@inproceedings{li-etal-2026-mm,
title = "{MM}-{B}rowse{C}omp: A Comprehensive Benchmark for Multimodal Browsing Agents",
author = "Li, Shilong and
Bu, Xingyuan and
Wang, Wenjie and
Liu, Jiaheng and
Dong, Jun and
He, Haoyang and
Lu, Hao and
Zhang, Haozhe and
Jing, Chenchen and
Li, Zhen and
Li, Chuanhao and
Tian, Jiayi and
Zhang, Chenchen and
Peng, Tianhao and
He, Yancheng and
Gu, Jihao and
Huang, Hui and
Zhou, Donghao and
Zhang, Yuanxing and
Yang, Jian and
Zhang, Ge and
Huang, Wenhao and
Zhang, Zhaoxiang and
Yang, Qiangpeng and
Wen, Shilei",
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.1169/",
pages = "26828--26847",
ISBN = "979-8-89176-459-0",
abstract = "AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However, existing benchmarks such as BrowseComp primarily focus on textual content, overlooking the prevalence of multimodal content. To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising 400 challenging, hand-crafted questions designed to evaluate multimodal retrieval and reasoning capabilities. Unlike prior work, MM-BrowseComp incorporates visual prompts and necessitates the extraction of key evidence from web images and videos to complete questions, rendering text-only approaches insufficient. Additionally, we provide a verified checklist for each question, enabling fine-grained analysis of multimodal dependencies and reasoning paths. Our comprehensive evaluation of 27 state-of-the-art models reveals that even leading models like GPT-5-High with tools achieve only 24.25{\%} accuracy, highlighting the suboptimal multimodal browsing capabilities, establishing MM-BrowseComp as a rigorous new standard for the field."
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<abstract>AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However, existing benchmarks such as BrowseComp primarily focus on textual content, overlooking the prevalence of multimodal content. To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising 400 challenging, hand-crafted questions designed to evaluate multimodal retrieval and reasoning capabilities. Unlike prior work, MM-BrowseComp incorporates visual prompts and necessitates the extraction of key evidence from web images and videos to complete questions, rendering text-only approaches insufficient. Additionally, we provide a verified checklist for each question, enabling fine-grained analysis of multimodal dependencies and reasoning paths. Our comprehensive evaluation of 27 state-of-the-art models reveals that even leading models like GPT-5-High with tools achieve only 24.25% accuracy, highlighting the suboptimal multimodal browsing capabilities, establishing MM-BrowseComp as a rigorous new standard for the field.</abstract>
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%0 Conference Proceedings
%T MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
%A Li, Shilong
%A Bu, Xingyuan
%A Wang, Wenjie
%A Liu, Jiaheng
%A Dong, Jun
%A He, Haoyang
%A Lu, Hao
%A Zhang, Haozhe
%A Jing, Chenchen
%A Li, Zhen
%A Li, Chuanhao
%A Tian, Jiayi
%A Zhang, Chenchen
%A Peng, Tianhao
%A He, Yancheng
%A Gu, Jihao
%A Huang, Hui
%A Zhou, Donghao
%A Zhang, Yuanxing
%A Yang, Jian
%A Zhang, Ge
%A Huang, Wenhao
%A Zhang, Zhaoxiang
%A Yang, Qiangpeng
%A Wen, Shilei
%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 li-etal-2026-mm
%X AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However, existing benchmarks such as BrowseComp primarily focus on textual content, overlooking the prevalence of multimodal content. To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising 400 challenging, hand-crafted questions designed to evaluate multimodal retrieval and reasoning capabilities. Unlike prior work, MM-BrowseComp incorporates visual prompts and necessitates the extraction of key evidence from web images and videos to complete questions, rendering text-only approaches insufficient. Additionally, we provide a verified checklist for each question, enabling fine-grained analysis of multimodal dependencies and reasoning paths. Our comprehensive evaluation of 27 state-of-the-art models reveals that even leading models like GPT-5-High with tools achieve only 24.25% accuracy, highlighting the suboptimal multimodal browsing capabilities, establishing MM-BrowseComp as a rigorous new standard for the field.
%U https://aclanthology.org/2026.emnlp-main.1169/
%P 26828-26847
Markdown (Informal)
[MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents](https://aclanthology.org/2026.emnlp-main.1169/) (Li et al., EMNLP 2026)
ACL
- Shilong Li, Xingyuan Bu, Wenjie Wang, Jiaheng Liu, Jun Dong, Haoyang He, Hao Lu, Haozhe Zhang, Chenchen Jing, Zhen Li, Chuanhao Li, Jiayi Tian, Chenchen Zhang, Tianhao Peng, Yancheng He, Jihao Gu, Hui Huang, Donghao Zhou, Yuanxing Zhang, Jian Yang, Ge Zhang, Wenhao Huang, Zhaoxiang Zhang, Qiangpeng Yang, and Shilei Wen. 2026. MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 26828–26847, Budapest, Hungary. Association for Computational Linguistics.