MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
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.
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Xukai Wang
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Xuanbo Liu
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2025-10-20T14:22:41.089Z
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Xuanbo Liu
user
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Mingrui Chen
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Haitian Zhong
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Xuanlin Yang
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Bohan Zeng
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2025-10-20T12:11:14.634Z
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bohan zeng
user
zbhpku
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Jinbo Hu
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Hao Liang
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Junbo Niu
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Xuchen Li
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2025-10-20T12:11:09.938Z
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Xuchen Li
user
Xuchen-Li
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Ruitao Wu
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Ruichuan An
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Yang Shi
claimed_verified
2025-10-20T12:11:17.061Z
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Yang Shi
user
DogNeverSleep
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Liu Liu
68f592bd8589920bf4d320cc
Xu-Yao Zhang
68f592bd8589920bf4d320cd
Qiang Liu
68f592bd8589920bf4d320ce
Zhouchen Lin
68f592bd8589920bf4d320cf
Wentao Zhang
68f592bd8589920bf4d320d0
Bin Dong
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
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.
MorphoBench is a benchmark that evaluates large models' reasoning capabilities using multidisciplinary questions, adaptive difficulty, and simulation-generated questions.
MorphoBench reasoning capabilities multidisciplinary questions adaptive difficulty simulation-generated questions