria for mathematical creativity and introduce DeepMath-Creative, a novel, high-quality benchmark comprising constructive problems across algebra, geometry, analysis, and other domains. We conduct a systematic evaluation of mainstream LLMs' creative problem-solving abilities using this dataset. Experimental results show that even under lenient scoring criteria -- emphasizing core solution components and disregarding minor inaccuracies, such as small logical gaps, incomplete justifications, or redundant explanations -- the best-performing model, O3 Mini, achieves merely 70% accuracy, primarily on basic undergraduate-level constructive tasks. Performance declines sharply on more complex problems, with models failing to provide substantive strategies for open problems. These findings suggest that, although current LLMs display a degree of constructive proficiency on familiar and lower-difficulty problems, such performance is likely attributable to the recombination of memorized patterns rather than authentic creative insight or novel synthesis.

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