@inproceedings{li-etal-2026-capgeo,
title = "{C}ap{G}eo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding",
author = "Li, Yuying and
Qian, Siyi and
Liang, Hao and
Zheng, Leqi and
An, Ruichuan and
Sun, Linzhuang and
Zhang, Jiajun and
Zhang, Wentao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1062/",
pages = "22475--22494",
ISBN = "979-8-89176-462-0",
abstract = "While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric problems. This discrepancy motivates us to investigate the root cause: is the bottleneck of multimodal geometric reasoning rooted in reasoning itself, or in the perception of geometric information from diagrams? To answer this question, we conduct an exploratory experiment and find that providing high-quality captions consistently and substantially boosts performance of MLLMs, empirically validating the visual perception bottleneck in geometric reasoning. However, MLLMs' capabilities in visual geometric perception remain insufficiently evaluated. To fill the gap, we propose \textbf{CapGeo-Bench}, a benchmark of 4,641 high-quality figure{--}caption pairs equipped with a fine-grained keypoint-based evaluation metric. Specifically, CapGeo-Bench encompasses three geometric classes and is devided into four difficulty levels according to the complexity of geometric primitives. Captions are scored at a fine-grained level by extracting keypoints along three dimensions-geometric elements, spatial relationships, and numerical attributes. CapGeo-Bench provides a reliable instrument and guideline for evaluating and advancing MLLMs' geometric perception capabilities. Our code and data are publicly available at: \url{https://github.com/YuYingLi0/CapGeo}."
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<abstract>While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric problems. This discrepancy motivates us to investigate the root cause: is the bottleneck of multimodal geometric reasoning rooted in reasoning itself, or in the perception of geometric information from diagrams? To answer this question, we conduct an exploratory experiment and find that providing high-quality captions consistently and substantially boosts performance of MLLMs, empirically validating the visual perception bottleneck in geometric reasoning. However, MLLMs’ capabilities in visual geometric perception remain insufficiently evaluated. To fill the gap, we propose CapGeo-Bench, a benchmark of 4,641 high-quality figure–caption pairs equipped with a fine-grained keypoint-based evaluation metric. Specifically, CapGeo-Bench encompasses three geometric classes and is devided into four difficulty levels according to the complexity of geometric primitives. Captions are scored at a fine-grained level by extracting keypoints along three dimensions-geometric elements, spatial relationships, and numerical attributes. CapGeo-Bench provides a reliable instrument and guideline for evaluating and advancing MLLMs’ geometric perception capabilities. Our code and data are publicly available at: https://github.com/YuYingLi0/CapGeo.</abstract>
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%0 Conference Proceedings
%T CapGeo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding
%A Li, Yuying
%A Qian, Siyi
%A Liang, Hao
%A Zheng, Leqi
%A An, Ruichuan
%A Sun, Linzhuang
%A Zhang, Jiajun
%A Zhang, Wentao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-capgeo
%X While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric problems. This discrepancy motivates us to investigate the root cause: is the bottleneck of multimodal geometric reasoning rooted in reasoning itself, or in the perception of geometric information from diagrams? To answer this question, we conduct an exploratory experiment and find that providing high-quality captions consistently and substantially boosts performance of MLLMs, empirically validating the visual perception bottleneck in geometric reasoning. However, MLLMs’ capabilities in visual geometric perception remain insufficiently evaluated. To fill the gap, we propose CapGeo-Bench, a benchmark of 4,641 high-quality figure–caption pairs equipped with a fine-grained keypoint-based evaluation metric. Specifically, CapGeo-Bench encompasses three geometric classes and is devided into four difficulty levels according to the complexity of geometric primitives. Captions are scored at a fine-grained level by extracting keypoints along three dimensions-geometric elements, spatial relationships, and numerical attributes. CapGeo-Bench provides a reliable instrument and guideline for evaluating and advancing MLLMs’ geometric perception capabilities. Our code and data are publicly available at: https://github.com/YuYingLi0/CapGeo.
%U https://aclanthology.org/2026.findings-emnlp.1062/
%P 22475-22494
Markdown (Informal)
[CapGeo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding](https://aclanthology.org/2026.findings-emnlp.1062/) (Li et al., Findings 2026)
ACL
- Yuying Li, Siyi Qian, Hao Liang, Leqi Zheng, Ruichuan An, Linzhuang Sun, Jiajun Zhang, and Wentao Zhang. 2026. CapGeo-Bench: Decoupling Visual Perception from Reasoning and Evaluating Geometric Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22475–22494, Budapest, Hungary. Association for Computational Linguistics.