@inproceedings{wang-etal-2026-morphobench,
title = "{M}orpho{B}ench: A Benchmark with Difficulty Adaptive to Model Reasoning",
author = "Wang, Xukai and
Liu, Xuanbo and
Chen, Mingrui and
Zhong, Haitian and
Yang, Xuanlin and
Zeng, Bohan and
Hu, Jinbo and
Liang, Hao and
Niu, Junbo and
Li, Xuchen and
Wu, Ruitao and
An, Ruichuan and
Shi, Yang and
Liu, Liu and
Liu, Qiang and
Lin, Zhouchen and
Zhang, Xu-Yao and
Zhang, Wentao and
Dong, Bin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1171/",
pages = "23387--23411",
ISBN = "979-8-89176-395-1",
abstract = "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 GPT-5 and Gemini-3-Pro. 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."
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<abstract>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 GPT-5 and Gemini-3-Pro. 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.</abstract>
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%0 Conference Proceedings
%T MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning
%A Wang, Xukai
%A Liu, Xuanbo
%A Chen, Mingrui
%A Zhong, Haitian
%A Yang, Xuanlin
%A Zeng, Bohan
%A Hu, Jinbo
%A Liang, Hao
%A Niu, Junbo
%A Li, Xuchen
%A Wu, Ruitao
%A An, Ruichuan
%A Shi, Yang
%A Liu, Liu
%A Liu, Qiang
%A Lin, Zhouchen
%A Zhang, Xu-Yao
%A Zhang, Wentao
%A Dong, Bin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wang-etal-2026-morphobench
%X 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 GPT-5 and Gemini-3-Pro. 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.
%U https://aclanthology.org/2026.findings-acl.1171/
%P 23387-23411
Markdown (Informal)
[MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning](https://aclanthology.org/2026.findings-acl.1171/) (Wang et al., Findings 2026)
ACL
- 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, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, and Bin Dong. 2026. MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23387–23411, San Diego, California, United States. Association for Computational Linguistics.