@inproceedings{zhang-etal-2026-geochallenge,
title = "{G}eo{C}hallenge-90{K}: A Multi-Answer Multiple-Choice Benchmark for Diagram-Grounded Geometric Reasoning",
author = "Zhang, Yushun and
Fu, Weiping and
Yang, Zesheng and
Zhao, Bo and
Zhang, Lingling and
Zhang, Jian and
Fu, Yumeng and
Huang, Jiaxing and
Liu, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2583/",
pages = "58710--58730",
ISBN = "979-8-89176-459-0",
abstract = "Evaluating the symbolic reasoning of large language models (LLMs) calls for geometry benchmarks that require multi-step proofs grounded in both text and diagrams. However, existing benchmarks are often limited in scale and rarely provide visually grounded multiple-choice questions, limiting reliable evaluation of complex reasoning. We introduce GeoChallenge, a dataset of 90K automatically generated multiple-choice geometry proof problems, each requiring multi-step reasoning over aligned textual descriptions and diagrams. GeoChallenge provides fine-grained complexity ratings and formal language annotations to enable controlled evaluation. Experiments on multiple advanced LLMs show a clear performance gap between models and humans (the best-performing model, GPT-5-nano, achieves 75.89 exact match vs. 94.74 for humans). Further analysis also reveals three common failure patterns of LLMs: (1) exact match failures under the multiple-choice setting; (2) weak visual reliance; and (3) overextended reasoning without convergence."
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<abstract>Evaluating the symbolic reasoning of large language models (LLMs) calls for geometry benchmarks that require multi-step proofs grounded in both text and diagrams. However, existing benchmarks are often limited in scale and rarely provide visually grounded multiple-choice questions, limiting reliable evaluation of complex reasoning. We introduce GeoChallenge, a dataset of 90K automatically generated multiple-choice geometry proof problems, each requiring multi-step reasoning over aligned textual descriptions and diagrams. GeoChallenge provides fine-grained complexity ratings and formal language annotations to enable controlled evaluation. Experiments on multiple advanced LLMs show a clear performance gap between models and humans (the best-performing model, GPT-5-nano, achieves 75.89 exact match vs. 94.74 for humans). Further analysis also reveals three common failure patterns of LLMs: (1) exact match failures under the multiple-choice setting; (2) weak visual reliance; and (3) overextended reasoning without convergence.</abstract>
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%0 Conference Proceedings
%T GeoChallenge-90K: A Multi-Answer Multiple-Choice Benchmark for Diagram-Grounded Geometric Reasoning
%A Zhang, Yushun
%A Fu, Weiping
%A Yang, Zesheng
%A Zhao, Bo
%A Zhang, Lingling
%A Zhang, Jian
%A Fu, Yumeng
%A Huang, Jiaxing
%A Liu, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-geochallenge
%X Evaluating the symbolic reasoning of large language models (LLMs) calls for geometry benchmarks that require multi-step proofs grounded in both text and diagrams. However, existing benchmarks are often limited in scale and rarely provide visually grounded multiple-choice questions, limiting reliable evaluation of complex reasoning. We introduce GeoChallenge, a dataset of 90K automatically generated multiple-choice geometry proof problems, each requiring multi-step reasoning over aligned textual descriptions and diagrams. GeoChallenge provides fine-grained complexity ratings and formal language annotations to enable controlled evaluation. Experiments on multiple advanced LLMs show a clear performance gap between models and humans (the best-performing model, GPT-5-nano, achieves 75.89 exact match vs. 94.74 for humans). Further analysis also reveals three common failure patterns of LLMs: (1) exact match failures under the multiple-choice setting; (2) weak visual reliance; and (3) overextended reasoning without convergence.
%U https://aclanthology.org/2026.emnlp-main.2583/
%P 58710-58730
Markdown (Informal)
[GeoChallenge-90K: A Multi-Answer Multiple-Choice Benchmark for Diagram-Grounded Geometric Reasoning](https://aclanthology.org/2026.emnlp-main.2583/) (Zhang et al., EMNLP 2026)
ACL
- Yushun Zhang, Weiping Fu, Zesheng Yang, Bo Zhao, Lingling Zhang, Jian Zhang, Yumeng Fu, Jiaxing Huang, and Jun Liu. 2026. GeoChallenge-90K: A Multi-Answer Multiple-Choice Benchmark for Diagram-Grounded Geometric Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 58710–58730, Budapest, Hungary. Association for Computational Linguistics.