Guji-Math: An Evaluation Benchmark for Assessing the Effectiveness of Reasoning Models in Solving Ancient Chinese Mathematical Problems

To promote the revitalization and utilization of ancient Chinese mathematical resources in the era of generative AI, this study designs Guji-Math, a specialized benchmark for evaluating ancient mathematical problems tailored to assess reasoning models. Guji-Math is constructed from the Suanjing Shishu (《算经十书》), the most renowned compendium of ancient Chinese mathematics.

Leveraging the unique "Question-Answer-Solution" textual structure, the benchmark creates verifiable question-answer pairs. Through semi-automatic annotation, each problem is assigned one of four difficulty levels and one of 15 problem types, resulting in a collection of 538 mathematical questions and 511 solution methods. The benchmark provides two evaluation modes—open-book and closed-book—to assess reasoning models' accuracy in solving problems either without external assistance or by referencing only the original solution methods from the texts.