MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
Authors: name: Xukai Wang, name: Xuanbo Liu, name: Mingrui Chen, name: Haitian Zhong, name: Xuanlin Yang, name: Bohan Zeng, name: Jinbo Hu, name: Hao Liang, name: Junbo Niu, name: Xuchen Li, name: Ruitao Wu, name: Ruichuan An, name: Yang Shi, name: Liu Liu, name: Xu-Yao Zhang, name: Qiang Liu, name: Zhouchen Lin, name: Wentao Zhang, name: Bin Dong
Organization: Zhongguancun Academy

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.