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    <abstract>Large-language-model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long-horizon navigation, large-scale information extraction, and reasoning under constraints. We present WebDART, a general framework that enables a single LLM to handle such complex chores. WebDART (i) dynamically decomposes each objective into three focused subtasks—navigation, information extraction, and execution—so the model concentrates on one skill at a time, and (ii) continuously re-plans the decomposition as new webpages are revealed, taking advantage of newly discovered filters or shortcuts and avoiding redundant exploration. Evaluated on WebChoreArena, WebDART lifts end-to-end success rates by up to 13.7 percentage points over previous state-of-the-art agents, while matching their performance on the easier WebArena suite and completing tasks with up to 14.7 fewer navigation steps. Code will be publicly available.</abstract>
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%0 Conference Proceedings
%T WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
%A Yang, Jingbo
%A Hou, Bairu
%A Wei, Wei
%A Chang, Shiyu
%A Bao, Yujia
%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 yang-etal-2026-webdart
%X Large-language-model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long-horizon navigation, large-scale information extraction, and reasoning under constraints. We present WebDART, a general framework that enables a single LLM to handle such complex chores. WebDART (i) dynamically decomposes each objective into three focused subtasks—navigation, information extraction, and execution—so the model concentrates on one skill at a time, and (ii) continuously re-plans the decomposition as new webpages are revealed, taking advantage of newly discovered filters or shortcuts and avoiding redundant exploration. Evaluated on WebChoreArena, WebDART lifts end-to-end success rates by up to 13.7 percentage points over previous state-of-the-art agents, while matching their performance on the easier WebArena suite and completing tasks with up to 14.7 fewer navigation steps. Code will be publicly available.
%U https://aclanthology.org/2026.findings-acl.568/
%P 11696-11715

Markdown (Informal)

[WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks](https://aclanthology.org/2026.findings-acl.568/) (Yang et al., Findings 2026)

• WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks (Yang et al., Findings 2026)

ACL

• Jingbo Yang, Bairu Hou, Wei Wei, Shiyu Chang, and Yujia Bao. 2026. WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks. In Findings of the Association for Computational Linguistics: ACL 2026, pages 11696–11715, San Diego, California, United States. Association for Computational Linguistics.