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    <abstract>Multimodal BrowseComp tasks require agents to combine perception, tool use, and long-horizon reasoning over dynamic web content, challenging their ability to handle compositional structure, open-world uncertainty, and multimodal integration across extended interactions. Crucially, real-world multimodal browsing involves three distinct information-flow patterns: text-only, image-to-text, and text-to-image, yet existing data construction methods cover only the text-only and image-to-text patterns, leaving text-to-image largely unaddressed and limiting agent generality and robustness. We introduce UniBrowse, a unified data pipeline that for the first time simultaneously generates training data covering all three patterns, augments curated knowledge graphs with live web retrieval for improved fidelity, and introduces a novel metric of exploration degree to filter low-signal instances for efficient reinforcement learning. Through this pipeline, we produce high-quality cold-start tool-use trajectories and exploration-rich QA pairs, and train a 35B-scale agent via supervised fine-tuning and exploration-aware RL. The resulting UniBrowse agent achieves state-of-the-art performance on multimodal BrowseComp benchmarks, attaining an average accuracy of 54.4 across five diverse benchmarks—an improvement of 10.5 points over its base model Qwen3.5-35B-A3B—and surpassing serveral closed-source agent workflows such as GPT-5 (42.9), Gemini-2.5 Pro (44.8), and Gemini-2.5 Flash (41.3).</abstract>
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%0 Conference Proceedings
%T UniBrowse: A Data-to-Agent Framework for Multimodal BrowseComp
%A Wei, Xiyu
%A Zong, Qingwei
%A Yu, Zhuocheng
%A Zhang, Feng
%A Jia, Kai
%A Li, Sujian
%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 wei-etal-2026-unibrowse
%X Multimodal BrowseComp tasks require agents to combine perception, tool use, and long-horizon reasoning over dynamic web content, challenging their ability to handle compositional structure, open-world uncertainty, and multimodal integration across extended interactions. Crucially, real-world multimodal browsing involves three distinct information-flow patterns: text-only, image-to-text, and text-to-image, yet existing data construction methods cover only the text-only and image-to-text patterns, leaving text-to-image largely unaddressed and limiting agent generality and robustness. We introduce UniBrowse, a unified data pipeline that for the first time simultaneously generates training data covering all three patterns, augments curated knowledge graphs with live web retrieval for improved fidelity, and introduces a novel metric of exploration degree to filter low-signal instances for efficient reinforcement learning. Through this pipeline, we produce high-quality cold-start tool-use trajectories and exploration-rich QA pairs, and train a 35B-scale agent via supervised fine-tuning and exploration-aware RL. The resulting UniBrowse agent achieves state-of-the-art performance on multimodal BrowseComp benchmarks, attaining an average accuracy of 54.4 across five diverse benchmarks—an improvement of 10.5 points over its base model Qwen3.5-35B-A3B—and surpassing serveral closed-source agent workflows such as GPT-5 (42.9), Gemini-2.5 Pro (44.8), and Gemini-2.5 Flash (41.3).
%U https://aclanthology.org/2026.findings-emnlp.667/
%P 14007-14023

Markdown (Informal)

[UniBrowse: A Data-to-Agent Framework for Multimodal BrowseComp](https://aclanthology.org/2026.findings-emnlp.667/) (Wei et al., Findings 2026)

• UniBrowse: A Data-to-Agent Framework for Multimodal BrowseComp (Wei et al., Findings 2026)

ACL

• Xiyu Wei, Qingwei Zong, Zhuocheng Yu, Feng Zhang, Kai Jia, and Sujian Li. 2026. UniBrowse: A Data-to-Agent Framework for Multimodal BrowseComp. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 14007–14023, Budapest, Hungary. Association for Computational Linguistics.