@inproceedings{huang-etal-2026-gui,
title = "Do {GUI} Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal {GUI} Agents",
author = "Huang, Zhaoyuan and
Ju, Tianjie and
Cheng, Pengzhou and
Wu, Zheng and
Li, Yansi and
Song, Chuanbiao and
Lan, Jun and
Zhu, Huijia and
Wang, Weiqiang and
Zhang, Zhuosheng",
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.1028/",
pages = "23630--23651",
ISBN = "979-8-89176-459-0",
abstract = "Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeasible instructions due to benign mistakes. A reliable agent should not only know how to act, but also when not to act. In this work, we introduce ConflictGUI, a benchmark covering instruction-internal conflicts and instruction-GUI context conflicts to study conflict-aware termination. Our evaluation reveals severe execution-biased over-compliance: agents that perform well on feasible tasks often continue to execute blindly under conflicting instructions. To mitigate this behavior, we propose ConflictGuard, an inference-time framework that aligns an agent{'}s feasibility awareness with its action generation. ConflictGuard contains two coupled components: a feasibility verification protocol that guides the agent to assess instruction logic and GUI-side evidence before acting, and a conditional action modulation mechanism that steers agent{'}s over-compliance into termination-oriented behavior. Experiments across five widely-used agents demonstrate that ConflictGuard improves average conflict task success rate significantly, while preserving normal GUI-task performance. These results validate that a lightweight inference-time intervention can substantially boost GUI Agent{'}s competence to identify inappropriate execution scenarios and refrain from unnecessary actions."
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%0 Conference Proceedings
%T Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents
%A Huang, Zhaoyuan
%A Ju, Tianjie
%A Cheng, Pengzhou
%A Wu, Zheng
%A Li, Yansi
%A Song, Chuanbiao
%A Lan, Jun
%A Zhu, Huijia
%A Wang, Weiqiang
%A Zhang, Zhuosheng
%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 huang-etal-2026-gui
%X Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeasible instructions due to benign mistakes. A reliable agent should not only know how to act, but also when not to act. In this work, we introduce ConflictGUI, a benchmark covering instruction-internal conflicts and instruction-GUI context conflicts to study conflict-aware termination. Our evaluation reveals severe execution-biased over-compliance: agents that perform well on feasible tasks often continue to execute blindly under conflicting instructions. To mitigate this behavior, we propose ConflictGuard, an inference-time framework that aligns an agent’s feasibility awareness with its action generation. ConflictGuard contains two coupled components: a feasibility verification protocol that guides the agent to assess instruction logic and GUI-side evidence before acting, and a conditional action modulation mechanism that steers agent’s over-compliance into termination-oriented behavior. Experiments across five widely-used agents demonstrate that ConflictGuard improves average conflict task success rate significantly, while preserving normal GUI-task performance. These results validate that a lightweight inference-time intervention can substantially boost GUI Agent’s competence to identify inappropriate execution scenarios and refrain from unnecessary actions.
%U https://aclanthology.org/2026.emnlp-main.1028/
%P 23630-23651
Markdown (Informal)
[Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents](https://aclanthology.org/2026.emnlp-main.1028/) (Huang et al., EMNLP 2026)
ACL
- Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng, Zheng Wu, Yansi Li, Chuanbiao Song, Jun Lan, Huijia Zhu, Weiqiang Wang, and Zhuosheng Zhang. 2026. Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 23630–23651, Budapest, Hungary. Association for Computational Linguistics.