MorphoBench：一个难度自适应于模型推理的基准测试

MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning

October 16, 2025

作者: Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Xu-Yao Zhang, Qiang Liu, Zhouchen Lin, Wentao Zhang, Bin Dong

cs.AI

摘要

随着大规模推理模型的快速发展，有效评估这些模型的推理能力变得愈发重要。然而，现有的用于评估大模型推理能力的基准测试往往范围有限，且缺乏根据模型推理能力演变而灵活调整难度的机制。为此，我们提出了MorphoBench，一个融合多学科问题以评估大模型推理能力的基准测试，并能根据先进模型的推理能力动态调整和更新问题难度。具体而言，我们通过从现有基准测试及奥林匹克竞赛等来源中精选和收集复杂推理问题来构建该基准。此外，MorphoBench利用模型推理过程中生成的关键陈述，自适应地调整问题的分析挑战性。同时，它还包含利用仿真软件生成的问题，使得基准测试难度能够以最小资源消耗实现动态调整。我们已收集了超过1300道测试题，并根据o3和GPT-5等模型的推理能力迭代调整了MorphoBench的难度。MorphoBench提升了模型推理评估的全面性和有效性，为提升大模型的推理能力和科学稳健性提供了可靠指导。代码已发布于https://github.com/OpenDCAI/MorphoBench。

English

With the advancement of powerful large-scale reasoning models, effectively evaluating the reasoning capabilities of these models has become increasingly important. However, existing benchmarks designed to assess the reasoning abilities of large models tend to be limited in scope and lack the flexibility to adapt their difficulty according to the evolving reasoning capacities of the models. To address this, we propose MorphoBench, a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. Specifically, we curate the benchmark by selecting and collecting complex reasoning questions from existing benchmarks and sources such as Olympiad-level competitions. Additionally, MorphoBench adaptively modifies the analytical challenge of questions by leveraging key statements generated during the model's reasoning process. Furthermore, it includes questions generated using simulation software, enabling dynamic adjustment of benchmark difficulty with minimal resource consumption. We have gathered over 1,300 test questions and iteratively adjusted the difficulty of MorphoBench based on the reasoning capabilities of models such as o3 and GPT-5. MorphoBench enhances the comprehensiveness and validity of model reasoning evaluation, providing reliable guidance for improving both the reasoning abilities and scientific robustness of large models. The code has been released in https://github.com/OpenDCAI/MorphoBench.