@inproceedings{lu-etal-2026-ui,
title = "{UI}-Copilot: Advancing Long-Horizon {GUI} Automation via Tool-Integrated Policy Optimization",
author = "Lu, Zhengxi and
Tang, Fei and
Liu, Guangyi and
Ma, Jin and
Song, Kaitao and
Tan, Xu and
Zhang, Wenqi and
Lu, Weiming and
Xiao, Jun and
Zhuang, Yueting and
Shen, Yongliang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.904/",
pages = "19741--19762",
ISBN = "979-8-89176-390-6",
abstract = "MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are burdened with tasks beyond their intrinsic capabilities, suffering from memory degradation, progress confusion, and math hallucination. To address these challenges, we present $\textbf{UI-Copilot}$, a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. We introduce memory decoupling to separate persistent observations from transient execution context, and train the policy agent to selectively invoke the copilot as $Retriever$ or $Calculator$ based on task demands. To enable effective tool invocation learning, we propose $\underline{\textbf{T}}$ool-$\underline{\textbf{I}}$ntegrated $\underline{\textbf{P}}$olicy $\underline{\textbf{O}}$ptimization ($\textbf{TIPO}$), which separately optimizes tool selection through single-turn prediction and task execution through on-policy multi-turn rollouts. Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UI-TARS-1.5-7B. Moreover, UI-Copilot-7B delivers a 17.1{\%} absolute improvement on AndroidWorld over the base Qwen model, highlighting UI-Copilot{'}s strong generalization to real-world GUI tasks. Code website: https://anonymous.4open.science/r/UI-Copilot-0535."
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<abstract>MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are burdened with tasks beyond their intrinsic capabilities, suffering from memory degradation, progress confusion, and math hallucination. To address these challenges, we present UI-Copilot, a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. We introduce memory decoupling to separate persistent observations from transient execution context, and train the policy agent to selectively invoke the copilot as Retriever or Calculator based on task demands. To enable effective tool invocation learning, we propose \underlineTool-\underlineIntegrated \underlinePolicy \underlineOptimization (TIPO), which separately optimizes tool selection through single-turn prediction and task execution through on-policy multi-turn rollouts. Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UI-TARS-1.5-7B. Moreover, UI-Copilot-7B delivers a 17.1% absolute improvement on AndroidWorld over the base Qwen model, highlighting UI-Copilot’s strong generalization to real-world GUI tasks. Code website: https://anonymous.4open.science/r/UI-Copilot-0535.</abstract>
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%0 Conference Proceedings
%T UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization
%A Lu, Zhengxi
%A Tang, Fei
%A Liu, Guangyi
%A Ma, Jin
%A Song, Kaitao
%A Tan, Xu
%A Zhang, Wenqi
%A Lu, Weiming
%A Xiao, Jun
%A Zhuang, Yueting
%A Shen, Yongliang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lu-etal-2026-ui
%X MLLM-based GUI agents have demonstrated strong capabilities in complex user interface interaction tasks. However, long-horizon scenarios remain challenging, as these agents are burdened with tasks beyond their intrinsic capabilities, suffering from memory degradation, progress confusion, and math hallucination. To address these challenges, we present UI-Copilot, a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation. We introduce memory decoupling to separate persistent observations from transient execution context, and train the policy agent to selectively invoke the copilot as Retriever or Calculator based on task demands. To enable effective tool invocation learning, we propose \underlineTool-\underlineIntegrated \underlinePolicy \underlineOptimization (TIPO), which separately optimizes tool selection through single-turn prediction and task execution through on-policy multi-turn rollouts. Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UI-TARS-1.5-7B. Moreover, UI-Copilot-7B delivers a 17.1% absolute improvement on AndroidWorld over the base Qwen model, highlighting UI-Copilot’s strong generalization to real-world GUI tasks. Code website: https://anonymous.4open.science/r/UI-Copilot-0535.
%U https://aclanthology.org/2026.acl-long.904/
%P 19741-19762
Markdown (Informal)
[UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization](https://aclanthology.org/2026.acl-long.904/) (Lu et al., ACL 2026)
ACL
- Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. 2026. UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19741–19762, San Diego, California, United States. Association for Computational Linguistics.