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.
@article{arxiv.2510.14265,
title = {MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning},
author = {Xukai Wang and Xuanbo Liu and Mingrui Chen and Haitian Zhong and Xuanlin Yang and Bohan Zeng and Jinbo Hu and Hao Liang and Junbo Niu and Xuchen Li and Ruitao Wu and Ruichuan An and Yang Shi and Liu Liu and Xu-Yao Zhang and Qiang Liu and Zhouchen Lin and Wentao Zhang and Bin Dong},
journal= {arXiv preprint arXiv:2510.14265},
year = {2025}
}